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from typing import Tuple
import torchvision
from torch import nn
import backbone.base
class ResNet50(backbone.base.Base):
def __init__(self, pretrained: bool):
super().__init__(pretrained)
def features(self) -> Tuple[nn.Module, nn.Module, int, int]:
resnet50 = torchvision.models.resnet50(pretrained=self._pretrained)
# list(resnet50.children()) consists of following modules
# [0] = Conv2d, [1] = BatchNorm2d, [2] = ReLU,
# [3] = MaxPool2d, [4] = Sequential(Bottleneck...),
# [5] = Sequential(Bottleneck...),
# [6] = Sequential(Bottleneck...),
# [7] = Sequential(Bottleneck...),
# [8] = AvgPool2d, [9] = Linear
children = list(resnet50.children())
features = children[:-3]
num_features_out = 1024
hidden = children[-3]
num_hidden_out = 2048
for parameters in [feature.parameters() for i, feature in enumerate(features) if i <= 4]:
for parameter in parameters:
parameter.requires_grad = False
features = nn.Sequential(*features)
return features, hidden, num_features_out, num_hidden_out
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