242 lines
11 KiB
Python
242 lines
11 KiB
Python
import torch.nn as nn
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import torch.nn.functional as F
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from torch import set_grad_enabled, flatten, Tensor
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from .nets_utils import EmbeddingRecorder
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from torchvision.models import resnet
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# Acknowledgement to
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# https://github.com/kuangliu/pytorch-cifar,
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# https://github.com/BIGBALLON/CIFAR-ZOO,
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def conv3x3(in_planes, out_planes, stride=1):
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return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
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class BasicBlock(nn.Module):
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expansion = 1
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def __init__(self, in_planes, planes, stride=1):
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super(BasicBlock, self).__init__()
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self.conv1 = conv3x3(in_planes, planes, stride)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = conv3x3(planes, planes)
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self.bn2 = nn.BatchNorm2d(planes)
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self.shortcut = nn.Sequential()
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if stride != 1 or in_planes != self.expansion * planes:
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self.shortcut = nn.Sequential(
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nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(self.expansion * planes)
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)
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def forward(self, x):
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out = F.relu(self.bn1(self.conv1(x)))
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out = self.bn2(self.conv2(out))
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out += self.shortcut(x)
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out = F.relu(out)
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return out
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class Bottleneck(nn.Module):
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expansion = 4
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def __init__(self, in_planes, planes, stride=1):
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super(Bottleneck, self).__init__()
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self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(planes)
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self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(self.expansion * planes)
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self.shortcut = nn.Sequential()
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if stride != 1 or in_planes != self.expansion * planes:
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self.shortcut = nn.Sequential(
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nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(self.expansion * planes)
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)
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def forward(self, x):
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out = F.relu(self.bn1(self.conv1(x)))
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out = F.relu(self.bn2(self.conv2(out)))
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out = self.bn3(self.conv3(out))
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out += self.shortcut(x)
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out = F.relu(out)
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return out
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class ResNet_32x32(nn.Module):
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def __init__(self, block, num_blocks, channel=3, num_classes=10, record_embedding: bool = False,
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no_grad: bool = False):
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super().__init__()
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self.in_planes = 64
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self.conv1 = conv3x3(channel, 64)
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self.bn1 = nn.BatchNorm2d(64)
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self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
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self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
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self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
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self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
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self.linear = nn.Linear(512 * block.expansion, num_classes)
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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.linear
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def _make_layer(self, block, planes, num_blocks, stride):
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strides = [stride] + [1] * (num_blocks - 1)
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layers = []
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for stride in strides:
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layers.append(block(self.in_planes, planes, stride))
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self.in_planes = planes * block.expansion
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return nn.Sequential(*layers)
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def forward(self, x):
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with set_grad_enabled(not self.no_grad):
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out = F.relu(self.bn1(self.conv1(x)))
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out = self.layer1(out)
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out = self.layer2(out)
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out = self.layer3(out)
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out = self.layer4(out)
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out = F.avg_pool2d(out, 4)
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out = out.view(out.size(0), -1)
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out = self.embedding_recorder(out)
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out = self.linear(out)
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return out
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class ResNet_224x224(resnet.ResNet):
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def __init__(self, block, layers, channel: int, num_classes: int, record_embedding: bool = False,
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no_grad: bool = False, **kwargs):
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super().__init__(block, layers, **kwargs)
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self.embedding_recorder = EmbeddingRecorder(record_embedding)
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if channel != 3:
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self.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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self.fc = nn.Linear(self.fc.in_features, num_classes)
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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_impl(self, x: Tensor) -> Tensor:
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# See note [TorchScript super()]
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with set_grad_enabled(not self.no_grad):
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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x = self.avgpool(x)
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x = flatten(x, 1)
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x = self.embedding_recorder(x)
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x = self.fc(x)
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return x
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def ResNet(arch: str, 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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arch = arch.lower()
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if pretrained:
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if arch == "resnet18":
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net = ResNet_224x224(resnet.BasicBlock, [2, 2, 2, 2], channel=3, num_classes=1000,
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record_embedding=record_embedding, no_grad=no_grad)
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elif arch == "resnet34":
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net = ResNet_224x224(resnet.BasicBlock, [3, 4, 6, 3], channel=3, num_classes=1000,
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record_embedding=record_embedding, no_grad=no_grad)
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elif arch == "resnet50":
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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)
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elif arch == "resnet101":
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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)
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elif arch == "resnet152":
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net = ResNet_224x224(resnet.Bottleneck, [3, 8, 36, 3], channel=3, num_classes=1000,
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record_embedding=record_embedding, no_grad=no_grad)
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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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if arch == "resnet18":
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net = ResNet_224x224(resnet.BasicBlock, [2, 2, 2, 2], channel=channel, num_classes=num_classes,
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record_embedding=record_embedding, no_grad=no_grad)
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elif arch == "resnet34":
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net = ResNet_224x224(resnet.BasicBlock, [3, 4, 6, 3], channel=channel, num_classes=num_classes,
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record_embedding=record_embedding, no_grad=no_grad)
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elif arch == "resnet50":
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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)
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elif arch == "resnet101":
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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)
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elif arch == "resnet152":
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net = ResNet_224x224(resnet.Bottleneck, [3, 8, 36, 3], channel=channel, num_classes=num_classes,
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record_embedding=record_embedding, no_grad=no_grad)
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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 == "resnet18":
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net = ResNet_32x32(BasicBlock, [2, 2, 2, 2], channel=channel, num_classes=num_classes,
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record_embedding=record_embedding, no_grad=no_grad)
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elif arch == "resnet34":
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net = ResNet_32x32(BasicBlock, [3, 4, 6, 3], channel=channel, num_classes=num_classes,
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record_embedding=record_embedding, no_grad=no_grad)
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elif arch == "resnet50":
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net = ResNet_32x32(Bottleneck, [3, 4, 6, 3], channel=channel, num_classes=num_classes,
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record_embedding=record_embedding, no_grad=no_grad)
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elif arch == "resnet101":
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net = ResNet_32x32(Bottleneck, [3, 4, 23, 3], channel=channel, num_classes=num_classes,
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record_embedding=record_embedding, no_grad=no_grad)
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elif arch == "resnet152":
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net = ResNet_32x32(Bottleneck, [3, 8, 36, 3], channel=channel, num_classes=num_classes,
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record_embedding=record_embedding, no_grad=no_grad)
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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 ResNet18(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 ResNet("resnet18", channel, num_classes, im_size, record_embedding, no_grad, pretrained)
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def ResNet34(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 ResNet("resnet34", channel, num_classes, im_size, record_embedding, no_grad, pretrained)
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def ResNet50(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 ResNet("resnet50", channel, num_classes, im_size, record_embedding, no_grad, pretrained)
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def ResNet101(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 ResNet("resnet101", channel, num_classes, im_size, record_embedding, no_grad, pretrained)
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def ResNet152(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 ResNet("resnet152", channel, num_classes, im_size, record_embedding, no_grad, pretrained)
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