70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
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"""
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Backbone modules.
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"""
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from collections import OrderedDict
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import torch
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import torch.nn.functional as F
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import torchvision
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from torch import nn
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import p2pnetUtils.vgg_ as models
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class BackboneBase_VGG(nn.Module):
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def __init__(self, backbone: nn.Module, num_channels: int, name: str, return_interm_layers: bool):
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super().__init__()
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features = list(backbone.features.children())
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if return_interm_layers:
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if name == 'vgg16_bn':
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self.body1 = nn.Sequential(*features[:13])
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self.body2 = nn.Sequential(*features[13:23])
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self.body3 = nn.Sequential(*features[23:33])
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self.body4 = nn.Sequential(*features[33:43])
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else:
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self.body1 = nn.Sequential(*features[:9])
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self.body2 = nn.Sequential(*features[9:16])
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self.body3 = nn.Sequential(*features[16:23])
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self.body4 = nn.Sequential(*features[23:30])
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else:
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if name == 'vgg16_bn':
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self.body = nn.Sequential(*features[:44]) # 16x down-sample
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elif name == 'vgg16':
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self.body = nn.Sequential(*features[:30]) # 16x down-sample
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self.num_channels = num_channels
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self.return_interm_layers = return_interm_layers
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def forward(self, tensor_list):
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out = []
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if self.return_interm_layers:
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xs = tensor_list
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for _, layer in enumerate([self.body1, self.body2, self.body3, self.body4]):
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xs = layer(xs)
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out.append(xs)
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else:
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xs = self.body(tensor_list)
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out.append(xs)
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return out
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class Backbone_VGG(BackboneBase_VGG):
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"""ResNet backbone with frozen BatchNorm."""
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def __init__(self, name: str, return_interm_layers: bool):
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if name == 'vgg16_bn':
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backbone = models.vgg16_bn(pretrained=True)
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elif name == 'vgg16':
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backbone = models.vgg16(pretrained=True)
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num_channels = 256
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super().__init__(backbone, num_channels, name, return_interm_layers)
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def build_backbone(args):
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backbone = Backbone_VGG(args['backbone'], True)
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return backbone
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if __name__ == '__main__':
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Backbone_VGG('vgg16', True)
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