98 lines
2.9 KiB
Python
98 lines
2.9 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
|
|
# Part of the implementation is borrowed and modified from RAFT,
|
|
# publicly available at https://github.com/princeton-vl/RAFT
|
|
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn.functional as F
|
|
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)
|