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123 lines
4.0 KiB
123 lines
4.0 KiB
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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# Ref: https://github.com/princeton-vl/RAFT/blob/master/core/extractor.py
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class ResidualBlock(nn.Module):
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def __init__(self, in_planes, planes, norm_fn='group', stride=1):
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super(ResidualBlock, self).__init__()
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self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, stride=stride)
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1)
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self.relu = nn.ReLU(inplace=True)
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num_groups = planes // 8
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if norm_fn == 'group':
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self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
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self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
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self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
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elif norm_fn == 'batch':
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self.norm1 = nn.BatchNorm2d(planes)
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self.norm2 = nn.BatchNorm2d(planes)
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self.norm3 = nn.BatchNorm2d(planes)
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elif norm_fn == 'instance':
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self.norm1 = nn.InstanceNorm2d(planes, affine=False)
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self.norm2 = nn.InstanceNorm2d(planes, affine=False)
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self.norm3 = nn.InstanceNorm2d(planes, affine=False)
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elif norm_fn == 'none':
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self.norm1 = nn.Sequential()
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self.norm2 = nn.Sequential()
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self.norm3 = nn.Sequential()
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self.downsample = nn.Sequential(
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nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3)
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def forward(self, x):
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y = x
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y = self.relu(self.norm1(self.conv1(y)))
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y = self.relu(self.norm2(self.conv2(y)))
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x = self.downsample(x)
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return self.relu(x+y)
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class BasicEncoder(nn.Module):
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def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0):
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super(BasicEncoder, self).__init__()
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self.norm_fn = norm_fn
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if self.norm_fn == 'group':
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self.norm1 = nn.GroupNorm(num_groups=8, num_channels=64)
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elif self.norm_fn == 'batch':
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self.norm1 = nn.BatchNorm2d(64)
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elif self.norm_fn == 'instance':
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self.norm1 = nn.InstanceNorm2d(64, affine=False)
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elif self.norm_fn == 'none':
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self.norm1 = nn.Sequential()
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
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self.relu1 = nn.ReLU(inplace=True)
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self.in_planes = 64
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self.layer1 = self._make_layer(64, stride=1)
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self.layer2 = self._make_layer(96, stride=2)
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self.layer3 = self._make_layer(128, stride=1)
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# output convolution
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self.conv2 = nn.Conv2d(128, output_dim, kernel_size=1)
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self.dropout = None
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if dropout > 0:
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self.dropout = nn.Dropout2d(p=dropout)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
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elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
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if m.weight is not None:
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nn.init.constant_(m.weight, 1)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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def _make_layer(self, dim, stride=1):
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layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride)
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layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1)
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layers = (layer1, layer2)
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self.in_planes = dim
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return nn.Sequential(*layers)
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def forward(self, x):
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# if input is list, combine batch dimension
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is_list = isinstance(x, tuple) or isinstance(x, list)
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if is_list:
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batch_dim = x[0].shape[0]
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x = torch.cat(x, dim=0)
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x = self.conv1(x)
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x = self.norm1(x)
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x = self.relu1(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.conv2(x)
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if self.dropout is not None:
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x = self.dropout(x)
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if is_list:
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x = torch.split(x, x.shape[0]//2, dim=0)
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return x |