add wandb, make compatible with ctd data

dataset.py

@@ -1,4 +1,5 @@
 import os
+import random
 import cv2
 import glob
 import numpy as np
@@ -48,8 +49,8 @@ class Augmentor:
 
     def __call__(self, left_img, right_img, left_disp):
         # 1. chromatic augmentation
-        left_img = self.chromatic_augmentation(left_img)
-        right_img = self.chromatic_augmentation(right_img)
+        # left_img = self.chromatic_augmentation(left_img)
+        # right_img = self.chromatic_augmentation(right_img)
 
         # 2. spatial augmentation
         # 2.1) rotate & vertical shift for right image
@@ -62,6 +63,7 @@ class Augmentor:
                 self.rng.uniform(0, right_img.shape[1]),
             )
             rot_mat = cv2.getRotationMatrix2D(image_center, ag, 1.0)
+            # right_img = right_img.transpose(2, 1, 0)
             right_img = cv2.warpAffine(
                 right_img, rot_mat, right_img.shape[1::-1], flags=cv2.INTER_LINEAR
             )
@@ -69,6 +71,7 @@ class Augmentor:
             right_img = cv2.warpAffine(
                 right_img, trans_mat, right_img.shape[1::-1], flags=cv2.INTER_LINEAR
             )
+            # right_img = right_img.transpose(1, 2, 0)
 
         # 2.2) random resize
         resize_scale = self.rng.uniform(self.scale_min, self.scale_max)
@@ -80,6 +83,10 @@ class Augmentor:
             fy=resize_scale,
             interpolation=cv2.INTER_LINEAR,
         )
+        if len(left_img.shape) == 2:
+            left_img.shape += 1,
+            # left_img = cv2.merge([left_img, left_img, left_img])
+ 
         right_img = cv2.resize(
             right_img,
             None,
@@ -87,6 +94,9 @@ class Augmentor:
             fy=resize_scale,
             interpolation=cv2.INTER_LINEAR,
         )
+        if len(left_img.shape) == 2:
+            left_img.shape += 1,
+            # left_img = cv2.merge([left_img, left_img, left_img])
 
         disp_mask = (left_disp < float(self.max_disp / resize_scale)) & (left_disp > 0)
         disp_mask = disp_mask.astype("float32")
@@ -110,7 +120,11 @@ class Augmentor:
         )
 
         # 2.3) random crop
-        h, w, c = left_img.shape
+        if len(left_img.shape) == 3:
+            h, w, c = left_img.shape
+        else:
+            h, w = left_img.shape
+            c = 1
         dx = w - self.image_width
         dy = h - self.image_height
         dy = self.rng.randint(min(0, dy), max(0, dy) + 1)
@@ -144,7 +158,7 @@ class Augmentor:
             (self.image_width, self.image_height),
             flags=cv2.INTER_LINEAR,
             borderValue=0,
-        )
+            )
 
         # 3. add random occlusion to right image
         if self.rng.binomial(1, 0.5):
@@ -156,6 +170,9 @@ class Augmentor:
                 np.mean(right_img, 0), 0
             )[np.newaxis, np.newaxis]
 
+        if len(left_img.shape) == 2:
+            left_img = cv2.merge([left_img, left_img, left_img])
+        
         return left_img, right_img, left_disp, disp_mask
 
 
@@ -197,7 +214,8 @@ class CREStereoDataset(Dataset):
         left_disp[left_disp == np.inf] = 0
 
         # augmentaion
-        left_img, right_img, left_disp, disp_mask = self.augmentor(
+        # left_img, right_img, left_disp, disp_mask = self.augmentor(
+        _, _, left_disp, disp_mask = self.augmentor(
             left_img, right_img, left_disp
         )
 
@@ -213,3 +231,96 @@ class CREStereoDataset(Dataset):
 
     def __len__(self):
         return len(self.imgs)
+
+
+class CTDDataset(Dataset):
+    def __init__(self, root, pattern_path: str, data_type: str = 'syn', augment=True, resize_pattern=True, blur=False):
+        super().__init__()
+        self.rng = np.random.RandomState(0)
+        self.augment = augment
+        self.blur = blur
+        self.imgs = glob.glob(os.path.join(root, f"{data_type}/*/im0_*.npy"), recursive=True)
+        self.pattern = cv2.imread(pattern_path)#, cv2.IMREAD_GRAYSCALE)
+
+        if resize_pattern and self.pattern.shape != (480, 640, 3):
+            # self.pattern = cv2.resize(self.pattern, (640, 480))
+            print(self.pattern.shape)
+            downsampled = cv2.pyrDown(self.pattern)
+            diff = (downsampled.shape[0] - 480) // 2
+            self.pattern = downsampled[diff:downsampled.shape[0]-diff, 0:downsampled.shape[1]]
+
+        self.augmentor = Augmentor(
+            image_height=480,
+            image_width=640,
+            max_disp=256,
+            scale_min=0.6,
+            scale_max=1.0,
+            seed=0,
+        )
+
+    def get_disp(self, path):
+        # disp = cv2.imread(path, cv2.IMREAD_UNCHANGED)
+        disp = np.load(path).transpose(1,2,0)
+        # return disp.astype(np.float32) / 32
+        return disp
+
+    def __getitem__(self, index):
+        # find path
+        left_path = self.imgs[index]
+        prefix = left_path[: left_path.rfind("_")]
+        # right_path = prefix + "_right.jpg"
+        left_disp_path = left_path.replace('im', 'disp')
+        # right_disp_path = prefix + "_right.disp.png"
+
+        # read img, disp
+        # left_img = cv2.imread(left_path, cv2.IMREAD_COLOR)
+        # right_img = cv2.imread(right_path, cv2.IMREAD_COLOR)
+        # left_img = np.load(left_path).transpose(1,2,0)
+        left_img = np.load(left_path)
+        # left_img = cv2.cvtColor(left_img, cv2.COLOR_GRAY2RGB)
+        # FIXME DO WE NEED THIS? OTHERWISE IT's PRETTY DARK
+        left_img = cv2.normalize(left_img, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
+        # left_img = cv2.merge([left_img, left_img, left_img]).reshape((3, 480, 640))
+        left_img = cv2.merge([left_img, left_img, left_img]).reshape((480, 640, 3))
+
+        right_img = self.pattern
+        # right_img = cv2.merge([right_img, right_img, right_img]).reshape((3, 480, 640))
+        left_disp = self.get_disp(left_disp_path)
+        # right_disp = self.get_disp(right_disp_path)
+
+        if False: # self.rng.binomial(1, 0.5):
+            left_img, right_img = np.fliplr(right_img), np.fliplr(left_img)
+            left_disp, right_disp = np.fliplr(right_disp), np.fliplr(left_disp)
+        left_disp[left_disp == np.inf] = 0
+
+        if self.blur:
+            kernel_size = random.sample([1,3,5,7,9], 1)[0]
+            kernel = (kernel_size, kernel_size)
+            left_img = cv2.GaussianBlur(left_img, kernel, 0)
+
+        # augmentation
+        if not self.augment:
+            _left_img, _right_img, _left_disp, disp_mask = self.augmentor(
+                left_img, right_img, left_disp
+            )
+        else:
+            left_img, right_img, left_disp, disp_mask = self.augmentor(
+                left_img, right_img, left_disp
+            )
+
+        # left_img = left_img.transpose(2, 0, 1).astype("uint8")
+        # left_img = left_img.transpose(2, 0, 1).astype("float32")
+        # left_img = left_img.astype("float32")
+        right_img = right_img.transpose(2, 0, 1).astype("uint8")
+        # right_img = right_img.astype("float32")
+        # print('post_augment', left_img)
+
+        return {
+            "left": left_img,
+            "right": right_img,
+            "disparity": left_disp,
+            "mask": disp_mask,
+        }
+
+    def __len__(self):
+        return len(self.imgs)

nets/crestereo.py

@@ -155,6 +155,8 @@ class CREStereo(nn.Module):
         flow = None
         flow_up = None
         if flow_init is not None:
+            if isinstance(flow_init, list):
+                flow_init = flow_init[0]
             scale = fmap1.shape[2] / flow_init.shape[2]
             flow = -scale * F.interpolate(
                 flow_init,

test_model.py

@@ -1,12 +1,22 @@
 import torch
+import torch.nn as nn
 import torch.nn.functional as F
 import numpy as np
 import cv2
-from imread_from_url import imread_from_url
 
 from nets import Model
 
+import wandb
+
+import random
+from torch.utils.data import DataLoader
+from dataset import CTDDataset
+from train import normalize_and_colormap, parse_yaml, inference as ctd_inference
+
 device = 'cuda'
+wandb.init(project="crestereo", entity="cpt-captain")
+
+
 
 #Ref: https://github.com/megvii-research/CREStereo/blob/master/test.py
 def inference(left, right, model, n_iter=20):
@@ -41,39 +51,198 @@ def inference(left, right, model, n_iter=20):
 
 	return pred_disp
 
+
+def inference_ctd(left, right, gt_disp, mask, model, epoch, n_iter=20):
+
+    print("Model Forwarding...")
+    # print(left.shape)
+    left = left.cpu().detach().numpy()
+    imgL = left
+    imgR = right.cpu().detach().numpy()
+    imgL = np.ascontiguousarray(imgL[None, :, :, :])
+    imgR = np.ascontiguousarray(imgR[None, :, :, :])
+    
+    # chosen for convenience
+    device = torch.device('cuda:0')
+
+    imgL = torch.tensor(imgL.astype("float32")).to(device)
+    imgR = torch.tensor(imgR.astype("float32")).to(device)
+    imgL = imgL.transpose(2,3).transpose(1,2)
+
+    imgL_dw2 = F.interpolate(
+            imgL,
+            size=(imgL.shape[2] // 2, imgL.shape[3] // 2),
+            mode="bilinear",
+            align_corners=True,
+            )
+    imgR_dw2 = F.interpolate(
+            imgR,
+            size=(imgL.shape[2] // 2, imgL.shape[3] // 2),
+            mode="bilinear",
+            align_corners=True,
+            )
+    with torch.inference_mode():
+        pred_flow_dw2 = model(image1=imgL_dw2, image2=imgR_dw2, iters=n_iter, flow_init=None)
+        pred_flow = model(imgL, imgR, iters=n_iter, flow_init=pred_flow_dw2)
+
+    log = {}
+    for i, (pf, pf_dw2) in enumerate(zip(pred_flow, pred_flow_dw2)):
+        pred_disp = torch.squeeze(pf[:, 0, :, :]).cpu().detach().numpy()
+        pred_disp_dw2 = torch.squeeze(pf_dw2[:, 0, :, :]).cpu().detach().numpy()
+        
+        pred_disp_norm = cv2.normalize(pred_disp, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
+        pred_disp_dw2_norm = cv2.normalize(pred_disp_dw2, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
+
+        log[f'pred_{i}'] = wandb.Image(
+                np.array([pred_disp.reshape(480, 640)]), 
+                caption=f"Pred. Disp. It {i}\n{pred_disp.min():.{2}f}/{pred_disp.max():.{2}f}",
+                )
+        log[f'pred_norm_{i}'] = wandb.Image(
+                np.array([pred_disp_norm.reshape(480, 640)]), 
+                caption=f"Pred. Disp. It {i}\n{pred_disp.min():.{2}f}/{pred_disp.max():.{2}f}",
+                )
+
+        log[f'pred_dw2_{i}'] = wandb.Image(
+                np.array([pred_disp_dw2.reshape(240, 320)]), 
+                caption=f"Pred. Disp. Dw2 It {i}\n{pred_disp_dw2.min():.{2}f}/{pred_disp_dw2.max():.{2}f}",
+                )
+        log[f'pred_dw2_norm_{i}'] = wandb.Image(
+                np.array([pred_disp_dw2_norm.reshape(240, 320)]), 
+                caption=f"Pred. Disp. Dw2 It {i}\n{pred_disp_dw2.min():.{2}f}/{pred_disp_dw2.max():.{2}f}",
+                )
+
+
+    log['input_left'] = wandb.Image(left.astype('uint8'), caption="Input Left")
+    log['input_right'] = wandb.Image(right.cpu().detach().numpy().transpose(1,2,0).astype('uint8'), caption="Input Right")
+
+    log['gt_disp'] = wandb.Image(gt_disp, caption=f"GT Disparity\n{gt_disp.min():.{2}f}/{gt_disp.max():.{2}f}")
+
+
+    disp_error = gt_disp - disp
+    log['disp_error'] = wandb.Image(
+            normalize_and_colormap(disp_error),
+            caption=f"Disp. Error\n{disp_error.min():.{2}f}/{disp_error.max():.{2}f}\n{disp_error.mean():.{2}f}",
+            )
+
+    wandb.log(log)
+
+
+def do_infer(left_img, right_img, gt_disp, model):
+        in_h, in_w = left_img.shape[:2]
+
+        # Resize image in case the GPU memory overflows
+        eval_h, eval_w = (in_h,in_w)
+
+        # FIXME borked for some reason, hopefully not very important
+        
+        imgL = left_img.cpu().detach().numpy() if isinstance(left_img, torch.Tensor) else left_img
+        imgR = right_img.cpu().detach().numpy() if isinstance(right_img, torch.Tensor) else right_img
+
+        imgL = cv2.resize(imgL, (eval_w, eval_h), interpolation=cv2.INTER_LINEAR)
+        imgR = cv2.resize(imgR, (eval_w, eval_h), interpolation=cv2.INTER_LINEAR)
+
+        # pred = ctd_inference(imgL, imgR, gt_disp, None, model, None, n_iter=20)
+        pred = ctd_inference(left_img, right_img, gt_disp, None, model, None, n_iter=20, wandb_log=False)
+
+        t = float(in_w) / float(eval_w)
+        disp = cv2.resize(pred, (in_w, in_h), interpolation=cv2.INTER_LINEAR) * t
+
+        disp_vis = normalize_and_colormap(disp)
+        gt_disp_vis = normalize_and_colormap(gt_disp)
+        if gt_disp.shape != disp.shape:
+            gt_disp = gt_disp.reshape(disp.shape)
+        disp_err = gt_disp - disp
+        disp_err = normalize_and_colormap(disp_err.abs())
+
+        wandb.log({
+            'disp_vis': wandb.Image(
+                disp_vis,
+                caption=f"Pred. Disparity \n{disp.min():.{2}f}/{disp.max():.{2}f}",
+                ),
+            'gt_disp_vis': wandb.Image(
+                gt_disp_vis,
+                caption=f"GT Disparity \n{gt_disp.min():.{2}f}/{gt_disp.max():.{2}f}",
+                ),
+            'disp_err': wandb.Image(
+                disp_err,
+                caption=f"Disparity Error\n{disp_err.min():.{2}f}/{disp_err.max():.{2}f}",
+                ),
+            'input_left': wandb.Image(
+                left_img.cpu().detach().numpy().astype('uint8'),
+                caption=f"Input left",
+                ),
+            'input_right': wandb.Image(
+                right_img.cpu().detach().numpy().astype('uint8'),
+                caption=f"Input right",
+                ),
+            })
+
+
+
 if __name__ == '__main__':
+        # model_path = "models/crestereo_eth3d.pth"
+        model_path = "train_log/models/latest.pth"
 
-	left_img = imread_from_url("https://raw.githubusercontent.com/megvii-research/CREStereo/master/img/test/left.png")
-	right_img = imread_from_url("https://raw.githubusercontent.com/megvii-research/CREStereo/master/img/test/right.png")
+        # reference_pattern_path = '/home/nils/kinect_reference_high_res_scaled_down.png'
+        reference_pattern_path = '/home/nils/kinect_reference_cropped.png'
+        # reference_pattern_path = '/home/nils/new_reference.png'
+        # reference_pattern_path = '/home/nils/kinect_reference_high_res.png'
+        # reference_pattern_path = '/home/nils/orig_ctd/connecting_the_dots/data/kinect_pattern.png'
 
-	in_h, in_w = left_img.shape[:2]
+        data_type = 'kinect'
+        augment = False
 
-	# Resize image in case the GPU memory overflows
-	eval_h, eval_w = (in_h,in_w)
-	imgL = cv2.resize(left_img, (eval_w, eval_h), interpolation=cv2.INTER_LINEAR)
-	imgR = cv2.resize(right_img, (eval_w, eval_h), interpolation=cv2.INTER_LINEAR)
+        args = parse_yaml("cfgs/train.yaml")
 
-	model_path = "models/crestereo_eth3d.pth"
+        wandb.config.update({'model_path': model_path, 'reference_pattern': reference_pattern_path, 'augment': augment})
 
-	model = Model(max_disp=256, mixed_precision=False, test_mode=True)
-	model.load_state_dict(torch.load(model_path), strict=True)
-	model.to(device)
-	model.eval()
+        model = Model(max_disp=256, mixed_precision=False, test_mode=True)
+        model = nn.DataParallel(model,device_ids=[device])
+        # model.load_state_dict(torch.load(model_path), strict=False)
+        state_dict = torch.load(model_path)['state_dict']
+        model.load_state_dict(state_dict, strict=True)
+        model.to(device)
+        model.eval()
 
-	pred = inference(imgL, imgR, model, n_iter=20)
+        CTD = True
+        if not CTD:
+            left_img = cv2.imread("../test_imgs/left.png")
+            right_img = cv2.imread("../test_imgs/right.png")
+            in_h, in_w = left_img.shape[:2]
 
-	t = float(in_w) / float(eval_w)
-	disp = cv2.resize(pred, (in_w, in_h), interpolation=cv2.INTER_LINEAR) * t
+            # Resize image in case the GPU memory overflows
+            eval_h, eval_w = (in_h,in_w)
 
-	disp_vis = (disp - disp.min()) / (disp.max() - disp.min()) * 255.0
-	disp_vis = disp_vis.astype("uint8")
-	disp_vis = cv2.applyColorMap(disp_vis, cv2.COLORMAP_INFERNO)
+            # FIXME borked for some reason, hopefully not very important
+            imgL = cv2.resize(left_img, (eval_w, eval_h), interpolation=cv2.INTER_LINEAR)
+            imgR = cv2.resize(right_img, (eval_w, eval_h), interpolation=cv2.INTER_LINEAR)
 
-	combined_img = np.hstack((left_img, disp_vis))
-	cv2.namedWindow("output", cv2.WINDOW_NORMAL)
-	cv2.imshow("output", combined_img)
-	cv2.imwrite("output.jpg", disp_vis)
-	cv2.waitKey(0)
+            pred = inference(imgL, imgR, model, n_iter=20)
 
+            t = float(in_w) / float(eval_w)
+            disp = cv2.resize(pred, (in_w, in_h), interpolation=cv2.INTER_LINEAR) * t
 
+            disp_vis = (disp - disp.min()) / (disp.max() - disp.min()) * 255.0
+            disp_vis = disp_vis.astype("uint8")
+            disp_vis = cv2.applyColorMap(disp_vis, cv2.COLORMAP_INFERNO)
+
+            combined_img = np.hstack((left_img, disp_vis))
+            # cv2.namedWindow("output", cv2.WINDOW_NORMAL)
+            # cv2.imshow("output", combined_img)
+            cv2.imwrite("output.jpg", disp_vis)
+            # cv2.waitKey(0)
+
+        else:
+            dataset = CTDDataset('/media/Data1/connecting_the_dots_data/ctd_data/', data_type=data_type, pattern_path=reference_pattern_path, augment=augment)
+            dataloader = DataLoader(dataset, args.batch_size, shuffle=True,
+                                    num_workers=0, drop_last=False, persistent_workers=False, pin_memory=True)
+            for batch in dataloader:
+                for left, right, disparity in zip(batch['left'], batch['right'], batch['disparity']): 
+                    right = right.transpose(0, 2).transpose(0, 1)
+                    left_img = left
+                    imgL = left.cpu().detach().numpy()
+                    right_img = right
+                    imgR = right.cpu().detach().numpy()
+                    gt_disp = disparity
+                    do_infer(left_img, right_img, gt_disp, model)
 

train.py

@@ -5,16 +5,131 @@ import logging
 from collections import namedtuple
 
 import yaml
-from tensorboardX import SummaryWriter
+# from tensorboardX import SummaryWriter
 
 from nets import Model
-from dataset import CREStereoDataset
+# from dataset import CREStereoDataset
+from dataset import CREStereoDataset, CTDDataset
 
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 import torch.optim as optim
 from torch.utils.data import DataLoader
 
+import wandb
+
+import numpy as np
+import cv2
+
+
+def normalize_and_colormap(img):
+    ret = (img - img.min()) / (img.max() - img.min()) * 255.0
+    if isinstance(ret, torch.Tensor):
+        ret = ret.cpu().detach().numpy()
+    ret = ret.astype("uint8")
+    ret = cv2.applyColorMap(ret, cv2.COLORMAP_INFERNO)
+    return ret
+
+
+def inference(left, right, gt_disp, mask, model, epoch, n_iter=20, wandb_log=True):
+
+    print("Model Forwarding...")
+    left = left.cpu().detach().numpy()
+    imgL = left
+    imgR = right.cpu().detach().numpy()
+    imgL = np.ascontiguousarray(imgL[None, :, :, :])
+    imgR = np.ascontiguousarray(imgR[None, :, :, :])
+    
+    # chosen for convenience
+    device = torch.device('cuda:0')
+
+    imgL = torch.tensor(imgL.astype("float32")).to(device)
+    imgR = torch.tensor(imgR.astype("float32")).to(device)
+    imgL = imgL.transpose(2,3).transpose(1,2)
+    if imgL.shape != imgR.shape:
+        imgR = imgR.transpose(2,3).transpose(1,2)
+
+    imgL_dw2 = F.interpolate(
+            imgL,
+            size=(imgL.shape[2] // 2, imgL.shape[3] // 2),
+            mode="bilinear",
+            align_corners=True,
+            )
+    imgR_dw2 = F.interpolate(
+            imgR,
+            size=(imgL.shape[2] // 2, imgL.shape[3] // 2),
+            mode="bilinear",
+            align_corners=True,
+            )
+    with torch.inference_mode():
+        pred_flow_dw2 = model(image1=imgL_dw2, image2=imgR_dw2, iters=n_iter, flow_init=None)
+        pred_flow = model(imgL, imgR, iters=n_iter, flow_init=pred_flow_dw2)
+
+
+    if not wandb_log:
+        return torch.squeeze(pred_flow[:, 0, :, :]).cpu().detach().numpy()
+
+    log = {}
+    in_h, in_w = left.shape[:2]
+
+    # Resize image in case the GPU memory overflows
+    eval_h, eval_w = (in_h,in_w)
+
+    for i, (pf, pf_dw2) in enumerate(zip(pred_flow, pred_flow_dw2)):
+        pred_disp = torch.squeeze(pf[:, 0, :, :]).cpu().detach().numpy()
+        pred_disp_dw2 = torch.squeeze(pf_dw2[:, 0, :, :]).cpu().detach().numpy()
+        
+        pred_disp_norm = cv2.normalize(pred_disp, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
+        pred_disp_dw2_norm = cv2.normalize(pred_disp_dw2, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
+
+        if i == n_iter-1:
+            t = float(in_w) / float(eval_w)
+            disp = cv2.resize(pred_disp, (in_w, in_h), interpolation=cv2.INTER_LINEAR) * t
+
+            log[f'disp_vis'] = wandb.Image(
+                    normalize_and_colormap(disp),
+                    caption=f"Disparity \n{pred_disp.min():.{2}f}/{pred_disp.max():.{2}f}",
+                    )
+
+        log[f'pred_{i}'] = wandb.Image(
+                np.array([pred_disp.reshape(480, 640)]), 
+                caption=f"Pred. Disp. It {i}\n{pred_disp.min():.{2}f}/{pred_disp.max():.{2}f}",
+                )
+        log[f'pred_norm_{i}'] = wandb.Image(
+                np.array([pred_disp_norm.reshape(480, 640)]), 
+                caption=f"Pred. Disp. It {i}\n{pred_disp.min():.{2}f}/{pred_disp.max():.{2}f}",
+                )
+
+        log[f'pred_dw2_{i}'] = wandb.Image(
+                np.array([pred_disp_dw2.reshape(240, 320)]), 
+                caption=f"Pred. Disp. Dw2 It {i}\n{pred_disp_dw2.min():.{2}f}/{pred_disp_dw2.max():.{2}f}",
+                )
+        log[f'pred_dw2_norm_{i}'] = wandb.Image(
+                np.array([pred_disp_dw2_norm.reshape(240, 320)]), 
+                caption=f"Pred. Disp. Dw2 It {i}\n{pred_disp_dw2.min():.{2}f}/{pred_disp_dw2.max():.{2}f}",
+                )
+
+
+    log['input_left'] = wandb.Image(left.astype('uint8'), caption="Input Left")
+    log['input_right'] = wandb.Image(right.cpu().detach().numpy().transpose(1,2,0).astype('uint8'), caption="Input Right")
+
+    log['gt_disp'] = wandb.Image(gt_disp, caption=f"GT Disparity\n{gt_disp.min():.{2}f}/{gt_disp.max():.{2}f}")
+
+    disp_error = gt_disp - disp
+    log['disp_error'] = wandb.Image(
+            normalize_and_colormap(disp_error.abs()),
+            caption=f"Disp. Error\n{disp_error.min():.{2}f}/{disp_error.max():.{2}f}\n{disp_error.abs().mean():.{2}f}",
+            )
+
+
+    log[f'gt_disp_vis'] = wandb.Image(
+        normalize_and_colormap(gt_disp),
+        caption=f"GT Disp Vis \n{gt_disp.min():.{2}f}/{gt_disp.max():.{2}f}",
+        )
+
+    wandb.log(log)
+
 
 def parse_yaml(file_path: str) -> namedtuple:
     """Parse yaml configuration file and return the object in `namedtuple`."""
@@ -65,6 +180,10 @@ def sequence_loss(flow_preds, flow_gt, valid, gamma=0.8):
     '''
     n_predictions = len(flow_preds)
     flow_loss = 0.0
+    
+    # TEST
+    flow_gt = torch.squeeze(flow_gt, dim=-1)
+
     for i in range(n_predictions):
         i_weight = gamma ** (n_predictions - i - 1)
         i_loss = torch.abs(flow_preds[i] - flow_gt)
@@ -93,7 +212,9 @@ def main(args):
     optimizer = optim.Adam(model.parameters(), lr=0.1, betas=(0.9, 0.999))
     # model = nn.DataParallel(model,device_ids=[0])
 
-    tb_log = SummaryWriter(os.path.join(args.log_dir, "train.events"))
+    # tb_log = SummaryWriter(os.path.join(args.log_dir, "train.events"))
+    wandb.watch(model)
+    metrics = {}
 
     # worklog
     logging.basicConfig(level=eval(args.log_level))
@@ -138,8 +259,12 @@ def main(args):
         start_epoch_idx = 1
         start_iters = 0
 
+    # pattern_path = '/home/nils/kinect_reference_high_res_scaled_down.png'
+    pattern_path = '/home/nils/kinect_reference_cropped.png'
+    # pattern_path = '/home/nils/orig_ctd/connecting_the_dots/data/kinect_pattern.png'
     # datasets
-    dataset = CREStereoDataset(args.training_data_path)
+    # dataset = CREStereoDataset(args.training_data_path)
+    dataset = CTDDataset(args.training_data_path, pattern_path=pattern_path)
     # if rank == 0:
     worklog.info(f"Dataset size: {len(dataset)}")
     dataloader = DataLoader(dataset, args.batch_size, shuffle=True,
@@ -183,6 +308,9 @@ def main(args):
             gt_flow = torch.cat([gt_disp, gt_disp * 0], dim=1)  # [2, 2, 384, 512]
 
             # forward
+            # left = left.transpose(1, 2).transpose(2, 3)
+            left = left.transpose(1, 3).transpose(2, 3)
+            right = right.transpose(2, 3).transpose(2, 3)#.transpose(1, 2)
             flow_predictions = model(left.cuda(), right.cuda())
 
             # loss & backword
@@ -190,6 +318,16 @@ def main(args):
                 flow_predictions, gt_flow, valid_mask, gamma=0.8
             )
 
+            if batch_idx % 128 == 0: 
+                inference(
+                    mini_batch_data['left'][0],
+                    mini_batch_data['right'][0],
+                    mini_batch_data['disparity'][0],
+                    mini_batch_data['mask'][0],
+                    model,
+                    batch_idx,
+                )
+
             # loss stats
             loss_item = loss.data.item()
             epoch_total_train_loss += loss_item
@@ -234,20 +372,25 @@ def main(args):
                 worklog.info("".join(info))
 
                 # minibatch loss
-                tb_log.add_scalar("train/loss_batch", loss_item, cur_iters)
-                tb_log.add_scalar(
-                    "train/lr", optimizer.param_groups[0]["lr"], cur_iters
-                )
-                tb_log.flush()
+                # tb_log.add_scalar("train/loss_batch", loss_item, cur_iters)
+                metrics['train/loss_batch'] = loss_item
+                # tb_log.add_scalar(
+                    # "train/lr", optimizer.param_groups[0]["lr"], cur_iters
+                # )
+                metrics['train/lr'] = optimizer.param_groups[0]["lr"]
+                # tb_log.flush()
+                wandb.log(metrics)
 
             t1 = time.perf_counter()
 
-        tb_log.add_scalar(
-            "train/loss",
-            epoch_total_train_loss / args.minibatch_per_epoch,
-            epoch_idx,
-        )
-        tb_log.flush()
+        # tb_log.add_scalar(
+            # "train/loss",
+            # epoch_total_train_loss / args.minibatch_per_epoch,
+            # epoch_idx,
+        # )
+        metrics['train/loss'] = epoch_total_train_loss / args.minibatch_per_epoch
+        # tb_log.flush()
+        wandb.log(metrics)
 
         # save model params
         ckp_data = {
@@ -271,4 +414,6 @@ def main(args):
 if __name__ == "__main__":
     # train configuration
     args = parse_yaml("cfgs/train.yaml")
+    wandb.init(project="crestereo", entity="cpt-captain")
+    wandb.config.update(args._asdict())
     main(args)