import torch import torch.nn as nn import torch.nn.functional as F from .nets_utils import EmbeddingRecorder from torchvision.models import resnet from .resnet import ResNet_224x224 # Acknowledgement to # https://github.com/xternalz/WideResNet-pytorch class BasicBlock(nn.Module): def __init__(self, in_planes, out_planes, stride, dropRate=0.0): super(BasicBlock, self).__init__() self.bn1 = nn.BatchNorm2d(in_planes) self.relu1 = nn.ReLU(inplace=True) self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(out_planes) self.relu2 = nn.ReLU(inplace=True) self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False) self.droprate = dropRate self.equalInOut = (in_planes == out_planes) self.convShortcut = (not self.equalInOut) and nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False) or None def forward(self, x): if not self.equalInOut: x = self.relu1(self.bn1(x)) else: out = self.relu1(self.bn1(x)) out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x))) if self.droprate > 0: out = F.dropout(out, p=self.droprate, training=self.training) out = self.conv2(out) return torch.add(x if self.equalInOut else self.convShortcut(x), out) class NetworkBlock(nn.Module): def __init__(self, nb_layers, in_planes, out_planes, block, stride, dropRate=0.0): super(NetworkBlock, self).__init__() self.layer = self._make_layer(block, in_planes, out_planes, nb_layers, stride, dropRate) def _make_layer(self, block, in_planes, out_planes, nb_layers, stride, dropRate): layers = [] for i in range(int(nb_layers)): layers.append(block(i == 0 and in_planes or out_planes, out_planes, i == 0 and stride or 1, dropRate)) return nn.Sequential(*layers) def forward(self, x): return self.layer(x) class WideResNet_32x32(nn.Module): def __init__(self, depth, num_classes, channel=3, widen_factor=1, drop_rate=0.0, record_embedding=False, no_grad=False): super(WideResNet_32x32, self).__init__() nChannels = [16, 16 * widen_factor, 32 * widen_factor, 64 * widen_factor] assert ((depth - 4) % 6 == 0) n = (depth - 4) / 6 block = BasicBlock # 1st conv before any network block self.conv1 = nn.Conv2d(channel, nChannels[0], kernel_size=3, stride=1, padding=3 if channel == 1 else 1, bias=False) # 1st block self.block1 = NetworkBlock(n, nChannels[0], nChannels[1], block, 1, drop_rate) # 2nd block self.block2 = NetworkBlock(n, nChannels[1], nChannels[2], block, 2, drop_rate) # 3rd block self.block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, 2, drop_rate) # global average pooling and classifier self.bn1 = nn.BatchNorm2d(nChannels[3]) self.relu = nn.ReLU(inplace=True) self.fc = nn.Linear(nChannels[3], num_classes) self.nChannels = nChannels[3] 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): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Linear): m.bias.data.zero_() self.embedding_recorder = EmbeddingRecorder(record_embedding) self.no_grad = no_grad def get_last_layer(self): return self.fc def forward(self, x): with torch.set_grad_enabled(not self.no_grad): out = self.conv1(x) out = self.block1(out) out = self.block2(out) out = self.block3(out) out = self.relu(self.bn1(out)) out = F.avg_pool2d(out, 8) out = out.view(-1, self.nChannels) out = self.embedding_recorder(out) return self.fc(out) def WideResNet(arch: str, channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, pretrained: bool = False): arch = arch.lower() if pretrained: if im_size[0] != 224 or im_size[1] != 224: raise NotImplementedError("torchvison pretrained models only accept inputs with size of 224*224") if arch == "wrn502": arch = "wide_resnet50_2" net = ResNet_224x224(resnet.Bottleneck, [3, 4, 6, 3], channel=3, num_classes=1000, record_embedding=record_embedding, no_grad=no_grad, width_per_group=64 * 2) elif arch == "wrn1012": arch = "wide_resnet101_2" net = ResNet_224x224(resnet.Bottleneck, [3, 4, 23, 3], channel=3, num_classes=1000, record_embedding=record_embedding, no_grad=no_grad, width_per_group=64 * 2) else: raise ValueError("Model architecture not found.") from torch.hub import load_state_dict_from_url state_dict = load_state_dict_from_url(resnet.model_urls[arch], progress=True) net.load_state_dict(state_dict) if channel != 3: net.conv1 = nn.Conv2d(channel, 64, kernel_size=7, stride=2, padding=3, bias=False) if num_classes != 1000: net.fc = nn.Linear(net.fc.in_features, num_classes) elif im_size[0] == 224 and im_size[1] == 224: # Use torchvision models without pretrained parameters if arch == "wrn502": arch = "wide_resnet50_2" net = ResNet_224x224(resnet.Bottleneck, [3, 4, 6, 3], channel=channel, num_classes=num_classes, record_embedding=record_embedding, no_grad=no_grad, width_per_group=64 * 2) elif arch == "wrn1012": arch = "wide_resnet101_2" net = ResNet_224x224(resnet.Bottleneck, [3, 4, 23, 3], channel=channel, num_classes=num_classes, record_embedding=record_embedding, no_grad=no_grad, width_per_group=64 * 2) else: raise ValueError("Model architecture not found.") elif (channel == 1 and im_size[0] == 28 and im_size[1] == 28) or ( channel == 3 and im_size[0] == 32 and im_size[1] == 32): if arch == "wrn168": net = WideResNet_32x32(16, num_classes, channel, 8) elif arch == "wrn2810": net = WideResNet_32x32(28, num_classes, channel, 10) elif arch == "wrn282": net = WideResNet_32x32(28, num_classes, channel, 2) else: raise ValueError("Model architecture not found.") else: raise NotImplementedError("Network Architecture for current dataset has not been implemented.") return net def WRN168(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, pretrained: bool = False): return WideResNet("wrn168", channel, num_classes, im_size, record_embedding, no_grad, pretrained) def WRN2810(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, pretrained: bool = False): return WideResNet("wrn2810", channel, num_classes, im_size, record_embedding, no_grad, pretrained) def WRN282(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, pretrained: bool = False): return WideResNet('wrn282', channel, num_classes, im_size, record_embedding, no_grad, pretrained) def WRN502(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, pretrained: bool = False): return WideResNet("wrn502", channel, num_classes, im_size, record_embedding, no_grad, pretrained) def WRN1012(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, pretrained: bool = False): return WideResNet("wrn1012", channel, num_classes, im_size, record_embedding, no_grad, pretrained)