self_defined_nn.py

import re
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from collections import OrderedDict
from torch import Tensor
from torch.jit.annotations import List


class conv_block(nn.Module):
    def __init__(self, input_channels, out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), pool=None,
                 bn_flag=True):
        super(conv_block, self).__init__()
        conv = nn.Conv2d(input_channels, out_channels, kernel_size=kernel_size, padding=padding, stride=stride,
                         groups=1, bias=False)
        bn = nn.BatchNorm2d(out_channels)
        if not bn_flag:
            bn = None
        relu = nn.ReLU6(inplace=True)
        # if pool is None:
        #     self.features = nn.Sequential(conv, bn, relu)
        # else:
        #     self.features = nn.Sequential(conv, bn, relu, pool)
        layers = [conv, bn, relu, pool]
        for i in layers:
            if i is None:
                layers.remove(i)
        self.features = nn.Sequential(*layers)

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


class separable_conv_block(nn.Module):
    def __init__(self, depthwise_channels, pointwise_channels, kernel_size=(3, 3), downsample=False, padding=(1, 1)):
        super(separable_conv_block, self).__init__()
        """Helper function to get a separable conv block"""
        if downsample:
            strides = (2, 2)
        else:
            strides = (1, 1)
        # depthwise convolution + bn + relu
        conv1 = nn.Conv2d(
            in_channels=depthwise_channels,
            out_channels=pointwise_channels,
            groups=depthwise_channels,
            kernel_size=kernel_size,
            stride=strides,
            padding=padding)
        bn1 = nn.BatchNorm2d(pointwise_channels)
        act1 = nn.ReLU6(inplace=True)
        # pointwise convolution + bn + relu
        conv2 = nn.Conv2d(
            pointwise_channels,
            out_channels=pointwise_channels,
            kernel_size=(1, 1),
            stride=(1, 1),
            padding=(0, 0), )
        bn2 = nn.BatchNorm2d(pointwise_channels)
        act2 = nn.ReLU6(inplace=True)
        self.features = nn.Sequential(conv1, bn1, act1, conv2, bn2, act2)

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


class Classifier(nn.Module):
    def __init__(self, input_planes, num_classes, pool_stride=(1, 1)):
        super(Classifier, self).__init__()
        self.avgpool = nn.AdaptiveAvgPool2d(pool_stride)
        self.fc = nn.Linear(input_planes, num_classes)

    def forward(self, x):
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)
        return x


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    expansion = 1
    __constants__ = ['downsample']

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4
    __constants__ = ['downsample']

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class one_layer(nn.Module):
    def __init__(self, num_classes=10):
        super(one_layer, self).__init__()
        self.conv1 = self.conv_block(3, 32, stride=(2, 2))
        self.conv2 = self.conv_block(32, 64, stride=(2, 2))
        # self.conv3 = self.conv_block(64, 128, stride=(2, 2))
        # self.conv4 = self.conv_block(128, 256, stride=(2, 2))
        # self.conv5 = self.conv_block(256, 512, stride=(2, 2))
        # self.conv6 = self.conv_block(512, 1024, stride=(2, 2))
        # self.conv7 = self.conv_block(1024, 2048, stride=(2, 2))
        # self.classifier = nn.Linear(1024, num_classes)

    # 模型计算时的前向过程，也就是按照这个过程进行计算
    def forward(self, x):
        out = self.conv1(x)
        out = self.conv2(out)
        # out = self.conv3(out)
        # out = self.conv4(out)
        # out = self.conv5(out)
        # out = self.conv6(out)
        # out = self.conv7(out)
        return out

    def conv_block(self, input_channels, out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)):
        conv = nn.Conv2d(input_channels, out_channels, kernel_size=kernel_size, padding=padding, stride=stride,
                         groups=1, bias=False)
        bn = nn.BatchNorm2d(out_channels)
        relu = nn.ReLU6(inplace=True)
        return nn.Sequential(conv, bn, relu)


class mobilenet1(nn.Module):
    def __init__(self, num_classes=10):
        super(mobilenet1, self).__init__()
        self.conv1 = conv_block(3, 32, stride=(2, 2))
        self.s1 = separable_conv_block(32, 64)
        # self.s2 = separable_conv_block(64, 128, downsample=True)
        # self.s3 = separable_conv_block(128, 128)
        # self.classifier = nn.Linear(1024, num_classes)

    # 模型计算时的前向过程，也就是按照这个过程进行计算
    def forward(self, x):
        out = self.conv1(x)
        out = self.s1(out)
        # out = self.s2(out)
        # out = self.s3(out)
        # out = self.conv5(out)
        # out = self.conv6(out)
        # out = self.conv7(out)
        return out


class mobilenet2(nn.Module):
    def __init__(self, num_classes=10):
        super(mobilenet2, self).__init__()
        self.s4 = separable_conv_block(64, 128, downsample=True)
        self.s5 = separable_conv_block(128, 128)
        self.s1 = separable_conv_block(128, 256, downsample=True)
        self.s2 = separable_conv_block(256, 256)
        self.s3 = separable_conv_block(256, 512, downsample=True)
        # self.classifier = nn.Linear(1024, num_classes)

    # 模型计算时的前向过程，也就是按照这个过程进行计算
    def forward(self, x):
        out = self.s4(x)
        out = self.s5(out)
        out = self.s1(out)
        out = self.s2(out)
        out = self.s3(out)
        # out = self.conv5(out)
        # out = self.conv6(out)
        # out = self.conv7(out)
        return out


class mobilenet3(nn.Module):
    def __init__(self, num_classes=10):
        super(mobilenet3, self).__init__()
        features = []
        for i in range(7, 12):
            features.append(separable_conv_block(512, 512))
        self.features = nn.Sequential(*features)
        # self.classifier = nn.Linear(1024, num_classes)

    # 模型计算时的前向过程，也就是按照这个过程进行计算
    def forward(self, x):
        out = self.features(x)
        return out


class mobilenet4(nn.Module):
    def __init__(self, num_classes=10):
        super(mobilenet4, self).__init__()
        self.s1 = separable_conv_block(512, 1024, downsample=True)
        self.s2 = separable_conv_block(1024, 1024)
        # building classifier
        self.classifier = nn.Sequential(
            nn.Dropout(0.2),
            nn.Linear(1024, num_classes),
        )

    # 模型计算时的前向过程，也就是按照这个过程进行计算
    def forward(self, x):
        out = self.s1(x)
        out = self.s2(out)
        out = out.mean([2, 3])
        out = self.classifier(out)
        # out = self.conv5(out)
        # out = self.conv6(out)
        # out = self.conv7(out)
        return out


class mobilenet(nn.Module):
    def __init__(self, num_classes=10):
        super(mobilenet, self).__init__()
        self.conv1 = conv_block(3, 32, stride=(2, 2))
        self.s1 = separable_conv_block(32, 64)
        self.s2 = separable_conv_block(64, 128, downsample=True)
        self.s3 = separable_conv_block(128, 128)
        self.s4 = separable_conv_block(128, 256, downsample=True)
        self.s5 = separable_conv_block(256, 256)
        self.s6 = separable_conv_block(256, 512, downsample=True)
        features = []
        for i in range(7, 12):
            features.append(separable_conv_block(512, 512))
        self.features = nn.Sequential(*features)
        self.s12 = separable_conv_block(512, 1024, downsample=True)
        self.s13 = separable_conv_block(1024, 1024)
        # self.pool = nn.AvgPool2d(7)
        # building classifier
        # self.classifier = nn.Sequential(
        #     nn.Dropout(0.2),
        #     nn.Linear(1024, num_classes),
        # )

    # 模型计算时的前向过程，也就是按照这个过程进行计算
    def forward(self, x):
        out = self.conv1(x)
        out = self.s1(out)
        out = self.s2(out)
        out = self.s3(out)
        out = self.s4(out)
        out = self.s5(out)
        out = self.s6(out)
        out = self.features(out)
        out = self.s12(out)
        out = self.s13(out)
        # out = out.mean([2, 3])
        # out = self.pool(out)
        out = out.view(-1, 1024)
        # out = self.classifier(out)
        return out
    # def separable_conv_block(self, depthwise_channels, pointwise_channels,
    #                          kernel_size=(3, 3), downsample=False, padding=(1, 1)):
    #     """Helper function to get a separable conv block"""
    #     if downsample:
    #         strides = (2, 2)
    #     else:
    #         strides = (1, 1)
    #     # depthwise convolution + bn + relu
    #     conv1 = nn.Conv2d(
    #         depthwise_channels,
    #         groups=pointwise_channels,
    #         kernel_size=kernel_size,
    #         stride=strides,
    #         padding=padding)
    #     bn1 = nn.BatchNorm2d(pointwise_channels)
    #     act1 = nn.ReLU6(inplace=True)
    #     # pointwise convolution + bn + relu
    #     conv2 = nn.Conv2d(
    #         pointwise_channels,
    #         kernel_size=(1, 1),
    #         stride=(1, 1),
    #         padding=(0, 0),)
    #     bn2 = nn.BatchNorm2d(pointwise_channels)
    #     act2 = nn.ReLU6(inplace=True)
    #     return nn.Sequential(conv1, bn1, act1, conv2, bn2, act2)


class ResNet1(nn.Module):

    def __init__(self, block, num_classes=1000, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 norm_layer=None):
        super(ResNet1, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group
        # self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
        #                        bias=False)
        # self.bn1 = norm_layer(self.inplanes)
        # self.relu = nn.ReLU(inplace=True)
        self.conv1 = conv_block(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                                pool=nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
        # self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, 2)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))

        return nn.Sequential(*layers)

    def _forward_impl(self, x):
        # See note [TorchScript super()]
        x = self.conv1(x)
        # x = self.bn1(x)
        # x = self.relu(x)
        # x = self.maxpool(x)

        x = self.layer1(x)

        return x

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


class ResNet2(nn.Module):

    def __init__(self, block, num_classes=1000, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 norm_layer=None):
        super(ResNet2, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group

        self.layer2 = self._make_layer(block, 128, 2, stride=2,
                                       dilate=replace_stride_with_dilation[0])

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))

        return nn.Sequential(*layers)

    def _forward_impl(self, x):
        # See note [TorchScript super()]
        x = self.layer2(x)

        return x

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


class ResNet3(nn.Module):

    def __init__(self, block, num_classes=1000, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 norm_layer=None):
        super(ResNet3, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 128
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group

        self.layer3 = self._make_layer(block, 256, 2, stride=2,
                                       dilate=replace_stride_with_dilation[1])

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))

        return nn.Sequential(*layers)

    def _forward_impl(self, x):
        # See note [TorchScript super()]
        x = self.layer3(x)

        return x

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


class ResNet4(nn.Module):

    def __init__(self, block, num_classes=1000, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 norm_layer=None):
        super(ResNet4, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 256
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group

        self.layer4 = self._make_layer(block, 512, 2, stride=2,
                                       dilate=replace_stride_with_dilation[2])
        # self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        # self.fc = nn.Linear(512 * block.expansion, num_classes)
        self.classifier = Classifier(512 * block.expansion, num_classes, (1, 1))

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))

        return nn.Sequential(*layers)

    def _forward_impl(self, x):
        # See note [TorchScript super()]
        x = self.layer4(x)

        # x = self.avgpool(x)
        # x = torch.flatten(x, 1)
        # x = self.fc(x)
        x = self.classifier(x)

        return x

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


class ResNet18(nn.Module):
    def __init__(self, num_classes=1000):
        super(ResNet18, self).__init__()
        block = BasicBlock
        self.r1 = ResNet1(block)
        self.r2 = ResNet2(block)
        self.r3 = ResNet3(block)
        self.r4 = ResNet4(block, num_classes=num_classes)

    def forward(self, x):
        x = self.r1(x)
        x = self.r2(x)
        x = self.r3(x)
        x = self.r4(x)
        return x


class ResNet(nn.Module):

    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(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group
        # self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
        #                        bias=False)
        # self.bn1 = norm_layer(self.inplanes)
        # self.relu = nn.ReLU(inplace=True)
        # self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.conv1 = conv_block(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                                pool=nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                       dilate=replace_stride_with_dilation[2])
        self.classifier = Classifier(512 * block.expansion, num_classes, (1, 1))

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))

        return nn.Sequential(*layers)

    def _forward_impl(self, x):
        # See note [TorchScript super()]
        # x = self.conv1(x)
        # x = self.bn1(x)
        # x = self.relu(x)
        # x = self.maxpool(x)
        x = self.conv1(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)
        # x = self.fc(x)
        x = self.classifier(x)

        return x

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


class vgg_pool(nn.Module):
    def __init__(self):
        super(vgg_pool, self).__init__()
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)

    def forward(self, x):
        x = self.pool(x)
        return x


class vgg_classifier(nn.Module):
    def __init__(self, num_classes=1000):
        super(vgg_classifier, self).__init__()
        self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
        self.classifier = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, num_classes),
        )

    def forward(self, x):
        # x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x


class VGG(nn.Module):

    def __init__(self, features, num_classes=1000, init_weights=True):
        super(VGG, self).__init__()
        self.features = features
        self.classifier = vgg_classifier(num_classes)
        if init_weights:
            self._initialize_weights()

    def forward(self, x):
        x = self.features(x)
        x = self.classifier(x)
        return x

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.constant_(m.bias, 0)


def make_layers(cfg, batch_norm=False):
    layers = []
    in_channels = 3
    for v in cfg:
        if v == 'M':
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
            # layers += [vgg_pool()]
            continue
        else:
            # conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
            if batch_norm:
                # layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
                layers += [conv_block(in_channels, v, kernel_size=3, stride=1, padding=1)]
            else:
                # layers += [conv2d, nn.ReLU(inplace=True)]
                layers += [conv_block(in_channels, v, kernel_size=3, stride=1, padding=1, bn_flag=False)]
            in_channels = v
    return nn.Sequential(*layers)


cfgs = {
    'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
    'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}

def get_vgg(cfg, batch_norm, **kwargs):
    kwargs['init_weights'] = True
    model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **kwargs)
    return model


def Conv3x3BNReLU(in_channels, out_channels, stride=1):
    return nn.Sequential(
        nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=stride, padding=1),
        nn.BatchNorm2d(out_channels),
        nn.ReLU6(inplace=True)
    )


def Conv1x1BNReLU(in_channels, out_channels):
    return nn.Sequential(
        nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, padding=0),
        nn.BatchNorm2d(out_channels),
        nn.ReLU6(inplace=True)
    )


class Residual(nn.Module):
    def __init__(self, nchannels):
        super(Residual, self).__init__()
        mid_channels = nchannels // 2
        self.conv1x1 = Conv1x1BNReLU(in_channels=nchannels, out_channels=mid_channels)
        self.conv3x3 = Conv3x3BNReLU(in_channels=mid_channels, out_channels=nchannels)

    def forward(self, x):
        out = self.conv3x3(self.conv1x1(x))
        return out + x


class Darknet53(nn.Module):
    def __init__(self, num_classes=1000):
        super(Darknet53, self).__init__()
        self.first_conv = Conv3x3BNReLU(in_channels=3, out_channels=32)

        self.block1 = self._make_layers(in_channels=32, out_channels=64, block_num=1)
        self.block2 = self._make_layers(in_channels=64, out_channels=128, block_num=2)
        self.block3 = self._make_layers(in_channels=128, out_channels=256, block_num=8)
        self.block4 = self._make_layers(in_channels=256, out_channels=512, block_num=8)
        self.block5 = self._make_layers(in_channels=512, out_channels=1024, block_num=4)

        self.avg_pool = nn.AvgPool2d(kernel_size=8, stride=1)
        self.linear = nn.Linear(in_features=1024, out_features=num_classes)
        self.softmax = nn.Softmax(dim=1)

    def _make_layers(self, in_channels, out_channels, block_num):
        _layers = []
        _layers.append(Conv3x3BNReLU(in_channels=in_channels, out_channels=out_channels, stride=2))
        for _ in range(block_num):
            _layers.append(Residual(nchannels=out_channels))
        return nn.Sequential(*_layers)

    def forward(self, x):
        x = self.first_conv(x)
        x = self.block1(x)
        x = self.block2(x)
        x = self.block3(x)
        x = self.block4(x)
        x = self.block5(x)

        x = self.avg_pool(x)
        x = x.view(x.size(0), -1)
        x = self.linear(x)
        out = self.softmax(x)
        return out


class _DenseLayer(nn.Module):
    def __init__(self, num_input_features, growth_rate, bn_size, drop_rate, memory_efficient=False):
        super(_DenseLayer, self).__init__()
        self.add_module('norm1', nn.BatchNorm2d(num_input_features)),
        self.add_module('relu1', nn.ReLU(inplace=True)),
        self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *
                                           growth_rate, kernel_size=1, stride=1,
                                           bias=False)),
        self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),
        self.add_module('relu2', nn.ReLU(inplace=True)),
        self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,
                                           kernel_size=3, stride=1, padding=1,
                                           bias=False)),
        self.drop_rate = float(drop_rate)
        self.memory_efficient = memory_efficient

    def bn_function(self, inputs):
        # type: (List[Tensor]) -> Tensor
        concated_features = torch.cat(inputs, 1)
        bottleneck_output = self.conv1(self.relu1(self.norm1(concated_features)))  # noqa: T484
        return bottleneck_output

    # todo: rewrite when torchscript supports any
    def any_requires_grad(self, input):
        # type: (List[Tensor]) -> bool
        for tensor in input:
            if tensor.requires_grad:
                return True
        return False

    @torch.jit.unused  # noqa: T484
    def call_checkpoint_bottleneck(self, input):
        # type: (List[Tensor]) -> Tensor
        def closure(*inputs):
            return self.bn_function(*inputs)

        return cp.checkpoint(closure, input)

    @torch.jit._overload_method  # noqa: F811
    def forward(self, input):
        # type: (List[Tensor]) -> (Tensor)
        pass

    @torch.jit._overload_method  # noqa: F811
    def forward(self, input):
        # type: (Tensor) -> (Tensor)
        pass

    # torchscript does not yet support *args, so we overload method
    # allowing it to take either a List[Tensor] or single Tensor
    def forward(self, input):  # noqa: F811
        if isinstance(input, Tensor):
            prev_features = [input]
        else:
            prev_features = input

        if self.memory_efficient and self.any_requires_grad(prev_features):
            if torch.jit.is_scripting():
                raise Exception("Memory Efficient not supported in JIT")

            bottleneck_output = self.call_checkpoint_bottleneck(prev_features)
        else:
            bottleneck_output = self.bn_function(prev_features)

        new_features = self.conv2(self.relu2(self.norm2(bottleneck_output)))
        if self.drop_rate > 0:
            new_features = F.dropout(new_features, p=self.drop_rate,
                                     training=self.training)
        return new_features


class DenseBlock(nn.ModuleDict):
    _version = 2

    def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate, memory_efficient=False):
        super(DenseBlock, self).__init__()
        for i in range(num_layers):
            layer = _DenseLayer(
                num_input_features + i * growth_rate,
                growth_rate=growth_rate,
                bn_size=bn_size,
                drop_rate=drop_rate,
                memory_efficient=memory_efficient,
            )
            self.add_module('denselayer%d' % (i + 1), layer)

    def forward(self, init_features):
        features = [init_features]
        for name, layer in self.items():
            new_features = layer(features)
            features.append(new_features)
        return torch.cat(features, 1)


class Transition(nn.Sequential):
    def __init__(self, num_input_features, num_output_features):
        super(Transition, self).__init__()
        self.add_module('norm', nn.BatchNorm2d(num_input_features))
        self.add_module('relu', nn.ReLU(inplace=True))
        self.add_module('conv', nn.Conv2d(num_input_features, num_output_features,
                                          kernel_size=1, stride=1, bias=False))
        self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))


class Dense_Classifier(nn.Module):
    def __init__(self, num_features, num_classes, pool_stride=(1, 1)):
        super(Dense_Classifier, self).__init__()
        self.norm = nn.BatchNorm2d(num_features)
        self.classifier = nn.Linear(num_features, num_classes)

    def forward(self, x):
        norm = self.norm(x)
        out = F.relu(norm, inplace=True)
        out = F.adaptive_avg_pool2d(out, (1, 1))
        out = torch.flatten(out, 1)
        out = self.classifier(out)
        return out


class DenseNet(nn.Module):
    r"""Densenet-BC model class, based on
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_

    Args:
        growth_rate (int) - how many filters to add each layer (`k` in paper)
        block_config (list of 4 ints) - how many layers in each pooling block
        num_init_features (int) - the number of filters to learn in the first convolution layer
        bn_size (int) - multiplicative factor for number of bottle neck layers
          (i.e. bn_size * k features in the bottleneck layer)
        drop_rate (float) - dropout rate after each dense layer
        num_classes (int) - number of classification classes
        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
    """

    __constants__ = ['features']

    def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),
                 num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000, memory_efficient=False):

        super(DenseNet, self).__init__()

        # First convolution
        self.features = nn.Sequential(OrderedDict([
            ('cb', conv_block(3, num_init_features, kernel_size=7, stride=2, padding=3,
                       pool=nn.MaxPool2d(kernel_size=3, stride=2, padding=1), bn_flag=True)
                       )]))
        # self.features = nn.Sequential(OrderedDict([
        #     ('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2,
        #                         padding=3, bias=False)),
        #     ('norm0', nn.BatchNorm2d(num_init_features)),
        #     ('relu0', nn.ReLU(inplace=True)),
        #     ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
        # ]))

        # Each denseblock
        num_features = num_init_features
        for i, num_layers in enumerate(block_config):
            block = DenseBlock(
                num_layers=num_layers,
                num_input_features=num_features,
                bn_size=bn_size,
                growth_rate=growth_rate,
                drop_rate=drop_rate,
                memory_efficient=memory_efficient
            )
            self.features.add_module('denseblock%d' % (i + 1), block)
            num_features = num_features + num_layers * growth_rate
            if i != len(block_config) - 1:
                trans = Transition(num_input_features=num_features,
                                    num_output_features=num_features // 2)
                self.features.add_module('transition%d' % (i + 1), trans)
                num_features = num_features // 2

        # Final batch norm
        # self.features.add_module('norm5', nn.BatchNorm2d(num_features))

        # Linear layer
        self.classifier = Dense_Classifier(num_features, num_classes)

        # Official init from torch repo.
        # for m in self.modules():
        #     if isinstance(m, nn.Conv2d):
        #         nn.init.kaiming_normal_(m.weight)
        #     elif isinstance(m, nn.BatchNorm2d):
        #         nn.init.constant_(m.weight, 1)
        #         nn.init.constant_(m.bias, 0)
        #     elif isinstance(m, nn.Linear):
        #         nn.init.constant_(m.bias, 0)

    def forward(self, x):
        features = self.features(x)
        # out = F.relu(features, inplace=True)
        # out = F.adaptive_avg_pool2d(out, (1, 1))
        # out = torch.flatten(out, 1)
        out = self.classifier(features)
        return out


class Inception3(nn.Module):

    def __init__(self, num_classes=1000, aux_logits=True, transform_input=False,
                 inception_blocks=None):
        super(Inception3, self).__init__()
        if inception_blocks is None:
            inception_blocks = [
                FirstBasicConv2d, InceptionA, InceptionB, InceptionC,
                InceptionD, InceptionE, InceptionAux
            ]
        assert len(inception_blocks) == 7
        conv_block = inception_blocks[0]
        inception_a = inception_blocks[1]
        inception_b = inception_blocks[2]
        inception_c = inception_blocks[3]
        inception_d = inception_blocks[4]
        inception_e = inception_blocks[5]
        inception_aux = inception_blocks[6]

        self.aux_logits = aux_logits
        self.transform_input = transform_input
        self.Conv2d_1a_3x3 = conv_block(3, 32, kernel_size=3, stride=2)
        self.Conv2d_2a_3x3 = conv_block(32, 32, kernel_size=3)
        self.Conv2d_2b_3x3 = conv_block(32, 64, kernel_size=3, padding=1)
        self.Conv2d_3b_1x1 = conv_block(64, 80, kernel_size=1)
        self.Conv2d_4a_3x3 = conv_block(80, 192, kernel_size=3)
        self.Mixed_5b = inception_a(192, pool_features=32)
        self.Mixed_5c = inception_a(256, pool_features=64)
        self.Mixed_5d = inception_a(288, pool_features=64)
        self.Mixed_6a = inception_b(288)
        self.Mixed_6b = inception_c(768, channels_7x7=128)
        self.Mixed_6c = inception_c(768, channels_7x7=160)
        self.Mixed_6d = inception_c(768, channels_7x7=160)
        self.Mixed_6e = inception_c(768, channels_7x7=192)
        if aux_logits:
            self.AuxLogits = inception_aux(768, num_classes)
        self.Mixed_7a = inception_d(768)
        self.Mixed_7b = inception_e(1280)
        self.Mixed_7c = inception_e(2048)
        self.pool1 = inception_pool()
        self.pool2 = inception_pool()
        self.classifier = inception_classifier(1000)
        # self.fc = nn.Linear(2048, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
                import scipy.stats as stats
                stddev = m.stddev if hasattr(m, 'stddev') else 0.1
                X = stats.truncnorm(-2, 2, scale=stddev)
                values = torch.as_tensor(X.rvs(m.weight.numel()), dtype=m.weight.dtype)
                values = values.view(m.weight.size())
                with torch.no_grad():
                    m.weight.copy_(values)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def _transform_input(self, x):
        if self.transform_input:
            x_ch0 = torch.unsqueeze(x[:, 0], 1) * (0.229 / 0.5) + (0.485 - 0.5) / 0.5
            x_ch1 = torch.unsqueeze(x[:, 1], 1) * (0.224 / 0.5) + (0.456 - 0.5) / 0.5
            x_ch2 = torch.unsqueeze(x[:, 2], 1) * (0.225 / 0.5) + (0.406 - 0.5) / 0.5
            x = torch.cat((x_ch0, x_ch1, x_ch2), 1)
        return x

    def _forward(self, x):
        # N x 3 x 299 x 299
        x = self.Conv2d_1a_3x3(x)
        # N x 32 x 149 x 149
        x = self.Conv2d_2a_3x3(x)
        # N x 32 x 147 x 147
        x = self.Conv2d_2b_3x3(x)
        # N x 64 x 147 x 147
        x = self.pool1(x)
        # N x 64 x 73 x 73
        x = self.Conv2d_3b_1x1(x)
        # N x 80 x 73 x 73
        x = self.Conv2d_4a_3x3(x)
        # N x 192 x 71 x 71
        x = self.pool2(x)
        # N x 192 x 35 x 35
        x = self.Mixed_5b(x)
        # N x 256 x 35 x 35
        x = self.Mixed_5c(x)
        # N x 288 x 35 x 35
        x = self.Mixed_5d(x)
        # N x 288 x 35 x 35
        x = self.Mixed_6a(x)
        # N x 768 x 17 x 17
        x = self.Mixed_6b(x)
        # N x 768 x 17 x 17
        x = self.Mixed_6c(x)
        # N x 768 x 17 x 17
        x = self.Mixed_6d(x)
        # N x 768 x 17 x 17
        x = self.Mixed_6e(x)
        # N x 768 x 17 x 17
        aux_defined = self.training and self.aux_logits
        if aux_defined:
            aux = self.AuxLogits(x)
        else:
            aux = None
        # N x 768 x 17 x 17
        x = self.Mixed_7a(x)
        # N x 1280 x 8 x 8
        x = self.Mixed_7b(x)
        # N x 2048 x 8 x 8
        x = self.Mixed_7c(x)
        # N x 2048 x 8 x 8
        # Adaptive average pooling
        # x = F.adaptive_avg_pool2d(x, (1, 1))
        # N x 2048 x 1 x 1
        # x = F.dropout(x, training=self.training)
        # N x 2048 x 1 x 1
        # x = torch.flatten(x, 1)
        # N x 2048
        x = self.classifier(x)
        # N x 1000 (num_classes)
        return x, aux

    @torch.jit.unused
    def eager_outputs(self, x, aux):
        # type: (Tensor, Optional[Tensor]) -> InceptionOutputs

        return x

    def forward(self, x):
        x = self._transform_input(x)
        x, aux = self._forward(x)
        # aux_defined = self.training and self.aux_logits
        return self.eager_outputs(x, aux)


class inception_pool(nn.Module):
    def __init__(self):
        super(inception_pool, self).__init__()
        self.pool = nn.MaxPool2d(kernel_size=3, stride=2)

    def forward(self, x):
        x = self.pool(x)
        return x


class inception_classifier(nn.Module):
    def __init__(self, num_classes):
        super(inception_classifier, self).__init__()
        self.pool = nn.AdaptiveAvgPool2d((1, 1))
        self.dropout = nn.Dropout()
        self.fc = nn.Linear(2048, num_classes)

    def forward(self, x):
        x = self.pool(x)
        # N x 2048 x 1 x 1
        x = self.dropout(x)
        # N x 2048 x 1 x 1
        x = torch.flatten(x, 1)
        # N x 2048
        x = self.fc(x)
        # N x 1000 (num_classes)
        return x


class InceptionA(nn.Module):

    def __init__(self, in_channels, pool_features, conv_block=None):
        super(InceptionA, self).__init__()
        if conv_block is None:
            conv_block = BasicConv2d
        self.branch1x1 = conv_block(in_channels, 64, kernel_size=1)

        self.branch5x5_1 = conv_block(in_channels, 48, kernel_size=1)
        self.branch5x5_2 = conv_block(48, 64, kernel_size=5, padding=2)

        self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1)
        self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1)
        self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, padding=1)

        self.branch_pool = conv_block(in_channels, pool_features, kernel_size=1)

    def _forward(self, x):
        branch1x1 = self.branch1x1(x)

        branch5x5 = self.branch5x5_1(x)
        branch5x5 = self.branch5x5_2(branch5x5)

        branch3x3dbl = self.branch3x3dbl_1(x)
        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)

        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
        branch_pool = self.branch_pool(branch_pool)

        outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
        return outputs

    def forward(self, x):
        outputs = self._forward(x)
        return torch.cat(outputs, 1)


class InceptionB(nn.Module):

    def __init__(self, in_channels, conv_block=None):
        super(InceptionB, self).__init__()
        if conv_block is None:
            conv_block = BasicConv2d
        self.branch3x3 = conv_block(in_channels, 384, kernel_size=3, stride=2)

        self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1)
        self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1)
        self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, stride=2)

    def _forward(self, x):
        branch3x3 = self.branch3x3(x)

        branch3x3dbl = self.branch3x3dbl_1(x)
        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)

        branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)

        outputs = [branch3x3, branch3x3dbl, branch_pool]
        return outputs

    def forward(self, x):
        outputs = self._forward(x)
        return torch.cat(outputs, 1)


class InceptionC(nn.Module):

    def __init__(self, in_channels, channels_7x7, conv_block=None):
        super(InceptionC, self).__init__()
        if conv_block is None:
            conv_block = BasicConv2d
        self.branch1x1 = conv_block(in_channels, 192, kernel_size=1)

        c7 = channels_7x7
        self.branch7x7_1 = conv_block(in_channels, c7, kernel_size=1)
        self.branch7x7_2 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3))
        self.branch7x7_3 = conv_block(c7, 192, kernel_size=(7, 1), padding=(3, 0))

        self.branch7x7dbl_1 = conv_block(in_channels, c7, kernel_size=1)
        self.branch7x7dbl_2 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0))
        self.branch7x7dbl_3 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3))
        self.branch7x7dbl_4 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0))
        self.branch7x7dbl_5 = conv_block(c7, 192, kernel_size=(1, 7), padding=(0, 3))

        self.branch_pool = conv_block(in_channels, 192, kernel_size=1)

    def _forward(self, x):
        branch1x1 = self.branch1x1(x)

        branch7x7 = self.branch7x7_1(x)
        branch7x7 = self.branch7x7_2(branch7x7)
        branch7x7 = self.branch7x7_3(branch7x7)

        branch7x7dbl = self.branch7x7dbl_1(x)
        branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
        branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
        branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
        branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)

        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
        branch_pool = self.branch_pool(branch_pool)

        outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
        return outputs

    def forward(self, x):
        outputs = self._forward(x)
        return torch.cat(outputs, 1)


class InceptionD(nn.Module):

    def __init__(self, in_channels, conv_block=None):
        super(InceptionD, self).__init__()
        if conv_block is None:
            conv_block = BasicConv2d
        self.branch3x3_1 = conv_block(in_channels, 192, kernel_size=1)
        self.branch3x3_2 = conv_block(192, 320, kernel_size=3, stride=2)

        self.branch7x7x3_1 = conv_block(in_channels, 192, kernel_size=1)
        self.branch7x7x3_2 = conv_block(192, 192, kernel_size=(1, 7), padding=(0, 3))
        self.branch7x7x3_3 = conv_block(192, 192, kernel_size=(7, 1), padding=(3, 0))
        self.branch7x7x3_4 = conv_block(192, 192, kernel_size=3, stride=2)

    def _forward(self, x):
        branch3x3 = self.branch3x3_1(x)
        branch3x3 = self.branch3x3_2(branch3x3)

        branch7x7x3 = self.branch7x7x3_1(x)
        branch7x7x3 = self.branch7x7x3_2(branch7x7x3)
        branch7x7x3 = self.branch7x7x3_3(branch7x7x3)
        branch7x7x3 = self.branch7x7x3_4(branch7x7x3)

        branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)
        outputs = [branch3x3, branch7x7x3, branch_pool]
        return outputs

    def forward(self, x):
        outputs = self._forward(x)
        return torch.cat(outputs, 1)


class InceptionE(nn.Module):

    def __init__(self, in_channels, conv_block=None):
        super(InceptionE, self).__init__()
        if conv_block is None:
            conv_block = BasicConv2d
        self.branch1x1 = conv_block(in_channels, 320, kernel_size=1)

        self.branch3x3_1 = conv_block(in_channels, 384, kernel_size=1)
        self.branch3x3_2a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1))
        self.branch3x3_2b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0))

        self.branch3x3dbl_1 = conv_block(in_channels, 448, kernel_size=1)
        self.branch3x3dbl_2 = conv_block(448, 384, kernel_size=3, padding=1)
        self.branch3x3dbl_3a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1))
        self.branch3x3dbl_3b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0))

        self.branch_pool = conv_block(in_channels, 192, kernel_size=1)

    def _forward(self, x):
        branch1x1 = self.branch1x1(x)

        branch3x3 = self.branch3x3_1(x)
        branch3x3 = [
            self.branch3x3_2a(branch3x3),
            self.branch3x3_2b(branch3x3),
        ]
        branch3x3 = torch.cat(branch3x3, 1)

        branch3x3dbl = self.branch3x3dbl_1(x)
        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
        branch3x3dbl = [
            self.branch3x3dbl_3a(branch3x3dbl),
            self.branch3x3dbl_3b(branch3x3dbl),
        ]
        branch3x3dbl = torch.cat(branch3x3dbl, 1)

        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
        branch_pool = self.branch_pool(branch_pool)

        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
        return outputs

    def forward(self, x):
        outputs = self._forward(x)
        return torch.cat(outputs, 1)


class InceptionAux(nn.Module):

    def __init__(self, in_channels, num_classes, conv_block=None):
        super(InceptionAux, self).__init__()
        if conv_block is None:
            conv_block = BasicConv2d
        self.conv0 = conv_block(in_channels, 128, kernel_size=1)
        self.conv1 = conv_block(128, 768, kernel_size=5)
        self.conv1.stddev = 0.01
        self.fc = nn.Linear(768, num_classes)
        self.fc.stddev = 0.001

    def forward(self, x):
        # N x 768 x 17 x 17
        x = F.avg_pool2d(x, kernel_size=5, stride=3)
        # N x 768 x 5 x 5
        x = self.conv0(x)
        # N x 128 x 5 x 5
        x = self.conv1(x)
        # N x 768 x 1 x 1
        # Adaptive average pooling
        x = F.adaptive_avg_pool2d(x, (1, 1))
        # N x 768 x 1 x 1
        x = torch.flatten(x, 1)
        # N x 768
        x = self.fc(x)
        # N x 1000
        return x


class BasicConv2d(nn.Module):

    def __init__(self, in_channels, out_channels, **kwargs):
        super(BasicConv2d, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
        self.bn = nn.BatchNorm2d(out_channels, eps=0.001)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        return F.relu(x, inplace=True)


class FirstBasicConv2d(nn.Module):

    def __init__(self, in_channels, out_channels, **kwargs):
        super(FirstBasicConv2d, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
        self.bn = nn.BatchNorm2d(out_channels, eps=0.001)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        return F.relu(x, inplace=True)
# if __name__ == '__main__':
#     model = Darknet53()
#     print(model)
#
#     input = torch.randn(1,3,256,256)
#     out = model(input)
#     print(out.shape)