287 lines
9.3 KiB
Python
287 lines
9.3 KiB
Python
import re
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from collections import OrderedDict
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from typing import Any, List, Tuple
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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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import torch.utils.checkpoint as cp
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from torch import Tensor
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class _DenseLayer(nn.Module):
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def __init__(
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self,
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input_c: int,
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growth_rate: int,
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bn_size: int,
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drop_rate: float,
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memory_efficient: bool = False,
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):
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super(_DenseLayer, self).__init__()
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self.add_module("norm1", nn.BatchNorm2d(input_c))
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self.add_module("relu1", nn.ReLU(inplace=True))
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self.add_module(
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"conv1",
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nn.Conv2d(
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in_channels=input_c,
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out_channels=bn_size * growth_rate,
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kernel_size=1,
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stride=1,
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bias=False,
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),
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)
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self.add_module("norm2", nn.BatchNorm2d(bn_size * growth_rate))
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self.add_module("relu2", nn.ReLU(inplace=True))
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self.add_module(
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"conv2",
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nn.Conv2d(
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bn_size * growth_rate,
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growth_rate,
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kernel_size=3,
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stride=1,
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padding=1,
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bias=False,
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),
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)
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self.drop_rate = drop_rate
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self.memory_efficient = memory_efficient
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def bn_function(self, inputs: List[Tensor]) -> Tensor:
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concat_features = torch.cat(inputs, 1)
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bottleneck_output = self.conv1(self.relu1(self.norm1(concat_features)))
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return bottleneck_output
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@staticmethod
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def any_requires_grad(inputs: List[Tensor]) -> bool:
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for tensor in inputs:
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if tensor.requires_grad:
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return True
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return False
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@torch.jit.unused
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def call_checkpoint_bottleneck(self, inputs: List[Tensor]) -> Tensor:
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def closure(*inp):
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return self.bn_function(inp)
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return cp.checkpoint(closure, *inputs)
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def forward(self, inputs: Tensor) -> Tensor:
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if isinstance(inputs, Tensor):
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prev_features = [inputs]
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else:
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prev_features = inputs
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if self.memory_efficient and self.any_requires_grad(prev_features):
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if torch.jit.is_scripting():
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raise Exception("memory efficient not supported in JIT")
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bottleneck_output = self.call_checkpoint_bottleneck(prev_features)
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else:
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bottleneck_output = self.bn_function(prev_features)
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new_features = self.conv2(self.relu2(self.norm2(bottleneck_output)))
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if self.drop_rate > 0:
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new_features = F.dropout(
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new_features, p=self.drop_rate, training=self.training
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)
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return new_features
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class _DenseBlock(nn.ModuleDict):
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_version = 2
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def __init__(
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self,
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num_layers: int,
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input_c: int,
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bn_size: int,
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growth_rate: int,
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drop_rate: float,
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memory_efficient: bool = False,
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):
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super(_DenseBlock, self).__init__()
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for i in range(num_layers):
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layer = _DenseLayer(
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input_c + i * growth_rate,
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growth_rate=growth_rate,
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bn_size=bn_size,
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drop_rate=drop_rate,
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memory_efficient=memory_efficient,
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)
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self.add_module("denselayer%d" % (i + 1), layer)
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def forward(self, init_features: Tensor) -> Tensor:
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features = [init_features]
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for name, layer in self.items():
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new_features = layer(features)
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features.append(new_features)
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return torch.cat(features, 1)
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class _Transition(nn.Sequential):
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def __init__(self, input_c: int, output_c: int):
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super(_Transition, self).__init__()
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self.add_module("norm", nn.BatchNorm2d(input_c))
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self.add_module("relu", nn.ReLU(inplace=True))
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self.add_module(
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"conv", nn.Conv2d(input_c, output_c, kernel_size=1, stride=1, bias=False)
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)
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self.add_module("pool", nn.AvgPool2d(kernel_size=2, stride=2))
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class DenseNet(nn.Module):
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"""
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Densenet-BC model class for imagenet
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Args:
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growth_rate (int) - how many filters to add each layer (`k` in paper)
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block_config (list of 4 ints) - how many layers in each pooling block
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num_init_features (int) - the number of filters to learn in the first convolution layer
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bn_size (int) - multiplicative factor for number of bottle neck layers
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(i.e. bn_size * k features in the bottleneck layer)
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drop_rate (float) - dropout rate after each dense layer
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num_classes (int) - number of classification classes
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memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient
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"""
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def __init__(
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self,
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growth_rate: int = 32,
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block_config: Tuple[int, int, int, int] = (6, 12, 24, 16),
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num_init_features: int = 64,
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bn_size: int = 4,
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drop_rate: float = 0,
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num_classes: int = 1000,
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memory_efficient: bool = False,
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):
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super(DenseNet, self).__init__()
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# first conv+bn+relu+pool
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self.features = nn.Sequential(
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OrderedDict(
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[
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(
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"conv0",
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nn.Conv2d(
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3,
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num_init_features,
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kernel_size=7,
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stride=2,
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padding=3,
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bias=False,
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),
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),
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("norm0", nn.BatchNorm2d(num_init_features)),
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("relu0", nn.ReLU(inplace=True)),
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("pool0", nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
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]
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)
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)
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# each dense block
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num_features = num_init_features
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for i, num_layers in enumerate(block_config):
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block = _DenseBlock(
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num_layers=num_layers,
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input_c=num_features,
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bn_size=bn_size,
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growth_rate=growth_rate,
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drop_rate=drop_rate,
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memory_efficient=memory_efficient,
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)
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self.features.add_module("denseblock%d" % (i + 1), block)
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num_features = num_features + num_layers * growth_rate
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if i != len(block_config) - 1:
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trans = _Transition(input_c=num_features, output_c=num_features // 2)
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self.features.add_module("transition%d" % (i + 1), trans)
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num_features = num_features // 2
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# finnal batch norm
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self.features.add_module("norm5", nn.BatchNorm2d(num_features))
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# fc layer
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self.classifier = nn.Linear(num_features, num_classes)
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# init weights
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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)
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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nn.init.constant_(m.bias, 0)
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def forward(self, x: Tensor) -> Tensor:
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features = self.features(x)
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out = F.relu(features, inplace=True)
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out = F.adaptive_avg_pool2d(out, (1, 1))
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out = torch.flatten(out, 1)
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out = self.classifier(out)
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return out
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def densenet121(**kwargs: Any) -> DenseNet:
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# Top-1 error: 25.35%
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# 'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth'
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return DenseNet(
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growth_rate=32, block_config=(6, 12, 24, 16), num_init_features=64, **kwargs
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)
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def densenet169(**kwargs: Any) -> DenseNet:
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# Top-1 error: 24.00%
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# 'densenet169': 'https://download.pytorch.org/models/densenet169-b2777c0a.pth'
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return DenseNet(
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growth_rate=32, block_config=(6, 12, 32, 32), num_init_features=64, **kwargs
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)
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def densenet201(**kwargs: Any) -> DenseNet:
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# Top-1 error: 22.80%
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# 'densenet201': 'https://download.pytorch.org/models/densenet201-c1103571.pth'
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return DenseNet(
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growth_rate=32, block_config=(6, 12, 48, 32), num_init_features=64, **kwargs
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)
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def densenet161(**kwargs: Any) -> DenseNet:
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# Top-1 error: 22.35%
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# 'densenet161': 'https://download.pytorch.org/models/densenet161-8d451a50.pth'
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return DenseNet(
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growth_rate=48, block_config=(6, 12, 36, 24), num_init_features=96, **kwargs
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)
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def load_state_dict(model: nn.Module, weights_path: str) -> None:
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# '.'s are no longer allowed in module names, but previous _DenseLayer
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# has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
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# They are also in the checkpoints in model_urls. This pattern is used
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# to find such keys.
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pattern = re.compile(
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r"^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$"
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)
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state_dict = torch.load(weights_path)
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num_classes = model.classifier.out_features
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load_fc = num_classes == 1000
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for key in list(state_dict.keys()):
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if load_fc is False:
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if "classifier" in key:
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del state_dict[key]
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res = pattern.match(key)
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if res:
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new_key = res.group(1) + res.group(2)
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state_dict[new_key] = state_dict[key]
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del state_dict[key]
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model.load_state_dict(state_dict, strict=load_fc)
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print("successfully load pretrain-weights.")
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