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