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