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| """ | |
| Modified by Nikita Selin (OPHoperHPO)[https://github.com/OPHoperHPO]. | |
| Source url: https://github.com/MarcoForte/FBA_Matting | |
| License: MIT License | |
| """ | |
| import torch.nn as nn | |
| import carvekit.ml.arch.fba_matting.layers_WS as L | |
| __all__ = ["ResNet", "l_resnet50"] | |
| def conv3x3(in_planes, out_planes, stride=1): | |
| """3x3 convolution with padding""" | |
| return L.Conv2d( | |
| in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False | |
| ) | |
| def conv1x1(in_planes, out_planes, stride=1): | |
| """1x1 convolution""" | |
| return L.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) | |
| class BasicBlock(nn.Module): | |
| expansion = 1 | |
| def __init__(self, inplanes, planes, stride=1, downsample=None): | |
| super(BasicBlock, self).__init__() | |
| self.conv1 = conv3x3(inplanes, planes, stride) | |
| self.bn1 = L.BatchNorm2d(planes) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.conv2 = conv3x3(planes, planes) | |
| self.bn2 = L.BatchNorm2d(planes) | |
| self.downsample = downsample | |
| self.stride = stride | |
| def forward(self, x): | |
| identity = x | |
| out = self.conv1(x) | |
| out = self.bn1(out) | |
| out = self.relu(out) | |
| out = self.conv2(out) | |
| out = self.bn2(out) | |
| if self.downsample is not None: | |
| identity = self.downsample(x) | |
| out += identity | |
| out = self.relu(out) | |
| return out | |
| class Bottleneck(nn.Module): | |
| expansion = 4 | |
| def __init__(self, inplanes, planes, stride=1, downsample=None): | |
| super(Bottleneck, self).__init__() | |
| self.conv1 = conv1x1(inplanes, planes) | |
| self.bn1 = L.BatchNorm2d(planes) | |
| self.conv2 = conv3x3(planes, planes, stride) | |
| self.bn2 = L.BatchNorm2d(planes) | |
| self.conv3 = conv1x1(planes, planes * self.expansion) | |
| self.bn3 = L.BatchNorm2d(planes * self.expansion) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.downsample = downsample | |
| self.stride = stride | |
| def forward(self, x): | |
| identity = x | |
| out = self.conv1(x) | |
| out = self.bn1(out) | |
| out = self.relu(out) | |
| out = self.conv2(out) | |
| out = self.bn2(out) | |
| out = self.relu(out) | |
| out = self.conv3(out) | |
| out = self.bn3(out) | |
| if self.downsample is not None: | |
| identity = self.downsample(x) | |
| out += identity | |
| out = self.relu(out) | |
| return out | |
| class ResNet(nn.Module): | |
| def __init__(self, block, layers, num_classes=1000): | |
| super(ResNet, self).__init__() | |
| self.inplanes = 64 | |
| self.conv1 = L.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) | |
| self.bn1 = L.BatchNorm2d(64) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.maxpool = nn.MaxPool2d( | |
| kernel_size=3, stride=2, padding=1, return_indices=True | |
| ) | |
| 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, 1)) | |
| self.fc = nn.Linear(512 * block.expansion, num_classes) | |
| def _make_layer(self, block, planes, blocks, stride=1): | |
| downsample = None | |
| if stride != 1 or self.inplanes != planes * block.expansion: | |
| downsample = nn.Sequential( | |
| conv1x1(self.inplanes, planes * block.expansion, stride), | |
| L.BatchNorm2d(planes * block.expansion), | |
| ) | |
| layers = [] | |
| layers.append(block(self.inplanes, planes, stride, downsample)) | |
| self.inplanes = planes * block.expansion | |
| for _ in range(1, blocks): | |
| layers.append(block(self.inplanes, planes)) | |
| return nn.Sequential(*layers) | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = self.bn1(x) | |
| x = self.relu(x) | |
| x = self.maxpool(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) | |
| x = self.fc(x) | |
| return x | |
| def l_resnet50(pretrained=False, **kwargs): | |
| """Constructs a ResNet-50 model. | |
| Args: | |
| pretrained (bool): If True, returns a model pre-trained on ImageNet | |
| """ | |
| model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) | |
| return model | |