261 lines
9.5 KiB
Python
261 lines
9.5 KiB
Python
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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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from torch import nn, Tensor
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from ..ops.misc import Conv2dNormActivation
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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 _make_divisible, _ovewrite_named_param, handle_legacy_interface
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__all__ = ["MobileNetV2", "MobileNet_V2_Weights", "mobilenet_v2"]
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# necessary for backwards compatibility
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class InvertedResidual(nn.Module):
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def __init__(
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self, inp: int, oup: int, stride: int, expand_ratio: int, norm_layer: Optional[Callable[..., nn.Module]] = None
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) -> None:
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super().__init__()
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self.stride = stride
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if stride not in [1, 2]:
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raise ValueError(f"stride should be 1 or 2 instead of {stride}")
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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hidden_dim = int(round(inp * expand_ratio))
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self.use_res_connect = self.stride == 1 and inp == oup
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layers: List[nn.Module] = []
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if expand_ratio != 1:
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# pw
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layers.append(
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Conv2dNormActivation(inp, hidden_dim, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.ReLU6)
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)
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layers.extend(
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[
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# dw
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Conv2dNormActivation(
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hidden_dim,
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hidden_dim,
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stride=stride,
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groups=hidden_dim,
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norm_layer=norm_layer,
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activation_layer=nn.ReLU6,
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),
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# pw-linear
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nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
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norm_layer(oup),
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]
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)
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self.conv = nn.Sequential(*layers)
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self.out_channels = oup
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self._is_cn = stride > 1
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def forward(self, x: Tensor) -> Tensor:
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if self.use_res_connect:
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return x + self.conv(x)
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else:
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return self.conv(x)
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class MobileNetV2(nn.Module):
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def __init__(
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self,
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num_classes: int = 1000,
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width_mult: float = 1.0,
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inverted_residual_setting: Optional[List[List[int]]] = None,
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round_nearest: int = 8,
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block: Optional[Callable[..., nn.Module]] = None,
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norm_layer: Optional[Callable[..., nn.Module]] = None,
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dropout: float = 0.2,
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) -> None:
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"""
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MobileNet V2 main class
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Args:
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num_classes (int): Number of classes
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width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
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inverted_residual_setting: Network structure
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round_nearest (int): Round the number of channels in each layer to be a multiple of this number
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Set to 1 to turn off rounding
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block: Module specifying inverted residual building block for mobilenet
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norm_layer: Module specifying the normalization layer to use
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dropout (float): The droupout probability
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"""
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super().__init__()
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_log_api_usage_once(self)
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if block is None:
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block = InvertedResidual
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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input_channel = 32
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last_channel = 1280
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if inverted_residual_setting is None:
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inverted_residual_setting = [
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# t, c, n, s
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[1, 16, 1, 1],
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[6, 24, 2, 2],
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[6, 32, 3, 2],
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[6, 64, 4, 2],
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[6, 96, 3, 1],
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[6, 160, 3, 2],
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[6, 320, 1, 1],
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]
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# only check the first element, assuming user knows t,c,n,s are required
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if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4:
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raise ValueError(
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f"inverted_residual_setting should be non-empty or a 4-element list, got {inverted_residual_setting}"
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)
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# building first layer
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input_channel = _make_divisible(input_channel * width_mult, round_nearest)
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self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
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features: List[nn.Module] = [
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Conv2dNormActivation(3, input_channel, stride=2, norm_layer=norm_layer, activation_layer=nn.ReLU6)
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]
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# building inverted residual blocks
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for t, c, n, s in inverted_residual_setting:
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output_channel = _make_divisible(c * width_mult, round_nearest)
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for i in range(n):
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stride = s if i == 0 else 1
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features.append(block(input_channel, output_channel, stride, expand_ratio=t, norm_layer=norm_layer))
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input_channel = output_channel
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# building last several layers
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features.append(
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Conv2dNormActivation(
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input_channel, self.last_channel, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.ReLU6
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)
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)
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# make it nn.Sequential
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self.features = nn.Sequential(*features)
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# building classifier
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self.classifier = nn.Sequential(
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nn.Dropout(p=dropout),
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nn.Linear(self.last_channel, num_classes),
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)
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# weight initialization
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode="fan_out")
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if m.bias is not None:
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nn.init.zeros_(m.bias)
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elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
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nn.init.ones_(m.weight)
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nn.init.zeros_(m.bias)
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elif isinstance(m, nn.Linear):
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nn.init.normal_(m.weight, 0, 0.01)
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nn.init.zeros_(m.bias)
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def _forward_impl(self, x: Tensor) -> Tensor:
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# This exists since TorchScript doesn't support inheritance, so the superclass method
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# (this one) needs to have a name other than `forward` that can be accessed in a subclass
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x = self.features(x)
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# Cannot use "squeeze" as batch-size can be 1
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x = nn.functional.adaptive_avg_pool2d(x, (1, 1))
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x = torch.flatten(x, 1)
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x = self.classifier(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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_COMMON_META = {
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"num_params": 3504872,
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"min_size": (1, 1),
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"categories": _IMAGENET_CATEGORIES,
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}
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class MobileNet_V2_Weights(WeightsEnum):
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IMAGENET1K_V1 = Weights(
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url="https://download.pytorch.org/models/mobilenet_v2-b0353104.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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"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv2",
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"_metrics": {
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"ImageNet-1K": {
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"acc@1": 71.878,
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"acc@5": 90.286,
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}
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},
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"_ops": 0.301,
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"_file_size": 13.555,
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"_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""",
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},
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)
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IMAGENET1K_V2 = Weights(
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url="https://download.pytorch.org/models/mobilenet_v2-7ebf99e0.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/issues/3995#new-recipe-with-reg-tuning",
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"_metrics": {
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"ImageNet-1K": {
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"acc@1": 72.154,
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"acc@5": 90.822,
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}
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},
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"_ops": 0.301,
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"_file_size": 13.598,
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"_docs": """
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These weights improve upon the results of the original paper by using a modified version of TorchVision's
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`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_V2
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@register_model()
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@handle_legacy_interface(weights=("pretrained", MobileNet_V2_Weights.IMAGENET1K_V1))
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def mobilenet_v2(
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*, weights: Optional[MobileNet_V2_Weights] = None, progress: bool = True, **kwargs: Any
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) -> MobileNetV2:
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"""MobileNetV2 architecture from the `MobileNetV2: Inverted Residuals and Linear
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Bottlenecks <https://arxiv.org/abs/1801.04381>`_ paper.
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Args:
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weights (:class:`~torchvision.models.MobileNet_V2_Weights`, optional): The
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pretrained weights to use. See
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:class:`~torchvision.models.MobileNet_V2_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.mobilenetv2.MobileNetV2``
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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/mobilenetv2.py>`_
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for more details about this class.
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.. autoclass:: torchvision.models.MobileNet_V2_Weights
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:members:
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"""
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weights = MobileNet_V2_Weights.verify(weights)
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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 = MobileNetV2(**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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