91 lines
3.1 KiB
Python
91 lines
3.1 KiB
Python
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from __future__ import print_function
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import torch
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import torch.nn as nn
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import torch.utils.data
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from torch.autograd import Variable
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from torch.autograd.function import Function
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import torch.nn.functional as F
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import math
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import numpy as np
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def convbn(in_channels, out_channels, kernel_size, stride, pad, dilation):
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return nn.Sequential(nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride,
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padding=dilation if dilation > 1 else pad, dilation=dilation, bias=False),
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nn.BatchNorm2d(out_channels))
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def convbn_3d(in_channels, out_channels, kernel_size, stride, pad):
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return nn.Sequential(nn.Conv3d(in_channels, out_channels, kernel_size=kernel_size, stride=stride,
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padding=pad, bias=False),
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nn.BatchNorm3d(out_channels))
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def disparity_regression(x, maxdisp):
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assert len(x.shape) == 4
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disp_values = torch.arange(0, maxdisp, dtype=x.dtype, device=x.device)
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disp_values = disp_values.view(1, maxdisp, 1, 1)
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return torch.sum(x * disp_values, 1, keepdim=False)
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def build_concat_volume(refimg_fea, targetimg_fea, maxdisp):
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B, C, H, W = refimg_fea.shape
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volume = refimg_fea.new_zeros([B, 2 * C, maxdisp, H, W])
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for i in range(maxdisp):
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if i > 0:
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volume[:, :C, i, :, i:] = refimg_fea[:, :, :, i:]
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volume[:, C:, i, :, i:] = targetimg_fea[:, :, :, :-i]
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else:
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volume[:, :C, i, :, :] = refimg_fea
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volume[:, C:, i, :, :] = targetimg_fea
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volume = volume.contiguous()
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return volume
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def groupwise_correlation(fea1, fea2, num_groups):
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B, C, H, W = fea1.shape
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assert C % num_groups == 0
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channels_per_group = C // num_groups
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cost = (fea1 * fea2).view([B, num_groups, channels_per_group, H, W]).mean(dim=2)
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assert cost.shape == (B, num_groups, H, W)
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return cost
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def build_gwc_volume(refimg_fea, targetimg_fea, maxdisp, num_groups):
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B, C, H, W = refimg_fea.shape
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volume = refimg_fea.new_zeros([B, num_groups, maxdisp, H, W])
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for i in range(maxdisp):
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if i > 0:
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volume[:, :, i, :, i:] = groupwise_correlation(refimg_fea[:, :, :, i:], targetimg_fea[:, :, :, :-i],
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num_groups)
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else:
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volume[:, :, i, :, :] = groupwise_correlation(refimg_fea, targetimg_fea, num_groups)
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volume = volume.contiguous()
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return volume
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class BasicBlock(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, stride, downsample, pad, dilation):
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super(BasicBlock, self).__init__()
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self.conv1 = nn.Sequential(convbn(inplanes, planes, 3, stride, pad, dilation),
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nn.ReLU(inplace=True))
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self.conv2 = convbn(planes, planes, 3, 1, pad, dilation)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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out = self.conv1(x)
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out = self.conv2(out)
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if self.downsample is not None:
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x = self.downsample(x)
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out += x
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return out
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