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| import torch.nn as nn | |
| import torch.nn.functional as F | |
| def conv1x1(in_planes, out_planes, stride=1): | |
| """1x1 convolution without padding""" | |
| return nn.Conv2d( | |
| in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False | |
| ) | |
| def conv3x3(in_planes, out_planes, stride=1): | |
| """3x3 convolution with padding""" | |
| return nn.Conv2d( | |
| in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False | |
| ) | |
| class ConvBlock(nn.Module): | |
| def __init__(self, in_planes, planes, stride=1, bn=True): | |
| super().__init__() | |
| self.conv = conv3x3(in_planes, planes, stride) | |
| self.bn = nn.BatchNorm2d(planes) if bn is True else None | |
| self.act = nn.GELU() | |
| def forward(self, x): | |
| y = self.conv(x) | |
| if self.bn: | |
| y = self.bn(y) # F.layer_norm(y, y.shape[1:]) | |
| y = self.act(y) | |
| return y | |
| class FPN(nn.Module): | |
| """ | |
| ResNet+FPN, output resolution are 1/8 and 1/2. | |
| Each block has 2 layers. | |
| """ | |
| def __init__(self, config): | |
| super().__init__() | |
| # Config | |
| block = ConvBlock | |
| initial_dim = config["initial_dim"] | |
| block_dims = config["block_dims"] | |
| # Class Variable | |
| self.in_planes = initial_dim | |
| # Networks | |
| self.conv1 = nn.Conv2d( | |
| 1, initial_dim, kernel_size=7, stride=2, padding=3, bias=False | |
| ) | |
| self.bn1 = nn.BatchNorm2d(initial_dim) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.layer1 = self._make_layer(block, block_dims[0], stride=1) # 1/2 | |
| self.layer2 = self._make_layer(block, block_dims[1], stride=2) # 1/4 | |
| self.layer3 = self._make_layer(block, block_dims[2], stride=2) # 1/8 | |
| self.layer4 = self._make_layer(block, block_dims[3], stride=2) # 1/16 | |
| # 3. FPN upsample | |
| self.layer3_outconv = conv1x1(block_dims[2], block_dims[3]) | |
| self.layer3_outconv2 = nn.Sequential( | |
| ConvBlock(block_dims[3], block_dims[2]), | |
| conv3x3(block_dims[2], block_dims[2]), | |
| ) | |
| self.layer2_outconv = conv1x1(block_dims[1], block_dims[2]) | |
| self.layer2_outconv2 = nn.Sequential( | |
| ConvBlock(block_dims[2], block_dims[1]), | |
| conv3x3(block_dims[1], block_dims[1]), | |
| ) | |
| self.layer1_outconv = conv1x1(block_dims[0], block_dims[1]) | |
| self.layer1_outconv2 = nn.Sequential( | |
| ConvBlock(block_dims[1], block_dims[0]), | |
| conv3x3(block_dims[0], block_dims[0]), | |
| ) | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") | |
| elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): | |
| nn.init.constant_(m.weight, 1) | |
| nn.init.constant_(m.bias, 0) | |
| def _make_layer(self, block, dim, stride=1): | |
| layer1 = block(self.in_planes, dim, stride=stride) | |
| layer2 = block(dim, dim, stride=1) | |
| layers = (layer1, layer2) | |
| self.in_planes = dim | |
| return nn.Sequential(*layers) | |
| def forward(self, x): | |
| # ResNet Backbone | |
| x0 = self.relu(self.bn1(self.conv1(x))) | |
| x1 = self.layer1(x0) # 1/2 | |
| x2 = self.layer2(x1) # 1/4 | |
| x3 = self.layer3(x2) # 1/8 | |
| x4 = self.layer4(x3) # 1/16 | |
| # FPN | |
| x4_out_2x = F.interpolate( | |
| x4, scale_factor=2.0, mode="bilinear", align_corners=True | |
| ) | |
| x3_out = self.layer3_outconv(x3) | |
| x3_out = self.layer3_outconv2(x3_out + x4_out_2x) | |
| x3_out_2x = F.interpolate( | |
| x3_out, scale_factor=2.0, mode="bilinear", align_corners=True | |
| ) | |
| x2_out = self.layer2_outconv(x2) | |
| x2_out = self.layer2_outconv2(x2_out + x3_out_2x) | |
| x2_out_2x = F.interpolate( | |
| x2_out, scale_factor=2.0, mode="bilinear", align_corners=True | |
| ) | |
| x1_out = self.layer1_outconv(x1) | |
| x1_out = self.layer1_outconv2(x1_out + x2_out_2x) | |
| return [x3_out, x1_out] | |