"""ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] <NAME>, <NAME>, <NAME>, <NAME>
    Deep Residual Learning for Image Recognition. arXiv:1512.03385

Pre-trained ImageNet models: 'deci-model-repository/resnet?/ckpt_best.pth' => ? = the type of resnet (e.g. 18, 34...)
Pre-trained CIFAR10 models: 'deci-model-repository/CIFAR_NAS_#?_????_?/ckpt_best.pth' => ? = num of model, structure, width_mult

Code adapted from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
"""

import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict

from super_gradients.training.models import SgModule


def width_multiplier(original, factor):
    return int(original * factor)


class BasicBlock(nn.Module):

    def __init__(self, in_planes, planes, stride=1, expansion=1, final_relu=True):
        super(BasicBlock, self).__init__()
        self.expansion = expansion
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.final_relu = final_relu

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion * planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        if self.final_relu:
            out = F.relu(out)
        return out


class Bottleneck(nn.Module):

    def __init__(self, in_planes, planes, stride=1, expansion=4, final_relu=True):
        super(Bottleneck, self).__init__()
        self.expansion = expansion
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(self.expansion * planes)
        self.final_relu = final_relu

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion * planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)

        if self.final_relu:
            out = F.relu(out)

        return out


class CifarResNet(SgModule):
    def __init__(self, block, num_blocks, num_classes=10, width_mult=1, expansion=1):
        super(CifarResNet, self).__init__()
        self.expansion = expansion
        self.structure = [num_blocks, width_mult]
        self.in_planes = width_multiplier(64, width_mult)
        self.conv1 = nn.Conv2d(3, width_multiplier(64, width_mult), kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(width_multiplier(64, width_mult))
        self.layer1 = self._make_layer(block, width_multiplier(64, width_mult), num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, width_multiplier(128, width_mult), num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, width_multiplier(256, width_mult), num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, width_multiplier(512, width_mult), num_blocks[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d(1)
        self.linear = nn.Linear(width_multiplier(512, width_mult) * self.expansion, num_classes)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        if num_blocks == 0:
            # When the number of blocks is zero but spatial dimension and/or number of filters about to change we put 1
            # 3X3 conv layer to make this change to the new dimensions.
            if stride != 1 or self.in_planes != planes:
                layers.append(nn.Sequential(
                    nn.Conv2d(self.in_planes, planes, kernel_size=3, stride=stride, bias=False, padding=1),
                    nn.BatchNorm2d(planes))
                )
                self.in_planes = planes

        else:
            for stride in strides:
                layers.append(block(self.in_planes, planes, stride))
                self.in_planes = planes * self.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.avgpool(out)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out


class ResNet(SgModule):
    def __init__(self, block, num_blocks: list, num_classes: int = 10, width_mult: float = 1, expansion: int = 1,
                 input_batchnorm: bool = False, backbone_mode: bool = False):
        super(ResNet, self).__init__()
        self.expansion = expansion
        self.backbone_mode = backbone_mode
        self.structure = [num_blocks, width_mult]
        self.in_planes = width_multiplier(64, width_mult)
        self.input_batchnorm = input_batchnorm
        if self.input_batchnorm:
            self.bn0 = nn.BatchNorm2d(3)

        self.conv1 = nn.Conv2d(3, width_multiplier(64, width_mult), kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(width_multiplier(64, width_mult))
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.layer1 = self._make_layer(block, width_multiplier(64, width_mult), num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, width_multiplier(128, width_mult), num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, width_multiplier(256, width_mult), num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, width_multiplier(512, width_mult), num_blocks[3], stride=2)

        if not self.backbone_mode:
            # IF RESNET IS IN BACK_BONE MODE WE DON'T NEED THE FINAL CLASSIFIER LAYERS, BUT ONLY THE NET BLOCK STRUCTURE
            self.linear = nn.Linear(width_multiplier(512, width_mult) * self.expansion, num_classes)
            self.avgpool = nn.AdaptiveAvgPool2d(1)

        self.width_mult = width_mult

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        if num_blocks == 0:
            # When the number of blocks is zero but spatial dimension and/or number of filters about to change we put 1
            # 3X3 conv layer to make this change to the new dimensions.
            if stride != 1 or self.in_planes != planes:
                layers.append(nn.Sequential(
                    nn.Conv2d(self.in_planes, planes, kernel_size=3, stride=stride, bias=False, padding=1),
                    nn.BatchNorm2d(planes))
                )
                self.in_planes = planes

        else:
            for stride in strides:
                layers.append(block(self.in_planes, planes, stride))
                self.in_planes = planes * self.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        if self.input_batchnorm:
            x = self.bn0(x)
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.maxpool(out)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)

        if not self.backbone_mode:
            # IF RESNET IS *NOT* IN BACK_BONE MODE WE  NEED THE FINAL CLASSIFIER LAYERS OUTPUTS
            out = self.avgpool(out)
            out = out.squeeze(dim=2).squeeze(dim=2)
            out = self.linear(out)

        return out

    def load_state_dict(self, state_dict, strict=True):
        """
        load_state_dict - Overloads the base method and calls it to load a modified dict for usage as a backbone
        :param state_dict:  The state_dict to load
        :param strict:      strict loading (see super() docs)
        """
        pretrained_model_weights_dict = state_dict.copy()

        if self.backbone_mode:
            # FIRST LET'S POP THE LAST TWO LAYERS - NO NEED TO LOAD THEIR VALUES SINCE THEY ARE IRRELEVANT AS A BACKBONE
            pretrained_model_weights_dict.popitem()
            pretrained_model_weights_dict.popitem()

            pretrained_backbone_weights_dict = OrderedDict()
            for layer_name, weights in pretrained_model_weights_dict.items():
                # GET THE LAYER NAME WITHOUT THE 'module.' PREFIX
                name_without_module_prefix = layer_name.split('module.')[1]

                # MAKE SURE THESE ARE NOT THE FINAL LAYERS
                pretrained_backbone_weights_dict[name_without_module_prefix] = weights

            # RETURNING THE UNMODIFIED/MODIFIED STATE DICT DEPENDING ON THE backbone_mode VALUE
            super().load_state_dict(pretrained_backbone_weights_dict, strict)
        else:
            super().load_state_dict(pretrained_model_weights_dict, strict)

    def replace_head(self, new_num_classes=None, new_head=None):
        if new_num_classes is None and new_head is None:
            raise ValueError("At least one of new_num_classes, new_head must be given to replace output layer.")
        if new_head is not None:
            self.linear = new_head
        else:
            self.linear = nn.Linear(width_multiplier(512, self.width_mult) * self.expansion, new_num_classes)


def ResNet18(arch_params, num_classes=None, backbone_mode=None):
    return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes or arch_params.num_classes,
                  backbone_mode=backbone_mode)


def ResNet18Cifar(arch_params, num_classes=None):
    return CifarResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes or arch_params.num_classes)


def ResNet34(arch_params, num_classes=None, backbone_mode=None):
    return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes or arch_params.num_classes,
                  backbone_mode=backbone_mode)


def ResNet50(arch_params, num_classes=None, backbone_mode=None):
    return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes or arch_params.num_classes,
                  backbone_mode=backbone_mode, expansion=4)


def ResNet50_3343(arch_params, num_classes=None, backbone_mode=None):
    return ResNet(Bottleneck, [3, 3, 4, 3], num_classes=num_classes or arch_params.num_classes,
                  backbone_mode=backbone_mode, expansion=4)


def ResNet101(arch_params, num_classes=None, backbone_mode=None):
    return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes or arch_params.num_classes,
                  backbone_mode=backbone_mode, expansion=4)


def ResNet152(arch_params, num_classes=None, backbone_mode=None):
    return ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes or arch_params.num_classes,
                  backbone_mode=backbone_mode, expansion=4)


def CustomizedResnetCifar(arch_params, num_classes=None):
    return CifarResNet(BasicBlock, arch_params.structure, width_mult=arch_params.width_mult,
                       num_classes=num_classes or arch_params.num_classes)


def CustomizedResnet50Cifar(arch_params, num_classes=None):
    return CifarResNet(Bottleneck, arch_params.structure, width_mult=arch_params.width_mult,
                       num_classes=num_classes or arch_params.num_classes, expansion=4)


def CustomizedResnet(arch_params, num_classes=None, backbone_mode=None):
    return ResNet(BasicBlock, arch_params.structure, width_mult=arch_params.width_mult,
                  num_classes=num_classes or arch_params.num_classes, backbone_mode=backbone_mode)


def CustomizedResnet50(arch_params, num_classes=None, backbone_mode=None):
    return ResNet(Bottleneck, arch_params.structure, width_mult=arch_params.width_mult,
                  num_classes=num_classes or arch_params.num_classes, backbone_mode=backbone_mode, expansion=4)

"""ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] <NAME>, <NAME>, <NAME>, <NAME>
    Deep Residual Learning for Image Recognition. arXiv:1512.03385

Pre-trained ImageNet models: 'deci-model-repository/resnet?/ckpt_best.pth' => ? = the type of resnet (e.g. 18, 34...)
Pre-trained CIFAR10 models: 'deci-model-repository/CIFAR_NAS_#?_????_?/ckpt_best.pth' => ? = num of model, structure, width_mult

Code adapted from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
"""

import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict

from super_gradients.training.models import SgModule


def width_multiplier(original, factor):
    return int(original * factor)


class BasicBlock(nn.Module):

    def __init__(self, in_planes, planes, stride=1, expansion=1, final_relu=True):
        super(BasicBlock, self).__init__()
        self.expansion = expansion
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.final_relu = final_relu

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion * planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        if self.final_relu:
            out = F.relu(out)
        return out


class Bottleneck(nn.Module):

    def __init__(self, in_planes, planes, stride=1, expansion=4, final_relu=True):
        super(Bottleneck, self).__init__()
        self.expansion = expansion
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(self.expansion * planes)
        self.final_relu = final_relu

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion * planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)

        if self.final_relu:
            out = F.relu(out)

        return out


class CifarResNet(SgModule):
    def __init__(self, block, num_blocks, num_classes=10, width_mult=1, expansion=1):
        super(CifarResNet, self).__init__()
        self.expansion = expansion
        self.structure = [num_blocks, width_mult]
        self.in_planes = width_multiplier(64, width_mult)
        self.conv1 = nn.Conv2d(3, width_multiplier(64, width_mult), kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(width_multiplier(64, width_mult))
        self.layer1 = self._make_layer(block, width_multiplier(64, width_mult), num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, width_multiplier(128, width_mult), num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, width_multiplier(256, width_mult), num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, width_multiplier(512, width_mult), num_blocks[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d(1)
        self.linear = nn.Linear(width_multiplier(512, width_mult) * self.expansion, num_classes)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        if num_blocks == 0:
            # When the number of blocks is zero but spatial dimension and/or number of filters about to change we put 1
            # 3X3 conv layer to make this change to the new dimensions.
            if stride != 1 or self.in_planes != planes:
                layers.append(nn.Sequential(
                    nn.Conv2d(self.in_planes, planes, kernel_size=3, stride=stride, bias=False, padding=1),
                    nn.BatchNorm2d(planes))
                )
                self.in_planes = planes

        else:
            for stride in strides:
                layers.append(block(self.in_planes, planes, stride))
                self.in_planes = planes * self.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.avgpool(out)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out


class ResNet(SgModule):
    def __init__(self, block, num_blocks: list, num_classes: int = 10, width_mult: float = 1, expansion: int = 1,
                 input_batchnorm: bool = False, backbone_mode: bool = False):
        super(ResNet, self).__init__()
        self.expansion = expansion
        self.backbone_mode = backbone_mode
        self.structure = [num_blocks, width_mult]
        self.in_planes = width_multiplier(64, width_mult)
        self.input_batchnorm = input_batchnorm
        if self.input_batchnorm:
            self.bn0 = nn.BatchNorm2d(3)

        self.conv1 = nn.Conv2d(3, width_multiplier(64, width_mult), kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(width_multiplier(64, width_mult))
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.layer1 = self._make_layer(block, width_multiplier(64, width_mult), num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, width_multiplier(128, width_mult), num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, width_multiplier(256, width_mult), num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, width_multiplier(512, width_mult), num_blocks[3], stride=2)

        if not self.backbone_mode:
            # IF RESNET IS IN BACK_BONE MODE WE DON'T NEED THE FINAL CLASSIFIER LAYERS, BUT ONLY THE NET BLOCK STRUCTURE
            self.linear = nn.Linear(width_multiplier(512, width_mult) * self.expansion, num_classes)
            self.avgpool = nn.AdaptiveAvgPool2d(1)

        self.width_mult = width_mult

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        if num_blocks == 0:
            # When the number of blocks is zero but spatial dimension and/or number of filters about to change we put 1
            # 3X3 conv layer to make this change to the new dimensions.
            if stride != 1 or self.in_planes != planes:
                layers.append(nn.Sequential(
                    nn.Conv2d(self.in_planes, planes, kernel_size=3, stride=stride, bias=False, padding=1),
                    nn.BatchNorm2d(planes))
                )
                self.in_planes = planes

        else:
            for stride in strides:
                layers.append(block(self.in_planes, planes, stride))
                self.in_planes = planes * self.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        if self.input_batchnorm:
            x = self.bn0(x)
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.maxpool(out)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)

        if not self.backbone_mode:
            # IF RESNET IS *NOT* IN BACK_BONE MODE WE  NEED THE FINAL CLASSIFIER LAYERS OUTPUTS
            out = self.avgpool(out)
            out = out.squeeze(dim=2).squeeze(dim=2)
            out = self.linear(out)

        return out

    def load_state_dict(self, state_dict, strict=True):
        """
        load_state_dict - Overloads the base method and calls it to load a modified dict for usage as a backbone
        :param state_dict:  The state_dict to load
        :param strict:      strict loading (see super() docs)
        """
        pretrained_model_weights_dict = state_dict.copy()

        if self.backbone_mode:
            # FIRST LET'S POP THE LAST TWO LAYERS - NO NEED TO LOAD THEIR VALUES SINCE THEY ARE IRRELEVANT AS A BACKBONE
            pretrained_model_weights_dict.popitem()
            pretrained_model_weights_dict.popitem()

            pretrained_backbone_weights_dict = OrderedDict()
            for layer_name, weights in pretrained_model_weights_dict.items():
                # GET THE LAYER NAME WITHOUT THE 'module.' PREFIX
                name_without_module_prefix = layer_name.split('module.')[1]

                # MAKE SURE THESE ARE NOT THE FINAL LAYERS
                pretrained_backbone_weights_dict[name_without_module_prefix] = weights

            # RETURNING THE UNMODIFIED/MODIFIED STATE DICT DEPENDING ON THE backbone_mode VALUE
            super().load_state_dict(pretrained_backbone_weights_dict, strict)
        else:
            super().load_state_dict(pretrained_model_weights_dict, strict)

    def replace_head(self, new_num_classes=None, new_head=None):
        if new_num_classes is None and new_head is None:
            raise ValueError("At least one of new_num_classes, new_head must be given to replace output layer.")
        if new_head is not None:
            self.linear = new_head
        else:
            self.linear = nn.Linear(width_multiplier(512, self.width_mult) * self.expansion, new_num_classes)


def ResNet18(arch_params, num_classes=None, backbone_mode=None):
    return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes or arch_params.num_classes,
                  backbone_mode=backbone_mode)


def ResNet18Cifar(arch_params, num_classes=None):
    return CifarResNet(BasicBlock, [2, 2, 2, 2], num_classes=num_classes or arch_params.num_classes)


def ResNet34(arch_params, num_classes=None, backbone_mode=None):
    return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes or arch_params.num_classes,
                  backbone_mode=backbone_mode)


def ResNet50(arch_params, num_classes=None, backbone_mode=None):
    return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes or arch_params.num_classes,
                  backbone_mode=backbone_mode, expansion=4)


def ResNet50_3343(arch_params, num_classes=None, backbone_mode=None):
    return ResNet(Bottleneck, [3, 3, 4, 3], num_classes=num_classes or arch_params.num_classes,
                  backbone_mode=backbone_mode, expansion=4)


def ResNet101(arch_params, num_classes=None, backbone_mode=None):
    return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes or arch_params.num_classes,
                  backbone_mode=backbone_mode, expansion=4)


def ResNet152(arch_params, num_classes=None, backbone_mode=None):
    return ResNet(Bottleneck, [3, 8, 36, 3], num_classes=num_classes or arch_params.num_classes,
                  backbone_mode=backbone_mode, expansion=4)


def CustomizedResnetCifar(arch_params, num_classes=None):
    return CifarResNet(BasicBlock, arch_params.structure, width_mult=arch_params.width_mult,
                       num_classes=num_classes or arch_params.num_classes)


def CustomizedResnet50Cifar(arch_params, num_classes=None):
    return CifarResNet(Bottleneck, arch_params.structure, width_mult=arch_params.width_mult,
                       num_classes=num_classes or arch_params.num_classes, expansion=4)


def CustomizedResnet(arch_params, num_classes=None, backbone_mode=None):
    return ResNet(BasicBlock, arch_params.structure, width_mult=arch_params.width_mult,
                  num_classes=num_classes or arch_params.num_classes, backbone_mode=backbone_mode)


def CustomizedResnet50(arch_params, num_classes=None, backbone_mode=None):
    return ResNet(Bottleneck, arch_params.structure, width_mult=arch_params.width_mult,
                  num_classes=num_classes or arch_params.num_classes, backbone_mode=backbone_mode, expansion=4)

ResNet in PyTorch. For Pre-activation ResNet, see 'preact_resnet.py'. Reference: [1] <NAME>, <NAME>, <NAME>, <NAME>     Deep Residual Learning for Image Recognition. arXiv:1512.03385  Pre-trained ImageNet models: 'deci-model-repository/resnet?/ckpt_best.pth' => ? = the type of resnet (e.g. 18, 34...) Pre-trained CIFAR10 models: 'deci-model-repository/CIFAR_NAS_#?_????_?/ckpt_best.pth' => ? = num of model, structure, width_mult  Code adapted from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py # When the number of blocks is zero but spatial dimension and/or number of filters about to change we put 1 # 3X3 conv layer to make this change to the new dimensions. # IF RESNET IS IN BACK_BONE MODE WE DON'T NEED THE FINAL CLASSIFIER LAYERS, BUT ONLY THE NET BLOCK STRUCTURE # When the number of blocks is zero but spatial dimension and/or number of filters about to change we put 1 # 3X3 conv layer to make this change to the new dimensions. # IF RESNET IS *NOT* IN BACK_BONE MODE WE  NEED THE FINAL CLASSIFIER LAYERS OUTPUTS load_state_dict - Overloads the base method and calls it to load a modified dict for usage as a backbone         :param state_dict:  The state_dict to load         :param strict:      strict loading (see super() docs) # FIRST LET'S POP THE LAST TWO LAYERS - NO NEED TO LOAD THEIR VALUES SINCE THEY ARE IRRELEVANT AS A BACKBONE # GET THE LAYER NAME WITHOUT THE 'module.' PREFIX # MAKE SURE THESE ARE NOT THE FINAL LAYERS # RETURNING THE UNMODIFIED/MODIFIED STATE DICT DEPENDING ON THE backbone_mode VALUE