From f5e5c4bd3fc826c06e0146d2065df0efacc3a705 Mon Sep 17 00:00:00 2001 From: Yiyi Liao Date: Thu, 13 Jun 2019 16:25:11 +0200 Subject: [PATCH] init --- README.md | 2 - co/__init__.py | 23 + co/args.py | 71 + co/cmap.py | 93 + co/geometry.py | 800 + co/gtimer.py | 32 + co/io3d.py | 267 + co/metric.py | 248 + co/plt.py | 99 + co/plt2d.py | 57 + co/plt3d.py | 38 + co/table.py | 445 + co/utils.py | 86 + config.json | 5 + create_syn_data.sh | 7 + data/__init__.py | 0 data/commons.py | 110 + data/create_syn_data.py | 259 + data/dataset.py | 148 + data/kinect_pattern.png | Bin 0 -> 599339 bytes data/lcn/img.png | Bin 0 -> 767724 bytes data/lcn/lcn.c | 20968 ++++++++++++++++++++++++ data/lcn/lcn.html | 720 + data/lcn/lcn.pyx | 58 + data/lcn/readme.txt | 5 + data/lcn/setup.py | 6 + data/lcn/test_lcn.py | 47 + hyperdepth/eval.cpp | 46 + hyperdepth/hyperdepth.h | 287 + hyperdepth/hyperdepth.pyx | 86 + hyperdepth/hyperparam_search.py | 65 + hyperdepth/rf/common.h | 18 + hyperdepth/rf/data.h | 72 + hyperdepth/rf/forest.h | 92 + hyperdepth/rf/leaffcn.h | 99 + hyperdepth/rf/node.h | 158 + hyperdepth/rf/serialization.h | 256 + hyperdepth/rf/spliteval.h | 71 + hyperdepth/rf/splitfcn.h | 106 + hyperdepth/rf/threadpool.h | 112 + hyperdepth/rf/train.h | 423 + hyperdepth/rf/tree.h | 55 + hyperdepth/setup.py | 45 + hyperdepth/train.cpp | 52 + hyperdepth/vis_eval.py | 15 + img/img.png | Bin 0 -> 1081568 bytes model/__init__.py | 0 model/exp_synph.py | 237 + model/exp_synphge.py | 298 + model/networks.py | 566 + readme.md | 95 + renderer/Makefile | 32 + renderer/__init__.py | 4 + renderer/cyrender.pyx | 200 + renderer/render/co_types.h | 10 + renderer/render/common.h | 135 + renderer/render/common_cpu.h | 26 + renderer/render/common_cuda.h | 173 + renderer/render/geometry.h | 294 + renderer/render/render.h | 369 + renderer/render/render_cpu.cpp | 22 + renderer/render/render_cpu.h | 23 + renderer/render/render_gpu.cu | 100 + renderer/render/render_gpu.h | 23 + renderer/render/render_gpu_dummy.cpp | 33 + renderer/render/stdlib_cuda.cu | 35 + renderer/render/stdlib_cuda.h | 18 + renderer/render/stdlib_cuda_dummy.cpp | 10 + renderer/setup.py | 49 + requirements.txt | 6 + torchext/__init__.py | 4 + torchext/dataset.py | 66 + torchext/ext/co_types.h | 10 + torchext/ext/common.h | 135 + torchext/ext/common_cuda.h | 173 + torchext/ext/ext.h | 347 + torchext/ext/ext_cpu.cpp | 198 + torchext/ext/ext_cuda.cpp | 135 + torchext/ext/ext_kernel.cu | 112 + torchext/functions.py | 147 + torchext/modules.py | 27 + torchext/setup.py | 24 + torchext/worker.py | 528 + train_val.py | 29 + 84 files changed, 31343 insertions(+), 2 deletions(-) delete mode 100644 README.md create mode 100644 co/__init__.py create mode 100644 co/args.py create mode 100644 co/cmap.py create mode 100644 co/geometry.py create mode 100644 co/gtimer.py create mode 100644 co/io3d.py create mode 100644 co/metric.py create mode 100644 co/plt.py create mode 100644 co/plt2d.py create mode 100644 co/plt3d.py create mode 100644 co/table.py create mode 100644 co/utils.py create mode 100644 config.json create mode 100755 create_syn_data.sh create mode 100644 data/__init__.py create mode 100644 data/commons.py create mode 100644 data/create_syn_data.py create mode 100644 data/dataset.py create mode 100644 data/kinect_pattern.png create mode 100644 data/lcn/img.png create mode 100644 data/lcn/lcn.c create mode 100644 data/lcn/lcn.html create mode 100644 data/lcn/lcn.pyx create mode 100644 data/lcn/readme.txt create mode 100644 data/lcn/setup.py create mode 100644 data/lcn/test_lcn.py create mode 100644 hyperdepth/eval.cpp create mode 100644 hyperdepth/hyperdepth.h create mode 100644 hyperdepth/hyperdepth.pyx create mode 100644 hyperdepth/hyperparam_search.py create mode 100644 hyperdepth/rf/common.h create mode 100644 hyperdepth/rf/data.h create mode 100644 hyperdepth/rf/forest.h create mode 100644 hyperdepth/rf/leaffcn.h create mode 100644 hyperdepth/rf/node.h create mode 100644 hyperdepth/rf/serialization.h create mode 100644 hyperdepth/rf/spliteval.h create mode 100644 hyperdepth/rf/splitfcn.h create mode 100644 hyperdepth/rf/threadpool.h create mode 100644 hyperdepth/rf/train.h create mode 100644 hyperdepth/rf/tree.h create mode 100644 hyperdepth/setup.py create mode 100644 hyperdepth/train.cpp create mode 100644 hyperdepth/vis_eval.py create mode 100644 img/img.png create mode 100644 model/__init__.py create mode 100644 model/exp_synph.py create mode 100644 model/exp_synphge.py create mode 100644 model/networks.py create mode 100644 readme.md create mode 100644 renderer/Makefile create mode 100644 renderer/__init__.py create mode 100644 renderer/cyrender.pyx create mode 100644 renderer/render/co_types.h create mode 100644 renderer/render/common.h create mode 100644 renderer/render/common_cpu.h create mode 100644 renderer/render/common_cuda.h create mode 100644 renderer/render/geometry.h create mode 100644 renderer/render/render.h create mode 100644 renderer/render/render_cpu.cpp create mode 100644 renderer/render/render_cpu.h create mode 100644 renderer/render/render_gpu.cu create mode 100644 renderer/render/render_gpu.h create mode 100644 renderer/render/render_gpu_dummy.cpp create mode 100644 renderer/render/stdlib_cuda.cu create mode 100644 renderer/render/stdlib_cuda.h create mode 100644 renderer/render/stdlib_cuda_dummy.cpp create mode 100644 renderer/setup.py create mode 100644 requirements.txt create mode 100644 torchext/__init__.py create mode 100644 torchext/dataset.py create mode 100644 torchext/ext/co_types.h create mode 100644 torchext/ext/common.h create mode 100644 torchext/ext/common_cuda.h create mode 100644 torchext/ext/ext.h create mode 100644 torchext/ext/ext_cpu.cpp create mode 100644 torchext/ext/ext_cuda.cpp create mode 100644 torchext/ext/ext_kernel.cu create mode 100644 torchext/functions.py create mode 100644 torchext/modules.py create mode 100644 torchext/setup.py create mode 100644 torchext/worker.py create mode 100644 train_val.py diff --git a/README.md b/README.md deleted file mode 100644 index ec63717..0000000 --- a/README.md +++ /dev/null @@ -1,2 +0,0 @@ -# connecting_the_dots -This repository contains the code for the paper "Connecting the Dots: Learning Representations for Active Monocular Depth Estimation" https://avg.is.tuebingen.mpg.de/publications/riegler2019cvpr diff --git a/co/__init__.py b/co/__init__.py new file mode 100644 index 0000000..3dcfa5d --- /dev/null +++ b/co/__init__.py @@ -0,0 +1,23 @@ +# import os +# this_dir = os.path.dirname(__file__) +# print(this_dir) +# import sys +# sys.path.append(this_dir) + +# set matplotlib backend depending on env +import os +import matplotlib +if os.name == 'posix' and "DISPLAY" not in os.environ: + matplotlib.use('Agg') + +from . import geometry +from . import plt +from . import plt2d +from . import plt3d +from . import metric +from . import table +from . import utils +from . import io3d +from . import gtimer +from . import cmap +from . import args diff --git a/co/args.py b/co/args.py new file mode 100644 index 0000000..c55ba7b --- /dev/null +++ b/co/args.py @@ -0,0 +1,71 @@ +import argparse +import os +from .utils import str2bool + + +def parse_args(): + parser = argparse.ArgumentParser() + # + parser.add_argument('--output_dir', + help='Output directory', + default='./output', type=str) + parser.add_argument('--loss', + help='Train with \'ph\' for the first stage without geometric loss, \ + train with \'phge\' for the second stage with geometric loss', + default='ph', choices=['ph','phge'], type=str) + parser.add_argument('--data_type', + default='syn', choices=['syn'], type=str) + # + parser.add_argument('--cmd', + help='Start training or test', + default='resume', choices=['retrain', 'resume', 'retest', 'test_init'], type=str) + parser.add_argument('--epoch', + help='If larger than -1, retest on the specified epoch', + default=-1, type=int) + parser.add_argument('--epochs', + help='Training epochs', + default=100, type=int) + + # + parser.add_argument('--ms', + help='If true, use multiscale loss', + default=True, type=str2bool) + parser.add_argument('--pattern_path', + help='Path of the pattern image', + default='./data/kinect_patttern.png', type=str) + # + parser.add_argument('--dp_weight', + help='Weight of the disparity loss', + default=0.02, type=float) + parser.add_argument('--ge_weight', + help='Weight of the geometric loss', + default=0.1, type=float) + # + parser.add_argument('--lcn_radius', + help='Radius of the window for LCN pre-processing', + default=5, type=int) + parser.add_argument('--max_disp', + help='Maximum disparity', + default=128, type=int) + # + parser.add_argument('--track_length', + help='Track length for geometric loss', + default=2, type=int) + # + parser.add_argument('--blend_im', + help='Parameter for adding texture', + default=0.6, type=float) + + args = parser.parse_args() + + args.exp_name = get_exp_name(args) + + return args + + +def get_exp_name(args): + name = f"exp_{args.data_type}" + return name + + + diff --git a/co/cmap.py b/co/cmap.py new file mode 100644 index 0000000..58a7e17 --- /dev/null +++ b/co/cmap.py @@ -0,0 +1,93 @@ +import numpy as np + +_color_map_errors = np.array([ + [149, 54, 49], #0: log2(x) = -infinity + [180, 117, 69], #0.0625: log2(x) = -4 + [209, 173, 116], #0.125: log2(x) = -3 + [233, 217, 171], #0.25: log2(x) = -2 + [248, 243, 224], #0.5: log2(x) = -1 + [144, 224, 254], #1.0: log2(x) = 0 + [97, 174, 253], #2.0: log2(x) = 1 + [67, 109, 244], #4.0: log2(x) = 2 + [39, 48, 215], #8.0: log2(x) = 3 + [38, 0, 165], #16.0: log2(x) = 4 + [38, 0, 165] #inf: log2(x) = inf +]).astype(float) + +def color_error_image(errors, scale=1, mask=None, BGR=True): + """ + Color an input error map. + + Arguments: + errors -- HxW numpy array of errors + [scale=1] -- scaling the error map (color change at unit error) + [mask=None] -- zero-pixels are masked white in the result + [BGR=True] -- toggle between BGR and RGB + + Returns: + colored_errors -- HxWx3 numpy array visualizing the errors + """ + + errors_flat = errors.flatten() + errors_color_indices = np.clip(np.log2(errors_flat / scale + 1e-5) + 5, 0, 9) + i0 = np.floor(errors_color_indices).astype(int) + f1 = errors_color_indices - i0.astype(float) + colored_errors_flat = _color_map_errors[i0, :] * (1-f1).reshape(-1,1) + _color_map_errors[i0+1, :] * f1.reshape(-1,1) + + if mask is not None: + colored_errors_flat[mask.flatten() == 0] = 255 + + if not BGR: + colored_errors_flat = colored_errors_flat[:,[2,1,0]] + + return colored_errors_flat.reshape(errors.shape[0], errors.shape[1], 3).astype(np.int) + +_color_map_depths = np.array([ + [0, 0, 0], # 0.000 + [0, 0, 255], # 0.114 + [255, 0, 0], # 0.299 + [255, 0, 255], # 0.413 + [0, 255, 0], # 0.587 + [0, 255, 255], # 0.701 + [255, 255, 0], # 0.886 + [255, 255, 255], # 1.000 + [255, 255, 255], # 1.000 +]).astype(float) +_color_map_bincenters = np.array([ + 0.0, + 0.114, + 0.299, + 0.413, + 0.587, + 0.701, + 0.886, + 1.000, + 2.000, # doesn't make a difference, just strictly higher than 1 +]) + +def color_depth_map(depths, scale=None): + """ + Color an input depth map. + + Arguments: + depths -- HxW numpy array of depths + [scale=None] -- scaling the values (defaults to the maximum depth) + + Returns: + colored_depths -- HxWx3 numpy array visualizing the depths + """ + + if scale is None: + scale = depths.max() + + values = np.clip(depths.flatten() / scale, 0, 1) + # for each value, figure out where they fit in in the bincenters: what is the last bincenter smaller than this value? + lower_bin = ((values.reshape(-1, 1) >= _color_map_bincenters.reshape(1,-1)) * np.arange(0,9)).max(axis=1) + lower_bin_value = _color_map_bincenters[lower_bin] + higher_bin_value = _color_map_bincenters[lower_bin + 1] + alphas = (values - lower_bin_value) / (higher_bin_value - lower_bin_value) + colors = _color_map_depths[lower_bin] * (1-alphas).reshape(-1,1) + _color_map_depths[lower_bin + 1] * alphas.reshape(-1,1) + return colors.reshape(depths.shape[0], depths.shape[1], 3).astype(np.uint8) + +#from utils.debug import save_color_numpy +#save_color_numpy(color_depth_map(np.matmul(np.ones((100,1)), np.arange(0,1200).reshape(1,1200)), scale=1000)) diff --git a/co/geometry.py b/co/geometry.py new file mode 100644 index 0000000..5cf194e --- /dev/null +++ b/co/geometry.py @@ -0,0 +1,800 @@ +import numpy as np +import scipy.spatial +import scipy.linalg + +def nullspace(A, atol=1e-13, rtol=0): + u, s, vh = np.linalg.svd(A) + tol = max(atol, rtol * s[0]) + nnz = (s >= tol).sum() + ns = vh[nnz:].conj().T + return ns + +def nearest_orthogonal_matrix(R): + U,S,Vt = np.linalg.svd(R) + return U @ np.eye(3,dtype=R.dtype) @ Vt + +def power_iters(A, n_iters=10): + b = np.random.uniform(-1,1, size=(A.shape[0], A.shape[1], 1)) + for iter in range(n_iters): + b = A @ b + b = b / np.linalg.norm(b, axis=1, keepdims=True) + return b + +def rayleigh_quotient(A, b): + return (b.transpose(0,2,1) @ A @ b) / (b.transpose(0,2,1) @ b) + + +def cross_prod_mat(x): + x = x.reshape(-1,3) + X = np.empty((x.shape[0],3,3), dtype=x.dtype) + X[:,0,0] = 0 + X[:,0,1] = -x[:,2] + X[:,0,2] = x[:,1] + X[:,1,0] = x[:,2] + X[:,1,1] = 0 + X[:,1,2] = -x[:,0] + X[:,2,0] = -x[:,1] + X[:,2,1] = x[:,0] + X[:,2,2] = 0 + return X.squeeze() + +def hat_operator(x): + return cross_prod_mat(x) + +def vee_operator(X): + X = X.reshape(-1,3,3) + x = np.empty((X.shape[0], 3), dtype=X.dtype) + x[:,0] = X[:,2,1] + x[:,1] = X[:,0,2] + x[:,2] = X[:,1,0] + return x.squeeze() + + +def rot_x(x, dtype=np.float32): + x = np.array(x, copy=False) + x = x.reshape(-1,1) + R = np.zeros((x.shape[0],3,3), dtype=dtype) + R[:,0,0] = 1 + R[:,1,1] = np.cos(x).ravel() + R[:,1,2] = -np.sin(x).ravel() + R[:,2,1] = np.sin(x).ravel() + R[:,2,2] = np.cos(x).ravel() + return R.squeeze() + +def rot_y(y, dtype=np.float32): + y = np.array(y, copy=False) + y = y.reshape(-1,1) + R = np.zeros((y.shape[0],3,3), dtype=dtype) + R[:,0,0] = np.cos(y).ravel() + R[:,0,2] = np.sin(y).ravel() + R[:,1,1] = 1 + R[:,2,0] = -np.sin(y).ravel() + R[:,2,2] = np.cos(y).ravel() + return R.squeeze() + +def rot_z(z, dtype=np.float32): + z = np.array(z, copy=False) + z = z.reshape(-1,1) + R = np.zeros((z.shape[0],3,3), dtype=dtype) + R[:,0,0] = np.cos(z).ravel() + R[:,0,1] = -np.sin(z).ravel() + R[:,1,0] = np.sin(z).ravel() + R[:,1,1] = np.cos(z).ravel() + R[:,2,2] = 1 + return R.squeeze() + +def xyz_from_rotm(R): + R = R.reshape(-1,3,3) + xyz = np.empty((R.shape[0],3), dtype=R.dtype) + for bidx in range(R.shape[0]): + if R[bidx,0,2] < 1: + if R[bidx,0,2] > -1: + xyz[bidx,1] = np.arcsin(R[bidx,0,2]) + xyz[bidx,0] = np.arctan2(-R[bidx,1,2], R[bidx,2,2]) + xyz[bidx,2] = np.arctan2(-R[bidx,0,1], R[bidx,0,0]) + else: + xyz[bidx,1] = -np.pi/2 + xyz[bidx,0] = -np.arctan2(R[bidx,1,0],R[bidx,1,1]) + xyz[bidx,2] = 0 + else: + xyz[bidx,1] = np.pi/2 + xyz[bidx,0] = np.arctan2(R[bidx,1,0], R[bidx,1,1]) + xyz[bidx,2] = 0 + return xyz.squeeze() + +def zyx_from_rotm(R): + R = R.reshape(-1,3,3) + zyx = np.empty((R.shape[0],3), dtype=R.dtype) + for bidx in range(R.shape[0]): + if R[bidx,2,0] < 1: + if R[bidx,2,0] > -1: + zyx[bidx,1] = np.arcsin(-R[bidx,2,0]) + zyx[bidx,0] = np.arctan2(R[bidx,1,0], R[bidx,0,0]) + zyx[bidx,2] = np.arctan2(R[bidx,2,1], R[bidx,2,2]) + else: + zyx[bidx,1] = np.pi / 2 + zyx[bidx,0] = -np.arctan2(-R[bidx,1,2], R[bidx,1,1]) + zyx[bidx,2] = 0 + else: + zyx[bidx,1] = -np.pi / 2 + zyx[bidx,0] = np.arctan2(-R[bidx,1,2], R[bidx,1,1]) + zyx[bidx,2] = 0 + return zyx.squeeze() + +def rotm_from_xyz(xyz): + xyz = np.array(xyz, copy=False).reshape(-1,3) + return (rot_x(xyz[:,0]) @ rot_y(xyz[:,1]) @ rot_z(xyz[:,2])).squeeze() + +def rotm_from_zyx(zyx): + zyx = np.array(zyx, copy=False).reshape(-1,3) + return (rot_z(zyx[:,0]) @ rot_y(zyx[:,1]) @ rot_x(zyx[:,2])).squeeze() + +def rotm_from_quat(q): + q = q.reshape(-1,4) + w, x, y, z = q[:,0], q[:,1], q[:,2], q[:,3] + R = np.array([ + [1 - 2*y*y - 2*z*z, 2*x*y - 2*z*w, 2*x*z + 2*y*w], + [2*x*y + 2*z*w, 1 - 2*x*x - 2*z*z, 2*y*z - 2*x*w], + [2*x*z - 2*y*w, 2*y*z + 2*x*w, 1 - 2*x*x - 2*y*y] + ], dtype=q.dtype) + R = R.transpose((2,0,1)) + return R.squeeze() + +def rotm_from_axisangle(a): + # exponential + a = a.reshape(-1,3) + phi = np.linalg.norm(a, axis=1).reshape(-1,1,1) + iphi = np.zeros_like(phi) + np.divide(1, phi, out=iphi, where=phi != 0) + A = cross_prod_mat(a) * iphi + R = np.eye(3, dtype=a.dtype) + np.sin(phi) * A + (1 - np.cos(phi)) * A @ A + return R.squeeze() + +def rotm_from_lookat(dir, up=None): + dir = dir.reshape(-1,3) + if up is None: + up = np.zeros_like(dir) + up[:,1] = 1 + dir /= np.linalg.norm(dir, axis=1, keepdims=True) + up /= np.linalg.norm(up, axis=1, keepdims=True) + x = dir[:,None,:] @ cross_prod_mat(up).transpose(0,2,1) + y = x @ cross_prod_mat(dir).transpose(0,2,1) + x = x.squeeze() + y = y.squeeze() + x /= np.linalg.norm(x, axis=1, keepdims=True) + y /= np.linalg.norm(y, axis=1, keepdims=True) + R = np.empty((dir.shape[0],3,3), dtype=dir.dtype) + R[:,0,0] = x[:,0] + R[:,0,1] = y[:,0] + R[:,0,2] = dir[:,0] + R[:,1,0] = x[:,1] + R[:,1,1] = y[:,1] + R[:,1,2] = dir[:,1] + R[:,2,0] = x[:,2] + R[:,2,1] = y[:,2] + R[:,2,2] = dir[:,2] + return R.transpose(0,2,1).squeeze() + +def rotm_distance_identity(R0, R1): + # https://link.springer.com/article/10.1007%2Fs10851-009-0161-2 + # in [0, 2*sqrt(2)] + R0 = R0.reshape(-1,3,3) + R1 = R1.reshape(-1,3,3) + dists = np.linalg.norm(np.eye(3,dtype=R0.dtype) - R0 @ R1.transpose(0,2,1), axis=(1,2)) + return dists.squeeze() + +def rotm_distance_geodesic(R0, R1): + # https://link.springer.com/article/10.1007%2Fs10851-009-0161-2 + # in [0, pi) + R0 = R0.reshape(-1,3,3) + R1 = R1.reshape(-1,3,3) + RtR = R0 @ R1.transpose(0,2,1) + aa = axisangle_from_rotm(RtR) + S = cross_prod_mat(aa).reshape(-1,3,3) + dists = np.linalg.norm(S, axis=(1,2)) + return dists.squeeze() + + + +def axisangle_from_rotm(R): + # logarithm of rotation matrix + # R = R.reshape(-1,3,3) + # tr = np.trace(R, axis1=1, axis2=2) + # phi = np.arccos(np.clip((tr - 1) / 2, -1, 1)) + # scale = np.zeros_like(phi) + # div = 2 * np.sin(phi) + # np.divide(phi, div, out=scale, where=np.abs(div) > 1e-6) + # A = (R - R.transpose(0,2,1)) * scale.reshape(-1,1,1) + # aa = np.stack((A[:,2,1], A[:,0,2], A[:,1,0]), axis=1) + # return aa.squeeze() + R = R.reshape(-1,3,3) + omega = np.empty((R.shape[0], 3), dtype=R.dtype) + omega[:,0] = R[:,2,1] - R[:,1,2] + omega[:,1] = R[:,0,2] - R[:,2,0] + omega[:,2] = R[:,1,0] - R[:,0,1] + r = np.linalg.norm(omega, axis=1).reshape(-1,1) + t = np.trace(R, axis1=1, axis2=2).reshape(-1,1) + omega = np.arctan2(r, t-1) * omega + aa = np.zeros_like(omega) + np.divide(omega, r, out=aa, where=r != 0) + return aa.squeeze() + +def axisangle_from_quat(q): + q = q.reshape(-1,4) + phi = 2 * np.arccos(q[:,0]) + denom = np.zeros_like(q[:,0]) + np.divide(1, np.sqrt(1 - q[:,0]**2), out=denom, where=q[:,0] != 1) + axis = q[:,1:] * denom.reshape(-1,1) + denom = np.linalg.norm(axis, axis=1).reshape(-1,1) + a = np.zeros_like(axis) + np.divide(phi.reshape(-1,1) * axis, denom, out=a, where=denom != 0) + aa = a.astype(q.dtype) + return aa.squeeze() + +def axisangle_apply(aa, x): + # working only with single aa and single x at the moment + xshape = x.shape + aa = aa.reshape(3,) + x = x.reshape(3,) + phi = np.linalg.norm(aa) + e = np.zeros_like(aa) + np.divide(aa, phi, out=e, where=phi != 0) + xr = np.cos(phi) * x + np.sin(phi) * np.cross(e, x) + (1 - np.cos(phi)) * (e.T @ x) * e + return xr.reshape(xshape) + + +def exp_so3(R): + w = axisangle_from_rotm(R) + return w + +def log_so3(w): + R = rotm_from_axisangle(w) + return R + +def exp_se3(R, t): + R = R.reshape(-1,3,3) + t = t.reshape(-1,3) + + w = exp_so3(R).reshape(-1,3) + + phi = np.linalg.norm(w, axis=1).reshape(-1,1,1) + A = cross_prod_mat(w) + Vi = np.eye(3, dtype=R.dtype) - A/2 + (1 - (phi * np.sin(phi) / (2 * (1 - np.cos(phi))))) / phi**2 * A @ A + u = t.reshape(-1,1,3) @ Vi.transpose(0,2,1) + + # v = (u, w) + v = np.empty((R.shape[0],6), dtype=R.dtype) + v[:,:3] = u.squeeze() + v[:,3:] = w + + return v.squeeze() + +def log_se3(v): + # v = (u, w) + v = v.reshape(-1,6) + u = v[:,:3] + w = v[:,3:] + + R = log_so3(w) + + phi = np.linalg.norm(w, axis=1).reshape(-1,1,1) + A = cross_prod_mat(w) + V = np.eye(3, dtype=v.dtype) + (1 - np.cos(phi)) / phi**2 * A + (phi - np.sin(phi)) / phi**3 * A @ A + t = u.reshape(-1,1,3) @ V.transpose(0,2,1) + + return R.squeeze(), t.squeeze() + + +def quat_from_rotm(R): + R = R.reshape(-1,3,3) + q = np.empty((R.shape[0], 4,), dtype=R.dtype) + q[:,0] = np.sqrt( np.maximum(0, 1 + R[:,0,0] + R[:,1,1] + R[:,2,2]) ) + q[:,1] = np.sqrt( np.maximum(0, 1 + R[:,0,0] - R[:,1,1] - R[:,2,2]) ) + q[:,2] = np.sqrt( np.maximum(0, 1 - R[:,0,0] + R[:,1,1] - R[:,2,2]) ) + q[:,3] = np.sqrt( np.maximum(0, 1 - R[:,0,0] - R[:,1,1] + R[:,2,2]) ) + q[:,1] *= np.sign(q[:,1] * (R[:,2,1] - R[:,1,2])) + q[:,2] *= np.sign(q[:,2] * (R[:,0,2] - R[:,2,0])) + q[:,3] *= np.sign(q[:,3] * (R[:,1,0] - R[:,0,1])) + q /= np.linalg.norm(q,axis=1,keepdims=True) + return q.squeeze() + +def quat_from_axisangle(a): + a = a.reshape(-1, 3) + phi = np.linalg.norm(a, axis=1) + iphi = np.zeros_like(phi) + np.divide(1, phi, out=iphi, where=phi != 0) + a = a * iphi.reshape(-1,1) + theta = phi / 2.0 + r = np.cos(theta) + stheta = np.sin(theta) + q = np.stack((r, stheta*a[:,0], stheta*a[:,1], stheta*a[:,2]), axis=1) + q /= np.linalg.norm(q, axis=1).reshape(-1,1) + return q.squeeze() + +def quat_identity(n=1, dtype=np.float32): + q = np.zeros((n,4), dtype=dtype) + q[:,0] = 1 + return q.squeeze() + +def quat_conjugate(q): + shape = q.shape + q = q.reshape(-1,4).copy() + q[:,1:] *= -1 + return q.reshape(shape) + +def quat_product(q1, q2): + # q1 . q2 is equivalent to R(q1) @ R(q2) + shape = q1.shape + q1, q2 = q1.reshape(-1,4), q2.reshape(-1, 4) + q = np.empty((max(q1.shape[0], q2.shape[0]), 4), dtype=q1.dtype) + a1,b1,c1,d1 = q1[:,0], q1[:,1], q1[:,2], q1[:,3] + a2,b2,c2,d2 = q2[:,0], q2[:,1], q2[:,2], q2[:,3] + q[:,0] = a1 * a2 - b1 * b2 - c1 * c2 - d1 * d2 + q[:,1] = a1 * b2 + b1 * a2 + c1 * d2 - d1 * c2 + q[:,2] = a1 * c2 - b1 * d2 + c1 * a2 + d1 * b2 + q[:,3] = a1 * d2 + b1 * c2 - c1 * b2 + d1 * a2 + return q.squeeze() + +def quat_apply(q, x): + xshape = x.shape + x = x.reshape(-1, 3) + qshape = q.shape + q = q.reshape(-1, 4) + + p = np.empty((x.shape[0], 4), dtype=x.dtype) + p[:,0] = 0 + p[:,1:] = x + + r = quat_product(quat_product(q, p), quat_conjugate(q)) + if r.ndim == 1: + return r[1:].reshape(xshape) + else: + return r[:,1:].reshape(xshape) + + +def quat_random(rng=None, n=1): + # http://planning.cs.uiuc.edu/node198.html + if rng is not None: + u = rng.uniform(0, 1, size=(3,n)) + else: + u = np.random.uniform(0, 1, size=(3,n)) + q = np.array(( + np.sqrt(1 - u[0]) * np.sin(2 * np.pi * u[1]), + np.sqrt(1 - u[0]) * np.cos(2 * np.pi * u[1]), + np.sqrt(u[0]) * np.sin(2 * np.pi * u[2]), + np.sqrt(u[0]) * np.cos(2 * np.pi * u[2]) + )).T + q /= np.linalg.norm(q,axis=1,keepdims=True) + return q.squeeze() + +def quat_distance_angle(q0, q1): + # https://math.stackexchange.com/questions/90081/quaternion-distance + # https://link.springer.com/article/10.1007%2Fs10851-009-0161-2 + q0 = q0.reshape(-1,4) + q1 = q1.reshape(-1,4) + dists = np.arccos(np.clip(2 * np.sum(q0 * q1, axis=1)**2 - 1, -1, 1)) + return dists + +def quat_distance_normdiff(q0, q1): + # https://link.springer.com/article/10.1007%2Fs10851-009-0161-2 + # \phi_4 + # [0, 1] + q0 = q0.reshape(-1,4) + q1 = q1.reshape(-1,4) + return 1 - np.sum(q0 * q1, axis=1)**2 + +def quat_distance_mineucl(q0, q1): + # https://link.springer.com/article/10.1007%2Fs10851-009-0161-2 + # http://users.cecs.anu.edu.au/~trumpf/pubs/Hartley_Trumpf_Dai_Li.pdf + q0 = q0.reshape(-1,4) + q1 = q1.reshape(-1,4) + diff0 = ((q0 - q1)**2).sum(axis=1) + diff1 = ((q0 + q1)**2).sum(axis=1) + return np.minimum(diff0, diff1) + +def quat_slerp_space(q0, q1, num=100, endpoint=True): + q0 = q0.ravel() + q1 = q1.ravel() + dot = q0.dot(q1) + if dot < 0: + q1 *= -1 + dot *= -1 + t = np.linspace(0, 1, num=num, endpoint=endpoint, dtype=q0.dtype) + t = t.reshape((-1,1)) + if dot > 0.9995: + ret = q0 + t * (q1 - q0) + return ret + dot = np.clip(dot, -1, 1) + theta0 = np.arccos(dot) + theta = theta0 * t + s0 = np.cos(theta) - dot * np.sin(theta) / np.sin(theta0) + s1 = np.sin(theta) / np.sin(theta0) + return (s0 * q0) + (s1 * q1) + +def cart_to_spherical(x): + shape = x.shape + x = x.reshape(-1,3) + y = np.empty_like(x) + y[:,0] = np.linalg.norm(x, axis=1) # r + y[:,1] = np.arccos(x[:,2] / y[:,0]) # theta + y[:,2] = np.arctan2(x[:,1], x[:,0]) # phi + return y.reshape(shape) + +def spherical_to_cart(x): + shape = x.shape + x = x.reshape(-1,3) + y = np.empty_like(x) + y[:,0] = x[:,0] * np.sin(x[:,1]) * np.cos(x[:,2]) + y[:,1] = x[:,0] * np.sin(x[:,1]) * np.sin(x[:,2]) + y[:,2] = x[:,0] * np.cos(x[:,1]) + return y.reshape(shape) + +def spherical_random(r=1, n=1): + # http://mathworld.wolfram.com/SpherePointPicking.html + # https://math.stackexchange.com/questions/1585975/how-to-generate-random-points-on-a-sphere + x = np.empty((n,3)) + x[:,0] = r + x[:,1] = 2 * np.pi * np.random.uniform(0,1, size=(n,)) + x[:,2] = np.arccos(2 * np.random.uniform(0,1, size=(n,)) - 1) + return x.squeeze() + +def color_pcl(pcl, K, im, color_axis=0, as_int=True, invalid_color=[0,0,0]): + uvd = K @ pcl.T + uvd /= uvd[2] + uvd = np.round(uvd).astype(np.int32) + mask = np.logical_and(uvd[0] >= 0, uvd[1] >= 0) + color = np.empty((pcl.shape[0], 3), dtype=im.dtype) + if color_axis == 0: + mask = np.logical_and(mask, uvd[0] < im.shape[2]) + mask = np.logical_and(mask, uvd[1] < im.shape[1]) + uvd = uvd[:,mask] + color[mask,:] = im[:,uvd[1],uvd[0]].T + elif color_axis == 2: + mask = np.logical_and(mask, uvd[0] < im.shape[1]) + mask = np.logical_and(mask, uvd[1] < im.shape[0]) + uvd = uvd[:,mask] + color[mask,:] = im[uvd[1],uvd[0], :] + else: + raise Exception('invalid color_axis') + color[np.logical_not(mask),:3] = invalid_color + if as_int: + color = (255.0 * color).astype(np.int32) + return color + +def center_pcl(pcl, robust=False, copy=False, axis=1): + if copy: + pcl = pcl.copy() + if robust: + mu = np.median(pcl, axis=axis, keepdims=True) + else: + mu = np.mean(pcl, axis=axis, keepdims=True) + return pcl - mu + +def to_homogeneous(x): + # return np.hstack((x, np.ones((x.shape[0],1),dtype=x.dtype))) + return np.concatenate((x, np.ones((*x.shape[:-1],1),dtype=x.dtype)), axis=-1) + +def from_homogeneous(x): + return x[:,:-1] / x[:,-1] + +def project_uvn(uv, Ki=None): + if uv.shape[1] == 2: + uvn = to_homogeneous(uv) + else: + uvn = uv + if uvn.shape[1] != 3: + raise Exception('uv should have shape Nx2 or Nx3') + if Ki is None: + return uvn + else: + return uvn @ Ki.T + +def project_uvd(uv, depth, K=np.eye(3), R=np.eye(3), t=np.zeros((3,1)), ignore_negative_depth=True, return_uvn=False): + Ki = np.linalg.inv(K) + + if ignore_negative_depth: + mask = depth >= 0 + uv = uv[mask,:] + d = depth[mask] + else: + d = depth.ravel() + + uv1 = to_homogeneous(uv) + + uvn1 = uv1 @ Ki.T + xyz = d.reshape(-1,1) * uvn1 + xyz = (xyz - t.reshape((1,3))) @ R + + if return_uvn: + return xyz, uvn1 + else: + return xyz + +def project_depth(depth, K, R=np.eye(3,3), t=np.zeros((3,1)), ignore_negative_depth=True, return_uvn=False): + u, v = np.meshgrid(range(depth.shape[1]), range(depth.shape[0])) + uv = np.hstack((u.reshape(-1,1), v.reshape(-1,1))) + return project_uvd(uv, depth.ravel(), K, R, t, ignore_negative_depth, return_uvn) + + +def project_xyz(xyz, K=np.eye(3), R=np.eye(3,3), t=np.zeros((3,1))): + uvd = K @ (R @ xyz.T + t.reshape((3,1))) + uvd[:2] /= uvd[2] + return uvd[:2].T, uvd[2] + + +def relative_motion(R0, t0, R1, t1, Rt_from_global=True): + t0 = t0.reshape((3,1)) + t1 = t1.reshape((3,1)) + if Rt_from_global: + Rr = R1 @ R0.T + tr = t1 - Rr @ t0 + else: + Rr = R1.T @ R0 + tr = R1.T @ (t0 - t1) + return Rr, tr.ravel() + + +def translation_to_cameracenter(R, t): + t = t.reshape(-1,3,1) + R = R.reshape(-1,3,3) + C = -R.transpose(0,2,1) @ t + return C.squeeze() + +def cameracenter_to_translation(R, C): + C = C.reshape(-1,3,1) + R = R.reshape(-1,3,3) + t = -R @ C + return t.squeeze() + +def decompose_projection_matrix(P, return_t=True): + if P.shape[0] != 3 or P.shape[1] != 4: + raise Exception('P has to be 3x4') + M = P[:, :3] + C = -np.linalg.inv(M) @ P[:, 3:] + + R,K = np.linalg.qr(np.flipud(M).T) + K = np.flipud(K.T) + K = np.fliplr(K) + R = np.flipud(R.T) + + T = np.diag(np.sign(np.diag(K))) + K = K @ T + R = T @ R + + if np.linalg.det(R) < 0: + R *= -1 + + K /= K[2,2] + if return_t: + return K, R, cameracenter_to_translation(R, C) + else: + return K, R, C + + +def compose_projection_matrix(K=np.eye(3), R=np.eye(3,3), t=np.zeros((3,1))): + return K @ np.hstack((R, t.reshape((3,1)))) + + + +def point_plane_distance(pts, plane): + pts = pts.reshape(-1,3) + return np.abs(np.sum(plane[:3] * pts, axis=1) + plane[3]) / np.linalg.norm(plane[:3]) + +def fit_plane(pts): + pts = pts.reshape(-1,3) + center = np.mean(pts, axis=0) + A = pts - center + u, s, vh = np.linalg.svd(A, full_matrices=False) + # if pts.shape[0] > 100: + # import ipdb; ipdb.set_trace() + plane = np.array([*vh[2], -vh[2].dot(center)]) + return plane + +def tetrahedron(dtype=np.float32): + verts = np.array([ + (np.sqrt(8/9), 0, -1/3), (-np.sqrt(2/9), np.sqrt(2/3), -1/3), + (-np.sqrt(2/9), -np.sqrt(2/3), -1/3), (0, 0, 1)], dtype=dtype) + faces = np.array([(0,1,2), (0,2,3), (0,1,3), (1,2,3)], dtype=np.int32) + normals = -np.mean(verts, axis=0) + verts + normals /= np.linalg.norm(normals, axis=1).reshape(-1,1) + return verts, faces, normals + +def cube(dtype=np.float32): + verts = np.array([ + [-0.5,-0.5,-0.5], [-0.5,0.5,-0.5], [0.5,0.5,-0.5], [0.5,-0.5,-0.5], + [-0.5,-0.5,0.5], [-0.5,0.5,0.5], [0.5,0.5,0.5], [0.5,-0.5,0.5]], dtype=dtype) + faces = np.array([ + (0,1,2), (0,2,3), (4,5,6), (4,6,7), + (0,4,7), (0,7,3), (1,5,6), (1,6,2), + (3,2,6), (3,6,7), (0,1,5), (0,5,4)], dtype=np.int32) + normals = -np.mean(verts, axis=0) + verts + normals /= np.linalg.norm(normals, axis=1).reshape(-1,1) + return verts, faces, normals + +def octahedron(dtype=np.float32): + verts = np.array([ + (+1,0,0), (0,+1,0), (0,0,+1), + (-1,0,0), (0,-1,0), (0,0,-1)], dtype=dtype) + faces = np.array([ + (0,1,2), (1,2,3), (3,2,4), (4,2,0), + (0,1,5), (1,5,3), (3,5,4), (4,5,0)], dtype=np.int32) + normals = -np.mean(verts, axis=0) + verts + normals /= np.linalg.norm(normals, axis=1).reshape(-1,1) + return verts, faces, normals + +def icosahedron(dtype=np.float32): + p = (1 + np.sqrt(5)) / 2 + verts = np.array([ + (-1,0,p), (1,0,p), (1,0,-p), (-1,0,-p), + (0,-p,1), (0,p,1), (0,p,-1), (0,-p,-1), + (-p,-1,0), (p,-1,0), (p,1,0), (-p,1,0) + ], dtype=dtype) + faces = np.array([ + (0,1,4), (0,1,5), (1,4,9), (1,9,10), (1,10,5), (0,4,8), (0,8,11), (0,11,5), + (5,6,11), (5,6,10), (4,7,8), (4,7,9), + (3,2,6), (3,2,7), (2,6,10), (2,10,9), (2,9,7), (3,6,11), (3,11,8), (3,8,7), + ], dtype=np.int32) + normals = -np.mean(verts, axis=0) + verts + normals /= np.linalg.norm(normals, axis=1).reshape(-1,1) + return verts, faces, normals + +def xyplane(dtype=np.float32, z=0, interleaved=False): + if interleaved: + eps = 1e-6 + verts = np.array([ + (-1,-1,z), (-1,1,z), (1,1,z), + (1-eps,1,z), (1-eps,-1,z), (-1-eps,-1,z)], dtype=dtype) + faces = np.array([(0,1,2), (3,4,5)], dtype=np.int32) + else: + verts = np.array([(-1,-1,z), (-1,1,z), (1,1,z), (1,-1,z)], dtype=dtype) + faces = np.array([(0,1,2), (0,2,3)], dtype=np.int32) + normals = np.zeros_like(verts) + normals[:,2] = -1 + return verts, faces, normals + +def mesh_independent_verts(verts, faces, normals=None): + new_verts = [] + new_normals = [] + for f in faces: + new_verts.append(verts[f[0]]) + new_verts.append(verts[f[1]]) + new_verts.append(verts[f[2]]) + if normals is not None: + new_normals.append(normals[f[0]]) + new_normals.append(normals[f[1]]) + new_normals.append(normals[f[2]]) + new_verts = np.array(new_verts) + new_faces = np.arange(0, faces.size, dtype=faces.dtype).reshape(-1,3) + if normals is None: + return new_verts, new_faces + else: + new_normals = np.array(new_normals) + return new_verts, new_faces, new_normals + + +def stack_mesh(verts, faces): + n_verts = 0 + mfaces = [] + for idx, f in enumerate(faces): + mfaces.append(f + n_verts) + n_verts += verts[idx].shape[0] + verts = np.vstack(verts) + faces = np.vstack(mfaces) + return verts, faces + +def normalize_mesh(verts): + # all the verts have unit distance to the center (0,0,0) + return verts / np.linalg.norm(verts, axis=1, keepdims=True) + + +def mesh_triangle_areas(verts, faces): + a = verts[faces[:,0]] + b = verts[faces[:,1]] + c = verts[faces[:,2]] + x = np.empty_like(a) + x = a - b + y = a - c + t = np.empty_like(a) + t[:,0] = (x[:,1] * y[:,2] - x[:,2] * y[:,1]); + t[:,1] = (x[:,2] * y[:,0] - x[:,0] * y[:,2]); + t[:,2] = (x[:,0] * y[:,1] - x[:,1] * y[:,0]); + return np.linalg.norm(t, axis=1) / 2 + +def subdivde_mesh(verts_in, faces_in, n=1): + for iter in range(n): + verts = [] + for v in verts_in: + verts.append(v) + faces = [] + verts_dict = {} + for f in faces_in: + f = np.sort(f) + i0,i1,i2 = f + v0,v1,v2 = verts_in[f] + + k = i0*len(verts_in)+i1 + if k in verts_dict: + i01 = verts_dict[k] + else: + i01 = len(verts) + verts_dict[k] = i01 + v01 = (v0 + v1) / 2 + verts.append(v01) + + k = i0*len(verts_in)+i2 + if k in verts_dict: + i02 = verts_dict[k] + else: + i02 = len(verts) + verts_dict[k] = i02 + v02 = (v0 + v2) / 2 + verts.append(v02) + + k = i1*len(verts_in)+i2 + if k in verts_dict: + i12 = verts_dict[k] + else: + i12 = len(verts) + verts_dict[k] = i12 + v12 = (v1 + v2) / 2 + verts.append(v12) + + faces.append((i0,i01,i02)) + faces.append((i01,i1,i12)) + faces.append((i12,i2,i02)) + faces.append((i01,i12,i02)) + + verts_in = np.array(verts, dtype=verts_in.dtype) + faces_in = np.array(faces, dtype=np.int32) + return verts_in, faces_in + + +def mesh_adjust_winding_order(verts, faces, normals): + n0 = normals[faces[:,0]] + n1 = normals[faces[:,1]] + n2 = normals[faces[:,2]] + fnormals = (n0 + n1 + n2) / 3 + + v0 = verts[faces[:,0]] + v1 = verts[faces[:,1]] + v2 = verts[faces[:,2]] + + e0 = v1 - v0 + e1 = v2 - v0 + fn = np.cross(e0, e1) + + dot = np.sum(fnormals * fn, axis=1) + ma = dot < 0 + + nfaces = faces.copy() + nfaces[ma,1], nfaces[ma,2] = nfaces[ma,2], nfaces[ma,1] + + return nfaces + + +def pcl_to_shapecl(verts, colors=None, shape='cube', width=1.0): + if shape == 'tetrahedron': + cverts, cfaces, _ = tetrahedron() + elif shape == 'cube': + cverts, cfaces, _ = cube() + elif shape == 'octahedron': + cverts, cfaces, _ = octahedron() + elif shape == 'icosahedron': + cverts, cfaces, _ = icosahedron() + else: + raise Exception('invalid shape') + + sverts = np.tile(cverts, (verts.shape[0], 1)) + sverts *= width + sverts += np.repeat(verts, cverts.shape[0], axis=0) + + sfaces = np.tile(cfaces, (verts.shape[0], 1)) + sfoffset = cverts.shape[0] * np.arange(0, verts.shape[0]) + sfaces += np.repeat(sfoffset, cfaces.shape[0]).reshape(-1,1) + + if colors is not None: + scolors = np.repeat(colors, cverts.shape[0], axis=0) + else: + scolors = None + + return sverts, sfaces, scolors diff --git a/co/gtimer.py b/co/gtimer.py new file mode 100644 index 0000000..5bad06b --- /dev/null +++ b/co/gtimer.py @@ -0,0 +1,32 @@ +import numpy as np + +from . import utils + +class StopWatch(utils.StopWatch): + def __del__(self): + print('='*80) + print('gtimer:') + total = ', '.join(['%s: %f[s]' % (k,v) for k,v in self.get(reduce=np.sum).items()]) + print(f' [total] {total}') + mean = ', '.join(['%s: %f[s]' % (k,v) for k,v in self.get(reduce=np.mean).items()]) + print(f' [mean] {mean}') + median = ', '.join(['%s: %f[s]' % (k,v) for k,v in self.get(reduce=np.median).items()]) + print(f' [median] {median}') + print('='*80) + +GTIMER = StopWatch() + +def start(name): + GTIMER.start(name) +def stop(name): + GTIMER.stop(name) + +class Ctx(object): + def __init__(self, name): + self.name = name + + def __enter__(self): + start(self.name) + + def __exit__(self, *args): + stop(self.name) diff --git a/co/io3d.py b/co/io3d.py new file mode 100644 index 0000000..54bc60a --- /dev/null +++ b/co/io3d.py @@ -0,0 +1,267 @@ +import struct +import numpy as np +import collections + +def _write_ply_point(fp, x,y,z, color=None, normal=None, binary=False): + args = [x,y,z] + if color is not None: + args += [int(color[0]), int(color[1]), int(color[2])] + if normal is not None: + args += [normal[0],normal[1],normal[2]] + if binary: + fmt = ' 1: + c = color[vidx] + else: + c = color[0] + else: + c = None + if normals is None: + n = None + else: + n = normals[vidx] + _write_ply_point(fp, v[0],v[1],v[2], c, n, binary) + + if trias is not None: + for t in trias: + _write_ply_triangle(fp, t[0],t[1],t[2], binary) + +def faces_to_triangles(faces): + new_faces = [] + for f in faces: + if f[0] == 3: + new_faces.append([f[1], f[2], f[3]]) + elif f[0] == 4: + new_faces.append([f[1], f[2], f[3]]) + new_faces.append([f[3], f[4], f[1]]) + else: + raise Exception('unknown face count %d', f[0]) + return new_faces + +def read_ply(path): + with open(path, 'rb') as f: + # parse header + line = f.readline().decode().strip() + if line != 'ply': + raise Exception('Header error') + n_verts = 0 + n_faces = 0 + vert_types = {} + vert_bin_format = [] + vert_bin_len = 0 + vert_bin_cols = 0 + line = f.readline().decode() + parse_vertex_prop = False + while line.strip() != 'end_header': + if 'format' in line: + if 'ascii' in line: + binary = False + elif 'binary_little_endian' in line: + binary = True + else: + raise Exception('invalid ply format') + if 'element face' in line: + splits = line.strip().split(' ') + n_faces = int(splits[-1]) + parse_vertex_prop = False + if 'element camera' in line: + parse_vertex_prop = False + if 'element vertex' in line: + splits = line.strip().split(' ') + n_verts = int(splits[-1]) + parse_vertex_prop = True + if parse_vertex_prop and 'property' in line: + prop = line.strip().split() + if prop[1] == 'float': + vert_bin_format.append('f4') + vert_bin_len += 4 + vert_bin_cols += 1 + elif prop[1] == 'uchar': + vert_bin_format.append('B') + vert_bin_len += 1 + vert_bin_cols += 1 + else: + raise Exception('invalid property') + vert_types[prop[2]] = len(vert_types) + line = f.readline().decode() + + # parse content + if binary: + sz = n_verts * vert_bin_len + fmt = ','.join(vert_bin_format) + verts = np.ndarray(shape=(1, n_verts), dtype=np.dtype(fmt), buffer=f.read(sz)) + verts = verts[0].astype(vert_bin_cols*'f4,').view(dtype='f4').reshape((n_verts,-1)) + faces = [] + for idx in range(n_faces): + fmt = '= 2 and len(parts[1]) > 0: + tidx = int(parts[1]) - 1 + else: + tidx = -1 + if len(parts) >= 3 and len(parts[2]) > 0: + nidx = int(parts[2]) - 1 + else: + nidx = -1 + return vidx, tidx, nidx + +def read_obj(path): + with open(path, 'r') as fp: + lines = fp.readlines() + + verts = [] + colors = [] + fnorms = [] + fnorm_map = collections.defaultdict(list) + faces = [] + for line in lines: + line = line.strip() + if line.startswith('#') or len(line) == 0: + continue + + parts = line.split() + if line.startswith('v '): + parts = parts[1:] + x,y,z = float(parts[0]), float(parts[1]), float(parts[2]) + if len(parts) == 4 or len(parts) == 7: + w = float(parts[3]) + x,y,z = x/w, y/w, z/w + verts.append((x,y,z)) + if len(parts) >= 6: + r,g,b = float(parts[-3]), float(parts[-2]), float(parts[-1]) + rgb.append((r,g,b)) + + elif line.startswith('vn '): + parts = parts[1:] + x,y,z = float(parts[0]), float(parts[1]), float(parts[2]) + fnorms.append((x,y,z)) + + elif line.startswith('f '): + parts = parts[1:] + if len(parts) != 3: + raise Exception('only triangle meshes supported atm') + vidx0, tidx0, nidx0 = _read_obj_split_f(parts[0]) + vidx1, tidx1, nidx1 = _read_obj_split_f(parts[1]) + vidx2, tidx2, nidx2 = _read_obj_split_f(parts[2]) + + faces.append((vidx0, vidx1, vidx2)) + if nidx0 >= 0: + fnorm_map[vidx0].append( nidx0 ) + if nidx1 >= 0: + fnorm_map[vidx1].append( nidx1 ) + if nidx2 >= 0: + fnorm_map[vidx2].append( nidx2 ) + + verts = np.array(verts) + colors = np.array(colors) + fnorms = np.array(fnorms) + faces = np.array(faces) + + # face normals to vertex normals + norms = np.zeros_like(verts) + for vidx in fnorm_map.keys(): + ind = fnorm_map[vidx] + norms[vidx] = fnorms[ind].sum(axis=0) + N = np.linalg.norm(norms, axis=1, keepdims=True) + np.divide(norms, N, out=norms, where=N != 0) + + return verts, faces, colors, norms diff --git a/co/metric.py b/co/metric.py new file mode 100644 index 0000000..26061da --- /dev/null +++ b/co/metric.py @@ -0,0 +1,248 @@ +import numpy as np +from . import geometry + +def _process_inputs(estimate, target, mask): + if estimate.shape != target.shape: + raise Exception('estimate and target have to be same shape') + if mask is None: + mask = np.ones(estimate.shape, dtype=np.bool) + else: + mask = mask != 0 + if estimate.shape != mask.shape: + raise Exception('estimate and mask have to be same shape') + return estimate, target, mask + +def mse(estimate, target, mask=None): + estimate, target, mask = _process_inputs(estimate, target, mask) + m = np.sum((estimate[mask] - target[mask])**2) / mask.sum() + return m + +def rmse(estimate, target, mask=None): + return np.sqrt(mse(estimate, target, mask)) + +def mae(estimate, target, mask=None): + estimate, target, mask = _process_inputs(estimate, target, mask) + m = np.abs(estimate[mask] - target[mask]).sum() / mask.sum() + return m + +def outlier_fraction(estimate, target, mask=None, threshold=0): + estimate, target, mask = _process_inputs(estimate, target, mask) + diff = np.abs(estimate[mask] - target[mask]) + m = (diff > threshold).sum() / mask.sum() + return m + + +class Metric(object): + def __init__(self, str_prefix=''): + self.str_prefix = str_prefix + self.reset() + + def reset(self): + pass + + def add(self, es, ta, ma=None): + pass + + def get(self): + return {} + + def items(self): + return self.get().items() + + def __str__(self): + return ', '.join([f'{self.str_prefix}{key}={value:.5f}' for key, value in self.get().items()]) + +class MultipleMetric(Metric): + def __init__(self, *metrics, **kwargs): + self.metrics = [*metrics] + super().__init__(**kwargs) + + def reset(self): + for m in self.metrics: + m.reset() + + def add(self, es, ta, ma=None): + for m in self.metrics: + m.add(es, ta, ma) + + def get(self): + ret = {} + for m in self.metrics: + vals = m.get() + for k in vals: + ret[k] = vals[k] + return ret + + def __str__(self): + return '\n'.join([str(m) for m in self.metrics]) + +class BaseDistanceMetric(Metric): + def __init__(self, name='', **kwargs): + super().__init__(**kwargs) + self.name = name + + def reset(self): + self.dists = [] + + def add(self, es, ta, ma=None): + pass + + def get(self): + dists = np.hstack(self.dists) + return { + f'dist{self.name}_mean': float(np.mean(dists)), + f'dist{self.name}_std': float(np.std(dists)), + f'dist{self.name}_median': float(np.median(dists)), + f'dist{self.name}_q10': float(np.percentile(dists, 10)), + f'dist{self.name}_q90': float(np.percentile(dists, 90)), + f'dist{self.name}_min': float(np.min(dists)), + f'dist{self.name}_max': float(np.max(dists)), + } + +class DistanceMetric(BaseDistanceMetric): + def __init__(self, vec_length, p=2, **kwargs): + super().__init__(name=f'{p}', **kwargs) + self.vec_length = vec_length + self.p = p + + def add(self, es, ta, ma=None): + if es.shape != ta.shape or es.shape[1] != self.vec_length or es.ndim != 2: + print(es.shape, ta.shape) + raise Exception('es and ta have to be of shape Nxdim') + if ma is not None: + es = es[ma != 0] + ta = ta[ma != 0] + dist = np.linalg.norm(es - ta, ord=self.p, axis=1) + self.dists.append( dist ) + +class OutlierFractionMetric(DistanceMetric): + def __init__(self, thresholds, *args, **kwargs): + super().__init__(*args, **kwargs) + self.thresholds = thresholds + + def get(self): + dists = np.hstack(self.dists) + ret = {} + for t in self.thresholds: + ma = dists > t + ret[f'of{t}'] = float(ma.sum() / ma.size) + return ret + +class RelativeDistanceMetric(BaseDistanceMetric): + def __init__(self, vec_length, p=2, **kwargs): + super().__init__(name=f'rel{p}', **kwargs) + self.vec_length = vec_length + self.p = p + + def add(self, es, ta, ma=None): + if es.shape != ta.shape or es.shape[1] != self.vec_length or es.ndim != 2: + raise Exception('es and ta have to be of shape Nxdim') + dist = np.linalg.norm(es - ta, ord=self.p, axis=1) + denom = np.linalg.norm(ta, ord=self.p, axis=1) + dist /= denom + if ma is not None: + dist = dist[ma != 0] + self.dists.append( dist ) + +class RotmDistanceMetric(BaseDistanceMetric): + def __init__(self, type='identity', **kwargs): + super().__init__(name=type, **kwargs) + self.type = type + + def add(self, es, ta, ma=None): + if es.shape != ta.shape or es.shape[1] != 3 or es.shape[2] != 3 or es.ndim != 3: + print(es.shape, ta.shape) + raise Exception('es and ta have to be of shape Nx3x3') + if ma is not None: + raise Exception('mask is not implemented') + if self.type == 'identity': + self.dists.append( geometry.rotm_distance_identity(es, ta) ) + elif self.type == 'geodesic': + self.dists.append( geometry.rotm_distance_geodesic_unit_sphere(es, ta) ) + else: + raise Exception('invalid distance type') + +class QuaternionDistanceMetric(BaseDistanceMetric): + def __init__(self, type='angle', **kwargs): + super().__init__(name=type, **kwargs) + self.type = type + + def add(self, es, ta, ma=None): + if es.shape != ta.shape or es.shape[1] != 4 or es.ndim != 2: + print(es.shape, ta.shape) + raise Exception('es and ta have to be of shape Nx4') + if ma is not None: + raise Exception('mask is not implemented') + if self.type == 'angle': + self.dists.append( geometry.quat_distance_angle(es, ta) ) + elif self.type == 'mineucl': + self.dists.append( geometry.quat_distance_mineucl(es, ta) ) + elif self.type == 'normdiff': + self.dists.append( geometry.quat_distance_normdiff(es, ta) ) + else: + raise Exception('invalid distance type') + + +class BinaryAccuracyMetric(Metric): + def __init__(self, thresholds=np.linspace(0.0, 1.0, num=101, dtype=np.float64)[:-1], **kwargs): + self.thresholds = thresholds + super().__init__(**kwargs) + + def reset(self): + self.tps = [0 for wp in self.thresholds] + self.fps = [0 for wp in self.thresholds] + self.fns = [0 for wp in self.thresholds] + self.tns = [0 for wp in self.thresholds] + self.n_pos = 0 + self.n_neg = 0 + + def add(self, es, ta, ma=None): + if ma is not None: + raise Exception('mask is not implemented') + es = es.ravel() + ta = ta.ravel() + if es.shape[0] != ta.shape[0]: + raise Exception('invalid shape of es, or ta') + if es.min() < 0 or es.max() > 1: + raise Exception('estimate has wrong value range') + ta_p = (ta == 1) + ta_n = (ta == 0) + es_p = es[ta_p] + es_n = es[ta_n] + for idx, wp in enumerate(self.thresholds): + wp = np.asscalar(wp) + self.tps[idx] += (es_p > wp).sum() + self.fps[idx] += (es_n > wp).sum() + self.fns[idx] += (es_p <= wp).sum() + self.tns[idx] += (es_n <= wp).sum() + self.n_pos += ta_p.sum() + self.n_neg += ta_n.sum() + + def get(self): + tps = np.array(self.tps).astype(np.float32) + fps = np.array(self.fps).astype(np.float32) + fns = np.array(self.fns).astype(np.float32) + tns = np.array(self.tns).astype(np.float32) + wp = self.thresholds + + ret = {} + + precisions = np.divide(tps, tps + fps, out=np.zeros_like(tps), where=tps + fps != 0) + recalls = np.divide(tps, tps + fns, out=np.zeros_like(tps), where=tps + fns != 0) # tprs + fprs = np.divide(fps, fps + tns, out=np.zeros_like(tps), where=fps + tns != 0) + + precisions = np.r_[0, precisions, 1] + recalls = np.r_[1, recalls, 0] + fprs = np.r_[1, fprs, 0] + + ret['auc'] = float(-np.trapz(recalls, fprs)) + ret['prauc'] = float(-np.trapz(precisions, recalls)) + ret['ap'] = float(-(np.diff(recalls) * precisions[:-1]).sum()) + + accuracies = np.divide(tps + tns, tps + tns + fps + fns) + aacc = np.mean(accuracies) + for t in np.linspace(0,1,num=11)[1:-1]: + idx = np.argmin(np.abs(t - wp)) + ret[f'acc{wp[idx]:.2f}'] = float(accuracies[idx]) + + return ret diff --git a/co/plt.py b/co/plt.py new file mode 100644 index 0000000..200ba25 --- /dev/null +++ b/co/plt.py @@ -0,0 +1,99 @@ +import numpy as np +import matplotlib as mpl +from matplotlib import _pylab_helpers +from matplotlib.rcsetup import interactive_bk as _interactive_bk +import matplotlib.pyplot as plt +import os +import time + +def save(path, remove_axis=False, dpi=300, fig=None): + if fig is None: + fig = plt.gcf() + dirname = os.path.dirname(path) + if dirname != '' and not os.path.exists(dirname): + os.makedirs(dirname) + if remove_axis: + for ax in fig.axes: + ax.axis('off') + ax.margins(0,0) + fig.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0) + for ax in fig.axes: + ax.xaxis.set_major_locator(plt.NullLocator()) + ax.yaxis.set_major_locator(plt.NullLocator()) + fig.savefig(path, dpi=dpi, bbox_inches='tight', pad_inches=0) + +def color_map(im_, cmap='viridis', vmin=None, vmax=None): + cm = plt.get_cmap(cmap) + im = im_.copy() + if vmin is None: + vmin = np.nanmin(im) + if vmax is None: + vmax = np.nanmax(im) + mask = np.logical_not(np.isfinite(im)) + im[mask] = vmin + im = (im.clip(vmin, vmax) - vmin) / (vmax - vmin) + im = cm(im) + im = im[...,:3] + for c in range(3): + im[mask, c] = 1 + return im + +def interactive_legend(leg=None, fig=None, all_axes=True): + if leg is None: + leg = plt.legend() + if fig is None: + fig = plt.gcf() + if all_axes: + axs = fig.get_axes() + else: + axs = [fig.gca()] + + # lined = dict() + # lines = ax.lines + # for legline, origline in zip(leg.get_lines(), ax.lines): + # legline.set_picker(5) + # lined[legline] = origline + lined = dict() + for lidx, legline in enumerate(leg.get_lines()): + legline.set_picker(5) + lined[legline] = [ax.lines[lidx] for ax in axs] + + def onpick(event): + if event.mouseevent.dblclick: + tmp = [(k,v) for k,v in lined.items()] + else: + tmp = [(event.artist, lined[event.artist])] + + for legline, origline in tmp: + for ol in origline: + vis = not ol.get_visible() + ol.set_visible(vis) + if vis: + legline.set_alpha(1.0) + else: + legline.set_alpha(0.2) + fig.canvas.draw() + + fig.canvas.mpl_connect('pick_event', onpick) + +def non_annoying_pause(interval, focus_figure=False): + # https://github.com/matplotlib/matplotlib/issues/11131 + backend = mpl.rcParams['backend'] + if backend in _interactive_bk: + figManager = _pylab_helpers.Gcf.get_active() + if figManager is not None: + canvas = figManager.canvas + if canvas.figure.stale: + canvas.draw() + if focus_figure: + plt.show(block=False) + canvas.start_event_loop(interval) + return + time.sleep(interval) + +def remove_all_ticks(fig=None): + if fig is None: + fig = plt.gcf() + for ax in fig.axes: + ax.axes.get_xaxis().set_visible(False) + ax.axes.get_yaxis().set_visible(False) diff --git a/co/plt2d.py b/co/plt2d.py new file mode 100644 index 0000000..de38aa1 --- /dev/null +++ b/co/plt2d.py @@ -0,0 +1,57 @@ +import numpy as np +import matplotlib.pyplot as plt + +from . import geometry + +def image_matrix(ims, bgval=0): + n = ims.shape[0] + m = int( np.ceil(np.sqrt(n)) ) + h = ims.shape[1] + w = ims.shape[2] + mat = np.empty((m*h, m*w), dtype=ims.dtype) + mat.fill(bgval) + idx = 0 + for r in range(m): + for c in range(m): + if idx < n: + mat[r*h:(r+1)*h, c*w:(c+1)*w] = ims[idx] + idx += 1 + return mat + +def image_cat(ims, vertical=False): + offx = [0] + offy = [0] + if vertical: + width = max([im.shape[1] for im in ims]) + offx += [0 for im in ims[:-1]] + offy += [im.shape[0] for im in ims[:-1]] + height = sum([im.shape[0] for im in ims]) + else: + height = max([im.shape[0] for im in ims]) + offx += [im.shape[1] for im in ims[:-1]] + offy += [0 for im in ims[:-1]] + width = sum([im.shape[1] for im in ims]) + offx = np.cumsum(offx) + offy = np.cumsum(offy) + + im = np.zeros((height,width,*ims[0].shape[2:]), dtype=ims[0].dtype) + for im0, ox, oy in zip(ims, offx, offy): + im[oy:oy + im0.shape[0], ox:ox + im0.shape[1]] = im0 + + return im, offx, offy + +def line(li, h, w, ax=None, *args, **kwargs): + if ax is None: + ax = plt.gca() + xs = (-li[2] - li[1] * np.array((0, h-1))) / li[0] + ys = (-li[2] - li[0] * np.array((0, w-1))) / li[1] + pts = np.array([(0,ys[0]), (w-1, ys[1]), (xs[0], 0), (xs[1], h-1)]) + pts = pts[np.logical_and(np.logical_and(pts[:,0] >= 0, pts[:,0] < w), np.logical_and(pts[:,1] >= 0, pts[:,1] < h))] + ax.plot(pts[:,0], pts[:,1], *args, **kwargs) + +def depthshow(depth, *args, ax=None, **kwargs): + if ax is None: + ax = plt.gca() + d = depth.copy() + d[d < 0] = np.NaN + ax.imshow(d, *args, **kwargs) diff --git a/co/plt3d.py b/co/plt3d.py new file mode 100644 index 0000000..8c3eb1c --- /dev/null +++ b/co/plt3d.py @@ -0,0 +1,38 @@ +import numpy as np +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D + +from . import geometry + +def ax3d(fig=None): + if fig is None: + fig = plt.gcf() + return fig.add_subplot(111, projection='3d') + +def plot_camera(ax=None, R=np.eye(3), t=np.zeros((3,)), size=25, marker_C='.', color='b', linestyle='-', linewidth=0.1, label=None, **kwargs): + if ax is None: + ax = plt.gca() + C0 = geometry.translation_to_cameracenter(R, t).ravel() + C1 = C0 + R.T.dot( np.array([[-size],[-size],[3*size]], dtype=np.float32) ).ravel() + C2 = C0 + R.T.dot( np.array([[-size],[+size],[3*size]], dtype=np.float32) ).ravel() + C3 = C0 + R.T.dot( np.array([[+size],[+size],[3*size]], dtype=np.float32) ).ravel() + C4 = C0 + R.T.dot( np.array([[+size],[-size],[3*size]], dtype=np.float32) ).ravel() + + if marker_C != '': + ax.plot([C0[0]], [C0[1]], [C0[2]], marker=marker_C, color=color, label=label, **kwargs) + ax.plot([C0[0], C1[0]], [C0[1], C1[1]], [C0[2], C1[2]], color=color, label='_nolegend_', linestyle=linestyle, linewidth=linewidth, **kwargs) + ax.plot([C0[0], C2[0]], [C0[1], C2[1]], [C0[2], C2[2]], color=color, label='_nolegend_', linestyle=linestyle, linewidth=linewidth, **kwargs) + ax.plot([C0[0], C3[0]], [C0[1], C3[1]], [C0[2], C3[2]], color=color, label='_nolegend_', linestyle=linestyle, linewidth=linewidth, **kwargs) + ax.plot([C0[0], C4[0]], [C0[1], C4[1]], [C0[2], C4[2]], color=color, label='_nolegend_', linestyle=linestyle, linewidth=linewidth, **kwargs) + ax.plot([C1[0], C2[0], C3[0], C4[0], C1[0]], [C1[1], C2[1], C3[1], C4[1], C1[1]], [C1[2], C2[2], C3[2], C4[2], C1[2]], color=color, label='_nolegend_', linestyle=linestyle, linewidth=linewidth, **kwargs) + +def axis_equal(ax=None): + if ax is None: + ax = plt.gca() + extents = np.array([getattr(ax, 'get_{}lim'.format(dim))() for dim in 'xyz']) + sz = extents[:,1] - extents[:,0] + centers = np.mean(extents, axis=1) + maxsize = max(abs(sz)) + r = maxsize/2 + for ctr, dim in zip(centers, 'xyz'): + getattr(ax, 'set_{}lim'.format(dim))(ctr - r, ctr + r) diff --git a/co/table.py b/co/table.py new file mode 100644 index 0000000..7b45aae --- /dev/null +++ b/co/table.py @@ -0,0 +1,445 @@ +import numpy as np +import pandas as pd +import enum +import itertools + +class Table(object): + def __init__(self, n_cols): + self.n_cols = n_cols + self.rows = [] + self.aligns = ['r' for c in range(n_cols)] + + def get_cell_align(self, r, c): + align = self.rows[r].cells[c].align + if align is None: + return self.aligns[c] + else: + return align + + def add_row(self, row): + if row.ncols() != self.n_cols: + raise Exception(f'row has invalid number of cols, {row.ncols()} vs. {self.n_cols}') + self.rows.append(row) + + def empty_row(self): + return Row.Empty(self.n_cols) + + def expand_rows(self, n_add_cols=1): + if n_add_cols < 0: raise Exception('n_add_cols has to be positive') + self.n_cols += n_add_cols + for row in self.rows: + row.cells.extend([Cell() for cidx in range(n_add_cols)]) + + def add_block(self, data, row=-1, col=0, fmt=None, expand=False): + if row < 0: row = len(self.rows) + while len(self.rows) < row + len(data): + self.add_row(self.empty_row()) + for r in range(len(data)): + cols = data[r] + if col + len(cols) > self.n_cols: + if expand: + self.expand_rows(col + len(cols) - self.n_cols) + else: + raise Exception('number of cols does not fit in table') + for c in range(len(cols)): + self.rows[row+r].cells[col+c] = Cell(data[r][c], fmt) + +class Row(object): + def __init__(self, cells, pre_separator=None, post_separator=None): + self.cells = cells + self.pre_separator = pre_separator + self.post_separator = post_separator + + @classmethod + def Empty(cls, n_cols): + return Row([Cell() for c in range(n_cols)]) + + def add_cell(self, cell): + self.cells.append(cell) + + def ncols(self): + return sum([c.span for c in self.cells]) + + + +class Color(object): + def __init__(self, color=(0,0,0), fmt='rgb'): + if fmt == 'rgb': + self.color = color + elif fmt == 'RGB': + self.color = tuple(c / 255 for c in color) + else: + return Exception('invalid color format') + + def as_rgb(self): + return self.color + + def as_RGB(self): + return tuple(int(c * 255) for c in self.color) + + @classmethod + def rgb(cls, r, g, b): + return Color(color=(r,g,b), fmt='rgb') + + @classmethod + def RGB(cls, r, g, b): + return Color(color=(r,g,b), fmt='RGB') + + +class CellFormat(object): + def __init__(self, fmt='%s', fgcolor=None, bgcolor=None, bold=False): + self.fmt = fmt + self.fgcolor = fgcolor + self.bgcolor = bgcolor + self.bold = bold + +class Cell(object): + def __init__(self, data=None, fmt=None, span=1, align=None): + self.data = data + if fmt is None: + fmt = CellFormat() + self.fmt = fmt + self.span = span + self.align = align + + def __str__(self): + return self.fmt.fmt % self.data + +class Separator(enum.Enum): + HEAD = 1 + BOTTOM = 2 + INNER = 3 + + +class Renderer(object): + def __init__(self): + pass + + def cell_str_len(self, cell): + return len(str(cell)) + + def col_widths(self, table): + widths = [0 for c in range(table.n_cols)] + for row in table.rows: + cidx = 0 + for cell in row.cells: + if cell.span == 1: + strlen = self.cell_str_len(cell) + widths[cidx] = max(widths[cidx], strlen) + cidx += cell.span + return widths + + def render(self, table): + raise NotImplementedError('not implemented') + + def __call__(self, table): + return self.render(table) + + def render_to_file_comment(self): + return '' + + def render_to_file(self, path, table): + txt = self.render(table) + with open(path, 'w') as fp: + fp.write(txt) + +class TerminalRenderer(Renderer): + def __init__(self, col_sep=' '): + super().__init__() + self.col_sep = col_sep + + def render_cell(self, table, row, col, widths): + cell = table.rows[row].cells[col] + str = cell.fmt.fmt % cell.data + str_width = len(str) + cell_width = sum([widths[idx] for idx in range(col, col+cell.span)]) + cell_width += len(self.col_sep) * (cell.span - 1) + if len(str) > cell_width: + str = str[:cell_width] + if cell.fmt.bold: + # str = sty.ef.bold + str + sty.rs.bold_dim + # str = sty.ef.bold + str + sty.rs.bold + pass + if cell.fmt.fgcolor is not None: + # color = cell.fmt.fgcolor.as_RGB() + # str = sty.fg(*color) + str + sty.rs.fg + pass + if str_width < cell_width: + n_ws = (cell_width - str_width) + if table.get_cell_align(row, col) == 'r': + str = ' '*n_ws + str + elif table.get_cell_align(row, col) == 'l': + str = str + ' '*n_ws + elif table.get_cell_align(row, col) == 'c': + n_ws1 = n_ws // 2 + n_ws0 = n_ws - n_ws1 + str = ' '*n_ws0 + str + ' '*n_ws1 + if cell.fmt.bgcolor is not None: + # color = cell.fmt.bgcolor.as_RGB() + # str = sty.bg(*color) + str + sty.rs.bg + pass + return str + + def render_separator(self, separator, tab, col_widths, total_width): + if separator == Separator.HEAD: + return '='*total_width + elif separator == Separator.INNER: + return '-'*total_width + elif separator == Separator.BOTTOM: + return '='*total_width + + def render(self, table): + widths = self.col_widths(table) + total_width = sum(widths) + len(self.col_sep) * (table.n_cols - 1) + lines = [] + for ridx, row in enumerate(table.rows): + if row.pre_separator is not None: + sepline = self.render_separator(row.pre_separator, table, widths, total_width) + if len(sepline) > 0: + lines.append(sepline) + line = [] + for cidx, cell in enumerate(row.cells): + line.append(self.render_cell(table, ridx, cidx, widths)) + lines.append(self.col_sep.join(line)) + if row.post_separator is not None: + sepline = self.render_separator(row.post_separator, table, widths, total_width) + if len(sepline) > 0: + lines.append(sepline) + return '\n'.join(lines) + +class MarkdownRenderer(TerminalRenderer): + def __init__(self): + super().__init__(col_sep='|') + self.printed_color_warning = False + + def print_color_warning(self): + if not self.printed_color_warning: + print('[WARNING] MarkdownRenderer does not support color yet') + self.printed_color_warning = True + + def cell_str_len(self, cell): + strlen = len(str(cell)) + if cell.fmt.bold: + strlen += 4 + strlen = max(5, strlen) + return strlen + + def render_cell(self, table, row, col, widths): + cell = table.rows[row].cells[col] + str = cell.fmt.fmt % cell.data + if cell.fmt.bold: + str = f'**{str}**' + + str_width = len(str) + cell_width = sum([widths[idx] for idx in range(col, col+cell.span)]) + cell_width += len(self.col_sep) * (cell.span - 1) + if len(str) > cell_width: + str = str[:cell_width] + else: + n_ws = (cell_width - str_width) + if table.get_cell_align(row, col) == 'r': + str = ' '*n_ws + str + elif table.get_cell_align(row, col) == 'l': + str = str + ' '*n_ws + elif table.get_cell_align(row, col) == 'c': + n_ws1 = n_ws // 2 + n_ws0 = n_ws - n_ws1 + str = ' '*n_ws0 + str + ' '*n_ws1 + + if col == 0: str = self.col_sep + str + if col == table.n_cols - 1: str += self.col_sep + + if cell.fmt.fgcolor is not None: + self.print_color_warning() + if cell.fmt.bgcolor is not None: + self.print_color_warning() + return str + + def render_separator(self, separator, tab, widths, total_width): + sep = '' + if separator == Separator.INNER: + sep = self.col_sep + for idx, width in enumerate(widths): + csep = '-' * (width - 2) + if tab.get_cell_align(1, idx) == 'r': + csep = '-' + csep + ':' + elif tab.get_cell_align(1, idx) == 'l': + csep = ':' + csep + '-' + elif tab.get_cell_align(1, idx) == 'c': + csep = ':' + csep + ':' + sep += csep + self.col_sep + return sep + + +class LatexRenderer(Renderer): + def __init__(self): + super().__init__() + + def render_cell(self, table, row, col): + cell = table.rows[row].cells[col] + str = cell.fmt.fmt % cell.data + if cell.fmt.bold: + str = '{\\bf '+ str + '}' + if cell.fmt.fgcolor is not None: + color = cell.fmt.fgcolor.as_rgb() + str = f'{{\\color[rgb]{{{color[0]},{color[1]},{color[2]}}} ' + str + '}' + if cell.fmt.bgcolor is not None: + color = cell.fmt.bgcolor.as_rgb() + str = f'\\cellcolor[rgb]{{{color[0]},{color[1]},{color[2]}}} ' + str + align = table.get_cell_align(row, col) + if cell.span != 1 or align != table.aligns[col]: + str = f'\\multicolumn{{{cell.span}}}{{{align}}}{{{str}}}' + return str + + def render_separator(self, separator): + if separator == Separator.HEAD: + return '\\toprule' + elif separator == Separator.INNER: + return '\\midrule' + elif separator == Separator.BOTTOM: + return '\\bottomrule' + + def render(self, table): + lines = ['\\begin{tabular}{' + ''.join(table.aligns) + '}'] + for ridx, row in enumerate(table.rows): + if row.pre_separator is not None: + lines.append(self.render_separator(row.pre_separator)) + line = [] + for cidx, cell in enumerate(row.cells): + line.append(self.render_cell(table, ridx, cidx)) + lines.append(' & '.join(line) + ' \\\\') + if row.post_separator is not None: + lines.append(self.render_separator(row.post_separator)) + lines.append('\\end{tabular}') + return '\n'.join(lines) + +class HtmlRenderer(Renderer): + def __init__(self, html_class='result_table'): + super().__init__() + self.html_class = html_class + + def render_cell(self, table, row, col): + cell = table.rows[row].cells[col] + str = cell.fmt.fmt % cell.data + styles = [] + if cell.fmt.bold: + styles.append('font-weight: bold;') + if cell.fmt.fgcolor is not None: + color = cell.fmt.fgcolor.as_RGB() + styles.append(f'color: rgb({color[0]},{color[1]},{color[2]});') + if cell.fmt.bgcolor is not None: + color = cell.fmt.bgcolor.as_RGB() + styles.append(f'background-color: rgb({color[0]},{color[1]},{color[2]});') + align = table.get_cell_align(row, col) + if align == 'l': align = 'left' + elif align == 'r': align = 'right' + elif align == 'c': align = 'center' + else: raise Exception('invalid align') + styles.append(f'text-align: {align};') + row = table.rows[row] + if row.pre_separator is not None: + styles.append(f'border-top: {self.render_separator(row.pre_separator)};') + if row.post_separator is not None: + styles.append(f'border-bottom: {self.render_separator(row.post_separator)};') + style = ' '.join(styles) + str = f' {str}\n' + return str + + def render_separator(self, separator): + if separator == Separator.HEAD: + return '1.5pt solid black' + elif separator == Separator.INNER: + return '0.75pt solid black' + elif separator == Separator.BOTTOM: + return '1.5pt solid black' + + def render(self, table): + lines = [f''] + for ridx, row in enumerate(table.rows): + line = [f' \n'] + for cidx, cell in enumerate(row.cells): + line.append(self.render_cell(table, ridx, cidx)) + line.append(' \n') + lines.append(' '.join(line)) + lines.append('
') + return '\n'.join(lines) + + +def pandas_to_table(rowname, colname, valname, data, val_cell_fmt=CellFormat(fmt='%.4f'), best_val_cell_fmt=CellFormat(fmt='%.4f', bold=True), best_is_max=[]): + rnames = data[rowname].unique() + cnames = data[colname].unique() + tab = Table(1+len(cnames)) + + header = [Cell('', align='r')] + header.extend([Cell(h, align='r') for h in cnames]) + header = Row(header, pre_separator=Separator.HEAD, post_separator=Separator.INNER) + tab.add_row(header) + + for rname in rnames: + cells = [Cell(rname, align='l')] + for cname in cnames: + cdata = data[data[colname] == cname] + if cname in best_is_max: + bestval = cdata[valname].max() + val = cdata[cdata[rowname] == rname][valname].max() + else: + bestval = cdata[valname].min() + val = cdata[cdata[rowname] == rname][valname].min() + if val == bestval: + fmt = best_val_cell_fmt + else: + fmt = val_cell_fmt + cells.append(Cell(val, align='r', fmt=fmt)) + tab.add_row(Row(cells)) + tab.rows[-1].post_separator = Separator.BOTTOM + return tab + + + +if __name__ == '__main__': + # df = pd.read_pickle('full.df') + # best_is_max = ['movF0.5', 'movF1.0'] + # tab = pandas_to_table(rowname='method', colname='metric', valname='val', data=df, best_is_max=best_is_max) + + # renderer = TerminalRenderer() + # print(renderer(tab)) + + tab = Table(7) + # header = Row([Cell('header', span=7, align='c')], pre_separator=Separator.HEAD, post_separator=Separator.INNER) + # tab.add_row(header) + # header2 = Row([Cell('thisisaverylongheader', span=4, align='c'), Cell('vals2', span=3, align='c')], post_separator=Separator.INNER) + # tab.add_row(header2) + tab.add_row(Row([Cell(f'c{c}') for c in range(7)])) + tab.rows[-1].post_separator = Separator.INNER + tab.add_block(np.arange(15*7).reshape(15,7)) + tab.rows[4].cells[2].fmt = CellFormat(bold=True) + tab.rows[2].cells[1].fmt = CellFormat(fgcolor=Color.rgb(0.2,0.6,0.1)) + tab.rows[2].cells[2].fmt = CellFormat(bgcolor=Color.rgb(0.7,0.1,0.5)) + tab.rows[5].cells[3].fmt = CellFormat(bold=True,bgcolor=Color.rgb(0.7,0.1,0.5),fgcolor=Color.rgb(0.1,0.1,0.1)) + tab.rows[-1].post_separator = Separator.BOTTOM + + renderer = TerminalRenderer() + print(renderer(tab)) + renderer = MarkdownRenderer() + print(renderer(tab)) + + # renderer = HtmlRenderer() + # html_tab = renderer(tab) + # print(html_tab) + # with open('test.html', 'w') as fp: + # fp.write(html_tab) + + # import latex + + # renderer = LatexRenderer() + # ltx_tab = renderer(tab) + # print(ltx_tab) + + # with open('test.tex', 'w') as fp: + # latex.write_doc_prefix(fp, document_class='article') + # fp.write('this is text that should appear before the table and should be long enough to wrap around.\n'*40) + # fp.write('\\begin{table}') + # fp.write(ltx_tab) + # fp.write('\\end{table}') + # fp.write('this is text that should appear after the table and should be long enough to wrap around.\n'*40) + # latex.write_doc_suffix(fp) diff --git a/co/utils.py b/co/utils.py new file mode 100644 index 0000000..c113e72 --- /dev/null +++ b/co/utils.py @@ -0,0 +1,86 @@ +import numpy as np +import pandas as pd +import time +from collections import OrderedDict +import argparse +import os +import re +import pickle +import subprocess + +def str2bool(v): + if v.lower() in ('yes', 'true', 't', 'y', '1'): + return True + elif v.lower() in ('no', 'false', 'f', 'n', '0'): + return False + else: + raise argparse.ArgumentTypeError('Boolean value expected.') + +class StopWatch(object): + def __init__(self): + self.timings = OrderedDict() + self.starts = {} + + def start(self, name): + self.starts[name] = time.time() + + def stop(self, name): + if name not in self.timings: + self.timings[name] = [] + self.timings[name].append(time.time() - self.starts[name]) + + def get(self, name=None, reduce=np.sum): + if name is not None: + return reduce(self.timings[name]) + else: + ret = {} + for k in self.timings: + ret[k] = reduce(self.timings[k]) + return ret + + def __repr__(self): + return ', '.join(['%s: %f[s]' % (k,v) for k,v in self.get().items()]) + def __str__(self): + return ', '.join(['%s: %f[s]' % (k,v) for k,v in self.get().items()]) + +class ETA(object): + def __init__(self, length): + self.length = length + self.start_time = time.time() + self.current_idx = 0 + self.current_time = time.time() + + def update(self, idx): + self.current_idx = idx + self.current_time = time.time() + + def get_elapsed_time(self): + return self.current_time - self.start_time + + def get_item_time(self): + return self.get_elapsed_time() / (self.current_idx + 1) + + def get_remaining_time(self): + return self.get_item_time() * (self.length - self.current_idx + 1) + + def format_time(self, seconds): + minutes, seconds = divmod(seconds, 60) + hours, minutes = divmod(minutes, 60) + hours = int(hours) + minutes = int(minutes) + return f'{hours:02d}:{minutes:02d}:{seconds:05.2f}' + + def get_elapsed_time_str(self): + return self.format_time(self.get_elapsed_time()) + + def get_remaining_time_str(self): + return self.format_time(self.get_remaining_time()) + +def git_hash(cwd=None): + ret = subprocess.run(['git', 'describe', '--always'], cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) + hash = ret.stdout + if hash is not None and 'fatal' not in hash.decode(): + return hash.decode().strip() + else: + return None + diff --git a/config.json b/config.json new file mode 100644 index 0000000..a3aeebd --- /dev/null +++ b/config.json @@ -0,0 +1,5 @@ +{ + "CUDA_LIBRARY_DIR": "/usr/local/cuda/lib64", + "DATA_ROOT": "/path/to/where/to/store/data/", + "SHAPENET_ROOT": "/path/to/shapenet/dataset" +} diff --git a/create_syn_data.sh b/create_syn_data.sh new file mode 100755 index 0000000..356514d --- /dev/null +++ b/create_syn_data.sh @@ -0,0 +1,7 @@ +#!/bin/bash + +cd data/lcn +python setup.py build_ext --inplace + +cd ../ +python create_syn_data.py diff --git a/data/__init__.py b/data/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/data/commons.py b/data/commons.py new file mode 100644 index 0000000..da88ca9 --- /dev/null +++ b/data/commons.py @@ -0,0 +1,110 @@ +import co +import numpy as np +import cv2 + + +def get_patterns(path='syn', imsizes=[], crop=True): + pattern_size = imsizes[0] + if path == 'syn': + np.random.seed(42) + pattern = np.random.uniform(0,1, size=pattern_size) + pattern = (pattern < 0.1).astype(np.float32) + pattern.reshape(*imsizes[0]) + else: + pattern = cv2.imread(path) + pattern = pattern.astype(np.float32) + pattern /= 255 + + if pattern.ndim == 2: + pattern = np.stack([pattern for idx in range(3)], axis=2) + + if crop and pattern.shape[0] > pattern_size[0] and pattern.shape[1] > pattern_size[1]: + r0 = (pattern.shape[0] - pattern_size[0]) // 2 + c0 = (pattern.shape[1] - pattern_size[1]) // 2 + pattern = pattern[r0:r0+imsizes[0][0], c0:c0+imsizes[0][1]] + + patterns = [] + for imsize in imsizes: + pat = cv2.resize(pattern, (imsize[1],imsize[0]), interpolation=cv2.INTER_LINEAR) + patterns.append(pat) + + return patterns + +def get_rotation_matrix(v0, v1): + v0 = v0/np.linalg.norm(v0) + v1 = v1/np.linalg.norm(v1) + v = np.cross(v0,v1) + c = np.dot(v0,v1) + s = np.linalg.norm(v) + I = np.eye(3) + vXStr = '{} {} {}; {} {} {}; {} {} {}'.format(0, -v[2], v[1], v[2], 0, -v[0], -v[1], v[0], 0) + k = np.matrix(vXStr) + r = I + k + k @ k * ((1 -c)/(s**2)) + return np.asarray(r.astype(np.float32)) + + +def augment_image(img,rng,disp=None,grad=None,max_shift=64,max_blur=1.5,max_noise=10.0,max_sp_noise=0.001): + + # get min/max values of image + min_val = np.min(img) + max_val = np.max(img) + + # init augmented image + img_aug = img + + # init disparity correction map + disp_aug = disp + grad_aug = grad + + # apply affine transformation + if max_shift>1: + + # affine parameters + rows,cols = img.shape + shear = 0 + shift = 0 + shear_correction = 0 + if rng.uniform(0,1)<0.75: shear = rng.uniform(-max_shift,max_shift) # shear with 75% probability + else: shift = rng.uniform(0,max_shift) # shift with 25% probability + if shear<0: shear_correction = -shear + + # affine transformation + a = shear/float(rows) + b = shift+shear_correction + + # warp image + T = np.float32([[1,a,b],[0,1,0]]) + img_aug = cv2.warpAffine(img_aug,T,(cols,rows)) + if grad is not None: + grad_aug = cv2.warpAffine(grad,T,(cols,rows)) + + # disparity correction map + col = a*np.array(range(rows))+b + disp_delta = np.tile(col,(cols,1)).transpose() + if disp is not None: + disp_aug = cv2.warpAffine(disp+disp_delta,T,(cols,rows)) + + # gaussian smoothing + if rng.uniform(0,1)<0.5: + img_aug = cv2.GaussianBlur(img_aug,(5,5),rng.uniform(0.2,max_blur)) + + # per-pixel gaussian noise + img_aug = img_aug + rng.randn(*img_aug.shape)*rng.uniform(0.0,max_noise)/255.0 + + # salt-and-pepper noise + if rng.uniform(0,1)<0.5: + ratio=rng.uniform(0.0,max_sp_noise) + img_shape = img_aug.shape + img_aug = img_aug.flatten() + coord = rng.choice(np.size(img_aug), int(np.size(img_aug)*ratio)) + img_aug[coord] = max_val + coord = rng.choice(np.size(img_aug), int(np.size(img_aug)*ratio)) + img_aug[coord] = min_val + img_aug = np.reshape(img_aug, img_shape) + + # clip intensities back to [0,1] + img_aug = np.maximum(img_aug,0.0) + img_aug = np.minimum(img_aug,1.0) + + # return image + return img_aug, disp_aug, grad_aug diff --git a/data/create_syn_data.py b/data/create_syn_data.py new file mode 100644 index 0000000..d637fd7 --- /dev/null +++ b/data/create_syn_data.py @@ -0,0 +1,259 @@ +import numpy as np +import matplotlib.pyplot as plt +import itertools +import pickle +from pathlib import Path +import multiprocessing +import time +import json +import cv2 +import os +import collections +import sys +sys.path.append('../') +import renderer +import co +from commons import get_patterns,get_rotation_matrix +from lcn import lcn + +def get_objs(shapenet_dir, obj_classes, num_perclass=100): + + shapenet = {'chair': '03001627', + 'airplane': '02691156', + 'car': '02958343', + 'watercraft': '04530566'} + + obj_paths = [] + for cls in obj_classes: + if cls not in shapenet.keys(): + raise Exception('unknown class name') + ids = shapenet[cls] + obj_path = sorted(Path(f'{shapenet_dir}/{ids}').glob('**/models/*.obj')) + obj_paths += obj_path[:num_perclass] + print(f'found {len(obj_paths)} object paths') + + objs = [] + for obj_path in obj_paths: + print(f'load {obj_path}') + v, f, _, n = co.io3d.read_obj(obj_path) + diffs = v.max(axis=0) - v.min(axis=0) + v /= (0.5 * diffs.max()) + v -= (v.min(axis=0) + 1) + f = f.astype(np.int32) + objs.append((v,f,n)) + print(f'loaded {len(objs)} objects') + + return objs + + +def get_mesh(rng, min_z=0): + # set up background board + verts, faces, normals, colors = [], [], [], [] + v, f, n = co.geometry.xyplane(z=0, interleaved=True) + v[:,2] += -v[:,2].min() + rng.uniform(2,7) + v[:,:2] *= 5e2 + v[:,2] = np.mean(v[:,2]) + (v[:,2] - np.mean(v[:,2])) * 5e2 + c = np.empty_like(v) + c[:] = rng.uniform(0,1, size=(3,)).astype(np.float32) + verts.append(v) + faces.append(f) + normals.append(n) + colors.append(c) + + # randomly sample 4 foreground objects for each scene + for shape_idx in range(4): + v, f, n = objs[rng.randint(0,len(objs))] + v, f, n = v.copy(), f.copy(), n.copy() + + s = rng.uniform(0.25, 1) + v *= s + R = co.geometry.rotm_from_quat(co.geometry.quat_random(rng=rng)) + v = v @ R.T + n = n @ R.T + v[:,2] += -v[:,2].min() + min_z + rng.uniform(0.5, 3) + v[:,:2] += rng.uniform(-1, 1, size=(1,2)) + + c = np.empty_like(v) + c[:] = rng.uniform(0,1, size=(3,)).astype(np.float32) + + verts.append(v.astype(np.float32)) + faces.append(f) + normals.append(n) + colors.append(c) + + verts, faces = co.geometry.stack_mesh(verts, faces) + normals = np.vstack(normals).astype(np.float32) + colors = np.vstack(colors).astype(np.float32) + return verts, faces, colors, normals + + +def create_data(out_root, idx, n_samples, imsize, patterns, K, baseline, blend_im, noise, track_length=4): + + tic = time.time() + rng = np.random.RandomState() + + rng.seed(idx) + + verts, faces, colors, normals = get_mesh(rng) + data = renderer.PyRenderInput(verts=verts.copy(), colors=colors.copy(), normals=normals.copy(), faces=faces.copy()) + print(f'loading mesh for sample {idx+1}/{n_samples} took {time.time()-tic}[s]') + + + # let the camera point to the center + center = np.array([0,0,3], dtype=np.float32) + + basevec = np.array([-baseline,0,0], dtype=np.float32) + unit = np.array([0,0,1],dtype=np.float32) + + cam_x_ = rng.uniform(-0.2,0.2) + cam_y_ = rng.uniform(-0.2,0.2) + cam_z_ = rng.uniform(-0.2,0.2) + + ret = collections.defaultdict(list) + blend_im_rnd = np.clip(blend_im + rng.uniform(-0.1,0.1), 0,1) + + # capture the same static scene from different view points as a track + for ind in range(track_length): + + cam_x = cam_x_ + rng.uniform(-0.1,0.1) + cam_y = cam_y_ + rng.uniform(-0.1,0.1) + cam_z = cam_z_ + rng.uniform(-0.1,0.1) + + tcam = np.array([cam_x, cam_y, cam_z], dtype=np.float32) + + if np.linalg.norm(tcam[0:2])<1e-9: + Rcam = np.eye(3, dtype=np.float32) + else: + Rcam = get_rotation_matrix(center, center-tcam) + + tproj = tcam + basevec + Rproj = Rcam + + ret['R'].append(Rcam) + ret['t'].append(tcam) + + cams = [] + projs = [] + + # render the scene at multiple scales + scales = [1, 0.5, 0.25, 0.125] + + for scale in scales: + fx = K[0,0] * scale + fy = K[1,1] * scale + px = K[0,2] * scale + py = K[1,2] * scale + im_height = imsize[0] * scale + im_width = imsize[1] * scale + cams.append( renderer.PyCamera(fx,fy,px,py, Rcam, tcam, im_width, im_height) ) + projs.append( renderer.PyCamera(fx,fy,px,py, Rproj, tproj, im_width, im_height) ) + + + for s, cam, proj, pattern in zip(itertools.count(), cams, projs, patterns): + fl = K[0,0] / (2**s) + + shader = renderer.PyShader(0.5,1.5,0.0,10) + pyrenderer = renderer.PyRenderer(cam, shader, engine='gpu') + pyrenderer.mesh_proj(data, proj, pattern, d_alpha=0, d_beta=0.35) + + # get the reflected laser pattern $R$ + im = pyrenderer.color().copy() + depth = pyrenderer.depth().copy() + disp = baseline * fl / depth + mask = depth > 0 + im = np.mean(im, axis=2) + + # get the ambient image $A$ + ambient = pyrenderer.normal().copy() + ambient = np.mean(ambient, axis=2) + + # get the noise free IR image $J$ + im = blend_im_rnd * im + (1 - blend_im_rnd) * ambient + ret[f'ambient{s}'].append( ambient[None].astype(np.float32) ) + + # get the gradient magnitude of the ambient image $|\nabla A|$ + ambient = ambient.astype(np.float32) + sobelx = cv2.Sobel(ambient,cv2.CV_32F,1,0,ksize=5) + sobely = cv2.Sobel(ambient,cv2.CV_32F,0,1,ksize=5) + grad = np.sqrt(sobelx**2 + sobely**2) + grad = np.maximum(grad-0.8,0.0) # parameter + + # get the local contract normalized grad LCN($|\nabla A|$) + grad_lcn, grad_std = lcn.normalize(grad,5,0.1) + grad_lcn = np.clip(grad_lcn,0.0,1.0) # parameter + ret[f'grad{s}'].append( grad_lcn[None].astype(np.float32)) + + ret[f'im{s}'].append( im[None].astype(np.float32)) + ret[f'mask{s}'].append(mask[None].astype(np.float32)) + ret[f'disp{s}'].append(disp[None].astype(np.float32)) + + for key in ret.keys(): + ret[key] = np.stack(ret[key], axis=0) + + # save to files + out_dir = out_root / f'{idx:08d}' + out_dir.mkdir(exist_ok=True, parents=True) + for k,val in ret.items(): + for tidx in range(track_length): + v = val[tidx] + out_path = out_dir / f'{k}_{tidx}.npy' + np.save(out_path, v) + np.save( str(out_dir /'blend_im.npy'), blend_im_rnd) + + print(f'create sample {idx+1}/{n_samples} took {time.time()-tic}[s]') + + + +if __name__=='__main__': + + np.random.seed(42) + + # output directory + with open('../config.json') as fp: + config = json.load(fp) + data_root = Path(config['DATA_ROOT']) + shapenet_root = config['SHAPENET_ROOT'] + + data_type = 'syn' + out_root = data_root / f'{data_type}' + out_root.mkdir(parents=True, exist_ok=True) + + # load shapenet models + obj_classes = ['chair'] + objs = get_objs(shapenet_root, obj_classes) + + # camera parameters + imsize = (480, 640) + imsizes = [(imsize[0]//(2**s), imsize[1]//(2**s)) for s in range(4)] + K = np.array([[567.6, 0, 324.7], [0, 570.2, 250.1], [0 ,0, 1]], dtype=np.float32) + focal_lengths = [K[0,0]/(2**s) for s in range(4)] + baseline=0.075 + blend_im = 0.6 + noise = 0 + + # capture the same static scene from different view points as a track + track_length = 4 + + # load pattern image + pattern_path = './kinect_pattern.png' + pattern_crop = True + patterns = get_patterns(pattern_path, imsizes, pattern_crop) + + # write settings to file + settings = { + 'imsizes': imsizes, + 'patterns': patterns, + 'focal_lengths': focal_lengths, + 'baseline': baseline, + 'K': K, + } + out_path = out_root / f'settings.pkl' + print(f'write settings to {out_path}') + with open(str(out_path), 'wb') as f: + pickle.dump(settings, f, pickle.HIGHEST_PROTOCOL) + + # start the job + n_samples = 2**10 + 2**13 + for idx in range(n_samples): + args = (out_root, idx, n_samples, imsize, patterns, K, baseline, blend_im, noise, track_length) + create_data(*args) diff --git a/data/dataset.py b/data/dataset.py new file mode 100644 index 0000000..ee57bd6 --- /dev/null +++ b/data/dataset.py @@ -0,0 +1,148 @@ +import torch +import torch.utils.data +import numpy as np +import matplotlib.pyplot as plt +import itertools +import pickle +import json +import time +from pathlib import Path +import collections +import cv2 +import sys +import os +import time +import glob + +import torchext +import renderer +import co +from .commons import get_patterns, augment_image + +from mpl_toolkits.mplot3d import Axes3D + +class TrackSynDataset(torchext.BaseDataset): + ''' + Load locally saved synthetic dataset + Please run ./create_syn_data.sh to generate the dataset + ''' + def __init__(self, settings_path, sample_paths, track_length=2, train=True, data_aug=False): + super().__init__(train=train) + + self.settings_path = settings_path + self.sample_paths = sample_paths + self.data_aug = data_aug + self.train = train + self.track_length=track_length + assert(track_length<=4) + + with open(str(settings_path), 'rb') as f: + settings = pickle.load(f) + self.imsizes = settings['imsizes'] + self.patterns = settings['patterns'] + self.focal_lengths = settings['focal_lengths'] + self.baseline = settings['baseline'] + self.K = settings['K'] + + self.scale = len(self.imsizes) + + self.max_shift=0 + self.max_blur=0.5 + self.max_noise=3.0 + self.max_sp_noise=0.0005 + + def __len__(self): + return len(self.sample_paths) + + def __getitem__(self, idx): + if not self.train: + rng = self.get_rng(idx) + else: + rng = np.random.RandomState() + sample_path = self.sample_paths[idx] + + if self.train: + track_ind = np.random.permutation(4)[0:self.track_length] + else: + track_ind = [0] + + ret = {} + ret['id'] = idx + + # load imgs, at all scales + for sidx in range(len(self.imsizes)): + imgs = [] + ambs = [] + grads = [] + for tidx in track_ind: + imgs.append(np.load(os.path.join(sample_path,f'im{sidx}_{tidx}.npy'))) + ambs.append(np.load(os.path.join(sample_path,f'ambient{sidx}_{tidx}.npy'))) + grads.append(np.load(os.path.join(sample_path,f'grad{sidx}_{tidx}.npy'))) + ret[f'im{sidx}'] = np.stack(imgs, axis=0) + ret[f'ambient{sidx}'] = np.stack(ambs, axis=0) + ret[f'grad{sidx}'] = np.stack(grads, axis=0) + + # load disp and grad only at full resolution + disps = [] + R = [] + t = [] + for tidx in track_ind: + disps.append(np.load(os.path.join(sample_path,f'disp0_{tidx}.npy'))) + R.append(np.load(os.path.join(sample_path,f'R_{tidx}.npy'))) + t.append(np.load(os.path.join(sample_path,f't_{tidx}.npy'))) + ret[f'disp0'] = np.stack(disps, axis=0) + ret['R'] = np.stack(R, axis=0) + ret['t'] = np.stack(t, axis=0) + + blend_im = np.load(os.path.join(sample_path,'blend_im.npy')) + ret['blend_im'] = blend_im.astype(np.float32) + + #### apply data augmentation at different scales seperately, only work for max_shift=0 + if self.data_aug: + for sidx in range(len(self.imsizes)): + if sidx==0: + img = ret[f'im{sidx}'] + disp = ret[f'disp{sidx}'] + grad = ret[f'grad{sidx}'] + img_aug = np.zeros_like(img) + disp_aug = np.zeros_like(img) + grad_aug = np.zeros_like(img) + for i in range(img.shape[0]): + img_aug_, disp_aug_, grad_aug_ = augment_image(img[i,0],rng, + disp=disp[i,0],grad=grad[i,0], + max_shift=self.max_shift, max_blur=self.max_blur, + max_noise=self.max_noise, max_sp_noise=self.max_sp_noise) + img_aug[i] = img_aug_[None].astype(np.float32) + disp_aug[i] = disp_aug_[None].astype(np.float32) + grad_aug[i] = grad_aug_[None].astype(np.float32) + ret[f'im{sidx}'] = img_aug + ret[f'disp{sidx}'] = disp_aug + ret[f'grad{sidx}'] = grad_aug + else: + img = ret[f'im{sidx}'] + img_aug = np.zeros_like(img) + for i in range(img.shape[0]): + img_aug_, _, _ = augment_image(img[i,0],rng, + max_shift=self.max_shift, max_blur=self.max_blur, + max_noise=self.max_noise, max_sp_noise=self.max_sp_noise) + img_aug[i] = img_aug_[None].astype(np.float32) + ret[f'im{sidx}'] = img_aug + + if len(track_ind)==1: + for key, val in ret.items(): + if key!='blend_im' and key!='id': + ret[key] = val[0] + + + return ret + + def getK(self, sidx=0): + K = self.K.copy() / (2**sidx) + K[2,2] = 1 + return K + + + +if __name__ == '__main__': + pass + diff --git a/data/kinect_pattern.png b/data/kinect_pattern.png new file mode 100644 index 0000000000000000000000000000000000000000..110f3c8c1fcb166d9ce40fb640c0bf45a33814c3 GIT binary patch literal 599339 zcmXtZ2CTW~BwvEP48b4{n#*=rBxZ1%c$ z@6UuQD@r3H5Fh{mfGjH`p$Y)d|81cFxc^ol zH(3Qq*d0U&BsNOnb}dZ+AOmD2zN-KDH|`f8qrUR`X`A{7-x3ct6rTtm9~L$#4xShb zTT)U*9VvlogiSpjsgF*U11Z6jx`0h*#EG5Zw>!ODd=x?-MOjf%sZ2AS9MA7aoP;QZ zg2~62*Vhf#%Qcn9_vZ}}4|~gt8rN*nn!|gcLw+zZ01nD{{EdDr!y5mwsu}<1Gm8k3 zj2*hdwcnpsbH4-y5Tz$ix@$m^05y^^$rr>(_rhhaQ7IfgF%MRC46p*6=k@cHI zWQWT=y2IF`14!e;gbg<E4@(Rk6aA==ymu4TEnTu0w5| z_#R>Yr!=a|7AAH}=YSbP?=IS!P2Z7u}#*M``l@*@GlgG|kw6bBR;`y~}5CLKqCiV>9)S91($e=y#= 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__cdecl + #endif + #ifndef __fastcall + #define __fastcall + #endif +#endif +#ifndef DL_IMPORT + #define DL_IMPORT(t) t +#endif +#ifndef DL_EXPORT + #define DL_EXPORT(t) t +#endif +#define __PYX_COMMA , +#ifndef HAVE_LONG_LONG + #if PY_VERSION_HEX >= 0x02070000 + #define HAVE_LONG_LONG + #endif +#endif +#ifndef PY_LONG_LONG + #define PY_LONG_LONG LONG_LONG +#endif +#ifndef Py_HUGE_VAL + #define Py_HUGE_VAL HUGE_VAL +#endif +#ifdef PYPY_VERSION + #define CYTHON_COMPILING_IN_PYPY 1 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #if PY_VERSION_HEX < 0x03050000 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 +#elif defined(PYSTON_VERSION) + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 1 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 +#else + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 1 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #if PY_VERSION_HEX < 0x02070000 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #elif !defined(CYTHON_USE_PYTYPE_LOOKUP) + #define CYTHON_USE_PYTYPE_LOOKUP 1 + #endif + #if PY_MAJOR_VERSION < 3 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #if PY_VERSION_HEX < 0x02070000 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #elif !defined(CYTHON_USE_PYLONG_INTERNALS) + #define CYTHON_USE_PYLONG_INTERNALS 1 + #endif + #ifndef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 1 + #endif + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #if PY_VERSION_HEX < 0x030300F0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #elif !defined(CYTHON_USE_UNICODE_WRITER) + #define CYTHON_USE_UNICODE_WRITER 1 + #endif + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #ifndef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 1 + #endif + #ifndef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 1 + #endif + #ifndef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT (PY_VERSION_HEX >= 0x03050000) + #endif + #ifndef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1) + #endif + #ifndef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX >= 0x030600B1) + #endif + #ifndef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK (PY_VERSION_HEX >= 0x030700A3) + #endif +#endif +#if !defined(CYTHON_FAST_PYCCALL) +#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) +#endif +#if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" + #undef SHIFT + #undef BASE + #undef MASK +#endif +#ifndef __has_attribute + #define __has_attribute(x) 0 +#endif +#ifndef __has_cpp_attribute + #define __has_cpp_attribute(x) 0 +#endif +#ifndef CYTHON_RESTRICT + #if defined(__GNUC__) + #define CYTHON_RESTRICT __restrict__ + #elif defined(_MSC_VER) && _MSC_VER >= 1400 + #define CYTHON_RESTRICT __restrict + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_RESTRICT restrict + #else + #define CYTHON_RESTRICT + #endif +#endif +#ifndef CYTHON_UNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_MAYBE_UNUSED_VAR +# if defined(__cplusplus) + template void CYTHON_MAYBE_UNUSED_VAR( const T& ) { } +# else +# define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x) +# endif +#endif +#ifndef CYTHON_NCP_UNUSED +# if CYTHON_COMPILING_IN_CPYTHON +# define CYTHON_NCP_UNUSED +# else +# define CYTHON_NCP_UNUSED CYTHON_UNUSED +# endif +#endif +#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) +#ifdef _MSC_VER + #ifndef _MSC_STDINT_H_ + #if _MSC_VER < 1300 + typedef unsigned char uint8_t; + typedef unsigned int uint32_t; + #else + typedef unsigned __int8 uint8_t; + typedef unsigned __int32 uint32_t; + #endif + #endif +#else + #include +#endif +#ifndef CYTHON_FALLTHROUGH + #if defined(__cplusplus) && __cplusplus >= 201103L + #if __has_cpp_attribute(fallthrough) + #define CYTHON_FALLTHROUGH [[fallthrough]] + #elif __has_cpp_attribute(clang::fallthrough) + #define CYTHON_FALLTHROUGH [[clang::fallthrough]] + #elif __has_cpp_attribute(gnu::fallthrough) + #define CYTHON_FALLTHROUGH [[gnu::fallthrough]] + #endif + #endif + #ifndef CYTHON_FALLTHROUGH + #if __has_attribute(fallthrough) + #define CYTHON_FALLTHROUGH __attribute__((fallthrough)) + #else + #define CYTHON_FALLTHROUGH + #endif + #endif + #if defined(__clang__ ) && defined(__apple_build_version__) + #if __apple_build_version__ < 7000000 + #undef CYTHON_FALLTHROUGH + #define CYTHON_FALLTHROUGH + #endif + #endif +#endif + +#ifndef CYTHON_INLINE + #if defined(__clang__) + #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) + #elif defined(__GNUC__) + #define CYTHON_INLINE __inline__ + #elif defined(_MSC_VER) + #define CYTHON_INLINE __inline + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_INLINE inline + #else + #define CYTHON_INLINE + #endif +#endif + +#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) + #define Py_OptimizeFlag 0 +#endif +#define __PYX_BUILD_PY_SSIZE_T "n" +#define CYTHON_FORMAT_SSIZE_T "z" +#if PY_MAJOR_VERSION < 3 + #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyClass_Type +#else + #define __Pyx_BUILTIN_MODULE_NAME "builtins" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyType_Type +#endif +#ifndef Py_TPFLAGS_CHECKTYPES + #define Py_TPFLAGS_CHECKTYPES 0 +#endif +#ifndef Py_TPFLAGS_HAVE_INDEX + #define Py_TPFLAGS_HAVE_INDEX 0 +#endif +#ifndef Py_TPFLAGS_HAVE_NEWBUFFER + #define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif +#ifndef Py_TPFLAGS_HAVE_FINALIZE + #define Py_TPFLAGS_HAVE_FINALIZE 0 +#endif +#ifndef METH_STACKLESS + #define METH_STACKLESS 0 +#endif +#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL) + #ifndef METH_FASTCALL + #define METH_FASTCALL 0x80 + #endif + typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs); + typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args, + Py_ssize_t nargs, PyObject *kwnames); +#else + #define __Pyx_PyCFunctionFast _PyCFunctionFast + #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords +#endif +#if CYTHON_FAST_PYCCALL +#define __Pyx_PyFastCFunction_Check(func)\ + ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))))) +#else +#define __Pyx_PyFastCFunction_Check(func) 0 +#endif +#if CYTHON_USE_DICT_VERSIONS +#define __PYX_GET_DICT_VERSION(dict) (((PyDictObject*)(dict))->ma_version_tag) +#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\ + (version_var) = __PYX_GET_DICT_VERSION(dict);\ + (cache_var) = (value); +#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\ + static PY_UINT64_T __pyx_dict_version = 0;\ + static PyObject *__pyx_dict_cached_value = NULL;\ + if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\ + (VAR) = __pyx_dict_cached_value;\ + } else {\ + (VAR) = __pyx_dict_cached_value = (LOOKUP);\ + __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\ + }\ + } +#else +#define __PYX_GET_DICT_VERSION(dict) (0) +#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var) +#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) (VAR) = (LOOKUP); +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) + #define PyObject_Malloc(s) PyMem_Malloc(s) + #define PyObject_Free(p) PyMem_Free(p) + #define PyObject_Realloc(p) PyMem_Realloc(p) +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030400A1 + #define PyMem_RawMalloc(n) PyMem_Malloc(n) + #define PyMem_RawRealloc(p, n) PyMem_Realloc(p, n) + #define PyMem_RawFree(p) PyMem_Free(p) +#endif +#if CYTHON_COMPILING_IN_PYSTON + #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno) +#else + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) +#endif +#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000 + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#elif PY_VERSION_HEX >= 0x03060000 + #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet() +#elif PY_VERSION_HEX >= 0x03000000 + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#else + #define __Pyx_PyThreadState_Current _PyThreadState_Current +#endif +#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT) +#include "pythread.h" +#define Py_tss_NEEDS_INIT 0 +typedef int Py_tss_t; +static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) { + *key = PyThread_create_key(); + return 0; // PyThread_create_key reports success always +} +static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) { + Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t)); + *key = Py_tss_NEEDS_INIT; + return key; +} +static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) { + PyObject_Free(key); +} +static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) { + return *key != Py_tss_NEEDS_INIT; +} +static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) { + PyThread_delete_key(*key); + *key = Py_tss_NEEDS_INIT; +} +static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) { + return PyThread_set_key_value(*key, value); +} +static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { + return PyThread_get_key_value(*key); +} +#endif // TSS (Thread Specific Storage) API +#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized) +#define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) +#else +#define __Pyx_PyDict_NewPresized(n) PyDict_New() +#endif +#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS +#define __Pyx_PyDict_GetItemStr(dict, name) _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash) +#else +#define __Pyx_PyDict_GetItemStr(dict, name) PyDict_GetItem(dict, name) +#endif +#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) + #define CYTHON_PEP393_ENABLED 1 + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) + #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) + #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) +#else + #define CYTHON_PEP393_ENABLED 0 + #define PyUnicode_1BYTE_KIND 1 + #define PyUnicode_2BYTE_KIND 2 + #define PyUnicode_4BYTE_KIND 4 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) + #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) + #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) +#endif +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) +#else + #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ + PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) + #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) + #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) + #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) +#endif +#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) +#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) +#else + #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) +#endif +#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) + #define PyObject_ASCII(o) PyObject_Repr(o) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyStringObject PyUnicodeObject + #define PyString_Type PyUnicode_Type + #define PyString_Check PyUnicode_Check + #define PyString_CheckExact PyUnicode_CheckExact + #define PyObject_Unicode PyObject_Str +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) + #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) +#else + #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) + #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) +#endif +#if CYTHON_ASSUME_SAFE_MACROS + #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq) +#else + #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyIntObject PyLongObject + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define PyNumber_Int PyNumber_Long +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBoolObject PyLongObject +#endif +#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY + #ifndef PyUnicode_InternFromString + #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) + #endif +#endif +#if PY_VERSION_HEX < 0x030200A4 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t PyInt_AsLong +#else + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : (Py_INCREF(func), func)) +#else + #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) +#endif +#if CYTHON_USE_ASYNC_SLOTS + #if PY_VERSION_HEX >= 0x030500B1 + #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods + #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) + #else + #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) + #endif +#else + #define __Pyx_PyType_AsAsync(obj) NULL +#endif +#ifndef __Pyx_PyAsyncMethodsStruct + typedef struct { + unaryfunc am_await; + unaryfunc am_aiter; + unaryfunc am_anext; + } __Pyx_PyAsyncMethodsStruct; +#endif + +#if defined(WIN32) || defined(MS_WINDOWS) + #define _USE_MATH_DEFINES +#endif +#include +#ifdef NAN +#define __PYX_NAN() ((float) NAN) +#else +static CYTHON_INLINE float __PYX_NAN() { + float value; + memset(&value, 0xFF, sizeof(value)); + return value; +} +#endif +#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) +#define __Pyx_truncl trunc +#else +#define __Pyx_truncl truncl +#endif + + +#define __PYX_ERR(f_index, lineno, Ln_error) \ +{ \ + __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \ +} + +#ifndef __PYX_EXTERN_C + #ifdef __cplusplus + #define __PYX_EXTERN_C extern "C" + #else + #define __PYX_EXTERN_C extern + #endif +#endif + +#define __PYX_HAVE__lcn +#define __PYX_HAVE_API__lcn +/* Early includes */ +#include "math.h" +#include "pythread.h" +#include +#include +#include +#include "pystate.h" +#ifdef _OPENMP +#include +#endif /* _OPENMP */ + +#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS) +#define CYTHON_WITHOUT_ASSERTIONS +#endif + +typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; + const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; + +#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 +#define __PYX_DEFAULT_STRING_ENCODING "" +#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString +#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#define __Pyx_uchar_cast(c) ((unsigned char)c) +#define __Pyx_long_cast(x) ((long)x) +#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ + (sizeof(type) < sizeof(Py_ssize_t)) ||\ + (sizeof(type) > sizeof(Py_ssize_t) &&\ + likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX) &&\ + (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ + v == (type)PY_SSIZE_T_MIN))) ||\ + (sizeof(type) == sizeof(Py_ssize_t) &&\ + (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX))) ) +static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) { + return (size_t) i < (size_t) limit; +} +#if defined (__cplusplus) && __cplusplus >= 201103L + #include + #define __Pyx_sst_abs(value) std::abs(value) +#elif SIZEOF_INT >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) abs(value) +#elif SIZEOF_LONG >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) labs(value) +#elif defined (_MSC_VER) + #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value)) +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define __Pyx_sst_abs(value) llabs(value) +#elif defined (__GNUC__) + #define __Pyx_sst_abs(value) __builtin_llabs(value) +#else + #define __Pyx_sst_abs(value) ((value<0) ? -value : value) +#endif +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*); +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); +#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) +#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#else + #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize +#endif +#define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyObject_AsWritableString(s) ((char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) +#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) +#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) +#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) +#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) { + const Py_UNICODE *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) +#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode +#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode +#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) +#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) +static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b); +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); +#define __Pyx_PySequence_Tuple(obj)\ + (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +#if CYTHON_ASSUME_SAFE_MACROS +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) +#else +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#endif +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) +#else +#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) +#endif +#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII +static int __Pyx_sys_getdefaultencoding_not_ascii; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + PyObject* ascii_chars_u = NULL; + PyObject* ascii_chars_b = NULL; + const char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + if (strcmp(default_encoding_c, "ascii") == 0) { + __Pyx_sys_getdefaultencoding_not_ascii = 0; + } else { + char ascii_chars[128]; + int c; + for (c = 0; c < 128; c++) { + ascii_chars[c] = c; + } + __Pyx_sys_getdefaultencoding_not_ascii = 1; + ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); + if (!ascii_chars_u) goto bad; + ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); + if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { + PyErr_Format( + PyExc_ValueError, + "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", + default_encoding_c); + goto bad; + } + Py_DECREF(ascii_chars_u); + Py_DECREF(ascii_chars_b); + } + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return -1; +} +#endif +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) +#else +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +static char* __PYX_DEFAULT_STRING_ENCODING; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1); + if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; + strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + return -1; +} +#endif +#endif + + +/* Test for GCC > 2.95 */ +#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) +#else /* !__GNUC__ or GCC < 2.95 */ + #define likely(x) (x) + #define unlikely(x) (x) +#endif /* __GNUC__ */ +static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; } + +static PyObject *__pyx_m = NULL; +static PyObject *__pyx_d; +static PyObject *__pyx_b; +static PyObject *__pyx_cython_runtime = NULL; +static PyObject *__pyx_empty_tuple; +static PyObject *__pyx_empty_bytes; +static PyObject *__pyx_empty_unicode; +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm= __FILE__; +static const char *__pyx_filename; + + +static const char *__pyx_f[] = { + "lcn.pyx", + "stringsource", +}; +/* MemviewSliceStruct.proto */ +struct __pyx_memoryview_obj; +typedef struct { + struct __pyx_memoryview_obj *memview; + char *data; + Py_ssize_t shape[8]; + Py_ssize_t strides[8]; + Py_ssize_t suboffsets[8]; +} __Pyx_memviewslice; +#define __Pyx_MemoryView_Len(m) (m.shape[0]) + +/* Atomics.proto */ +#include +#ifndef CYTHON_ATOMICS + #define CYTHON_ATOMICS 1 +#endif +#define __pyx_atomic_int_type int +#if CYTHON_ATOMICS && __GNUC__ >= 4 && (__GNUC_MINOR__ > 1 ||\ + (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL >= 2)) &&\ + !defined(__i386__) + #define __pyx_atomic_incr_aligned(value, lock) __sync_fetch_and_add(value, 1) + #define __pyx_atomic_decr_aligned(value, lock) __sync_fetch_and_sub(value, 1) + #ifdef __PYX_DEBUG_ATOMICS + #warning "Using GNU atomics" + #endif +#elif CYTHON_ATOMICS && defined(_MSC_VER) && 0 + #include + #undef __pyx_atomic_int_type + #define __pyx_atomic_int_type LONG + #define __pyx_atomic_incr_aligned(value, lock) InterlockedIncrement(value) + #define __pyx_atomic_decr_aligned(value, lock) InterlockedDecrement(value) + #ifdef __PYX_DEBUG_ATOMICS + #pragma message ("Using MSVC atomics") + #endif +#elif CYTHON_ATOMICS && (defined(__ICC) || defined(__INTEL_COMPILER)) && 0 + #define __pyx_atomic_incr_aligned(value, lock) _InterlockedIncrement(value) + #define __pyx_atomic_decr_aligned(value, lock) _InterlockedDecrement(value) + #ifdef __PYX_DEBUG_ATOMICS + #warning "Using Intel atomics" + #endif +#else + #undef CYTHON_ATOMICS + #define CYTHON_ATOMICS 0 + #ifdef __PYX_DEBUG_ATOMICS + #warning "Not using atomics" + #endif +#endif +typedef volatile __pyx_atomic_int_type __pyx_atomic_int; +#if CYTHON_ATOMICS + #define __pyx_add_acquisition_count(memview)\ + __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) + #define __pyx_sub_acquisition_count(memview)\ + __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) +#else + #define __pyx_add_acquisition_count(memview)\ + __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) + #define __pyx_sub_acquisition_count(memview)\ + __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) +#endif + +/* ForceInitThreads.proto */ +#ifndef __PYX_FORCE_INIT_THREADS + #define __PYX_FORCE_INIT_THREADS 0 +#endif + +/* NoFastGil.proto */ +#define __Pyx_PyGILState_Ensure PyGILState_Ensure +#define __Pyx_PyGILState_Release PyGILState_Release +#define __Pyx_FastGIL_Remember() +#define __Pyx_FastGIL_Forget() +#define __Pyx_FastGilFuncInit() + +/* BufferFormatStructs.proto */ +#define IS_UNSIGNED(type) (((type) -1) > 0) +struct __Pyx_StructField_; +#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) +typedef struct { + const char* name; + struct __Pyx_StructField_* fields; + size_t size; + size_t arraysize[8]; + int ndim; + char typegroup; + char is_unsigned; + int flags; +} __Pyx_TypeInfo; +typedef struct __Pyx_StructField_ { + __Pyx_TypeInfo* type; + const char* name; + size_t offset; +} __Pyx_StructField; +typedef struct { + __Pyx_StructField* field; + size_t parent_offset; +} __Pyx_BufFmt_StackElem; +typedef struct { + __Pyx_StructField root; + __Pyx_BufFmt_StackElem* head; + size_t fmt_offset; + size_t new_count, enc_count; + size_t struct_alignment; + int is_complex; + char enc_type; + char new_packmode; + char enc_packmode; + char is_valid_array; +} __Pyx_BufFmt_Context; + + +/*--- Type declarations ---*/ +struct __pyx_array_obj; +struct __pyx_MemviewEnum_obj; +struct __pyx_memoryview_obj; +struct __pyx_memoryviewslice_obj; + +/* "View.MemoryView":104 + * + * @cname("__pyx_array") + * cdef class array: # <<<<<<<<<<<<<< + * + * cdef: + */ +struct __pyx_array_obj { + PyObject_HEAD + struct __pyx_vtabstruct_array *__pyx_vtab; + char *data; + Py_ssize_t len; + char *format; + int ndim; + Py_ssize_t *_shape; + Py_ssize_t *_strides; + Py_ssize_t itemsize; + PyObject *mode; + PyObject *_format; + void (*callback_free_data)(void *); + int free_data; + int dtype_is_object; +}; + + +/* "View.MemoryView":278 + * + * @cname('__pyx_MemviewEnum') + * cdef class Enum(object): # <<<<<<<<<<<<<< + * cdef object name + * def __init__(self, name): + */ +struct __pyx_MemviewEnum_obj { + PyObject_HEAD + PyObject *name; +}; + + +/* "View.MemoryView":329 + * + * @cname('__pyx_memoryview') + * cdef class memoryview(object): # <<<<<<<<<<<<<< + * + * cdef object obj + */ +struct __pyx_memoryview_obj { + PyObject_HEAD + struct __pyx_vtabstruct_memoryview *__pyx_vtab; + PyObject *obj; + PyObject *_size; + PyObject *_array_interface; + PyThread_type_lock lock; + __pyx_atomic_int acquisition_count[2]; + __pyx_atomic_int *acquisition_count_aligned_p; + Py_buffer view; + int flags; + int dtype_is_object; + __Pyx_TypeInfo *typeinfo; +}; + + +/* "View.MemoryView":960 + * + * @cname('__pyx_memoryviewslice') + * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< + * "Internal class for passing memoryview slices to Python" + * + */ +struct __pyx_memoryviewslice_obj { + struct __pyx_memoryview_obj __pyx_base; + __Pyx_memviewslice from_slice; + PyObject *from_object; + PyObject *(*to_object_func)(char *); + int (*to_dtype_func)(char *, PyObject *); +}; + + + +/* "View.MemoryView":104 + * + * @cname("__pyx_array") + * cdef class array: # <<<<<<<<<<<<<< + * + * cdef: + */ + +struct __pyx_vtabstruct_array { + PyObject *(*get_memview)(struct __pyx_array_obj *); +}; +static struct __pyx_vtabstruct_array *__pyx_vtabptr_array; + + +/* "View.MemoryView":329 + * + * @cname('__pyx_memoryview') + * cdef class memoryview(object): # <<<<<<<<<<<<<< + * + * cdef object obj + */ + +struct __pyx_vtabstruct_memoryview { + char *(*get_item_pointer)(struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*is_slice)(struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*setitem_slice_assignment)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); + PyObject *(*setitem_slice_assign_scalar)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*setitem_indexed)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); + PyObject *(*convert_item_to_object)(struct __pyx_memoryview_obj *, char *); + PyObject *(*assign_item_from_object)(struct __pyx_memoryview_obj *, char *, PyObject *); +}; +static struct __pyx_vtabstruct_memoryview *__pyx_vtabptr_memoryview; + + +/* "View.MemoryView":960 + * + * @cname('__pyx_memoryviewslice') + * cdef class _memoryviewslice(memoryview): # <<<<<<<<<<<<<< + * "Internal class for passing memoryview slices to Python" + * + */ + +struct __pyx_vtabstruct__memoryviewslice { + struct __pyx_vtabstruct_memoryview __pyx_base; +}; +static struct __pyx_vtabstruct__memoryviewslice *__pyx_vtabptr__memoryviewslice; + +/* --- Runtime support code (head) --- */ +/* Refnanny.proto */ +#ifndef CYTHON_REFNANNY + #define CYTHON_REFNANNY 0 +#endif +#if CYTHON_REFNANNY + typedef struct { + void (*INCREF)(void*, PyObject*, int); + void (*DECREF)(void*, PyObject*, int); + void (*GOTREF)(void*, PyObject*, int); + void (*GIVEREF)(void*, PyObject*, int); + void* (*SetupContext)(const char*, int, const char*); + void (*FinishContext)(void**); + } __Pyx_RefNannyAPIStruct; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); + #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL; +#ifdef WITH_THREAD + #define __Pyx_RefNannySetupContext(name, acquire_gil)\ + if (acquire_gil) {\ + PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\ + PyGILState_Release(__pyx_gilstate_save);\ + } else {\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\ + } +#else + #define __Pyx_RefNannySetupContext(name, acquire_gil)\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__) +#endif + #define __Pyx_RefNannyFinishContext()\ + __Pyx_RefNanny->FinishContext(&__pyx_refnanny) + #define __Pyx_INCREF(r) __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_DECREF(r) __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_GOTREF(r) __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_XINCREF(r) do { if((r) != NULL) {__Pyx_INCREF(r); }} while(0) + #define __Pyx_XDECREF(r) do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0) + #define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0) + #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0) +#else + #define __Pyx_RefNannyDeclarations + #define __Pyx_RefNannySetupContext(name, acquire_gil) + #define __Pyx_RefNannyFinishContext() + #define __Pyx_INCREF(r) Py_INCREF(r) + #define __Pyx_DECREF(r) Py_DECREF(r) + #define __Pyx_GOTREF(r) + #define __Pyx_GIVEREF(r) + #define __Pyx_XINCREF(r) Py_XINCREF(r) + #define __Pyx_XDECREF(r) Py_XDECREF(r) + #define __Pyx_XGOTREF(r) + #define __Pyx_XGIVEREF(r) +#endif +#define __Pyx_XDECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_XDECREF(tmp);\ + } while (0) +#define __Pyx_DECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_DECREF(tmp);\ + } while (0) +#define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0) +#define __Pyx_XCLEAR(r) do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0) + +/* PyObjectGetAttrStr.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) +#endif + +/* GetBuiltinName.proto */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name); + +/* RaiseDoubleKeywords.proto */ +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); + +/* ParseKeywords.proto */ +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\ + PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\ + const char* function_name); + +/* RaiseArgTupleInvalid.proto */ +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); + +/* GetModuleGlobalName.proto */ +#if CYTHON_USE_DICT_VERSIONS +#define __Pyx_GetModuleGlobalName(var, name) {\ + static PY_UINT64_T __pyx_dict_version = 0;\ + static PyObject *__pyx_dict_cached_value = NULL;\ + (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\ + (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\ + __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ +} +#define __Pyx_GetModuleGlobalNameUncached(var, name) {\ + PY_UINT64_T __pyx_dict_version;\ + PyObject *__pyx_dict_cached_value;\ + (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ +} +static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value); +#else +#define __Pyx_GetModuleGlobalName(var, name) (var) = __Pyx__GetModuleGlobalName(name) +#define __Pyx_GetModuleGlobalNameUncached(var, name) (var) = __Pyx__GetModuleGlobalName(name) +static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name); +#endif + +/* PyObjectCall.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); +#else +#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) +#endif + +/* MemviewSliceInit.proto */ +#define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d +#define __Pyx_MEMVIEW_DIRECT 1 +#define __Pyx_MEMVIEW_PTR 2 +#define __Pyx_MEMVIEW_FULL 4 +#define __Pyx_MEMVIEW_CONTIG 8 +#define __Pyx_MEMVIEW_STRIDED 16 +#define __Pyx_MEMVIEW_FOLLOW 32 +#define __Pyx_IS_C_CONTIG 1 +#define __Pyx_IS_F_CONTIG 2 +static int __Pyx_init_memviewslice( + struct __pyx_memoryview_obj *memview, + int ndim, + __Pyx_memviewslice *memviewslice, + int memview_is_new_reference); +static CYTHON_INLINE int __pyx_add_acquisition_count_locked( + __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); +static CYTHON_INLINE int __pyx_sub_acquisition_count_locked( + __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); +#define __pyx_get_slice_count_pointer(memview) (memview->acquisition_count_aligned_p) +#define __pyx_get_slice_count(memview) (*__pyx_get_slice_count_pointer(memview)) +#define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__) +#define __PYX_XDEC_MEMVIEW(slice, have_gil) __Pyx_XDEC_MEMVIEW(slice, have_gil, __LINE__) +static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int); +static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *, int, int); + +/* ArgTypeTest.proto */ +#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\ + ((likely((Py_TYPE(obj) == type) | (none_allowed && (obj == Py_None)))) ? 1 :\ + __Pyx__ArgTypeTest(obj, type, name, exact)) +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact); + +/* PyThreadStateGet.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; +#define __Pyx_PyThreadState_assign __pyx_tstate = __Pyx_PyThreadState_Current; +#define __Pyx_PyErr_Occurred() __pyx_tstate->curexc_type +#else +#define __Pyx_PyThreadState_declare +#define __Pyx_PyThreadState_assign +#define __Pyx_PyErr_Occurred() PyErr_Occurred() +#endif + +/* PyErrFetchRestore.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL) +#define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL)) +#else +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#endif +#else +#define __Pyx_PyErr_Clear() PyErr_Clear() +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestoreInState(tstate, type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchInState(tstate, type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) +#endif + +/* RaiseException.proto */ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); + +/* PyCFunctionFastCall.proto */ +#if CYTHON_FAST_PYCCALL +static CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs); +#else +#define __Pyx_PyCFunction_FastCall(func, args, nargs) (assert(0), NULL) +#endif + +/* PyFunctionFastCall.proto */ +#if CYTHON_FAST_PYCALL +#define __Pyx_PyFunction_FastCall(func, args, nargs)\ + __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) +#if 1 || PY_VERSION_HEX < 0x030600B1 +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs); +#else +#define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs) +#endif +#define __Pyx_BUILD_ASSERT_EXPR(cond)\ + (sizeof(char [1 - 2*!(cond)]) - 1) +#ifndef Py_MEMBER_SIZE +#define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member) +#endif + static size_t __pyx_pyframe_localsplus_offset = 0; + #include "frameobject.h" + #define __Pxy_PyFrame_Initialize_Offsets()\ + ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\ + (void)(__pyx_pyframe_localsplus_offset = PyFrame_Type.tp_basicsize - Py_MEMBER_SIZE(PyFrameObject, f_localsplus))) + #define __Pyx_PyFrame_GetLocalsplus(frame)\ + (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset)) +#endif + +/* PyObjectCall2Args.proto */ +static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2); + +/* PyObjectCallMethO.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); +#endif + +/* PyObjectCallOneArg.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); + +/* IncludeStringH.proto */ +#include + +/* BytesEquals.proto */ +static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); + +/* UnicodeEquals.proto */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); + +/* StrEquals.proto */ +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals +#else +#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals +#endif + +/* None.proto */ +static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t); + +/* UnaryNegOverflows.proto */ +#define UNARY_NEG_WOULD_OVERFLOW(x)\ + (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x))) + +static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/ +/* GetAttr.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *); + +/* GetItemInt.proto */ +#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ + (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ + __Pyx_GetItemInt_Generic(o, to_py_func(i)))) +#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, + int is_list, int wraparound, int boundscheck); + +/* ObjectGetItem.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key); +#else +#define __Pyx_PyObject_GetItem(obj, key) PyObject_GetItem(obj, key) +#endif + +/* decode_c_string_utf16.proto */ +static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16(const char *s, Py_ssize_t size, const char *errors) { + int byteorder = 0; + return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); +} +static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16LE(const char *s, Py_ssize_t size, const char *errors) { + int byteorder = -1; + return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); +} +static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16BE(const char *s, Py_ssize_t size, const char *errors) { + int byteorder = 1; + return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); +} + +/* decode_c_string.proto */ +static CYTHON_INLINE PyObject* __Pyx_decode_c_string( + const char* cstring, Py_ssize_t start, Py_ssize_t stop, + const char* encoding, const char* errors, + PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)); + +/* PyErrExceptionMatches.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err) +static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err); +#else +#define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err) +#endif + +/* GetAttr3.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *); + +/* RaiseTooManyValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); + +/* RaiseNeedMoreValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); + +/* RaiseNoneIterError.proto */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); + +/* ExtTypeTest.proto */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); + +/* GetTopmostException.proto */ +#if CYTHON_USE_EXC_INFO_STACK +static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate); +#endif + +/* SaveResetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +#else +#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) +#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) +#endif + +/* GetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* SwapException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* Import.proto */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); + +/* FastTypeChecks.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); +#else +#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) +#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) +#endif +#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) + +static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +/* ListCompAppend.proto */ +#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS +static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { + PyListObject* L = (PyListObject*) list; + Py_ssize_t len = Py_SIZE(list); + if (likely(L->allocated > len)) { + Py_INCREF(x); + PyList_SET_ITEM(list, len, x); + Py_SIZE(list) = len+1; + return 0; + } + return PyList_Append(list, x); +} +#else +#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x) +#endif + +/* PyIntBinop.proto */ +#if !CYTHON_COMPILING_IN_PYPY +static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace); +#else +#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace)\ + (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2)) +#endif + +/* ListExtend.proto */ +static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) { +#if CYTHON_COMPILING_IN_CPYTHON + PyObject* none = _PyList_Extend((PyListObject*)L, v); + if (unlikely(!none)) + return -1; + Py_DECREF(none); + return 0; +#else + return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v); +#endif +} + +/* ListAppend.proto */ +#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS +static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) { + PyListObject* L = (PyListObject*) list; + Py_ssize_t len = Py_SIZE(list); + if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) { + Py_INCREF(x); + PyList_SET_ITEM(list, len, x); + Py_SIZE(list) = len+1; + return 0; + } + return PyList_Append(list, x); +} +#else +#define __Pyx_PyList_Append(L,x) PyList_Append(L,x) +#endif + +/* None.proto */ +static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname); + +/* None.proto */ +static CYTHON_INLINE long __Pyx_div_long(long, long); + +/* WriteUnraisableException.proto */ +static void __Pyx_WriteUnraisable(const char *name, int clineno, + int lineno, const char *filename, + int full_traceback, int nogil); + +/* ImportFrom.proto */ +static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); + +/* HasAttr.proto */ +static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *); + +/* PyObject_GenericGetAttrNoDict.proto */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr +#endif + +/* PyObject_GenericGetAttr.proto */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr +#endif + +/* SetVTable.proto */ +static int __Pyx_SetVtable(PyObject *dict, void *vtable); + +/* SetupReduce.proto */ +static int __Pyx_setup_reduce(PyObject* type_obj); + +/* CLineInTraceback.proto */ +#ifdef CYTHON_CLINE_IN_TRACEBACK +#define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0) +#else +static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line); +#endif + +/* CodeObjectCache.proto */ +typedef struct { + PyCodeObject* code_object; + int code_line; +} __Pyx_CodeObjectCacheEntry; +struct __Pyx_CodeObjectCache { + int count; + int max_count; + __Pyx_CodeObjectCacheEntry* entries; +}; +static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); +static PyCodeObject *__pyx_find_code_object(int code_line); +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); + +/* AddTraceback.proto */ +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename); + +#if PY_MAJOR_VERSION < 3 + static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); + static void __Pyx_ReleaseBuffer(Py_buffer *view); +#else + #define __Pyx_GetBuffer PyObject_GetBuffer + #define __Pyx_ReleaseBuffer PyBuffer_Release +#endif + + +/* BufferStructDeclare.proto */ +typedef struct { + Py_ssize_t shape, strides, suboffsets; +} __Pyx_Buf_DimInfo; +typedef struct { + size_t refcount; + Py_buffer pybuffer; +} __Pyx_Buffer; +typedef struct { + __Pyx_Buffer *rcbuffer; + char *data; + __Pyx_Buf_DimInfo diminfo[8]; +} __Pyx_LocalBuf_ND; + +/* MemviewSliceIsContig.proto */ +static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim); + +/* OverlappingSlices.proto */ +static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, + __Pyx_memviewslice *slice2, + int ndim, size_t itemsize); + +/* Capsule.proto */ +static CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig); + +/* IsLittleEndian.proto */ +static CYTHON_INLINE int __Pyx_Is_Little_Endian(void); + +/* BufferFormatCheck.proto */ +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type); + +/* TypeInfoCompare.proto */ +static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b); + +/* MemviewSliceValidateAndInit.proto */ +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_float(PyObject *, int writable_flag); + +/* None.proto */ +static CYTHON_INLINE Py_ssize_t __Pyx_pow_Py_ssize_t(Py_ssize_t, Py_ssize_t); + +/* MemviewSliceCopyTemplate.proto */ +static __Pyx_memviewslice +__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, + const char *mode, int ndim, + size_t sizeof_dtype, int contig_flag, + int dtype_is_object); + +/* CIntFromPy.proto */ +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); + +/* CIntFromPy.proto */ +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); + +/* CIntFromPy.proto */ +static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *); + +/* CheckBinaryVersion.proto */ +static int __Pyx_check_binary_version(void); + +/* InitStrings.proto */ +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); + +static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/ +static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/ +static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/ +static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/ +static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ +static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ +static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ + +/* Module declarations from 'cython.view' */ + +/* Module declarations from 'cython' */ + +/* Module declarations from 'lcn' */ +static PyTypeObject *__pyx_array_type = 0; +static PyTypeObject *__pyx_MemviewEnum_type = 0; +static PyTypeObject *__pyx_memoryview_type = 0; +static PyTypeObject *__pyx_memoryviewslice_type = 0; +static PyObject *generic = 0; +static PyObject *strided = 0; +static PyObject *indirect = 0; +static PyObject *contiguous = 0; +static PyObject *indirect_contiguous = 0; +static int __pyx_memoryview_thread_locks_used; +static PyThread_type_lock __pyx_memoryview_thread_locks[8]; +static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/ +static void *__pyx_align_pointer(void *, size_t); /*proto*/ +static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/ +static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/ +static PyObject *_unellipsify(PyObject *, int); /*proto*/ +static PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/ +static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/ +static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/ +static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/ +static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/ +static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/ +static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/ +static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/ +static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/ +static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/ +static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/ +static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/ +static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/ +static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/ +static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/ +static int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/ +static int __pyx_memoryview_err(PyObject *, char *); /*proto*/ +static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/ +static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/ +static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/ +static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ +static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ +static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/ +static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/ +static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/ +static __Pyx_TypeInfo __Pyx_TypeInfo_float = { "float", NULL, sizeof(float), { 0 }, 0, 'R', 0, 0 }; +#define __Pyx_MODULE_NAME "lcn" +extern int __pyx_module_is_main_lcn; +int __pyx_module_is_main_lcn = 0; + +/* Implementation of 'lcn' */ +static PyObject *__pyx_builtin_range; +static PyObject *__pyx_builtin_ValueError; +static PyObject *__pyx_builtin_MemoryError; +static PyObject *__pyx_builtin_enumerate; +static PyObject *__pyx_builtin_TypeError; +static PyObject *__pyx_builtin_Ellipsis; +static PyObject *__pyx_builtin_id; +static PyObject *__pyx_builtin_IndexError; +static const char __pyx_k_M[] = "M"; +static const char __pyx_k_N[] = "N"; +static const char __pyx_k_O[] = "O"; +static const char __pyx_k_c[] = "c"; +static const char __pyx_k_i[] = "i"; +static const char __pyx_k_j[] = "j"; +static const char __pyx_k_m[] = "m"; +static const char __pyx_k_n[] = "n"; +static const char __pyx_k_id[] = "id"; +static const char __pyx_k_ks[] = "ks"; +static const char __pyx_k_np[] = "np"; +static const char __pyx_k_eps[] = "eps"; +static const char __pyx_k_img[] = "img"; +static const char __pyx_k_lcn[] = "lcn"; +static const char __pyx_k_new[] = "__new__"; +static const char __pyx_k_num[] = "num"; +static const char __pyx_k_obj[] = "obj"; +static const char __pyx_k_tmp[] = "tmp"; +static const char __pyx_k_base[] = "base"; +static const char __pyx_k_dict[] = "__dict__"; +static const char __pyx_k_main[] = "__main__"; +static const char __pyx_k_mean[] = "mean"; +static const char __pyx_k_mode[] = "mode"; +static const char __pyx_k_name[] = "name"; +static const char __pyx_k_ndim[] = "ndim"; +static const char __pyx_k_pack[] = "pack"; +static const char __pyx_k_size[] = "size"; +static const char __pyx_k_step[] = "step"; +static const char __pyx_k_stop[] = "stop"; +static const char __pyx_k_test[] = "__test__"; +static const char __pyx_k_ASCII[] = "ASCII"; +static const char __pyx_k_class[] = "__class__"; +static const char __pyx_k_dtype[] = "dtype"; +static const char __pyx_k_error[] = "error"; +static const char __pyx_k_flags[] = "flags"; +static const char __pyx_k_numpy[] = "numpy"; +static const char __pyx_k_range[] = "range"; +static const char __pyx_k_shape[] = "shape"; +static const char __pyx_k_start[] = "start"; +static const char __pyx_k_zeros[] = "zeros"; +static const char __pyx_k_encode[] = "encode"; +static const char __pyx_k_format[] = "format"; +static const char __pyx_k_import[] = "__import__"; +static const char __pyx_k_name_2[] = "__name__"; +static const char __pyx_k_pickle[] = "pickle"; +static const char __pyx_k_reduce[] = "__reduce__"; +static const char __pyx_k_stddev[] = "stddev"; +static const char __pyx_k_struct[] = "struct"; +static const char __pyx_k_unpack[] = "unpack"; +static const char __pyx_k_update[] = "update"; +static const char __pyx_k_epsilon[] = "epsilon"; +static const char __pyx_k_float32[] = "float32"; +static const char __pyx_k_fortran[] = "fortran"; +static const char __pyx_k_img_lcn[] = "img_lcn"; +static const char __pyx_k_img_std[] = "img_std"; +static const char __pyx_k_lcn_pyx[] = "lcn.pyx"; +static const char __pyx_k_memview[] = "memview"; +static const char __pyx_k_Ellipsis[] = "Ellipsis"; +static const char __pyx_k_getstate[] = "__getstate__"; +static const char __pyx_k_itemsize[] = "itemsize"; +static const char __pyx_k_pyx_type[] = "__pyx_type"; +static const char __pyx_k_setstate[] = "__setstate__"; +static const char __pyx_k_TypeError[] = "TypeError"; +static const char __pyx_k_enumerate[] = "enumerate"; +static const char __pyx_k_normalize[] = "normalize"; +static const char __pyx_k_pyx_state[] = "__pyx_state"; +static const char __pyx_k_reduce_ex[] = "__reduce_ex__"; +static const char __pyx_k_IndexError[] = "IndexError"; +static const char __pyx_k_ValueError[] = "ValueError"; +static const char __pyx_k_pyx_result[] = "__pyx_result"; +static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__"; +static const char __pyx_k_MemoryError[] = "MemoryError"; +static const char __pyx_k_PickleError[] = "PickleError"; +static const char __pyx_k_kernel_size[] = "kernel_size"; +static const char __pyx_k_img_lcn_view[] = "img_lcn_view"; +static const char __pyx_k_img_std_view[] = "img_std_view"; +static const char __pyx_k_pyx_checksum[] = "__pyx_checksum"; +static const char __pyx_k_stringsource[] = "stringsource"; +static const char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer"; +static const char __pyx_k_reduce_cython[] = "__reduce_cython__"; +static const char __pyx_k_View_MemoryView[] = "View.MemoryView"; +static const char __pyx_k_allocate_buffer[] = "allocate_buffer"; +static const char __pyx_k_dtype_is_object[] = "dtype_is_object"; +static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError"; +static const char __pyx_k_setstate_cython[] = "__setstate_cython__"; +static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum"; +static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; +static const char __pyx_k_strided_and_direct[] = ""; +static const char __pyx_k_strided_and_indirect[] = ""; +static const char __pyx_k_contiguous_and_direct[] = ""; +static const char __pyx_k_MemoryView_of_r_object[] = ""; +static const char __pyx_k_MemoryView_of_r_at_0x_x[] = ""; +static const char __pyx_k_contiguous_and_indirect[] = ""; +static const char __pyx_k_Cannot_index_with_type_s[] = "Cannot index with type '%s'"; +static const char __pyx_k_Invalid_shape_in_axis_d_d[] = "Invalid shape in axis %d: %d."; +static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array"; +static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data."; +static const char __pyx_k_strided_and_direct_or_indirect[] = ""; +static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides"; +static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory."; +static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview"; +static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview"; +static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array"; +static const char __pyx_k_Incompatible_checksums_s_vs_0xb0[] = "Incompatible checksums (%s vs 0xb068931 = (name))"; +static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported"; +static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s"; +static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis %d)"; +static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object"; +static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension %d (got %d and %d)"; +static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__"; +static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides."; +static PyObject *__pyx_n_s_ASCII; +static PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri; +static PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is; +static PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor; +static PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi; +static PyObject *__pyx_kp_s_Cannot_index_with_type_s; +static PyObject *__pyx_n_s_Ellipsis; +static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr; +static PyObject *__pyx_kp_s_Incompatible_checksums_s_vs_0xb0; +static PyObject *__pyx_n_s_IndexError; +static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte; +static PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr; +static PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d; +static PyObject *__pyx_n_s_M; +static PyObject *__pyx_n_s_MemoryError; +static PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x; +static PyObject *__pyx_kp_s_MemoryView_of_r_object; +static PyObject *__pyx_n_s_N; +static PyObject *__pyx_n_b_O; +static PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a; +static PyObject *__pyx_n_s_PickleError; +static PyObject *__pyx_n_s_TypeError; +static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object; +static PyObject *__pyx_n_s_ValueError; +static PyObject *__pyx_n_s_View_MemoryView; +static PyObject *__pyx_n_s_allocate_buffer; +static PyObject *__pyx_n_s_base; +static PyObject *__pyx_n_s_c; +static PyObject *__pyx_n_u_c; +static PyObject *__pyx_n_s_class; +static PyObject *__pyx_n_s_cline_in_traceback; +static PyObject *__pyx_kp_s_contiguous_and_direct; +static PyObject *__pyx_kp_s_contiguous_and_indirect; +static PyObject *__pyx_n_s_dict; +static PyObject *__pyx_n_s_dtype; +static PyObject *__pyx_n_s_dtype_is_object; +static PyObject *__pyx_n_s_encode; +static PyObject *__pyx_n_s_enumerate; +static PyObject *__pyx_n_s_eps; +static PyObject *__pyx_n_s_epsilon; +static PyObject *__pyx_n_s_error; +static PyObject *__pyx_n_s_flags; +static PyObject *__pyx_n_s_float32; +static PyObject *__pyx_n_s_format; +static PyObject *__pyx_n_s_fortran; +static PyObject *__pyx_n_u_fortran; +static PyObject *__pyx_n_s_getstate; +static PyObject *__pyx_kp_s_got_differing_extents_in_dimensi; +static PyObject *__pyx_n_s_i; +static PyObject *__pyx_n_s_id; +static PyObject *__pyx_n_s_img; +static PyObject *__pyx_n_s_img_lcn; +static PyObject *__pyx_n_s_img_lcn_view; +static PyObject *__pyx_n_s_img_std; +static PyObject *__pyx_n_s_img_std_view; +static PyObject *__pyx_n_s_import; +static PyObject *__pyx_n_s_itemsize; +static PyObject *__pyx_kp_s_itemsize_0_for_cython_array; +static PyObject *__pyx_n_s_j; +static PyObject *__pyx_n_s_kernel_size; +static PyObject *__pyx_n_s_ks; +static PyObject *__pyx_n_s_lcn; +static PyObject *__pyx_kp_s_lcn_pyx; +static PyObject *__pyx_n_s_m; +static PyObject *__pyx_n_s_main; +static PyObject *__pyx_n_s_mean; +static PyObject *__pyx_n_s_memview; +static PyObject *__pyx_n_s_mode; +static PyObject *__pyx_n_s_n; +static PyObject *__pyx_n_s_name; +static PyObject *__pyx_n_s_name_2; +static PyObject *__pyx_n_s_ndim; +static PyObject *__pyx_n_s_new; +static PyObject *__pyx_kp_s_no_default___reduce___due_to_non; +static PyObject *__pyx_n_s_normalize; +static PyObject *__pyx_n_s_np; +static PyObject *__pyx_n_s_num; +static PyObject *__pyx_n_s_numpy; +static PyObject *__pyx_n_s_obj; +static PyObject *__pyx_n_s_pack; +static PyObject *__pyx_n_s_pickle; +static PyObject *__pyx_n_s_pyx_PickleError; +static PyObject *__pyx_n_s_pyx_checksum; +static PyObject *__pyx_n_s_pyx_getbuffer; +static PyObject *__pyx_n_s_pyx_result; +static PyObject *__pyx_n_s_pyx_state; +static PyObject *__pyx_n_s_pyx_type; +static PyObject *__pyx_n_s_pyx_unpickle_Enum; +static PyObject *__pyx_n_s_pyx_vtable; +static PyObject *__pyx_n_s_range; +static PyObject *__pyx_n_s_reduce; +static PyObject *__pyx_n_s_reduce_cython; +static PyObject *__pyx_n_s_reduce_ex; +static PyObject *__pyx_n_s_setstate; +static PyObject *__pyx_n_s_setstate_cython; +static PyObject *__pyx_n_s_shape; +static PyObject *__pyx_n_s_size; +static PyObject *__pyx_n_s_start; +static PyObject *__pyx_n_s_stddev; +static PyObject *__pyx_n_s_step; +static PyObject *__pyx_n_s_stop; +static PyObject *__pyx_kp_s_strided_and_direct; +static PyObject *__pyx_kp_s_strided_and_direct_or_indirect; +static PyObject *__pyx_kp_s_strided_and_indirect; +static PyObject *__pyx_kp_s_stringsource; +static PyObject *__pyx_n_s_struct; +static PyObject *__pyx_n_s_test; +static PyObject *__pyx_n_s_tmp; +static PyObject *__pyx_kp_s_unable_to_allocate_array_data; +static PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str; +static PyObject *__pyx_n_s_unpack; +static PyObject *__pyx_n_s_update; +static PyObject *__pyx_n_s_zeros; +static PyObject *__pyx_pf_3lcn_normalize(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_img, int __pyx_v_kernel_size, float __pyx_v_epsilon); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */ +static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */ +static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */ +static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */ +static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject 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(((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1126 + * for i in range(ndim): + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] # <<<<<<<<<<<<<< + * break + * + */ + __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]); + + /* "View.MemoryView":1127 + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] + * break # <<<<<<<<<<<<<< + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): + */ + goto __pyx_L7_break; + + /* "View.MemoryView":1125 + * + * for i in range(ndim): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * f_stride = mslice.strides[i] + * break + */ + } + } + __pyx_L7_break:; + + /* "View.MemoryView":1129 + * break + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< + * return 'C' + * else: + */ + __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1130 + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): + * return 'C' # <<<<<<<<<<<<<< + * else: + * return 'F' + */ + __pyx_r = 'C'; + goto __pyx_L0; + + /* "View.MemoryView":1129 + * break + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< + * return 'C' + * else: + */ + } + + /* "View.MemoryView":1132 + * return 'C' + * else: + * return 'F' # <<<<<<<<<<<<<< + * + * @cython.cdivision(True) + */ + /*else*/ { + __pyx_r = 'F'; + goto __pyx_L0; + } + + /* "View.MemoryView":1111 + * + * @cname('__pyx_get_best_slice_order') + * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< + * """ + * Figure out the best memory access order for a given slice. + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1135 + * + * @cython.cdivision(True) + * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< + * char *dst_data, Py_ssize_t *dst_strides, + * Py_ssize_t *src_shape, 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dst_shape[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t src_stride = src_strides[0] + * cdef Py_ssize_t dst_stride = dst_strides[0] + */ + __pyx_v_dst_extent = (__pyx_v_dst_shape[0]); + + /* "View.MemoryView":1144 + * cdef Py_ssize_t src_extent = src_shape[0] + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + */ + __pyx_v_src_stride = (__pyx_v_src_strides[0]); + + /* "View.MemoryView":1145 + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] + * cdef Py_ssize_t dst_stride = dst_strides[0] # <<<<<<<<<<<<<< + * + * if ndim == 1: + */ + __pyx_v_dst_stride = (__pyx_v_dst_strides[0]); + + /* "View.MemoryView":1147 + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + */ + __pyx_t_1 = ((__pyx_v_ndim == 1) != 0); + if (__pyx_t_1) { + + /* "View.MemoryView":1148 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + __pyx_t_2 = ((__pyx_v_src_stride > 0) != 0); + if (__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L5_bool_binop_done; + } + __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0); + if (__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L5_bool_binop_done; + } + + /* "View.MemoryView":1149 + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<< + * memcpy(dst_data, src_data, itemsize * dst_extent) + * else: + */ + __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize); + if (__pyx_t_2) { + __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride)); + } + __pyx_t_3 = (__pyx_t_2 != 0); + __pyx_t_1 = __pyx_t_3; + __pyx_L5_bool_binop_done:; + + /* "View.MemoryView":1148 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + if (__pyx_t_1) { + + /* "View.MemoryView":1150 + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<< + * else: + * for i in range(dst_extent): + */ + (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent))); + + /* "View.MemoryView":1148 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + goto __pyx_L4; + } + + /* "View.MemoryView":1152 + * memcpy(dst_data, src_data, itemsize * dst_extent) + * else: + * for i in range(dst_extent): # <<<<<<<<<<<<<< + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride + */ + /*else*/ { + __pyx_t_4 = __pyx_v_dst_extent; + __pyx_t_5 = __pyx_t_4; + for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { + __pyx_v_i = __pyx_t_6; + + /* "View.MemoryView":1153 + * else: + * for i in range(dst_extent): + * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<< + * src_data += src_stride + * dst_data += dst_stride + */ + (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize)); + + /* "View.MemoryView":1154 + * for i in range(dst_extent): + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride # <<<<<<<<<<<<<< + * dst_data += dst_stride + * else: + */ + __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); + + /* "View.MemoryView":1155 + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride + * dst_data += dst_stride # <<<<<<<<<<<<<< + * else: + * for i in range(dst_extent): + */ + __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); + } + } + __pyx_L4:; + + /* "View.MemoryView":1147 + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + */ + goto __pyx_L3; + } + + /* "View.MemoryView":1157 + * dst_data += dst_stride + * else: + * for i in range(dst_extent): # <<<<<<<<<<<<<< + * _copy_strided_to_strided(src_data, src_strides + 1, + * dst_data, dst_strides + 1, + */ + /*else*/ { + __pyx_t_4 = __pyx_v_dst_extent; + __pyx_t_5 = __pyx_t_4; + for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { + __pyx_v_i = __pyx_t_6; + + /* "View.MemoryView":1158 + * else: + * for i in range(dst_extent): + * _copy_strided_to_strided(src_data, src_strides + 1, # <<<<<<<<<<<<<< + * dst_data, dst_strides + 1, + * src_shape + 1, dst_shape + 1, + */ + _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize); + + /* "View.MemoryView":1162 + * src_shape + 1, dst_shape + 1, + * ndim - 1, itemsize) + * src_data += src_stride # <<<<<<<<<<<<<< + * dst_data += dst_stride + * + */ + __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); + + /* "View.MemoryView":1163 + * ndim - 1, itemsize) + * src_data += src_stride + * dst_data += dst_stride # <<<<<<<<<<<<<< + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, + */ + __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); + } + } + __pyx_L3:; + + /* "View.MemoryView":1135 + * + * @cython.cdivision(True) + * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< + * char *dst_data, Py_ssize_t *dst_strides, + * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, + */ + + /* function exit code */ +} + +/* "View.MemoryView":1165 + * dst_data += dst_stride + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice *dst, + * int ndim, size_t itemsize) nogil: + */ + +static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) { + + /* "View.MemoryView":1168 + * __Pyx_memviewslice *dst, + * int ndim, size_t itemsize) nogil: + * _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides, # <<<<<<<<<<<<<< + * src.shape, dst.shape, ndim, itemsize) + * + */ + _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize); + + /* "View.MemoryView":1165 + * dst_data += dst_stride + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice *dst, + * int ndim, size_t itemsize) nogil: + */ + + /* function exit code */ +} + +/* "View.MemoryView":1172 + * + * @cname('__pyx_memoryview_slice_get_size') + * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< + * "Return the size of the memory occupied by the slice in number of bytes" + * cdef int i + */ + +static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) { + int __pyx_v_i; + Py_ssize_t __pyx_v_size; + Py_ssize_t __pyx_r; + Py_ssize_t __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + int __pyx_t_4; + + /* "View.MemoryView":1175 + * "Return the size of the memory occupied by the slice in number of bytes" + * cdef int i + * cdef Py_ssize_t size = src.memview.view.itemsize # <<<<<<<<<<<<<< + * + * for i in range(ndim): + */ + __pyx_t_1 = __pyx_v_src->memview->view.itemsize; + __pyx_v_size = __pyx_t_1; + + /* "View.MemoryView":1177 + * cdef Py_ssize_t size = src.memview.view.itemsize + * + * for i in range(ndim): # <<<<<<<<<<<<<< + * size *= src.shape[i] + * + */ + __pyx_t_2 = __pyx_v_ndim; + __pyx_t_3 = __pyx_t_2; + for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { + __pyx_v_i = __pyx_t_4; + + /* "View.MemoryView":1178 + * + * for i in range(ndim): + * size *= src.shape[i] # <<<<<<<<<<<<<< + * + * return size + */ + __pyx_v_size = (__pyx_v_size * (__pyx_v_src->shape[__pyx_v_i])); + } + + /* "View.MemoryView":1180 + * size *= src.shape[i] + * + * return size # <<<<<<<<<<<<<< + * + * @cname('__pyx_fill_contig_strides_array') + */ + __pyx_r = __pyx_v_size; + goto __pyx_L0; + + /* "View.MemoryView":1172 + * + * @cname('__pyx_memoryview_slice_get_size') + * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< + * "Return the size of the memory occupied by the slice in number of bytes" + * cdef int i + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1183 + * + * @cname('__pyx_fill_contig_strides_array') + * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< + * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, + * int ndim, char order) nogil: + */ + +static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) { + int __pyx_v_idx; + Py_ssize_t __pyx_r; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + int __pyx_t_4; + + /* "View.MemoryView":1192 + * cdef int idx + * + * if order == 'F': # <<<<<<<<<<<<<< + * for idx in range(ndim): + * strides[idx] = stride + */ + __pyx_t_1 = ((__pyx_v_order == 'F') != 0); + if (__pyx_t_1) { + + /* "View.MemoryView":1193 + * + * if order == 'F': + * for idx in range(ndim): # <<<<<<<<<<<<<< + * strides[idx] = stride + * stride = stride * shape[idx] + */ + __pyx_t_2 = __pyx_v_ndim; + __pyx_t_3 = __pyx_t_2; + for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { + __pyx_v_idx = __pyx_t_4; + + /* "View.MemoryView":1194 + * if order == 'F': + * for idx in range(ndim): + * strides[idx] = stride # <<<<<<<<<<<<<< + * stride = stride * shape[idx] + * else: + */ + (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; + + /* "View.MemoryView":1195 + * for idx in range(ndim): + * strides[idx] = stride + * stride = stride * shape[idx] # <<<<<<<<<<<<<< + * else: + * for idx in range(ndim - 1, -1, -1): + */ + __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); + } + + /* "View.MemoryView":1192 + * cdef int idx + * + * if order == 'F': # <<<<<<<<<<<<<< + * for idx in range(ndim): + * strides[idx] = stride + */ + goto __pyx_L3; + } + + /* "View.MemoryView":1197 + * stride = stride * shape[idx] + * else: + * for idx in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< + * strides[idx] = stride + * stride = stride * shape[idx] + */ + /*else*/ { + for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) { + __pyx_v_idx = __pyx_t_2; + + /* "View.MemoryView":1198 + * else: + * for idx in range(ndim - 1, -1, -1): + * strides[idx] = stride # <<<<<<<<<<<<<< + * stride = stride * shape[idx] + * + */ + (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; + + /* "View.MemoryView":1199 + * for idx in range(ndim - 1, -1, -1): + * strides[idx] = stride + * stride = stride * shape[idx] # <<<<<<<<<<<<<< + * + * return stride + */ + __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); + } + } + __pyx_L3:; + + /* "View.MemoryView":1201 + * stride = stride * shape[idx] + * + * return stride # <<<<<<<<<<<<<< + * + * @cname('__pyx_memoryview_copy_data_to_temp') + */ + __pyx_r = __pyx_v_stride; + goto __pyx_L0; + + /* "View.MemoryView":1183 + * + * @cname('__pyx_fill_contig_strides_array') + * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< + * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, + * int ndim, char order) nogil: + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1204 + * + * @cname('__pyx_memoryview_copy_data_to_temp') + * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice *tmpslice, + * char order, + */ + +static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) { + int __pyx_v_i; + void *__pyx_v_result; + size_t __pyx_v_itemsize; + size_t __pyx_v_size; + void *__pyx_r; + Py_ssize_t __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + struct __pyx_memoryview_obj *__pyx_t_4; + int __pyx_t_5; + int __pyx_t_6; + + /* "View.MemoryView":1215 + * cdef void *result + * + * cdef size_t itemsize = src.memview.view.itemsize # <<<<<<<<<<<<<< + * cdef size_t size = slice_get_size(src, ndim) + * + */ + __pyx_t_1 = __pyx_v_src->memview->view.itemsize; + __pyx_v_itemsize = __pyx_t_1; + + /* "View.MemoryView":1216 + * + * cdef size_t itemsize = src.memview.view.itemsize + * cdef size_t size = slice_get_size(src, ndim) # <<<<<<<<<<<<<< + * + * result = malloc(size) + */ + __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim); + + /* "View.MemoryView":1218 + * cdef size_t size = slice_get_size(src, ndim) + * + * result = malloc(size) # <<<<<<<<<<<<<< + * if not result: + * _err(MemoryError, NULL) + */ + __pyx_v_result = malloc(__pyx_v_size); + + /* "View.MemoryView":1219 + * + * result = malloc(size) + * if not result: # <<<<<<<<<<<<<< + * _err(MemoryError, NULL) + * + */ + __pyx_t_2 = ((!(__pyx_v_result != 0)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1220 + * result = malloc(size) + * if not result: + * _err(MemoryError, NULL) # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_3 = __pyx_memoryview_err(__pyx_builtin_MemoryError, NULL); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 1220, __pyx_L1_error) + + /* "View.MemoryView":1219 + * + * result = malloc(size) + * if not result: # <<<<<<<<<<<<<< + * _err(MemoryError, NULL) + * + */ + } + + /* "View.MemoryView":1223 + * + * + * tmpslice.data = result # <<<<<<<<<<<<<< + * tmpslice.memview = src.memview + * for i in range(ndim): + */ + __pyx_v_tmpslice->data = ((char *)__pyx_v_result); + + /* "View.MemoryView":1224 + * + * tmpslice.data = result + * tmpslice.memview = src.memview # <<<<<<<<<<<<<< + * for i in range(ndim): + * tmpslice.shape[i] = src.shape[i] + */ + __pyx_t_4 = __pyx_v_src->memview; + __pyx_v_tmpslice->memview = __pyx_t_4; + + /* "View.MemoryView":1225 + * tmpslice.data = result + * tmpslice.memview = src.memview + * for i in range(ndim): # <<<<<<<<<<<<<< + * tmpslice.shape[i] = src.shape[i] + * tmpslice.suboffsets[i] = -1 + */ + __pyx_t_3 = __pyx_v_ndim; + __pyx_t_5 = __pyx_t_3; + for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { + __pyx_v_i = __pyx_t_6; + + /* "View.MemoryView":1226 + * tmpslice.memview = src.memview + * for i in range(ndim): + * tmpslice.shape[i] = src.shape[i] # <<<<<<<<<<<<<< + * tmpslice.suboffsets[i] = -1 + * + */ + (__pyx_v_tmpslice->shape[__pyx_v_i]) = (__pyx_v_src->shape[__pyx_v_i]); + + /* "View.MemoryView":1227 + * for i in range(ndim): + * tmpslice.shape[i] = src.shape[i] + * tmpslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< + * + * fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, + */ + (__pyx_v_tmpslice->suboffsets[__pyx_v_i]) = -1L; + } + + /* "View.MemoryView":1229 + * tmpslice.suboffsets[i] = -1 + * + * fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, # <<<<<<<<<<<<<< + * ndim, order) + * + */ + (void)(__pyx_fill_contig_strides_array((&(__pyx_v_tmpslice->shape[0])), (&(__pyx_v_tmpslice->strides[0])), __pyx_v_itemsize, __pyx_v_ndim, __pyx_v_order)); + + /* "View.MemoryView":1233 + * + * + * for i in range(ndim): # <<<<<<<<<<<<<< + * if tmpslice.shape[i] == 1: + * tmpslice.strides[i] = 0 + */ + __pyx_t_3 = __pyx_v_ndim; + __pyx_t_5 = __pyx_t_3; + for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { + __pyx_v_i = __pyx_t_6; + + /* "View.MemoryView":1234 + * + * for i in range(ndim): + * if tmpslice.shape[i] == 1: # <<<<<<<<<<<<<< + * tmpslice.strides[i] = 0 + * + */ + __pyx_t_2 = (((__pyx_v_tmpslice->shape[__pyx_v_i]) == 1) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1235 + * for i in range(ndim): + * if tmpslice.shape[i] == 1: + * tmpslice.strides[i] = 0 # <<<<<<<<<<<<<< + * + * if slice_is_contig(src[0], order, ndim): + */ + (__pyx_v_tmpslice->strides[__pyx_v_i]) = 0; + + /* "View.MemoryView":1234 + * + * for i in range(ndim): + * if tmpslice.shape[i] == 1: # <<<<<<<<<<<<<< + * tmpslice.strides[i] = 0 + * + */ + } + } + + /* "View.MemoryView":1237 + * tmpslice.strides[i] = 0 + * + * if slice_is_contig(src[0], order, ndim): # <<<<<<<<<<<<<< + * memcpy(result, src.data, size) + * else: + */ + __pyx_t_2 = (__pyx_memviewslice_is_contig((__pyx_v_src[0]), __pyx_v_order, __pyx_v_ndim) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1238 + * + * if slice_is_contig(src[0], order, ndim): + * memcpy(result, src.data, size) # <<<<<<<<<<<<<< + * else: + * copy_strided_to_strided(src, tmpslice, ndim, itemsize) + */ + (void)(memcpy(__pyx_v_result, __pyx_v_src->data, __pyx_v_size)); + + /* "View.MemoryView":1237 + * tmpslice.strides[i] = 0 + * + * if slice_is_contig(src[0], order, ndim): # <<<<<<<<<<<<<< + * memcpy(result, src.data, size) + * else: + */ + goto __pyx_L9; + } + + /* "View.MemoryView":1240 + * memcpy(result, src.data, size) + * else: + * copy_strided_to_strided(src, tmpslice, ndim, itemsize) # <<<<<<<<<<<<<< + * + * return result + */ + /*else*/ { + copy_strided_to_strided(__pyx_v_src, __pyx_v_tmpslice, __pyx_v_ndim, __pyx_v_itemsize); + } + __pyx_L9:; + + /* "View.MemoryView":1242 + * copy_strided_to_strided(src, tmpslice, ndim, itemsize) + * + * return result # <<<<<<<<<<<<<< + * + * + */ + __pyx_r = __pyx_v_result; + goto __pyx_L0; + + /* "View.MemoryView":1204 + * + * @cname('__pyx_memoryview_copy_data_to_temp') + * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice *tmpslice, + * char order, + */ + + /* function exit code */ + __pyx_L1_error:; + { + #ifdef WITH_THREAD + PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); + #endif + __Pyx_AddTraceback("View.MemoryView.copy_data_to_temp", __pyx_clineno, __pyx_lineno, 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_err_extents(i, dst.shape[i], src.shape[i]) + * + * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< + * _err_dim(ValueError, "Dimension %d is not direct", i) + * + */ + __pyx_t_2 = (((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1296 + * + * if src.suboffsets[i] >= 0: + * _err_dim(ValueError, "Dimension %d is not direct", i) # <<<<<<<<<<<<<< + * + * if slices_overlap(&src, &dst, ndim, itemsize): + */ + __pyx_t_6 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Dimension %d is not direct"), __pyx_v_i); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1296, __pyx_L1_error) + + /* "View.MemoryView":1295 + * _err_extents(i, dst.shape[i], src.shape[i]) + * + * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< + * _err_dim(ValueError, "Dimension %d is not direct", i) + * + */ + } + } + + /* "View.MemoryView":1298 + * _err_dim(ValueError, "Dimension %d is not direct", i) + * + * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< + * + * if not slice_is_contig(src, order, ndim): + */ + __pyx_t_2 = (__pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1300 + * if slices_overlap(&src, &dst, ndim, itemsize): + * + * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< + * order = get_best_order(&dst, ndim) + * + */ + __pyx_t_2 = ((!(__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim) != 0)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1301 + * + * if not slice_is_contig(src, order, ndim): + * order = get_best_order(&dst, ndim) # <<<<<<<<<<<<<< + * + * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) + */ + __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim); + + /* "View.MemoryView":1300 + * if slices_overlap(&src, &dst, ndim, itemsize): + * + * if not slice_is_contig(src, order, ndim): # <<<<<<<<<<<<<< + * order = get_best_order(&dst, ndim) + * + */ + } + + /* "View.MemoryView":1303 + * order = get_best_order(&dst, ndim) + * + * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) # <<<<<<<<<<<<<< + * src = tmp + * + */ + __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(1, 1303, __pyx_L1_error) + __pyx_v_tmpdata = __pyx_t_7; + + /* "View.MemoryView":1304 + * + * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) + * src = tmp # <<<<<<<<<<<<<< + * + * if not broadcasting: + */ + __pyx_v_src = __pyx_v_tmp; + + /* "View.MemoryView":1298 + * _err_dim(ValueError, "Dimension %d is not direct", i) + * + * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< + * + * if not slice_is_contig(src, order, ndim): + */ + } + + /* "View.MemoryView":1306 + * src = tmp + * + * if not broadcasting: # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1309 + * + * + * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): + */ + __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1310 + * + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) # <<<<<<<<<<<<<< + * elif slice_is_contig(src, 'F', ndim): + * direct_copy = slice_is_contig(dst, 'F', ndim) + */ + __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim); + + /* "View.MemoryView":1309 + * + * + * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): + */ + goto __pyx_L12; + } + + /* "View.MemoryView":1311 + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + */ + __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1312 + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): + * direct_copy = slice_is_contig(dst, 'F', ndim) # <<<<<<<<<<<<<< + * + * if direct_copy: + */ + __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim); + + /* "View.MemoryView":1311 + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + */ + } + __pyx_L12:; + + /* "View.MemoryView":1314 + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + * if direct_copy: # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, False) + */ + __pyx_t_2 = (__pyx_v_direct_copy != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1316 + * if direct_copy: + * + * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, True) + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); + + /* "View.MemoryView":1317 + * + * refcount_copying(&dst, dtype_is_object, ndim, False) + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) # <<<<<<<<<<<<<< + * refcount_copying(&dst, dtype_is_object, ndim, True) + * free(tmpdata) + */ + (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim))); + + /* "View.MemoryView":1318 + * refcount_copying(&dst, dtype_is_object, ndim, False) + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< + * free(tmpdata) + * return 0 + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); + + /* "View.MemoryView":1319 + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, True) + * free(tmpdata) # <<<<<<<<<<<<<< + * return 0 + * + */ + free(__pyx_v_tmpdata); + + /* "View.MemoryView":1320 + * refcount_copying(&dst, dtype_is_object, ndim, True) + * free(tmpdata) + * return 0 # <<<<<<<<<<<<<< + * + * if order == 'F' == get_best_order(&dst, ndim): + */ + __pyx_r = 0; + goto __pyx_L0; + + /* "View.MemoryView":1314 + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + * if direct_copy: # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, False) + */ + } + + /* "View.MemoryView":1306 + * src = tmp + * + * if not broadcasting: # <<<<<<<<<<<<<< + * + * + */ + } + + /* "View.MemoryView":1322 + * return 0 + * + * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_2 = (__pyx_v_order == 'F'); + if (__pyx_t_2) { + __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim)); + } + __pyx_t_8 = (__pyx_t_2 != 0); + if (__pyx_t_8) { + + /* "View.MemoryView":1325 + * + * + * transpose_memslice(&src) # <<<<<<<<<<<<<< + * transpose_memslice(&dst) + * + */ + __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(1, 1325, __pyx_L1_error) + + /* "View.MemoryView":1326 + * + * transpose_memslice(&src) + * transpose_memslice(&dst) # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, False) + */ + __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(1, 1326, __pyx_L1_error) + + /* "View.MemoryView":1322 + * return 0 + * + * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< + * + * + */ + } + + /* "View.MemoryView":1328 + * transpose_memslice(&dst) + * + * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< + * copy_strided_to_strided(&src, &dst, ndim, itemsize) + * refcount_copying(&dst, dtype_is_object, ndim, True) + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); + + /* "View.MemoryView":1329 + * + * refcount_copying(&dst, dtype_is_object, ndim, False) + * copy_strided_to_strided(&src, &dst, ndim, itemsize) # <<<<<<<<<<<<<< + * refcount_copying(&dst, dtype_is_object, ndim, True) + * + */ + copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize); + + /* "View.MemoryView":1330 + * refcount_copying(&dst, dtype_is_object, ndim, False) + * copy_strided_to_strided(&src, &dst, ndim, itemsize) + * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< + * + * free(tmpdata) + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); + + /* "View.MemoryView":1332 + * refcount_copying(&dst, dtype_is_object, ndim, True) + * + * free(tmpdata) # <<<<<<<<<<<<<< + * return 0 + * + */ + free(__pyx_v_tmpdata); + + /* "View.MemoryView":1333 + * + * free(tmpdata) + * return 0 # <<<<<<<<<<<<<< + * + * @cname('__pyx_memoryview_broadcast_leading') + */ + __pyx_r = 0; + goto __pyx_L0; + + /* "View.MemoryView":1264 + * + * @cname('__pyx_memoryview_copy_contents') + * cdef int memoryview_copy_contents(__Pyx_memviewslice src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice dst, + * int src_ndim, int dst_ndim, + */ + + /* function exit code */ + __pyx_L1_error:; + { + #ifdef WITH_THREAD + PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); + #endif + __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename); + #ifdef WITH_THREAD + __Pyx_PyGILState_Release(__pyx_gilstate_save); + #endif + } + __pyx_r = -1; + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1336 + * + * @cname('__pyx_memoryview_broadcast_leading') + * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< + * int ndim, + * int ndim_other) nogil: + */ + +static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim, int __pyx_v_ndim_other) { + int __pyx_v_i; + int __pyx_v_offset; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + + /* "View.MemoryView":1340 + * int ndim_other) nogil: + * cdef int i + * cdef int offset = ndim_other - ndim # <<<<<<<<<<<<<< + * + * for i in range(ndim - 1, -1, -1): + */ + __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim); + + /* "View.MemoryView":1342 + * cdef int offset = ndim_other - ndim + * + * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< + * mslice.shape[i + offset] = mslice.shape[i] + * mslice.strides[i + offset] = mslice.strides[i] + */ + for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { + __pyx_v_i = __pyx_t_1; + + /* "View.MemoryView":1343 + * + * for i in range(ndim - 1, -1, -1): + * mslice.shape[i + offset] = mslice.shape[i] # <<<<<<<<<<<<<< + * mslice.strides[i + offset] = mslice.strides[i] + * mslice.suboffsets[i + offset] = mslice.suboffsets[i] + */ + (__pyx_v_mslice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->shape[__pyx_v_i]); + + /* "View.MemoryView":1344 + * for i in range(ndim - 1, -1, -1): + * mslice.shape[i + offset] = mslice.shape[i] + * mslice.strides[i + offset] = mslice.strides[i] # <<<<<<<<<<<<<< + * mslice.suboffsets[i + offset] = mslice.suboffsets[i] + * + */ + (__pyx_v_mslice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->strides[__pyx_v_i]); + + /* "View.MemoryView":1345 + * mslice.shape[i + offset] = mslice.shape[i] + * mslice.strides[i + offset] = mslice.strides[i] + * mslice.suboffsets[i + offset] = mslice.suboffsets[i] # <<<<<<<<<<<<<< + * + * for i in range(offset): + */ + (__pyx_v_mslice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->suboffsets[__pyx_v_i]); + } + + /* "View.MemoryView":1347 + * mslice.suboffsets[i + offset] = mslice.suboffsets[i] + * + * for i in range(offset): # <<<<<<<<<<<<<< + * mslice.shape[i] = 1 + * mslice.strides[i] = mslice.strides[0] + */ + __pyx_t_1 = __pyx_v_offset; + __pyx_t_2 = __pyx_t_1; + for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { + __pyx_v_i = __pyx_t_3; + + /* "View.MemoryView":1348 + * + * for i in range(offset): + * mslice.shape[i] = 1 # <<<<<<<<<<<<<< + * mslice.strides[i] = mslice.strides[0] + * mslice.suboffsets[i] = -1 + */ + (__pyx_v_mslice->shape[__pyx_v_i]) = 1; + + /* "View.MemoryView":1349 + * for i in range(offset): + * mslice.shape[i] = 1 + * mslice.strides[i] = mslice.strides[0] # <<<<<<<<<<<<<< + * mslice.suboffsets[i] = -1 + * + */ + (__pyx_v_mslice->strides[__pyx_v_i]) = (__pyx_v_mslice->strides[0]); + + /* "View.MemoryView":1350 + * mslice.shape[i] = 1 + * mslice.strides[i] = mslice.strides[0] + * mslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< + * + * + */ + (__pyx_v_mslice->suboffsets[__pyx_v_i]) = -1L; + } + + /* "View.MemoryView":1336 + * + * @cname('__pyx_memoryview_broadcast_leading') + * cdef void broadcast_leading(__Pyx_memviewslice *mslice, # <<<<<<<<<<<<<< + * int ndim, + * int ndim_other) nogil: + */ + + /* function exit code */ +} + +/* "View.MemoryView":1358 + * + * @cname('__pyx_memoryview_refcount_copying') + * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, # <<<<<<<<<<<<<< + * int ndim, bint inc) nogil: + * + */ + +static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_dtype_is_object, int __pyx_v_ndim, int __pyx_v_inc) { + int __pyx_t_1; + + /* "View.MemoryView":1362 + * + * + * if dtype_is_object: # <<<<<<<<<<<<<< + * refcount_objects_in_slice_with_gil(dst.data, dst.shape, + * dst.strides, ndim, inc) + */ + __pyx_t_1 = (__pyx_v_dtype_is_object != 0); + if (__pyx_t_1) { + + /* "View.MemoryView":1363 + * + * if dtype_is_object: + * refcount_objects_in_slice_with_gil(dst.data, dst.shape, # <<<<<<<<<<<<<< + * dst.strides, ndim, inc) + * + */ + __pyx_memoryview_refcount_objects_in_slice_with_gil(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, 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+ 0, /*tp_finalize*/ + #endif +}; + +static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { + struct __pyx_MemviewEnum_obj *p; + PyObject *o; + if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { + o = (*t->tp_alloc)(t, 0); + } else { + o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); + } + if (unlikely(!o)) return 0; + p = ((struct __pyx_MemviewEnum_obj *)o); + p->name = Py_None; Py_INCREF(Py_None); + return o; +} + +static void __pyx_tp_dealloc_Enum(PyObject *o) { + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + #endif + PyObject_GC_UnTrack(o); + Py_CLEAR(p->name); + (*Py_TYPE(o)->tp_free)(o); +} + +static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + if (p->name) { + e = (*v)(p->name, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear_Enum(PyObject *o) { + PyObject* tmp; + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + tmp = ((PyObject*)p->name); + p->name = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + return 0; +} + +static PyMethodDef __pyx_methods_Enum[] = { + {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_1__reduce_cython__, METH_NOARGS, 0}, + {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, METH_O, 0}, + {0, 0, 0, 0} +}; + +static PyTypeObject __pyx_type___pyx_MemviewEnum = { + PyVarObject_HEAD_INIT(0, 0) + "lcn.Enum", /*tp_name*/ + sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_Enum, /*tp_dealloc*/ + 0, /*tp_print*/ + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + __pyx_MemviewEnum___repr__, /*tp_repr*/ + 0, /*tp_as_number*/ + 0, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + 0, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + 0, /*tp_doc*/ + __pyx_tp_traverse_Enum, /*tp_traverse*/ + __pyx_tp_clear_Enum, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_Enum, /*tp_methods*/ + 0, /*tp_members*/ + 0, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + 0, /*tp_dictoffset*/ + __pyx_MemviewEnum___init__, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_Enum, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + 0, /*tp_finalize*/ + #endif +}; +static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview; + +static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) { + struct __pyx_memoryview_obj *p; + PyObject *o; + if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) { + o = (*t->tp_alloc)(t, 0); + } else { + o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); + } + if (unlikely(!o)) return 0; + p = ((struct __pyx_memoryview_obj *)o); + p->__pyx_vtab = __pyx_vtabptr_memoryview; + p->obj = Py_None; Py_INCREF(Py_None); + p->_size = Py_None; Py_INCREF(Py_None); + p->_array_interface = Py_None; Py_INCREF(Py_None); + p->view.obj = NULL; + if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad; + return o; + bad: + Py_DECREF(o); o = 0; + return NULL; +} + +static void __pyx_tp_dealloc_memoryview(PyObject *o) { + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + #endif + PyObject_GC_UnTrack(o); + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + ++Py_REFCNT(o); + __pyx_memoryview___dealloc__(o); + --Py_REFCNT(o); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->obj); + Py_CLEAR(p->_size); + Py_CLEAR(p->_array_interface); + (*Py_TYPE(o)->tp_free)(o); +} + +static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + if (p->obj) { + e = (*v)(p->obj, a); if (e) return e; + } + if (p->_size) { + e = (*v)(p->_size, a); if (e) return e; + } + if (p->_array_interface) { + e = (*v)(p->_array_interface, a); if (e) return e; + } + if (p->view.obj) { + e = (*v)(p->view.obj, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear_memoryview(PyObject *o) { + PyObject* tmp; + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + tmp = ((PyObject*)p->obj); + p->obj = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->_size); + p->_size = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->_array_interface); + p->_array_interface = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + Py_CLEAR(p->view.obj); + return 0; +} +static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) { + PyObject *r; + PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; + r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); + Py_DECREF(x); + return r; +} + +static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) { + if (v) { + return __pyx_memoryview___setitem__(o, i, v); + } + else { + PyErr_Format(PyExc_NotImplementedError, + "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name); + return -1; + } +} + +static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o); +} + +static PyMethodDef __pyx_methods_memoryview[] = { + {"is_c_contig", (PyCFunction)__pyx_memoryview_is_c_contig, METH_NOARGS, 0}, + {"is_f_contig", (PyCFunction)__pyx_memoryview_is_f_contig, METH_NOARGS, 0}, + {"copy", (PyCFunction)__pyx_memoryview_copy, METH_NOARGS, 0}, + {"copy_fortran", (PyCFunction)__pyx_memoryview_copy_fortran, METH_NOARGS, 0}, + {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_1__reduce_cython__, METH_NOARGS, 0}, + {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_3__setstate_cython__, METH_O, 0}, + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets_memoryview[] = { + {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0}, + {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0}, + {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0}, + {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0}, + {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0}, + {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0}, + {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0}, + {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0}, + {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0}, + {0, 0, 0, 0, 0} +}; + +static PySequenceMethods __pyx_tp_as_sequence_memoryview = { + __pyx_memoryview___len__, /*sq_length*/ + 0, /*sq_concat*/ + 0, /*sq_repeat*/ + __pyx_sq_item_memoryview, /*sq_item*/ + 0, /*sq_slice*/ + 0, /*sq_ass_item*/ + 0, /*sq_ass_slice*/ + 0, /*sq_contains*/ + 0, /*sq_inplace_concat*/ + 0, /*sq_inplace_repeat*/ +}; + +static PyMappingMethods __pyx_tp_as_mapping_memoryview = { + __pyx_memoryview___len__, /*mp_length*/ + __pyx_memoryview___getitem__, /*mp_subscript*/ + __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/ +}; + +static PyBufferProcs __pyx_tp_as_buffer_memoryview = { + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getreadbuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getwritebuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getsegcount*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getcharbuffer*/ + #endif + __pyx_memoryview_getbuffer, /*bf_getbuffer*/ + 0, /*bf_releasebuffer*/ +}; + +static PyTypeObject __pyx_type___pyx_memoryview = { + PyVarObject_HEAD_INIT(0, 0) + "lcn.memoryview", /*tp_name*/ + sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_memoryview, /*tp_dealloc*/ + 0, /*tp_print*/ + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + __pyx_memoryview___repr__, /*tp_repr*/ + 0, /*tp_as_number*/ + &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/ + &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + __pyx_memoryview___str__, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + 0, /*tp_doc*/ + __pyx_tp_traverse_memoryview, /*tp_traverse*/ + __pyx_tp_clear_memoryview, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_memoryview, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets_memoryview, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + 0, /*tp_dictoffset*/ + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_memoryview, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + 0, /*tp_finalize*/ + #endif +}; +static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice; + +static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) { + struct __pyx_memoryviewslice_obj *p; + PyObject *o = __pyx_tp_new_memoryview(t, a, k); + if (unlikely(!o)) return 0; + p = ((struct __pyx_memoryviewslice_obj *)o); + p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice; + p->from_object = Py_None; Py_INCREF(Py_None); + p->from_slice.memview = NULL; + return o; +} + +static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) { + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + #endif + PyObject_GC_UnTrack(o); + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + ++Py_REFCNT(o); + __pyx_memoryviewslice___dealloc__(o); + --Py_REFCNT(o); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->from_object); + PyObject_GC_Track(o); + __pyx_tp_dealloc_memoryview(o); +} + +static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e; + if (p->from_object) { + e = (*v)(p->from_object, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear__memoryviewslice(PyObject *o) { + PyObject* tmp; + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + __pyx_tp_clear_memoryview(o); + tmp = ((PyObject*)p->from_object); + p->from_object = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + __PYX_XDEC_MEMVIEW(&p->from_slice, 1); + return 0; +} + +static PyObject *__pyx_getprop___pyx_memoryviewslice_base(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(o); +} + +static PyMethodDef __pyx_methods__memoryviewslice[] = { + {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, METH_NOARGS, 0}, + {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, METH_O, 0}, + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets__memoryviewslice[] = { + {(char *)"base", __pyx_getprop___pyx_memoryviewslice_base, 0, (char *)0, 0}, + {0, 0, 0, 0, 0} +}; + +static PyTypeObject __pyx_type___pyx_memoryviewslice = { + PyVarObject_HEAD_INIT(0, 0) + "lcn._memoryviewslice", /*tp_name*/ + sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/ + 0, /*tp_print*/ + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + #if CYTHON_COMPILING_IN_PYPY + __pyx_memoryview___repr__, /*tp_repr*/ + #else + 0, /*tp_repr*/ + #endif + 0, /*tp_as_number*/ + 0, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + #if CYTHON_COMPILING_IN_PYPY + __pyx_memoryview___str__, /*tp_str*/ + #else + 0, /*tp_str*/ + #endif + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + "Internal class for passing memoryview slices to Python", /*tp_doc*/ + __pyx_tp_traverse__memoryviewslice, /*tp_traverse*/ + __pyx_tp_clear__memoryviewslice, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods__memoryviewslice, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets__memoryviewslice, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + 0, /*tp_dictoffset*/ + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new__memoryviewslice, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + 0, /*tp_finalize*/ + #endif +}; + +static PyMethodDef __pyx_methods[] = { + {0, 0, 0, 0} +}; + +#if PY_MAJOR_VERSION >= 3 +#if CYTHON_PEP489_MULTI_PHASE_INIT +static PyObject* __pyx_pymod_create(PyObject *spec, PyModuleDef *def); /*proto*/ +static int __pyx_pymod_exec_lcn(PyObject* module); /*proto*/ +static PyModuleDef_Slot __pyx_moduledef_slots[] = { + {Py_mod_create, (void*)__pyx_pymod_create}, + {Py_mod_exec, (void*)__pyx_pymod_exec_lcn}, + {0, NULL} +}; +#endif + +static struct PyModuleDef __pyx_moduledef = { + PyModuleDef_HEAD_INIT, + "lcn", + 0, /* m_doc */ + #if CYTHON_PEP489_MULTI_PHASE_INIT + 0, /* m_size */ + #else + -1, /* m_size */ + #endif + __pyx_methods /* m_methods */, + #if CYTHON_PEP489_MULTI_PHASE_INIT + __pyx_moduledef_slots, /* m_slots */ + #else + NULL, /* m_reload */ + #endif + NULL, /* m_traverse */ + NULL, /* m_clear */ + NULL /* m_free */ +}; +#endif +#ifndef CYTHON_SMALL_CODE +#if defined(__clang__) + #define CYTHON_SMALL_CODE +#elif defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 3)) + #define CYTHON_SMALL_CODE __attribute__((cold)) +#else + #define CYTHON_SMALL_CODE +#endif +#endif + +static __Pyx_StringTabEntry __pyx_string_tab[] = { + {&__pyx_n_s_ASCII, __pyx_k_ASCII, sizeof(__pyx_k_ASCII), 0, 0, 1, 1}, + {&__pyx_kp_s_Buffer_view_does_not_expose_stri, __pyx_k_Buffer_view_does_not_expose_stri, sizeof(__pyx_k_Buffer_view_does_not_expose_stri), 0, 0, 1, 0}, + {&__pyx_kp_s_Can_only_create_a_buffer_that_is, __pyx_k_Can_only_create_a_buffer_that_is, sizeof(__pyx_k_Can_only_create_a_buffer_that_is), 0, 0, 1, 0}, + {&__pyx_kp_s_Cannot_assign_to_read_only_memor, __pyx_k_Cannot_assign_to_read_only_memor, sizeof(__pyx_k_Cannot_assign_to_read_only_memor), 0, 0, 1, 0}, + {&__pyx_kp_s_Cannot_create_writable_memory_vi, __pyx_k_Cannot_create_writable_memory_vi, sizeof(__pyx_k_Cannot_create_writable_memory_vi), 0, 0, 1, 0}, + {&__pyx_kp_s_Cannot_index_with_type_s, __pyx_k_Cannot_index_with_type_s, sizeof(__pyx_k_Cannot_index_with_type_s), 0, 0, 1, 0}, + {&__pyx_n_s_Ellipsis, __pyx_k_Ellipsis, sizeof(__pyx_k_Ellipsis), 0, 0, 1, 1}, + {&__pyx_kp_s_Empty_shape_tuple_for_cython_arr, __pyx_k_Empty_shape_tuple_for_cython_arr, sizeof(__pyx_k_Empty_shape_tuple_for_cython_arr), 0, 0, 1, 0}, + {&__pyx_kp_s_Incompatible_checksums_s_vs_0xb0, __pyx_k_Incompatible_checksums_s_vs_0xb0, sizeof(__pyx_k_Incompatible_checksums_s_vs_0xb0), 0, 0, 1, 0}, + {&__pyx_n_s_IndexError, __pyx_k_IndexError, sizeof(__pyx_k_IndexError), 0, 0, 1, 1}, + {&__pyx_kp_s_Indirect_dimensions_not_supporte, __pyx_k_Indirect_dimensions_not_supporte, sizeof(__pyx_k_Indirect_dimensions_not_supporte), 0, 0, 1, 0}, + {&__pyx_kp_s_Invalid_mode_expected_c_or_fortr, __pyx_k_Invalid_mode_expected_c_or_fortr, sizeof(__pyx_k_Invalid_mode_expected_c_or_fortr), 0, 0, 1, 0}, + {&__pyx_kp_s_Invalid_shape_in_axis_d_d, __pyx_k_Invalid_shape_in_axis_d_d, sizeof(__pyx_k_Invalid_shape_in_axis_d_d), 0, 0, 1, 0}, + {&__pyx_n_s_M, __pyx_k_M, sizeof(__pyx_k_M), 0, 0, 1, 1}, + {&__pyx_n_s_MemoryError, __pyx_k_MemoryError, 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{&__pyx_n_s_View_MemoryView, __pyx_k_View_MemoryView, sizeof(__pyx_k_View_MemoryView), 0, 0, 1, 1}, + {&__pyx_n_s_allocate_buffer, __pyx_k_allocate_buffer, sizeof(__pyx_k_allocate_buffer), 0, 0, 1, 1}, + {&__pyx_n_s_base, __pyx_k_base, sizeof(__pyx_k_base), 0, 0, 1, 1}, + {&__pyx_n_s_c, __pyx_k_c, sizeof(__pyx_k_c), 0, 0, 1, 1}, + {&__pyx_n_u_c, __pyx_k_c, sizeof(__pyx_k_c), 0, 1, 0, 1}, + {&__pyx_n_s_class, __pyx_k_class, sizeof(__pyx_k_class), 0, 0, 1, 1}, + {&__pyx_n_s_cline_in_traceback, __pyx_k_cline_in_traceback, sizeof(__pyx_k_cline_in_traceback), 0, 0, 1, 1}, + {&__pyx_kp_s_contiguous_and_direct, __pyx_k_contiguous_and_direct, sizeof(__pyx_k_contiguous_and_direct), 0, 0, 1, 0}, + {&__pyx_kp_s_contiguous_and_indirect, __pyx_k_contiguous_and_indirect, sizeof(__pyx_k_contiguous_and_indirect), 0, 0, 1, 0}, + {&__pyx_n_s_dict, __pyx_k_dict, sizeof(__pyx_k_dict), 0, 0, 1, 1}, + {&__pyx_n_s_dtype, __pyx_k_dtype, sizeof(__pyx_k_dtype), 0, 0, 1, 1}, + {&__pyx_n_s_dtype_is_object, 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sizeof(__pyx_k_got_differing_extents_in_dimensi), 0, 0, 1, 0}, + {&__pyx_n_s_i, __pyx_k_i, sizeof(__pyx_k_i), 0, 0, 1, 1}, + {&__pyx_n_s_id, __pyx_k_id, sizeof(__pyx_k_id), 0, 0, 1, 1}, + {&__pyx_n_s_img, __pyx_k_img, sizeof(__pyx_k_img), 0, 0, 1, 1}, + {&__pyx_n_s_img_lcn, __pyx_k_img_lcn, sizeof(__pyx_k_img_lcn), 0, 0, 1, 1}, + {&__pyx_n_s_img_lcn_view, __pyx_k_img_lcn_view, sizeof(__pyx_k_img_lcn_view), 0, 0, 1, 1}, + {&__pyx_n_s_img_std, __pyx_k_img_std, sizeof(__pyx_k_img_std), 0, 0, 1, 1}, + {&__pyx_n_s_img_std_view, __pyx_k_img_std_view, sizeof(__pyx_k_img_std_view), 0, 0, 1, 1}, + {&__pyx_n_s_import, __pyx_k_import, sizeof(__pyx_k_import), 0, 0, 1, 1}, + {&__pyx_n_s_itemsize, __pyx_k_itemsize, sizeof(__pyx_k_itemsize), 0, 0, 1, 1}, + {&__pyx_kp_s_itemsize_0_for_cython_array, __pyx_k_itemsize_0_for_cython_array, sizeof(__pyx_k_itemsize_0_for_cython_array), 0, 0, 1, 0}, + {&__pyx_n_s_j, __pyx_k_j, sizeof(__pyx_k_j), 0, 0, 1, 1}, + {&__pyx_n_s_kernel_size, __pyx_k_kernel_size, 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{&__pyx_kp_s_unable_to_allocate_array_data, __pyx_k_unable_to_allocate_array_data, sizeof(__pyx_k_unable_to_allocate_array_data), 0, 0, 1, 0}, + {&__pyx_kp_s_unable_to_allocate_shape_and_str, __pyx_k_unable_to_allocate_shape_and_str, sizeof(__pyx_k_unable_to_allocate_shape_and_str), 0, 0, 1, 0}, + {&__pyx_n_s_unpack, __pyx_k_unpack, sizeof(__pyx_k_unpack), 0, 0, 1, 1}, + {&__pyx_n_s_update, __pyx_k_update, sizeof(__pyx_k_update), 0, 0, 1, 1}, + {&__pyx_n_s_zeros, __pyx_k_zeros, sizeof(__pyx_k_zeros), 0, 0, 1, 1}, + {0, 0, 0, 0, 0, 0, 0} +}; +static CYTHON_SMALL_CODE int __Pyx_InitCachedBuiltins(void) { + __pyx_builtin_range = __Pyx_GetBuiltinName(__pyx_n_s_range); if (!__pyx_builtin_range) __PYX_ERR(0, 36, __pyx_L1_error) + __pyx_builtin_ValueError = __Pyx_GetBuiltinName(__pyx_n_s_ValueError); if (!__pyx_builtin_ValueError) __PYX_ERR(1, 132, __pyx_L1_error) + __pyx_builtin_MemoryError = __Pyx_GetBuiltinName(__pyx_n_s_MemoryError); if (!__pyx_builtin_MemoryError) __PYX_ERR(1, 147, 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PyMODINIT_FUNC +#endif +#else +#ifdef CYTHON_NO_PYINIT_EXPORT +#define __Pyx_PyMODINIT_FUNC PyObject * +#else +#define __Pyx_PyMODINIT_FUNC PyMODINIT_FUNC +#endif +#endif + + +#if PY_MAJOR_VERSION < 3 +__Pyx_PyMODINIT_FUNC initlcn(void) CYTHON_SMALL_CODE; /*proto*/ +__Pyx_PyMODINIT_FUNC initlcn(void) +#else +__Pyx_PyMODINIT_FUNC PyInit_lcn(void) CYTHON_SMALL_CODE; /*proto*/ +__Pyx_PyMODINIT_FUNC PyInit_lcn(void) +#if CYTHON_PEP489_MULTI_PHASE_INIT +{ + return PyModuleDef_Init(&__pyx_moduledef); +} +static CYTHON_SMALL_CODE int __Pyx_check_single_interpreter(void) { + #if PY_VERSION_HEX >= 0x030700A1 + static PY_INT64_T main_interpreter_id = -1; + PY_INT64_T current_id = PyInterpreterState_GetID(PyThreadState_Get()->interp); + if (main_interpreter_id == -1) { + main_interpreter_id = current_id; + return (unlikely(current_id == -1)) ? -1 : 0; + } else if (unlikely(main_interpreter_id != current_id)) + #else + static PyInterpreterState *main_interpreter = NULL; + PyInterpreterState 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+ if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + int cmp = (**argname == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**argname, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) goto arg_passed_twice; + argname++; + } + } + } else + goto invalid_keyword_type; + if (kwds2) { + if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; + } else { + goto invalid_keyword; + } + } + return 0; +arg_passed_twice: + __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + goto bad; +invalid_keyword: + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION < 3 + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif +bad: + return -1; +} + +/* RaiseArgTupleInvalid */ +static void __Pyx_RaiseArgtupleInvalid( + const char* func_name, + int exact, + Py_ssize_t num_min, + Py_ssize_t num_max, + Py_ssize_t num_found) +{ + Py_ssize_t num_expected; + const char *more_or_less; + if (num_found < num_min) { + num_expected = num_min; + more_or_less = "at least"; + } else { + num_expected = num_max; + more_or_less = "at most"; + } + if (exact) { + more_or_less = "exactly"; + } + PyErr_Format(PyExc_TypeError, + "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", + func_name, more_or_less, num_expected, + (num_expected == 1) ? "" : "s", num_found); +} + +/* GetModuleGlobalName */ +#if CYTHON_USE_DICT_VERSIONS +static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value) +#else +static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name) +#endif +{ + PyObject *result; +#if !CYTHON_AVOID_BORROWED_REFS +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 + result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } else if (unlikely(PyErr_Occurred())) { + return NULL; + } +#else + result = PyDict_GetItem(__pyx_d, name); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } +#endif +#else + result = PyObject_GetItem(__pyx_d, name); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } + PyErr_Clear(); +#endif + return __Pyx_GetBuiltinName(name); +} + +/* PyObjectCall */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *result; + ternaryfunc call = func->ob_type->tp_call; + if (unlikely(!call)) + return PyObject_Call(func, arg, kw); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = (*call)(func, arg, kw); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* MemviewSliceInit */ +static int +__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview, + int ndim, + __Pyx_memviewslice *memviewslice, + int memview_is_new_reference) +{ + __Pyx_RefNannyDeclarations + int i, retval=-1; + Py_buffer *buf = &memview->view; + __Pyx_RefNannySetupContext("init_memviewslice", 0); + if (!buf) { + PyErr_SetString(PyExc_ValueError, + "buf is NULL."); + goto fail; + } else if (memviewslice->memview || memviewslice->data) { + PyErr_SetString(PyExc_ValueError, + "memviewslice is already initialized!"); + goto fail; + } + if (buf->strides) { + for (i = 0; i < ndim; i++) { + memviewslice->strides[i] = buf->strides[i]; + } + } else { + Py_ssize_t stride = buf->itemsize; + for (i = ndim - 1; i >= 0; i--) { + memviewslice->strides[i] = stride; + stride *= buf->shape[i]; + } + } + for (i = 0; i < ndim; i++) { + memviewslice->shape[i] = buf->shape[i]; + if (buf->suboffsets) { + memviewslice->suboffsets[i] = buf->suboffsets[i]; + } else { + memviewslice->suboffsets[i] = -1; + } + } + memviewslice->memview = memview; + memviewslice->data = (char *)buf->buf; + if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) { + Py_INCREF(memview); + } + retval = 0; + goto no_fail; +fail: + memviewslice->memview = 0; + memviewslice->data = 0; + retval = -1; +no_fail: + __Pyx_RefNannyFinishContext(); + return retval; +} +#ifndef Py_NO_RETURN +#define Py_NO_RETURN +#endif +static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN { + va_list vargs; + char msg[200]; +#ifdef HAVE_STDARG_PROTOTYPES + va_start(vargs, fmt); +#else + va_start(vargs); +#endif + vsnprintf(msg, 200, fmt, vargs); + va_end(vargs); + Py_FatalError(msg); +} +static CYTHON_INLINE int +__pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count, + PyThread_type_lock lock) +{ + int result; + PyThread_acquire_lock(lock, 1); + result = (*acquisition_count)++; + PyThread_release_lock(lock); + return result; +} +static CYTHON_INLINE int +__pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count, + PyThread_type_lock lock) +{ + int result; + PyThread_acquire_lock(lock, 1); + result = (*acquisition_count)--; + PyThread_release_lock(lock); + return result; +} +static CYTHON_INLINE void +__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) +{ + int first_time; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (!memview || (PyObject *) memview == Py_None) + return; + if (__pyx_get_slice_count(memview) < 0) + __pyx_fatalerror("Acquisition count is %d (line %d)", + __pyx_get_slice_count(memview), lineno); + first_time = __pyx_add_acquisition_count(memview) == 0; + if (first_time) { + if (have_gil) { + Py_INCREF((PyObject *) memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_INCREF((PyObject *) memview); + PyGILState_Release(_gilstate); + } + } +} +static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice, + int have_gil, int lineno) { + int last_time; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (!memview ) { + return; + } else if ((PyObject *) memview == Py_None) { + memslice->memview = NULL; + return; + } + if (__pyx_get_slice_count(memview) <= 0) + __pyx_fatalerror("Acquisition count is %d (line %d)", + __pyx_get_slice_count(memview), lineno); + last_time = __pyx_sub_acquisition_count(memview) == 1; + memslice->data = NULL; + if (last_time) { + if (have_gil) { + Py_CLEAR(memslice->memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_CLEAR(memslice->memview); + PyGILState_Release(_gilstate); + } + } else { + memslice->memview = NULL; + } +} + +/* ArgTypeTest */ +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact) +{ + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + else if (exact) { + #if PY_MAJOR_VERSION == 2 + if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; + #endif + } + else { + if (likely(__Pyx_TypeCheck(obj, type))) return 1; + } + PyErr_Format(PyExc_TypeError, + "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", + name, type->tp_name, Py_TYPE(obj)->tp_name); + return 0; +} + +/* PyErrFetchRestore */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + tmp_type = tstate->curexc_type; + tmp_value = tstate->curexc_value; + tmp_tb = tstate->curexc_traceback; + tstate->curexc_type = type; + tstate->curexc_value = value; + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +} +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + *type = tstate->curexc_type; + *value = tstate->curexc_value; + *tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +} +#endif + +/* RaiseException */ +#if PY_MAJOR_VERSION < 3 +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, + CYTHON_UNUSED PyObject *cause) { + __Pyx_PyThreadState_declare + Py_XINCREF(type); + if (!value || value == Py_None) + value = NULL; + else + Py_INCREF(value); + if (!tb || tb == Py_None) + tb = NULL; + else { + Py_INCREF(tb); + if (!PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } + } + if (PyType_Check(type)) { +#if CYTHON_COMPILING_IN_PYPY + if (!value) { + Py_INCREF(Py_None); + value = Py_None; + } +#endif + PyErr_NormalizeException(&type, &value, &tb); + } else { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto raise_error; + } + value = type; + type = (PyObject*) Py_TYPE(type); + Py_INCREF(type); + if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto raise_error; + } + } + __Pyx_PyThreadState_assign + __Pyx_ErrRestore(type, value, tb); + return; +raise_error: + Py_XDECREF(value); + Py_XDECREF(type); + Py_XDECREF(tb); + return; +} +#else +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + PyObject* owned_instance = NULL; + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto bad; + } + if (value == Py_None) + value = 0; + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; + } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (PyExceptionClass_Check(type)) { + PyObject *instance_class = NULL; + if (value && PyExceptionInstance_Check(value)) { + instance_class = (PyObject*) Py_TYPE(value); + if (instance_class != type) { + int is_subclass = PyObject_IsSubclass(instance_class, type); + if (!is_subclass) { + instance_class = NULL; + } else if (unlikely(is_subclass == -1)) { + goto bad; + } else { + type = instance_class; + } + } + } + if (!instance_class) { + PyObject *args; + if (!value) + args = PyTuple_New(0); + else if (PyTuple_Check(value)) { + Py_INCREF(value); + args = value; + } else + args = PyTuple_Pack(1, value); + if (!args) + goto bad; + owned_instance = PyObject_Call(type, args, NULL); + Py_DECREF(args); + if (!owned_instance) + goto bad; + value = owned_instance; + if (!PyExceptionInstance_Check(value)) { + PyErr_Format(PyExc_TypeError, + "calling %R should have returned an instance of " + "BaseException, not %R", + type, Py_TYPE(value)); + goto bad; + } + } + } else { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; + } + if (cause) { + PyObject *fixed_cause; + if (cause == Py_None) { + fixed_cause = NULL; + } else if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } + PyErr_SetObject(type, value); + if (tb) { +#if CYTHON_COMPILING_IN_PYPY + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); + Py_INCREF(tb); + PyErr_Restore(tmp_type, tmp_value, tb); + Py_XDECREF(tmp_tb); +#else + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } +#endif + } +bad: + Py_XDECREF(owned_instance); + return; +} +#endif + +/* PyCFunctionFastCall */ +#if CYTHON_FAST_PYCCALL +static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { + PyCFunctionObject *func = (PyCFunctionObject*)func_obj; + PyCFunction meth = PyCFunction_GET_FUNCTION(func); + PyObject *self = PyCFunction_GET_SELF(func); + int flags = PyCFunction_GET_FLAGS(func); + assert(PyCFunction_Check(func)); + assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))); + assert(nargs >= 0); + assert(nargs == 0 || args != NULL); + /* _PyCFunction_FastCallDict() must not be called with an exception set, + because it may clear it (directly or indirectly) and so the + caller loses its exception */ + assert(!PyErr_Occurred()); + if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) { + return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL); + } else { + return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs); + } +} +#endif + +/* PyFunctionFastCall */ +#if CYTHON_FAST_PYCALL +static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, + PyObject *globals) { + PyFrameObject *f; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject **fastlocals; + Py_ssize_t i; + PyObject *result; + assert(globals != NULL); + /* XXX Perhaps we should create a specialized + PyFrame_New() that doesn't take locals, but does + take builtins without sanity checking them. + */ + assert(tstate != NULL); + f = PyFrame_New(tstate, co, globals, NULL); + if (f == NULL) { + return NULL; + } + fastlocals = __Pyx_PyFrame_GetLocalsplus(f); + for (i = 0; i < na; i++) { + Py_INCREF(*args); + fastlocals[i] = *args++; + } + result = PyEval_EvalFrameEx(f,0); + ++tstate->recursion_depth; + Py_DECREF(f); + --tstate->recursion_depth; + return result; +} +#if 1 || PY_VERSION_HEX < 0x030600B1 +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs) { + PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); + PyObject *globals = PyFunction_GET_GLOBALS(func); + PyObject *argdefs = PyFunction_GET_DEFAULTS(func); + PyObject *closure; +#if PY_MAJOR_VERSION >= 3 + PyObject *kwdefs; +#endif + PyObject *kwtuple, **k; + PyObject **d; + Py_ssize_t nd; + Py_ssize_t nk; + PyObject *result; + assert(kwargs == NULL || PyDict_Check(kwargs)); + nk = kwargs ? PyDict_Size(kwargs) : 0; + if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { + return NULL; + } + if ( +#if PY_MAJOR_VERSION >= 3 + co->co_kwonlyargcount == 0 && +#endif + likely(kwargs == NULL || nk == 0) && + co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { + if (argdefs == NULL && co->co_argcount == nargs) { + result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); + goto done; + } + else if (nargs == 0 && argdefs != NULL + && co->co_argcount == Py_SIZE(argdefs)) { + /* function called with no arguments, but all parameters have + a default value: use default values as arguments .*/ + args = &PyTuple_GET_ITEM(argdefs, 0); + result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); + goto done; + } + } + if (kwargs != NULL) { + Py_ssize_t pos, i; + kwtuple = PyTuple_New(2 * nk); + if (kwtuple == NULL) { + result = NULL; + goto done; + } + k = &PyTuple_GET_ITEM(kwtuple, 0); + pos = i = 0; + while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { + Py_INCREF(k[i]); + Py_INCREF(k[i+1]); + i += 2; + } + nk = i / 2; + } + else { + kwtuple = NULL; + k = NULL; + } + closure = PyFunction_GET_CLOSURE(func); +#if PY_MAJOR_VERSION >= 3 + kwdefs = PyFunction_GET_KW_DEFAULTS(func); +#endif + if (argdefs != NULL) { + d = &PyTuple_GET_ITEM(argdefs, 0); + nd = Py_SIZE(argdefs); + } + else { + d = NULL; + nd = 0; + } +#if PY_MAJOR_VERSION >= 3 + result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, + args, nargs, + k, (int)nk, + d, (int)nd, kwdefs, closure); +#else + result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, + args, nargs, + k, (int)nk, + d, (int)nd, closure); +#endif + Py_XDECREF(kwtuple); +done: + Py_LeaveRecursiveCall(); + return result; +} +#endif +#endif + +/* PyObjectCall2Args */ +static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) { + PyObject *args, *result = NULL; + #if CYTHON_FAST_PYCALL + if (PyFunction_Check(function)) { + PyObject *args[2] = {arg1, arg2}; + return __Pyx_PyFunction_FastCall(function, args, 2); + } + #endif + #if CYTHON_FAST_PYCCALL + if (__Pyx_PyFastCFunction_Check(function)) { + PyObject *args[2] = {arg1, arg2}; + return __Pyx_PyCFunction_FastCall(function, args, 2); + } + #endif + args = PyTuple_New(2); + if (unlikely(!args)) goto done; + Py_INCREF(arg1); + PyTuple_SET_ITEM(args, 0, arg1); + Py_INCREF(arg2); + PyTuple_SET_ITEM(args, 1, arg2); + Py_INCREF(function); + result = __Pyx_PyObject_Call(function, args, NULL); + Py_DECREF(args); + Py_DECREF(function); +done: + return result; +} + +/* PyObjectCallMethO */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { + PyObject *self, *result; + PyCFunction cfunc; + cfunc = PyCFunction_GET_FUNCTION(func); + self = PyCFunction_GET_SELF(func); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = cfunc(self, arg); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyObjectCallOneArg */ +#if CYTHON_COMPILING_IN_CPYTHON +static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_New(1); + if (unlikely(!args)) return NULL; + Py_INCREF(arg); + PyTuple_SET_ITEM(args, 0, arg); + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { +#if CYTHON_FAST_PYCALL + if (PyFunction_Check(func)) { + return __Pyx_PyFunction_FastCall(func, &arg, 1); + } +#endif + if (likely(PyCFunction_Check(func))) { + if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { + return __Pyx_PyObject_CallMethO(func, arg); +#if CYTHON_FAST_PYCCALL + } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) { + return __Pyx_PyCFunction_FastCall(func, &arg, 1); +#endif + } + } + return __Pyx__PyObject_CallOneArg(func, arg); +} +#else +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_Pack(1, arg); + if (unlikely(!args)) return NULL; + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +#endif + +/* BytesEquals */ +static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY + return PyObject_RichCompareBool(s1, s2, equals); +#else + if (s1 == s2) { + return (equals == Py_EQ); + } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) { + const char *ps1, *ps2; + Py_ssize_t length = PyBytes_GET_SIZE(s1); + if (length != PyBytes_GET_SIZE(s2)) + return (equals == Py_NE); + ps1 = PyBytes_AS_STRING(s1); + ps2 = PyBytes_AS_STRING(s2); + if (ps1[0] != ps2[0]) { + return (equals == Py_NE); + } else if (length == 1) { + return (equals == Py_EQ); + } else { + int result; +#if CYTHON_USE_UNICODE_INTERNALS + Py_hash_t hash1, hash2; + hash1 = ((PyBytesObject*)s1)->ob_shash; + hash2 = ((PyBytesObject*)s2)->ob_shash; + if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { + return (equals == Py_NE); + } +#endif + result = memcmp(ps1, ps2, (size_t)length); + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { + return (equals == Py_NE); + } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { + return (equals == Py_NE); + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +#endif +} + +/* UnicodeEquals */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY + return PyObject_RichCompareBool(s1, s2, equals); +#else +#if PY_MAJOR_VERSION < 3 + PyObject* owned_ref = NULL; +#endif + int s1_is_unicode, s2_is_unicode; + if (s1 == s2) { + goto return_eq; + } + s1_is_unicode = PyUnicode_CheckExact(s1); + s2_is_unicode = PyUnicode_CheckExact(s2); +#if PY_MAJOR_VERSION < 3 + if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) { + owned_ref = PyUnicode_FromObject(s2); + if (unlikely(!owned_ref)) + return -1; + s2 = owned_ref; + s2_is_unicode = 1; + } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) { + owned_ref = PyUnicode_FromObject(s1); + if (unlikely(!owned_ref)) + return -1; + s1 = owned_ref; + s1_is_unicode = 1; + } else if (((!s2_is_unicode) & (!s1_is_unicode))) { + return __Pyx_PyBytes_Equals(s1, s2, equals); + } +#endif + if (s1_is_unicode & s2_is_unicode) { + Py_ssize_t length; + int kind; + void *data1, *data2; + if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0)) + return -1; + length = __Pyx_PyUnicode_GET_LENGTH(s1); + if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) { + goto return_ne; + } +#if CYTHON_USE_UNICODE_INTERNALS + { + Py_hash_t hash1, hash2; + #if CYTHON_PEP393_ENABLED + hash1 = ((PyASCIIObject*)s1)->hash; + hash2 = ((PyASCIIObject*)s2)->hash; + #else + hash1 = ((PyUnicodeObject*)s1)->hash; + hash2 = ((PyUnicodeObject*)s2)->hash; + #endif + if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { + goto return_ne; + } + } +#endif + kind = __Pyx_PyUnicode_KIND(s1); + if (kind != __Pyx_PyUnicode_KIND(s2)) { + goto return_ne; + } + data1 = __Pyx_PyUnicode_DATA(s1); + data2 = __Pyx_PyUnicode_DATA(s2); + if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) { + goto return_ne; + } else if (length == 1) { + goto return_eq; + } else { + int result = memcmp(data1, data2, (size_t)(length * kind)); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & s2_is_unicode) { + goto return_ne; + } else if ((s2 == Py_None) & s1_is_unicode) { + goto return_ne; + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +return_eq: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ); +return_ne: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_NE); +#endif +} + +/* None */ +static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) { + Py_ssize_t q = a / b; + Py_ssize_t r = a - q*b; + q -= ((r != 0) & ((r ^ b) < 0)); + return q; +} + +/* GetAttr */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { +#if CYTHON_USE_TYPE_SLOTS +#if PY_MAJOR_VERSION >= 3 + if (likely(PyUnicode_Check(n))) +#else + if (likely(PyString_Check(n))) +#endif + return __Pyx_PyObject_GetAttrStr(o, n); +#endif + return PyObject_GetAttr(o, n); +} + +/* GetItemInt */ +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { + PyObject *r; + if (!j) return NULL; + r = PyObject_GetItem(o, j); + Py_DECREF(j); + return r; +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyList_GET_SIZE(o); + } + if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) { + PyObject *r = PyList_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyTuple_GET_SIZE(o); + } + if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS + if (is_list || PyList_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); + if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) { + PyObject *r = PyList_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } + else if (PyTuple_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); + if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } else { + PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; + if (likely(m && m->sq_item)) { + if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { + Py_ssize_t l = m->sq_length(o); + if (likely(l >= 0)) { + i += l; + } else { + if (!PyErr_ExceptionMatches(PyExc_OverflowError)) + return NULL; + PyErr_Clear(); + } + } + return m->sq_item(o, i); + } + } +#else + if (is_list || PySequence_Check(o)) { + return PySequence_GetItem(o, i); + } +#endif + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +} + +/* ObjectGetItem */ +#if CYTHON_USE_TYPE_SLOTS +static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) { + PyObject *runerr; + Py_ssize_t key_value; + PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence; + if (unlikely(!(m && m->sq_item))) { + PyErr_Format(PyExc_TypeError, "'%.200s' object is not subscriptable", Py_TYPE(obj)->tp_name); + return NULL; + } + key_value = __Pyx_PyIndex_AsSsize_t(index); + if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) { + return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1); + } + if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) { + PyErr_Clear(); + PyErr_Format(PyExc_IndexError, "cannot fit '%.200s' into an index-sized integer", Py_TYPE(index)->tp_name); + } + return NULL; +} +static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) { + PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping; + if (likely(m && m->mp_subscript)) { + return m->mp_subscript(obj, key); + } + return __Pyx_PyObject_GetIndex(obj, key); +} +#endif + +/* decode_c_string */ +static CYTHON_INLINE PyObject* __Pyx_decode_c_string( + const char* cstring, Py_ssize_t start, Py_ssize_t stop, + const char* encoding, const char* errors, + PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) { + Py_ssize_t length; + if (unlikely((start < 0) | (stop < 0))) { + size_t slen = strlen(cstring); + if (unlikely(slen > (size_t) PY_SSIZE_T_MAX)) { + PyErr_SetString(PyExc_OverflowError, + "c-string too long to convert to Python"); + return NULL; + } + length = (Py_ssize_t) slen; + if (start < 0) { + start += length; + if (start < 0) + start = 0; + } + if (stop < 0) + stop += length; + } + length = stop - start; + if (unlikely(length <= 0)) + return PyUnicode_FromUnicode(NULL, 0); + cstring += start; + if (decode_func) { + return decode_func(cstring, length, errors); + } else { + return PyUnicode_Decode(cstring, length, encoding, errors); + } +} + +/* PyErrExceptionMatches */ +#if CYTHON_FAST_THREAD_STATE +static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(tuple); +#if PY_MAJOR_VERSION >= 3 + for (i=0; icurexc_type; + if (exc_type == err) return 1; + if (unlikely(!exc_type)) return 0; + if (unlikely(PyTuple_Check(err))) + return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err); + return __Pyx_PyErr_GivenExceptionMatches(exc_type, err); +} +#endif + +/* GetAttr3 */ +static PyObject *__Pyx_GetAttr3Default(PyObject *d) { + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) + return NULL; + __Pyx_PyErr_Clear(); + Py_INCREF(d); + return d; +} +static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) { + PyObject *r = __Pyx_GetAttr(o, n); + return (likely(r)) ? r : __Pyx_GetAttr3Default(d); +} + +/* RaiseTooManyValuesToUnpack */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { + PyErr_Format(PyExc_ValueError, + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); +} + +/* RaiseNeedMoreValuesToUnpack */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", + index, (index == 1) ? "" : "s"); +} + +/* RaiseNoneIterError */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +/* ExtTypeTest */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (likely(__Pyx_TypeCheck(obj, type))) + return 1; + PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", + Py_TYPE(obj)->tp_name, type->tp_name); + return 0; +} + +/* GetTopmostException */ +#if CYTHON_USE_EXC_INFO_STACK +static _PyErr_StackItem * +__Pyx_PyErr_GetTopmostException(PyThreadState *tstate) +{ + _PyErr_StackItem *exc_info = tstate->exc_info; + while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) && + exc_info->previous_item != NULL) + { + exc_info = exc_info->previous_item; + } + return exc_info; +} +#endif + +/* SaveResetException */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + #if CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); + *type = exc_info->exc_type; + *value = exc_info->exc_value; + *tb = exc_info->exc_traceback; + #else + *type = tstate->exc_type; + *value = tstate->exc_value; + *tb = tstate->exc_traceback; + #endif + Py_XINCREF(*type); + Py_XINCREF(*value); + Py_XINCREF(*tb); +} +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = type; + exc_info->exc_value = value; + exc_info->exc_traceback = tb; + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = type; + tstate->exc_value = value; + tstate->exc_traceback = tb; + #endif + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +} +#endif + +/* GetException */ +#if CYTHON_FAST_THREAD_STATE +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) +#endif +{ + PyObject *local_type, *local_value, *local_tb; +#if CYTHON_FAST_THREAD_STATE + PyObject *tmp_type, *tmp_value, *tmp_tb; + local_type = tstate->curexc_type; + local_value = tstate->curexc_value; + local_tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +#else + PyErr_Fetch(&local_type, &local_value, &local_tb); +#endif + PyErr_NormalizeException(&local_type, &local_value, &local_tb); +#if CYTHON_FAST_THREAD_STATE + if (unlikely(tstate->curexc_type)) +#else + if (unlikely(PyErr_Occurred())) +#endif + goto bad; + #if PY_MAJOR_VERSION >= 3 + if (local_tb) { + if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) + goto bad; + } + #endif + Py_XINCREF(local_tb); + Py_XINCREF(local_type); + Py_XINCREF(local_value); + *type = local_type; + *value = local_value; + *tb = local_tb; +#if CYTHON_FAST_THREAD_STATE + #if CYTHON_USE_EXC_INFO_STACK + { + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = local_type; + exc_info->exc_value = local_value; + exc_info->exc_traceback = local_tb; + } + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = local_type; + tstate->exc_value = local_value; + tstate->exc_traceback = local_tb; + #endif + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#else + PyErr_SetExcInfo(local_type, local_value, local_tb); +#endif + return 0; +bad: + *type = 0; + *value = 0; + *tb = 0; + Py_XDECREF(local_type); + Py_XDECREF(local_value); + Py_XDECREF(local_tb); + return -1; +} + +/* SwapException */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = *type; + exc_info->exc_value = *value; + exc_info->exc_traceback = *tb; + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = *type; + tstate->exc_value = *value; + tstate->exc_traceback = *tb; + #endif + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); + PyErr_SetExcInfo(*type, *value, *tb); + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#endif + +/* Import */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { + PyObject *empty_list = 0; + PyObject *module = 0; + PyObject *global_dict = 0; + PyObject *empty_dict = 0; + PyObject *list; + #if PY_MAJOR_VERSION < 3 + PyObject *py_import; + py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); + if (!py_import) + goto bad; + #endif + if (from_list) + list = from_list; + else { + empty_list = PyList_New(0); + if (!empty_list) + goto bad; + list = empty_list; + } + global_dict = PyModule_GetDict(__pyx_m); + if (!global_dict) + goto bad; + empty_dict = PyDict_New(); + if (!empty_dict) + goto bad; + { + #if PY_MAJOR_VERSION >= 3 + if (level == -1) { + if (strchr(__Pyx_MODULE_NAME, '.')) { + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, 1); + if (!module) { + if (!PyErr_ExceptionMatches(PyExc_ImportError)) + goto bad; + PyErr_Clear(); + } + } + level = 0; + } + #endif + if (!module) { + #if PY_MAJOR_VERSION < 3 + PyObject *py_level = PyInt_FromLong(level); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, (PyObject *)NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, level); + #endif + } + } +bad: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(py_import); + #endif + Py_XDECREF(empty_list); + Py_XDECREF(empty_dict); + return module; +} + +/* FastTypeChecks */ +#if CYTHON_COMPILING_IN_CPYTHON +static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { + while (a) { + a = a->tp_base; + if (a == b) + return 1; + } + return b == &PyBaseObject_Type; +} +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { + PyObject *mro; + if (a == b) return 1; + mro = a->tp_mro; + if (likely(mro)) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(mro); + for (i = 0; i < n; i++) { + if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) + return 1; + } + return 0; + } + return __Pyx_InBases(a, b); +} +#if PY_MAJOR_VERSION == 2 +static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { + PyObject *exception, *value, *tb; + int res; + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + __Pyx_ErrFetch(&exception, &value, &tb); + res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + if (!res) { + res = PyObject_IsSubclass(err, exc_type2); + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + } + __Pyx_ErrRestore(exception, value, tb); + return res; +} +#else +static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { + int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0; + if (!res) { + res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); + } + return res; +} +#endif +static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { + Py_ssize_t i, n; + assert(PyExceptionClass_Check(exc_type)); + n = PyTuple_GET_SIZE(tuple); +#if PY_MAJOR_VERSION >= 3 + for (i=0; i= 0 || (x^b) >= 0)) + return PyInt_FromLong(x); + return PyLong_Type.tp_as_number->nb_add(op1, op2); + } + #endif + #if CYTHON_USE_PYLONG_INTERNALS + if (likely(PyLong_CheckExact(op1))) { + const long b = intval; + long a, x; +#ifdef HAVE_LONG_LONG + const PY_LONG_LONG llb = intval; + PY_LONG_LONG lla, llx; +#endif + const digit* digits = ((PyLongObject*)op1)->ob_digit; + const Py_ssize_t size = Py_SIZE(op1); + if (likely(__Pyx_sst_abs(size) <= 1)) { + a = likely(size) ? digits[0] : 0; + if (size == -1) a = -a; + } else { + switch (size) { + case -2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case 2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case -3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case 3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case -4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case 4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + default: return PyLong_Type.tp_as_number->nb_add(op1, op2); + } + } + x = a + b; + return PyLong_FromLong(x); +#ifdef HAVE_LONG_LONG + long_long: + llx = lla + llb; + return PyLong_FromLongLong(llx); +#endif + + + } + #endif + if (PyFloat_CheckExact(op1)) { + const long b = intval; + double a = PyFloat_AS_DOUBLE(op1); + double result; + PyFPE_START_PROTECT("add", return NULL) + result = ((double)a) + (double)b; + PyFPE_END_PROTECT(result) + return PyFloat_FromDouble(result); + } + return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2); +} +#endif + +/* None */ +static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) { + PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); +} + +/* None */ +static CYTHON_INLINE long __Pyx_div_long(long a, long b) { + long q = a / b; + long r = a - q*b; + q -= ((r != 0) & ((r ^ b) < 0)); + return q; +} + +/* WriteUnraisableException */ +static void __Pyx_WriteUnraisable(const char *name, CYTHON_UNUSED int clineno, + CYTHON_UNUSED int lineno, CYTHON_UNUSED const char *filename, + int full_traceback, CYTHON_UNUSED int nogil) { + PyObject *old_exc, *old_val, *old_tb; + PyObject *ctx; + __Pyx_PyThreadState_declare +#ifdef WITH_THREAD + PyGILState_STATE state; + if (nogil) + state = PyGILState_Ensure(); +#ifdef _MSC_VER + else state = (PyGILState_STATE)-1; +#endif +#endif + __Pyx_PyThreadState_assign + __Pyx_ErrFetch(&old_exc, &old_val, &old_tb); + if (full_traceback) { + Py_XINCREF(old_exc); + Py_XINCREF(old_val); + Py_XINCREF(old_tb); + __Pyx_ErrRestore(old_exc, old_val, old_tb); + PyErr_PrintEx(1); + } + #if PY_MAJOR_VERSION < 3 + ctx = PyString_FromString(name); + #else + ctx = PyUnicode_FromString(name); + #endif + __Pyx_ErrRestore(old_exc, old_val, old_tb); + if (!ctx) { + PyErr_WriteUnraisable(Py_None); + } else { + PyErr_WriteUnraisable(ctx); + Py_DECREF(ctx); + } +#ifdef WITH_THREAD + if (nogil) + PyGILState_Release(state); +#endif +} + +/* ImportFrom */ +static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { + PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); + if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { + PyErr_Format(PyExc_ImportError, + #if PY_MAJOR_VERSION < 3 + "cannot import name %.230s", PyString_AS_STRING(name)); + #else + "cannot import name %S", name); + #endif + } + return value; +} + +/* HasAttr */ +static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) { + PyObject *r; + if (unlikely(!__Pyx_PyBaseString_Check(n))) { + PyErr_SetString(PyExc_TypeError, + "hasattr(): attribute name must be string"); + return -1; + } + r = __Pyx_GetAttr(o, n); + if (unlikely(!r)) { + PyErr_Clear(); + return 0; + } else { + Py_DECREF(r); + return 1; + } +} + +/* PyObject_GenericGetAttrNoDict */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) { + PyErr_Format(PyExc_AttributeError, +#if PY_MAJOR_VERSION >= 3 + "'%.50s' object has no attribute '%U'", + tp->tp_name, attr_name); +#else + "'%.50s' object has no attribute '%.400s'", + tp->tp_name, PyString_AS_STRING(attr_name)); +#endif + return NULL; +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) { + PyObject *descr; + PyTypeObject *tp = Py_TYPE(obj); + if (unlikely(!PyString_Check(attr_name))) { + return PyObject_GenericGetAttr(obj, attr_name); + } + assert(!tp->tp_dictoffset); + descr = _PyType_Lookup(tp, attr_name); + if (unlikely(!descr)) { + return __Pyx_RaiseGenericGetAttributeError(tp, attr_name); + } + Py_INCREF(descr); + #if PY_MAJOR_VERSION < 3 + if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS))) + #endif + { + descrgetfunc f = Py_TYPE(descr)->tp_descr_get; + if (unlikely(f)) { + PyObject *res = f(descr, obj, (PyObject *)tp); + Py_DECREF(descr); + return res; + } + } + return descr; +} +#endif + +/* PyObject_GenericGetAttr */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) { + if (unlikely(Py_TYPE(obj)->tp_dictoffset)) { + return PyObject_GenericGetAttr(obj, attr_name); + } + return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name); +} +#endif + +/* SetVTable */ +static int __Pyx_SetVtable(PyObject *dict, void *vtable) { +#if PY_VERSION_HEX >= 0x02070000 + PyObject *ob = PyCapsule_New(vtable, 0, 0); +#else + PyObject *ob = PyCObject_FromVoidPtr(vtable, 0); +#endif + if (!ob) + goto bad; + if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0) + goto bad; + Py_DECREF(ob); + return 0; +bad: + Py_XDECREF(ob); + return -1; +} + +/* SetupReduce */ +static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { + int ret; + PyObject *name_attr; + name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name_2); + if (likely(name_attr)) { + ret = PyObject_RichCompareBool(name_attr, name, Py_EQ); + } else { + ret = -1; + } + if (unlikely(ret < 0)) { + PyErr_Clear(); + ret = 0; + } + Py_XDECREF(name_attr); + return ret; +} +static int __Pyx_setup_reduce(PyObject* type_obj) { + int ret = 0; + PyObject *object_reduce = NULL; + PyObject *object_reduce_ex = NULL; + PyObject *reduce = NULL; + PyObject *reduce_ex = NULL; + PyObject *reduce_cython = NULL; + PyObject *setstate = NULL; + PyObject *setstate_cython = NULL; +#if CYTHON_USE_PYTYPE_LOOKUP + if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto GOOD; +#else + if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto GOOD; +#endif +#if CYTHON_USE_PYTYPE_LOOKUP + object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto BAD; +#else + object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto BAD; +#endif + reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto BAD; + if (reduce_ex == object_reduce_ex) { +#if CYTHON_USE_PYTYPE_LOOKUP + object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto BAD; +#else + object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto BAD; +#endif + reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto BAD; + if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) { + reduce_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_cython); if (unlikely(!reduce_cython)) goto BAD; + ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto BAD; + ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto BAD; + setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate); + if (!setstate) PyErr_Clear(); + if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) { + setstate_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate_cython); if (unlikely(!setstate_cython)) goto BAD; + ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto BAD; + ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto BAD; + } + PyType_Modified((PyTypeObject*)type_obj); + } + } + goto GOOD; +BAD: + if (!PyErr_Occurred()) + PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name); + ret = -1; +GOOD: +#if !CYTHON_USE_PYTYPE_LOOKUP + Py_XDECREF(object_reduce); + Py_XDECREF(object_reduce_ex); +#endif + Py_XDECREF(reduce); + Py_XDECREF(reduce_ex); + Py_XDECREF(reduce_cython); + Py_XDECREF(setstate); + Py_XDECREF(setstate_cython); + return ret; +} + +/* CLineInTraceback */ +#ifndef CYTHON_CLINE_IN_TRACEBACK +static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line) { + PyObject *use_cline; + PyObject *ptype, *pvalue, *ptraceback; +#if CYTHON_COMPILING_IN_CPYTHON + PyObject **cython_runtime_dict; +#endif + if (unlikely(!__pyx_cython_runtime)) { + return c_line; + } + __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); +#if CYTHON_COMPILING_IN_CPYTHON + cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime); + if (likely(cython_runtime_dict)) { + __PYX_PY_DICT_LOOKUP_IF_MODIFIED( + use_cline, *cython_runtime_dict, + __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback)) + } else +#endif + { + PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback); + if (use_cline_obj) { + use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True; + Py_DECREF(use_cline_obj); + } else { + PyErr_Clear(); + use_cline = NULL; + } + } + if (!use_cline) { + c_line = 0; + PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); + } + else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) { + c_line = 0; + } + __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); + return c_line; +} +#endif + +/* CodeObjectCache */ +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { + int start = 0, mid = 0, end = count - 1; + if (end >= 0 && code_line > entries[end].code_line) { + return count; + } + while (start < end) { + mid = start + (end - start) / 2; + if (code_line < entries[mid].code_line) { + end = mid; + } else if (code_line > entries[mid].code_line) { + start = mid + 1; + } else { + return mid; + } + } + if (code_line <= entries[mid].code_line) { + return mid; + } else { + return mid + 1; + } +} +static PyCodeObject *__pyx_find_code_object(int code_line) { + PyCodeObject* code_object; + int pos; + if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { + return NULL; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { + return NULL; + } + code_object = __pyx_code_cache.entries[pos].code_object; + Py_INCREF(code_object); + return code_object; +} +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { + int pos, i; + __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; + if (unlikely(!code_line)) { + return; + } + if (unlikely(!entries)) { + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); + if (likely(entries)) { + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = 64; + __pyx_code_cache.count = 1; + entries[0].code_line = code_line; + entries[0].code_object = code_object; + Py_INCREF(code_object); + } + return; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { + PyCodeObject* tmp = entries[pos].code_object; + entries[pos].code_object = code_object; + Py_DECREF(tmp); + return; + } + if (__pyx_code_cache.count == __pyx_code_cache.max_count) { + int new_max = __pyx_code_cache.max_count + 64; + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( + __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); + if (unlikely(!entries)) { + return; + } + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = new_max; + } + for (i=__pyx_code_cache.count; i>pos; i--) { + entries[i] = entries[i-1]; + } + entries[pos].code_line = code_line; + entries[pos].code_object = code_object; + __pyx_code_cache.count++; + Py_INCREF(code_object); +} + +/* AddTraceback */ +#include "compile.h" +#include "frameobject.h" +#include "traceback.h" +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( + const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyObject *py_srcfile = 0; + PyObject *py_funcname = 0; + #if PY_MAJOR_VERSION < 3 + py_srcfile = PyString_FromString(filename); + #else + py_srcfile = PyUnicode_FromString(filename); + #endif + if (!py_srcfile) goto bad; + if (c_line) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + #else + py_funcname = PyUnicode_FromString(funcname); + #endif + } + if (!py_funcname) goto bad; + py_code = __Pyx_PyCode_New( + 0, + 0, + 0, + 0, + 0, + __pyx_empty_bytes, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + py_line, + __pyx_empty_bytes /*PyObject *lnotab*/ + ); + Py_DECREF(py_srcfile); + Py_DECREF(py_funcname); + return py_code; +bad: + Py_XDECREF(py_srcfile); + Py_XDECREF(py_funcname); + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyFrameObject *py_frame = 0; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + if (c_line) { + c_line = __Pyx_CLineForTraceback(tstate, c_line); + } + py_code = __pyx_find_code_object(c_line ? -c_line : py_line); + if (!py_code) { + py_code = __Pyx_CreateCodeObjectForTraceback( + funcname, c_line, py_line, filename); + if (!py_code) goto bad; + __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); + } + py_frame = PyFrame_New( + tstate, /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + __pyx_d, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + __Pyx_PyFrame_SetLineNumber(py_frame, py_line); + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} + +#if PY_MAJOR_VERSION < 3 +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { + if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); + if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags); + if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags); + PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); + return -1; +} +static void __Pyx_ReleaseBuffer(Py_buffer *view) { + PyObject *obj = view->obj; + if (!obj) return; + if (PyObject_CheckBuffer(obj)) { + PyBuffer_Release(view); + return; + } + if ((0)) {} + view->obj = NULL; + Py_DECREF(obj); +} +#endif + + +/* MemviewSliceIsContig */ +static int +__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim) +{ + int i, index, step, start; + Py_ssize_t itemsize = mvs.memview->view.itemsize; + if (order == 'F') { + step = 1; + start = 0; + } else { + step = -1; + start = ndim - 1; + } + for (i = 0; i < ndim; i++) { + index = start + step * i; + if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize) + return 0; + itemsize *= mvs.shape[index]; + } + return 1; +} + +/* OverlappingSlices */ +static void +__pyx_get_array_memory_extents(__Pyx_memviewslice *slice, + void **out_start, void **out_end, + int ndim, size_t itemsize) +{ + char *start, *end; + int i; + start = end = slice->data; + for (i = 0; i < ndim; i++) { + Py_ssize_t stride = slice->strides[i]; + Py_ssize_t extent = slice->shape[i]; + if (extent == 0) { + *out_start = *out_end = start; + return; + } else { + if (stride > 0) + end += stride * (extent - 1); + else + start += stride * (extent - 1); + } + } + *out_start = start; + *out_end = end + itemsize; +} +static int +__pyx_slices_overlap(__Pyx_memviewslice *slice1, + __Pyx_memviewslice *slice2, + int ndim, size_t itemsize) +{ + void *start1, *end1, *start2, *end2; + __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize); + __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize); + return (start1 < end2) && (start2 < end1); +} + +/* Capsule */ +static CYTHON_INLINE PyObject * +__pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig) +{ + PyObject *cobj; +#if PY_VERSION_HEX >= 0x02070000 + cobj = PyCapsule_New(p, sig, NULL); +#else + cobj = PyCObject_FromVoidPtr(p, NULL); +#endif + return cobj; +} + +/* IsLittleEndian */ +static CYTHON_INLINE int __Pyx_Is_Little_Endian(void) +{ + union { + uint32_t u32; + uint8_t u8[4]; + } S; + S.u32 = 0x01020304; + return S.u8[0] == 4; +} + +/* BufferFormatCheck */ +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type) { + stack[0].field = &ctx->root; + stack[0].parent_offset = 0; + ctx->root.type = type; + ctx->root.name = "buffer dtype"; + ctx->root.offset = 0; + ctx->head = stack; + ctx->head->field = &ctx->root; + ctx->fmt_offset = 0; + ctx->head->parent_offset = 0; + ctx->new_packmode = '@'; + ctx->enc_packmode = '@'; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->is_complex = 0; + ctx->is_valid_array = 0; + ctx->struct_alignment = 0; + while (type->typegroup == 'S') { + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = 0; + type = type->fields->type; + } +} +static int __Pyx_BufFmt_ParseNumber(const char** ts) { + int count; + const char* t = *ts; + if (*t < '0' || *t > '9') { + return -1; + } else { + count = *t++ - '0'; + while (*t >= '0' && *t < '9') { + count *= 10; + count += *t++ - '0'; + } + } + *ts = t; + return count; +} +static int __Pyx_BufFmt_ExpectNumber(const char **ts) { + int number = __Pyx_BufFmt_ParseNumber(ts); + if (number == -1) + PyErr_Format(PyExc_ValueError,\ + "Does not understand character buffer dtype format string ('%c')", **ts); + return number; +} +static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { + PyErr_Format(PyExc_ValueError, + "Unexpected format string character: '%c'", ch); +} +static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { + switch (ch) { + case 'c': return "'char'"; + case 'b': return "'signed char'"; + case 'B': return "'unsigned char'"; + case 'h': return "'short'"; + case 'H': return "'unsigned short'"; + case 'i': return "'int'"; + case 'I': return "'unsigned int'"; + case 'l': return "'long'"; + case 'L': return "'unsigned long'"; + case 'q': return "'long long'"; + case 'Q': return "'unsigned long long'"; + case 'f': return (is_complex ? "'complex float'" : "'float'"); + case 'd': return (is_complex ? "'complex double'" : "'double'"); + case 'g': return (is_complex ? "'complex long double'" : "'long double'"); + case 'T': return "a struct"; + case 'O': return "Python object"; + case 'P': return "a pointer"; + case 's': case 'p': return "a string"; + case 0: return "end"; + default: return "unparseable format string"; + } +} +static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return 2; + case 'i': case 'I': case 'l': case 'L': return 4; + case 'q': case 'Q': return 8; + case 'f': return (is_complex ? 8 : 4); + case 'd': return (is_complex ? 16 : 8); + case 'g': { + PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); + return 0; + } + case 'O': case 'P': return sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { + switch (ch) { + case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(short); + case 'i': case 'I': return sizeof(int); + case 'l': case 'L': return sizeof(long); + #ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(PY_LONG_LONG); + #endif + case 'f': return sizeof(float) * (is_complex ? 2 : 1); + case 'd': return sizeof(double) * (is_complex ? 2 : 1); + case 'g': return sizeof(long double) * (is_complex ? 2 : 1); + case 'O': case 'P': return sizeof(void*); + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +typedef struct { char c; short x; } __Pyx_st_short; +typedef struct { char c; int x; } __Pyx_st_int; +typedef struct { char c; long x; } __Pyx_st_long; +typedef struct { char c; float x; } __Pyx_st_float; +typedef struct { char c; double x; } __Pyx_st_double; +typedef struct { char c; long double x; } __Pyx_st_longdouble; +typedef struct { char c; void *x; } __Pyx_st_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_st_float) - sizeof(float); + case 'd': return sizeof(__Pyx_st_double) - sizeof(double); + case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +/* These are for computing the padding at the end of the struct to align + on the first member of the struct. This will probably the same as above, + but we don't have any guarantees. + */ +typedef struct { short x; char c; } __Pyx_pad_short; +typedef struct { int x; char c; } __Pyx_pad_int; +typedef struct { long x; char c; } __Pyx_pad_long; +typedef struct { float x; char c; } __Pyx_pad_float; +typedef struct { double x; char c; } __Pyx_pad_double; +typedef struct { long double x; char c; } __Pyx_pad_longdouble; +typedef struct { void *x; char c; } __Pyx_pad_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); + case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); + case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { + switch (ch) { + case 'c': + return 'H'; + case 'b': case 'h': case 'i': + case 'l': case 'q': case 's': case 'p': + return 'I'; + case 'B': case 'H': case 'I': case 'L': case 'Q': + return 'U'; + case 'f': case 'd': case 'g': + return (is_complex ? 'C' : 'R'); + case 'O': + return 'O'; + case 'P': + return 'P'; + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { + if (ctx->head == NULL || ctx->head->field == &ctx->root) { + const char* expected; + const char* quote; + if (ctx->head == NULL) { + expected = "end"; + quote = ""; + } else { + expected = ctx->head->field->type->name; + quote = "'"; + } + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected %s%s%s but got %s", + quote, expected, quote, + __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); + } else { + __Pyx_StructField* field = ctx->head->field; + __Pyx_StructField* parent = (ctx->head - 1)->field; + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", + field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), + parent->type->name, field->name); + } +} +static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { + char group; + size_t size, offset, arraysize = 1; + if (ctx->enc_type == 0) return 0; + if (ctx->head->field->type->arraysize[0]) { + int i, ndim = 0; + if (ctx->enc_type == 's' || ctx->enc_type == 'p') { + ctx->is_valid_array = ctx->head->field->type->ndim == 1; + ndim = 1; + if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %zu", + ctx->head->field->type->arraysize[0], ctx->enc_count); + return -1; + } + } + if (!ctx->is_valid_array) { + PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", + ctx->head->field->type->ndim, ndim); + return -1; + } + for (i = 0; i < ctx->head->field->type->ndim; i++) { + arraysize *= ctx->head->field->type->arraysize[i]; + } + ctx->is_valid_array = 0; + ctx->enc_count = 1; + } + group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); + do { + __Pyx_StructField* field = ctx->head->field; + __Pyx_TypeInfo* type = field->type; + if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { + size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); + } else { + size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); + } + if (ctx->enc_packmode == '@') { + size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); + size_t align_mod_offset; + if (align_at == 0) return -1; + align_mod_offset = ctx->fmt_offset % align_at; + if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; + if (ctx->struct_alignment == 0) + ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, + ctx->is_complex); + } + if (type->size != size || type->typegroup != group) { + if (type->typegroup == 'C' && type->fields != NULL) { + size_t parent_offset = ctx->head->parent_offset + field->offset; + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = parent_offset; + continue; + } + if ((type->typegroup == 'H' || group == 'H') && type->size == size) { + } else { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + } + offset = ctx->head->parent_offset + field->offset; + if (ctx->fmt_offset != offset) { + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", + (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); + return -1; + } + ctx->fmt_offset += size; + if (arraysize) + ctx->fmt_offset += (arraysize - 1) * size; + --ctx->enc_count; + while (1) { + if (field == &ctx->root) { + ctx->head = NULL; + if (ctx->enc_count != 0) { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + break; + } + ctx->head->field = ++field; + if (field->type == NULL) { + --ctx->head; + field = ctx->head->field; + continue; + } else if (field->type->typegroup == 'S') { + size_t parent_offset = ctx->head->parent_offset + field->offset; + if (field->type->fields->type == NULL) continue; + field = field->type->fields; + ++ctx->head; + ctx->head->field = field; + ctx->head->parent_offset = parent_offset; + break; + } else { + break; + } + } + } while (ctx->enc_count); + ctx->enc_type = 0; + ctx->is_complex = 0; + return 0; +} +static PyObject * +__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) +{ + const char *ts = *tsp; + int i = 0, number; + int ndim = ctx->head->field->type->ndim; +; + ++ts; + if (ctx->new_count != 1) { + PyErr_SetString(PyExc_ValueError, + "Cannot handle repeated arrays in format string"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + while (*ts && *ts != ')') { + switch (*ts) { + case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; + default: break; + } + number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) + return PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %d", + ctx->head->field->type->arraysize[i], number); + if (*ts != ',' && *ts != ')') + return PyErr_Format(PyExc_ValueError, + "Expected a comma in format string, got '%c'", *ts); + if (*ts == ',') ts++; + i++; + } + if (i != ndim) + return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", + ctx->head->field->type->ndim, i); + if (!*ts) { + PyErr_SetString(PyExc_ValueError, + "Unexpected end of format string, expected ')'"); + return NULL; + } + ctx->is_valid_array = 1; + ctx->new_count = 1; + *tsp = ++ts; + return Py_None; +} +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { + int got_Z = 0; + while (1) { + switch(*ts) { + case 0: + if (ctx->enc_type != 0 && ctx->head == NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + if (ctx->head != NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + return ts; + case ' ': + case '\r': + case '\n': + ++ts; + break; + case '<': + if (!__Pyx_Is_Little_Endian()) { + PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '>': + case '!': + if (__Pyx_Is_Little_Endian()) { + PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '=': + case '@': + case '^': + ctx->new_packmode = *ts++; + break; + case 'T': + { + const char* ts_after_sub; + size_t i, struct_count = ctx->new_count; + size_t struct_alignment = ctx->struct_alignment; + ctx->new_count = 1; + ++ts; + if (*ts != '{') { + PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + ctx->enc_count = 0; + ctx->struct_alignment = 0; + ++ts; + ts_after_sub = ts; + for (i = 0; i != struct_count; ++i) { + ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); + if (!ts_after_sub) return NULL; + } + ts = ts_after_sub; + if (struct_alignment) ctx->struct_alignment = struct_alignment; + } + break; + case '}': + { + size_t alignment = ctx->struct_alignment; + ++ts; + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + if (alignment && ctx->fmt_offset % alignment) { + ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); + } + } + return ts; + case 'x': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->fmt_offset += ctx->new_count; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->enc_packmode = ctx->new_packmode; + ++ts; + break; + case 'Z': + got_Z = 1; + ++ts; + if (*ts != 'f' && *ts != 'd' && *ts != 'g') { + __Pyx_BufFmt_RaiseUnexpectedChar('Z'); + return NULL; + } + CYTHON_FALLTHROUGH; + case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': + case 'l': case 'L': case 'q': case 'Q': + case 'f': case 'd': case 'g': + case 'O': case 'p': + if (ctx->enc_type == *ts && got_Z == ctx->is_complex && + ctx->enc_packmode == ctx->new_packmode) { + ctx->enc_count += ctx->new_count; + ctx->new_count = 1; + got_Z = 0; + ++ts; + break; + } + CYTHON_FALLTHROUGH; + case 's': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_count = ctx->new_count; + ctx->enc_packmode = ctx->new_packmode; + ctx->enc_type = *ts; + ctx->is_complex = got_Z; + ++ts; + ctx->new_count = 1; + got_Z = 0; + break; + case ':': + ++ts; + while(*ts != ':') ++ts; + ++ts; + break; + case '(': + if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; + break; + default: + { + int number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + ctx->new_count = (size_t)number; + } + } + } +} + +/* TypeInfoCompare */ + static int +__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) +{ + int i; + if (!a || !b) + return 0; + if (a == b) + return 1; + if (a->size != b->size || a->typegroup != b->typegroup || + a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { + if (a->typegroup == 'H' || b->typegroup == 'H') { + return a->size == b->size; + } else { + return 0; + } + } + if (a->ndim) { + for (i = 0; i < a->ndim; i++) + if (a->arraysize[i] != b->arraysize[i]) + return 0; + } + if (a->typegroup == 'S') { + if (a->flags != b->flags) + return 0; + if (a->fields || b->fields) { + if (!(a->fields && b->fields)) + return 0; + for (i = 0; a->fields[i].type && b->fields[i].type; i++) { + __Pyx_StructField *field_a = a->fields + i; + __Pyx_StructField *field_b = b->fields + i; + if (field_a->offset != field_b->offset || + !__pyx_typeinfo_cmp(field_a->type, field_b->type)) + return 0; + } + return !a->fields[i].type && !b->fields[i].type; + } + } + return 1; +} + +/* MemviewSliceValidateAndInit */ + static int +__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) +{ + if (buf->shape[dim] <= 1) + return 1; + if (buf->strides) { + if (spec & __Pyx_MEMVIEW_CONTIG) { + if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { + if (buf->strides[dim] != sizeof(void *)) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly contiguous " + "in dimension %d.", dim); + goto fail; + } + } else if (buf->strides[dim] != buf->itemsize) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_FOLLOW) { + Py_ssize_t stride = buf->strides[dim]; + if (stride < 0) + stride = -stride; + if (stride < buf->itemsize) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + } else { + if (spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not contiguous in " + "dimension %d", dim); + goto fail; + } else if (spec & (__Pyx_MEMVIEW_PTR)) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not indirect in " + "dimension %d", dim); + goto fail; + } else if (buf->suboffsets) { + PyErr_SetString(PyExc_ValueError, + "Buffer exposes suboffsets but no strides"); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec) +{ + if (spec & __Pyx_MEMVIEW_DIRECT) { + if (buf->suboffsets && buf->suboffsets[dim] >= 0) { + PyErr_Format(PyExc_ValueError, + "Buffer not compatible with direct access " + "in dimension %d.", dim); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_PTR) { + if (!buf->suboffsets || (buf->suboffsets && buf->suboffsets[dim] < 0)) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly accessible " + "in dimension %d.", dim); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) +{ + int i; + if (c_or_f_flag & __Pyx_IS_F_CONTIG) { + Py_ssize_t stride = 1; + for (i = 0; i < ndim; i++) { + if (stride * buf->itemsize != buf->strides[i] && + buf->shape[i] > 1) + { + PyErr_SetString(PyExc_ValueError, + "Buffer not fortran contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { + Py_ssize_t stride = 1; + for (i = ndim - 1; i >- 1; i--) { + if (stride * buf->itemsize != buf->strides[i] && + buf->shape[i] > 1) { + PyErr_SetString(PyExc_ValueError, + "Buffer not C contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } + return 1; +fail: + return 0; +} +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj) +{ + struct __pyx_memoryview_obj *memview, *new_memview; + __Pyx_RefNannyDeclarations + Py_buffer *buf; + int i, spec = 0, retval = -1; + __Pyx_BufFmt_Context ctx; + int from_memoryview = __pyx_memoryview_check(original_obj); + __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); + if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) + original_obj)->typeinfo)) { + memview = (struct __pyx_memoryview_obj *) original_obj; + new_memview = NULL; + } else { + memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( + original_obj, buf_flags, 0, dtype); + new_memview = memview; + if (unlikely(!memview)) + goto fail; + } + buf = &memview->view; + if (buf->ndim != ndim) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + ndim, buf->ndim); + goto fail; + } + if (new_memview) { + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; + } + if ((unsigned) buf->itemsize != dtype->size) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " + "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", + buf->itemsize, + (buf->itemsize > 1) ? "s" : "", + dtype->name, + dtype->size, + (dtype->size > 1) ? "s" : ""); + goto fail; + } + for (i = 0; i < ndim; i++) { + spec = axes_specs[i]; + if (!__pyx_check_strides(buf, i, ndim, spec)) + goto fail; + if (!__pyx_check_suboffsets(buf, i, ndim, spec)) + goto fail; + } + if (buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag)) + goto fail; + if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, + new_memview != NULL) == -1)) { + goto fail; + } + retval = 0; + goto no_fail; +fail: + Py_XDECREF(new_memview); + retval = -1; +no_fail: + __Pyx_RefNannyFinishContext(); + return retval; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dsds_float(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, + PyBUF_RECORDS_RO | writable_flag, 2, + &__Pyx_TypeInfo_float, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* CIntFromPyVerify */ + #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) +#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) +#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ + {\ + func_type value = func_value;\ + if (sizeof(target_type) < sizeof(func_type)) {\ + if (unlikely(value != (func_type) (target_type) value)) {\ + func_type zero = 0;\ + if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ + return (target_type) -1;\ + if (is_unsigned && unlikely(value < zero))\ + goto raise_neg_overflow;\ + else\ + goto raise_overflow;\ + }\ + }\ + return (target_type) value;\ + } + +/* None */ + static CYTHON_INLINE Py_ssize_t __Pyx_pow_Py_ssize_t(Py_ssize_t b, Py_ssize_t e) { + Py_ssize_t t = b; + switch (e) { + case 3: + t *= b; + CYTHON_FALLTHROUGH; + case 2: + t *= b; + CYTHON_FALLTHROUGH; + case 1: + return t; + case 0: + return 1; + } + #if 1 + if (unlikely(e<0)) return 0; + #endif + t = 1; + while (likely(e)) { + t *= (b * (e&1)) | ((~e)&1); + b *= b; + e >>= 1; + } + return t; +} + +/* MemviewSliceCopyTemplate */ + static __Pyx_memviewslice +__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, + const char *mode, int ndim, + size_t sizeof_dtype, int contig_flag, + int dtype_is_object) +{ + __Pyx_RefNannyDeclarations + int i; + __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } }; + struct __pyx_memoryview_obj *from_memview = from_mvs->memview; + Py_buffer *buf = &from_memview->view; + PyObject *shape_tuple = NULL; + PyObject *temp_int = NULL; + struct __pyx_array_obj *array_obj = NULL; + struct __pyx_memoryview_obj *memview_obj = NULL; + __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); + for (i = 0; i < ndim; i++) { + if (from_mvs->suboffsets[i] >= 0) { + PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " + "indirect dimensions (axis %d)", i); + goto fail; + } + } + shape_tuple = PyTuple_New(ndim); + if (unlikely(!shape_tuple)) { + goto fail; + } + __Pyx_GOTREF(shape_tuple); + for(i = 0; i < ndim; i++) { + temp_int = PyInt_FromSsize_t(from_mvs->shape[i]); + if(unlikely(!temp_int)) { + goto fail; + } else { + PyTuple_SET_ITEM(shape_tuple, i, temp_int); + temp_int = NULL; + } + } + array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL); + if (unlikely(!array_obj)) { + goto fail; + } + __Pyx_GOTREF(array_obj); + memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( + (PyObject *) array_obj, contig_flag, + dtype_is_object, + from_mvs->memview->typeinfo); + if (unlikely(!memview_obj)) + goto fail; + if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0)) + goto fail; + if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim, + dtype_is_object) < 0)) + goto fail; + goto no_fail; +fail: + __Pyx_XDECREF(new_mvs.memview); + new_mvs.memview = NULL; + new_mvs.data = NULL; +no_fail: + __Pyx_XDECREF(shape_tuple); + __Pyx_XDECREF(temp_int); + __Pyx_XDECREF(array_obj); + __Pyx_RefNannyFinishContext(); + return new_mvs; +} + +/* CIntFromPy */ + static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { + const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(int) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { + return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { + return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { + return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(int) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) + case -2: + if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + } +#endif + if (sizeof(int) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + int val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (int) -1; + } + } else { + int val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (int) -1; + val = __Pyx_PyInt_As_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to int"); + return (int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; +} + +/* CIntFromPy */ + static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { + const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(long) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (long) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { + return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { + return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { + return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(long) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) + case -2: + if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + } +#endif + if (sizeof(long) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + long val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (long) -1; + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (long) -1; + val = __Pyx_PyInt_As_long(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to long"); + return (long) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { + const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(int) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(int), + little, !is_unsigned); + } +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { + const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(long) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(long) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(long), + little, !is_unsigned); + } +} + +/* CIntFromPy */ + static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) { + const char neg_one = (char) ((char) 0 - (char) 1), const_zero = (char) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(char) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (char) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (char) 0; + case 1: __PYX_VERIFY_RETURN_INT(char, digit, digits[0]) + case 2: + if (8 * sizeof(char) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) >= 2 * PyLong_SHIFT) { + return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(char) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) >= 3 * PyLong_SHIFT) { + return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(char) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) >= 4 * PyLong_SHIFT) { + return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (char) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(char) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (char) 0; + case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(char, digit, +digits[0]) + case -2: + if (8 * sizeof(char) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { + return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(char) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { + return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { + return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(char) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { + return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { + return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(char) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { + return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + } +#endif + if (sizeof(char) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + char val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (char) -1; + } + } else { + char val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (char) -1; + val = __Pyx_PyInt_As_char(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to char"); + return (char) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to char"); + return (char) -1; +} + +/* CheckBinaryVersion */ + static int __Pyx_check_binary_version(void) { + char ctversion[4], rtversion[4]; + PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); + PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); + if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { + char message[200]; + PyOS_snprintf(message, sizeof(message), + "compiletime version %s of module '%.100s' " + "does not match runtime version %s", + ctversion, __Pyx_MODULE_NAME, rtversion); + return PyErr_WarnEx(NULL, message, 1); + } + return 0; +} + +/* InitStrings */ + static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { + while (t->p) { + #if PY_MAJOR_VERSION < 3 + if (t->is_unicode) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + #else + if (t->is_unicode | t->is_str) { + if (t->intern) { + *t->p = PyUnicode_InternFromString(t->s); + } else if (t->encoding) { + *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); + } else { + *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } + } else { + *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); + } + #endif + if (!*t->p) + return -1; + if (PyObject_Hash(*t->p) == -1) + return -1; + ++t; + } + return 0; +} + +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { + return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); +} +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +#if !CYTHON_PEP393_ENABLED +static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + char* defenc_c; + PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); + if (!defenc) return NULL; + defenc_c = PyBytes_AS_STRING(defenc); +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + { + char* end = defenc_c + PyBytes_GET_SIZE(defenc); + char* c; + for (c = defenc_c; c < end; c++) { + if ((unsigned char) (*c) >= 128) { + PyUnicode_AsASCIIString(o); + return NULL; + } + } + } +#endif + *length = PyBytes_GET_SIZE(defenc); + return defenc_c; +} +#else +static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (likely(PyUnicode_IS_ASCII(o))) { + *length = PyUnicode_GET_LENGTH(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else + return PyUnicode_AsUTF8AndSize(o, length); +#endif +} +#endif +#endif +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT + if ( +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + __Pyx_sys_getdefaultencoding_not_ascii && +#endif + PyUnicode_Check(o)) { + return __Pyx_PyUnicode_AsStringAndSize(o, length); + } else +#endif +#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) + if (PyByteArray_Check(o)) { + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); + } else +#endif + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) { + int retval; + if (unlikely(!x)) return -1; + retval = __Pyx_PyObject_IsTrue(x); + Py_DECREF(x); + return retval; +} +static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) { +#if PY_MAJOR_VERSION >= 3 + if (PyLong_Check(result)) { + if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, + "__int__ returned non-int (type %.200s). " + "The ability to return an instance of a strict subclass of int " + "is deprecated, and may be removed in a future version of Python.", + Py_TYPE(result)->tp_name)) { + Py_DECREF(result); + return NULL; + } + return result; + } +#endif + PyErr_Format(PyExc_TypeError, + "__%.4s__ returned non-%.4s (type %.200s)", + type_name, type_name, Py_TYPE(result)->tp_name); + Py_DECREF(result); + return NULL; +} +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { +#if CYTHON_USE_TYPE_SLOTS + PyNumberMethods *m; +#endif + const char *name = NULL; + PyObject *res = NULL; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x) || PyLong_Check(x))) +#else + if (likely(PyLong_Check(x))) +#endif + return __Pyx_NewRef(x); +#if CYTHON_USE_TYPE_SLOTS + m = Py_TYPE(x)->tp_as_number; + #if PY_MAJOR_VERSION < 3 + if (m && m->nb_int) { + name = "int"; + res = m->nb_int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = m->nb_long(x); + } + #else + if (likely(m && m->nb_int)) { + name = "int"; + res = m->nb_int(x); + } + #endif +#else + if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { + res = PyNumber_Int(x); + } +#endif + if (likely(res)) { +#if PY_MAJOR_VERSION < 3 + if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { +#else + if (unlikely(!PyLong_CheckExact(res))) { +#endif + return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); + } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; +} +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject *x; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(b))) { + if (sizeof(Py_ssize_t) >= sizeof(long)) + return PyInt_AS_LONG(b); + else + return PyInt_AsSsize_t(b); + } +#endif + if (likely(PyLong_CheckExact(b))) { + #if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)b)->ob_digit; + const Py_ssize_t size = Py_SIZE(b); + if (likely(__Pyx_sst_abs(size) <= 1)) { + ival = likely(size) ? digits[0] : 0; + if (size == -1) ival = -ival; + return ival; + } else { + switch (size) { + case 2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + } + } + #endif + return PyLong_AsSsize_t(b); + } + x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} +static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) { + return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False); +} +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { + return PyInt_FromSize_t(ival); +} + + +#endif /* Py_PYTHON_H */ diff --git a/data/lcn/lcn.html b/data/lcn/lcn.html new file mode 100644 index 0000000..805940b --- /dev/null +++ b/data/lcn/lcn.html @@ -0,0 +1,720 @@ + + + + + + Cython: lcn.pyx + + + +

Generated by Cython 0.29

+

+ Yellow lines hint at Python interaction.
+ Click on a line that starts with a "+" to see the C code that Cython generated for it. +

+ +
+01: import numpy as np
+
  __pyx_t_1 = __Pyx_Import(__pyx_n_s_numpy, 0, -1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 1, __pyx_L1_error)
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+
 02: cimport cython
+
 03:
+
 04: # use c square root function
+
 05: cdef extern from "math.h":
+
 06:     float sqrt(float x)
+
 07:
+
 08: @cython.boundscheck(False)
+
 09: @cython.wraparound(False)
+
 10: @cython.cdivision(True)
+
 11:
+
 12: # 3 parameters:
+
 13: # - float image
+
 14: # - kernel size (actually this is the radius, kernel is 2*k+1)
+
 15: # - small constant epsilon that is used to avoid division by zero
+
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+25:     cdef float[:, :] img_lcn_view = img_lcn
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 28:     # temporary c variables
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 29:     cdef float tmp, mean, stddev
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 30:     cdef Py_ssize_t m, n, i, j
+
+31:     cdef Py_ssize_t ks = kernel_size
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+32:     cdef float eps = epsilon
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+33:     cdef float num = (ks*2+1)**2
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+  __pyx_t_8 = __pyx_t_7;
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 38:
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 39:             # calculate mean
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+40:             mean = 0;
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+41:             for i in range(-ks,ks+1):
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      __pyx_t_13 = (__pyx_v_ks + 1);
+      __pyx_t_14 = __pyx_t_13;
+      for (__pyx_t_15 = (-__pyx_v_ks); __pyx_t_15 < __pyx_t_14; __pyx_t_15+=1) {
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+42:                 for j in range(-ks,ks+1):
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+43:                     mean += img[m+i, n+j]
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          __pyx_t_19 = (__pyx_v_m + __pyx_v_i);
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+        }
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+44:             mean = mean/num
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 45:
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 46:             # calculate std dev
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      __pyx_v_stddev = 0.0;
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+48:             for i in range(-ks,ks+1):
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      __pyx_t_13 = (__pyx_v_ks + 1);
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+49:                 for j in range(-ks,ks+1):
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        __pyx_t_16 = (__pyx_v_ks + 1);
+        __pyx_t_17 = __pyx_t_16;
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+          __pyx_v_j = __pyx_t_18;
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+50:                     stddev = stddev + (img[m+i, n+j]-mean)*(img[m+i, n+j]-mean)
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          __pyx_t_21 = (__pyx_v_m + __pyx_v_i);
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+          __pyx_t_24 = (__pyx_v_n + __pyx_v_j);
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+        }
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+51:             stddev = sqrt(stddev/num)
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      __pyx_v_stddev = sqrt((__pyx_v_stddev / __pyx_v_num));
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 52:
+
 53:             # compute normalized image (add epsilon) and std dev image
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+54:             img_lcn_view[m, n] = (img[m, n]-mean)/(stddev+eps)
+
      __pyx_t_25 = __pyx_v_m;
+      __pyx_t_26 = __pyx_v_n;
+      __pyx_t_27 = __pyx_v_m;
+      __pyx_t_28 = __pyx_v_n;
+      *((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_img_lcn_view.data + __pyx_t_27 * __pyx_v_img_lcn_view.strides[0]) ) + __pyx_t_28 * __pyx_v_img_lcn_view.strides[1]) )) = (((*((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_img.data + __pyx_t_25 * __pyx_v_img.strides[0]) ) + __pyx_t_26 * __pyx_v_img.strides[1]) ))) - __pyx_v_mean) / (__pyx_v_stddev + __pyx_v_eps));
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+55:             img_std_view[m, n] = stddev
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+      __pyx_t_30 = __pyx_v_n;
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+    }
+  }
+
 56:
+
 57:     # return both
+
+58:     return img_lcn, img_std
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diff --git a/data/lcn/lcn.pyx b/data/lcn/lcn.pyx new file mode 100644 index 0000000..c1a322a --- /dev/null +++ b/data/lcn/lcn.pyx @@ -0,0 +1,58 @@ +import numpy as np +cimport cython + +# use c square root function +cdef extern from "math.h": + float sqrt(float x) + +@cython.boundscheck(False) +@cython.wraparound(False) +@cython.cdivision(True) + +# 3 parameters: +# - float image +# - kernel size (actually this is the radius, kernel is 2*k+1) +# - small constant epsilon that is used to avoid division by zero +def normalize(float[:, :] img, int kernel_size = 4, float epsilon = 0.01): + + # image dimensions + cdef Py_ssize_t M = img.shape[0] + cdef Py_ssize_t N = img.shape[1] + + # create outputs and output views + img_lcn = np.zeros((M, N), dtype=np.float32) + img_std = np.zeros((M, N), dtype=np.float32) + cdef float[:, :] img_lcn_view = img_lcn + cdef float[:, :] img_std_view = img_std + + # temporary c variables + cdef float tmp, mean, stddev + cdef Py_ssize_t m, n, i, j + cdef Py_ssize_t ks = kernel_size + cdef float eps = epsilon + cdef float num = (ks*2+1)**2 + + # for all pixels do + for m in range(ks,M-ks): + for n in range(ks,N-ks): + + # calculate mean + mean = 0; + for i in range(-ks,ks+1): + for j in range(-ks,ks+1): + mean += img[m+i, n+j] + mean = mean/num + + # calculate std dev + stddev = 0; + for i in range(-ks,ks+1): + for j in range(-ks,ks+1): + stddev = stddev + (img[m+i, n+j]-mean)*(img[m+i, n+j]-mean) + stddev = sqrt(stddev/num) + + # compute normalized image (add epsilon) and std dev image + img_lcn_view[m, n] = (img[m, n]-mean)/(stddev+eps) + img_std_view[m, n] = stddev + + # return both + return img_lcn, img_std diff --git a/data/lcn/readme.txt b/data/lcn/readme.txt new file mode 100644 index 0000000..1c6aa51 --- /dev/null +++ b/data/lcn/readme.txt @@ -0,0 +1,5 @@ +compile: +python setup.py build_ext --inplace + +run: +python test_lcn.py diff --git a/data/lcn/setup.py b/data/lcn/setup.py new file mode 100644 index 0000000..e1d4d0e --- /dev/null +++ b/data/lcn/setup.py @@ -0,0 +1,6 @@ +from distutils.core import setup +from Cython.Build import cythonize + +setup( + ext_modules = cythonize("lcn.pyx",annotate=True) +) diff --git a/data/lcn/test_lcn.py b/data/lcn/test_lcn.py new file mode 100644 index 0000000..eff0289 --- /dev/null +++ b/data/lcn/test_lcn.py @@ -0,0 +1,47 @@ +import numpy as np +import matplotlib.pyplot as plt +import lcn +from scipy import misc + +# load and convert to float +img = misc.imread('img.png') +img = img.astype(np.float32)/255.0 + +# normalize +img_lcn, img_std = lcn.normalize(img,5,0.05) + +# normalize to reasonable range between 0 and 1 +#img_lcn = img_lcn/3.0 +#img_lcn = np.maximum(img_lcn,0.0) +#img_lcn = np.minimum(img_lcn,1.0) + +# save to file +#misc.imsave('lcn2.png',img_lcn) + +print ("Orig Image: %d x %d (%s), Min: %f, Max: %f" % \ +(img.shape[0], img.shape[1], img.dtype, img.min(), img.max())) +print ("Norm Image: %d x %d (%s), Min: %f, Max: %f" % \ +(img_lcn.shape[0], img_lcn.shape[1], img_lcn.dtype, img_lcn.min(), img_lcn.max())) + +# plot original image +plt.figure(1) +img_plot = plt.imshow(img) +img_plot.set_cmap('gray') +plt.clim(0, 1) # fix range +plt.tight_layout() + +# plot normalized image +plt.figure(2) +img_lcn_plot = plt.imshow(img_lcn) +img_lcn_plot.set_cmap('gray') +#plt.clim(0, 1) # fix range +plt.tight_layout() + +# plot stddev image +plt.figure(3) +img_std_plot = plt.imshow(img_std) +img_std_plot.set_cmap('gray') +#plt.clim(0, 0.1) # fix range +plt.tight_layout() + +plt.show() diff --git a/hyperdepth/eval.cpp b/hyperdepth/eval.cpp new file mode 100644 index 0000000..2da06f6 --- /dev/null +++ b/hyperdepth/eval.cpp @@ -0,0 +1,46 @@ +#include "hyperdepth.h" + + +int main() { + cv::Mat_ im = read_im(0); + cv::Mat_ disp = read_disp(0); + int im_rows = im.rows; + int im_cols = im.cols; + std::cout << im.rows << "/" << im.cols << std::endl; + std::cout << disp.rows << "/" << disp.cols << std::endl; + + cv::Mat_ ta_disp(im_rows, im_cols); + cv::Mat_ es_disp(im_rows, im_cols); + + int n_disp_bins = 16; + + for(int row = 0; row < im_rows; ++row) { + std::vector data; + extract_row_samples(im, disp, row, data, false, n_disp_bins); + + std::ostringstream forest_path; + forest_path << "cforest_" << row << ".bin"; + BinarySerializationIn fin(forest_path.str()); + HDForest forest; + forest.Load(fin); + + auto res = forest.inferencemt(data, 18); + for(int col = 0; col < im_cols; ++col) { + auto fcn = res[col]; + auto target = std::static_pointer_cast(data[col].target); + + float ta = col - float(target->cl()) / n_disp_bins; + float es = col - float(fcn->argmax()) / n_disp_bins; + es = std::max(0.f, es); + + ta_disp(row, col) = int(ta * 16); + es_disp(row, col) = int(es * 16); + } + } + + cv::imwrite("disp_orig.png", disp); + cv::imwrite("disp_ta.png", ta_disp); + cv::imwrite("disp_es.png", es_disp); +} + + diff --git a/hyperdepth/hyperdepth.h b/hyperdepth/hyperdepth.h new file mode 100644 index 0000000..72bd1c6 --- /dev/null +++ b/hyperdepth/hyperdepth.h @@ -0,0 +1,287 @@ +#include +#include + +#include "rf/forest.h" +#include "rf/spliteval.h" + +class HyperdepthSplitEvaluator : public SplitEvaluator { +public: + HyperdepthSplitEvaluator(bool normalize, int n_classes, int n_disp_bins, int depth_switch) + : SplitEvaluator(normalize), n_classes_(n_classes), n_disp_bins_(n_disp_bins), depth_switch_(depth_switch) {} + virtual ~HyperdepthSplitEvaluator() {} + +protected: + virtual float Purity(const std::vector& targets, int depth) const { + if(targets.size() == 0) return 0; + + int n_classes = n_classes_; + if(depth >= depth_switch_) { + n_classes *= n_disp_bins_; + } + + std::vector ps; + ps.resize(n_classes, 0); + for(auto target : targets) { + auto ctarget = std::static_pointer_cast(target.optimize_target); + int cl = ctarget->cl(); + if(depth < depth_switch_) { + cl /= n_disp_bins_; + } + ps[cl] += 1; + } + + float h = 0; + for(int cl = 0; cl < n_classes; ++cl) { + float fi = float(ps[cl]) / float(targets.size()); + if(fi > 0) { + h = h - fi * std::log(fi); + } + } + + return h; + } + +private: + int n_classes_; + int n_disp_bins_; + int depth_switch_; +}; + + +class HyperdepthLeafFunction { +public: + HyperdepthLeafFunction() : n_classes_(-1) {} + HyperdepthLeafFunction(int n_classes) : n_classes_(n_classes) {} + virtual ~HyperdepthLeafFunction() {} + + virtual std::shared_ptr Copy() const { + auto fcn = std::make_shared(); + fcn->n_classes_ = n_classes_; + fcn->counts_.resize(counts_.size()); + for(size_t idx = 0; idx < counts_.size(); ++idx) { + fcn->counts_[idx] = counts_[idx]; + } + fcn->sum_counts_ = sum_counts_; + + return fcn; + } + + virtual std::shared_ptr Create(const std::vector& samples) { + auto stat = std::make_shared(); + + stat->counts_.resize(n_classes_, 0); + for(auto sample : samples) { + auto ctarget = std::static_pointer_cast(sample.target); + stat->counts_[ctarget->cl()] += 1; + } + stat->sum_counts_ = samples.size(); + + return stat; + } + + virtual std::shared_ptr Reduce(const std::vector>& fcns) const { + auto stat = std::make_shared(); + auto cfcn0 = std::static_pointer_cast(fcns[0]); + stat->counts_.resize(cfcn0->counts_.size(), 0); + stat->sum_counts_ = 0; + + for(auto fcn : fcns) { + auto cfcn = std::static_pointer_cast(fcn); + for(size_t cl = 0; cl < stat->counts_.size(); ++cl) { + stat->counts_[cl] += cfcn->counts_[cl]; + } + stat->sum_counts_ += cfcn->sum_counts_; + } + + return stat; + } + + virtual std::tuple argmax() const { + int max_idx = 0; + int max_count = counts_[0]; + int max2_idx = -1; + int max2_count = -1; + for(size_t idx = 1; idx < counts_.size(); ++idx) { + if(counts_[idx] > max_count) { + max2_count = max_count; + max2_idx = max_idx; + max_count = counts_[idx]; + max_idx = idx; + } + else if(counts_[idx] > max2_count) { + max2_count = counts_[idx]; + max2_idx = idx; + } + } + return std::make_tuple(max_idx, max2_idx); + } + + virtual std::vector prob_vec() const { + std::vector probs(counts_.size(), 0.f); + int sum = 0; + for(int cnt : counts_) { + sum += cnt; + } + for(size_t idx = 0; idx < counts_.size(); ++idx) { + probs[idx] = float(counts_[idx]) / sum; + } + return probs; + } + + virtual void Save(SerializationOut& ar) const { + ar << n_classes_; + int n_counts = counts_.size(); + ar << n_counts; + for(int idx = 0; idx < n_counts; ++idx) { + ar << counts_[idx]; + } + ar << sum_counts_; + } + + virtual void Load(SerializationIn& ar) { + ar >> n_classes_; + int n_counts; + ar >> n_counts; + counts_.resize(n_counts); + for(int idx = 0; idx < n_counts; ++idx) { + ar >> counts_[idx]; + } + ar >> sum_counts_; + } + +public: + int n_classes_; + + std::vector counts_; + int sum_counts_; + +DISABLE_COPY_AND_ASSIGN(HyperdepthLeafFunction); +}; + + +typedef SplitFunctionPixelDifference HDSplitFunctionT; +typedef HyperdepthLeafFunction HDLeafFunctionT; +typedef HyperdepthSplitEvaluator HDSplitEvaluatorT; +typedef Forest HDForest; + + + +template +class Raw { +public: + const T* raw; + const int nsamples; + const int rows; + const int cols; + Raw(const T* raw, int nsamples, int rows, int cols) + : raw(raw), nsamples(nsamples), rows(rows), cols(cols) {} + + T operator()(int n, int r, int c) const { + return raw[(n * rows + r) * cols + c]; + } +}; + + +class RawSample : public Sample { +public: + RawSample(const Raw& raw, int n, int rc, int cc, int patch_height, int patch_width) + : Sample(1, patch_height, patch_width), raw(raw), n(n), rc(rc), cc(cc) {} + + virtual float at(int ch, int r, int c) const { + r += rc - height_ / 2; + c += cc - width_ / 2; + r = std::max(0, std::min(raw.rows-1, r)); + c = std::max(0, std::min(raw.cols-1, c)); + return raw(n, r, c); + } + +protected: + const Raw& raw; + int n; + int rc; + int cc; +}; + +void extract_row_samples(const Raw& im, const Raw& disp, int row, int n_disp_bins, bool only_valid, std::vector& data) { + for(int n = 0; n < im.nsamples; ++n) { + for(int col = 0; col < im.cols; ++col) { + float d = disp(n, row, col); + float pos = col - d; + int cl = pos * n_disp_bins; + if((d < 0 || cl < 0) && only_valid) continue; + + auto sample = std::make_shared(im, n, row, col, 32, 32); + auto target = std::make_shared(cl); + auto datum = TrainDatum(sample, target); + data.push_back(datum); + } + } + std::cout << "extracted " << data.size() << " train samples" << std::endl; + std::cout << "n_classes (" << im.cols << ") * n_disp_bins (" << n_disp_bins << ") = " << (im.cols * n_disp_bins) << std::endl; +} + + +void train(int row_from, int row_to, TrainParameters params, const uint8_t* ims, const float* disps, int n, int h, int w, int n_disp_bins, int depth_switch, int n_threads, std::string forest_prefix) { + Raw raw_ims(ims, n, h, w); + Raw raw_disps(disps, n, h, w); + + int n_classes = w; + + auto gen_split_fcn = std::make_shared(); + auto gen_leaf_fcn = std::make_shared(n_classes * n_disp_bins); + auto split_eval = std::make_shared(true, n_classes, n_disp_bins, depth_switch); + + for(int row = row_from; row < row_to; ++row) { + std::cout << "train row " << row << std::endl; + + std::vector data; + extract_row_samples(raw_ims, raw_disps, row, n_disp_bins, true, data); + + TrainForestQueued train(params, gen_split_fcn, gen_leaf_fcn, split_eval, n_threads, true); + + auto forest = train.Train(data, TrainType::TRAIN, nullptr); + + std::ostringstream forest_path; + forest_path << forest_prefix << row << ".bin"; + std::cout << "save forest of row " << row << " to " << forest_path.str() << std::endl; + BinarySerializationOut fout(forest_path.str()); + forest->Save(fout); + } +} + + +void eval(int row_from, int row_to, const uint8_t* ims, const float* disps, int n, int h, int w, int n_disp_bins, int depth_switch, int n_threads, std::string forest_prefix, float* out) { + Raw raw_ims(ims, n, h, w); + Raw raw_disps(disps, n, h, w); + + for(int row = row_from; row < row_to; ++row) { + std::vector data; + extract_row_samples(raw_ims, raw_disps, row, n_disp_bins, false, data); + + std::ostringstream forest_path; + forest_path << forest_prefix << row << ".bin"; + std::cout << "eval row " << row << " - " << forest_path.str() << std::endl; + + BinarySerializationIn fin(forest_path.str()); + HDForest forest; + forest.Load(fin); + + auto res = forest.inferencemt(data, n_threads); + + for(int nidx = 0; nidx < n; ++nidx) { + for(int col = 0; col < w; ++col) { + auto fcn = res[nidx * w + col]; + int pos, pos2; + std::tie(pos, pos2) = fcn->argmax(); + float disp = col - float(pos) / n_disp_bins; + float disp2 = col - float(pos2) / n_disp_bins; + + float prob = fcn->prob_vec()[pos]; + + out[((nidx * h + row) * w + col) * 3 + 0] = disp; + out[((nidx * h + row) * w + col) * 3 + 1] = prob; + out[((nidx * h + row) * w + col) * 3 + 2] = std::abs(disp - disp2); + } + } + } +} diff --git a/hyperdepth/hyperdepth.pyx b/hyperdepth/hyperdepth.pyx new file mode 100644 index 0000000..abc741a --- /dev/null +++ b/hyperdepth/hyperdepth.pyx @@ -0,0 +1,86 @@ +cimport cython +import numpy as np +cimport numpy as np + +from libc.stdlib cimport free, malloc +from libcpp cimport bool +from libcpp.string cimport string +from cpython cimport PyObject, Py_INCREF + +CREATE_INIT = True # workaround, so cython builds a init function + +np.import_array() + + +ctypedef unsigned char uint8_t + +cdef extern from "rf/train.h": + cdef cppclass TrainParameters: + int n_trees; + int max_tree_depth; + int n_test_split_functions; + int n_test_thresholds; + int n_test_samples; + int min_samples_to_split; + int min_samples_for_leaf; + int print_node_info; + TrainParameters(); + + +cdef extern from "hyperdepth.h": + void train(int row_from, int row_to, TrainParameters params, const uint8_t* ims, const float* disps, int n, int h, int w, int n_disp_bins, int depth_switch, int n_threads, string forest_prefix); + void eval(int row_from, int row_to, const uint8_t* ims, const float* disps, int n, int h, int w, int n_disp_bins, int depth_switch, int n_threads, string forest_prefix, float* out); + + + + +cdef class TrainParams: + cdef TrainParameters params; + + def __cinit__(self, int n_trees=6, int max_tree_depth=8, int n_test_split_functions=50, int n_test_thresholds=10, int n_test_samples=4096, int min_samples_to_split=16, int min_samples_for_leaf=8, int print_node_info=100): + self.params.n_trees = n_trees + self.params.max_tree_depth = max_tree_depth + self.params.n_test_split_functions = n_test_split_functions + self.params.n_test_thresholds = n_test_thresholds + self.params.n_test_samples = n_test_samples + self.params.min_samples_to_split = min_samples_to_split + self.params.min_samples_for_leaf = min_samples_for_leaf + self.params.print_node_info = print_node_info + + def __str__(self): + return f'n_trees={self.params.n_trees}, max_tree_depth={self.params.max_tree_depth}, n_test_split_functions={self.params.n_test_split_functions}, n_test_thresholds={self.params.n_test_thresholds}, n_test_samples={self.params.n_test_samples}, min_samples_to_split={self.params.min_samples_to_split}, min_samples_for_leaf={self.params.min_samples_for_leaf}' + + +def train_forest(TrainParams params, uint8_t[:,:,::1] ims, float[:,:,::1] disps, int n_disp_bins=10, int depth_switch=0, int n_threads=18, str forest_prefix='forest', int row_from=-1, int row_to=-1): + cdef int n = ims.shape[0] + cdef int h = ims.shape[1] + cdef int w = ims.shape[2] + + if row_from < 0: + row_from = 0 + if row_to > h or row_to < 0: + row_to = h + + if n != disps.shape[0] or h != disps.shape[1] or w != disps.shape[2]: + raise Exception('ims.shape != disps.shape') + + train(row_from, row_to, params.params, &ims[0,0,0], &disps[0,0,0], n, h, w, n_disp_bins, depth_switch, n_threads, forest_prefix.encode()) + + +def eval_forest(uint8_t[:,:,::1] ims, float[:,:,::1] disps, int n_disp_bins=10, int depth_switch=0, int n_threads=18, str forest_prefix='forest', int row_from=-1, int row_to=-1): + cdef int n = ims.shape[0] + cdef int h = ims.shape[1] + cdef int w = ims.shape[2] + + if n != disps.shape[0] or h != disps.shape[1] or w != disps.shape[2]: + raise Exception('ims.shape != disps.shape') + + if row_from < 0: + row_from = 0 + if row_to > h or row_to < 0: + row_to = h + + out = np.empty((n, h, w, 3), dtype=np.float32) + cdef float[:,:,:,::1] out_view = out + eval(row_from, row_to, &ims[0,0,0], &disps[0,0,0], n, h, w, n_disp_bins, depth_switch, n_threads, forest_prefix.encode(), &out_view[0,0,0,0]) + return out diff --git a/hyperdepth/hyperparam_search.py b/hyperdepth/hyperparam_search.py new file mode 100644 index 0000000..54e9475 --- /dev/null +++ b/hyperdepth/hyperparam_search.py @@ -0,0 +1,65 @@ +import numpy as np +import matplotlib.pyplot as plt +import cv2 +from pathlib import Path +import sys + +import hyperdepth as hd + +sys.path.append('../') +import dataset + + +def get_data(n, row_from, row_to, train): + imsizes = [(256,384)] + focal_lengths = [160] + dset = dataset.SynDataset(n, imsizes=imsizes, focal_lengths=focal_lengths, train=train) + ims = np.empty((n, row_to-row_from, imsizes[0][1]), dtype=np.uint8) + disps = np.empty((n, row_to-row_from, imsizes[0][1]), dtype=np.float32) + for idx in range(n): + print(f'load sample {idx} train={train}') + sample = dset[idx] + ims[idx] = (sample['im0'][0,row_from:row_to] * 255).astype(np.uint8) + disps[idx] = sample['disp0'][0,row_from:row_to] + return ims, disps + + + +params = hd.TrainParams( + n_trees=4, + max_tree_depth=, + n_test_split_functions=50, + n_test_thresholds=10, + n_test_samples=4096, + min_samples_to_split=16, + min_samples_for_leaf=8) + +n_disp_bins = 20 +depth_switch = 0 + +row_from = 100 +row_to = 108 +n_train_samples = 1024 +n_test_samples = 32 + +train_ims, train_disps = get_data(n_train_samples, row_from, row_to, True) +test_ims, test_disps = get_data(n_test_samples, row_from, row_to, False) + +for tree_depth in [8,10,12,14,16]: + depth_switch = tree_depth - 4 + + prefix = f'td{tree_depth}_ds{depth_switch}' + prefix = Path(f'./forests/{prefix}/') + prefix.mkdir(parents=True, exist_ok=True) + + hd.train_forest(params, train_ims, train_disps, n_disp_bins=n_disp_bins, depth_switch=depth_switch, forest_prefix=str(prefix / 'fr')) + + es = hd.eval_forest(test_ims, test_disps, n_disp_bins=n_disp_bins, depth_switch=depth_switch, forest_prefix=str(prefix / 'fr')) + + np.save(str(prefix / 'ta.npy'), test_disps) + np.save(str(prefix / 'es.npy'), es) + + # plt.figure(); + # plt.subplot(2,1,1); plt.imshow(test_disps[0], vmin=0, vmax=4); + # plt.subplot(2,1,2); plt.imshow(es[0], vmin=0, vmax=4); + # plt.show() diff --git a/hyperdepth/rf/common.h b/hyperdepth/rf/common.h new file mode 100644 index 0000000..e93f1c7 --- /dev/null +++ b/hyperdepth/rf/common.h @@ -0,0 +1,18 @@ +#ifndef COMMON_H +#define COMMON_H + +#include +#include + +#if defined(_OPENMP) +#include +#endif + + +#define DISABLE_COPY_AND_ASSIGN(classname) \ +private:\ + classname(const classname&) = delete;\ + classname& operator=(const classname&) = delete; + + +#endif diff --git a/hyperdepth/rf/data.h b/hyperdepth/rf/data.h new file mode 100644 index 0000000..1c34426 --- /dev/null +++ b/hyperdepth/rf/data.h @@ -0,0 +1,72 @@ +#pragma once + +#include + + +class Sample { +public: + Sample(int channels, int height, int width) + : channels_(channels), height_(height), width_(width) {} + + virtual ~Sample() {} + + virtual float at(int c, int h, int w) const = 0; + + virtual float operator()(int c, int h, int w) const { + return at(c,h,w); + } + + virtual int channels() const { return channels_; } + virtual int height() const { return height_; } + virtual int width() const { return width_; } + +protected: + int channels_; + int height_; + int width_; +}; + +typedef std::shared_ptr SamplePtr; + + + + +class Target { +public: + Target() {} + virtual ~Target() {} +}; + +typedef std::shared_ptr TargetPtr; +typedef std::vector VecTargetPtr; +typedef std::shared_ptr VecPtrTargetPtr; + + +class ClassificationTarget : public Target { +public: + ClassificationTarget(int cl) : cl_(cl) {} + virtual ~ClassificationTarget() {} + int cl() const { return cl_; } + +private: + int cl_; +}; + +typedef std::shared_ptr ClassificationTargetPtr; + + + + +struct TrainDatum { + SamplePtr sample; + TargetPtr target; + TargetPtr optimize_target; + + TrainDatum() : sample(nullptr), target(nullptr), optimize_target(nullptr) {} + + TrainDatum(SamplePtr sample, TargetPtr target) + : sample(sample), target(target), optimize_target(target) {} + + TrainDatum(SamplePtr sample, TargetPtr target, TargetPtr optimize_target) + : sample(sample), target(target), optimize_target(optimize_target) {} +}; diff --git a/hyperdepth/rf/forest.h b/hyperdepth/rf/forest.h new file mode 100644 index 0000000..34bbf89 --- /dev/null +++ b/hyperdepth/rf/forest.h @@ -0,0 +1,92 @@ +#pragma once + +#include "tree.h" + +template +class Forest { +public: + Forest() {} + virtual ~Forest() {} + + std::shared_ptr inferencest(const SamplePtr& sample) const { + int n_trees = trees_.size(); + + std::vector> fcns; + + //inference of individual trees + for(int tree_idx = 0; tree_idx < n_trees; ++tree_idx) { + std::shared_ptr tree_fcn = trees_[tree_idx]->inference(sample); + fcns.push_back(tree_fcn); + } + + //combine tree fcns/results and collect all results + return fcns[0]->Reduce(fcns); + } + + std::vector> inferencemt(const std::vector& samples, int n_threads) const { + std::vector> targets(samples.size()); + + omp_set_num_threads(n_threads); + #pragma omp parallel for + for(size_t sample_idx = 0; sample_idx < samples.size(); ++sample_idx) { + targets[sample_idx] = inferencest(samples[sample_idx]); + } + + return targets; + } + + std::vector> inferencemt(const std::vector& samples, int n_threads) const { + std::vector> targets(samples.size()); + + omp_set_num_threads(n_threads); + #pragma omp parallel for + for(size_t sample_idx = 0; sample_idx < samples.size(); ++sample_idx) { + targets[sample_idx] = inferencest(samples[sample_idx].sample); + } + + return targets; + } + + void AddTree(std::shared_ptr> tree) { + trees_.push_back(tree); + } + + size_t trees_size() const { return trees_.size(); } + // TreePtr trees(int idx) const { return trees_[idx]; } + + virtual void Save(SerializationOut& ar) const { + size_t n_trees = trees_.size(); + std::cout << "[DEBUG] write " << n_trees << " trees" << std::endl; + ar << n_trees; + + if(true) std::cout << "[Forest][write] write number of trees " << n_trees << std::endl; + + for(size_t tree_idx = 0; tree_idx < trees_.size(); ++tree_idx) { + if(true) std::cout << "[Forest][write] write tree nb. " << tree_idx << std::endl; + trees_[tree_idx]->Save(ar); + } + } + + virtual void Load(SerializationIn& ar) { + size_t n_trees; + ar >> n_trees; + + if(true) std::cout << "[Forest][read] nTrees: " << n_trees << std::endl; + + trees_.clear(); + for(size_t i = 0; i < n_trees; ++i) { + if(true) std::cout << "[Forest][read] read tree " << (i+1) << " of " << n_trees << " - " << std::endl; + + auto tree = std::make_shared>(); + tree->Load(ar); + trees_.push_back(tree); + + if(true) std::cout << "[Forest][read] finished read tree " << (i+1) << " of " << n_trees << std::endl; + } + } + + +private: + std::vector>> trees_; +}; + diff --git a/hyperdepth/rf/leaffcn.h b/hyperdepth/rf/leaffcn.h new file mode 100644 index 0000000..db0cac9 --- /dev/null +++ b/hyperdepth/rf/leaffcn.h @@ -0,0 +1,99 @@ +#pragma once + +#include + +#include "common.h" +#include "data.h" + + +class ClassProbabilitiesLeafFunction { +public: + ClassProbabilitiesLeafFunction() : n_classes_(-1) {} + ClassProbabilitiesLeafFunction(int n_classes) : n_classes_(n_classes) {} + virtual ~ClassProbabilitiesLeafFunction() {} + + virtual std::shared_ptr Copy() const { + auto fcn = std::make_shared(); + fcn->n_classes_ = n_classes_; + fcn->counts_.resize(counts_.size()); + for(size_t idx = 0; idx < counts_.size(); ++idx) { + fcn->counts_[idx] = counts_[idx]; + } + fcn->sum_counts_ = sum_counts_; + + return fcn; + } + + virtual std::shared_ptr Create(const std::vector& samples) { + auto stat = std::make_shared(); + + stat->counts_.resize(n_classes_, 0); + for(auto sample : samples) { + auto ctarget = std::static_pointer_cast(sample.target); + stat->counts_[ctarget->cl()] += 1; + } + stat->sum_counts_ = samples.size(); + + return stat; + } + + virtual std::shared_ptr Reduce(const std::vector>& fcns) const { + auto stat = std::make_shared(); + auto cfcn0 = std::static_pointer_cast(fcns[0]); + stat->counts_.resize(cfcn0->counts_.size(), 0); + stat->sum_counts_ = 0; + + for(auto fcn : fcns) { + auto cfcn = std::static_pointer_cast(fcn); + for(size_t cl = 0; cl < stat->counts_.size(); ++cl) { + stat->counts_[cl] += cfcn->counts_[cl]; + } + stat->sum_counts_ += cfcn->sum_counts_; + } + + return stat; + } + + virtual int argmax() const { + int max_idx = 0; + int max_count = counts_[0]; + for(size_t idx = 1; idx < counts_.size(); ++idx) { + if(counts_[idx] > max_count) { + max_count = counts_[idx]; + max_idx = idx; + } + } + return max_idx; + } + + virtual void Save(SerializationOut& ar) const { + ar << n_classes_; + int n_counts = counts_.size(); + ar << n_counts; + for(int idx = 0; idx < n_counts; ++idx) { + ar << counts_[idx]; + } + ar << sum_counts_; + } + + virtual void Load(SerializationIn& ar) { + ar >> n_classes_; + int n_counts; + ar >> n_counts; + counts_.resize(n_counts); + for(int idx = 0; idx < n_counts; ++idx) { + ar >> counts_[idx]; + } + ar >> sum_counts_; + } + +public: + int n_classes_; + + std::vector counts_; + int sum_counts_; + +DISABLE_COPY_AND_ASSIGN(ClassProbabilitiesLeafFunction); +}; + + diff --git a/hyperdepth/rf/node.h b/hyperdepth/rf/node.h new file mode 100644 index 0000000..bef200a --- /dev/null +++ b/hyperdepth/rf/node.h @@ -0,0 +1,158 @@ +#pragma once + +#include + +#include "serialization.h" +#include "leaffcn.h" +#include "splitfcn.h" + +class Node { +public: + Node() {} + virtual ~Node() {} + + virtual std::shared_ptr Copy() const = 0; + + virtual int type() const = 0; + + virtual void Save(SerializationOut& ar) const = 0; + virtual void Load(SerializationIn& ar) = 0; + +}; + +typedef std::shared_ptr NodePtr; + + +template +class LeafNode : public Node { +public: + static const int TYPE = 0; + + LeafNode() {} + LeafNode(std::shared_ptr leaf_node_fcn) : leaf_node_fcn_(leaf_node_fcn) {} + + virtual ~LeafNode() {} + + virtual NodePtr Copy() const { + auto node = std::make_shared(); + node->leaf_node_fcn_ = leaf_node_fcn_->Copy(); + return node; + } + + virtual void Save(SerializationOut& ar) const { + leaf_node_fcn_->Save(ar); + } + + virtual void Load(SerializationIn& ar) { + leaf_node_fcn_ = std::make_shared(); + leaf_node_fcn_->Load(ar); + } + + virtual int type() const { return TYPE; }; + std::shared_ptr leaf_node_fcn() const { return leaf_node_fcn_; } + +private: + std::shared_ptr leaf_node_fcn_; + +DISABLE_COPY_AND_ASSIGN(LeafNode); +}; + + +template +class SplitNode : public Node { +public: + static const int TYPE = 1; + + SplitNode() {} + + SplitNode(NodePtr left, NodePtr right, std::shared_ptr split_fcn) : + left_(left), right_(right), split_fcn_(split_fcn) + {} + + virtual ~SplitNode() {} + + virtual std::shared_ptr Copy() const { + std::shared_ptr node = std::make_shared(); + node->left_ = left_->Copy(); + node->right_ = right_->Copy(); + node->split_fcn_ = split_fcn_->Copy(); + + return node; + } + + bool Split(SamplePtr sample) { + return split_fcn_->Split(sample); + } + + virtual void Save(SerializationOut& ar) const { + split_fcn_->Save(ar); + + //left + int type = left_->type(); + ar << type; + left_->Save(ar); + + //right + type = right_->type(); + ar << type; + right_->Save(ar); + } + + virtual void Load(SerializationIn& ar); + + + virtual int type() const { return TYPE; } + + NodePtr left() const { return left_; } + NodePtr right() const { return right_; } + std::shared_ptr split_fcn() const { return split_fcn_; } + + void set_left(NodePtr left) { left_ = left; } + void set_right(NodePtr right) { right_ = right; } + void set_split_fcn(std::shared_ptr split_fcn) { split_fcn_ = split_fcn; } + +public: + NodePtr left_; + NodePtr right_; + std::shared_ptr split_fcn_; + +DISABLE_COPY_AND_ASSIGN(SplitNode); +}; + + +template +NodePtr MakeNode(int type) { + NodePtr node; + if(type == LeafNode::TYPE) { + node = std::make_shared>(); + } + else if(type == SplitNode::TYPE) { + node = std::make_shared>(); + } + else { + std::cout << "[ERROR] unknown node type" << std::endl; + exit(-1); + } + + return node; +} + + +template +void SplitNode::Load(SerializationIn& ar) { + + split_fcn_ = std::make_shared(); + split_fcn_->Load(ar); + + //left + int left_type; + ar >> left_type; + left_ = MakeNode(left_type); + left_->Load(ar); + + //right + int right_type; + ar >> right_type; + right_ = MakeNode(right_type); + right_->Load(ar); +} diff --git a/hyperdepth/rf/serialization.h b/hyperdepth/rf/serialization.h new file mode 100644 index 0000000..8bd20c4 --- /dev/null +++ b/hyperdepth/rf/serialization.h @@ -0,0 +1,256 @@ +#pragma once + +#include + +class SerializationOut { +public: + SerializationOut(const std::string& path) : path_(path) {} + virtual ~SerializationOut() {} + + virtual SerializationOut& operator<<(const bool& v) = 0; + virtual SerializationOut& operator<<(const char& v) = 0; + virtual SerializationOut& operator<<(const int& v) = 0; + virtual SerializationOut& operator<<(const unsigned int& v) = 0; + virtual SerializationOut& operator<<(const long int& v) = 0; + virtual SerializationOut& operator<<(const unsigned long int& v) = 0; + virtual SerializationOut& operator<<(const long long int& v) = 0; + virtual SerializationOut& operator<<(const unsigned long long int& v) = 0; + virtual SerializationOut& operator<<(const float& v) = 0; + virtual SerializationOut& operator<<(const double& v) = 0; + +protected: + const std::string& path_; +}; + +class SerializationIn { +public: + SerializationIn(const std::string& path) : path_(path) {} + virtual ~SerializationIn() {} + + virtual SerializationIn& operator>>(bool& v) = 0; + virtual SerializationIn& operator>>(char& v) = 0; + virtual SerializationIn& operator>>(int& v) = 0; + virtual SerializationIn& operator>>(unsigned int& v) = 0; + virtual SerializationIn& operator>>(long int& v) = 0; + virtual SerializationIn& operator>>(unsigned long int& v) = 0; + virtual SerializationIn& operator>>(long long int& v) = 0; + virtual SerializationIn& operator>>(unsigned long long int& v) = 0; + virtual SerializationIn& operator>>(float& v) = 0; + virtual SerializationIn& operator>>(double& v) = 0; + +protected: + const std::string& path_; +}; + +class TextSerializationOut : public SerializationOut { +public: + TextSerializationOut(const std::string& path) : SerializationOut(path), + f_(path.c_str()) {} + virtual ~TextSerializationOut() { + f_.close(); + } + + virtual SerializationOut& operator<<(const bool& v) { + f_ << v << std::endl; + return (*this); + } + virtual SerializationOut& operator<<(const char& v) { + f_ << v << std::endl; + return (*this); + } + virtual SerializationOut& operator<<(const int& v) { + f_ << v << std::endl; + return (*this); + } + virtual SerializationOut& operator<<(const unsigned int& v) { + f_ << v << std::endl; + return (*this); + } + virtual SerializationOut& operator<<(const long int& v) { + f_ << v << std::endl; + return (*this); + } + virtual SerializationOut& operator<<(const unsigned long int& v) { + f_ << v << std::endl; + return (*this); + } + virtual SerializationOut& operator<<(const long long int& v) { + f_ << v << std::endl; + return (*this); + } + virtual SerializationOut& operator<<(const unsigned long long int& v) { + f_ << v << std::endl; + return (*this); + } + virtual SerializationOut& operator<<(const float& v) { + f_ << v << std::endl; + return (*this); + } + virtual SerializationOut& operator<<(const double& v) { + f_ << v << std::endl; + return (*this); + } + +protected: + std::ofstream f_; +}; + +class TextSerializationIn : public SerializationIn { +public: + TextSerializationIn(const std::string& path) : SerializationIn(path), + f_(path.c_str()) {} + virtual ~TextSerializationIn() { + f_.close(); + } + + virtual SerializationIn& operator>>(bool& v) { + f_ >> v; + return (*this); + } + virtual SerializationIn& operator>>(char& v) { + f_ >> v; + return (*this); + } + virtual SerializationIn& operator>>(int& v) { + f_ >> v; + return (*this); + } + virtual SerializationIn& operator>>(unsigned int& v) { + f_ >> v; + return (*this); + } + virtual SerializationIn& operator>>(long int& v) { + f_ >> v; + return (*this); + } + virtual SerializationIn& operator>>(unsigned long int& v) { + f_ >> v; + return (*this); + } + virtual SerializationIn& operator>>(long long int& v) { + f_ >> v; + return (*this); + } + virtual SerializationIn& operator>>(unsigned long long int& v) { + f_ >> v; + return (*this); + } + virtual SerializationIn& operator>>(float& v) { + f_ >> v; + return (*this); + } + virtual SerializationIn& operator>>(double& v) { + f_ >> v; + return (*this); + } + +protected: + std::ifstream f_; +}; + +class BinarySerializationOut : public SerializationOut { +public: + BinarySerializationOut(const std::string& path) : SerializationOut(path), + f_(path.c_str(), std::ios::binary) {} + virtual ~BinarySerializationOut() { + f_.close(); + } + + virtual SerializationOut& operator<<(const bool& v) { + f_.write((char*)&v, sizeof(bool)); + return (*this); + } + virtual SerializationOut& operator<<(const char& v) { + f_.write((char*)&v, sizeof(char)); + return (*this); + } + virtual SerializationOut& operator<<(const int& v) { + f_.write((char*)&v, sizeof(int)); + return (*this); + } + virtual SerializationOut& operator<<(const unsigned int& v) { + f_.write((char*)&v, sizeof(unsigned int)); + return (*this); + } + virtual SerializationOut& operator<<(const long int& v) { + f_.write((char*)&v, sizeof(long int)); + return (*this); + } + virtual SerializationOut& operator<<(const unsigned long int& v) { + f_.write((char*)&v, sizeof(unsigned long int)); + return (*this); + } + virtual SerializationOut& operator<<(const long long int& v) { + f_.write((char*)&v, sizeof(long long int)); + return (*this); + } + virtual SerializationOut& operator<<(const unsigned long long int& v) { + f_.write((char*)&v, sizeof(unsigned long long int)); + return (*this); + } + virtual SerializationOut& operator<<(const float& v) { + f_.write((char*)&v, sizeof(float)); + return (*this); + } + virtual SerializationOut& operator<<(const double& v) { + f_.write((char*)&v, sizeof(double)); + return (*this); + } + +protected: + std::ofstream f_; +}; + +class BinarySerializationIn : public SerializationIn { +public: + BinarySerializationIn(const std::string& path) : SerializationIn(path), + f_(path.c_str(), std::ios::binary) {} + virtual ~BinarySerializationIn() { + f_.close(); + } + + virtual SerializationIn& operator>>(bool& v) { + f_.read((char*)&v, sizeof(bool)); + return (*this); + } + virtual SerializationIn& operator>>(char& v) { + f_.read((char*)&v, sizeof(char)); + return (*this); + } + virtual SerializationIn& operator>>(int& v) { + f_.read((char*)&v, sizeof(int)); + return (*this); + } + virtual SerializationIn& operator>>(unsigned int& v) { + f_.read((char*)&v, sizeof(unsigned int)); + return (*this); + } + virtual SerializationIn& operator>>(long int& v) { + f_.read((char*)&v, sizeof(long int)); + return (*this); + } + virtual SerializationIn& operator>>(unsigned long int& v) { + f_.read((char*)&v, sizeof(unsigned long int)); + return (*this); + } + virtual SerializationIn& operator>>(long long int& v) { + f_.read((char*)&v, sizeof(long long int)); + return (*this); + } + virtual SerializationIn& operator>>(unsigned long long int& v) { + f_.read((char*)&v, sizeof(unsigned long long int)); + return (*this); + } + virtual SerializationIn& operator>>(float& v) { + f_.read((char*)&v, sizeof(float)); + return (*this); + } + virtual SerializationIn& operator>>(double& v) { + f_.read((char*)&v, sizeof(double)); + return (*this); + } + +protected: + std::ifstream f_; +}; + diff --git a/hyperdepth/rf/spliteval.h b/hyperdepth/rf/spliteval.h new file mode 100644 index 0000000..dc08d69 --- /dev/null +++ b/hyperdepth/rf/spliteval.h @@ -0,0 +1,71 @@ +#pragma once + +class SplitEvaluator { +public: + SplitEvaluator(bool normalize) + : normalize_(normalize) {} + + virtual ~SplitEvaluator() {} + + virtual float Eval(const std::vector& lefttargets, const std::vector& righttargets, int depth) const { + float purity_left = Purity(lefttargets, depth); + float purity_right = Purity(righttargets, depth); + + float normalize_left = 1.0; + float normalize_right = 1.0; + + if(normalize_) { + unsigned int n_left = lefttargets.size(); + unsigned int n_right = righttargets.size(); + unsigned int n_total = n_left + n_right; + + normalize_left = float(n_left) / float(n_total); + normalize_right = float(n_right) / float(n_total); + } + + float purity = purity_left * normalize_left + purity_right * normalize_right; + + return purity; + } + +protected: + virtual float Purity(const std::vector& targets, int depth) const = 0; + +protected: + bool normalize_; +}; + + + +class ClassificationIGSplitEvaluator : public SplitEvaluator { +public: + ClassificationIGSplitEvaluator(bool normalize, int n_classes) + : SplitEvaluator(normalize), n_classes_(n_classes) {} + virtual ~ClassificationIGSplitEvaluator() {} + +protected: + virtual float Purity(const std::vector& targets, int depth) const { + if(targets.size() == 0) return 0; + + std::vector ps; + ps.resize(n_classes_, 0); + for(auto target : targets) { + auto ctarget = std::static_pointer_cast(target.optimize_target); + ps[ctarget->cl()] += 1; + } + + float h = 0; + for(int cl = 0; cl < n_classes_; ++cl) { + float fi = float(ps[cl]) / float(targets.size()); + if(fi > 0) { + h = h - fi * std::log(fi); + } + } + + return h; + } + +private: + int n_classes_; +}; + diff --git a/hyperdepth/rf/splitfcn.h b/hyperdepth/rf/splitfcn.h new file mode 100644 index 0000000..966b1cd --- /dev/null +++ b/hyperdepth/rf/splitfcn.h @@ -0,0 +1,106 @@ +#pragma once + +#include + +class SplitFunction { +public: + SplitFunction() {} + virtual ~SplitFunction() {} + + virtual float Compute(SamplePtr sample) const = 0; + + virtual bool Split(SamplePtr sample) const { + return Compute(sample) < threshold_; + } + + virtual void Save(SerializationOut& ar) const { + ar << threshold_; + } + + virtual void Load(SerializationIn& ar) { + ar >> threshold_; + } + + virtual float threshold() const { return threshold_; } + virtual void set_threshold(float threshold) { threshold_ = threshold; } + +protected: + float threshold_; +}; + + + +class SplitFunctionPixelDifference : public SplitFunction { +public: + + SplitFunctionPixelDifference() {} + virtual ~SplitFunctionPixelDifference() {} + + virtual std::shared_ptr Copy() const { + std::shared_ptr split_fcn = std::make_shared(); + split_fcn->threshold_ = threshold_; + split_fcn->c0_ = c0_; + split_fcn->c1_ = c1_; + split_fcn->h0_ = h0_; + split_fcn->h1_ = h1_; + split_fcn->w0_ = w0_; + split_fcn->w1_ = w1_; + + return split_fcn; + } + + virtual std::shared_ptr Generate(std::mt19937& rng, const SamplePtr sample) const { + std::shared_ptr split_fcn = std::make_shared(); + + std::uniform_int_distribution cdist(0, sample->channels()-1); + split_fcn->c0_ = cdist(rng); + split_fcn->c1_ = cdist(rng); + + std::uniform_int_distribution hdist(0, sample->height()-1); + split_fcn->h0_ = hdist(rng); + split_fcn->h1_ = hdist(rng); + + std::uniform_int_distribution wdist(0, sample->width()-1); + split_fcn->w0_ = wdist(rng); + split_fcn->w1_ = wdist(rng); + + return split_fcn; + } + + virtual float Compute(SamplePtr sample) const { + return (*sample)(c0_, h0_, w0_) - (*sample)(c1_, h1_, w1_); + } + + virtual void Save(SerializationOut& ar) const { + SplitFunction::Save(ar); + ar << c0_; + ar << c1_; + ar << h0_; + ar << h1_; + ar << w0_; + ar << w1_; + } + + virtual void Load(SerializationIn& ar) { + SplitFunction::Load(ar); + + ar >> c0_; + ar >> c1_; + ar >> h0_; + ar >> h1_; + ar >> w0_; + ar >> w1_; + } + +private: + int c0_; + int c1_; + int h0_; + int h1_; + int w0_; + int w1_; + +DISABLE_COPY_AND_ASSIGN(SplitFunctionPixelDifference); +}; + + diff --git a/hyperdepth/rf/threadpool.h b/hyperdepth/rf/threadpool.h new file mode 100644 index 0000000..b9d9f0f --- /dev/null +++ b/hyperdepth/rf/threadpool.h @@ -0,0 +1,112 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +class ThreadPool { +public: + ThreadPool(size_t); + template + auto enqueue(F&& f, Args&&... args) + -> std::future::type>; + ~ThreadPool(); + + bool has_running_tasks() { + std::unique_lock lock(running_tasks_mutex); + return n_running_tasks > 0; + } + +private: + // need to keep track of threads so we can join them + std::vector< std::thread > workers; + // the task queue + std::queue< std::function > tasks; + + int n_running_tasks; + + // synchronization + std::mutex queue_mutex; + std::mutex running_tasks_mutex; + std::condition_variable condition; + bool stop; +}; + +// the constructor just launches some amount of workers +inline ThreadPool::ThreadPool(size_t threads) + : n_running_tasks(0), stop(false) +{ + for(size_t i = 0;i task; + + { + std::unique_lock lock(this->queue_mutex); + this->condition.wait(lock, + [this]{ return this->stop || !this->tasks.empty(); }); + if(this->stop && this->tasks.empty()) + return; + task = std::move(this->tasks.front()); + this->tasks.pop(); + } + + { + std::unique_lock lock(this->running_tasks_mutex); + n_running_tasks++; + } + task(); + { + std::unique_lock lock(this->running_tasks_mutex); + n_running_tasks--; + } + } + } + ); +} + +// add new work item to the pool +template +auto ThreadPool::enqueue(F&& f, Args&&... args) + -> std::future::type> +{ + using return_type = typename std::result_of::type; + + auto task = std::make_shared< std::packaged_task >( + std::bind(std::forward(f), std::forward(args)...) + ); + + std::future res = task->get_future(); + { + std::unique_lock lock(queue_mutex); + + // don't allow enqueueing after stopping the pool + if(stop) + throw std::runtime_error("enqueue on stopped ThreadPool"); + + tasks.emplace([task](){ (*task)(); }); + } + condition.notify_one(); + return res; +} + +// the destructor joins all threads +inline ThreadPool::~ThreadPool() +{ + { + std::unique_lock lock(queue_mutex); + stop = true; + } + condition.notify_all(); + for(std::thread &worker: workers) + worker.join(); +} diff --git a/hyperdepth/rf/train.h b/hyperdepth/rf/train.h new file mode 100644 index 0000000..4f9d610 --- /dev/null +++ b/hyperdepth/rf/train.h @@ -0,0 +1,423 @@ +#pragma once + +#include +#include +#include + +#include "threadpool.h" + +#include "forest.h" +#include "spliteval.h" + + +enum class TrainType : int { + TRAIN = 0, + RETRAIN = 1, + RETRAIN_WITH_REPLACEMENT = 2 +}; + +struct TrainParameters { + TrainType train_type; + int n_trees; + int max_tree_depth; + int n_test_split_functions; + int n_test_thresholds; + int n_test_samples; + int min_samples_to_split; + int min_samples_for_leaf; + int print_node_info; + + TrainParameters() : + train_type(TrainType::TRAIN), + n_trees(5), + max_tree_depth(7), + n_test_split_functions(50), + n_test_thresholds(10), + n_test_samples(100), + min_samples_to_split(14), + min_samples_for_leaf(7), + print_node_info(100) + {} +}; + + +template +class TrainForest { +public: + TrainForest(const TrainParameters& params, const std::shared_ptr gen_split_fcn, const std::shared_ptr gen_leaf_fcn, const std::shared_ptr split_eval, int n_threads, bool verbose) + : params_(params), gen_split_fcn_(gen_split_fcn), gen_leaf_fcn_(gen_leaf_fcn), split_eval_(split_eval), n_threads(n_threads), verbose_(verbose) { + + n_created_nodes_ = 0; + n_max_nodes_ = 1; + unsigned long n_nodes_d = 1; + for(int depth = 0; depth < params.max_tree_depth; ++depth) { + n_nodes_d *= 2; + n_max_nodes_ += n_nodes_d; + } + n_max_nodes_ *= params.n_trees; + } + + virtual ~TrainForest() {} + + virtual std::shared_ptr> Train(const std::vector& samples, TrainType train_type, const std::shared_ptr>& old_forest) = 0; + +protected: + virtual void PrintParams() { + if(verbose_){ + #pragma omp critical (TrainForest_train) + { + std::cout << "[TRAIN] training forest " << std::endl; + std::cout << "[TRAIN] n_trees : " << params_.n_trees << std::endl; + std::cout << "[TRAIN] max_tree_depth : " << params_.max_tree_depth << std::endl; + std::cout << "[TRAIN] n_test_split_functions: " << params_.n_test_split_functions << std::endl; + std::cout << "[TRAIN] n_test_thresholds : " << params_.n_test_thresholds << std::endl; + std::cout << "[TRAIN] n_test_samples : " << params_.n_test_samples << std::endl; + std::cout << "[TRAIN] min_samples_to_split : " << params_.min_samples_to_split << std::endl; + } + } + } + + virtual void UpdateNodeInfo(unsigned int depth, bool leaf) { + if(verbose_) { + n_created_nodes_ += 1; + + if(leaf) { + unsigned long n_nodes_d = 1; + unsigned int n_remove_max_nodes = 0; + for(int d = depth; d < params_.max_tree_depth; ++d) { + n_nodes_d *= 2; + n_remove_max_nodes += n_nodes_d; + } + n_max_nodes_ -= n_remove_max_nodes; + } + + if(n_created_nodes_ % params_.print_node_info == 0 || n_created_nodes_ == n_max_nodes_) { + std::cout << "[Forest]" + << " created node number " << n_created_nodes_ + << " @ depth " << depth + << ", max. " << n_max_nodes_ << " left" + << " => " << (double(n_created_nodes_) / double(n_max_nodes_)) + << " done" << std::endl; + } + } + } + + virtual void SampleData(const std::vector& all, std::vector& sampled, std::mt19937& rng) { + unsigned int n = all.size(); + unsigned int k = params_.n_test_samples; + k = n < k ? n : k; + + std::set indices; + std::uniform_int_distribution udist(0, all.size()-1); + while(indices.size() < k) { + int idx = udist(rng); + indices.insert(idx); + } + + sampled.resize(k); + int sidx = 0; + for(int idx : indices) { + sampled[sidx] = all[idx]; + sidx += 1; + } + } + + virtual void Split(const std::shared_ptr& split_function, const std::vector& samples, std::vector& left, std::vector& right) { + for(auto sample : samples) { + if(split_function->Split(sample.sample)) { + left.push_back(sample); + } + else { + right.push_back(sample); + } + } + } + + + virtual std::shared_ptr OptimizeSplitFunction(const std::vector& samples, int depth, std::mt19937& rng) { + std::vector split_samples; + SampleData(samples, split_samples, rng); + + unsigned int min_samples_for_leaf = params_.min_samples_for_leaf; + + float min_cost = std::numeric_limits::max(); + std::shared_ptr best_split_fcn; + float best_threshold = 0; + + for(int split_fcn_idx = 0; split_fcn_idx < params_.n_test_split_functions; ++split_fcn_idx) { + auto split_fcn = gen_split_fcn_->Generate(rng, samples[0].sample); + + for(int threshold_idx = 0; threshold_idx < params_.n_test_thresholds; ++threshold_idx) { + std::uniform_int_distribution udist(0, split_samples.size()-1); + int rand_split_sample_idx = udist(rng); + float threshold = split_fcn->Compute(split_samples[rand_split_sample_idx].sample); + split_fcn->set_threshold(threshold); + + std::vector left; + std::vector right; + Split(split_fcn, split_samples, left, right); + if(left.size() < min_samples_for_leaf || right.size() < min_samples_for_leaf) { + continue; + } + + // std::cout << "split done " << left.size() << "," << right.size() << std::endl; + float split_cost = split_eval_->Eval(left, right, depth); + // std::cout << ", " << split_cost << ", " << threshold << "; " << std::endl; + + if(split_cost < min_cost) { + min_cost = split_cost; + best_split_fcn = split_fcn; + best_threshold = threshold; //need theshold extra because of pointer + } + } + } + + if(best_split_fcn != nullptr) { + best_split_fcn->set_threshold(best_threshold); + } + + return best_split_fcn; + } + + + virtual NodePtr CreateLeafNode(const std::vector& samples, unsigned int depth) { + auto leaf_fct = gen_leaf_fcn_->Create(samples); + auto node = std::make_shared>(leaf_fct); + + UpdateNodeInfo(depth, true); + + return node; + } + +protected: + const TrainParameters& params_; + const std::shared_ptr gen_split_fcn_; + const std::shared_ptr gen_leaf_fcn_; + const std::shared_ptr split_eval_; + int n_threads; + bool verbose_; + + unsigned long n_created_nodes_; + unsigned long n_max_nodes_; +}; + + +template +class TrainForestRecursive : public TrainForest { +public: + TrainForestRecursive(const TrainParameters& params, const std::shared_ptr gen_split_fcn, const std::shared_ptr gen_leaf_fcn, const std::shared_ptr split_eval, int n_threads, bool verbose) + : TrainForest(params, gen_split_fcn, gen_leaf_fcn, split_eval, n_threads, verbose) {} + + virtual ~TrainForestRecursive() {} + + virtual std::shared_ptr> Train(const std::vector& samples, TrainType train_type, const std::shared_ptr>& old_forest) { + + this->PrintParams(); + + auto tim = std::chrono::system_clock::now(); + auto forest = std::make_shared>(); + + omp_set_num_threads(this->n_threads); + #pragma omp parallel for ordered + for(size_t treeIdx = 0; treeIdx < this->params_.n_trees; ++treeIdx) { + auto treetim = std::chrono::system_clock::now(); + + #pragma omp critical (TrainForest_train) + { + if(this->verbose_){ + std::cout << "[TRAIN][START] training tree " << treeIdx << " of " << this->params_.n_trees << std::endl; + } + } + + std::shared_ptr> old_tree; + if(old_forest != 0 && treeIdx < old_forest->trees_size()) { + old_tree = old_forest->trees(treeIdx); + } + + std::random_device rd; + std::mt19937 rng(rd()); + + auto tree = Train(samples, train_type, old_tree,rng); + + #pragma omp critical (TrainForest_train) + { + forest->AddTree(tree); + if(this->verbose_){ + auto now = std::chrono::system_clock::now(); + auto ms = std::chrono::duration_cast(now - treetim); + std::cout << "[TRAIN][FINISHED] training tree " << treeIdx << " of " << this->params_.n_trees << " - took " << (ms.count() * 1e-3) << "[s]" << std::endl; + std::cout << "[TRAIN][FINISHED] " << (this->params_.n_trees - forest->trees_size()) << " left for training" << std::endl; + } + } + } + + if(this->verbose_){ + auto now = std::chrono::system_clock::now(); + auto ms = std::chrono::duration_cast(now - tim); + std::cout << "[TRAIN][FINISHED] training forest - took " << (ms.count() * 1e-3) << "[s]" << std::endl; + } + + return forest; + } + +private: + virtual std::shared_ptr> Train(const std::vector& samples, TrainType train_type, const std::shared_ptr>& old_tree, std::mt19937& rng) { + NodePtr old_root; + if(old_tree != nullptr) { + old_root = old_tree->root(); + } + + NodePtr root = Train(samples, train_type, old_root, 0, rng); + return std::make_shared>(root); + } + + virtual NodePtr Train(const std::vector& samples, TrainType train_type, const NodePtr& old_node, unsigned int depth, std::mt19937& rng) { + + if(depth < this->params_.max_tree_depth && samples.size() > this->params_.min_samples_to_split) { + std::shared_ptr best_split_fcn; + bool was_split_node = false; + if(old_node == nullptr || old_node->type() == LeafNode::TYPE) { + best_split_fcn = this->OptimizeSplitFunction(samples, depth, rng); + was_split_node = false; + } + else if(old_node->type() == SplitNode::TYPE) { + auto split_node = std::static_pointer_cast>(old_node); + best_split_fcn = split_node->split_fcn()->Copy(); + was_split_node = true; + } + + if(best_split_fcn == nullptr) { + if(old_node == nullptr || train_type == TrainType::TRAIN || train_type == TrainType::RETRAIN_WITH_REPLACEMENT) { + return this->CreateLeafNode(samples, depth); + } + else if(train_type == TrainType::RETRAIN) { + return old_node->Copy(); + } + else { + std::cout << "[ERROR] unknown train type" << std::endl; + exit(-1); + } + } + + // (1) split samples + std::vector leftsamples, rightsamples; + this->Split(best_split_fcn, samples, leftsamples, rightsamples); + + //output node information + this->UpdateNodeInfo(depth, false); + + //create split node - recursively train the siblings + if(was_split_node) { + auto split_node = std::static_pointer_cast>(old_node); + NodePtr left = this->Train(leftsamples, train_type, split_node->left(), depth + 1, rng); + NodePtr right = this->Train(rightsamples, train_type, split_node->right(), depth + 1, rng); + auto new_node = std::make_shared>(left, right, best_split_fcn); + return new_node; + } + else { + NodePtr left = this->Train(leftsamples, train_type, nullptr, depth + 1, rng); + NodePtr right = this->Train(rightsamples, train_type, nullptr, depth + 1, rng); + auto new_node = std::make_shared>(left, right, best_split_fcn); + return new_node; + } + } // if samples < min_samples || depth >= max_depth then make leaf node + else { + if(old_node == 0 || train_type == TrainType::TRAIN || train_type == TrainType::RETRAIN_WITH_REPLACEMENT) { + return this->CreateLeafNode(samples, depth); + } + else if(train_type == TrainType::RETRAIN) { + return old_node->Copy(); + } + else { + std::cout << "[ERROR] unknown train type" << std::endl; + exit(-1); + } + } + } + +}; + + +struct QueueTuple { + int depth; + std::vector train_data; + NodePtr* parent; + + QueueTuple() : depth(-1), train_data(), parent(nullptr) {} + QueueTuple(int depth, std::vector train_data, NodePtr* parent) : + depth(depth), train_data(train_data), parent(parent) {} +}; + +template +class TrainForestQueued : public TrainForest { +public: + TrainForestQueued(const TrainParameters& params, const std::shared_ptr gen_split_fcn, const std::shared_ptr gen_leaf_fcn, const std::shared_ptr split_eval, int n_threads, bool verbose) + : TrainForest(params, gen_split_fcn, gen_leaf_fcn, split_eval, n_threads, verbose) {} + + virtual ~TrainForestQueued() {} + + virtual std::shared_ptr> Train(const std::vector& samples, TrainType train_type, const std::shared_ptr>& old_forest) { + this->PrintParams(); + + auto tim = std::chrono::system_clock::now(); + auto forest = std::make_shared>(); + + std::cout << "[TRAIN] create pool with " << this->n_threads << " threads" << std::endl; + auto pool = std::make_shared(this->n_threads); + for(int treeidx = 0; treeidx < this->params_.n_trees; ++treeidx) { + auto tree = std::make_shared>(); + forest->AddTree(tree); + AddJob(pool, QueueTuple(0, samples, &(tree->root_))); + } + + while(pool->has_running_tasks()) { + std::this_thread::sleep_for(std::chrono::milliseconds(100)); + } + + if(this->verbose_){ + auto now = std::chrono::system_clock::now(); + auto ms = std::chrono::duration_cast(now - tim); + std::cout << "[TRAIN][FINISHED] training forest - took " << (ms.count() * 1e-3) << "[s]" << std::endl; + } + + return forest; + } + +private: + virtual void AddJob(std::shared_ptr pool, QueueTuple data) { + pool->enqueue([this](std::shared_ptr pool, QueueTuple data) { + std::random_device rd; + std::mt19937 rng(rd()); + + std::shared_ptr best_split_fcn = nullptr; + + if(data.depth < this->params_.max_tree_depth && int(data.train_data.size()) > this->params_.min_samples_to_split) { + best_split_fcn = this->OptimizeSplitFunction(data.train_data, data.depth, rng); + } + + if(best_split_fcn == nullptr) { + auto node = this->CreateLeafNode(data.train_data, data.depth); + *(data.parent) = node; + } + else { + this->UpdateNodeInfo(data.depth, false); + auto node = std::make_shared>(); + node->split_fcn_ = best_split_fcn; + *(data.parent) = node; + + QueueTuple left; + QueueTuple right; + this->Split(best_split_fcn, data.train_data, left.train_data, right.train_data); + + left.depth = data.depth + 1; + right.depth = data.depth + 1; + + left.parent = &(node->left_); + right.parent = &(node->right_); + + this->AddJob(pool, left); + this->AddJob(pool, right); + } + }, pool, data); + } +}; diff --git a/hyperdepth/rf/tree.h b/hyperdepth/rf/tree.h new file mode 100644 index 0000000..bced027 --- /dev/null +++ b/hyperdepth/rf/tree.h @@ -0,0 +1,55 @@ +#pragma once + +#include "node.h" + +template +class Tree { +public: + Tree() : root_(nullptr) {} + Tree(NodePtr root) : root_(root) {} + + virtual ~Tree() {} + + std::shared_ptr inference(const SamplePtr sample) const { + if(root_ == nullptr) { + std::cout << "[ERROR] tree inference root node is NULL"; + exit(-1); + } + + NodePtr node = root_; + while(node->type() == SplitNode::TYPE) { + auto splitNode = std::static_pointer_cast>(node); + bool left = splitNode->Split(sample); + if(left) { + node = splitNode->left(); + } + else { + node = splitNode->right(); + } + } + + auto leaf_node = std::static_pointer_cast>(node); + return leaf_node->leaf_node_fcn(); + } + + NodePtr root() const { return root_; } + void set_root(NodePtr root) { root_ = root; } + + virtual void Save(SerializationOut& ar) const { + int type = root_->type(); + ar << type; + root_->Save(ar); + } + + virtual void Load(SerializationIn& ar) { + int type; + ar >> type; + root_ = MakeNode(type); + root_->Load(ar); + } + + +public: + NodePtr root_; +}; + diff --git a/hyperdepth/setup.py b/hyperdepth/setup.py new file mode 100644 index 0000000..00933cc --- /dev/null +++ b/hyperdepth/setup.py @@ -0,0 +1,45 @@ +from distutils.core import setup +from Cython.Build import cythonize +from distutils.extension import Extension +from Cython.Distutils import build_ext +import numpy as np +import platform +import os + +this_dir = os.path.dirname(__file__) + + +extra_compile_args = ['-O3', '-std=c++11'] +extra_link_args = [] + +print('using openmp') +extra_compile_args.append('-fopenmp') +extra_link_args.append('-fopenmp') + +sources = ['hyperdepth.pyx'] +extra_objects = [] +library_dirs = [] +libraries = ['m'] + +setup( + name="hyperdepth", + cmdclass= {'build_ext': build_ext}, + ext_modules=[ + Extension('hyperdepth', + sources, + extra_objects=extra_objects, + language='c++', + library_dirs=library_dirs, + libraries=libraries, + include_dirs=[ + np.get_include(), + ], + extra_compile_args=extra_compile_args, + extra_link_args=extra_link_args + ) + ] +) + + + + diff --git a/hyperdepth/train.cpp b/hyperdepth/train.cpp new file mode 100644 index 0000000..05a82c5 --- /dev/null +++ b/hyperdepth/train.cpp @@ -0,0 +1,52 @@ +#include "hyperdepth.h" +#include "rf/train.h" + +int main() { + cv::Mat_ im = read_im(0); + cv::Mat_ disp = read_disp(0); + int im_rows = im.rows; + int im_cols = im.cols; + std::cout << im.rows << "/" << im.cols << std::endl; + std::cout << disp.rows << "/" << disp.cols << std::endl; + + TrainParameters params; + params.n_trees = 6; + params.n_test_samples = 2048; + params.min_samples_to_split = 16; + params.min_samples_for_leaf = 8; + params.n_test_split_functions = 50; + params.n_test_thresholds = 10; + params.max_tree_depth = 8; + + int n_classes = im_cols; + int n_disp_bins = 16; + int depth_switch = 4; + + auto gen_split_fcn = std::make_shared(); + auto gen_leaf_fcn = std::make_shared(n_classes * n_disp_bins); + auto split_eval = std::make_shared(true, n_classes, n_disp_bins, depth_switch); + + for(int row = 0; row < im_rows; ++row) { + std::vector train_data; + for(int idx = 0; idx < 12; ++idx) { + std::cout << "read sample " << idx << std::endl; + im = read_im(idx); + disp = read_disp(idx); + + extract_row_samples(im, disp, row, train_data, true, n_disp_bins); + } + std::cout << "extracted " << train_data.size() << " train samples" << std::endl; + std::cout << "n_classes (" << n_classes << ") * n_disp_bins (" << n_disp_bins << ") = " << (n_classes * n_disp_bins) << std::endl; + + TrainForestQueued train(params, gen_split_fcn, gen_leaf_fcn, split_eval, true); + + auto forest = train.Train(train_data, TrainType::TRAIN, nullptr); + std::cout << "training done" << std::endl; + + std::ostringstream forest_path; + forest_path << "cforest_" << row << ".bin"; + BinarySerializationOut fout(forest_path.str()); + forest->Save(fout); + } +} + diff --git a/hyperdepth/vis_eval.py b/hyperdepth/vis_eval.py new file mode 100644 index 0000000..d6d9a92 --- /dev/null +++ b/hyperdepth/vis_eval.py @@ -0,0 +1,15 @@ +import numpy as np +import matplotlib.pyplot as plt +import cv2 + +orig = cv2.imread('disp_orig.png', cv2.IMREAD_ANYDEPTH).astype(np.float32) +ta = cv2.imread('disp_ta.png', cv2.IMREAD_ANYDEPTH).astype(np.float32) +es = cv2.imread('disp_es.png', cv2.IMREAD_ANYDEPTH).astype(np.float32) + + +plt.figure() +plt.subplot(2,2,1); plt.imshow(orig / 16, vmin=0, vmax=4, cmap='magma') +plt.subplot(2,2,2); plt.imshow(ta / 16, vmin=0, vmax=4, cmap='magma') +plt.subplot(2,2,3); 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y{>Ll$g`b5aLGu57k%sDn1@TDykDnd>+ms2R>53IsvKfhgP*PBre0: + vals.append(val * self.dp_weight) + + # apply edge loss on a subset of training samples + for s,e in zip(itertools.count(), edge): + # inversed ground truth edge where 0 means edge + grad = self.data[f'grad{s}']<0.2 + grad = grad.to(torch.float32) + ids = self.data['id'] + mask = ids>self.train_edge + if mask.sum()>0: + val = self.edge_loss(e[mask], grad[mask]) + else: + val = torch.zeros_like(vals[0]) + if s == 0: + self.edge = e.detach() + self.edge = torch.sigmoid(self.edge) + self.edge_gt = grad.detach() + vals.append(val) + + return vals + + def numpy_in_out(self, output): + output, edge = output + if not(isinstance(output, tuple) or isinstance(output, list)): + output = [output] + es = output[0].detach().to('cpu').numpy() + gt = self.data['disp0'].to('cpu').numpy().astype(np.float32) + im = self.data['im0'][:,0:1,...].detach().to('cpu').numpy() + + ma = gt>0 + return es, gt, im, ma + + def write_img(self, out_path, es, gt, im, ma): + logging.info(f'write img {out_path}') + u_pos, _ = np.meshgrid(range(es.shape[1]), range(es.shape[0])) + + diff = np.abs(es - gt) + + vmin, vmax = np.nanmin(gt), np.nanmax(gt) + vmin = vmin - 0.2*(vmax-vmin) + vmax = vmax + 0.2*(vmax-vmin) + + pattern_proj = self.pattern_proj.to('cpu').numpy()[0,0] + im_orig = self.data['im0'].detach().to('cpu').numpy()[0,0] + pattern_diff = np.abs(im_orig - pattern_proj) + + + fig = plt.figure(figsize=(16,16)) + es_ = co.cmap.color_depth_map(es, scale=vmax) + gt_ = co.cmap.color_depth_map(gt, scale=vmax) + diff_ = co.cmap.color_error_image(diff, BGR=True) + + # plot disparities, ground truth disparity is shown only for reference + ax = plt.subplot(3,3,1); plt.imshow(es_[...,[2,1,0]]); plt.xticks([]); plt.yticks([]); ax.set_title(f'Disparity Est. {es.min():.4f}/{es.max():.4f}') + ax = plt.subplot(3,3,2); plt.imshow(gt_[...,[2,1,0]]); plt.xticks([]); plt.yticks([]); ax.set_title(f'Disparity GT {np.nanmin(gt):.4f}/{np.nanmax(gt):.4f}') + ax = plt.subplot(3,3,3); plt.imshow(diff_[...,[2,1,0]]); plt.xticks([]); plt.yticks([]); ax.set_title(f'Disparity Err. {diff.mean():.5f}') + + # plot edges + edge = self.edge.to('cpu').numpy()[0,0] + edge_gt = self.edge_gt.to('cpu').numpy()[0,0] + edge_err = np.abs(edge - edge_gt) + ax = plt.subplot(3,3,4); plt.imshow(edge, cmap='gray'); plt.xticks([]); plt.yticks([]); ax.set_title(f'Edge Est. {edge.min():.5f}/{edge.max():.5f}') + ax = plt.subplot(3,3,5); plt.imshow(edge_gt, cmap='gray'); plt.xticks([]); plt.yticks([]); ax.set_title(f'Edge GT {edge_gt.min():.5f}/{edge_gt.max():.5f}') + ax = plt.subplot(3,3,6); plt.imshow(edge_err, cmap='gray'); plt.xticks([]); plt.yticks([]); ax.set_title(f'Edge Err. {edge_err.mean():.5f}') + + # plot normalized IR input and warped pattern + ax = plt.subplot(3,3,7); plt.imshow(im, vmin=im.min(), vmax=im.max(), cmap='gray'); plt.xticks([]); plt.yticks([]); ax.set_title(f'IR input {im.mean():.5f}/{im.std():.5f}') + ax = plt.subplot(3,3,8); plt.imshow(pattern_proj, vmin=im.min(), vmax=im.max(), cmap='gray'); plt.xticks([]); plt.yticks([]); ax.set_title(f'Warped Pattern {pattern_proj.mean():.5f}/{pattern_proj.std():.5f}') + im_std = self.data['std0'].to('cpu').numpy()[0,0] + ax = plt.subplot(3,3,9); plt.imshow(im_std, cmap='gray'); plt.xticks([]); plt.yticks([]); ax.set_title(f'IR std {im_std.min():.5f}/{im_std.max():.5f}') + + plt.tight_layout() + plt.savefig(str(out_path)) + plt.close(fig) + + + def callback_train_post_backward(self, net, errs, output, epoch, batch_idx, masks=[]): + if batch_idx % 512 == 0: + out_path = self.exp_out_root / f'train_{epoch:03d}_{batch_idx:04d}.png' + es, gt, im, ma = self.numpy_in_out(output) + self.write_img(out_path, es[0,0], gt[0,0], im[0,0], ma[0,0]) + + + def callback_test_start(self, epoch, set_idx): + self.metric = co.metric.MultipleMetric( + co.metric.DistanceMetric(vec_length=1), + co.metric.OutlierFractionMetric(vec_length=1, thresholds=[0.1, 0.5, 1, 2, 5]) + ) + + def callback_test_add(self, epoch, set_idx, batch_idx, n_batches, output, masks=[]): + es, gt, im, ma = self.numpy_in_out(output) + + if batch_idx % 8 == 0: + out_path = self.exp_out_root / f'test_{epoch:03d}_{batch_idx:04d}.png' + self.write_img(out_path, es[0,0], gt[0,0], im[0,0], ma[0,0]) + + es, gt, im, ma = self.crop_output(es, gt, im, ma) + + es = es.reshape(-1,1) + gt = gt.reshape(-1,1) + ma = ma.ravel() + self.metric.add(es, gt, ma) + + def callback_test_stop(self, epoch, set_idx, loss): + logging.info(f'{self.metric}') + for k, v in self.metric.items(): + self.metric_add_test(epoch, set_idx, k, v) + + def crop_output(self, es, gt, im, ma): + bs = es.shape[0] + es = np.reshape(es[:,:,self.eval_mask], [bs, 1, self.eval_h, self.eval_w]) + gt = np.reshape(gt[:,:,self.eval_mask], [bs, 1, self.eval_h, self.eval_w]) + im = np.reshape(im[:,:,self.eval_mask], [bs, 1, self.eval_h, self.eval_w]) + ma = np.reshape(ma[:,:,self.eval_mask], [bs, 1, self.eval_h, self.eval_w]) + return es, gt, im, ma + + + +if __name__ == '__main__': + pass diff --git a/model/exp_synphge.py b/model/exp_synphge.py new file mode 100644 index 0000000..9794450 --- /dev/null +++ b/model/exp_synphge.py @@ -0,0 +1,298 @@ +import torch +import numpy as np +import time +from pathlib import Path +import logging +import sys +import itertools +import json +import matplotlib.pyplot as plt +import co +import torchext +from model import networks +from data import dataset + +class Worker(torchext.Worker): + def __init__(self, args, num_workers=18, train_batch_size=8, test_batch_size=8, save_frequency=1, **kwargs): + super().__init__(args.output_dir, args.exp_name, epochs=args.epochs, num_workers=num_workers, train_batch_size=train_batch_size, test_batch_size=test_batch_size, save_frequency=save_frequency, **kwargs) + + self.ms = args.ms + self.pattern_path = args.pattern_path + self.lcn_radius = args.lcn_radius + self.dp_weight = args.dp_weight + self.ge_weight = args.ge_weight + self.track_length = args.track_length + self.data_type = args.data_type + assert(self.track_length>1) + + self.imsizes = [(480,640)] + for iter in range(3): + self.imsizes.append((int(self.imsizes[-1][0]/2), int(self.imsizes[-1][1]/2))) + + with open('config.json') as fp: + config = json.load(fp) + data_root = Path(config['DATA_ROOT']) + self.settings_path = data_root / self.data_type / 'settings.pkl' + sample_paths = sorted((data_root / self.data_type).glob('0*/')) + + self.train_paths = sample_paths[2**10:] + self.test_paths = sample_paths[:2**8] + + # supervise the edge encoder with only 2**8 samples + self.train_edge = len(self.train_paths) - 2**8 + + self.lcn_in = networks.LCN(self.lcn_radius, 0.05) + self.disparity_loss = networks.DisparityLoss() + self.edge_loss = torch.nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([0.1]).to(self.train_device)) + + # evaluate in the region where opencv Block Matching has valid values + self.eval_mask = np.zeros(self.imsizes[0]) + self.eval_mask[13:self.imsizes[0][0]-13, 140:self.imsizes[0][1]-13]=1 + self.eval_mask = self.eval_mask.astype(np.bool) + self.eval_h = self.imsizes[0][0]-2*13 + self.eval_w = self.imsizes[0][1]-13-140 + + + def get_train_set(self): + train_set = dataset.TrackSynDataset(self.settings_path, self.train_paths, train=True, data_aug=True, track_length=self.track_length) + return train_set + + def get_test_sets(self): + test_sets = torchext.TestSets() + test_set = dataset.TrackSynDataset(self.settings_path, self.test_paths, train=False, data_aug=True, track_length=1) + test_sets.append('simple', test_set, test_frequency=1) + + self.ph_losses = [] + self.ge_losses = [] + self.d2ds = [] + + self.lcn_in = self.lcn_in.to('cuda') + for sidx in range(len(test_set.imsizes)): + imsize = test_set.imsizes[sidx] + pat = test_set.patterns[sidx] + pat = pat.mean(axis=2) + pat = torch.from_numpy(pat[None][None].astype(np.float32)).to('cuda') + pat,_ = self.lcn_in(pat) + pat = torch.cat([pat for idx in range(3)], dim=1) + ph_loss = networks.RectifiedPatternSimilarityLoss(imsize[0],imsize[1], pattern=pat) + + K = test_set.getK(sidx) + Ki = np.linalg.inv(K) + K = torch.from_numpy(K) + Ki = torch.from_numpy(Ki) + ge_loss = networks.ProjectionDepthSimilarityLoss(K, Ki, imsize[0], imsize[1], clamp=0.1) + + self.ph_losses.append( ph_loss ) + self.ge_losses.append( ge_loss ) + + d2d = networks.DispToDepth(float(test_set.focal_lengths[sidx]), float(test_set.baseline)) + self.d2ds.append( d2d ) + + return test_sets + + def copy_data(self, data, device, requires_grad, train): + self.data = {} + + self.lcn_in = self.lcn_in.to(device) + for key, val in data.items(): + # from + # batch_size x track_length x ... + # to + # track_length x batch_size x ... + if len(val.shape)>2: + if train: + val = val.transpose(0,1) + else: + val = val.unsqueeze(0) + grad = 'im' in key and requires_grad + self.data[key] = val.to(device).requires_grad_(requires_grad=grad) + if 'im' in key and 'blend' not in key: + im = self.data[key] + tl = im.shape[0] + bs = im.shape[1] + im_lcn,im_std = self.lcn_in(im.contiguous().view(-1, *im.shape[2:])) + key_std = key.replace('im','std') + self.data[key_std] = im_std.view(tl, bs, *im.shape[2:]).to(device) + im_cat = torch.cat((im_lcn.view(tl, bs, *im.shape[2:]), im), dim=2) + self.data[key] = im_cat + + def net_forward(self, net, train): + im0 = self.data['im0'] + tl = im0.shape[0] + bs = im0.shape[1] + im0 = im0.view(-1, *im0.shape[2:]) + out, edge = net(im0) + if not(isinstance(out, tuple) or isinstance(out, list)): + out = out.view(tl, bs, *out.shape[1:]) + edge = edge.view(tl, bs, *out.shape[1:]) + else: + out = [o.view(tl, bs, *o.shape[1:]) for o in out] + edge = [e.view(tl, bs, *e.shape[1:]) for e in edge] + return out, edge + + def loss_forward(self, out, train): + out, edge = out + if not(isinstance(out, tuple) or isinstance(out, list)): + out = [out] + vals = [] + diffs = [] + + # apply photometric loss + for s,l,o in zip(itertools.count(), self.ph_losses, out): + im = self.data[f'im{s}'] + im = im.view(-1, *im.shape[2:]) + o = o.view(-1, *o.shape[2:]) + std = self.data[f'std{s}'] + std = std.view(-1, *std.shape[2:]) + val, pattern_proj = l(o, im[:,0:1,...], std) + vals.append(val) + if s == 0: + self.pattern_proj = pattern_proj.detach() + + # apply disparity loss + # 1-edge as ground truth edge if inversed + edge0 = 1-torch.sigmoid(edge[0]) + edge0 = edge0.view(-1, *edge0.shape[2:]) + out0 = out[0].view(-1, *out[0].shape[2:]) + val = self.disparity_loss(out0, edge0) + if self.dp_weight>0: + vals.append(val * self.dp_weight) + + # apply edge loss on a subset of training samples + for s,e in zip(itertools.count(), edge): + # inversed ground truth edge where 0 means edge + grad = self.data[f'grad{s}']<0.2 + grad = grad.to(torch.float32) + ids = self.data['id'] + mask = ids>self.train_edge + if mask.sum()>0: + e = e[:,mask,:] + grad = grad[:,mask,:] + e = e.view(-1, *e.shape[2:]) + grad = grad.view(-1, *grad.shape[2:]) + val = self.edge_loss(e, grad) + else: + val = torch.zeros_like(vals[0]) + vals.append(val) + + if train is False: + return vals + + # apply geometric loss + R = self.data['R'] + t = self.data['t'] + ge_num = self.track_length * (self.track_length-1) / 2 + for sidx in range(len(out)): + d2d = self.d2ds[sidx] + depth = d2d(out[sidx]) + ge_loss = self.ge_losses[sidx] + imsize = self.imsizes[sidx] + for tidx0 in range(depth.shape[0]): + for tidx1 in range(tidx0+1, depth.shape[0]): + depth0 = depth[tidx0] + R0 = R[tidx0] + t0 = t[tidx0] + depth1 = depth[tidx1] + R1 = R[tidx1] + t1 = t[tidx1] + + val = ge_loss(depth0, depth1, R0, t0, R1, t1) + vals.append(val * self.ge_weight / ge_num) + + return vals + + def numpy_in_out(self, output): + output, edge = output + if not(isinstance(output, tuple) or isinstance(output, list)): + output = [output] + es = output[0].detach().to('cpu').numpy() + gt = self.data['disp0'].to('cpu').numpy().astype(np.float32) + im = self.data['im0'][:,:,0:1,...].detach().to('cpu').numpy() + ma = gt>0 + return es, gt, im, ma + + def write_img(self, out_path, es, gt, im, ma): + logging.info(f'write img {out_path}') + u_pos, _ = np.meshgrid(range(es.shape[1]), range(es.shape[0])) + + diff = np.abs(es - gt) + + vmin, vmax = np.nanmin(gt), np.nanmax(gt) + vmin = vmin - 0.2*(vmax-vmin) + vmax = vmax + 0.2*(vmax-vmin) + + pattern_proj = self.pattern_proj.to('cpu').numpy()[0,0] + im_orig = self.data['im0'].detach().to('cpu').numpy()[0,0,0] + pattern_diff = np.abs(im_orig - pattern_proj) + + fig = plt.figure(figsize=(16,16)) + es0 = co.cmap.color_depth_map(es[0], scale=vmax) + gt0 = co.cmap.color_depth_map(gt[0], scale=vmax) + diff0 = co.cmap.color_error_image(diff[0], BGR=True) + + # plot disparities, ground truth disparity is shown only for reference + ax = plt.subplot(3,3,1); plt.imshow(es0[...,[2,1,0]]); plt.xticks([]); plt.yticks([]); ax.set_title(f'F0 Disparity Est. {es0.min():.4f}/{es0.max():.4f}') + ax = plt.subplot(3,3,2); plt.imshow(gt0[...,[2,1,0]]); plt.xticks([]); plt.yticks([]); ax.set_title(f'F0 Disparity GT {np.nanmin(gt0):.4f}/{np.nanmax(gt0):.4f}') + ax = plt.subplot(3,3,3); plt.imshow(diff0[...,[2,1,0]]); plt.xticks([]); plt.yticks([]); ax.set_title(f'F0 Disparity Err. {diff0.mean():.5f}') + + # plot disparities of the second frame in the track if exists + if es.shape[0]>=2: + es1 = co.cmap.color_depth_map(es[1], scale=vmax) + gt1 = co.cmap.color_depth_map(gt[1], scale=vmax) + diff1 = co.cmap.color_error_image(diff[1], BGR=True) + ax = plt.subplot(3,3,4); plt.imshow(es1[...,[2,1,0]]); plt.xticks([]); plt.yticks([]); ax.set_title(f'F1 Disparity Est. {es1.min():.4f}/{es1.max():.4f}') + ax = plt.subplot(3,3,5); plt.imshow(gt1[...,[2,1,0]]); plt.xticks([]); plt.yticks([]); ax.set_title(f'F1 Disparity GT {np.nanmin(gt1):.4f}/{np.nanmax(gt1):.4f}') + ax = plt.subplot(3,3,6); plt.imshow(diff1[...,[2,1,0]]); plt.xticks([]); plt.yticks([]); ax.set_title(f'F1 Disparity Err. {diff1.mean():.5f}') + + # plot normalized IR inputs + ax = plt.subplot(3,3,7); plt.imshow(im[0], vmin=im.min(), vmax=im.max(), cmap='gray'); plt.xticks([]); plt.yticks([]); ax.set_title(f'F0 IR input {im[0].mean():.5f}/{im[0].std():.5f}') + if es.shape[0]>=2: + ax = plt.subplot(3,3,8); plt.imshow(im[1], vmin=im.min(), vmax=im.max(), cmap='gray'); plt.xticks([]); plt.yticks([]); ax.set_title(f'F1 IR input {im[1].mean():.5f}/{im[1].std():.5f}') + + plt.tight_layout() + plt.savefig(str(out_path)) + plt.close(fig) + + def callback_train_post_backward(self, net, errs, output, epoch, batch_idx, masks): + if batch_idx % 512 == 0: + out_path = self.exp_out_root / f'train_{epoch:03d}_{batch_idx:04d}.png' + es, gt, im, ma = self.numpy_in_out(output) + masks = [ m.detach().to('cpu').numpy() for m in masks ] + self.write_img(out_path, es[:,0,0], gt[:,0,0], im[:,0,0], ma[:,0,0]) + + def callback_test_start(self, epoch, set_idx): + self.metric = co.metric.MultipleMetric( + co.metric.DistanceMetric(vec_length=1), + co.metric.OutlierFractionMetric(vec_length=1, thresholds=[0.1, 0.5, 1, 2, 5]) + ) + + def callback_test_add(self, epoch, set_idx, batch_idx, n_batches, output, masks): + es, gt, im, ma = self.numpy_in_out(output) + + if batch_idx % 8 == 0: + out_path = self.exp_out_root / f'test_{epoch:03d}_{batch_idx:04d}.png' + self.write_img(out_path, es[:,0,0], gt[:,0,0], im[:,0,0], ma[:,0,0]) + + es, gt, im, ma = self.crop_output(es, gt, im, ma) + + es = es.reshape(-1,1) + gt = gt.reshape(-1,1) + ma = ma.ravel() + self.metric.add(es, gt, ma) + + def callback_test_stop(self, epoch, set_idx, loss): + logging.info(f'{self.metric}') + for k, v in self.metric.items(): + self.metric_add_test(epoch, set_idx, k, v) + + def crop_output(self, es, gt, im, ma): + tl = es.shape[0] + bs = es.shape[1] + es = np.reshape(es[...,self.eval_mask], [tl*bs, 1, self.eval_h, self.eval_w]) + gt = np.reshape(gt[...,self.eval_mask], [tl*bs, 1, self.eval_h, self.eval_w]) + im = np.reshape(im[...,self.eval_mask], [tl*bs, 1, self.eval_h, self.eval_w]) + ma = np.reshape(ma[...,self.eval_mask], [tl*bs, 1, self.eval_h, self.eval_w]) + return es, gt, im, ma + +if __name__ == '__main__': + pass diff --git a/model/networks.py b/model/networks.py new file mode 100644 index 0000000..4781706 --- /dev/null +++ b/model/networks.py @@ -0,0 +1,566 @@ +import torch +import torch.nn.functional as F +import numpy as np +import matplotlib.pyplot as plt + +import torchext +import co + + +class TimedModule(torch.nn.Module): + def __init__(self, mod_name): + super().__init__() + self.mod_name = mod_name + + def tforward(self, *args, **kwargs): + raise Exception('not implemented') + + def forward(self, *args, **kwargs): + torch.cuda.synchronize() + with co.gtimer.Ctx(self.mod_name): + x = self.tforward(*args, **kwargs) + torch.cuda.synchronize() + return x + + +class PosOutput(TimedModule): + def __init__(self, channels_in, type, im_height, im_width, alpha=1, beta=0, gamma=1, offset=0): + super().__init__(mod_name='PosOutput') + self.im_width = im_width + self.im_width = im_width + + if type == 'pos': + self.layer = torch.nn.Sequential( + torch.nn.Conv2d(channels_in, 1, kernel_size=3, padding=1), + SigmoidAffine(alpha=alpha, beta=beta, gamma=gamma, offset=offset) + ) + elif type == 'pos_row': + self.layer = torch.nn.Sequential( + MultiLinear(im_height, channels_in, 1), + SigmoidAffine(alpha=alpha, beta=beta, gamma=gamma, offset=offset) + ) + + self.u_pos = None + + def tforward(self, x): + if self.u_pos is None: + self.u_pos = torch.arange(x.shape[3], dtype=torch.float32).view(1,1,1,-1) + self.u_pos = self.u_pos.to(x.device) + pos = self.layer(x) + disp = self.u_pos - pos + return disp + + +class OutputLayerFactory(object): + ''' + Define type of output + type options: + linear: apply only conv channel, used for the edge decoder + disp: estimate the disparity + disp_row: independently estimate the disparity per row + pos: estimate the absolute location + pos_row: independently estimate the absolute location per row + ''' + def __init__(self, type='disp', params={}): + self.type = type + self.params = params + + def __call__(self, channels_in, imsize): + + if self.type == 'linear': + return torch.nn.Conv2d(channels_in, 1, kernel_size=3, padding=1) + + elif self.type == 'disp': + return torch.nn.Sequential( + torch.nn.Conv2d(channels_in, 1, kernel_size=3, padding=1), + SigmoidAffine(**self.params) + ) + + elif self.type == 'disp_row': + return torch.nn.Sequential( + MultiLinear(imsize[0], channels_in, 1), + SigmoidAffine(**self.params) + ) + + elif self.type == 'pos' or self.type == 'pos_row': + return PosOutput(channels_in, **self.params) + + else: + raise Exception('unknown output layer type') + + +class SigmoidAffine(TimedModule): + def __init__(self, alpha=1, beta=0, gamma=1, offset=0): + super().__init__(mod_name='SigmoidAffine') + self.alpha = alpha + self.beta = beta + self.gamma = gamma + self.offset = offset + + def tforward(self, x): + return torch.sigmoid(x/self.gamma - self.offset) * self.alpha + self.beta + + +class MultiLinear(TimedModule): + def __init__(self, n, channels_in, channels_out): + super().__init__(mod_name='MultiLinear') + self.channels_out = channels_out + self.mods = torch.nn.ModuleList() + for idx in range(n): + self.mods.append(torch.nn.Linear(channels_in, channels_out)) + + def tforward(self, x): + x = x.permute(2,0,3,1) # BxCxHxW => HxBxWxC + y = x.new_empty(*x.shape[:-1], self.channels_out) + for hidx in range(x.shape[0]): + y[hidx] = self.mods[hidx](x[hidx]) + y = y.permute(1,3,0,2) # HxBxWxC => BxCxHxW + return y + + + +class DispNetS(TimedModule): + ''' + Disparity Decoder based on DispNetS + ''' + def __init__(self, channels_in, imsizes, output_facs, output_ms=True, coordconv=False, weight_init=False, channel_multiplier=1): + super(DispNetS, self).__init__(mod_name='DispNetS') + + self.output_ms = output_ms + self.coordconv = coordconv + + conv_planes = channel_multiplier * np.array( [32, 64, 128, 256, 512, 512, 512] ) + self.conv1 = self.downsample_conv(channels_in, conv_planes[0], kernel_size=7) + self.conv2 = self.downsample_conv(conv_planes[0], conv_planes[1], kernel_size=5) + self.conv3 = self.downsample_conv(conv_planes[1], conv_planes[2]) + self.conv4 = self.downsample_conv(conv_planes[2], conv_planes[3]) + self.conv5 = self.downsample_conv(conv_planes[3], conv_planes[4]) + self.conv6 = self.downsample_conv(conv_planes[4], conv_planes[5]) + self.conv7 = self.downsample_conv(conv_planes[5], conv_planes[6]) + + upconv_planes = channel_multiplier * np.array( [512, 512, 256, 128, 64, 32, 16] ) + self.upconv7 = self.upconv(conv_planes[6], upconv_planes[0]) + self.upconv6 = self.upconv(upconv_planes[0], upconv_planes[1]) + self.upconv5 = self.upconv(upconv_planes[1], upconv_planes[2]) + self.upconv4 = self.upconv(upconv_planes[2], upconv_planes[3]) + self.upconv3 = self.upconv(upconv_planes[3], upconv_planes[4]) + self.upconv2 = self.upconv(upconv_planes[4], upconv_planes[5]) + self.upconv1 = self.upconv(upconv_planes[5], upconv_planes[6]) + + self.iconv7 = self.conv(upconv_planes[0] + conv_planes[5], upconv_planes[0]) + self.iconv6 = self.conv(upconv_planes[1] + conv_planes[4], upconv_planes[1]) + self.iconv5 = self.conv(upconv_planes[2] + conv_planes[3], upconv_planes[2]) + self.iconv4 = self.conv(upconv_planes[3] + conv_planes[2], upconv_planes[3]) + self.iconv3 = self.conv(1 + upconv_planes[4] + conv_planes[1], upconv_planes[4]) + self.iconv2 = self.conv(1 + upconv_planes[5] + conv_planes[0], upconv_planes[5]) + self.iconv1 = self.conv(1 + upconv_planes[6], upconv_planes[6]) + + if isinstance(output_facs, list): + self.predict_disp4 = output_facs[3](upconv_planes[3], imsizes[3]) + self.predict_disp3 = output_facs[2](upconv_planes[4], imsizes[2]) + self.predict_disp2 = output_facs[1](upconv_planes[5], imsizes[1]) + self.predict_disp1 = output_facs[0](upconv_planes[6], imsizes[0]) + else: + self.predict_disp4 = output_facs(upconv_planes[3], imsizes[3]) + self.predict_disp3 = output_facs(upconv_planes[4], imsizes[2]) + self.predict_disp2 = output_facs(upconv_planes[5], imsizes[1]) + self.predict_disp1 = output_facs(upconv_planes[6], imsizes[0]) + + + def init_weights(self): + for m in self.modules(): + if isinstance(m, torch.nn.Conv2d) or isinstance(m, torch.nn.ConvTranspose2d): + torch.nn.init.xavier_uniform_(m.weight, gain=0.1) + if m.bias is not None: + torch.nn.init.zeros_(m.bias) + + def downsample_conv(self, in_planes, out_planes, kernel_size=3): + if self.coordconv: + conv = torchext.CoordConv2d(in_planes, out_planes, kernel_size=kernel_size, stride=2, padding=(kernel_size-1)//2) + else: + conv = torch.nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=2, padding=(kernel_size-1)//2) + return torch.nn.Sequential( + conv, + torch.nn.ReLU(inplace=True), + torch.nn.Conv2d(out_planes, out_planes, kernel_size=kernel_size, padding=(kernel_size-1)//2), + torch.nn.ReLU(inplace=True) + ) + + def conv(self, in_planes, out_planes): + return torch.nn.Sequential( + torch.nn.Conv2d(in_planes, out_planes, kernel_size=3, padding=1), + torch.nn.ReLU(inplace=True) + ) + + def upconv(self, in_planes, out_planes): + return torch.nn.Sequential( + torch.nn.ConvTranspose2d(in_planes, out_planes, kernel_size=3, stride=2, padding=1, output_padding=1), + torch.nn.ReLU(inplace=True) + ) + + def crop_like(self, input, ref): + assert(input.size(2) >= ref.size(2) and input.size(3) >= ref.size(3)) + return input[:, :, :ref.size(2), :ref.size(3)] + + def tforward(self, x): + out_conv1 = self.conv1(x) + out_conv2 = self.conv2(out_conv1) + out_conv3 = self.conv3(out_conv2) + out_conv4 = self.conv4(out_conv3) + out_conv5 = self.conv5(out_conv4) + out_conv6 = self.conv6(out_conv5) + out_conv7 = self.conv7(out_conv6) + + out_upconv7 = self.crop_like(self.upconv7(out_conv7), out_conv6) + concat7 = torch.cat((out_upconv7, out_conv6), 1) + out_iconv7 = self.iconv7(concat7) + + out_upconv6 = self.crop_like(self.upconv6(out_iconv7), out_conv5) + concat6 = torch.cat((out_upconv6, out_conv5), 1) + out_iconv6 = self.iconv6(concat6) + + out_upconv5 = self.crop_like(self.upconv5(out_iconv6), out_conv4) + concat5 = torch.cat((out_upconv5, out_conv4), 1) + out_iconv5 = self.iconv5(concat5) + + out_upconv4 = self.crop_like(self.upconv4(out_iconv5), out_conv3) + concat4 = torch.cat((out_upconv4, out_conv3), 1) + out_iconv4 = self.iconv4(concat4) + disp4 = self.predict_disp4(out_iconv4) + + out_upconv3 = self.crop_like(self.upconv3(out_iconv4), out_conv2) + disp4_up = self.crop_like(torch.nn.functional.interpolate(disp4, scale_factor=2, mode='bilinear', align_corners=False), out_conv2) + concat3 = torch.cat((out_upconv3, out_conv2, disp4_up), 1) + out_iconv3 = self.iconv3(concat3) + disp3 = self.predict_disp3(out_iconv3) + + out_upconv2 = self.crop_like(self.upconv2(out_iconv3), out_conv1) + disp3_up = self.crop_like(torch.nn.functional.interpolate(disp3, scale_factor=2, mode='bilinear', align_corners=False), out_conv1) + concat2 = torch.cat((out_upconv2, out_conv1, disp3_up), 1) + out_iconv2 = self.iconv2(concat2) + disp2 = self.predict_disp2(out_iconv2) + + out_upconv1 = self.crop_like(self.upconv1(out_iconv2), x) + disp2_up = self.crop_like(torch.nn.functional.interpolate(disp2, scale_factor=2, mode='bilinear', align_corners=False), x) + concat1 = torch.cat((out_upconv1, disp2_up), 1) + out_iconv1 = self.iconv1(concat1) + disp1 = self.predict_disp1(out_iconv1) + + if self.output_ms: + return disp1, disp2, disp3, disp4 + else: + return disp1 + + +class DispNetShallow(DispNetS): + ''' + Edge Decoder based on DispNetS with fewer layers + ''' + def __init__(self, channels_in, imsizes, output_facs, output_ms=True, coordconv=False, weight_init=False): + super(DispNetShallow, self).__init__(channels_in, imsizes, output_facs, output_ms, coordconv, weight_init) + self.mod_name = 'DispNetShallow' + conv_planes = [32, 64, 128, 256, 512, 512, 512] + upconv_planes = [512, 512, 256, 128, 64, 32, 16] + self.iconv3 = self.conv(upconv_planes[4] + conv_planes[1], upconv_planes[4]) + + def tforward(self, x): + out_conv1 = self.conv1(x) + out_conv2 = self.conv2(out_conv1) + out_conv3 = self.conv3(out_conv2) + + out_upconv3 = self.crop_like(self.upconv3(out_conv3), out_conv2) + concat3 = torch.cat((out_upconv3, out_conv2), 1) + out_iconv3 = self.iconv3(concat3) + disp3 = self.predict_disp3(out_iconv3) + + out_upconv2 = self.crop_like(self.upconv2(out_iconv3), out_conv1) + disp3_up = self.crop_like(torch.nn.functional.interpolate(disp3, scale_factor=2, mode='bilinear', align_corners=False), out_conv1) + concat2 = torch.cat((out_upconv2, out_conv1, disp3_up), 1) + out_iconv2 = self.iconv2(concat2) + disp2 = self.predict_disp2(out_iconv2) + + out_upconv1 = self.crop_like(self.upconv1(out_iconv2), x) + disp2_up = self.crop_like(torch.nn.functional.interpolate(disp2, scale_factor=2, mode='bilinear', align_corners=False), x) + concat1 = torch.cat((out_upconv1, disp2_up), 1) + out_iconv1 = self.iconv1(concat1) + disp1 = self.predict_disp1(out_iconv1) + + if self.output_ms: + return disp1, disp2, disp3 + else: + return disp1 + + +class DispEdgeDecoders(TimedModule): + ''' + Disparity Decoder and Edge Decoder + ''' + def __init__(self, *args, max_disp=128, **kwargs): + super(DispEdgeDecoders, self).__init__(mod_name='DispEdgeDecoders') + + output_facs = [OutputLayerFactory( type='disp', params={ 'alpha': max_disp/(2**s), 'beta': 0, 'gamma': 1, 'offset': 3}) for s in range(4)] + self.disp_decoder = DispNetS(*args, output_facs=output_facs, **kwargs) + + output_facs = [OutputLayerFactory( type='linear' ) for s in range(4)] + self.edge_decoder = DispNetShallow(*args, output_facs=output_facs, **kwargs) + + def tforward(self, x): + disp = self.disp_decoder(x) + edge = self.edge_decoder(x) + return disp, edge + + +class DispToDepth(TimedModule): + def __init__(self, focal_length, baseline): + super().__init__(mod_name='DispToDepth') + self.baseline_focal_length = baseline * focal_length + + def tforward(self, disp): + disp = torch.nn.functional.relu(disp) + 1e-12 + depth = self.baseline_focal_length / disp + return depth + + +class PosToDepth(DispToDepth): + def __init__(self, focal_length, baseline, im_height, im_width): + super().__init__(focal_length, baseline) + self.mod_name = 'PosToDepth' + + self.im_height = im_height + self.im_width = im_width + self.u_pos = torch.arange(im_width, dtype=torch.float32).view(1,1,1,-1) + + def tforward(self, pos): + self.u_pos = self.u_pos.to(pos.device) + disp = self.u_pos - pos + return super().forward(disp) + + + +class RectifiedPatternSimilarityLoss(TimedModule): + ''' + Photometric Loss + ''' + def __init__(self, im_height, im_width, pattern, loss_type='census_sad', loss_eps=0.5): + super().__init__(mod_name='RectifiedPatternSimilarityLoss') + self.im_height = im_height + self.im_width = im_width + self.pattern = pattern.mean(dim=1, keepdim=True).contiguous() + + u, v = np.meshgrid(range(im_width), range(im_height)) + uv0 = np.stack((u,v), axis=2).reshape(-1,1) + uv0 = uv0.astype(np.float32).reshape(1,-1,2) + self.uv0 = torch.from_numpy(uv0) + + self.loss_type = loss_type + self.loss_eps = loss_eps + + def tforward(self, disp0, im, std=None): + self.pattern = self.pattern.to(disp0.device) + self.uv0 = self.uv0.to(disp0.device) + + uv0 = self.uv0.expand(disp0.shape[0], *self.uv0.shape[1:]) + uv1 = torch.empty_like(uv0) + uv1[...,0] = uv0[...,0] - disp0.contiguous().view(disp0.shape[0],-1) + uv1[...,1] = uv0[...,1] + + uv1[..., 0] = 2 * (uv1[..., 0] / (self.im_width-1) - 0.5) + uv1[..., 1] = 2 * (uv1[..., 1] / (self.im_height-1) - 0.5) + uv1 = uv1.view(-1, self.im_height, self.im_width, 2).clone() + pattern = self.pattern.expand(disp0.shape[0], *self.pattern.shape[1:]) + pattern_proj = torch.nn.functional.grid_sample(pattern, uv1, padding_mode='border') + mask = torch.ones_like(im) + if std is not None: + mask = mask*std + + diff = torchext.photometric_loss(pattern_proj.contiguous(), im.contiguous(), 9, self.loss_type, self.loss_eps) + val = (mask*diff).sum() / mask.sum() + return val, pattern_proj + +class DisparityLoss(TimedModule): + ''' + Disparity Loss + ''' + def __init__(self): + super().__init__(mod_name='DisparityLoss') + self.sobel = SobelFilter(norm=False) + + #if not edge_gt: + self.b0=0.0503428816795 + self.b1=1.07274045944 + #else: + # self.b0=0.0587115108967 + # self.b1=1.51931190491 + + def tforward(self, disp, edge=None): + self.sobel=self.sobel.to(disp.device) + + if edge is not None: + grad = self.sobel(disp) + grad = torch.sqrt(grad[:,0:1,...]**2 + grad[:,1:2,...]**2 + 1e-8) + pdf = (1-edge)/self.b0 * torch.exp(-torch.abs(grad)/self.b0) + \ + edge/self.b1 * torch.exp(-torch.abs(grad)/self.b1) + val = torch.mean(-torch.log(pdf.clamp(min=1e-4))) + else: + # on qifeng's data we don't have ambient info + # therefore we supress edge everywhere + grad = self.sobel(disp) + grad = torch.sqrt(grad[:,0:1,...]**2 + grad[:,1:2,...]**2 + 1e-8) + grad= torch.clamp(grad, 0, 1.0) + val = torch.mean(grad) + + return val + + + +class ProjectionBaseLoss(TimedModule): + ''' + Base module of the Geometric Loss + ''' + def __init__(self, K, Ki, im_height, im_width): + super().__init__(mod_name='ProjectionBaseLoss') + + self.K = K.view(-1,3,3) + + self.im_height = im_height + self.im_width = im_width + + u, v = np.meshgrid(range(im_width), range(im_height)) + uv = np.stack((u,v,np.ones_like(u)), axis=2).reshape(-1,3) + + ray = uv @ Ki.numpy().T + + ray = ray.reshape(1,-1,3).astype(np.float32) + self.ray = torch.from_numpy(ray) + + def transform(self, xyz, R=None, t=None): + if t is not None: + bs = xyz.shape[0] + xyz = xyz - t.reshape(bs,1,3) + if R is not None: + xyz = torch.bmm(xyz, R) + return xyz + + def unproject(self, depth, R=None, t=None): + self.ray = self.ray.to(depth.device) + bs = depth.shape[0] + + xyz = depth.reshape(bs,-1,1) * self.ray + xyz = self.transform(xyz, R, t) + return xyz + + def project(self, xyz, R, t): + self.K = self.K.to(xyz.device) + bs = xyz.shape[0] + + xyz = torch.bmm(xyz, R.transpose(1,2)) + xyz = xyz + t.reshape(bs,1,3) + + Kt = self.K.transpose(1,2).expand(bs,-1,-1) + uv = torch.bmm(xyz, Kt) + + d = uv[:,:,2:3] + + # avoid division by zero + uv = uv[:,:,:2] / (torch.nn.functional.relu(d) + 1e-12) + return uv, d + + + def tforward(self, depth0, R0, t0, R1, t1): + xyz = self.unproject(depth0, R0, t0) + return self.project(xyz, R1, t1) + + +class ProjectionDepthSimilarityLoss(ProjectionBaseLoss): + ''' + Geometric Loss + ''' + def __init__(self, *args, clamp=-1): + super().__init__(*args) + self.mod_name = 'ProjectionDepthSimilarityLoss' + self.clamp = clamp + + def fwd(self, depth0, depth1, R0, t0, R1, t1): + uv1, d1 = super().tforward(depth0, R0, t0, R1, t1) + + uv1[..., 0] = 2 * (uv1[..., 0] / (self.im_width-1) - 0.5) + uv1[..., 1] = 2 * (uv1[..., 1] / (self.im_height-1) - 0.5) + uv1 = uv1.view(-1, self.im_height, self.im_width, 2).clone() + + depth10 = torch.nn.functional.grid_sample(depth1, uv1, padding_mode='border') + + diff = torch.abs(d1.view(-1) - depth10.view(-1)) + + if self.clamp > 0: + diff = torch.clamp(diff, 0, self.clamp) + + # return diff without clamping for debugging + return diff.mean() + + def tforward(self, depth0, depth1, R0, t0, R1, t1): + l0 = self.fwd(depth0, depth1, R0, t0, R1, t1) + l1 = self.fwd(depth1, depth0, R1, t1, R0, t0) + return l0+l1 + + + +class LCN(TimedModule): + ''' + Local Contract Normalization + ''' + def __init__(self, radius, epsilon): + super().__init__(mod_name='LCN') + self.box_conv = torch.nn.Sequential( + torch.nn.ReflectionPad2d(radius), + torch.nn.Conv2d(1, 1, kernel_size=2*radius+1, bias=False) + ) + self.box_conv[1].weight.requires_grad=False + self.box_conv[1].weight.fill_(1.) + + self.epsilon = epsilon + self.radius = radius + + def tforward(self, data): + boxs = self.box_conv(data) + + avgs = boxs / (2*self.radius+1)**2 + boxs_n2 = boxs**2 + boxs_2n = self.box_conv(data**2) + + stds = torch.sqrt(boxs_2n / (2*self.radius+1)**2 - avgs**2 + 1e-6) + stds = stds + self.epsilon + + return (data - avgs) / stds, stds + + + +class SobelFilter(TimedModule): + ''' + Sobel Filter + ''' + def __init__(self, norm=False): + super(SobelFilter, self).__init__(mod_name='SobelFilter') + kx = np.array([[-5, -4, 0, 4, 5], + [-8, -10, 0, 10, 8], + [-10, -20, 0, 20, 10], + [-8, -10, 0, 10, 8], + [-5, -4, 0, 4, 5]])/240.0 + ky = kx.copy().transpose(1,0) + + self.conv_x=torch.nn.Conv2d(1, 1, kernel_size=5, stride=1, padding=0, bias=False) + self.conv_x.weight=torch.nn.Parameter(torch.from_numpy(kx).float().unsqueeze(0).unsqueeze(0)) + + self.conv_y=torch.nn.Conv2d(1, 1, kernel_size=5, stride=1, padding=0, bias=False) + self.conv_y.weight=torch.nn.Parameter(torch.from_numpy(ky).float().unsqueeze(0).unsqueeze(0)) + + self.norm=norm + + def tforward(self,x): + x = F.pad(x, (2,2,2,2), "replicate") + gx = self.conv_x(x) + gy = self.conv_y(x) + if self.norm: + return torch.sqrt(gx**2 + gy**2 + 1e-8) + else: + return torch.cat((gx, gy), dim=1) + diff --git a/readme.md b/readme.md new file mode 100644 index 0000000..6e2b487 --- /dev/null +++ b/readme.md @@ -0,0 +1,95 @@ +# Connecting the Dots: Learning Representations for Active Monocular Depth Estimation + +![example](img/img.png) + +This repository contains the code for the paper + +**[Connecting the Dots: Learning Representations for Active Monocular Depth Estimation](TODO)** +
+[Gernot Riegler](https://griegler.github.io/), [Yiyi Liao](https://yiyiliao.github.io/), [Simon Donne](https://avg.is.tuebingen.mpg.de/person/sdonne), [Vladlen Koltun](http://vladlen.info/), and [Andreas Geiger](http://www.cvlibs.net/) +
+[CVPR 2019](http://cvpr2019.thecvf.com/) + +> We propose a technique for depth estimation with a monocular structured-light camera, i.e., a calibrated stereo set-up with one camera and one laser projector. Instead of formulating the depth estimation via a correspondence search problem, we show that a simple convolutional architecture is sufficient for high-quality disparity estimates in this setting. As accurate ground-truth is hard to obtain, we train our model in a self-supervised fashion with a combination of photometric and geometric losses. Further, we demonstrate that the projected pattern of the structured light sensor can be reliably separated from the ambient information. This can then be used to improve depth boundaries in a weakly supervised fashion by modeling the joint statistics of image and depth edges. The model trained in this fashion compares favorably to the state-of-the-art on challenging synthetic and real-world datasets. In addition, we contribute a novel simulator, which allows to benchmark active depth prediction algorithms in controlled conditions. + + +If you find this code useful for your research, please cite + +``` +@inproceedings{Riegler2019Connecting, + title={Connecting the Dots: Learning Representations for Active Monocular Depth Estimation}, + author={Riegler, Gernot and Liao, Yiyi and Donne, Simon and Koltun, Vladlen and Geiger, Andreas}, + booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, + year={2019} +} +``` + + +## Dependencies + +The network training/evaluation code is based on `Pytorch`. +``` +PyTorch>=1.1 +Cuda>=10.0 +``` + +The other python packages can be installed with `anaconda`: +``` +conda install --file requirements.txt +``` + +### Structured Light Renderer +To train and evaluate our method in a controlled setting, we implemented an structured light renderer. +It can be used to render a virtual scene (arbitrary triangle mesh) with the structured light pattern projected from a customizable projector location. +To build it, first make sure the correct `CUDA_LIBRARY_PATH` is set in `config.json`. +Afterwards, the renderer can be build by running `make` within the `renderer` directory. + +### PyTorch Extensions +The network training/evaluation code is based on `PyTorch`. +We implemented some custom layers that need to be built in the `torchext` directory. +Simply change into this directory and run + +``` +python setup.py build_ext --inplace +``` + +### Baseline HyperDepth +As baseline we partially re-implemented the random forest based method [HyperDepth](http://openaccess.thecvf.com/content_cvpr_2016/papers/Fanello_HyperDepth_Learning_Depth_CVPR_2016_paper.pdf). +The code resided in the `hyperdepth` directory and is implemented in `C++11` with a Python wrapper written in `Cython`. +To build it change into the directory and run + +``` +python setup.py build_ext --inplace +``` + +## Running + + +### Creating synthetic data +To create synthetic data and save it locally, download [ShapeNet V2](https://www.shapenet.org/) and correct `SHAPENET_ROOT` in `config.json`. Then the data can be generated and saved to `DATA_ROOT` in `config.json` by running +``` +./create_syn_data.sh +``` + +### Training Network + +As a first stage, it is recommended to train the disparity decoder and edge decoder without the geometric loss. To train the network on synthetic data for the first stage run +``` +python train_val.py +``` + +After the model is pretrained without the geometric loss, the full model can be trained from the initialized weights by running +``` +python train_val.py --loss phge +``` + + +### Evaluating Network +To evaluate a specific checkpoint, e.g. the 50th epoch, one can run +``` +python train_val.py --cmd retest --epoch 50 +``` + + +## Acknowledgement +This work was supported by the Intel Network on Intelligent Systems. diff --git a/renderer/Makefile b/renderer/Makefile new file mode 100644 index 0000000..aaf82d3 --- /dev/null +++ b/renderer/Makefile @@ -0,0 +1,32 @@ +INCLUDE_DIR = +C = gcc -c +C_FLAGS = -O3 -msse -msse2 -msse3 -msse4.2 -fPIC -Wall +CXX = g++ -c +CXX_FLAGS = -O3 -std=c++11 -msse -msse2 -msse3 -msse4.2 -fPIC -Wall +CUDA = nvcc -c +CUDA_FLAGS = -x cu -Xcompiler -fPIC -arch=sm_30 -std=c++11 --expt-extended-lambda + + +PYRENDER_DEPENDENCIES = setup.py \ + render/render_cpu.cpp.o \ + render/stdlib_cuda_dummy.cpp.o \ + render/render_gpu_dummy.cpp.o + +PYRENDER_DEPENDENCIES += render/render_gpu.cu.o \ + render/stdlib_cuda.cu.o + +all: pyrender + +clean: + rm render/*.o + +pyrender: $(PYRENDER_DEPENDENCIES) + cd pyrender; \ + python setup.py build_ext --inplace + +%.c.o: %.c + $(C) $(C_FLAGS) -o $@ $< $(INCLUDE_DIR) +%.cpp.o: %.cpp + $(CXX) $(CXX_FLAGS) -o $@ $< $(INCLUDE_DIR) +%.cu.o: %.cu + $(CUDA) -o $@ $< $(CUDA_FLAGS) $(INCLUDE_DIR) diff --git a/renderer/__init__.py b/renderer/__init__.py new file mode 100644 index 0000000..ea77263 --- /dev/null +++ b/renderer/__init__.py @@ -0,0 +1,4 @@ +import ctypes +import os + +from .cyrender import * diff --git a/renderer/cyrender.pyx b/renderer/cyrender.pyx new file mode 100644 index 0000000..8aa53b3 --- /dev/null +++ b/renderer/cyrender.pyx @@ -0,0 +1,200 @@ +cimport cython +import numpy as np +cimport numpy as np + +from libc.stdlib cimport free, malloc +from libcpp cimport bool +from cpython cimport PyObject, Py_INCREF + +CREATE_INIT = True # workaround, so cython builds a init function + +np.import_array() + + +ctypedef unsigned char uint8_t + +cdef extern from "render/render.h": + cdef cppclass Camera[T]: + const T fx; + const T fy; + const T px; + const T py; + const T R0, R1, R2, R3, R4, R5, R6, R7, R8; + const T t0, t1, t2; + const T C0, C1, C2; + const int height; + const int width; + Camera(const T fx, const T fy, const T px, const T py, const T* R, const T* t, int width, int height) + + cdef cppclass RenderInput[T]: + T* verts; + T* radii; + T* colors; + T* normals; + int n_verts; + int* faces; + int n_faces; + + T* tex_coords; + T* tex; + int tex_height; + int tex_width; + int tex_channels; + + RenderInput(); + + cdef cppclass Buffer[T]: + T* depth; + T* color; + T* normal; + Buffer(); + + cdef cppclass Shader[T]: + const T ka; + const T kd; + const T ks; + const T alpha; + Shader(T ka, T kd, T ks, T alpha) + + cdef cppclass BaseRenderer[T]: + const Camera[T] cam; + const Shader[T] shader; + Buffer[T] buffer; + BaseRenderer(const Camera[T] cam, const Shader[T] shader, Buffer[T] buffer) + void render_mesh(const RenderInput[T] input); + void render_mesh_proj(const RenderInput[T] input, const Camera[T] proj, const float* pattern, float d_alpha, float d_beta); + + +cdef extern from "render/render_cpu.h": + cdef cppclass RendererCpu[T](BaseRenderer[T]): + RendererCpu(const Camera[T] cam, const Shader[T] shader, Buffer[T] buffer, int n_threads) + void render_mesh(const RenderInput[T] input); + void render_mesh_proj(const RenderInput[T] input, const Camera[T] proj, const float* pattern, float d_alpha, float d_beta); + +cdef extern from "render/render_gpu.h": + cdef cppclass RendererGpu[T](BaseRenderer[T]): + RendererGpu(const Camera[T] cam, const Shader[T] shader, Buffer[T] buffer) + void render_mesh(const RenderInput[T] input); + void render_mesh_proj(const RenderInput[T] input, const Camera[T] proj, const float* pattern, float d_alpha, float d_beta); + + +cdef class PyCamera: + cdef Camera[float]* cam; + + def __cinit__(self, float fx, float fy, float px, float py, float[:,::1] R, float[::1] t, int width, int height): + if R.shape[0] != 3 or R.shape[1] != 3: + raise Exception('invalid R matrix') + if t.shape[0] != 3: + raise Exception('invalid t vector') + + self.cam = new Camera[float](fx,fy, px,py, &R[0,0], &t[0], width, height) + + def __dealloc__(self): + del self.cam + + +cdef class PyRenderInput: + cdef RenderInput[float] input; + cdef verts + cdef colors + cdef normals + cdef faces + + def __cinit__(self, float[:,::1] verts=None, float[:,::1] colors=None, float[:,::1] normals=None, int[:,::1] faces=None): + self.input = RenderInput[float]() + if verts is not None: + self.set_verts(verts) + if normals is not None: + self.set_normals(normals) + if colors is not None: + self.set_colors(colors) + if faces is not None: + self.set_faces(faces) + + def set_verts(self, float[:,::1] verts): + if verts.shape[1] != 3: + raise Exception('verts has to be a Nx3 matrix') + self.verts = verts + cdef float[:,::1] verts_view = self.verts + self.input.verts = &verts_view[0,0] + self.input.n_verts = self.verts.shape[0] + + def set_colors(self, float[:,::1] colors): + if colors.shape[1] != 3: + raise Exception('colors has to be a Nx3 matrix') + self.colors = colors + cdef float[:,::1] colors_view = self.colors + self.input.colors = &colors_view[0,0] + + def set_normals(self, float[:,::1] normals): + if normals.shape[1] != 3: + raise Exception('normals has to be a Nx3 matrix') + self.normals = normals + cdef float[:,::1] normals_view = self.normals + self.input.normals = &normals_view[0,0] + + def set_faces(self, int[:,::1] faces): + if faces.shape[1] != 3: + raise Exception('faces has to be a Nx3 matrix') + self.faces = faces + cdef int[:,::1] faces_view = self.faces + self.input.faces = &faces_view[0,0] + self.input.n_faces = self.faces.shape[0] + +cdef class PyShader: + cdef Shader[float]* shader + + def __cinit__(self, float ka, float kd, float ks, float alpha): + self.shader = new Shader[float](ka, kd, ks, alpha) + + def __dealloc__(self): + del self.shader + + +cdef class PyRenderer: + cdef BaseRenderer[float]* renderer + + cdef Buffer[float] buffer + cdef depth_buffer + cdef color_buffer + cdef normal_buffer + + def depth(self): + return self.depth_buffer + + def color(self): + return self.color_buffer + + def normal(self): + return self.normal_buffer + + def __cinit__(self, PyCamera cam, PyShader shader, engine='cpu', int n_threads=1): + self.depth_buffer = np.empty((cam.cam[0].height, cam.cam[0].width), dtype=np.float32) + self.color_buffer = np.empty((cam.cam[0].height, cam.cam[0].width, 3), dtype=np.float32) + self.normal_buffer = np.empty((cam.cam[0].height, cam.cam[0].width, 3), dtype=np.float32) + + cdef float[:,::1] dbv = self.depth_buffer + cdef float[:,:,::1] cbv = self.color_buffer + cdef float[:,:,::1] nbv = self.normal_buffer + self.buffer.depth = &dbv[0,0] + self.buffer.color = &cbv[0,0,0] + self.buffer.normal = &nbv[0,0,0] + + if engine == 'cpu': + self.renderer = new RendererCpu[float](cam.cam[0], shader.shader[0], self.buffer, n_threads) + elif engine == 'gpu': + self.renderer = new RendererGpu[float](cam.cam[0], shader.shader[0], self.buffer) + else: + raise Exception('invalid engine') + + def __dealloc__(self): + del self.renderer + + def mesh(self, PyRenderInput input): + self.renderer.render_mesh(input.input) + + def mesh_proj(self, PyRenderInput input, PyCamera proj, float[:,:,::1] pattern, float d_alpha=1, float d_beta=0): + if pattern.shape[0] != proj.cam[0].height or pattern.shape[1] != proj.cam[0].width or pattern.shape[2] != 3: + raise Exception(f'pattern has to be a {proj.cam[0].height}x{proj.cam[0].width}x3 tensor') + self.renderer.render_mesh_proj(input.input, proj.cam[0], &pattern[0,0,0], d_alpha, d_beta) + diff --git a/renderer/render/co_types.h b/renderer/render/co_types.h new file mode 100644 index 0000000..6a9b9bb --- /dev/null +++ b/renderer/render/co_types.h @@ -0,0 +1,10 @@ +#ifndef TYPES_H +#define TYPES_H + +#ifdef __CUDA_ARCH__ +#define CPU_GPU_FUNCTION __host__ __device__ +#else +#define CPU_GPU_FUNCTION +#endif + +#endif diff --git a/renderer/render/common.h b/renderer/render/common.h new file mode 100644 index 0000000..d94501f --- /dev/null +++ b/renderer/render/common.h @@ -0,0 +1,135 @@ +#ifndef COMMON_H +#define COMMON_H + +#include "co_types.h" +#include +#include + +#if defined(_OPENMP) +#include +#endif + + +#define DISABLE_COPY_AND_ASSIGN(classname) \ +private:\ + classname(const classname&) = delete;\ + classname& operator=(const classname&) = delete; + + +template +CPU_GPU_FUNCTION +void fill(T* arr, int N, T val) { + for(int idx = 0; idx < N; ++idx) { + arr[idx] = val; + } +} + +template +CPU_GPU_FUNCTION +void fill_zero(T* arr, int N) { + for(int idx = 0; idx < N; ++idx) { + arr[idx] = 0; + } +} + +template +CPU_GPU_FUNCTION +inline T distance_euclidean(const T* q, const T* t, int N) { + T out = 0; + for(int idx = 0; idx < N; idx++) { + T diff = q[idx] - t[idx]; + out += diff * diff; + } + return out; +} + +template +CPU_GPU_FUNCTION +inline T distance_l2(const T* q, const T* t, int N) { + T out = distance_euclidean(q, t, N); + out = std::sqrt(out); + return out; +} + + + + +template +struct FillFunctor { + T* arr; + const T val; + + FillFunctor(T* arr, const T val) : arr(arr), val(val) {} + CPU_GPU_FUNCTION void operator()(const int idx) { + arr[idx] = val; + } +}; + +template +CPU_GPU_FUNCTION +T mmin(const T& a, const T& b) { +#ifdef __CUDA_ARCH__ + return min(a, b); +#else + return std::min(a, b); +#endif +} + +template +CPU_GPU_FUNCTION +T mmax(const T& a, const T& b) { +#ifdef __CUDA_ARCH__ + return max(a, b); +#else + return std::max(a, b); +#endif +} + +template +CPU_GPU_FUNCTION +T mround(const T& a) { +#ifdef __CUDA_ARCH__ + return round(a); +#else + return round(a); +#endif +} + + +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ < 600 +__device__ double atomicAdd(double* address, double val) +{ + unsigned long long int* address_as_ull = + (unsigned long long int*)address; + unsigned long long int old = *address_as_ull, assumed; + + do { + assumed = old; + old = atomicCAS(address_as_ull, assumed, + __double_as_longlong(val + + __longlong_as_double(assumed))); + + // Note: uses integer comparison to avoid hang in case of NaN (since NaN != NaN) + } while (assumed != old); + + return __longlong_as_double(old); +} +#endif +#endif + + +template +CPU_GPU_FUNCTION +void matomic_add(T* addr, T val) { +#ifdef __CUDA_ARCH__ + atomicAdd(addr, val); +#else +#if defined(_OPENMP) +#pragma omp atomic +#endif + *addr += val; +#endif +} + +#endif diff --git a/renderer/render/common_cpu.h b/renderer/render/common_cpu.h new file mode 100644 index 0000000..227a842 --- /dev/null +++ b/renderer/render/common_cpu.h @@ -0,0 +1,26 @@ +#ifndef COMMON_CPU +#define COMMON_CPU + +#if defined(_OPENMP) +#include +#endif + +template +void iterate_cpu(FunctorT functor, int N) { + for(int idx = 0; idx < N; ++idx) { + functor(idx); + } +} + +template +void iterate_omp_cpu(FunctorT functor, int N, int n_threads) { +#if defined(_OPENMP) + omp_set_num_threads(n_threads); + #pragma omp parallel for +#endif + for(int idx = 0; idx < N; ++idx) { + functor(idx); + } +} + +#endif diff --git a/renderer/render/common_cuda.h b/renderer/render/common_cuda.h new file mode 100644 index 0000000..4fe0b8a --- /dev/null +++ b/renderer/render/common_cuda.h @@ -0,0 +1,173 @@ +#ifndef COMMON_CUDA +#define COMMON_CUDA + +#include +#include + +#define DEBUG 0 +#define CUDA_DEBUG_DEVICE_SYNC 0 + +// cuda check for cudaMalloc and so on +#define CUDA_CHECK(condition) \ + /* Code block avoids redefinition of cudaError_t error */ \ + do { \ + if(CUDA_DEBUG_DEVICE_SYNC) { cudaDeviceSynchronize(); } \ + cudaError_t error = condition; \ + if(error != cudaSuccess) { \ + printf("%s in %s at %d\n", cudaGetErrorString(error), __FILE__, __LINE__); \ + exit(-1); \ + } \ + } while (0) + +/// Get error string for error code. +/// @param error +inline const char* cublasGetErrorString(cublasStatus_t error) { + switch (error) { + case CUBLAS_STATUS_SUCCESS: + return "CUBLAS_STATUS_SUCCESS"; + case CUBLAS_STATUS_NOT_INITIALIZED: + return "CUBLAS_STATUS_NOT_INITIALIZED"; + case CUBLAS_STATUS_ALLOC_FAILED: + return "CUBLAS_STATUS_ALLOC_FAILED"; + case CUBLAS_STATUS_INVALID_VALUE: + return "CUBLAS_STATUS_INVALID_VALUE"; + case CUBLAS_STATUS_ARCH_MISMATCH: + return "CUBLAS_STATUS_ARCH_MISMATCH"; + case CUBLAS_STATUS_MAPPING_ERROR: + return "CUBLAS_STATUS_MAPPING_ERROR"; + case CUBLAS_STATUS_EXECUTION_FAILED: + return "CUBLAS_STATUS_EXECUTION_FAILED"; + case CUBLAS_STATUS_INTERNAL_ERROR: + return "CUBLAS_STATUS_INTERNAL_ERROR"; + case CUBLAS_STATUS_NOT_SUPPORTED: + return "CUBLAS_STATUS_NOT_SUPPORTED"; + case CUBLAS_STATUS_LICENSE_ERROR: + return "CUBLAS_STATUS_LICENSE_ERROR"; + } + return "Unknown cublas status"; +} + +#define CUBLAS_CHECK(condition) \ + do { \ + if(CUDA_DEBUG_DEVICE_SYNC) { cudaDeviceSynchronize(); } \ + cublasStatus_t status = condition; \ + if(status != CUBLAS_STATUS_SUCCESS) { \ + printf("%s in %s at %d\n", cublasGetErrorString(status), __FILE__, __LINE__); \ + exit(-1); \ + } \ + } while (0) + +// check if there is a error after kernel execution +#define CUDA_POST_KERNEL_CHECK \ + CUDA_CHECK(cudaPeekAtLastError()); \ + CUDA_CHECK(cudaGetLastError()); + +#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; + +inline int GET_BLOCKS(const int N, const int N_THREADS=CUDA_NUM_THREADS) { + return (N + N_THREADS - 1) / N_THREADS; +} + +template +T* device_malloc(long N) { + T* dptr; + CUDA_CHECK(cudaMalloc(&dptr, N * sizeof(T))); + if(DEBUG) { printf("[DEBUG] device_malloc %p, %ld\n", dptr, N); } + return dptr; +} + +template +void device_free(T* dptr) { + if(DEBUG) { printf("[DEBUG] device_free %p\n", dptr); } + CUDA_CHECK(cudaFree(dptr)); +} + +template +void host_to_device(const T* hptr, T* dptr, long N) { + if(DEBUG) { printf("[DEBUG] host_to_device %p => %p, %ld\n", hptr, dptr, N); } + CUDA_CHECK(cudaMemcpy(dptr, hptr, N * sizeof(T), cudaMemcpyHostToDevice)); +} + +template +T* host_to_device_malloc(const T* hptr, long N) { + T* dptr = device_malloc(N); + host_to_device(hptr, dptr, N); + return dptr; +} + +template +void device_to_host(const T* dptr, T* hptr, long N) { + if(DEBUG) { printf("[DEBUG] device_to_host %p => %p, %ld\n", dptr, hptr, N); } + CUDA_CHECK(cudaMemcpy(hptr, dptr, N * sizeof(T), cudaMemcpyDeviceToHost)); +} + +template +T* device_to_host_malloc(const T* dptr, long N) { + T* hptr = new T[N]; + device_to_host(dptr, hptr, N); + return hptr; +} + +template +void device_to_device(const T* dptr, T* hptr, long N) { + if(DEBUG) { printf("[DEBUG] device_to_device %p => %p, %ld\n", dptr, hptr, N); } + CUDA_CHECK(cudaMemcpy(hptr, dptr, N * sizeof(T), cudaMemcpyDeviceToDevice)); +} + +// https://github.com/parallel-forall/code-samples/blob/master/posts/cuda-aware-mpi-example/src/Device.cu +// https://github.com/treecode/Bonsai/blob/master/runtime/profiling/derived_atomic_functions.h +__device__ __forceinline__ void atomicMaxF(float * const address, const float value) { + if (*address >= value) { + return; + } + + int * const address_as_i = (int *)address; + int old = * address_as_i, assumed; + + do { + assumed = old; + if (__int_as_float(assumed) >= value) { + break; + } + + old = atomicCAS(address_as_i, assumed, __float_as_int(value)); + } while (assumed != old); +} + +__device__ __forceinline__ void atomicMinF(float * const address, const float value) { + if (*address <= value) { + return; + } + + int * const address_as_i = (int *)address; + int old = * address_as_i, assumed; + + do { + assumed = old; + if (__int_as_float(assumed) <= value) { + break; + } + + old = atomicCAS(address_as_i, assumed, __float_as_int(value)); + } while (assumed != old); +} + + +template +__global__ void iterate_kernel(FunctorT functor, int N) { + CUDA_KERNEL_LOOP(idx, N) { + functor(idx); + } +} + +template +void iterate_cuda(FunctorT functor, int N, int N_THREADS=CUDA_NUM_THREADS) { + iterate_kernel<<>>(functor, N); + CUDA_POST_KERNEL_CHECK; +} + + +#endif diff --git a/renderer/render/geometry.h b/renderer/render/geometry.h new file mode 100644 index 0000000..8f97851 --- /dev/null +++ b/renderer/render/geometry.h @@ -0,0 +1,294 @@ +#ifndef GEOMETRY_H +#define GEOMETRY_H + +#include +#include +#include + +#include "co_types.h" + +template +CPU_GPU_FUNCTION +inline void vec_fill(T* v, const T fill) { + for(int idx = 0; idx < N; ++idx) { + v[idx] = fill; + } +} + +template <> +CPU_GPU_FUNCTION +inline void vec_fill(float* v, const float fill) { + v[0] = fill; + v[1] = fill; + v[2] = fill; +} + +template +CPU_GPU_FUNCTION +inline void vec_add(const T* in1, const T* in2, T* out) { + for(int idx = 0; idx < N; ++idx) { + out[idx] = in1[idx] + in2[idx]; + } +} + +template <> +CPU_GPU_FUNCTION +inline void vec_add(const float* in1, const float* in2, float* out) { + out[0] = in1[0] + in2[0]; + out[1] = in1[1] + in2[1]; + out[2] = in1[2] + in2[2]; +} + +template +CPU_GPU_FUNCTION +inline void vec_add(const T lam1, const T* in1, const T lam2, const T* in2, T* out) { + for(int idx = 0; idx < N; ++idx) { + out[idx] = lam1 * in1[idx] + lam2 * in2[idx]; + } +} + +template <> +CPU_GPU_FUNCTION +inline void vec_add(const float lam1, const float* in1, const float lam2, const float* in2, float* out) { + out[0] = lam1 * in1[0] + lam2 * in2[0]; + out[1] = lam1 * in1[1] + lam2 * in2[1]; + out[2] = lam1 * in1[2] + lam2 * in2[2]; +} + +template +CPU_GPU_FUNCTION +inline void vec_sub(const T* in1, const T* in2, T* out) { + for(int idx = 0; idx < N; ++idx) { + out[idx] = in1[idx] - in2[idx]; + } +} + +template <> +CPU_GPU_FUNCTION +inline void vec_sub(const float* in1, const float* in2, float* out) { + out[0] = in1[0] - in2[0]; + out[1] = in1[1] - in2[1]; + out[2] = in1[2] - in2[2]; +} + +template +CPU_GPU_FUNCTION +inline void vec_add_scalar(const T* in, const T lam, T* out) { + for(int idx = 0; idx < N; ++idx) { + out[idx] = in[idx] + lam; + } +} + +template <> +CPU_GPU_FUNCTION +inline void vec_add_scalar(const float* in, const float lam, float* out) { + out[0] = in[0] + lam; + out[1] = in[1] + lam; + out[2] = in[2] + lam; +} + +template +CPU_GPU_FUNCTION +inline void vec_mul_scalar(const T* in, const T lam, T* out) { + for(int idx = 0; idx < N; ++idx) { + out[idx] = in[idx] * lam; + } +} + +template <> +CPU_GPU_FUNCTION +inline void vec_mul_scalar(const float* in, const float lam, float* out) { + out[0] = in[0] * lam; + out[1] = in[1] * lam; + out[2] = in[2] * lam; +} + +template +CPU_GPU_FUNCTION +inline void vec_div_scalar(const T* in, const T lam, T* out) { + for(int idx = 0; idx < N; ++idx) { + out[idx] = in[idx] / lam; + } +} + +template <> +CPU_GPU_FUNCTION +inline void vec_div_scalar(const float* in, const float lam, float* out) { + out[0] = in[0] / lam; + out[1] = in[1] / lam; + out[2] = in[2] / lam; +} + +template +CPU_GPU_FUNCTION +inline void mat_dot_vec3(const T* M, const T* v, T* w) { + w[0] = M[0] * v[0] + M[1] * v[1] + M[2] * v[2]; + w[1] = M[3] * v[0] + M[4] * v[1] + M[5] * v[2]; + w[2] = M[6] * v[0] + M[7] * v[1] + M[8] * v[2]; +} + +template +CPU_GPU_FUNCTION +inline void matT_dot_vec3(const T* M, const T* v, T* w) { + w[0] = M[0] * v[0] + M[3] * v[1] + M[6] * v[2]; + w[1] = M[1] * v[0] + M[4] * v[1] + M[7] * v[2]; + w[2] = M[2] * v[0] + M[5] * v[1] + M[8] * v[2]; +} + +template +CPU_GPU_FUNCTION +inline T vec_dot(const T* in1, const T* in2) { + T out = T(0); + for(int idx = 0; idx < N; ++idx) { + out += in1[idx] * in2[idx]; + } + return out; +} + +template <> +CPU_GPU_FUNCTION +inline float vec_dot(const float* in1, const float* in2) { + return in1[0] * in2[0] + in1[1] * in2[1] + in1[2] * in2[2]; +} + +template +CPU_GPU_FUNCTION +inline void vec_cross3(const T* u, const T* v, T* out) { + out[0] = u[1] * v[2] - u[2] * v[1]; + out[1] = u[2] * v[0] - u[0] * v[2]; + out[2] = u[0] * v[1] - u[1] * v[0]; +} + +template +CPU_GPU_FUNCTION +inline T vec_norm(const T* u) { + T norm = T(0); + for(int idx = 0; idx < N; ++idx) { + norm += u[idx] * u[idx]; + } + return std::sqrt(norm); +} + +template <> +CPU_GPU_FUNCTION +inline float vec_norm(const float* u) { + return std::sqrt(u[0] * u[0] + u[1] * u[1] + u[2] * u[2]); +} + +template +CPU_GPU_FUNCTION +inline void vec_normalize(const T* u, T* v) { + T denom = vec_norm(u); + vec_div_scalar(u, denom, v); +} + +template <> +CPU_GPU_FUNCTION +inline void vec_normalize(const float* u, float* v) { + vec_div_scalar(u, vec_norm(u), v); +} + +template +CPU_GPU_FUNCTION +void vertex_normal_3d(const T* a, const T* b, const T* c, T* no) { + T e1[3]; + T e2[3]; + vec_sub(a, b, e1); + vec_sub(c, b, e2); + vec_cross3(e1, e2, no); +} + +template +CPU_GPU_FUNCTION +bool ray_triangle_intersect_3d(const T* orig, const T* dir, const T* v0, const T* v1, const T* v2, T* t, T* u, T* v, T eps = 1e-6) { + T v0v1[3]; + vec_sub(v1, v0, v0v1); + T v0v2[3]; + vec_sub(v2, v0, v0v2); + T pvec[3]; + vec_cross3(dir, v0v2, pvec); + T det = vec_dot(v0v1, pvec); + + if(fabs(det) < eps) return false; + + T inv_det = 1 / det; + + T tvec[3]; + vec_sub(orig, v0, tvec); + *u = vec_dot(tvec, pvec) * inv_det; + if(*u < 0 || *u > 1) return false; + + T qvec[3]; + vec_cross3(tvec, v0v1, qvec); + *v = vec_dot(dir, qvec) * inv_det; + if(*v < 0 || (*u + *v) > 1) return false; + + *t = vec_dot(v0v2, qvec) * inv_det; + T w = 1 - *u - *v; + *v = *u; + *u = w; + + return true; +} + +template +CPU_GPU_FUNCTION +bool ray_triangle_mesh_intersect_3d(const T* orig, const T* dir, const int* faces, int n_faces, const T* vertices, int* face_idx, T* t, T* u, T* v) { +#ifdef __CUDA_ARCH__ + *t = 1e9; +#else + *t = std::numeric_limits::max(); +#endif + bool valid = false; + for(int fidx = 0; fidx < n_faces; ++fidx) { + const T* v0 = vertices + faces[fidx * 3 + 0] * 3; + const T* v1 = vertices + faces[fidx * 3 + 1] * 3; + const T* v2 = vertices + faces[fidx * 3 + 2] * 3; + + T ft, fu, fv; + bool inter = ray_triangle_intersect_3d(orig, dir, v0,v1,v2, &ft,&fu,&fv); + if(inter && ft < *t) { + *face_idx = fidx; + *t = ft; + *u = fu; + *v = fv; + valid = true; + } + } + + return valid; +} + +template +CPU_GPU_FUNCTION +void reflectance_light_dir(const T* sp, const T* lp, T* l) { + vec_sub(lp, sp, l); + vec_normalize(l, l); +} + +template +CPU_GPU_FUNCTION +T reflectance_lambartian(const T* sp, const T* lp, const T* n) { + T l[3]; + reflectance_light_dir(sp, lp, l); + return vec_dot(l, n); +} + +template +CPU_GPU_FUNCTION +T reflectance_phong(const T* orig, const T* sp, const T* lp, const T* n, const T ka, const T kd, const T ks, const T alpha) { + T l[3]; + reflectance_light_dir(sp, lp, l); + + T r[3]; + vec_add(2 * vec_dot(l, n), n, -1.f, l, r); + vec_normalize(r,r); //needed? + + T v[3]; + vec_sub(orig, sp, v); + vec_normalize(v, v); + + return ka + kd * vec_dot(l, n) + ks * std::pow(vec_dot(r, v), alpha); +} + +#endif diff --git a/renderer/render/render.h b/renderer/render/render.h new file mode 100644 index 0000000..fba2fc6 --- /dev/null +++ b/renderer/render/render.h @@ -0,0 +1,369 @@ +#ifndef RENDER_H +#define RENDER_H + +#include +#include + +#include "co_types.h" +#include "common.h" +#include "geometry.h" + + +template +struct Camera { + const T fx; + const T fy; + const T px; + const T py; + const T R0, R1, R2, R3, R4, R5, R6, R7, R8; + const T t0, t1, t2; + const T C0, C1, C2; + const int height; + const int width; + + Camera(const T fx, const T fy, const T px, const T py, const T* R, const T* t, int width, int height) : + fx(fx), fy(fy), px(px), py(py), + R0(R[0]), R1(R[1]), R2(R[2]), R3(R[3]), R4(R[4]), R5(R[5]), R6(R[6]), R7(R[7]), R8(R[8]), + t0(t[0]), t1(t[1]), t2(t[2]), + C0(-(R[0] * t[0] + R[3] * t[1] + R[6] * t[2])), + C1(-(R[1] * t[0] + R[4] * t[1] + R[7] * t[2])), + C2(-(R[2] * t[0] + R[5] * t[1] + R[8] * t[2])), + height(height), width(width) + { + } + + CPU_GPU_FUNCTION + inline void to_cam(const T* x, T* y) const { + y[0] = R0 * x[0] + R1 * x[1] + R2 * x[2] + t0; + y[1] = R3 * x[0] + R4 * x[1] + R5 * x[2] + t1; + y[2] = R6 * x[0] + R7 * x[1] + R8 * x[2] + t2; + } + + CPU_GPU_FUNCTION + inline void to_world(const T* x, T* y) const { + y[0] = R0 * (x[0] - t0) + R3 * (x[1] - t1) + R6 * (x[2] - t2); + y[1] = R1 * (x[0] - t0) + R4 * (x[1] - t1) + R7 * (x[2] - t2); + y[2] = R2 * (x[0] - t0) + R5 * (x[1] - t1) + R8 * (x[2] - t2); + } + + CPU_GPU_FUNCTION + inline void to_ray(const int h, const int w, T* dir) const { + T uhat[2]; + uhat[0] = (w - px) / fx; + uhat[1] = (h - py) / fy; + dir[0] = R0 * (uhat[0]) + R3 * (uhat[1]) + R6; + dir[1] = R1 * (uhat[0]) + R4 * (uhat[1]) + R7; + dir[2] = R2 * (uhat[0]) + R5 * (uhat[1]) + R8; + } + + CPU_GPU_FUNCTION + inline void to_2d(const T* xyz, T* u, T* v, T* d) const { + T xyz_t[3]; + to_cam(xyz, xyz_t); + *u = fx * xyz_t[0] + px * xyz_t[2]; + *v = fy * xyz_t[1] + py * xyz_t[2]; + *d = xyz_t[2]; + *u /= *d; + *v /= *d; + } + + CPU_GPU_FUNCTION + inline void get_C(T* C) const { + C[0] = C0; + C[1] = C1; + C[2] = C2; + } + + CPU_GPU_FUNCTION + inline int num_pixel() const { + return height * width; + } +}; + + +template +struct RenderInput { + T* verts; + T* colors; + T* normals; + int n_verts; + int* faces; + int n_faces; + + RenderInput() : verts(nullptr), colors(nullptr), normals(nullptr), n_verts(0), faces(nullptr), n_faces(0) {} +}; + +template +struct Buffer { + T* depth; + T* color; + T* normal; + + Buffer() : depth(nullptr), color(nullptr), normal(nullptr) {} +}; + +template +struct Shader { + const T ka; + const T kd; + const T ks; + const T alpha; + + Shader(T ka, T kd, T ks, T alpha) : ka(ka), kd(kd), ks(ks), alpha(alpha) {} + + CPU_GPU_FUNCTION + T operator()(const T* orig, const T* sp, const T* lp, const T* norm) const { + return reflectance_phong(orig, sp, lp, norm, ka, kd, ks, alpha); + } +}; + + + +template +class BaseRenderer { +public: + const Camera cam; + const Shader shader; + Buffer buffer; + + BaseRenderer(const Camera cam, const Shader shader, Buffer buffer) : cam(cam), shader(shader), buffer(buffer) { + } + + virtual ~BaseRenderer() {} + + virtual void render_mesh(const RenderInput input) = 0; + virtual void render_mesh_proj(const RenderInput input, const Camera proj, const float* pattern, float d_alpha, float d_beta) = 0; +}; + + + +template +struct RenderFunctor { + const Camera cam; + const Shader shader; + Buffer buffer; + + RenderFunctor(const Camera cam, const Shader shader, Buffer buffer) : cam(cam), shader(shader), buffer(buffer) {} +}; + + +template +struct RenderMeshFunctor : public RenderFunctor { + const RenderInput input; + + RenderMeshFunctor(const RenderInput input, const Shader shader, const Camera cam, Buffer buffer) : RenderFunctor(cam, shader,buffer), input(input) { + } + + CPU_GPU_FUNCTION void operator()(const int idx) { + int h = idx / this->cam.width; + int w = idx % this->cam.width; + + T orig[3]; + this->cam.get_C(orig); + T dir[3]; + this->cam.to_ray(h, w, dir); + + int face_idx; + T t, tu, tv; + bool valid = ray_triangle_mesh_intersect_3d(orig, dir, this->input.faces, this->input.n_faces, this->input.verts, &face_idx, &t, &tu, &tv); + + if(this->buffer.depth != nullptr) { + this->buffer.depth[idx] = valid ? t : -1; + } + + if(!valid) { + if(this->buffer.color != nullptr) { + this->buffer.color[idx * 3 + 0] = 0; + this->buffer.color[idx * 3 + 1] = 0; + this->buffer.color[idx * 3 + 2] = 0; + } + if(this->buffer.normal != nullptr) { + this->buffer.normal[idx * 3 + 0] = 0; + this->buffer.normal[idx * 3 + 1] = 0; + this->buffer.normal[idx * 3 + 2] = 0; + } + } + else if(this->buffer.normal != nullptr || this->buffer.color != nullptr) { + const int* face = input.faces + face_idx * 3; + T tw = 1 - tu - tv; + + T norm[3]; + vec_fill(norm, 0.f); + vec_add(1.f, norm, tu, this->input.normals + face[0] * 3, norm); + vec_add(1.f, norm, tv, this->input.normals + face[1] * 3, norm); + vec_add(1.f, norm, tw, this->input.normals + face[2] * 3, norm); + if(vec_dot(norm, dir) > 0) { + vec_mul_scalar(norm, -1.f, norm); + } + + if(this->buffer.normal != nullptr) { + this->buffer.normal[idx * 3 + 0] = norm[0]; + this->buffer.normal[idx * 3 + 1] = norm[1]; + this->buffer.normal[idx * 3 + 2] = norm[2]; + } + + if(this->buffer.color != nullptr) { + T color[3]; + vec_fill(color, 0.f); + vec_add(1.f, color, tu, this->input.colors + face[0] * 3, color); + vec_add(1.f, color, tv, this->input.colors + face[1] * 3, color); + vec_add(1.f, color, tw, this->input.colors + face[2] * 3, color); + + T sp[3]; + vec_add(1.f, orig, t, dir, sp); + T reflectance = this->shader(orig, sp, orig, norm); + + this->buffer.color[idx * 3 + 0] = mmin(1.f, mmax(0.f, reflectance * color[0])); + this->buffer.color[idx * 3 + 1] = mmin(1.f, mmax(0.f, reflectance * color[1])); + this->buffer.color[idx * 3 + 2] = mmin(1.f, mmax(0.f, reflectance * color[2])); + } + } + } +}; + +template +CPU_GPU_FUNCTION +inline void interpolate_linear(const T* im, T x, T y, int height, int width, T* out_vec) { + int x1 = int(x); + int y1 = int(y); + int x2 = x1 + 1; + int y2 = y1 + 1; + + T denom = (x2 - x1) * (y2 - y1); + T t11 = (x2 - x) * (y2 - y); + T t21 = (x - x1) * (y2 - y); + T t12 = (x2 - x) * (y - y1); + T t22 = (x - x1) * (y - y1); + + x1 = mmin(mmax(x1, int(0)), width-1); + x2 = mmin(mmax(x2, int(0)), width-1); + y1 = mmin(mmax(y1, int(0)), height-1); + y2 = mmin(mmax(y2, int(0)), height-1); + + for(int idx = 0; idx < n; ++idx) { + out_vec[idx] = (im[(y1 * width + x1) * 3 + idx] * t11 + + im[(y2 * width + x1) * 3 + idx] * t12 + + im[(y1 * width + x2) * 3 + idx] * t21 + + im[(y2 * width + x2) * 3 + idx] * t22) / denom; + } +} + +template +struct RenderProjectorFunctor : public RenderFunctor { + const RenderInput input; + const Camera proj; + const float* pattern; + const float d_alpha; + const float d_beta; + + RenderProjectorFunctor(const RenderInput input, const Shader shader, const Camera cam, const Camera proj, const float* pattern, float d_alpha, float d_beta, Buffer buffer) : RenderFunctor(cam, shader, buffer), input(input), proj(proj), pattern(pattern), d_alpha(d_alpha), d_beta(d_beta) { + } + + CPU_GPU_FUNCTION void operator()(const int idx) { + int h = idx / this->cam.width; + int w = idx % this->cam.width; + + T orig[3]; + this->cam.get_C(orig); + T dir[3]; + this->cam.to_ray(h, w, dir); + + int face_idx; + T t, tu, tv; + bool valid = ray_triangle_mesh_intersect_3d(orig, dir, this->input.faces, this->input.n_faces, this->input.verts, &face_idx, &t, &tu, &tv); + if(this->buffer.depth != nullptr) { + this->buffer.depth[idx] = valid ? t : -1; + } + + this->buffer.color[idx * 3 + 0] = 0; + this->buffer.color[idx * 3 + 1] = 0; + this->buffer.color[idx * 3 + 2] = 0; + + if(valid) { + if(this->buffer.normal != nullptr) { + const int* face = input.faces + face_idx * 3; + T tw = 1 - tu - tv; + + T norm[3]; + vertex_normal_3d( + this->input.verts + face[0] * 3, + this->input.verts + face[1] * 3, + this->input.verts + face[2] * 3, + norm); + vec_normalize(norm, norm); + + if(vec_dot(norm, dir) > 0) { + vec_mul_scalar(norm, -1.f, norm); + } + + T color[3]; + vec_fill(color, 0.f); + vec_add(1.f, color, tu, this->input.colors + face[0] * 3, color); + vec_add(1.f, color, tv, this->input.colors + face[1] * 3, color); + vec_add(1.f, color, tw, this->input.colors + face[2] * 3, color); + + T sp[3]; + vec_add(1.f, orig, t, dir, sp); + T reflectance = this->shader(orig, sp, orig, norm); + + this->buffer.normal[idx * 3 + 0] = mmin(1.f, mmax(0.f, reflectance * color[0])); + this->buffer.normal[idx * 3 + 1] = mmin(1.f, mmax(0.f, reflectance * color[1])); + this->buffer.normal[idx * 3 + 2] = mmin(1.f, mmax(0.f, reflectance * color[2])); + } + + // get 3D point + T pt[3]; + vec_mul_scalar(dir, t, pt); + vec_add(orig, pt, pt); + + // get dir from proj + T proj_orig[3]; + proj.get_C(proj_orig); + T proj_dir[3]; + vec_sub(pt, proj_orig, proj_dir); + vec_div_scalar(proj_dir, proj_dir[2], proj_dir); + + // check if it hit same tria + int p_face_idx; + T p_t, p_tu, p_tv; + valid = ray_triangle_mesh_intersect_3d(proj_orig, proj_dir, this->input.faces, this->input.n_faces, this->input.verts, &p_face_idx, &p_t, &p_tu, &p_tv); + // if(!valid || p_face_idx != face_idx) { + // return; + // } + + T p_pt[3]; + vec_mul_scalar(proj_dir, p_t, p_pt); + vec_add(proj_orig, p_pt, p_pt); + T diff[3]; + vec_sub(p_pt, pt, diff); + if(!valid || vec_norm(diff) > 1e-5) { + return; + } + + // get uv in proj + T u,v,d; + proj.to_2d(p_pt, &u,&v,&d); + + // if valid u,v than use it to inpaint + if(u >= 0 && v >= 0 && u < this->proj.width && v < this->proj.height) { + // int pattern_idx = ((int(v) * this->proj.width) + int(u)) * 3; + // this->buffer.color[idx * 3 + 0] = pattern[pattern_idx + 0]; + // this->buffer.color[idx * 3 + 1] = pattern[pattern_idx + 1]; + // this->buffer.color[idx * 3 + 2] = pattern[pattern_idx + 2]; + interpolate_linear(pattern, u, v, this->proj.height, this->proj.width, this->buffer.color + idx * 3); + + // decay based on distance + T decay = d_alpha + d_beta * d; + decay *= decay; + decay = mmax(decay, T(1)); + vec_div_scalar(this->buffer.color + idx * 3, decay, this->buffer.color + idx * 3); + } + } + + } +}; + + + + +#endif diff --git a/renderer/render/render_cpu.cpp b/renderer/render/render_cpu.cpp new file mode 100644 index 0000000..4dcd1bc --- /dev/null +++ b/renderer/render/render_cpu.cpp @@ -0,0 +1,22 @@ +#include + +#if defined(_OPENMP) +#include +#endif + +#include "render_cpu.h" +#include "common_cpu.h" + +template +void RendererCpu::render_mesh(RenderInput input) { + RenderMeshFunctor functor(input, this->shader, this->cam, this->buffer); + iterate_omp_cpu(functor, this->cam.num_pixel(), n_threads); +} + +template +void RendererCpu::render_mesh_proj(const RenderInput input, const Camera proj, const float* pattern, float d_alpha, float d_beta) { + RenderProjectorFunctor functor(input, this->shader, this->cam, proj, pattern, d_alpha, d_beta, this->buffer); + iterate_omp_cpu(functor, this->cam.num_pixel(), this->n_threads); +} + +template class RendererCpu; diff --git a/renderer/render/render_cpu.h b/renderer/render/render_cpu.h new file mode 100644 index 0000000..c228450 --- /dev/null +++ b/renderer/render/render_cpu.h @@ -0,0 +1,23 @@ +#ifndef RENDER_CPU_H +#define RENDER_CPU_H + +#include "render.h" + + + +template +class RendererCpu : public BaseRenderer { +public: + const int n_threads; + + RendererCpu(const Camera cam, const Shader shader, Buffer buffer, int n_threads) : BaseRenderer(cam, shader, buffer), n_threads(n_threads) { + } + + virtual ~RendererCpu() { + } + + virtual void render_mesh(const RenderInput input); + virtual void render_mesh_proj(const RenderInput input, const Camera proj, const float* pattern, float d_alpha, float d_beta); +}; + +#endif diff --git a/renderer/render/render_gpu.cu b/renderer/render/render_gpu.cu new file mode 100644 index 0000000..03d32c8 --- /dev/null +++ b/renderer/render/render_gpu.cu @@ -0,0 +1,100 @@ +#include "common_cuda.h" +#include "render_gpu.h" + +template +RendererGpu::RendererGpu(const Camera cam, const Shader shader, Buffer buffer) : BaseRenderer(cam, shader, buffer) { + if(buffer.depth != nullptr) { + buffer_gpu.depth = device_malloc(cam.num_pixel()); + } + + if(buffer.color != nullptr) { + buffer_gpu.color = device_malloc(cam.num_pixel() * 3); + } + + if(buffer.normal != nullptr) { + buffer_gpu.normal = device_malloc(cam.num_pixel() * 3); + } +} + +template +RendererGpu::~RendererGpu() { + device_free(buffer_gpu.depth); + device_free(buffer_gpu.color); + device_free(buffer_gpu.normal); +} + +template +void RendererGpu::gpu_to_cpu() { + if(buffer_gpu.depth != nullptr && this->buffer.depth != nullptr) { + device_to_host(buffer_gpu.depth, this->buffer.depth, this->cam.num_pixel()); + } + if(buffer_gpu.color != nullptr && this->buffer.color != nullptr) { + device_to_host(buffer_gpu.color, this->buffer.color, this->cam.num_pixel() * 3); + } + if(buffer_gpu.normal != nullptr && this->buffer.normal != nullptr) { + device_to_host(buffer_gpu.normal, this->buffer.normal, this->cam.num_pixel() * 3); + } +} + +template +RenderInput RendererGpu::input_to_device(const RenderInput input) { + RenderInput input_gpu; + input_gpu.n_verts = input.n_verts; + input_gpu.n_faces = input.n_faces; + + if(input.verts != nullptr) { + input_gpu.verts = host_to_device_malloc(input.verts, input.n_verts * 3); + } + if(input.colors != nullptr) { + input_gpu.colors = host_to_device_malloc(input.colors, input.n_verts * 3); + } + if(input.normals != nullptr) { + input_gpu.normals = host_to_device_malloc(input.normals, input.n_verts * 3); + } + if(input.faces != nullptr) { + input_gpu.faces = host_to_device_malloc(input.faces, input.n_faces * 3); + } + + return input_gpu; +} + +template +void RendererGpu::input_free_device(const RenderInput input) { + if(input.verts != nullptr) { + device_free(input.verts); + } + if(input.colors != nullptr) { + device_free(input.colors); + } + if(input.normals != nullptr) { + device_free(input.normals); + } + if(input.faces != nullptr) { + device_free(input.faces); + } +} + + +template +void RendererGpu::render_mesh(RenderInput input) { + RenderInput input_gpu = this->input_to_device(input); + RenderMeshFunctor functor(input_gpu, this->shader, this->cam, this->buffer_gpu); + iterate_cuda(functor, this->cam.num_pixel()); + gpu_to_cpu(); + this->input_free_device(input_gpu); +} + +template +void RendererGpu::render_mesh_proj(const RenderInput input, const Camera proj, const float* pattern, float d_alpha, float d_beta) { + RenderInput input_gpu = this->input_to_device(input); + float* pattern_gpu = host_to_device_malloc(pattern, proj.num_pixel()*3); + + RenderProjectorFunctor functor(input_gpu, this->shader, this->cam, proj, pattern_gpu, d_alpha, d_beta, this->buffer_gpu); + iterate_cuda(functor, this->cam.num_pixel()); + + gpu_to_cpu(); + this->input_free_device(input_gpu); + device_free(pattern_gpu); +} + +template class RendererGpu; diff --git a/renderer/render/render_gpu.h b/renderer/render/render_gpu.h new file mode 100644 index 0000000..4c9f630 --- /dev/null +++ b/renderer/render/render_gpu.h @@ -0,0 +1,23 @@ +#ifndef RENDER_RENDER_GPU_H +#define RENDER_RENDER_GPU_H + +#include "render.h" + +template +class RendererGpu : public BaseRenderer { +public: + Buffer buffer_gpu; + + RendererGpu(const Camera cam, const Shader shader, Buffer buffer); + + virtual ~RendererGpu(); + + virtual void gpu_to_cpu(); + virtual RenderInput input_to_device(const RenderInput input); + virtual void input_free_device(const RenderInput input); + + virtual void render_mesh(const RenderInput input); + virtual void render_mesh_proj(const RenderInput input, const Camera proj, const float* pattern, float d_alpha, float d_beta); +}; + +#endif diff --git a/renderer/render/render_gpu_dummy.cpp b/renderer/render/render_gpu_dummy.cpp new file mode 100644 index 0000000..66cb55e --- /dev/null +++ b/renderer/render/render_gpu_dummy.cpp @@ -0,0 +1,33 @@ +#include "render_gpu.h" + +template +RendererGpu::RendererGpu(const Camera cam, const Shader shader, Buffer buffer) : BaseRenderer(cam, shader, buffer) { +} + +template +RendererGpu::~RendererGpu() { +} + +template +void RendererGpu::gpu_to_cpu() {} + +template +RenderInput RendererGpu::input_to_device(const RenderInput input) { return RenderInput(); } + +template +void RendererGpu::input_free_device(const RenderInput input) { + throw std::logic_error("Not implemented"); +} + +template +void RendererGpu::render_mesh(const RenderInput input) { + throw std::logic_error("Not implemented"); +} + +template +void RendererGpu::render_mesh_proj(const RenderInput input, const Camera proj, const float* pattern, float d_alpha, float d_beta) { + throw std::logic_error("Not implemented"); +} + + +template class RendererGpu; diff --git a/renderer/render/stdlib_cuda.cu b/renderer/render/stdlib_cuda.cu new file mode 100644 index 0000000..6e56c60 --- /dev/null +++ b/renderer/render/stdlib_cuda.cu @@ -0,0 +1,35 @@ +#include "common_cuda.h" +#include "stdlib_cuda.h" + +void device_synchronize() { + cudaDeviceSynchronize(); +} + +float* device_malloc_f32(long N) { + return device_malloc(N); +} +int* device_malloc_i32(long N) { + return device_malloc(N); +} + +void device_free_f32(float* dptr) { + device_free(dptr); +} +void device_free_i32(int* dptr) { + device_free(dptr); +} + +void device_to_host_f32(const float* dptr, float* hptr, long N) { + device_to_host(dptr, hptr, N); +} +void device_to_host_i32(const int* dptr, int* hptr, long N) { + device_to_host(dptr, hptr, N); +} + +float* host_to_device_malloc_f32(const float* hptr, long N) { + return host_to_device_malloc(hptr, N); +} + +int* host_to_device_malloc_i32(const int* hptr, long N) { + return host_to_device_malloc(hptr, N); +} diff --git a/renderer/render/stdlib_cuda.h b/renderer/render/stdlib_cuda.h new file mode 100644 index 0000000..fca096f --- /dev/null +++ b/renderer/render/stdlib_cuda.h @@ -0,0 +1,18 @@ +#ifndef STDLIB_CUDA +#define STDLIB_CUDA + +void device_synchronize(); + +float* device_malloc_f32(long N); +int* device_malloc_i32(long N); + +void device_free_f32(float* dptr); +void device_free_i32(int* dptr); + +float* host_to_device_malloc_f32(const float* hptr, long N); +int* host_to_device_malloc_i32(const int* hptr, long N); + +void device_to_host_f32(const float* dptr, float* hptr, long N); +void device_to_host_i32(const int* dptr, int* hptr, long N); + +#endif diff --git a/renderer/render/stdlib_cuda_dummy.cpp b/renderer/render/stdlib_cuda_dummy.cpp new file mode 100644 index 0000000..265ab52 --- /dev/null +++ b/renderer/render/stdlib_cuda_dummy.cpp @@ -0,0 +1,10 @@ +#include "stdlib_cuda.h" + +float* device_malloc_f32(long N) { return nullptr; } +int* device_malloc_i32(long N) { return nullptr; } +void device_free_f32(float* dptr) {} +void device_free_i32(int* dptr) {} +float* host_to_device_malloc_f32(const float* hptr, long N) { return nullptr; } +int* host_to_device_malloc_i32(const int* hptr, long N) { return nullptr; } +void device_to_host_f32(const float* dptr, float* hptr, long N) {} +void device_to_host_i32(const int* dptr, int* hptr, long N) {} diff --git a/renderer/setup.py b/renderer/setup.py new file mode 100644 index 0000000..393f160 --- /dev/null +++ b/renderer/setup.py @@ -0,0 +1,49 @@ +from distutils.core import setup +from Cython.Build import cythonize +from distutils.extension import Extension +from Cython.Distutils import build_ext +import numpy as np +import platform +import os +import json + +this_dir = os.path.dirname(__file__) + +with open('../config.json') as fp: + config = json.load(fp) + +extra_compile_args = ['-O3', '-std=c++11'] + +print('using cuda') +cuda_lib_dir = config['CUDA_LIBRARY_DIR'] +cuda_lib = 'cudart' + +sources = ['cyrender.pyx'] +extra_objects = [ + os.path.join(this_dir, 'render/render_cpu.cpp.o'), +] +library_dirs = [] +libraries = ['m'] +extra_objects.append(os.path.join(this_dir, 'render/render_gpu.cu.o')) +extra_objects.append(os.path.join(this_dir, 'render/stdlib_cuda.cu.o')) +library_dirs.append(cuda_lib_dir) +libraries.append(cuda_lib) + +setup( + name="cyrender", + cmdclass= {'build_ext': build_ext}, + ext_modules=[ + Extension('cyrender', + sources, + extra_objects=extra_objects, + language='c++', + library_dirs=library_dirs, + libraries=libraries, + include_dirs=[ + np.get_include(), + ], + extra_compile_args=extra_compile_args, + # extra_link_args=extra_link_args + ) + ] +) diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..a9b54f9 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,6 @@ +cython +numpy +matplotlib +pandas +scipy +opencv diff --git a/torchext/__init__.py b/torchext/__init__.py new file mode 100644 index 0000000..7001ba5 --- /dev/null +++ b/torchext/__init__.py @@ -0,0 +1,4 @@ +from .dataset import * +from .worker import * +from .functions import * +from .modules import * diff --git a/torchext/dataset.py b/torchext/dataset.py new file mode 100644 index 0000000..2c606df --- /dev/null +++ b/torchext/dataset.py @@ -0,0 +1,66 @@ +import torch +import torch.utils.data +import numpy as np + +class TestSet(object): + def __init__(self, name, dset, test_frequency=1): + self.name = name + self.dset = dset + self.test_frequency = test_frequency + +class TestSets(list): + def append(self, name, dset, test_frequency=1): + super().append(TestSet(name, dset, test_frequency)) + + + +class MultiDataset(torch.utils.data.Dataset): + def __init__(self, *datasets): + self.current_epoch = 0 + + self.datasets = [] + self.cum_n_samples = [0] + + for dataset in datasets: + self.append(dataset) + + def append(self, dataset): + self.datasets.append(dataset) + self.__update_cum_n_samples(dataset) + + def __update_cum_n_samples(self, dataset): + n_samples = self.cum_n_samples[-1] + len(dataset) + self.cum_n_samples.append(n_samples) + + def dataset_updated(self): + self.cum_n_samples = [0] + for dset in self.datasets: + self.__update_cum_n_samples(dset) + + def __len__(self): + return self.cum_n_samples[-1] + + def __getitem__(self, idx): + didx = np.searchsorted(self.cum_n_samples, idx, side='right') - 1 + sidx = idx - self.cum_n_samples[didx] + return self.datasets[didx][sidx] + + + +class BaseDataset(torch.utils.data.Dataset): + def __init__(self, train=True, fix_seed_per_epoch=False): + self.current_epoch = 0 + self.train = train + self.fix_seed_per_epoch = fix_seed_per_epoch + + def get_rng(self, idx): + rng = np.random.RandomState() + if self.train: + if self.fix_seed_per_epoch: + seed = 1 * len(self) + idx + else: + seed = (self.current_epoch + 1) * len(self) + idx + rng.seed(seed) + else: + rng.seed(idx) + return rng diff --git a/torchext/ext/co_types.h b/torchext/ext/co_types.h new file mode 100644 index 0000000..6a9b9bb --- /dev/null +++ b/torchext/ext/co_types.h @@ -0,0 +1,10 @@ +#ifndef TYPES_H +#define TYPES_H + +#ifdef __CUDA_ARCH__ +#define CPU_GPU_FUNCTION __host__ __device__ +#else +#define CPU_GPU_FUNCTION +#endif + +#endif diff --git a/torchext/ext/common.h b/torchext/ext/common.h new file mode 100644 index 0000000..d94501f --- /dev/null +++ b/torchext/ext/common.h @@ -0,0 +1,135 @@ +#ifndef COMMON_H +#define COMMON_H + +#include "co_types.h" +#include +#include + +#if defined(_OPENMP) +#include +#endif + + +#define DISABLE_COPY_AND_ASSIGN(classname) \ +private:\ + classname(const classname&) = delete;\ + classname& operator=(const classname&) = delete; + + +template +CPU_GPU_FUNCTION +void fill(T* arr, int N, T val) { + for(int idx = 0; idx < N; ++idx) { + arr[idx] = val; + } +} + +template +CPU_GPU_FUNCTION +void fill_zero(T* arr, int N) { + for(int idx = 0; idx < N; ++idx) { + arr[idx] = 0; + } +} + +template +CPU_GPU_FUNCTION +inline T distance_euclidean(const T* q, const T* t, int N) { + T out = 0; + for(int idx = 0; idx < N; idx++) { + T diff = q[idx] - t[idx]; + out += diff * diff; + } + return out; +} + +template +CPU_GPU_FUNCTION +inline T distance_l2(const T* q, const T* t, int N) { + T out = distance_euclidean(q, t, N); + out = std::sqrt(out); + return out; +} + + + + +template +struct FillFunctor { + T* arr; + const T val; + + FillFunctor(T* arr, const T val) : arr(arr), val(val) {} + CPU_GPU_FUNCTION void operator()(const int idx) { + arr[idx] = val; + } +}; + +template +CPU_GPU_FUNCTION +T mmin(const T& a, const T& b) { +#ifdef __CUDA_ARCH__ + return min(a, b); +#else + return std::min(a, b); +#endif +} + +template +CPU_GPU_FUNCTION +T mmax(const T& a, const T& b) { +#ifdef __CUDA_ARCH__ + return max(a, b); +#else + return std::max(a, b); +#endif +} + +template +CPU_GPU_FUNCTION +T mround(const T& a) { +#ifdef __CUDA_ARCH__ + return round(a); +#else + return round(a); +#endif +} + + +#ifdef __CUDA_ARCH__ +#if __CUDA_ARCH__ < 600 +__device__ double atomicAdd(double* address, double val) +{ + unsigned long long int* address_as_ull = + (unsigned long long int*)address; + unsigned long long int old = *address_as_ull, assumed; + + do { + assumed = old; + old = atomicCAS(address_as_ull, assumed, + __double_as_longlong(val + + __longlong_as_double(assumed))); + + // Note: uses integer comparison to avoid hang in case of NaN (since NaN != NaN) + } while (assumed != old); + + return __longlong_as_double(old); +} +#endif +#endif + + +template +CPU_GPU_FUNCTION +void matomic_add(T* addr, T val) { +#ifdef __CUDA_ARCH__ + atomicAdd(addr, val); +#else +#if defined(_OPENMP) +#pragma omp atomic +#endif + *addr += val; +#endif +} + +#endif diff --git a/torchext/ext/common_cuda.h b/torchext/ext/common_cuda.h new file mode 100644 index 0000000..4fe0b8a --- /dev/null +++ b/torchext/ext/common_cuda.h @@ -0,0 +1,173 @@ +#ifndef COMMON_CUDA +#define COMMON_CUDA + +#include +#include + +#define DEBUG 0 +#define CUDA_DEBUG_DEVICE_SYNC 0 + +// cuda check for cudaMalloc and so on +#define CUDA_CHECK(condition) \ + /* Code block avoids redefinition of cudaError_t error */ \ + do { \ + if(CUDA_DEBUG_DEVICE_SYNC) { cudaDeviceSynchronize(); } \ + cudaError_t error = condition; \ + if(error != cudaSuccess) { \ + printf("%s in %s at %d\n", cudaGetErrorString(error), __FILE__, __LINE__); \ + exit(-1); \ + } \ + } while (0) + +/// Get error string for error code. +/// @param error +inline const char* cublasGetErrorString(cublasStatus_t error) { + switch (error) { + case CUBLAS_STATUS_SUCCESS: + return "CUBLAS_STATUS_SUCCESS"; + case CUBLAS_STATUS_NOT_INITIALIZED: + return "CUBLAS_STATUS_NOT_INITIALIZED"; + case CUBLAS_STATUS_ALLOC_FAILED: + return "CUBLAS_STATUS_ALLOC_FAILED"; + case CUBLAS_STATUS_INVALID_VALUE: + return "CUBLAS_STATUS_INVALID_VALUE"; + case CUBLAS_STATUS_ARCH_MISMATCH: + return "CUBLAS_STATUS_ARCH_MISMATCH"; + case CUBLAS_STATUS_MAPPING_ERROR: + return "CUBLAS_STATUS_MAPPING_ERROR"; + case CUBLAS_STATUS_EXECUTION_FAILED: + return "CUBLAS_STATUS_EXECUTION_FAILED"; + case CUBLAS_STATUS_INTERNAL_ERROR: + return "CUBLAS_STATUS_INTERNAL_ERROR"; + case CUBLAS_STATUS_NOT_SUPPORTED: + return "CUBLAS_STATUS_NOT_SUPPORTED"; + case CUBLAS_STATUS_LICENSE_ERROR: + return "CUBLAS_STATUS_LICENSE_ERROR"; + } + return "Unknown cublas status"; +} + +#define CUBLAS_CHECK(condition) \ + do { \ + if(CUDA_DEBUG_DEVICE_SYNC) { cudaDeviceSynchronize(); } \ + cublasStatus_t status = condition; \ + if(status != CUBLAS_STATUS_SUCCESS) { \ + printf("%s in %s at %d\n", cublasGetErrorString(status), __FILE__, __LINE__); \ + exit(-1); \ + } \ + } while (0) + +// check if there is a error after kernel execution +#define CUDA_POST_KERNEL_CHECK \ + CUDA_CHECK(cudaPeekAtLastError()); \ + CUDA_CHECK(cudaGetLastError()); + +#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; + +inline int GET_BLOCKS(const int N, const int N_THREADS=CUDA_NUM_THREADS) { + return (N + N_THREADS - 1) / N_THREADS; +} + +template +T* device_malloc(long N) { + T* dptr; + CUDA_CHECK(cudaMalloc(&dptr, N * sizeof(T))); + if(DEBUG) { printf("[DEBUG] device_malloc %p, %ld\n", dptr, N); } + return dptr; +} + +template +void device_free(T* dptr) { + if(DEBUG) { printf("[DEBUG] device_free %p\n", dptr); } + CUDA_CHECK(cudaFree(dptr)); +} + +template +void host_to_device(const T* hptr, T* dptr, long N) { + if(DEBUG) { printf("[DEBUG] host_to_device %p => %p, %ld\n", hptr, dptr, N); } + CUDA_CHECK(cudaMemcpy(dptr, hptr, N * sizeof(T), cudaMemcpyHostToDevice)); +} + +template +T* host_to_device_malloc(const T* hptr, long N) { + T* dptr = device_malloc(N); + host_to_device(hptr, dptr, N); + return dptr; +} + +template +void device_to_host(const T* dptr, T* hptr, long N) { + if(DEBUG) { printf("[DEBUG] device_to_host %p => %p, %ld\n", dptr, hptr, N); } + CUDA_CHECK(cudaMemcpy(hptr, dptr, N * sizeof(T), cudaMemcpyDeviceToHost)); +} + +template +T* device_to_host_malloc(const T* dptr, long N) { + T* hptr = new T[N]; + device_to_host(dptr, hptr, N); + return hptr; +} + +template +void device_to_device(const T* dptr, T* hptr, long N) { + if(DEBUG) { printf("[DEBUG] device_to_device %p => %p, %ld\n", dptr, hptr, N); } + CUDA_CHECK(cudaMemcpy(hptr, dptr, N * sizeof(T), cudaMemcpyDeviceToDevice)); +} + +// https://github.com/parallel-forall/code-samples/blob/master/posts/cuda-aware-mpi-example/src/Device.cu +// https://github.com/treecode/Bonsai/blob/master/runtime/profiling/derived_atomic_functions.h +__device__ __forceinline__ void atomicMaxF(float * const address, const float value) { + if (*address >= value) { + return; + } + + int * const address_as_i = (int *)address; + int old = * address_as_i, assumed; + + do { + assumed = old; + if (__int_as_float(assumed) >= value) { + break; + } + + old = atomicCAS(address_as_i, assumed, __float_as_int(value)); + } while (assumed != old); +} + +__device__ __forceinline__ void atomicMinF(float * const address, const float value) { + if (*address <= value) { + return; + } + + int * const address_as_i = (int *)address; + int old = * address_as_i, assumed; + + do { + assumed = old; + if (__int_as_float(assumed) <= value) { + break; + } + + old = atomicCAS(address_as_i, assumed, __float_as_int(value)); + } while (assumed != old); +} + + +template +__global__ void iterate_kernel(FunctorT functor, int N) { + CUDA_KERNEL_LOOP(idx, N) { + functor(idx); + } +} + +template +void iterate_cuda(FunctorT functor, int N, int N_THREADS=CUDA_NUM_THREADS) { + iterate_kernel<<>>(functor, N); + CUDA_POST_KERNEL_CHECK; +} + + +#endif diff --git a/torchext/ext/ext.h b/torchext/ext/ext.h new file mode 100644 index 0000000..21285ec --- /dev/null +++ b/torchext/ext/ext.h @@ -0,0 +1,347 @@ +#pragma once + +#include "common.h" + + +#define CHECK_CUDA(x) AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) AT_ASSERTM(x.is_contiguous(), #x " must be contiguous") + +#define CHECK_INPUT_CPU(x) CHECK_CONTIGUOUS(x) +#define CHECK_INPUT_CUDA(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) + + +template +struct NNFunctor { + const T* in0; // nelem0 x dim + const T* in1; // nelem1 x dim + const long nelem0; + const long nelem1; + long* out; // nelem0 + + NNFunctor(const T* in0, const T* in1, long nelem0, long nelem1, long* out) : in0(in0), in1(in1), nelem0(nelem0), nelem1(nelem1), out(out) {} + + CPU_GPU_FUNCTION void operator()(long idx0) { + // idx0 \in [nelem0] + + const T* vec0 = in0 + idx0 * dim; + + T min_dist = 1e9; + long min_arg = -1; + for(long idx1 = 0; idx1 < nelem1; ++idx1) { + const T* vec1 = in1 + idx1 * dim; + T dist = 0; + for(long didx = 0; didx < dim; ++didx) { + T diff = vec0[didx] - vec1[didx]; + dist += diff * diff; + } + + if(dist < min_dist) { + min_dist = dist; + min_arg = idx1; + } + } + + out[idx0] = min_arg; + } +}; + +struct CrossCheckFunctor { + const long* in0; // nelem0 + const long* in1; // nelem1 + const long nelem0; + const long nelem1; + uint8_t* out; // nelem0 + + CrossCheckFunctor(const long* in0, const long* in1, long nelem0, long nelem1, uint8_t* out) : in0(in0), in1(in1), nelem0(nelem0), nelem1(nelem1), out(out) {} + + CPU_GPU_FUNCTION void operator()(long idx0) { + // idx0 \in [nelem0] + int idx1 = in0[idx0]; + out[idx0] = idx1 >=0 && in1[idx1] >= 0 && idx0 == in1[idx1]; + // out[idx0] = idx0 == in1[in0[idx0]]; + } +}; + +template +struct ProjNNFunctor { + // xyz0, xyz1 in coord sys of 1 + const T* xyz0; // bs x height x width x 3 + const T* xyz1; // bs x height x width x 3 + const T* K; // 3 x 3 + const long batch_size; + const long height; + const long width; + const long patch_size; + long* out; // bs x height x width + + ProjNNFunctor(const T* xyz0, const T* xyz1, const T* K, long batch_size, long height, long width, long patch_size, long* out) + : xyz0(xyz0), xyz1(xyz1), K(K), batch_size(batch_size), height(height), width(width), patch_size(patch_size), out(out) {} + + CPU_GPU_FUNCTION void operator()(long idx0) { + // idx0 \in [0, bs x height x width] + + const long bs = idx0 / (height * width); + + const T x = xyz0[idx0 * 3 + 0]; + const T y = xyz0[idx0 * 3 + 1]; + const T z = xyz0[idx0 * 3 + 2]; + const T d = K[6] * x + K[7] * y + K[8] * z; + const T u = (K[0] * x + K[1] * y + K[2] * z) / d; + const T v = (K[3] * x + K[4] * y + K[5] * z) / d; + + int u0 = u + 0.5; + int v0 = v + 0.5; + + long min_idx1 = -1; + T min_dist = 1e9; + for(int pidx = 0; pidx < patch_size*patch_size; ++pidx) { + int pu = pidx % patch_size; + int pv = pidx / patch_size; + + int u1 = u0 + pu - patch_size/2; + int v1 = v0 + pv - patch_size/2; + + if(u1 >= 0 && v1 >= 0 && u1 < width && v1 < height) { + const long idx1 = (bs * height + v1) * width + u1; + const T* xyz1n = xyz1 + idx1 * 3; + const T d = (x-xyz1n[0]) * (x-xyz1n[0]) + (y-xyz1n[1]) * (y-xyz1n[1]) + (z-xyz1n[2]) * (z-xyz1n[2]); + if(d < min_dist) { + min_dist = d; + min_idx1 = idx1; + } + } + } + + out[idx0] = min_idx1; + } +}; + + +template +struct XCorrVolFunctor { + const T* in0; // channels x height x width + const T* in1; // channels x height x width + const long channels; + const long height; + const long width; + const long n_disps; + const long block_size; + T* out; // nelem0 + + XCorrVolFunctor(const T* in0, const T* in1, long channels, long height, long width, long n_disps, long block_size, T* out) : in0(in0), in1(in1), channels(channels), height(height), width(width), n_disps(n_disps), block_size(block_size), out(out) {} + + CPU_GPU_FUNCTION void operator()(long oidx) { + // idx0 \in [n_disps x height x width] + + auto d = oidx / (height * width); + auto h = (oidx / width) % height; + auto w = oidx % width; + + long block_size2 = block_size * block_size; + + T val = 0; + for(int c = 0; c < channels; ++c) { + // compute means + T mu0 = 0; + T mu1 = 0; + for(int bh = 0; bh < block_size; ++bh) { + long h0 = h + bh - block_size / 2; + h0 = mmax(long(0), mmin(height-1, h0)); + for(int bw = 0; bw < block_size; ++bw) { + long w0 = w + bw - block_size / 2; + long w1 = w0 - d; + w0 = mmax(long(0), mmin(width-1, w0)); + w1 = mmax(long(0), mmin(width-1, w1)); + long idx0 = (c * height + h0) * width + w0; + long idx1 = (c * height + h0) * width + w1; + mu0 += in0[idx0] / block_size2; + mu1 += in1[idx1] / block_size2; + } + } + + // compute stds and dot product + T sigma0 = 0; + T sigma1 = 0; + T dot = 0; + for(int bh = 0; bh < block_size; ++bh) { + long h0 = h + bh - block_size / 2; + h0 = mmax(long(0), mmin(height-1, h0)); + for(int bw = 0; bw < block_size; ++bw) { + long w0 = w + bw - block_size / 2; + long w1 = w0 - d; + w0 = mmax(long(0), mmin(width-1, w0)); + w1 = mmax(long(0), mmin(width-1, w1)); + long idx0 = (c * height + h0) * width + w0; + long idx1 = (c * height + h0) * width + w1; + T v0 = in0[idx0] - mu0; + T v1 = in1[idx1] - mu1; + + dot += v0 * v1; + sigma0 += v0 * v0; + sigma1 += v1 * v1; + } + } + + T norm = sqrt(sigma0 * sigma1) + 1e-8; + val += dot / norm; + } + + out[oidx] = val; + } +}; + + + + +const int PHOTOMETRIC_LOSS_MSE = 0; +const int PHOTOMETRIC_LOSS_SAD = 1; +const int PHOTOMETRIC_LOSS_CENSUS_MSE = 2; +const int PHOTOMETRIC_LOSS_CENSUS_SAD = 3; + +template +struct PhotometricLossForward { + const T* es; // batch_size x channels x height x width; + const T* ta; + const int block_size; + const int block_size2; + const T eps; + const int batch_size; + const int channels; + const int height; + const int width; + T* out; // batch_size x channels x height x width; + + PhotometricLossForward(const T* es, const T* ta, int block_size, T eps, int batch_size, int channels, int height, int width, T* out) : + es(es), ta(ta), block_size(block_size), block_size2(block_size*block_size), eps(eps), batch_size(batch_size), channels(channels), height(height), width(width), out(out) {} + + CPU_GPU_FUNCTION void operator()(int outidx) { + // outidx \in [0, batch_size x height x width] + + int w = outidx % width; + int h = (outidx / width) % height; + int n = outidx / (height * width); + + T loss = 0; + for(int bidx = 0; bidx < block_size2; ++bidx) { + int bh = bidx / block_size; + int bw = bidx % block_size; + int h0 = h + bh - block_size / 2; + int w0 = w + bw - block_size / 2; + + h0 = mmin(height-1, mmax(0, h0)); + w0 = mmin(width-1, mmax(0, w0)); + + for(int c = 0; c < channels; ++c) { + int inidx = ((n * channels + c) * height + h0) * width + w0; + if(type == PHOTOMETRIC_LOSS_SAD || type == PHOTOMETRIC_LOSS_MSE) { + T diff = es[inidx] - ta[inidx]; + if(type == PHOTOMETRIC_LOSS_MSE) { + loss += diff * diff / block_size2; + } + else if(type == PHOTOMETRIC_LOSS_SAD) { + loss += fabs(diff) / block_size2; + } + } + else if(type == PHOTOMETRIC_LOSS_CENSUS_SAD || type == PHOTOMETRIC_LOSS_CENSUS_MSE) { + int inidxc = ((n * channels + c) * height + h) * width + w; + T des = es[inidx] - es[inidxc]; + T dta = ta[inidx] - ta[inidxc]; + T h_des = 0.5 * (1 + des / sqrt(des * des + eps)); + T h_dta = 0.5 * (1 + dta / sqrt(dta * dta + eps)); + T diff = h_des - h_dta; + // printf("%d,%d %d,%d: des=%f, dta=%f, h_des=%f, h_dta=%f, diff=%f\n", h,w, h0,w0, des,dta, h_des,h_dta, diff); + // printf("%d,%d %d,%d: h_des=%f = 0.5 * (1 + %f / %f); %f, %f, %f\n", h,w, h0,w0, h_des, des, sqrt(des * des + eps), des*des, des*des+eps, eps); + if(type == PHOTOMETRIC_LOSS_CENSUS_MSE) { + loss += diff * diff / block_size2; + } + else if(type == PHOTOMETRIC_LOSS_CENSUS_SAD) { + loss += fabs(diff) / block_size2; + } + } + } + } + + out[outidx] = loss; + } +}; + +template +struct PhotometricLossBackward { + const T* es; // batch_size x channels x height x width; + const T* ta; + const T* grad_out; + const int block_size; + const int block_size2; + const T eps; + const int batch_size; + const int channels; + const int height; + const int width; + T* grad_in; // batch_size x channels x height x width; + + PhotometricLossBackward(const T* es, const T* ta, const T* grad_out, int block_size, T eps, int batch_size, int channels, int height, int width, T* grad_in) : + es(es), ta(ta), grad_out(grad_out), block_size(block_size), block_size2(block_size*block_size), eps(eps), batch_size(batch_size), channels(channels), height(height), width(width), grad_in(grad_in) {} + + CPU_GPU_FUNCTION void operator()(int outidx) { + // outidx \in [0, batch_size x height x width] + + int w = outidx % width; + int h = (outidx / width) % height; + int n = outidx / (height * width); + + for(int bidx = 0; bidx < block_size2; ++bidx) { + int bh = bidx / block_size; + int bw = bidx % block_size; + int h0 = h + bh - block_size / 2; + int w0 = w + bw - block_size / 2; + + h0 = mmin(height-1, mmax(0, h0)); + w0 = mmin(width-1, mmax(0, w0)); + + const T go = grad_out[outidx]; + + for(int c = 0; c < channels; ++c) { + int inidx = ((n * channels + c) * height + h0) * width + w0; + if(type == PHOTOMETRIC_LOSS_SAD || type == PHOTOMETRIC_LOSS_MSE) { + T diff = es[inidx] - ta[inidx]; + T grad = 0; + if(type == PHOTOMETRIC_LOSS_MSE) { + grad = 2 * diff; + } + else if(type == PHOTOMETRIC_LOSS_SAD) { + grad = diff < 0 ? -1 : (diff > 0 ? 1 : 0); + } + grad = grad / block_size2 * go; + matomic_add(grad_in + inidx, grad); + } + else if(type == PHOTOMETRIC_LOSS_CENSUS_SAD || type == PHOTOMETRIC_LOSS_CENSUS_MSE) { + int inidxc = ((n * channels + c) * height + h) * width + w; + T des = es[inidx] - es[inidxc]; + T dta = ta[inidx] - ta[inidxc]; + T h_des = 0.5 * (1 + des / sqrt(des * des + eps)); + T h_dta = 0.5 * (1 + dta / sqrt(dta * dta + eps)); + T diff = h_des - h_dta; + + T grad_loss = 0; + if(type == PHOTOMETRIC_LOSS_CENSUS_MSE) { + grad_loss = 2 * diff; + } + else if(type == PHOTOMETRIC_LOSS_CENSUS_SAD) { + grad_loss = diff < 0 ? -1 : (diff > 0 ? 1 : 0); + } + grad_loss = grad_loss / block_size2; + + T tmp = des * des + eps; + T grad_heaviside = 0.5 * eps / sqrt(tmp * tmp * tmp); + + T grad = go * grad_loss * grad_heaviside; + matomic_add(grad_in + inidx, grad); + matomic_add(grad_in + inidxc, -grad); + } + } + } + } +}; + + + diff --git a/torchext/ext/ext_cpu.cpp b/torchext/ext/ext_cpu.cpp new file mode 100644 index 0000000..ca4c795 --- /dev/null +++ b/torchext/ext/ext_cpu.cpp @@ -0,0 +1,198 @@ +#include + +#include + +#include "ext.h" + +template +void iterate_cpu(FunctorT functor, int N) { + for(int idx = 0; idx < N; ++idx) { + functor(idx); + } +} + +at::Tensor nn_cpu(at::Tensor in0, at::Tensor in1) { + CHECK_INPUT_CPU(in0) + CHECK_INPUT_CPU(in1) + + auto nelem0 = in0.size(0); + auto nelem1 = in1.size(0); + auto dim = in0.size(1); + + AT_ASSERTM(dim == in1.size(1), "in0 and in1 have to be the same shape") + AT_ASSERTM(dim == 3, "dim hast to be 3") + AT_ASSERTM(in0.dim() == 2, "in0 has to be N0 x 3") + AT_ASSERTM(in1.dim() == 2, "in1 has to be N1 x 3") + + auto out = at::empty({nelem0}, torch::CPU(at::kLong)); + + AT_DISPATCH_FLOATING_TYPES(in0.scalar_type(), "nn", ([&] { + iterate_cpu( + NNFunctor(in0.data(), in1.data(), nelem0, nelem1, out.data()), + nelem0); + })); + + return out; +} + + +at::Tensor crosscheck_cpu(at::Tensor in0, at::Tensor in1) { + CHECK_INPUT_CPU(in0) + CHECK_INPUT_CPU(in1) + + AT_ASSERTM(in0.dim() == 1, "") + AT_ASSERTM(in1.dim() == 1, "") + + auto nelem0 = in0.size(0); + auto nelem1 = in1.size(0); + + auto out = at::empty({nelem0}, torch::CPU(at::kByte)); + + iterate_cpu( + CrossCheckFunctor(in0.data(), in1.data(), nelem0, nelem1, out.data()), + nelem0); + + return out; +} + + +at::Tensor proj_nn_cpu(at::Tensor xyz0, at::Tensor xyz1, at::Tensor K, int patch_size) { + CHECK_INPUT_CPU(xyz0) + CHECK_INPUT_CPU(xyz1) + CHECK_INPUT_CPU(K) + + auto batch_size = xyz0.size(0); + auto height = xyz0.size(1); + auto width = xyz0.size(2); + + AT_ASSERTM(xyz0.size(0) == xyz1.size(0), "") + AT_ASSERTM(xyz0.size(1) == xyz1.size(1), "") + AT_ASSERTM(xyz0.size(2) == xyz1.size(2), "") + AT_ASSERTM(xyz0.size(3) == xyz1.size(3), "") + AT_ASSERTM(xyz0.size(3) == 3, "") + AT_ASSERTM(xyz0.dim() == 4, "") + AT_ASSERTM(xyz1.dim() == 4, "") + + auto out = at::empty({batch_size, height, width}, torch::CPU(at::kLong)); + + AT_DISPATCH_FLOATING_TYPES(xyz0.scalar_type(), "proj_nn", ([&] { + iterate_cpu( + ProjNNFunctor(xyz0.data(), xyz1.data(), K.data(), batch_size, height, width, patch_size, out.data()), + batch_size * height * width); + })); + + return out; +} + + +at::Tensor xcorrvol_cpu(at::Tensor in0, at::Tensor in1, int n_disps, int block_size) { + CHECK_INPUT_CPU(in0) + CHECK_INPUT_CPU(in1) + + auto channels = in0.size(0); + auto height = in0.size(1); + auto width = in0.size(2); + + auto out = at::empty({n_disps, height, width}, in0.options()); + + AT_DISPATCH_FLOATING_TYPES(in0.scalar_type(), "xcorrvol", ([&] { + iterate_cpu( + XCorrVolFunctor(in0.data(), in1.data(), channels, height, width, n_disps, block_size, out.data()), + n_disps * height * width); + })); + + return out; +} + + + + +at::Tensor photometric_loss_forward(at::Tensor es, at::Tensor ta, int block_size, int type, float eps) { + CHECK_INPUT_CPU(es) + CHECK_INPUT_CPU(ta) + + auto batch_size = es.size(0); + auto channels = es.size(1); + auto height = es.size(2); + auto width = es.size(3); + + auto out = at::empty({batch_size, 1, height, width}, es.options()); + + AT_DISPATCH_FLOATING_TYPES(es.scalar_type(), "photometric_loss_forward_cpu", ([&] { + if(type == PHOTOMETRIC_LOSS_MSE) { + iterate_cpu( + PhotometricLossForward(es.data(), ta.data(), block_size, eps, batch_size, channels, height, width, out.data()), + out.numel()); + } + else if(type == PHOTOMETRIC_LOSS_SAD) { + iterate_cpu( + PhotometricLossForward(es.data(), ta.data(), block_size, eps, batch_size, channels, height, width, out.data()), + out.numel()); + } + else if(type == PHOTOMETRIC_LOSS_CENSUS_MSE) { + iterate_cpu( + PhotometricLossForward(es.data(), ta.data(), block_size, eps, batch_size, channels, height, width, out.data()), + out.numel()); + } + else if(type == PHOTOMETRIC_LOSS_CENSUS_SAD) { + iterate_cpu( + PhotometricLossForward(es.data(), ta.data(), block_size, eps, batch_size, channels, height, width, out.data()), + out.numel()); + } + })); + + return out; +} + +at::Tensor photometric_loss_backward(at::Tensor es, at::Tensor ta, at::Tensor grad_out, int block_size, int type, float eps) { + CHECK_INPUT_CPU(es) + CHECK_INPUT_CPU(ta) + CHECK_INPUT_CPU(grad_out) + + auto batch_size = es.size(0); + auto channels = es.size(1); + auto height = es.size(2); + auto width = es.size(3); + + CHECK_INPUT_CPU(ta) + auto grad_in = at::zeros({batch_size, channels, height, width}, grad_out.options()); + + AT_DISPATCH_FLOATING_TYPES(es.scalar_type(), "photometric_loss_backward_cpu", ([&] { + if(type == PHOTOMETRIC_LOSS_MSE) { + iterate_cpu( + PhotometricLossBackward(es.data(), ta.data(), grad_out.data(), block_size, eps, batch_size, channels, height, width, grad_in.data()), + grad_out.numel()); + } + else if(type == PHOTOMETRIC_LOSS_SAD) { + iterate_cpu( + PhotometricLossBackward(es.data(), ta.data(), grad_out.data(), block_size, eps, batch_size, channels, height, width, grad_in.data()), + grad_out.numel()); + } + else if(type == PHOTOMETRIC_LOSS_CENSUS_MSE) { + iterate_cpu( + PhotometricLossBackward(es.data(), ta.data(), grad_out.data(), block_size, eps, batch_size, channels, height, width, grad_in.data()), + grad_out.numel()); + } + else if(type == PHOTOMETRIC_LOSS_CENSUS_SAD) { + iterate_cpu( + PhotometricLossBackward(es.data(), ta.data(), grad_out.data(), block_size, eps, batch_size, channels, height, width, grad_in.data()), + grad_out.numel()); + } + })); + + return grad_in; +} + + + + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("nn_cpu", &nn_cpu, "nn_cpu"); + m.def("crosscheck_cpu", &crosscheck_cpu, "crosscheck_cpu"); + m.def("proj_nn_cpu", &proj_nn_cpu, "proj_nn_cpu"); + + m.def("xcorrvol_cpu", &xcorrvol_cpu, "xcorrvol_cpu"); + + m.def("photometric_loss_forward", &photometric_loss_forward); + m.def("photometric_loss_backward", &photometric_loss_backward); +} diff --git a/torchext/ext/ext_cuda.cpp b/torchext/ext/ext_cuda.cpp new file mode 100644 index 0000000..d98c9d6 --- /dev/null +++ b/torchext/ext/ext_cuda.cpp @@ -0,0 +1,135 @@ +#include + +#include + +#include "ext.h" + +void nn_kernel(at::Tensor in0, at::Tensor in1, at::Tensor out); + +at::Tensor nn_cuda(at::Tensor in0, at::Tensor in1) { + CHECK_INPUT_CUDA(in0) + CHECK_INPUT_CUDA(in1) + + auto nelem0 = in0.size(0); + auto dim = in0.size(1); + + AT_ASSERTM(dim == in1.size(1), "in0 and in1 have to be the same shape") + AT_ASSERTM(dim == 3, "dim hast to be 3") + AT_ASSERTM(in0.dim() == 2, "in0 has to be N0 x 3") + AT_ASSERTM(in1.dim() == 2, "in1 has to be N1 x 3") + + auto out = at::empty({nelem0}, torch::CUDA(at::kLong)); + + nn_kernel(in0, in1, out); + + return out; +} + + +void crosscheck_kernel(at::Tensor in0, at::Tensor in1, at::Tensor out); + +at::Tensor crosscheck_cuda(at::Tensor in0, at::Tensor in1) { + CHECK_INPUT_CUDA(in0) + CHECK_INPUT_CUDA(in1) + + AT_ASSERTM(in0.dim() == 1, "") + AT_ASSERTM(in1.dim() == 1, "") + + auto nelem0 = in0.size(0); + auto out = at::empty({nelem0}, torch::CUDA(at::kByte)); + crosscheck_kernel(in0, in1, out); + + return out; +} + +void proj_nn_kernel(at::Tensor xyz0, at::Tensor xyz1, at::Tensor K, int patch_size, at::Tensor out); + +at::Tensor proj_nn_cuda(at::Tensor xyz0, at::Tensor xyz1, at::Tensor K, int patch_size) { + CHECK_INPUT_CUDA(xyz0) + CHECK_INPUT_CUDA(xyz1) + CHECK_INPUT_CUDA(K) + + auto batch_size = xyz0.size(0); + auto height = xyz0.size(1); + auto width = xyz0.size(2); + + AT_ASSERTM(xyz0.size(0) == xyz1.size(0), "") + AT_ASSERTM(xyz0.size(1) == xyz1.size(1), "") + AT_ASSERTM(xyz0.size(2) == xyz1.size(2), "") + AT_ASSERTM(xyz0.size(3) == xyz1.size(3), "") + AT_ASSERTM(xyz0.size(3) == 3, "") + AT_ASSERTM(xyz0.dim() == 4, "") + AT_ASSERTM(xyz1.dim() == 4, "") + + auto out = at::empty({batch_size, height, width}, torch::CUDA(at::kLong)); + + proj_nn_kernel(xyz0, xyz1, K, patch_size, out); + + return out; +} + +void xcorrvol_kernel(at::Tensor in0, at::Tensor in1, int n_disps, int block_size, at::Tensor out); + +at::Tensor xcorrvol_cuda(at::Tensor in0, at::Tensor in1, int n_disps, int block_size) { + CHECK_INPUT_CUDA(in0) + CHECK_INPUT_CUDA(in1) + + // auto channels = in0.size(0); + auto height = in0.size(1); + auto width = in0.size(2); + + auto out = at::empty({n_disps, height, width}, in0.options()); + + xcorrvol_kernel(in0, in1, n_disps, block_size, out); + + return out; +} + + + +void photometric_loss_forward_kernel(at::Tensor es, at::Tensor ta, int block_size, int type, float eps, at::Tensor out); + +at::Tensor photometric_loss_forward(at::Tensor es, at::Tensor ta, int block_size, int type, float eps) { + CHECK_INPUT_CUDA(es) + CHECK_INPUT_CUDA(ta) + + auto batch_size = es.size(0); + auto height = es.size(2); + auto width = es.size(3); + + auto out = at::empty({batch_size, 1, height, width}, es.options()); + photometric_loss_forward_kernel(es, ta, block_size, type, eps, out); + + return out; +} + + +void photometric_loss_backward_kernel(at::Tensor es, at::Tensor ta, at::Tensor grad_out, int block_size, int type, float eps, at::Tensor grad_in); + +at::Tensor photometric_loss_backward(at::Tensor es, at::Tensor ta, at::Tensor grad_out, int block_size, int type, float eps) { + CHECK_INPUT_CUDA(es) + CHECK_INPUT_CUDA(ta) + CHECK_INPUT_CUDA(grad_out) + + auto batch_size = es.size(0); + auto channels = es.size(1); + auto height = es.size(2); + auto width = es.size(3); + + auto grad_in = at::zeros({batch_size, channels, height, width}, grad_out.options()); + photometric_loss_backward_kernel(es, ta, grad_out, block_size, type, eps, grad_in); + + return grad_in; +} + + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("nn_cuda", &nn_cuda, "nn_cuda"); + m.def("crosscheck_cuda", &crosscheck_cuda, "crosscheck_cuda"); + m.def("proj_nn_cuda", &proj_nn_cuda, "proj_nn_cuda"); + + m.def("xcorrvol_cuda", &xcorrvol_cuda, "xcorrvol_cuda"); + + m.def("photometric_loss_forward", &photometric_loss_forward); + m.def("photometric_loss_backward", &photometric_loss_backward); +} diff --git a/torchext/ext/ext_kernel.cu b/torchext/ext/ext_kernel.cu new file mode 100644 index 0000000..46df268 --- /dev/null +++ b/torchext/ext/ext_kernel.cu @@ -0,0 +1,112 @@ +#include + +#include "ext.h" +#include "common_cuda.h" + +void nn_kernel(at::Tensor in0, at::Tensor in1, at::Tensor out) { + auto nelem0 = in0.size(0); + auto nelem1 = in1.size(0); + auto dim = in0.size(1); + + AT_DISPATCH_FLOATING_TYPES(in0.scalar_type(), "nn", ([&] { + iterate_cuda( + NNFunctor(in0.data(), in1.data(), nelem0, nelem1, out.data()), + nelem0); + })); +} + + +void crosscheck_kernel(at::Tensor in0, at::Tensor in1, at::Tensor out) { + auto nelem0 = in0.size(0); + auto nelem1 = in1.size(0); + + iterate_cuda( + CrossCheckFunctor(in0.data(), in1.data(), nelem0, nelem1, out.data()), + nelem0); +} + +void proj_nn_kernel(at::Tensor xyz0, at::Tensor xyz1, at::Tensor K, int patch_size, at::Tensor out) { + auto batch_size = xyz0.size(0); + auto height = xyz0.size(1); + auto width = xyz0.size(2); + + AT_DISPATCH_FLOATING_TYPES(xyz0.scalar_type(), "proj_nn", ([&] { + iterate_cuda( + ProjNNFunctor(xyz0.data(), xyz1.data(), K.data(), batch_size, height, width, patch_size, out.data()), + batch_size * height * width); + })); +} + +void xcorrvol_kernel(at::Tensor in0, at::Tensor in1, int n_disps, int block_size, at::Tensor out) { + auto channels = in0.size(0); + auto height = in0.size(1); + auto width = in0.size(2); + + AT_DISPATCH_FLOATING_TYPES(in0.scalar_type(), "xcorrvol", ([&] { + iterate_cuda( + XCorrVolFunctor(in0.data(), in1.data(), channels, height, width, n_disps, block_size, out.data()), + n_disps * height * width, 512); + })); +} + + + +void photometric_loss_forward_kernel(at::Tensor es, at::Tensor ta, int block_size, int type, float eps, at::Tensor out) { + auto batch_size = es.size(0); + auto channels = es.size(1); + auto height = es.size(2); + auto width = es.size(3); + + AT_DISPATCH_FLOATING_TYPES(es.scalar_type(), "photometric_loss_forward_cuda", ([&] { + if(type == PHOTOMETRIC_LOSS_MSE) { + iterate_cuda( + PhotometricLossForward(es.data(), ta.data(), block_size, eps, batch_size, channels, height, width, out.data()), + out.numel()); + } + else if(type == PHOTOMETRIC_LOSS_SAD) { + iterate_cuda( + PhotometricLossForward(es.data(), ta.data(), block_size, eps, batch_size, channels, height, width, out.data()), + out.numel()); + } + else if(type == PHOTOMETRIC_LOSS_CENSUS_MSE) { + iterate_cuda( + PhotometricLossForward(es.data(), ta.data(), block_size, eps, batch_size, channels, height, width, out.data()), + out.numel()); + } + else if(type == PHOTOMETRIC_LOSS_CENSUS_SAD) { + iterate_cuda( + PhotometricLossForward(es.data(), ta.data(), block_size, eps, batch_size, channels, height, width, out.data()), + out.numel()); + } + })); +} + +void photometric_loss_backward_kernel(at::Tensor es, at::Tensor ta, at::Tensor grad_out, int block_size, int type, float eps, at::Tensor grad_in) { + auto batch_size = es.size(0); + auto channels = es.size(1); + auto height = es.size(2); + auto width = es.size(3); + + AT_DISPATCH_FLOATING_TYPES(es.scalar_type(), "photometric_loss_backward_cuda", ([&] { + if(type == PHOTOMETRIC_LOSS_MSE) { + iterate_cuda( + PhotometricLossBackward(es.data(), ta.data(), grad_out.data(), block_size, eps, batch_size, channels, height, width, grad_in.data()), + grad_out.numel()); + } + else if(type == PHOTOMETRIC_LOSS_SAD) { + iterate_cuda( + PhotometricLossBackward(es.data(), ta.data(), grad_out.data(), block_size, eps, batch_size, channels, height, width, grad_in.data()), + grad_out.numel()); + } + else if(type == PHOTOMETRIC_LOSS_CENSUS_MSE) { + iterate_cuda( + PhotometricLossBackward(es.data(), ta.data(), grad_out.data(), block_size, eps, batch_size, channels, height, width, grad_in.data()), + grad_out.numel()); + } + else if(type == PHOTOMETRIC_LOSS_CENSUS_SAD) { + iterate_cuda( + PhotometricLossBackward(es.data(), ta.data(), grad_out.data(), block_size, eps, batch_size, channels, height, width, grad_in.data()), + grad_out.numel()); + } + })); +} diff --git a/torchext/functions.py b/torchext/functions.py new file mode 100644 index 0000000..73dc7ae --- /dev/null +++ b/torchext/functions.py @@ -0,0 +1,147 @@ +import torch +from . import ext_cpu +from . import ext_cuda + +class NNFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, in0, in1): + args = (in0, in1) + if in0.is_cuda: + out = ext_cuda.nn_cuda(*args) + else: + out = ext_cpu.nn_cpu(*args) + return out + + @staticmethod + def backward(ctx, grad_out): + return None, None + +def nn(in0, in1): + return NNFunction.apply(in0, in1) + + +class CrossCheckFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, in0, in1): + args = (in0, in1) + if in0.is_cuda: + out = ext_cuda.crosscheck_cuda(*args) + else: + out = ext_cpu.crosscheck_cpu(*args) + return out + + @staticmethod + def backward(ctx, grad_out): + return None, None + +def crosscheck(in0, in1): + return CrossCheckFunction.apply(in0, in1) + +class ProjNNFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, xyz0, xyz1, K, patch_size): + args = (xyz0, xyz1, K, patch_size) + if xyz0.is_cuda: + out = ext_cuda.proj_nn_cuda(*args) + else: + out = ext_cpu.proj_nn_cpu(*args) + return out + + @staticmethod + def backward(ctx, grad_out): + return None, None, None, None + +def proj_nn(xyz0, xyz1, K, patch_size): + return ProjNNFunction.apply(xyz0, xyz1, K, patch_size) + + + +class XCorrVolFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, in0, in1, n_disps, block_size): + args = (in0, in1, n_disps, block_size) + if in0.is_cuda: + out = ext_cuda.xcorrvol_cuda(*args) + else: + out = ext_cpu.xcorrvol_cpu(*args) + return out + + @staticmethod + def backward(ctx, grad_out): + return None, None, None, None + +def xcorrvol(in0, in1, n_disps, block_size): + return XCorrVolFunction.apply(in0, in1, n_disps, block_size) + + + + +class PhotometricLossFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, es, ta, block_size, type, eps): + args = (es, ta, block_size, type, eps) + ctx.save_for_backward(es, ta) + ctx.block_size = block_size + ctx.type = type + ctx.eps = eps + if es.is_cuda: + out = ext_cuda.photometric_loss_forward(*args) + else: + out = ext_cpu.photometric_loss_forward(*args) + return out + + @staticmethod + def backward(ctx, grad_out): + es, ta = ctx.saved_tensors + block_size = ctx.block_size + type = ctx.type + eps = ctx.eps + args = (es, ta, grad_out.contiguous(), block_size, type, eps) + if grad_out.is_cuda: + grad_es = ext_cuda.photometric_loss_backward(*args) + else: + grad_es = ext_cpu.photometric_loss_backward(*args) + return grad_es, None, None, None, None + +def photometric_loss(es, ta, block_size, type='mse', eps=0.1): + type = type.lower() + if type == 'mse': + type = 0 + elif type == 'sad': + type = 1 + elif type == 'census_mse': + type = 2 + elif type == 'census_sad': + type = 3 + else: + raise Exception('invalid loss type') + return PhotometricLossFunction.apply(es, ta, block_size, type, eps) + +def photometric_loss_pytorch(es, ta, block_size, type='mse', eps=0.1): + type = type.lower() + p = block_size // 2 + es_pad = torch.nn.functional.pad(es, (p,p,p,p), mode='replicate') + ta_pad = torch.nn.functional.pad(ta, (p,p,p,p), mode='replicate') + es_uf = torch.nn.functional.unfold(es_pad, kernel_size=block_size) + ta_uf = torch.nn.functional.unfold(ta_pad, kernel_size=block_size) + es_uf = es_uf.view(es.shape[0], es.shape[1], -1, es.shape[2], es.shape[3]) + ta_uf = ta_uf.view(ta.shape[0], ta.shape[1], -1, ta.shape[2], ta.shape[3]) + if type == 'mse': + ref = (es_uf - ta_uf)**2 + elif type == 'sad': + ref = torch.abs(es_uf - ta_uf) + elif type == 'census_mse' or type == 'census_sad': + des = es_uf - es.unsqueeze(2) + dta = ta_uf - ta.unsqueeze(2) + h_des = 0.5 * (1 + des / torch.sqrt(des * des + eps)) + h_dta = 0.5 * (1 + dta / torch.sqrt(dta * dta + eps)) + diff = h_des - h_dta + if type == 'census_mse': + ref = diff * diff + elif type == 'census_sad': + ref = torch.abs(diff) + else: + raise Exception('invalid loss type') + ref = ref.view(es.shape[0], -1, es.shape[2], es.shape[3]) + ref = torch.sum(ref, dim=1, keepdim=True) / block_size**2 + return ref diff --git a/torchext/modules.py b/torchext/modules.py new file mode 100644 index 0000000..c7c1bfd --- /dev/null +++ b/torchext/modules.py @@ -0,0 +1,27 @@ +import torch +import math +import numpy as np + +from .functions import * + +class CoordConv2d(torch.nn.Module): + def __init__(self, channels_in, channels_out, kernel_size, stride, padding): + super().__init__() + + self.conv = torch.nn.Conv2d(channels_in+2, channels_out, kernel_size=kernel_size, padding=padding, stride=stride) + + self.uv = None + + def forward(self, x): + if self.uv is None: + height, width = x.shape[2], x.shape[3] + u, v = np.meshgrid(range(width), range(height)) + u = 2 * u / (width - 1) - 1 + v = 2 * v / (height - 1) - 1 + uv = np.stack((u, v)).reshape(1, 2, height, width) + self.uv = torch.from_numpy( uv.astype(np.float32) ) + self.uv = self.uv.to(x.device) + uv = self.uv.expand(x.shape[0], *self.uv.shape[1:]) + xuv = torch.cat((x, uv), dim=1) + y = self.conv(xuv) + return y diff --git a/torchext/setup.py b/torchext/setup.py new file mode 100644 index 0000000..bde6e37 --- /dev/null +++ b/torchext/setup.py @@ -0,0 +1,24 @@ +from setuptools import setup +from torch.utils.cpp_extension import CppExtension, CUDAExtension, BuildExtension +import os + +this_dir = os.path.dirname(os.path.realpath(__file__)) + +include_dirs = [ +] + +nvcc_args = [ + '-arch=sm_30', + '-gencode=arch=compute_30,code=sm_30', + '-gencode=arch=compute_35,code=sm_35', +] + +setup( + name='ext', + ext_modules=[ + CppExtension('ext_cpu', ['ext/ext_cpu.cpp']), + CUDAExtension('ext_cuda', ['ext/ext_cuda.cpp', 'ext/ext_kernel.cu'], extra_compile_args={'cxx': [], 'nvcc': nvcc_args}), + ], + cmdclass={'build_ext': BuildExtension}, + include_dirs=include_dirs +) diff --git a/torchext/worker.py b/torchext/worker.py new file mode 100644 index 0000000..a668f93 --- /dev/null +++ b/torchext/worker.py @@ -0,0 +1,528 @@ +import numpy as np +import torch +import random +import logging +import datetime +from pathlib import Path +import argparse +import subprocess +import socket +import sys +import os +import gc +import json +import matplotlib.pyplot as plt +import time +from collections import OrderedDict + + +class StopWatch(object): + def __init__(self): + self.timings = OrderedDict() + self.starts = {} + + def start(self, name): + self.starts[name] = time.time() + + def stop(self, name): + if name not in self.timings: + self.timings[name] = [] + self.timings[name].append(time.time() - self.starts[name]) + + def get(self, name=None, reduce=np.sum): + if name is not None: + return reduce(self.timings[name]) + else: + ret = {} + for k in self.timings: + ret[k] = reduce(self.timings[k]) + return ret + + def __repr__(self): + return ', '.join(['%s: %f[s]' % (k,v) for k,v in self.get().items()]) + def __str__(self): + return ', '.join(['%s: %f[s]' % (k,v) for k,v in self.get().items()]) + + +class ETA(object): + def __init__(self, length): + self.length = length + self.start_time = time.time() + self.current_idx = 0 + self.current_time = time.time() + + def update(self, idx): + self.current_idx = idx + self.current_time = time.time() + + def get_elapsed_time(self): + return self.current_time - self.start_time + + def get_item_time(self): + return self.get_elapsed_time() / (self.current_idx + 1) + + def get_remaining_time(self): + return self.get_item_time() * (self.length - self.current_idx + 1) + + def format_time(self, seconds): + minutes, seconds = divmod(seconds, 60) + hours, minutes = divmod(minutes, 60) + hours = int(hours) + minutes = int(minutes) + return f'{hours:02d}:{minutes:02d}:{seconds:05.2f}' + + def get_elapsed_time_str(self): + return self.format_time(self.get_elapsed_time()) + + def get_remaining_time_str(self): + return self.format_time(self.get_remaining_time()) + +class Worker(object): + def __init__(self, out_root, experiment_name, epochs=10, seed=42, train_batch_size=8, test_batch_size=16, num_workers=16, save_frequency=1, train_device='cuda:0', test_device='cuda:0', max_train_iter=-1): + self.out_root = Path(out_root) + self.experiment_name = experiment_name + self.epochs = epochs + self.seed = seed + self.train_batch_size = train_batch_size + self.test_batch_size = test_batch_size + self.num_workers = num_workers + self.save_frequency = save_frequency + self.train_device = train_device + self.test_device = test_device + self.max_train_iter = max_train_iter + + self.errs_list=[] + + self.setup_experiment() + + def setup_experiment(self): + self.exp_out_root = self.out_root / self.experiment_name + self.exp_out_root.mkdir(parents=True, exist_ok=True) + + if logging.root: del logging.root.handlers[:] + logging.basicConfig( + level=logging.INFO, + handlers=[ + logging.FileHandler( str(self.exp_out_root / 'train.log') ), + logging.StreamHandler() + ], + format='%(relativeCreated)d:%(levelname)s:%(process)d-%(processName)s: %(message)s' + ) + + logging.info('='*80) + logging.info(f'Start of experiment: {self.experiment_name}') + logging.info(socket.gethostname()) + self.log_datetime() + logging.info('='*80) + + self.metric_path = self.exp_out_root / 'metrics.json' + if self.metric_path.exists(): + with open(str(self.metric_path), 'r') as fp: + self.metric_data = json.load(fp) + else: + self.metric_data = {} + + self.init_seed() + + def metric_add_train(self, epoch, key, val): + epoch = str(epoch) + key = str(key) + if epoch not in self.metric_data: + self.metric_data[epoch] = {} + if 'train' not in self.metric_data[epoch]: + self.metric_data[epoch]['train'] = {} + self.metric_data[epoch]['train'][key] = val + + def metric_add_test(self, epoch, set_idx, key, val): + epoch = str(epoch) + set_idx = str(set_idx) + key = str(key) + if epoch not in self.metric_data: + self.metric_data[epoch] = {} + if 'test' not in self.metric_data[epoch]: + self.metric_data[epoch]['test'] = {} + if set_idx not in self.metric_data[epoch]['test']: + self.metric_data[epoch]['test'][set_idx] = {} + self.metric_data[epoch]['test'][set_idx][key] = val + + def metric_save(self): + with open(str(self.metric_path), 'w') as fp: + json.dump(self.metric_data, fp, indent=2) + + def init_seed(self, seed=None): + if seed is not None: + self.seed = seed + logging.info(f'Set seed to {self.seed}') + np.random.seed(self.seed) + random.seed(self.seed) + torch.manual_seed(self.seed) + torch.cuda.manual_seed(self.seed) + + def log_datetime(self): + logging.info(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')) + + def mem_report(self): + for obj in gc.get_objects(): + if torch.is_tensor(obj): + print(type(obj), obj.shape) + + def get_net_path(self, epoch, root=None): + if root is None: + root = self.exp_out_root + return root / f'net_{epoch:04d}.params' + + def get_do_parser_cmds(self): + return ['retrain', 'resume', 'retest', 'test_init'] + + def get_do_parser(self): + parser = argparse.ArgumentParser() + parser.add_argument('--cmd', type=str, default='resume', choices=self.get_do_parser_cmds()) + parser.add_argument('--epoch', type=int, default=-1) + return parser + + def do_cmd(self, args, net, optimizer, scheduler=None): + if args.cmd == 'retrain': + self.train(net, optimizer, resume=False, scheduler=scheduler) + elif args.cmd == 'resume': + self.train(net, optimizer, resume=True, scheduler=scheduler) + elif args.cmd == 'retest': + self.retest(net, epoch=args.epoch) + elif args.cmd == 'test_init': + test_sets = self.get_test_sets() + self.test(-1, net, test_sets) + else: + raise Exception('invalid cmd') + + def do(self, net, optimizer, load_net_optimizer=None, scheduler=None): + parser = self.get_do_parser() + args, _ = parser.parse_known_args() + + if load_net_optimizer is not None and args.cmd not in ['schedule']: + net, optimizer = load_net_optimizer() + + self.do_cmd(args, net, optimizer, scheduler=scheduler) + + def retest(self, net, epoch=-1): + if epoch < 0: + epochs = range(self.epochs) + else: + epochs = [epoch] + + test_sets = self.get_test_sets() + + for epoch in epochs: + net_path = self.get_net_path(epoch) + if net_path.exists(): + state_dict = torch.load(str(net_path)) + net.load_state_dict(state_dict) + self.test(epoch, net, test_sets) + + def format_err_str(self, errs, div=1): + err = sum(errs) + if len(errs) > 1: + err_str = f'{err/div:0.4f}=' + '+'.join([f'{e/div:0.4f}' for e in errs]) + else: + err_str = f'{err/div:0.4f}' + return err_str + + def write_err_img(self): + err_img_path = self.exp_out_root / 'errs.png' + fig = plt.figure(figsize=(16,16)) + lines=[] + for idx,errs in enumerate(self.errs_list): + line,=plt.plot(range(len(errs)), errs, label=f'error{idx}') + lines.append(line) + plt.tight_layout() + plt.legend(handles=lines) + plt.savefig(str(err_img_path)) + plt.close(fig) + + + def callback_train_new_epoch(self, epoch, net, optimizer): + pass + + def train(self, net, optimizer, resume=False, scheduler=None): + logging.info('='*80) + logging.info('Start training') + self.log_datetime() + logging.info('='*80) + + train_set = self.get_train_set() + test_sets = self.get_test_sets() + + net = net.to(self.train_device) + + epoch = 0 + min_err = {ts.name: 1e9 for ts in test_sets} + + state_path = self.exp_out_root / 'state.dict' + if resume and state_path.exists(): + logging.info('='*80) + logging.info(f'Loading state from {state_path}') + logging.info('='*80) + state = torch.load(str(state_path)) + epoch = state['epoch'] + 1 + if 'min_err' in state: + min_err = state['min_err'] + + curr_state = net.state_dict() + curr_state.update(state['state_dict']) + net.load_state_dict(curr_state) + + + try: + optimizer.load_state_dict(state['optimizer']) + except: + logging.info('Warning: cannot load optimizer from state_dict') + pass + if 'cpu_rng_state' in state: + torch.set_rng_state(state['cpu_rng_state']) + if 'gpu_rng_state' in state: + torch.cuda.set_rng_state(state['gpu_rng_state']) + + for epoch in range(epoch, self.epochs): + self.callback_train_new_epoch(epoch, net, optimizer) + + # train epoch + self.train_epoch(epoch, net, optimizer, train_set) + + # test epoch + errs = self.test(epoch, net, test_sets) + + if (epoch + 1) % self.save_frequency == 0: + net = net.to(self.train_device) + + # store state + state_dict = { + 'epoch': epoch, + 'min_err': min_err, + 'state_dict': net.state_dict(), + 'optimizer': optimizer.state_dict(), + 'cpu_rng_state': torch.get_rng_state(), + 'gpu_rng_state': torch.cuda.get_rng_state(), + } + logging.info(f'save state to {state_path}') + state_path = self.exp_out_root / 'state.dict' + torch.save(state_dict, str(state_path)) + + for test_set_name in errs: + err = sum(errs[test_set_name]) + if err < min_err[test_set_name]: + min_err[test_set_name] = err + state_path = self.exp_out_root / f'state_set{test_set_name}_best.dict' + logging.info(f'save state to {state_path}') + torch.save(state_dict, str(state_path)) + + # store network + net_path = self.get_net_path(epoch) + logging.info(f'save network to {net_path}') + torch.save(net.state_dict(), str(net_path)) + + if scheduler is not None: + scheduler.step() + + logging.info('='*80) + logging.info('Finished training') + self.log_datetime() + logging.info('='*80) + + def get_train_set(self): + # returns train_set + raise NotImplementedError() + + def get_test_sets(self): + # returns test_sets + raise NotImplementedError() + + def copy_data(self, data, device, requires_grad, train): + raise NotImplementedError() + + def net_forward(self, net, train): + raise NotImplementedError() + + def loss_forward(self, output, train): + raise NotImplementedError() + + def callback_train_post_backward(self, net, errs, output, epoch, batch_idx, masks): + # err = False + # for name, param in net.named_parameters(): + # if not torch.isfinite(param.grad).all(): + # print(name) + # err = True + # if err: + # import ipdb; ipdb.set_trace() + pass + + def callback_train_start(self, epoch): + pass + + def callback_train_stop(self, epoch, loss): + pass + + def train_epoch(self, epoch, net, optimizer, dset): + self.callback_train_start(epoch) + stopwatch = StopWatch() + + logging.info('='*80) + logging.info('Train epoch %d' % epoch) + + dset.current_epoch = epoch + train_loader = torch.utils.data.DataLoader(dset, batch_size=self.train_batch_size, shuffle=True, num_workers=self.num_workers, drop_last=True, pin_memory=False) + + net = net.to(self.train_device) + net.train() + + mean_loss = None + + n_batches = self.max_train_iter if self.max_train_iter > 0 else len(train_loader) + bar = ETA(length=n_batches) + + stopwatch.start('total') + stopwatch.start('data') + for batch_idx, data in enumerate(train_loader): + if self.max_train_iter > 0 and batch_idx > self.max_train_iter: break + self.copy_data(data, device=self.train_device, requires_grad=True, train=True) + stopwatch.stop('data') + + optimizer.zero_grad() + + stopwatch.start('forward') + output = self.net_forward(net, train=True) + if 'cuda' in self.train_device: torch.cuda.synchronize() + stopwatch.stop('forward') + + stopwatch.start('loss') + errs = self.loss_forward(output, train=True) + if isinstance(errs, dict): + masks = errs['masks'] + errs = errs['errs'] + else: + masks = [] + if not isinstance(errs, list) and not isinstance(errs, tuple): + errs = [errs] + err = sum(errs) + if 'cuda' in self.train_device: torch.cuda.synchronize() + stopwatch.stop('loss') + + stopwatch.start('backward') + err.backward() + self.callback_train_post_backward(net, errs, output, epoch, batch_idx, masks) + if 'cuda' in self.train_device: torch.cuda.synchronize() + stopwatch.stop('backward') + + stopwatch.start('optimizer') + optimizer.step() + if 'cuda' in self.train_device: torch.cuda.synchronize() + stopwatch.stop('optimizer') + + bar.update(batch_idx) + if (epoch <= 1 and batch_idx < 128) or batch_idx % 16 == 0: + err_str = self.format_err_str(errs) + logging.info(f'train e{epoch}: {batch_idx+1}/{len(train_loader)}: loss={err_str} | {bar.get_elapsed_time_str()} / {bar.get_remaining_time_str()}') + #self.write_err_img() + + + if mean_loss is None: + mean_loss = [0 for e in errs] + for erridx, err in enumerate(errs): + mean_loss[erridx] += err.item() + + stopwatch.start('data') + stopwatch.stop('total') + logging.info('timings: %s' % stopwatch) + + mean_loss = [l / len(train_loader) for l in mean_loss] + self.callback_train_stop(epoch, mean_loss) + self.metric_add_train(epoch, 'loss', mean_loss) + + # save metrics + self.metric_save() + + err_str = self.format_err_str(mean_loss) + logging.info(f'avg train_loss={err_str}') + return mean_loss + + def callback_test_start(self, epoch, set_idx): + pass + + def callback_test_add(self, epoch, set_idx, batch_idx, n_batches, output, masks): + pass + + def callback_test_stop(self, epoch, set_idx, loss): + pass + + def test(self, epoch, net, test_sets): + errs = {} + for test_set_idx, test_set in enumerate(test_sets): + if (epoch + 1) % test_set.test_frequency == 0: + logging.info('='*80) + logging.info(f'testing set {test_set.name}') + err = self.test_epoch(epoch, test_set_idx, net, test_set.dset) + errs[test_set.name] = err + return errs + + def test_epoch(self, epoch, set_idx, net, dset): + logging.info('-'*80) + logging.info('Test epoch %d' % epoch) + dset.current_epoch = epoch + test_loader = torch.utils.data.DataLoader(dset, batch_size=self.test_batch_size, shuffle=False, num_workers=self.num_workers, drop_last=False, pin_memory=False) + + net = net.to(self.test_device) + net.eval() + + with torch.no_grad(): + mean_loss = None + + self.callback_test_start(epoch, set_idx) + + bar = ETA(length=len(test_loader)) + stopwatch = StopWatch() + stopwatch.start('total') + stopwatch.start('data') + for batch_idx, data in enumerate(test_loader): + # if batch_idx == 10: break + self.copy_data(data, device=self.test_device, requires_grad=False, train=False) + stopwatch.stop('data') + + stopwatch.start('forward') + output = self.net_forward(net, train=False) + if 'cuda' in self.test_device: torch.cuda.synchronize() + stopwatch.stop('forward') + + stopwatch.start('loss') + errs = self.loss_forward(output, train=False) + if isinstance(errs, dict): + masks = errs['masks'] + errs = errs['errs'] + else: + masks = [] + if not isinstance(errs, list) and not isinstance(errs, tuple): + errs = [errs] + + bar.update(batch_idx) + if batch_idx % 25 == 0: + err_str = self.format_err_str(errs) + logging.info(f'test e{epoch}: {batch_idx+1}/{len(test_loader)}: loss={err_str} | {bar.get_elapsed_time_str()} / {bar.get_remaining_time_str()}') + + if mean_loss is None: + mean_loss = [0 for e in errs] + for erridx, err in enumerate(errs): + mean_loss[erridx] += err.item() + stopwatch.stop('loss') + + self.callback_test_add(epoch, set_idx, batch_idx, len(test_loader), output, masks) + + stopwatch.start('data') + stopwatch.stop('total') + logging.info('timings: %s' % stopwatch) + + mean_loss = [l / len(test_loader) for l in mean_loss] + self.callback_test_stop(epoch, set_idx, mean_loss) + self.metric_add_test(epoch, set_idx, 'loss', mean_loss) + + # save metrics + self.metric_save() + + err_str = self.format_err_str(mean_loss) + logging.info(f'test epoch {epoch}: avg test_loss={err_str}') + return mean_loss diff --git a/train_val.py b/train_val.py new file mode 100644 index 0000000..9c92610 --- /dev/null +++ b/train_val.py @@ -0,0 +1,29 @@ +import os +import torch +from model import exp_synph +from model import exp_synphge +from model import networks +from co.args import parse_args + + +# parse args +args = parse_args() + +# loss types +if args.loss=='ph': + worker = exp_synph.Worker(args) +elif args.loss=='phge': + worker = exp_synphge.Worker(args) + +# concatenation of original image and lcn image +channels_in=2 + +# set up network +net = networks.DispEdgeDecoders(channels_in=channels_in, max_disp=args.max_disp, imsizes=worker.imsizes, output_ms=worker.ms) + +# optimizer +optimizer = torch.optim.Adam(net.parameters(), lr=1e-4) + +# start the work +worker.do(net, optimizer) +

Raw output: lcn.c