# A direct copy of torchvision's resnet.py modified to support gradient checkpointing.

import torch
import torch.nn as nn
from torchvision.models.resnet import BasicBlock, Bottleneck
from torchvision.models.utils import load_state_dict_from_url
import torchvision


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

from trainer.networks import register_model
from utils.util import checkpoint

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 Backbone(torchvision.models.resnet.ResNet):

    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 norm_layer=None):
        super().__init__(block, layers, num_classes, zero_init_residual, groups, width_per_group,
                         replace_stride_with_dilation, norm_layer)
        del self.fc
        del self.avgpool

    def _forward_impl(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        l1 = checkpoint(self.layer1, x)
        l2 = checkpoint(self.layer2, l1)
        l3 = checkpoint(self.layer3, l2)
        l4 = checkpoint(self.layer4, l3)

        return l1, l2, l3, l4

    def forward(self, x):
        return self._forward_impl(x)


def _backbone(arch, block, layers, pretrained, progress, **kwargs):
    model = Backbone(block, layers, **kwargs)
    if pretrained:
        state_dict = load_state_dict_from_url(model_urls[arch],
                                              progress=progress)
        model.load_state_dict(state_dict)
    return model


def backbone18(pretrained=False, progress=True, **kwargs):
    r"""ResNet-18 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 _backbone('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
                   **kwargs)


def backbone34(pretrained=False, progress=True, **kwargs):
    r"""ResNet-34 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 _backbone('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
                   **kwargs)


def backbone50(pretrained=False, progress=True, **kwargs):
    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 _backbone('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
                   **kwargs)


def backbone101(pretrained=False, progress=True, **kwargs):
    r"""ResNet-101 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 _backbone('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
                   **kwargs)


def backbone152(pretrained=False, progress=True, **kwargs):
    r"""ResNet-152 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 _backbone('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
                   **kwargs)


@register_model
def register_resnet50(opt_net, opt):
    model = resnet50(pretrained=opt_net['pretrained'])
    if opt_net['custom_head_logits']:
        model.fc = nn.Linear(512 * 4, opt_net['custom_head_logits'])
    return model