f129eaa39e
- Remove option to aug in dataset (there's really no reason for this now that kornia works on GPU on windows) - Other stufff
125 lines
4.7 KiB
Python
125 lines
4.7 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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from torchvision.models.utils import load_state_dict_from_url
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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 Backbone(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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del self.fc
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del self.avgpool
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def _forward_impl(self, x):
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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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l1 = checkpoint(self.layer1, x)
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l2 = checkpoint(self.layer2, l1)
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l3 = checkpoint(self.layer3, l2)
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l4 = checkpoint(self.layer4, l3)
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return l1, l2, l3, l4
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def forward(self, x):
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return self._forward_impl(x)
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def _backbone(arch, block, layers, pretrained, progress, **kwargs):
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model = Backbone(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 backbone18(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 _backbone('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
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**kwargs)
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def backbone34(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 _backbone('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
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**kwargs)
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def backbone50(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 _backbone('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
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**kwargs)
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def backbone101(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 _backbone('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
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**kwargs)
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def backbone152(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 _backbone('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
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**kwargs)
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