409 lines
15 KiB
Python
409 lines
15 KiB
Python
from functools import partial
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from typing import Any, Callable, List, Optional
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import torch
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import torch.nn as nn
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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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"ShuffleNetV2",
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"ShuffleNet_V2_X0_5_Weights",
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"ShuffleNet_V2_X1_0_Weights",
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"ShuffleNet_V2_X1_5_Weights",
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"ShuffleNet_V2_X2_0_Weights",
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"shufflenet_v2_x0_5",
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"shufflenet_v2_x1_0",
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"shufflenet_v2_x1_5",
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"shufflenet_v2_x2_0",
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]
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def channel_shuffle(x: Tensor, groups: int) -> Tensor:
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batchsize, num_channels, height, width = x.size()
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channels_per_group = num_channels // groups
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# reshape
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x = x.view(batchsize, groups, channels_per_group, height, width)
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x = torch.transpose(x, 1, 2).contiguous()
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# flatten
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x = x.view(batchsize, num_channels, height, width)
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return x
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class InvertedResidual(nn.Module):
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def __init__(self, inp: int, oup: int, stride: int) -> None:
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super().__init__()
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if not (1 <= stride <= 3):
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raise ValueError("illegal stride value")
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self.stride = stride
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branch_features = oup // 2
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if (self.stride == 1) and (inp != branch_features << 1):
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raise ValueError(
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f"Invalid combination of stride {stride}, inp {inp} and oup {oup} values. If stride == 1 then inp should be equal to oup // 2 << 1."
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)
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if self.stride > 1:
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self.branch1 = nn.Sequential(
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self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1),
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nn.BatchNorm2d(inp),
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nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
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nn.BatchNorm2d(branch_features),
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nn.ReLU(inplace=True),
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)
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else:
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self.branch1 = nn.Sequential()
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self.branch2 = nn.Sequential(
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nn.Conv2d(
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inp if (self.stride > 1) else branch_features,
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branch_features,
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kernel_size=1,
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stride=1,
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padding=0,
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bias=False,
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),
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nn.BatchNorm2d(branch_features),
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nn.ReLU(inplace=True),
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self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1),
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nn.BatchNorm2d(branch_features),
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nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
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nn.BatchNorm2d(branch_features),
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nn.ReLU(inplace=True),
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)
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@staticmethod
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def depthwise_conv(
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i: int, o: int, kernel_size: int, stride: int = 1, padding: int = 0, bias: bool = False
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) -> nn.Conv2d:
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return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i)
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def forward(self, x: Tensor) -> Tensor:
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if self.stride == 1:
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x1, x2 = x.chunk(2, dim=1)
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out = torch.cat((x1, self.branch2(x2)), dim=1)
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else:
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out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
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out = channel_shuffle(out, 2)
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return out
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class ShuffleNetV2(nn.Module):
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def __init__(
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self,
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stages_repeats: List[int],
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stages_out_channels: List[int],
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num_classes: int = 1000,
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inverted_residual: Callable[..., nn.Module] = InvertedResidual,
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) -> None:
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super().__init__()
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_log_api_usage_once(self)
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if len(stages_repeats) != 3:
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raise ValueError("expected stages_repeats as list of 3 positive ints")
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if len(stages_out_channels) != 5:
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raise ValueError("expected stages_out_channels as list of 5 positive ints")
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self._stage_out_channels = stages_out_channels
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input_channels = 3
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output_channels = self._stage_out_channels[0]
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self.conv1 = nn.Sequential(
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nn.Conv2d(input_channels, output_channels, 3, 2, 1, bias=False),
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nn.BatchNorm2d(output_channels),
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nn.ReLU(inplace=True),
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)
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input_channels = output_channels
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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# Static annotations for mypy
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self.stage2: nn.Sequential
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self.stage3: nn.Sequential
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self.stage4: nn.Sequential
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stage_names = [f"stage{i}" for i in [2, 3, 4]]
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for name, repeats, output_channels in zip(stage_names, stages_repeats, self._stage_out_channels[1:]):
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seq = [inverted_residual(input_channels, output_channels, 2)]
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for i in range(repeats - 1):
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seq.append(inverted_residual(output_channels, output_channels, 1))
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setattr(self, name, nn.Sequential(*seq))
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input_channels = output_channels
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output_channels = self._stage_out_channels[-1]
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self.conv5 = nn.Sequential(
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nn.Conv2d(input_channels, output_channels, 1, 1, 0, bias=False),
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nn.BatchNorm2d(output_channels),
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nn.ReLU(inplace=True),
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)
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self.fc = nn.Linear(output_channels, num_classes)
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def _forward_impl(self, x: Tensor) -> Tensor:
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# See note [TorchScript super()]
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x = self.conv1(x)
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x = self.maxpool(x)
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x = self.stage2(x)
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x = self.stage3(x)
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x = self.stage4(x)
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x = self.conv5(x)
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x = x.mean([2, 3]) # globalpool
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x = self.fc(x)
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return x
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def forward(self, x: Tensor) -> Tensor:
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return self._forward_impl(x)
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def _shufflenetv2(
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weights: Optional[WeightsEnum],
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progress: bool,
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*args: Any,
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**kwargs: Any,
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) -> ShuffleNetV2:
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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 = ShuffleNetV2(*args, **kwargs)
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if weights is not None:
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model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
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return model
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_COMMON_META = {
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"min_size": (1, 1),
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"categories": _IMAGENET_CATEGORIES,
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"recipe": "https://github.com/ericsun99/Shufflenet-v2-Pytorch",
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}
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class ShuffleNet_V2_X0_5_Weights(WeightsEnum):
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IMAGENET1K_V1 = Weights(
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# Weights ported from https://github.com/ericsun99/Shufflenet-v2-Pytorch
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url="https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.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": 1366792,
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"_metrics": {
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"ImageNet-1K": {
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"acc@1": 60.552,
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"acc@5": 81.746,
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}
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},
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"_ops": 0.04,
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"_file_size": 5.282,
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"_docs": """These weights were trained from scratch to reproduce closely the results of the paper.""",
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},
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)
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DEFAULT = IMAGENET1K_V1
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class ShuffleNet_V2_X1_0_Weights(WeightsEnum):
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IMAGENET1K_V1 = Weights(
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# Weights ported from https://github.com/ericsun99/Shufflenet-v2-Pytorch
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url="https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.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": 2278604,
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"_metrics": {
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"ImageNet-1K": {
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"acc@1": 69.362,
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"acc@5": 88.316,
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}
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},
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"_ops": 0.145,
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"_file_size": 8.791,
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"_docs": """These weights were trained from scratch to reproduce closely the results of the paper.""",
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},
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)
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DEFAULT = IMAGENET1K_V1
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class ShuffleNet_V2_X1_5_Weights(WeightsEnum):
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IMAGENET1K_V1 = Weights(
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url="https://download.pytorch.org/models/shufflenetv2_x1_5-3c479a10.pth",
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transforms=partial(ImageClassification, crop_size=224, resize_size=232),
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meta={
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**_COMMON_META,
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"recipe": "https://github.com/pytorch/vision/pull/5906",
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"num_params": 3503624,
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"_metrics": {
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"ImageNet-1K": {
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"acc@1": 72.996,
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"acc@5": 91.086,
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}
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},
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"_ops": 0.296,
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"_file_size": 13.557,
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"_docs": """
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These weights were trained from scratch by using TorchVision's `new training recipe
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<https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_.
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""",
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},
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)
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DEFAULT = IMAGENET1K_V1
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class ShuffleNet_V2_X2_0_Weights(WeightsEnum):
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IMAGENET1K_V1 = Weights(
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url="https://download.pytorch.org/models/shufflenetv2_x2_0-8be3c8ee.pth",
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transforms=partial(ImageClassification, crop_size=224, resize_size=232),
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meta={
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**_COMMON_META,
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"recipe": "https://github.com/pytorch/vision/pull/5906",
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"num_params": 7393996,
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"_metrics": {
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"ImageNet-1K": {
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"acc@1": 76.230,
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"acc@5": 93.006,
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}
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},
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"_ops": 0.583,
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"_file_size": 28.433,
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"_docs": """
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These weights were trained from scratch by using TorchVision's `new training recipe
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<https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_.
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""",
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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", ShuffleNet_V2_X0_5_Weights.IMAGENET1K_V1))
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def shufflenet_v2_x0_5(
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*, weights: Optional[ShuffleNet_V2_X0_5_Weights] = None, progress: bool = True, **kwargs: Any
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) -> ShuffleNetV2:
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"""
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Constructs a ShuffleNetV2 architecture with 0.5x output channels, as described in
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`ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
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<https://arxiv.org/abs/1807.11164>`__.
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Args:
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weights (:class:`~torchvision.models.ShuffleNet_V2_X0_5_Weights`, optional): The
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pretrained weights to use. See
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:class:`~torchvision.models.ShuffleNet_V2_X0_5_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
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download to stderr. Default is True.
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**kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2``
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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/shufflenetv2.py>`_
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for more details about this class.
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.. autoclass:: torchvision.models.ShuffleNet_V2_X0_5_Weights
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:members:
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"""
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weights = ShuffleNet_V2_X0_5_Weights.verify(weights)
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return _shufflenetv2(weights, progress, [4, 8, 4], [24, 48, 96, 192, 1024], **kwargs)
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@register_model()
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@handle_legacy_interface(weights=("pretrained", ShuffleNet_V2_X1_0_Weights.IMAGENET1K_V1))
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def shufflenet_v2_x1_0(
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*, weights: Optional[ShuffleNet_V2_X1_0_Weights] = None, progress: bool = True, **kwargs: Any
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) -> ShuffleNetV2:
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"""
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Constructs a ShuffleNetV2 architecture with 1.0x output channels, as described in
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`ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
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<https://arxiv.org/abs/1807.11164>`__.
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Args:
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weights (:class:`~torchvision.models.ShuffleNet_V2_X1_0_Weights`, optional): The
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pretrained weights to use. See
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:class:`~torchvision.models.ShuffleNet_V2_X1_0_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
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download to stderr. Default is True.
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**kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2``
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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/shufflenetv2.py>`_
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for more details about this class.
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.. autoclass:: torchvision.models.ShuffleNet_V2_X1_0_Weights
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:members:
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"""
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weights = ShuffleNet_V2_X1_0_Weights.verify(weights)
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return _shufflenetv2(weights, progress, [4, 8, 4], [24, 116, 232, 464, 1024], **kwargs)
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@register_model()
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@handle_legacy_interface(weights=("pretrained", ShuffleNet_V2_X1_5_Weights.IMAGENET1K_V1))
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def shufflenet_v2_x1_5(
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*, weights: Optional[ShuffleNet_V2_X1_5_Weights] = None, progress: bool = True, **kwargs: Any
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) -> ShuffleNetV2:
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"""
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Constructs a ShuffleNetV2 architecture with 1.5x output channels, as described in
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`ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
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<https://arxiv.org/abs/1807.11164>`__.
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Args:
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weights (:class:`~torchvision.models.ShuffleNet_V2_X1_5_Weights`, optional): The
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pretrained weights to use. See
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:class:`~torchvision.models.ShuffleNet_V2_X1_5_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
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download to stderr. Default is True.
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**kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2``
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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/shufflenetv2.py>`_
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for more details about this class.
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.. autoclass:: torchvision.models.ShuffleNet_V2_X1_5_Weights
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:members:
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"""
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weights = ShuffleNet_V2_X1_5_Weights.verify(weights)
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return _shufflenetv2(weights, progress, [4, 8, 4], [24, 176, 352, 704, 1024], **kwargs)
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@register_model()
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@handle_legacy_interface(weights=("pretrained", ShuffleNet_V2_X2_0_Weights.IMAGENET1K_V1))
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def shufflenet_v2_x2_0(
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*, weights: Optional[ShuffleNet_V2_X2_0_Weights] = None, progress: bool = True, **kwargs: Any
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) -> ShuffleNetV2:
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"""
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Constructs a ShuffleNetV2 architecture with 2.0x output channels, as described in
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`ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
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<https://arxiv.org/abs/1807.11164>`__.
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Args:
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weights (:class:`~torchvision.models.ShuffleNet_V2_X2_0_Weights`, optional): The
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pretrained weights to use. See
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:class:`~torchvision.models.ShuffleNet_V2_X2_0_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
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download to stderr. Default is True.
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**kwargs: parameters passed to the ``torchvision.models.shufflenetv2.ShuffleNetV2``
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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/shufflenetv2.py>`_
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for more details about this class.
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.. autoclass:: torchvision.models.ShuffleNet_V2_X2_0_Weights
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:members:
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"""
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weights = ShuffleNet_V2_X2_0_Weights.verify(weights)
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return _shufflenetv2(weights, progress, [4, 8, 4], [24, 244, 488, 976, 2048], **kwargs)
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