# This code is built from the PyTorch examples repository: https://github.com/pytorch/vision/tree/master/torchvision/models. # Copyright (c) 2017 Torch Contributors. # The Pytorch examples are available under the BSD 3-Clause License. # # ========================================================================================== # # Adobe’s modifications are Copyright 2019 Adobe. All rights reserved. # Adobe’s modifications are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike # 4.0 International Public License (CC-NC-SA-4.0). To view a copy of the license, visit # https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode. # # ========================================================================================== # # BSD-3 License # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # * Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE import torch.nn as nn import torch.utils.model_zoo as model_zoo from .lpf import * __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnext50_32x4d', 'resnext101_32x8d'] # model_urls = { # 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', # 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', # 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', # 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', # 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', # } def conv3x3(in_planes, out_planes, stride=1, groups=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False) def conv1x1(in_planes, out_planes, stride=1): """1x1 convolution""" return nn.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, groups=1, norm_layer=None, filter_size=1): super(BasicBlock, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d if groups != 1: raise ValueError('BasicBlock only supports groups=1') # Both self.conv1 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv3x3(inplanes, planes) self.bn1 = norm_layer(planes) self.relu = nn.ReLU(inplace=True) if(stride==1): self.conv2 = conv3x3(planes,planes) else: self.conv2 = nn.Sequential(Downsample(filt_size=filter_size, stride=stride, channels=planes), conv3x3(planes, planes),) self.bn2 = norm_layer(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, groups=1, norm_layer=None, filter_size=1): super(Bottleneck, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d # Both self.conv2 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv1x1(inplanes, planes) self.bn1 = norm_layer(planes) self.conv2 = conv3x3(planes, planes, groups) # stride moved self.bn2 = norm_layer(planes) if(stride==1): self.conv3 = conv1x1(planes, planes * self.expansion) else: self.conv3 = nn.Sequential(Downsample(filt_size=filter_size, stride=stride, channels=planes), conv1x1(planes, planes * self.expansion)) self.bn3 = norm_layer(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, zero_init_residual=False, groups=1, width_per_group=64, norm_layer=None, filter_size=1, pool_only=True): super(ResNet, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d planes = [int(width_per_group * groups * 2 ** i) for i in range(4)] self.inplanes = planes[0] if(pool_only): self.conv1 = nn.Conv2d(3, planes[0], kernel_size=7, stride=2, padding=3, bias=False) else: self.conv1 = nn.Conv2d(3, planes[0], kernel_size=7, stride=1, padding=3, bias=False) self.bn1 = norm_layer(planes[0]) self.relu = nn.ReLU(inplace=True) if(pool_only): self.maxpool = nn.Sequential(*[nn.MaxPool2d(kernel_size=2, stride=1), Downsample(filt_size=filter_size, stride=2, channels=planes[0])]) else: self.maxpool = nn.Sequential(*[Downsample(filt_size=filter_size, stride=2, channels=planes[0]), nn.MaxPool2d(kernel_size=2, stride=1), Downsample(filt_size=filter_size, stride=2, channels=planes[0])]) self.layer1 = self._make_layer(block, planes[0], layers[0], groups=groups, norm_layer=norm_layer) self.layer2 = self._make_layer(block, planes[1], layers[1], stride=2, groups=groups, norm_layer=norm_layer, filter_size=filter_size) self.layer3 = self._make_layer(block, planes[2], layers[2], stride=2, groups=groups, norm_layer=norm_layer, filter_size=filter_size) self.layer4 = self._make_layer(block, planes[3], layers[3], stride=2, groups=groups, norm_layer=norm_layer, filter_size=filter_size) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(planes[3] * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): if(m.in_channels!=m.out_channels or m.out_channels!=m.groups or m.bias is not None): # don't want to reinitialize downsample layers, code assuming normal conv layers will not have these characteristics nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') else: print('Not initializing') elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) # Zero-initialize the last BN in each residual branch, # so that the residual branch starts with zeros, and each residual block behaves like an identity. # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 if zero_init_residual: for m in self.modules(): if isinstance(m, Bottleneck): nn.init.constant_(m.bn3.weight, 0) elif isinstance(m, BasicBlock): nn.init.constant_(m.bn2.weight, 0) def _make_layer(self, block, planes, blocks, stride=1, groups=1, norm_layer=None, filter_size=1): if norm_layer is None: norm_layer = nn.BatchNorm2d downsample = None if stride != 1 or self.inplanes != planes * block.expansion: # downsample = nn.Sequential( # conv1x1(self.inplanes, planes * block.expansion, stride, filter_size=filter_size), # norm_layer(planes * block.expansion), # ) downsample = [Downsample(filt_size=filter_size, stride=stride, channels=self.inplanes),] if(stride !=1) else [] downsample += [conv1x1(self.inplanes, planes * block.expansion, 1), norm_layer(planes * block.expansion)] # print(downsample) downsample = nn.Sequential(*downsample) layers = [] layers.append(block(self.inplanes, planes, stride, downsample, groups, norm_layer, filter_size=filter_size)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes, groups=groups, norm_layer=norm_layer, filter_size=filter_size)) 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 resnet18(pretrained=False, filter_size=1, pool_only=True, **kwargs): """Constructs a ResNet-18 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(BasicBlock, [2, 2, 2, 2], filter_size=filter_size, pool_only=pool_only, **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet18'])) return model def resnet34(pretrained=False, filter_size=1, pool_only=True, **kwargs): """Constructs a ResNet-34 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(BasicBlock, [3, 4, 6, 3], filter_size=filter_size, pool_only=pool_only, **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet34'])) return model def resnet50(pretrained=False, filter_size=1, pool_only=True, **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], filter_size=filter_size, pool_only=pool_only, **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet50'])) return model def resnet101(pretrained=False, filter_size=1, pool_only=True, **kwargs): """Constructs a ResNet-101 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(Bottleneck, [3, 4, 23, 3], filter_size=filter_size, pool_only=pool_only, **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet101'])) return model def resnet152(pretrained=False, filter_size=1, pool_only=True, **kwargs): """Constructs a ResNet-152 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(Bottleneck, [3, 8, 36, 3], filter_size=filter_size, pool_only=pool_only, **kwargs) if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['resnet152'])) return model def resnext50_32x4d(pretrained=False, filter_size=1, pool_only=True, **kwargs): model = ResNet(Bottleneck, [3, 4, 6, 3], groups=4, width_per_group=32, filter_size=filter_size, pool_only=pool_only, **kwargs) # if pretrained: # model.load_state_dict(model_zoo.load_url(model_urls['resnet50'])) return model def resnext101_32x8d(pretrained=False, filter_size=1, pool_only=True, **kwargs): model = ResNet(Bottleneck, [3, 4, 23, 3], groups=8, width_per_group=32, filter_size=filter_size, pool_only=pool_only, **kwargs) # if pretrained: # model.load_state_dict(model_zoo.load_url(model_urls['resnet50'])) return model