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import torch
import torch.nn.functional as F
import numpy as np
from scipy import interpolate
class InputPadder:
""" Pads images such that dimensions are divisible by 8 """
def __init__(self, dims, mode='sintel'):
self.ht, self.wd = dims[-2:]
pad_ht = (((self.ht // 8) + 1) * 8 - self.ht) % 8
pad_wd = (((self.wd // 8) + 1) * 8 - self.wd) % 8
if mode == 'sintel':
self._pad = [pad_wd//2, pad_wd - pad_wd//2, pad_ht//2, pad_ht - pad_ht//2]
else:
self._pad = [pad_wd//2, pad_wd - pad_wd//2, 0, pad_ht]
def pad(self, *inputs):
return [F.pad(x, self._pad, mode='replicate') for x in inputs]
def unpad(self,x):
ht, wd = x.shape[-2:]
c = [self._pad[2], ht-self._pad[3], self._pad[0], wd-self._pad[1]]
return x[..., c[0]:c[1], c[2]:c[3]]
def forward_interpolate(flow):
flow = flow.detach().cpu().numpy()
dx, dy = flow[0], flow[1]
ht, wd = dx.shape
x0, y0 = np.meshgrid(np.arange(wd), np.arange(ht))
x1 = x0 + dx
y1 = y0 + dy
x1 = x1.reshape(-1)
y1 = y1.reshape(-1)
dx = dx.reshape(-1)
dy = dy.reshape(-1)
valid = (x1 > 0) & (x1 < wd) & (y1 > 0) & (y1 < ht)
x1 = x1[valid]
y1 = y1[valid]
dx = dx[valid]
dy = dy[valid]
flow_x = interpolate.griddata(
(x1, y1), dx, (x0, y0), method='nearest', fill_value=0)
flow_y = interpolate.griddata(
(x1, y1), dy, (x0, y0), method='nearest', fill_value=0)
flow = np.stack([flow_x, flow_y], axis=0)
return torch.from_numpy(flow).float()
def bilinear_sampler(img, coords, mode='bilinear', mask=False):
""" Wrapper for grid_sample, uses pixel coordinates """
H, W = img.shape[-2:]
xgrid, ygrid = coords.split([1,1], dim=-1)
xgrid = 2*xgrid/(W-1) - 1
ygrid = 2*ygrid/(H-1) - 1
grid = torch.cat([xgrid, ygrid], dim=-1)
img = F.grid_sample(img, grid, align_corners=True)
if mask:
mask = (xgrid > -1) & (ygrid > -1) & (xgrid < 1) & (ygrid < 1)
return img, mask.float()
return img
def coords_grid(batch, ht, wd, device):
coords = torch.meshgrid(torch.arange(ht, device=device), torch.arange(wd, device=device))
coords = torch.stack(coords[::-1], dim=0).float()
return coords[None].repeat(batch, 1, 1, 1)
def upflow8(flow, mode='bilinear'):
new_size = (8 * flow.shape[2], 8 * flow.shape[3])
return 8 * F.interpolate(flow, size=new_size, mode=mode, align_corners=True)
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