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import re |
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from collections import OrderedDict |
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from functools import partial |
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from typing import Any, List, Optional, 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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from ..transforms._presets import ImageClassification |
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from ..utils import _log_api_usage_once |
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from ._api import register_model, Weights, WeightsEnum |
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from ._meta import _IMAGENET_CATEGORIES |
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from ._utils import _ovewrite_named_param, handle_legacy_interface |
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__all__ = [ |
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"DenseNet", |
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"DenseNet121_Weights", |
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"DenseNet161_Weights", |
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"DenseNet169_Weights", |
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"DenseNet201_Weights", |
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"densenet121", |
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"densenet161", |
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"densenet169", |
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"densenet201", |
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] |
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class _DenseLayer(nn.Module): |
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def __init__( |
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self, num_input_features: int, growth_rate: int, bn_size: int, drop_rate: float, memory_efficient: bool = False |
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) -> None: |
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super().__init__() |
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self.norm1 = nn.BatchNorm2d(num_input_features) |
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self.relu1 = nn.ReLU(inplace=True) |
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self.conv1 = nn.Conv2d(num_input_features, bn_size * growth_rate, kernel_size=1, stride=1, bias=False) |
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self.norm2 = nn.BatchNorm2d(bn_size * growth_rate) |
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self.relu2 = nn.ReLU(inplace=True) |
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self.conv2 = nn.Conv2d(bn_size * growth_rate, growth_rate, kernel_size=3, stride=1, padding=1, bias=False) |
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self.drop_rate = float(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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concated_features = torch.cat(inputs, 1) |
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bottleneck_output = self.conv1(self.relu1(self.norm1(concated_features))) |
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return bottleneck_output |
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def any_requires_grad(self, input: List[Tensor]) -> bool: |
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for tensor in input: |
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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, input: List[Tensor]) -> Tensor: |
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def closure(*inputs): |
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return self.bn_function(inputs) |
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return cp.checkpoint(closure, *input, use_reentrant=False) |
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@torch.jit._overload_method |
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def forward(self, input: List[Tensor]) -> Tensor: |
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pass |
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@torch.jit._overload_method |
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def forward(self, input: Tensor) -> Tensor: |
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pass |
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def forward(self, input: Tensor) -> Tensor: |
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if isinstance(input, Tensor): |
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prev_features = [input] |
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else: |
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prev_features = input |
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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(new_features, p=self.drop_rate, training=self.training) |
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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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num_input_features: 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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) -> None: |
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super().__init__() |
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for i in range(num_layers): |
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layer = _DenseLayer( |
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num_input_features + 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, num_input_features: int, num_output_features: int) -> None: |
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super().__init__() |
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self.norm = nn.BatchNorm2d(num_input_features) |
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self.relu = nn.ReLU(inplace=True) |
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self.conv = nn.Conv2d(num_input_features, num_output_features, kernel_size=1, stride=1, bias=False) |
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self.pool = nn.AvgPool2d(kernel_size=2, stride=2) |
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class DenseNet(nn.Module): |
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r"""Densenet-BC model class, based on |
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`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_. |
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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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but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_. |
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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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) -> None: |
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super().__init__() |
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_log_api_usage_once(self) |
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self.features = nn.Sequential( |
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OrderedDict( |
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[ |
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("conv0", nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)), |
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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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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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num_input_features=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(num_input_features=num_features, num_output_features=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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self.features.add_module("norm5", nn.BatchNorm2d(num_features)) |
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self.classifier = nn.Linear(num_features, num_classes) |
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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 _load_state_dict(model: nn.Module, weights: WeightsEnum, progress: bool) -> None: |
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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 = weights.get_state_dict(progress=progress, check_hash=True) |
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for key in list(state_dict.keys()): |
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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) |
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def _densenet( |
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growth_rate: int, |
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block_config: Tuple[int, int, int, int], |
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num_init_features: int, |
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weights: Optional[WeightsEnum], |
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progress: bool, |
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**kwargs: Any, |
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) -> DenseNet: |
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if weights is not None: |
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_ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) |
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model = DenseNet(growth_rate, block_config, num_init_features, **kwargs) |
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if weights is not None: |
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_load_state_dict(model=model, weights=weights, progress=progress) |
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return model |
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_COMMON_META = { |
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"min_size": (29, 29), |
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"categories": _IMAGENET_CATEGORIES, |
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"recipe": "https://github.com/pytorch/vision/pull/116", |
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"_docs": """These weights are ported from LuaTorch.""", |
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} |
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class DenseNet121_Weights(WeightsEnum): |
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IMAGENET1K_V1 = Weights( |
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url="https://download.pytorch.org/models/densenet121-a639ec97.pth", |
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transforms=partial(ImageClassification, crop_size=224), |
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meta={ |
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**_COMMON_META, |
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"num_params": 7978856, |
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"_metrics": { |
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"ImageNet-1K": { |
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"acc@1": 74.434, |
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"acc@5": 91.972, |
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} |
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}, |
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"_ops": 2.834, |
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"_file_size": 30.845, |
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}, |
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) |
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DEFAULT = IMAGENET1K_V1 |
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class DenseNet161_Weights(WeightsEnum): |
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IMAGENET1K_V1 = Weights( |
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url="https://download.pytorch.org/models/densenet161-8d451a50.pth", |
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transforms=partial(ImageClassification, crop_size=224), |
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meta={ |
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**_COMMON_META, |
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"num_params": 28681000, |
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"_metrics": { |
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"ImageNet-1K": { |
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"acc@1": 77.138, |
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"acc@5": 93.560, |
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} |
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}, |
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"_ops": 7.728, |
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"_file_size": 110.369, |
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}, |
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) |
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DEFAULT = IMAGENET1K_V1 |
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class DenseNet169_Weights(WeightsEnum): |
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IMAGENET1K_V1 = Weights( |
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url="https://download.pytorch.org/models/densenet169-b2777c0a.pth", |
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transforms=partial(ImageClassification, crop_size=224), |
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meta={ |
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**_COMMON_META, |
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"num_params": 14149480, |
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"_metrics": { |
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"ImageNet-1K": { |
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"acc@1": 75.600, |
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"acc@5": 92.806, |
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} |
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}, |
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"_ops": 3.36, |
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"_file_size": 54.708, |
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}, |
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) |
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DEFAULT = IMAGENET1K_V1 |
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class DenseNet201_Weights(WeightsEnum): |
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IMAGENET1K_V1 = Weights( |
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url="https://download.pytorch.org/models/densenet201-c1103571.pth", |
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transforms=partial(ImageClassification, crop_size=224), |
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meta={ |
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**_COMMON_META, |
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"num_params": 20013928, |
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"_metrics": { |
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"ImageNet-1K": { |
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"acc@1": 76.896, |
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"acc@5": 93.370, |
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} |
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}, |
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"_ops": 4.291, |
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"_file_size": 77.373, |
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}, |
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) |
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DEFAULT = IMAGENET1K_V1 |
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@register_model() |
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@handle_legacy_interface(weights=("pretrained", DenseNet121_Weights.IMAGENET1K_V1)) |
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def densenet121(*, weights: Optional[DenseNet121_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet: |
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r"""Densenet-121 model from |
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`Densely Connected Convolutional Networks <https://arxiv.org/abs/1608.06993>`_. |
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Args: |
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weights (:class:`~torchvision.models.DenseNet121_Weights`, optional): The |
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pretrained weights to use. See |
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:class:`~torchvision.models.DenseNet121_Weights` below for |
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more details, and possible values. By default, no pre-trained |
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weights are used. |
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progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. |
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**kwargs: parameters passed to the ``torchvision.models.densenet.DenseNet`` |
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base class. Please refer to the `source code |
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<https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py>`_ |
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for more details about this class. |
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.. autoclass:: torchvision.models.DenseNet121_Weights |
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:members: |
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""" |
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weights = DenseNet121_Weights.verify(weights) |
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return _densenet(32, (6, 12, 24, 16), 64, weights, progress, **kwargs) |
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@register_model() |
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@handle_legacy_interface(weights=("pretrained", DenseNet161_Weights.IMAGENET1K_V1)) |
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def densenet161(*, weights: Optional[DenseNet161_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet: |
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r"""Densenet-161 model from |
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`Densely Connected Convolutional Networks <https://arxiv.org/abs/1608.06993>`_. |
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Args: |
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weights (:class:`~torchvision.models.DenseNet161_Weights`, optional): The |
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pretrained weights to use. See |
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:class:`~torchvision.models.DenseNet161_Weights` below for |
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more details, and possible values. By default, no pre-trained |
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weights are used. |
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progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. |
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**kwargs: parameters passed to the ``torchvision.models.densenet.DenseNet`` |
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base class. Please refer to the `source code |
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<https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py>`_ |
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for more details about this class. |
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.. autoclass:: torchvision.models.DenseNet161_Weights |
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:members: |
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""" |
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weights = DenseNet161_Weights.verify(weights) |
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return _densenet(48, (6, 12, 36, 24), 96, weights, progress, **kwargs) |
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@register_model() |
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@handle_legacy_interface(weights=("pretrained", DenseNet169_Weights.IMAGENET1K_V1)) |
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def densenet169(*, weights: Optional[DenseNet169_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet: |
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r"""Densenet-169 model from |
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`Densely Connected Convolutional Networks <https://arxiv.org/abs/1608.06993>`_. |
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Args: |
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weights (:class:`~torchvision.models.DenseNet169_Weights`, optional): The |
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pretrained weights to use. See |
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:class:`~torchvision.models.DenseNet169_Weights` below for |
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more details, and possible values. By default, no pre-trained |
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weights are used. |
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progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. |
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**kwargs: parameters passed to the ``torchvision.models.densenet.DenseNet`` |
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base class. Please refer to the `source code |
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<https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py>`_ |
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for more details about this class. |
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.. autoclass:: torchvision.models.DenseNet169_Weights |
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:members: |
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""" |
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weights = DenseNet169_Weights.verify(weights) |
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return _densenet(32, (6, 12, 32, 32), 64, weights, progress, **kwargs) |
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@register_model() |
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@handle_legacy_interface(weights=("pretrained", DenseNet201_Weights.IMAGENET1K_V1)) |
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def densenet201(*, weights: Optional[DenseNet201_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet: |
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r"""Densenet-201 model from |
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`Densely Connected Convolutional Networks <https://arxiv.org/abs/1608.06993>`_. |
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Args: |
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weights (:class:`~torchvision.models.DenseNet201_Weights`, optional): The |
|
pretrained weights to use. See |
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:class:`~torchvision.models.DenseNet201_Weights` below for |
|
more details, and possible values. By default, no pre-trained |
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weights are used. |
|
progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. |
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**kwargs: parameters passed to the ``torchvision.models.densenet.DenseNet`` |
|
base class. Please refer to the `source code |
|
<https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py>`_ |
|
for more details about this class. |
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|
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.. autoclass:: torchvision.models.DenseNet201_Weights |
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:members: |
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""" |
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weights = DenseNet201_Weights.verify(weights) |
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return _densenet(32, (6, 12, 48, 32), 64, weights, progress, **kwargs) |
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