148 lines
5.6 KiB
Python
148 lines
5.6 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.init as init
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from .utils import load_state_dict_from_url
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from typing import Any
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__all__ = ['SqueezeNet', 'squeezenet1_0', 'squeezenet1_1']
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model_urls = {
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'squeezenet1_0': 'https://download.pytorch.org/models/squeezenet1_0-a815701f.pth',
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'squeezenet1_1': 'https://download.pytorch.org/models/squeezenet1_1-f364aa15.pth',
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}
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class Fire(nn.Module):
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def __init__(
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self,
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inplanes: int,
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squeeze_planes: int,
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expand1x1_planes: int,
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expand3x3_planes: int
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) -> None:
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super(Fire, self).__init__()
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self.inplanes = inplanes
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self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
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self.squeeze_activation = nn.ReLU(inplace=True)
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self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes,
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kernel_size=1)
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self.expand1x1_activation = nn.ReLU(inplace=True)
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self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes,
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kernel_size=3, padding=1)
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self.expand3x3_activation = nn.ReLU(inplace=True)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = self.squeeze_activation(self.squeeze(x))
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return torch.cat([
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self.expand1x1_activation(self.expand1x1(x)),
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self.expand3x3_activation(self.expand3x3(x))
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], 1)
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class SqueezeNet(nn.Module):
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def __init__(
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self,
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version: str = '1_0',
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num_classes: int = 1000
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) -> None:
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super(SqueezeNet, self).__init__()
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self.num_classes = num_classes
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if version == '1_0':
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self.features = nn.Sequential(
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nn.Conv2d(3, 96, kernel_size=7, stride=2),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
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Fire(96, 16, 64, 64),
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Fire(128, 16, 64, 64),
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Fire(128, 32, 128, 128),
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nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
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Fire(256, 32, 128, 128),
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Fire(256, 48, 192, 192),
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Fire(384, 48, 192, 192),
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Fire(384, 64, 256, 256),
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nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
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Fire(512, 64, 256, 256),
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)
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elif version == '1_1':
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self.features = nn.Sequential(
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nn.Conv2d(3, 64, kernel_size=3, stride=2),
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nn.ReLU(inplace=True),
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nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
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Fire(64, 16, 64, 64),
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Fire(128, 16, 64, 64),
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nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
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Fire(128, 32, 128, 128),
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Fire(256, 32, 128, 128),
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nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
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Fire(256, 48, 192, 192),
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Fire(384, 48, 192, 192),
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Fire(384, 64, 256, 256),
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Fire(512, 64, 256, 256),
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)
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else:
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# FIXME: Is this needed? SqueezeNet should only be called from the
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# FIXME: squeezenet1_x() functions
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# FIXME: This checking is not done for the other models
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raise ValueError("Unsupported SqueezeNet version {version}:"
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"1_0 or 1_1 expected".format(version=version))
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# Final convolution is initialized differently from the rest
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final_conv = nn.Conv2d(512, self.num_classes, kernel_size=1)
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self.classifier = nn.Sequential(
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nn.Dropout(p=0.5),
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final_conv,
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nn.ReLU(inplace=True),
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nn.AdaptiveAvgPool2d((1, 1))
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)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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if m is final_conv:
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init.normal_(m.weight, mean=0.0, std=0.01)
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else:
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init.kaiming_uniform_(m.weight)
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if m.bias is not None:
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init.constant_(m.bias, 0)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = self.features(x)
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x = self.classifier(x)
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return torch.flatten(x, 1)
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def _squeezenet(version: str, pretrained: bool, progress: bool, **kwargs: Any) -> SqueezeNet:
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model = SqueezeNet(version, **kwargs)
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if pretrained:
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arch = 'squeezenet' + version
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state_dict = load_state_dict_from_url(model_urls[arch],
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progress=progress)
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model.load_state_dict(state_dict)
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return model
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def squeezenet1_0(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> SqueezeNet:
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r"""SqueezeNet model architecture from the `"SqueezeNet: AlexNet-level
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accuracy with 50x fewer parameters and <0.5MB model size"
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<https://arxiv.org/abs/1602.07360>`_ paper.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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return _squeezenet('1_0', pretrained, progress, **kwargs)
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def squeezenet1_1(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> SqueezeNet:
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r"""SqueezeNet 1.1 model from the `official SqueezeNet repo
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<https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_.
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SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters
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than SqueezeNet 1.0, without sacrificing accuracy.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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return _squeezenet('1_1', pretrained, progress, **kwargs)
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