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import torch |
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import torch.nn.functional as F |
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from .utils.utils import bilinear_sampler, coords_grid |
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try: |
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import alt_cuda_corr |
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except: |
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pass |
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class CorrBlock: |
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def __init__(self, fmap1, fmap2, num_levels=4, radius=4): |
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self.num_levels = num_levels |
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self.radius = radius |
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self.corr_pyramid = [] |
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corr = CorrBlock.corr(fmap1, fmap2) |
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batch, h1, w1, dim, h2, w2 = corr.shape |
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corr = corr.reshape(batch*h1*w1, dim, h2, w2) |
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self.corr_pyramid.append(corr) |
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for i in range(self.num_levels-1): |
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corr = F.avg_pool2d(corr, 2, stride=2) |
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self.corr_pyramid.append(corr) |
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def __call__(self, coords): |
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r = self.radius |
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coords = coords.permute(0, 2, 3, 1) |
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batch, h1, w1, _ = coords.shape |
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out_pyramid = [] |
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for i in range(self.num_levels): |
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corr = self.corr_pyramid[i] |
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dx = torch.linspace(-r, r, 2*r+1) |
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dy = torch.linspace(-r, r, 2*r+1) |
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delta = torch.stack(torch.meshgrid(dy, dx), axis=-1).to(coords.device) |
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centroid_lvl = coords.reshape(batch*h1*w1, 1, 1, 2) / 2**i |
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delta_lvl = delta.view(1, 2*r+1, 2*r+1, 2) |
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coords_lvl = centroid_lvl + delta_lvl |
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corr = bilinear_sampler(corr, coords_lvl) |
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corr = corr.view(batch, h1, w1, -1) |
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out_pyramid.append(corr) |
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out = torch.cat(out_pyramid, dim=-1) |
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return out.permute(0, 3, 1, 2).contiguous().float() |
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@staticmethod |
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def corr(fmap1, fmap2): |
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batch, dim, ht, wd = fmap1.shape |
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fmap1 = fmap1.view(batch, dim, ht*wd) |
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fmap2 = fmap2.view(batch, dim, ht*wd) |
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corr = torch.matmul(fmap1.transpose(1,2), fmap2) |
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corr = corr.view(batch, ht, wd, 1, ht, wd) |
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return corr / torch.sqrt(torch.tensor(dim).float()) |
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class CorrLayer(torch.autograd.Function): |
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@staticmethod |
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def forward(ctx, fmap1, fmap2, coords, r): |
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fmap1 = fmap1.contiguous() |
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fmap2 = fmap2.contiguous() |
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coords = coords.contiguous() |
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ctx.save_for_backward(fmap1, fmap2, coords) |
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ctx.r = r |
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corr, = correlation_cudaz.forward(fmap1, fmap2, coords, ctx.r) |
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return corr |
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@staticmethod |
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def backward(ctx, grad_corr): |
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fmap1, fmap2, coords = ctx.saved_tensors |
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grad_corr = grad_corr.contiguous() |
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fmap1_grad, fmap2_grad, coords_grad = \ |
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correlation_cudaz.backward(fmap1, fmap2, coords, grad_corr, ctx.r) |
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return fmap1_grad, fmap2_grad, coords_grad, None |
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class AlternateCorrBlock: |
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def __init__(self, fmap1, fmap2, num_levels=4, radius=4): |
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self.num_levels = num_levels |
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self.radius = radius |
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self.pyramid = [(fmap1, fmap2)] |
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for i in range(self.num_levels): |
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fmap1 = F.avg_pool2d(fmap1, 2, stride=2) |
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fmap2 = F.avg_pool2d(fmap2, 2, stride=2) |
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self.pyramid.append((fmap1, fmap2)) |
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def __call__(self, coords): |
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coords = coords.permute(0, 2, 3, 1) |
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B, H, W, _ = coords.shape |
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corr_list = [] |
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for i in range(self.num_levels): |
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r = self.radius |
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fmap1_i = self.pyramid[0][0].permute(0, 2, 3, 1) |
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fmap2_i = self.pyramid[i][1].permute(0, 2, 3, 1) |
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coords_i = (coords / 2**i).reshape(B, 1, H, W, 2).contiguous() |
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corr = alt_cuda_corr(fmap1_i, fmap2_i, coords_i, r) |
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corr_list.append(corr.squeeze(1)) |
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corr = torch.stack(corr_list, dim=1) |
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corr = corr.reshape(B, -1, H, W) |
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return corr / 16.0 |
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