199 lines
7.7 KiB
Python
199 lines
7.7 KiB
Python
# A direct copy of torchvision's resnet.py modified to support gradient checkpointing.
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import torch
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import torch.nn as nn
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from torchvision.models.resnet import BasicBlock, Bottleneck
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import torchvision
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__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
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'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
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'wide_resnet50_2', 'wide_resnet101_2']
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from trainer.networks import register_model
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from utils.util import checkpoint
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model_urls = {
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'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
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'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
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'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
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'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
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'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
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'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
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'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
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'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
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'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
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}
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class ResNet(torchvision.models.resnet.ResNet):
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def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
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groups=1, width_per_group=64, replace_stride_with_dilation=None,
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norm_layer=None):
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super().__init__(block, layers, num_classes, zero_init_residual, groups, width_per_group,
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replace_stride_with_dilation, norm_layer)
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def _forward_impl(self, x):
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# Should be the exact same implementation of torchvision.models.resnet.ResNet.forward_impl,
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# except using checkpoints on the body conv layers.
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)
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x = checkpoint(self.layer1, x)
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x = checkpoint(self.layer2, x)
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x = checkpoint(self.layer3, x)
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x = checkpoint(self.layer4, x)
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x = self.avgpool(x)
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x = torch.flatten(x, 1)
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x = self.fc(x)
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return x
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def forward(self, x):
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return self._forward_impl(x)
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def _resnet(arch, block, layers, pretrained, progress, **kwargs):
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model = ResNet(block, layers, **kwargs)
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if pretrained:
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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 resnet18(pretrained=False, progress=True, **kwargs):
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r"""ResNet-18 model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
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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 _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
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**kwargs)
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def resnet34(pretrained=False, progress=True, **kwargs):
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r"""ResNet-34 model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
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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 _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
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**kwargs)
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def resnet50(pretrained=False, progress=True, **kwargs):
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r"""ResNet-50 model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
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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 _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
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**kwargs)
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def resnet101(pretrained=False, progress=True, **kwargs):
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r"""ResNet-101 model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
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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 _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
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**kwargs)
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def resnet152(pretrained=False, progress=True, **kwargs):
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r"""ResNet-152 model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
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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 _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
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**kwargs)
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def resnext50_32x4d(pretrained=False, progress=True, **kwargs):
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r"""ResNeXt-50 32x4d model from
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`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
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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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kwargs['groups'] = 32
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kwargs['width_per_group'] = 4
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return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
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pretrained, progress, **kwargs)
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def resnext101_32x8d(pretrained=False, progress=True, **kwargs):
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r"""ResNeXt-101 32x8d model from
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`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
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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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kwargs['groups'] = 32
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kwargs['width_per_group'] = 8
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return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
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pretrained, progress, **kwargs)
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def wide_resnet50_2(pretrained=False, progress=True, **kwargs):
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r"""Wide ResNet-50-2 model from
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`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
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The model is the same as ResNet except for the bottleneck number of channels
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which is twice larger in every block. The number of channels in outer 1x1
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convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
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channels, and in Wide ResNet-50-2 has 2048-1024-2048.
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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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kwargs['width_per_group'] = 64 * 2
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return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
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pretrained, progress, **kwargs)
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def wide_resnet101_2(pretrained=False, progress=True, **kwargs):
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r"""Wide ResNet-101-2 model from
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`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
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The model is the same as ResNet except for the bottleneck number of channels
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which is twice larger in every block. The number of channels in outer 1x1
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convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
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channels, and in Wide ResNet-50-2 has 2048-1024-2048.
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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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kwargs['width_per_group'] = 64 * 2
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return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
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pretrained, progress, **kwargs)
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@register_model
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def register_resnet50(opt_net, opt):
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model = resnet50(pretrained=opt_net['pretrained'])
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if opt_net['custom_head_logits']:
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model.fc = nn.Linear(512 * 4, opt_net['custom_head_logits'])
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return model
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