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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from .update import BasicUpdateBlock
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from .extractor import BasicEncoder
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from .corr import AGCL
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from .attention import PositionEncodingSine, LocalFeatureTransformer
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try:
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autocast = torch.cuda.amp.autocast
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except:
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# dummy autocast for PyTorch < 1.6
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class autocast:
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def __init__(self, enabled):
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pass
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def __enter__(self):
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pass
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def __exit__(self, *args):
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pass
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#Ref: https://github.com/princeton-vl/RAFT/blob/master/core/raft.py
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class CREStereo(nn.Module):
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def __init__(self, max_disp=192, mixed_precision=False, test_mode=False):
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super(CREStereo, self).__init__()
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self.max_flow = max_disp
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self.mixed_precision = mixed_precision
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self.test_mode = test_mode
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self.hidden_dim = 128
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self.context_dim = 128
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self.dropout = 0
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self.fnet = BasicEncoder(output_dim=256, norm_fn='instance', dropout=self.dropout)
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self.update_block = BasicUpdateBlock(hidden_dim=self.hidden_dim, cor_planes=4 * 9, mask_size=4)
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# loftr
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self.self_att_fn = LocalFeatureTransformer(
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d_model=256, nhead=8, layer_names=["self"] * 1, attention="linear"
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)
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self.cross_att_fn = LocalFeatureTransformer(
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d_model=256, nhead=8, layer_names=["cross"] * 1, attention="linear"
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)
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# adaptive search
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self.search_num = 9
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self.conv_offset_16 = nn.Conv2d(
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256, self.search_num * 2, kernel_size=3, stride=1, padding=1
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)
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self.conv_offset_8 = nn.Conv2d(
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256, self.search_num * 2, kernel_size=3, stride=1, padding=1
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)
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self.range_16 = 1
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self.range_8 = 1
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def freeze_bn(self):
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for m in self.modules():
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if isinstance(m, nn.BatchNorm2d):
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m.eval()
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def convex_upsample(self, flow, mask, rate=4):
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""" Upsample flow field [H/8, W/8, 2] -> [H, W, 2] using convex combination """
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N, _, H, W = flow.shape
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# print(flow.shape, mask.shape, rate)
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mask = mask.view(N, 1, 9, rate, rate, H, W)
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mask = torch.softmax(mask, dim=2)
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up_flow = F.unfold(rate * flow, [3,3], padding=1)
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up_flow = up_flow.view(N, 2, 9, 1, 1, H, W)
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up_flow = torch.sum(mask * up_flow, dim=2)
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up_flow = up_flow.permute(0, 1, 4, 2, 5, 3)
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return up_flow.reshape(N, 2, rate*H, rate*W)
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def zero_init(self, fmap):
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N, C, H, W = fmap.shape
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_x = torch.zeros([N, 1, H, W], dtype=torch.float32)
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_y = torch.zeros([N, 1, H, W], dtype=torch.float32)
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zero_flow = torch.cat((_x, _y), dim=1).to(fmap.device)
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return zero_flow
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def forward(self, image1, image2, flow_init=None, iters=10, upsample=True, test_mode=False):
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""" Estimate optical flow between pair of frames """
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image1 = 2 * (image1 / 255.0) - 1.0
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image2 = 2 * (image2 / 255.0) - 1.0
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image1 = image1.contiguous()
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image2 = image2.contiguous()
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hdim = self.hidden_dim
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cdim = self.context_dim
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# run the feature network
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with autocast(enabled=self.mixed_precision):
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fmap1, fmap2 = self.fnet([image1, image2])
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fmap1 = fmap1.float()
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fmap2 = fmap2.float()
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with autocast(enabled=self.mixed_precision):
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# 1/4 -> 1/8
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# feature
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fmap1_dw8 = F.avg_pool2d(fmap1, 2, stride=2)
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fmap2_dw8 = F.avg_pool2d(fmap2, 2, stride=2)
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# offset
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offset_dw8 = self.conv_offset_8(fmap1_dw8)
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offset_dw8 = self.range_8 * (torch.sigmoid(offset_dw8) - 0.5) * 2.0
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# context
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net, inp = torch.split(fmap1, [hdim,hdim], dim=1)
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net = torch.tanh(net)
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inp = F.relu(inp)
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net_dw8 = F.avg_pool2d(net, 2, stride=2)
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inp_dw8 = F.avg_pool2d(inp, 2, stride=2)
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# 1/4 -> 1/16
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# feature
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fmap1_dw16 = F.avg_pool2d(fmap1, 4, stride=4)
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fmap2_dw16 = F.avg_pool2d(fmap2, 4, stride=4)
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offset_dw16 = self.conv_offset_16(fmap1_dw16)
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offset_dw16 = self.range_16 * (torch.sigmoid(offset_dw16) - 0.5) * 2.0
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# context
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net_dw16 = F.avg_pool2d(net, 4, stride=4)
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inp_dw16 = F.avg_pool2d(inp, 4, stride=4)
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# positional encoding and self-attention
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pos_encoding_fn_small = PositionEncodingSine(
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d_model=256, max_shape=(image1.shape[2] // 16, image1.shape[3] // 16)
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)
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# 'n c h w -> n (h w) c'
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x_tmp = pos_encoding_fn_small(fmap1_dw16)
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fmap1_dw16 = x_tmp.permute(0, 2, 3, 1).reshape(x_tmp.shape[0], x_tmp.shape[2] * x_tmp.shape[3], x_tmp.shape[1])
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# 'n c h w -> n (h w) c'
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x_tmp = pos_encoding_fn_small(fmap2_dw16)
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fmap2_dw16 = x_tmp.permute(0, 2, 3, 1).reshape(x_tmp.shape[0], x_tmp.shape[2] * x_tmp.shape[3], x_tmp.shape[1])
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fmap1_dw16, fmap2_dw16 = self.self_att_fn(fmap1_dw16, fmap2_dw16)
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fmap1_dw16, fmap2_dw16 = [
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x.reshape(x.shape[0], image1.shape[2] // 16, -1, x.shape[2]).permute(0, 3, 1, 2)
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for x in [fmap1_dw16, fmap2_dw16]
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]
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corr_fn = AGCL(fmap1, fmap2)
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corr_fn_dw8 = AGCL(fmap1_dw8, fmap2_dw8)
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corr_fn_att_dw16 = AGCL(fmap1_dw16, fmap2_dw16, att=self.cross_att_fn)
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# Cascaded refinement (1/16 + 1/8 + 1/4)
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predictions = []
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flow = None
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flow_up = None
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if flow_init is not None:
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scale = fmap1.shape[2] / flow_init.shape[2]
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flow = -scale * F.interpolate(
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flow_init,
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size=(fmap1.shape[2], fmap1.shape[3]),
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mode="bilinear",
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align_corners=True,
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)
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else:
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# zero initialization
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flow_dw16 = self.zero_init(fmap1_dw16)
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# Recurrent Update Module
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# RUM: 1/16
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for itr in range(iters // 2):
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if itr % 2 == 0:
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small_patch = False
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else:
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small_patch = True
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flow_dw16 = flow_dw16.detach()
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out_corrs = corr_fn_att_dw16(
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flow_dw16, offset_dw16, small_patch=small_patch
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)
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with autocast(enabled=self.mixed_precision):
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net_dw16, up_mask, delta_flow = self.update_block(
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net_dw16, inp_dw16, out_corrs, flow_dw16
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)
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flow_dw16 = flow_dw16 + delta_flow
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flow = self.convex_upsample(flow_dw16, up_mask, rate=4)
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flow_up = -4 * F.interpolate(
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flow,
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size=(4 * flow.shape[2], 4 * flow.shape[3]),
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mode="bilinear",
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align_corners=True,
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)
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predictions.append(flow_up)
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scale = fmap1_dw8.shape[2] / flow.shape[2]
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flow_dw8 = -scale * F.interpolate(
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flow,
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size=(fmap1_dw8.shape[2], fmap1_dw8.shape[3]),
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mode="bilinear",
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align_corners=True,
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)
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# RUM: 1/8
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for itr in range(iters // 2):
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if itr % 2 == 0:
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small_patch = False
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else:
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small_patch = True
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flow_dw8 = flow_dw8.detach()
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out_corrs = corr_fn_dw8(flow_dw8, offset_dw8, small_patch=small_patch)
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with autocast(enabled=self.mixed_precision):
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net_dw8, up_mask, delta_flow = self.update_block(
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net_dw8, inp_dw8, out_corrs, flow_dw8
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)
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flow_dw8 = flow_dw8 + delta_flow
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flow = self.convex_upsample(flow_dw8, up_mask, rate=4)
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flow_up = -2 * F.interpolate(
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flow,
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size=(2 * flow.shape[2], 2 * flow.shape[3]),
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mode="bilinear",
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align_corners=True,
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)
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predictions.append(flow_up)
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scale = fmap1.shape[2] / flow.shape[2]
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flow = -scale * F.interpolate(
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flow,
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size=(fmap1.shape[2], fmap1.shape[3]),
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mode="bilinear",
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align_corners=True,
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)
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# RUM: 1/4
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for itr in range(iters):
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if itr % 2 == 0:
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small_patch = False
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else:
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small_patch = True
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flow = flow.detach()
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out_corrs = corr_fn(flow, None, small_patch=small_patch, iter_mode=True)
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with autocast(enabled=self.mixed_precision):
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net, up_mask, delta_flow = self.update_block(net, inp, out_corrs, flow)
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flow = flow + delta_flow
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flow_up = -self.convex_upsample(flow, up_mask, rate=4)
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predictions.append(flow_up)
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if self.test_mode:
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return flow_up
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return predictions
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