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import torch |
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import numpy as np |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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backwarp_tenGrid = {} |
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def warp(tenInput, tenFlow): |
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with torch.cuda.amp.autocast(enabled=False): |
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k = (str(tenFlow.device), str(tenFlow.size())) |
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if k not in backwarp_tenGrid: |
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tenHorizontal = torch.linspace(-1.0, 1.0, tenFlow.shape[3], device=device).view( |
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1, 1, 1, tenFlow.shape[3]).expand(tenFlow.shape[0], -1, tenFlow.shape[2], -1) |
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tenVertical = torch.linspace(-1.0, 1.0, tenFlow.shape[2], device=device).view( |
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1, 1, tenFlow.shape[2], 1).expand(tenFlow.shape[0], -1, -1, tenFlow.shape[3]) |
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backwarp_tenGrid[k] = torch.cat( |
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[tenHorizontal, tenVertical], 1).to(device) |
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tenFlow = torch.cat([tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0), |
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tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0)], 1) |
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g = (backwarp_tenGrid[k] + tenFlow).permute(0, 2, 3, 1) |
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if tenInput.dtype != g.dtype: |
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g = g.to(tenInput.dtype) |
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return torch.nn.functional.grid_sample(input=tenInput, grid=g, mode='bilinear', padding_mode='border', align_corners=True) |
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def warp_features(inp, flow, ): |
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groups = flow.shape[1]//2 |
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samples = inp.shape[0] |
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h = inp.shape[2] |
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w = inp.shape[3] |
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assert(flow.shape[0] == samples and flow.shape[2] |
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== h and flow.shape[3] == w) |
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chns = inp.shape[1] |
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chns_per_group = chns // groups |
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assert(flow.shape[1] % 2 == 0) |
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assert(chns % groups == 0) |
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inp = inp.contiguous().view(samples*groups, chns_per_group, h, w) |
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flow = flow.contiguous().view(samples*groups, 2, h, w) |
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feat = warp(inp, flow) |
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feat = feat.view(samples, chns, h, w) |
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return feat |
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def flow2rgb(flow_map_np): |
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h, w, _ = flow_map_np.shape |
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rgb_map = np.ones((h, w, 3)).astype(np.float32)/2.0 |
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normalized_flow_map = np.concatenate( |
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(flow_map_np[:, :, 0:1]/h/2.0, flow_map_np[:, :, 1:2]/w/2.0), axis=2) |
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rgb_map[:, :, 0] += normalized_flow_map[:, :, 0] |
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rgb_map[:, :, 1] -= 0.5 * \ |
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(normalized_flow_map[:, :, 0] + normalized_flow_map[:, :, 1]) |
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rgb_map[:, :, 2] += normalized_flow_map[:, :, 1] |
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return (rgb_map.clip(0, 1)*255.0).astype(np.uint8) |
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