data_utils/resnet.py

# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
# From PyTorch:
#
# Copyright (c) 2016-     Facebook, Inc            (Adam Paszke)
# Copyright (c) 2014-     Facebook, Inc            (Soumith Chintala)
# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
# Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
# Copyright (c) 2011-2013 NYU                      (Clement Farabet)
# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
# Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
#
# From Caffe2:
#
# Copyright (c) 2016-present, Facebook Inc. All rights reserved.
#
# All contributions by Facebook:
# Copyright (c) 2016 Facebook Inc.
#
# All contributions by Google:
# Copyright (c) 2015 Google Inc.
# All rights reserved.
#
# All contributions by Yangqing Jia:
# Copyright (c) 2015 Yangqing Jia
# All rights reserved.
#
# All contributions by Kakao Brain:
# Copyright 2019-2020 Kakao Brain
#
# All contributions from Caffe:
# Copyright(c) 2013, 2014, 2015, the respective contributors
# All rights reserved.
#
# All other contributions:
# Copyright(c) 2015, 2016 the respective contributors
# All rights reserved.
import torch
from torchvision.models.utils import load_state_dict_from_url
from typing import Type, Any, Callable, Union, List, Optional
from torchvision.models.resnet import BasicBlock, Bottleneck, ResNet


__all__ = [
    "ResNet",
    "resnet18",
    "resnet34",
    "resnet50",
    "resnet101",
    "resnet152",
    "resnext50_32x4d",
    "resnext101_32x8d",
    "wide_resnet50_2",
    "wide_resnet101_2",
]


model_urls = {
    "resnet18": "https://download.pytorch.org/models/resnet18-5c106cde.pth",
    "resnet34": "https://download.pytorch.org/models/resnet34-333f7ec4.pth",
    "resnet50": "https://download.pytorch.org/models/resnet50-19c8e357.pth",
    "resnet101": "https://download.pytorch.org/models/resnet101-5d3b4d8f.pth",
    "resnet152": "https://download.pytorch.org/models/resnet152-b121ed2d.pth",
    "resnext50_32x4d": "https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth",
    "resnext101_32x8d": "https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth",
    "wide_resnet50_2": "https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth",
    "wide_resnet101_2": "https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth",
}


class ResNet_mine(ResNet):
    def __init__(self, block, layers, classifier_run=True, **kwargs):
        super().__init__(block, layers, **kwargs)
        self.classifier_run = classifier_run

    def _forward_impl(self, x: torch.Tensor) -> (torch.Tensor, torch.Tensor):
        # See note [TorchScript super()]
        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)

        x = self.avgpool(x_)
        x = torch.flatten(x, 1)
        if self.classifier_run:
            x = self.fc(x)

        return x, x_

    def forward(self, x: torch.Tensor) -> (torch.Tensor, torch.Tensor):
        return self._forward_impl(x)


def pnorm(weights, p):
    normB = torch.norm(weights, 2, 1)
    ws = weights.clone()
    for i in range(weights.size(0)):
        ws[i] = ws[i] / torch.pow(normB[i], p)
    return ws


def _resnet(
    arch: str,
    block: Type[Union[BasicBlock, Bottleneck]],
    layers: List[int],
    pretrained: bool,
    progress: bool,
    **kwargs: Any
) -> ResNet:
    model = ResNet_mine(block, layers, **kwargs)
    if pretrained:
        print("Inside resnet function, using ImageNet pretrained from model url!")
        state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)
        model.load_state_dict(state_dict)
    return model


def resnext50_32x4d(
    pretrained: bool = False, progress: bool = True, **kwargs: Any
) -> ResNet:
    r"""ResNeXt-50 32x4d model from
    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    kwargs["groups"] = 32
    kwargs["width_per_group"] = 4
    return _resnet(
        "resnext50_32x4d", Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs
    )


def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
    r"""ResNet-50 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet("resnet50", Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs)