97 lines
3.1 KiB
Python
97 lines
3.1 KiB
Python
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import torch
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import torch.nn.functional as F
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import numpy as np
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from scipy import interpolate
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class InputPadder:
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""" Pads images such that dimensions are divisible by 8 """
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def __init__(self, dims, mode='sintel', divis_by=8):
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self.ht, self.wd = dims[-2:]
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pad_ht = (((self.ht // divis_by) + 1) * divis_by - self.ht) % divis_by
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pad_wd = (((self.wd // divis_by) + 1) * divis_by - self.wd) % divis_by
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if mode == 'sintel':
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self._pad = [pad_wd//2, pad_wd - pad_wd//2, pad_ht//2, pad_ht - pad_ht//2]
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else:
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self._pad = [pad_wd//2, pad_wd - pad_wd//2, 0, pad_ht]
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def pad(self, *inputs):
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assert all((x.ndim == 4) for x in inputs)
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return [F.pad(x, self._pad, mode='replicate') for x in inputs]
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def unpad(self, x):
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assert x.ndim == 4
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ht, wd = x.shape[-2:]
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c = [self._pad[2], ht-self._pad[3], self._pad[0], wd-self._pad[1]]
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return x[..., c[0]:c[1], c[2]:c[3]]
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def forward_interpolate(flow):
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flow = flow.detach().cpu().numpy()
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dx, dy = flow[0], flow[1]
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ht, wd = dx.shape
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x0, y0 = np.meshgrid(np.arange(wd), np.arange(ht))
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x1 = x0 + dx
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y1 = y0 + dy
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x1 = x1.reshape(-1)
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y1 = y1.reshape(-1)
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dx = dx.reshape(-1)
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dy = dy.reshape(-1)
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valid = (x1 > 0) & (x1 < wd) & (y1 > 0) & (y1 < ht)
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x1 = x1[valid]
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y1 = y1[valid]
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dx = dx[valid]
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dy = dy[valid]
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flow_x = interpolate.griddata(
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(x1, y1), dx, (x0, y0), method='nearest', fill_value=0)
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flow_y = interpolate.griddata(
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(x1, y1), dy, (x0, y0), method='nearest', fill_value=0)
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flow = np.stack([flow_x, flow_y], axis=0)
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return torch.from_numpy(flow).float()
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def bilinear_sampler(img, coords, mode='bilinear', mask=False):
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""" Wrapper for grid_sample, uses pixel coordinates """
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H, W = img.shape[-2:]
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# print("$$$55555", img.shape, coords.shape)
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xgrid, ygrid = coords.split([1,1], dim=-1)
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xgrid = 2*xgrid/(W-1) - 1
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# print("######88888", xgrid)
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assert torch.unique(ygrid).numel() == 1 and H == 1 # This is a stereo problem
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grid = torch.cat([xgrid, ygrid], dim=-1)
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# print("###37777", grid.shape)
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img = F.grid_sample(img, grid, align_corners=True)
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if mask:
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mask = (xgrid > -1) & (ygrid > -1) & (xgrid < 1) & (ygrid < 1)
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return img, mask.float()
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return img
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def coords_grid(batch, ht, wd):
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coords = torch.meshgrid(torch.arange(ht), torch.arange(wd))
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coords = torch.stack(coords[::-1], dim=0).float()
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return coords[None].repeat(batch, 1, 1, 1)
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def upflow8(flow, mode='bilinear'):
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new_size = (8 * flow.shape[2], 8 * flow.shape[3])
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return 8 * F.interpolate(flow, size=new_size, mode=mode, align_corners=True)
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def gauss_blur(input, N=5, std=1):
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B, D, H, W = input.shape
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x, y = torch.meshgrid(torch.arange(N).float() - N//2, torch.arange(N).float() - N//2)
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unnormalized_gaussian = torch.exp(-(x.pow(2) + y.pow(2)) / (2 * std ** 2))
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weights = unnormalized_gaussian / unnormalized_gaussian.sum().clamp(min=1e-4)
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weights = weights.view(1,1,N,N).to(input)
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output = F.conv2d(input.reshape(B*D,1,H,W), weights, padding=N//2)
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return output.view(B, D, H, W)
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