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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import logging | |
| import math | |
| import torch.nn as nn | |
| import pdb | |
| logger = logging.getLogger(__name__) | |
| def conv3x3(in_planes, out_planes, stride=1): | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, | |
| padding=1, bias=False) | |
| def downsample_basic_block( inplanes, outplanes, stride ): | |
| return nn.Sequential( | |
| nn.Conv2d(inplanes, outplanes, kernel_size=1, stride=stride, bias=False), | |
| nn.BatchNorm2d(outplanes), | |
| ) | |
| def downsample_basic_block_v2( inplanes, outplanes, stride ): | |
| return nn.Sequential( | |
| nn.AvgPool2d(kernel_size=stride, stride=stride, ceil_mode=True, count_include_pad=False), | |
| nn.Conv2d(inplanes, outplanes, kernel_size=1, stride=1, bias=False), | |
| nn.BatchNorm2d(outplanes), | |
| ) | |
| class BasicBlock(nn.Module): | |
| expansion = 1 | |
| def __init__(self, inplanes, planes, stride=1, downsample=None, relu_type = 'relu' ): | |
| super(BasicBlock, self).__init__() | |
| assert relu_type in ['relu','prelu'] | |
| self.conv1 = conv3x3(inplanes, planes, stride) | |
| self.bn1 = nn.BatchNorm2d(planes) | |
| 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=planes) | |
| self.relu2 = nn.PReLU(num_parameters=planes) | |
| else: | |
| raise Exception('relu type not implemented') | |
| self.conv2 = conv3x3(planes, planes) | |
| self.bn2 = nn.BatchNorm2d(planes) | |
| self.downsample = downsample | |
| self.stride = stride | |
| def forward(self, x): | |
| 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, layers, num_classes=1000, relu_type = 'relu', gamma_zero = False, avg_pool_downsample = False): | |
| self.inplanes = 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. / 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, planes, blocks, stride=1): | |
| downsample = None | |
| if stride != 1 or self.inplanes != planes * block.expansion: | |
| downsample = self.downsample_block( inplanes = self.inplanes, | |
| outplanes = planes * block.expansion, | |
| stride = stride ) | |
| layers = [] | |
| layers.append(block(self.inplanes, planes, stride, downsample, relu_type = self.relu_type)) | |
| self.inplanes = planes * block.expansion | |
| for i in range(1, blocks): | |
| layers.append(block(self.inplanes, planes, relu_type = self.relu_type)) | |
| return nn.Sequential(*layers) | |
| def forward(self, x): | |
| 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 ResEncoder(nn.Module): | |
| def __init__(self, relu_type, weights): | |
| super(ResEncoder, self).__init__() | |
| self.frontend_nout = 64 | |
| self.backend_out = 512 | |
| frontend_relu = nn.PReLU(num_parameters=self.frontend_nout) if relu_type == 'prelu' else nn.ReLU() | |
| self.frontend3D = nn.Sequential( | |
| nn.Conv3d(1, self.frontend_nout, kernel_size=(5, 7, 7), stride=(1, 2, 2), padding=(2, 3, 3), bias=False), | |
| nn.BatchNorm3d(self.frontend_nout), | |
| 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 weights is not None: | |
| logger.info(f"Load {weights} for resnet") | |
| std = torch.load(weights, map_location=torch.device('cpu'))['model_state_dict'] | |
| frontend_std, trunk_std = OrderedDict(), OrderedDict() | |
| for key, val in std.items(): | |
| new_key = '.'.join(key.split('.')[1:]) | |
| if 'frontend3D' in key: | |
| frontend_std[new_key] = val | |
| if 'trunk' in key: | |
| trunk_std[new_key] = val | |
| self.frontend3D.load_state_dict(frontend_std) | |
| self.trunk.load_state_dict(trunk_std) | |
| def forward(self, x): | |
| B, C, T, H, W = x.size() | |
| x = self.frontend3D(x) | |
| Tnew = x.shape[2] | |
| x = self.threeD_to_2D_tensor(x) | |
| x = self.trunk(x) | |
| x = x.view(B, Tnew, x.size(1)) | |
| x = x.transpose(1, 2).contiguous() | |
| return x | |
| def threeD_to_2D_tensor(self, x): | |
| n_batch, n_channels, s_time, sx, sy = x.shape | |
| x = x.transpose(1, 2).contiguous() | |
| return x.reshape(n_batch*s_time, n_channels, sx, sy) | |