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import torch
def flow_prop(feat, flow, mode='forward'):
"""
Args:
feat: features to be aligned
flow: the filled current flow
mode: `forward` or `backward`, indicates the propagation direction
Returns: feature after warping
"""
assert mode in ['forward', 'backward'], 'Invalid mode: {}'.format(mode)
feat = warp(feat, flow, mode)
return feat
def warp(feat, flow, mode):
device = feat.device
c = feat.shape[1]
y = flow[:, 0:1, :, :]
x = flow[:, 1:2, :, :]
x = x.repeat(1, c, 1, 1) # [b, c, h, w]
y = y.repeat(1, c, 1, 1)
x1 = torch.floor(x)
x2 = x1 + 1
y1 = torch.floor(y)
y2 = y1 + 1
w11, w12, w21, w22 = get_gaussian_weights(x, y, x1, y1, x2, y2)
feat11, o11 = sample_one(feat, x1, y1, w11, mode)
feat12, o12 = sample_one(feat, x1, y2, w12, mode)
feat21, o21 = sample_one(feat, x2, y1, w21, mode)
feat22, o22 = sample_one(feat, x2, y2, w22, mode)
feat_o = feat11 + feat12 + feat21 + feat22
o = o11 + o12 + o21 + o22
feat_o[o > 0] = feat_o[o > 0] / o[o > 0]
return feat_o
def sample_one(feat, shiftx, shifty, weight, mode):
device = feat.device
b, c, h, w = feat.shape
flat_shiftx = shiftx.view(-1) # [b * c * h * w]
flat_shifty = shifty.view(-1)
flat_basex = torch.arange(0, h, requires_grad=False).view(-1, 1).long().repeat(b, c, 1, w).view(-1)
flat_basey = torch.arange(0, w, requires_grad=False).view(-1, 1).long().repeat(b, c, h, 1).view(-1)
flat_basex = flat_basex.to(device)
flat_basey = flat_basey.to(device)
flat_weight = weight.reshape(-1)
flat_feat = feat.reshape(-1)
idxn = torch.arange(0, b, requires_grad=False).view(b, 1, 1, 1).long().repeat(1, c, h, w).view(-1)
idxc = torch.arange(0, c, requires_grad=False).view(1, c, 1, 1).long().repeat(b, 1, h, w).view(-1)
idxn = idxn.to(device)
idxc = idxc.to(device)
if mode == 'forward':
idxx = flat_shiftx.long() + flat_basex # size [-1]
idxy = flat_shifty.long() + flat_basey # size [-1]
else: # backward propagation
idxx = -flat_shiftx.long() + flat_basex # size [-1]
idxy = -flat_shifty.long() + flat_basey # size [-1]
# record the shifted pixels inside the image boundaries
mask = idxx.ge(0) & idxx.lt(h) & idxy.ge(0) & idxy.lt(w)
# mask off points out of boundaries
ids = idxn * c * h * w + idxc * h * w + idxx * w + idxy
ids_mask = torch.masked_select(ids, mask).clone()
# put the value into corresponding regions
feat_warp = torch.zeros([b * c * h * w])
feat_warp = feat_warp.to(device)
feat_warp.put_(ids_mask, torch.masked_select(flat_feat * flat_weight, mask), accumulate=True)
one_warp = torch.zeros([b * c * h * w])
one_warp = one_warp.to(device)
one_warp.put_(ids_mask, torch.masked_select(flat_weight, mask), accumulate=True)
return feat_warp.view(b, c, h, w), one_warp.view(b, c, h, w)
def get_gaussian_weights(x, y, x1, y1, x2, y2):
sigma = 1
w11 = torch.exp(-((x - x1) ** 2 + (y - y1) ** 2) / (sigma ** 2))
w12 = torch.exp(-((x - x1) ** 2 + (y - y2) ** 2) / (sigma ** 2))
w21 = torch.exp(-((x - x2) ** 2 + (y - y1) ** 2) / (sigma ** 2))
w22 = torch.exp(-((x - x2) ** 2 + (y - y2) ** 2) / (sigma ** 2))
return w11, w12, w21, w22
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