| 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):
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| self.ht, self.wd = dims[-2:]
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| pad_ht = (((self.ht // 8) + 1) * 8 - self.ht) % 8
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| pad_wd = (((self.wd // 8) + 1) * 8 - self.wd) % 8
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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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| return [F.pad(x, self._pad, mode='replicate') for x in inputs]
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|
|
| def unpad(self,x):
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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='cubic', fill_value=0)
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|
|
| flow_y = interpolate.griddata(
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| (x1, y1), dy, (x0, y0), method='cubic', 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):
|
| """ Wrapper for grid_sample, uses pixel coordinates """
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| H, W = img.shape[-2:]
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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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| ygrid = 2*ygrid/(H-1) - 1
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|
|
| grid = torch.cat([xgrid, ygrid], dim=-1)
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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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|
|
