resnet101.py

import keras
from keras import models

class ResNet101(models.ResNet):
    def __init__(self, num_classes=1000, pretrained=True, **kwargs):
        # Start with the standard resnet101
        super().__init__(
            block=models.resnet.Bottleneck,
            layers=[3, 4, 23, 3],
            num_classes=num_classes,
            **kwargs
        )
        if pretrained:
            state_dict = load_state_dict_from_url(
                models.resnet.model_urls['resnet101'],
                progress=True
            )
            self.load_state_dict(state_dict)
 
    # Reimplementing forward pass.
    # Replacing the forward inference defined here 
    # http://tiny.cc/23pmmz
    def _forward_impl(self, x):
        # Standard forward for resnet
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
 
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
 
        # Notice there is no forward pass through the original classifier.
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
 
        return x