| import math | |
| import torch | |
| import torch.nn as nn | |
| from collections import OrderedDict | |
| def conv3x3(in_channels: int, out_channels: int, stride: int = 1) -> nn.Conv2d: | |
| return nn.Conv2d( | |
| in_channels=in_channels, | |
| out_channels=out_channels, | |
| kernel_size=3, | |
| stride=stride, | |
| padding=1, | |
| bias=False | |
| ) | |
| def downsample_basic_block( | |
| in_channels: int, | |
| out_channels: int, | |
| stride: int, | |
| ) -> nn.Sequential: | |
| return nn.Sequential( | |
| nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False), | |
| nn.BatchNorm2d(out_channels), | |
| ) | |
| def downsample_basic_block_v2( | |
| in_channels: int, | |
| out_channels: int, | |
| stride: int, | |
| ) -> nn.Sequential: | |
| return nn.Sequential( | |
| nn.AvgPool2d( | |
| kernel_size=stride, | |
| stride=stride, | |
| ceil_mode=True, | |
| count_include_pad=False, | |
| ), | |
| nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False), | |
| nn.BatchNorm2d(out_channels), | |
| ) | |
| class BasicBlock(nn.Module): | |
| expansion = 1 | |
| def __init__( | |
| self, | |
| in_channels: int, | |
| channels: int, | |
| stride: int = 1, | |
| downsample: nn.Sequential = None, | |
| relu_type: str = "relu", | |
| ) -> None: | |
| super(BasicBlock, self).__init__() | |
| assert relu_type in ["relu", "prelu"] | |
| self.conv1 = conv3x3(in_channels, channels, stride) | |
| self.bn1 = nn.BatchNorm2d(channels) | |
| if relu_type == "relu": | |
| self.relu1 = nn.ReLU(inplace=True) | |
| self.relu2 = nn.ReLU(inplace=True) | |
| elif relu_type == "prelu": | |
| self.relu1 = nn.PReLU(num_parameters=channels) | |
| self.relu2 = nn.PReLU(num_parameters=channels) | |
| else: | |
| raise Exception("relu type not implemented") | |
| self.conv2 = conv3x3(channels, channels) | |
| self.bn2 = nn.BatchNorm2d(channels) | |
| self.downsample = downsample | |
| self.stride = stride | |
| def forward(self, x: torch.Tensor) -> torch.Tensor: | |
| residual = x | |
| out = self.conv1(x) | |
| out = self.bn1(out) | |
| out = self.relu1(out) | |
| out = self.conv2(out) | |
| out = self.bn2(out) | |
| if self.downsample is not None: | |
| residual = self.downsample(x) | |
| out += residual | |
| out = self.relu2(out) | |
| return out | |
| class ResNet(nn.Module): | |
| def __init__( | |
| self, | |
| block: nn.Module, | |
| layers: list, | |
| relu_type: str = "relu", | |
| gamma_zero: bool = False, | |
| avg_pool_downsample: bool = False, | |
| ) -> None: | |
| self.in_channels = 64 | |
| self.relu_type = relu_type | |
| self.gamma_zero = gamma_zero | |
| self.downsample_block = ( | |
| downsample_basic_block_v2 if avg_pool_downsample else downsample_basic_block | |
| ) | |
| super(ResNet, self).__init__() | |
| self.layer1 = self._make_layer(block, 64, layers[0]) | |
| self.layer2 = self._make_layer(block, 128, layers[1], stride=2) | |
| self.layer3 = self._make_layer(block, 256, layers[2], stride=2) | |
| self.layer4 = self._make_layer(block, 512, layers[3], stride=2) | |
| self.avgpool = nn.AdaptiveAvgPool2d(1) | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels | |
| m.weight.data.normal_(0, math.sqrt(2.0 / n)) | |
| elif isinstance(m, nn.BatchNorm2d): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| if self.gamma_zero: | |
| for m in self.modules(): | |
| if isinstance(m, BasicBlock): | |
| m.bn2.weight.data.zero_() | |
| def _make_layer( | |
| self, | |
| block: nn.Module, | |
| channels: int, | |
| n_blocks: int, | |
| stride: int = 1, | |
| ) -> nn.Sequential: | |
| downsample = None | |
| if stride != 1 or self.in_channels != channels * block.expansion: | |
| downsample = self.downsample_block( | |
| in_channels=self.in_channels, | |
| out_channels=channels * block.expansion, | |
| stride=stride, | |
| ) | |
| layers = [ | |
| block( | |
| self.in_channels, channels, stride, downsample, relu_type=self.relu_type | |
| ) | |
| ] | |
| self.in_channels = channels * block.expansion | |
| for _ in range(1, n_blocks): | |
| layers.append(block(self.in_channels, channels, relu_type=self.relu_type)) | |
| return nn.Sequential(*layers) | |
| def forward(self, x: torch.Tensor) -> torch.Tensor: | |
| x = self.layer1(x) | |
| x = self.layer2(x) | |
| x = self.layer3(x) | |
| x = self.layer4(x) | |
| x = self.avgpool(x) | |
| x = x.view(x.size(0), -1) | |
| return x | |
| class ResNetEncoder(nn.Module): | |
| def __init__(self, relu_type: str, weight_file: str = None) -> None: | |
| super(ResNetEncoder, self).__init__() | |
| self.frontend_out = 64 | |
| self.backend_out = 512 | |
| frontend_relu = ( | |
| nn.PReLU(num_parameters=self.frontend_out) | |
| if relu_type == "prelu" | |
| else nn.ReLU() | |
| ) | |
| self.frontend3D = nn.Sequential( | |
| nn.Conv3d( | |
| 1, | |
| self.frontend_out, | |
| kernel_size=(5, 7, 7), | |
| stride=(1, 2, 2), | |
| padding=(2, 3, 3), | |
| bias=False, | |
| ), | |
| nn.BatchNorm3d(self.frontend_out), | |
| frontend_relu, | |
| nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1)), | |
| ) | |
| self.trunk = ResNet(BasicBlock, [2, 2, 2, 2], relu_type=relu_type) | |
| if weight_file is not None: | |
| model_state_dict = torch.load(weight_file, map_location=torch.device("cpu")) | |
| model_state_dict = model_state_dict["model_state_dict"] | |
| frontend_state_dict, trunk_state_dict = OrderedDict(), OrderedDict() | |
| for key, val in model_state_dict.items(): | |
| new_key = ".".join(key.split(".")[1:]) | |
| if "frontend3D" in key: | |
| frontend_state_dict[new_key] = val | |
| if "trunk" in key: | |
| trunk_state_dict[new_key] = val | |
| self.frontend3D.load_state_dict(frontend_state_dict) | |
| self.trunk.load_state_dict(trunk_state_dict) | |
| def forward(self, x: torch.Tensor) -> torch.Tensor: | |
| B, C, T, H, W = x.size() | |
| x = self.frontend3D(x) | |
| Tnew = x.shape[2] | |
| x = self.convert_3D_to_2D(x) | |
| x = self.trunk(x) | |
| x = x.view(B, Tnew, x.size(1)) | |
| x = x.transpose(1, 2).contiguous() | |
| return x | |
| def convert_3D_to_2D(self, x: torch.Tensor) -> torch.Tensor: | |
| n_batches, n_channels, s_time, sx, sy = x.shape | |
| x = x.transpose(1, 2).contiguous() | |
| return x.reshape(n_batches * s_time, n_channels, sx, sy) | |