pytorch-image-models/timm/models/nasnet.py

import torch
import torch.nn as nn
import torch.nn.functional as F

from .helpers import load_pretrained
from .layers import SelectAdaptivePool2d
from .registry import register_model

__all__ = ['NASNetALarge']

default_cfgs = {
    'nasnetalarge': {
        'url': 'http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth',
        'input_size': (3, 331, 331),
        'pool_size': (11, 11),
        'crop_pct': 0.875,
        'interpolation': 'bicubic',
        'mean': (0.5, 0.5, 0.5),
        'std': (0.5, 0.5, 0.5),
        'num_classes': 1001,
        'first_conv': 'conv0.conv',
        'classifier': 'last_linear',
    },
}


class MaxPoolPad(nn.Module):

    def __init__(self):
        super(MaxPoolPad, self).__init__()
        self.pad = nn.ZeroPad2d((1, 0, 1, 0))
        self.pool = nn.MaxPool2d(3, stride=2, padding=1)

    def forward(self, x):
        x = self.pad(x)
        x = self.pool(x)
        x = x[:, :, 1:, 1:]
        return x


class AvgPoolPad(nn.Module):

    def __init__(self, stride=2, padding=1):
        super(AvgPoolPad, self).__init__()
        self.pad = nn.ZeroPad2d((1, 0, 1, 0))
        self.pool = nn.AvgPool2d(3, stride=stride, padding=padding, count_include_pad=False)

    def forward(self, x):
        x = self.pad(x)
        x = self.pool(x)
        x = x[:, :, 1:, 1:]
        return x


class SeparableConv2d(nn.Module):

    def __init__(self, in_channels, out_channels, dw_kernel, dw_stride, dw_padding, bias=False):
        super(SeparableConv2d, self).__init__()
        self.depthwise_conv2d = nn.Conv2d(
            in_channels, in_channels, dw_kernel,
            stride=dw_stride, padding=dw_padding,
            bias=bias, groups=in_channels)
        self.pointwise_conv2d = nn.Conv2d(in_channels, out_channels, 1, stride=1, bias=bias)

    def forward(self, x):
        x = self.depthwise_conv2d(x)
        x = self.pointwise_conv2d(x)
        return x


class BranchSeparables(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, bias=False):
        super(BranchSeparables, self).__init__()
        self.relu = nn.ReLU()
        self.separable_1 = SeparableConv2d(in_channels, in_channels, kernel_size, stride, padding, bias=bias)
        self.bn_sep_1 = nn.BatchNorm2d(in_channels, eps=0.001, momentum=0.1, affine=True)
        self.relu1 = nn.ReLU()
        self.separable_2 = SeparableConv2d(in_channels, out_channels, kernel_size, 1, padding, bias=bias)
        self.bn_sep_2 = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0.1, affine=True)

    def forward(self, x):
        x = self.relu(x)
        x = self.separable_1(x)
        x = self.bn_sep_1(x)
        x = self.relu1(x)
        x = self.separable_2(x)
        x = self.bn_sep_2(x)
        return x


class BranchSeparablesStem(nn.Module):

    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, bias=False):
        super(BranchSeparablesStem, self).__init__()
        self.relu = nn.ReLU()
        self.separable_1 = SeparableConv2d(in_channels, out_channels, kernel_size, stride, padding, bias=bias)
        self.bn_sep_1 = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0.1, affine=True)
        self.relu1 = nn.ReLU()
        self.separable_2 = SeparableConv2d(out_channels, out_channels, kernel_size, 1, padding, bias=bias)
        self.bn_sep_2 = nn.BatchNorm2d(out_channels, eps=0.001, momentum=0.1, affine=True)

    def forward(self, x):
        x = self.relu(x)
        x = self.separable_1(x)
        x = self.bn_sep_1(x)
        x = self.relu1(x)
        x = self.separable_2(x)
        x = self.bn_sep_2(x)
        return x


class BranchSeparablesReduction(BranchSeparables):

    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, z_padding=1, bias=False):
        BranchSeparables.__init__(self, in_channels, out_channels, kernel_size, stride, padding, bias)
        self.padding = nn.ZeroPad2d((z_padding, 0, z_padding, 0))

    def forward(self, x):
        x = self.relu(x)
        x = self.padding(x)
        x = self.separable_1(x)
        x = x[:, :, 1:, 1:].contiguous()
        x = self.bn_sep_1(x)
        x = self.relu1(x)
        x = self.separable_2(x)
        x = self.bn_sep_2(x)
        return x


class CellStem0(nn.Module):
    def __init__(self, stem_size, num_channels=42):
        super(CellStem0, self).__init__()
        self.num_channels = num_channels
        self.stem_size = stem_size
        self.conv_1x1 = nn.Sequential()
        self.conv_1x1.add_module('relu', nn.ReLU())
        self.conv_1x1.add_module('conv', nn.Conv2d(self.stem_size, self.num_channels, 1, stride=1, bias=False))
        self.conv_1x1.add_module('bn', nn.BatchNorm2d(self.num_channels, eps=0.001, momentum=0.1, affine=True))

        self.comb_iter_0_left = BranchSeparables(self.num_channels, self.num_channels, 5, 2, 2)
        self.comb_iter_0_right = BranchSeparablesStem(self.stem_size, self.num_channels, 7, 2, 3, bias=False)

        self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, padding=1)
        self.comb_iter_1_right = BranchSeparablesStem(self.stem_size, self.num_channels, 7, 2, 3, bias=False)

        self.comb_iter_2_left = nn.AvgPool2d(3, stride=2, padding=1, count_include_pad=False)
        self.comb_iter_2_right = BranchSeparablesStem(self.stem_size, self.num_channels, 5, 2, 2, bias=False)

        self.comb_iter_3_right = nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False)

        self.comb_iter_4_left = BranchSeparables(self.num_channels, self.num_channels, 3, 1, 1, bias=False)
        self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, padding=1)

    def forward(self, x):
        x1 = self.conv_1x1(x)

        x_comb_iter_0_left = self.comb_iter_0_left(x1)
        x_comb_iter_0_right = self.comb_iter_0_right(x)
        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right

        x_comb_iter_1_left = self.comb_iter_1_left(x1)
        x_comb_iter_1_right = self.comb_iter_1_right(x)
        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right

        x_comb_iter_2_left = self.comb_iter_2_left(x1)
        x_comb_iter_2_right = self.comb_iter_2_right(x)
        x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right

        x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
        x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1

        x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
        x_comb_iter_4_right = self.comb_iter_4_right(x1)
        x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right

        x_out = torch.cat([x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1)
        return x_out


class CellStem1(nn.Module):

    def __init__(self, stem_size, num_channels):
        super(CellStem1, self).__init__()
        self.num_channels = num_channels
        self.stem_size = stem_size
        self.conv_1x1 = nn.Sequential()
        self.conv_1x1.add_module('relu', nn.ReLU())
        self.conv_1x1.add_module('conv', nn.Conv2d(2 * self.num_channels, self.num_channels, 1, stride=1, bias=False))
        self.conv_1x1.add_module('bn', nn.BatchNorm2d(self.num_channels, eps=0.001, momentum=0.1, affine=True))

        self.relu = nn.ReLU()
        self.path_1 = nn.Sequential()
        self.path_1.add_module('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False))
        self.path_1.add_module('conv', nn.Conv2d(self.stem_size, self.num_channels // 2, 1, stride=1, bias=False))
        self.path_2 = nn.ModuleList()
        self.path_2.add_module('pad', nn.ZeroPad2d((0, 1, 0, 1)))
        self.path_2.add_module('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False))
        self.path_2.add_module('conv', nn.Conv2d(self.stem_size, self.num_channels // 2, 1, stride=1, bias=False))

        self.final_path_bn = nn.BatchNorm2d(self.num_channels, eps=0.001, momentum=0.1, affine=True)

        self.comb_iter_0_left = BranchSeparables(self.num_channels, self.num_channels, 5, 2, 2, bias=False)
        self.comb_iter_0_right = BranchSeparables(self.num_channels, self.num_channels, 7, 2, 3, bias=False)

        self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, padding=1)
        self.comb_iter_1_right = BranchSeparables(self.num_channels, self.num_channels, 7, 2, 3, bias=False)

        self.comb_iter_2_left = nn.AvgPool2d(3, stride=2, padding=1, count_include_pad=False)
        self.comb_iter_2_right = BranchSeparables(self.num_channels, self.num_channels, 5, 2, 2, bias=False)

        self.comb_iter_3_right = nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False)

        self.comb_iter_4_left = BranchSeparables(self.num_channels, self.num_channels, 3, 1, 1, bias=False)
        self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, padding=1)

    def forward(self, x_conv0, x_stem_0):
        x_left = self.conv_1x1(x_stem_0)

        x_relu = self.relu(x_conv0)
        # path 1
        x_path1 = self.path_1(x_relu)
        # path 2
        x_path2 = self.path_2.pad(x_relu)
        x_path2 = x_path2[:, :, 1:, 1:]
        x_path2 = self.path_2.avgpool(x_path2)
        x_path2 = self.path_2.conv(x_path2)
        # final path
        x_right = self.final_path_bn(torch.cat([x_path1, x_path2], 1))

        x_comb_iter_0_left = self.comb_iter_0_left(x_left)
        x_comb_iter_0_right = self.comb_iter_0_right(x_right)
        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right

        x_comb_iter_1_left = self.comb_iter_1_left(x_left)
        x_comb_iter_1_right = self.comb_iter_1_right(x_right)
        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right

        x_comb_iter_2_left = self.comb_iter_2_left(x_left)
        x_comb_iter_2_right = self.comb_iter_2_right(x_right)
        x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right

        x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
        x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1

        x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
        x_comb_iter_4_right = self.comb_iter_4_right(x_left)
        x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right

        x_out = torch.cat([x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1)
        return x_out


class FirstCell(nn.Module):

    def __init__(self, in_channels_left, out_channels_left, in_channels_right, out_channels_right):
        super(FirstCell, self).__init__()
        self.conv_1x1 = nn.Sequential()
        self.conv_1x1.add_module('relu', nn.ReLU())
        self.conv_1x1.add_module('conv', nn.Conv2d(in_channels_right, out_channels_right, 1, stride=1, bias=False))
        self.conv_1x1.add_module('bn', nn.BatchNorm2d(out_channels_right, eps=0.001, momentum=0.1, affine=True))

        self.relu = nn.ReLU()
        self.path_1 = nn.Sequential()
        self.path_1.add_module('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False))
        self.path_1.add_module('conv', nn.Conv2d(in_channels_left, out_channels_left, 1, stride=1, bias=False))
        self.path_2 = nn.ModuleList()
        self.path_2.add_module('pad', nn.ZeroPad2d((0, 1, 0, 1)))
        self.path_2.add_module('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False))
        self.path_2.add_module('conv', nn.Conv2d(in_channels_left, out_channels_left, 1, stride=1, bias=False))

        self.final_path_bn = nn.BatchNorm2d(out_channels_left * 2, eps=0.001, momentum=0.1, affine=True)

        self.comb_iter_0_left = BranchSeparables(out_channels_right, out_channels_right, 5, 1, 2, bias=False)
        self.comb_iter_0_right = BranchSeparables(out_channels_right, out_channels_right, 3, 1, 1, bias=False)

        self.comb_iter_1_left = BranchSeparables(out_channels_right, out_channels_right, 5, 1, 2, bias=False)
        self.comb_iter_1_right = BranchSeparables(out_channels_right, out_channels_right, 3, 1, 1, bias=False)

        self.comb_iter_2_left = nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False)

        self.comb_iter_3_left = nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False)
        self.comb_iter_3_right = nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False)

        self.comb_iter_4_left = BranchSeparables(out_channels_right, out_channels_right, 3, 1, 1, bias=False)

    def forward(self, x, x_prev):
        x_relu = self.relu(x_prev)
        # path 1
        x_path1 = self.path_1(x_relu)
        # path 2
        x_path2 = self.path_2.pad(x_relu)
        x_path2 = x_path2[:, :, 1:, 1:]
        x_path2 = self.path_2.avgpool(x_path2)
        x_path2 = self.path_2.conv(x_path2)
        # final path
        x_left = self.final_path_bn(torch.cat([x_path1, x_path2], 1))

        x_right = self.conv_1x1(x)

        x_comb_iter_0_left = self.comb_iter_0_left(x_right)
        x_comb_iter_0_right = self.comb_iter_0_right(x_left)
        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right

        x_comb_iter_1_left = self.comb_iter_1_left(x_left)
        x_comb_iter_1_right = self.comb_iter_1_right(x_left)
        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right

        x_comb_iter_2_left = self.comb_iter_2_left(x_right)
        x_comb_iter_2 = x_comb_iter_2_left + x_left

        x_comb_iter_3_left = self.comb_iter_3_left(x_left)
        x_comb_iter_3_right = self.comb_iter_3_right(x_left)
        x_comb_iter_3 = x_comb_iter_3_left + x_comb_iter_3_right

        x_comb_iter_4_left = self.comb_iter_4_left(x_right)
        x_comb_iter_4 = x_comb_iter_4_left + x_right

        x_out = torch.cat([x_left, x_comb_iter_0, x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1)
        return x_out


class NormalCell(nn.Module):

    def __init__(self, in_channels_left, out_channels_left, in_channels_right, out_channels_right):
        super(NormalCell, self).__init__()
        self.conv_prev_1x1 = nn.Sequential()
        self.conv_prev_1x1.add_module('relu', nn.ReLU())
        self.conv_prev_1x1.add_module('conv', nn.Conv2d(in_channels_left, out_channels_left, 1, stride=1, bias=False))
        self.conv_prev_1x1.add_module('bn', nn.BatchNorm2d(out_channels_left, eps=0.001, momentum=0.1, affine=True))

        self.conv_1x1 = nn.Sequential()
        self.conv_1x1.add_module('relu', nn.ReLU())
        self.conv_1x1.add_module('conv', nn.Conv2d(in_channels_right, out_channels_right, 1, stride=1, bias=False))
        self.conv_1x1.add_module('bn', nn.BatchNorm2d(out_channels_right, eps=0.001, momentum=0.1, affine=True))

        self.comb_iter_0_left = BranchSeparables(out_channels_right, out_channels_right, 5, 1, 2, bias=False)
        self.comb_iter_0_right = BranchSeparables(out_channels_left, out_channels_left, 3, 1, 1, bias=False)

        self.comb_iter_1_left = BranchSeparables(out_channels_left, out_channels_left, 5, 1, 2, bias=False)
        self.comb_iter_1_right = BranchSeparables(out_channels_left, out_channels_left, 3, 1, 1, bias=False)

        self.comb_iter_2_left = nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False)

        self.comb_iter_3_left = nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False)
        self.comb_iter_3_right = nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False)

        self.comb_iter_4_left = BranchSeparables(out_channels_right, out_channels_right, 3, 1, 1, bias=False)

    def forward(self, x, x_prev):
        x_left = self.conv_prev_1x1(x_prev)
        x_right = self.conv_1x1(x)

        x_comb_iter_0_left = self.comb_iter_0_left(x_right)
        x_comb_iter_0_right = self.comb_iter_0_right(x_left)
        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right

        x_comb_iter_1_left = self.comb_iter_1_left(x_left)
        x_comb_iter_1_right = self.comb_iter_1_right(x_left)
        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right

        x_comb_iter_2_left = self.comb_iter_2_left(x_right)
        x_comb_iter_2 = x_comb_iter_2_left + x_left

        x_comb_iter_3_left = self.comb_iter_3_left(x_left)
        x_comb_iter_3_right = self.comb_iter_3_right(x_left)
        x_comb_iter_3 = x_comb_iter_3_left + x_comb_iter_3_right

        x_comb_iter_4_left = self.comb_iter_4_left(x_right)
        x_comb_iter_4 = x_comb_iter_4_left + x_right

        x_out = torch.cat([x_left, x_comb_iter_0, x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1)
        return x_out


class ReductionCell0(nn.Module):

    def __init__(self, in_channels_left, out_channels_left, in_channels_right, out_channels_right):
        super(ReductionCell0, self).__init__()
        self.conv_prev_1x1 = nn.Sequential()
        self.conv_prev_1x1.add_module('relu', nn.ReLU())
        self.conv_prev_1x1.add_module('conv', nn.Conv2d(in_channels_left, out_channels_left, 1, stride=1, bias=False))
        self.conv_prev_1x1.add_module('bn', nn.BatchNorm2d(out_channels_left, eps=0.001, momentum=0.1, affine=True))

        self.conv_1x1 = nn.Sequential()
        self.conv_1x1.add_module('relu', nn.ReLU())
        self.conv_1x1.add_module('conv', nn.Conv2d(in_channels_right, out_channels_right, 1, stride=1, bias=False))
        self.conv_1x1.add_module('bn', nn.BatchNorm2d(out_channels_right, eps=0.001, momentum=0.1, affine=True))

        self.comb_iter_0_left = BranchSeparablesReduction(out_channels_right, out_channels_right, 5, 2, 2, bias=False)
        self.comb_iter_0_right = BranchSeparablesReduction(out_channels_right, out_channels_right, 7, 2, 3, bias=False)

        self.comb_iter_1_left = MaxPoolPad()
        self.comb_iter_1_right = BranchSeparablesReduction(out_channels_right, out_channels_right, 7, 2, 3, bias=False)

        self.comb_iter_2_left = AvgPoolPad()
        self.comb_iter_2_right = BranchSeparablesReduction(out_channels_right, out_channels_right, 5, 2, 2, bias=False)

        self.comb_iter_3_right = nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False)

        self.comb_iter_4_left = BranchSeparablesReduction(out_channels_right, out_channels_right, 3, 1, 1, bias=False)
        self.comb_iter_4_right = MaxPoolPad()

    def forward(self, x, x_prev):
        x_left = self.conv_prev_1x1(x_prev)
        x_right = self.conv_1x1(x)

        x_comb_iter_0_left = self.comb_iter_0_left(x_right)
        x_comb_iter_0_right = self.comb_iter_0_right(x_left)
        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right

        x_comb_iter_1_left = self.comb_iter_1_left(x_right)
        x_comb_iter_1_right = self.comb_iter_1_right(x_left)
        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right

        x_comb_iter_2_left = self.comb_iter_2_left(x_right)
        x_comb_iter_2_right = self.comb_iter_2_right(x_left)
        x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right

        x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
        x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1

        x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
        x_comb_iter_4_right = self.comb_iter_4_right(x_right)
        x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right

        x_out = torch.cat([x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1)
        return x_out


class ReductionCell1(nn.Module):

    def __init__(self, in_channels_left, out_channels_left, in_channels_right, out_channels_right):
        super(ReductionCell1, self).__init__()
        self.conv_prev_1x1 = nn.Sequential()
        self.conv_prev_1x1.add_module('relu', nn.ReLU())
        self.conv_prev_1x1.add_module('conv', nn.Conv2d(in_channels_left, out_channels_left, 1, stride=1, bias=False))
        self.conv_prev_1x1.add_module('bn', nn.BatchNorm2d(out_channels_left, eps=0.001, momentum=0.1, affine=True))

        self.conv_1x1 = nn.Sequential()
        self.conv_1x1.add_module('relu', nn.ReLU())
        self.conv_1x1.add_module('conv', nn.Conv2d(in_channels_right, out_channels_right, 1, stride=1, bias=False))
        self.conv_1x1.add_module('bn', nn.BatchNorm2d(out_channels_right, eps=0.001, momentum=0.1, affine=True))

        self.comb_iter_0_left = BranchSeparables(out_channels_right, out_channels_right, 5, 2, 2, bias=False)
        self.comb_iter_0_right = BranchSeparables(out_channels_right, out_channels_right, 7, 2, 3, bias=False)

        self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, padding=1)
        self.comb_iter_1_right = BranchSeparables(out_channels_right, out_channels_right, 7, 2, 3, bias=False)

        self.comb_iter_2_left = nn.AvgPool2d(3, stride=2, padding=1, count_include_pad=False)
        self.comb_iter_2_right = BranchSeparables(out_channels_right, out_channels_right, 5, 2, 2, bias=False)

        self.comb_iter_3_right = nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False)

        self.comb_iter_4_left = BranchSeparables(out_channels_right, out_channels_right, 3, 1, 1, bias=False)
        self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, padding=1)

    def forward(self, x, x_prev):
        x_left = self.conv_prev_1x1(x_prev)
        x_right = self.conv_1x1(x)

        x_comb_iter_0_left = self.comb_iter_0_left(x_right)
        x_comb_iter_0_right = self.comb_iter_0_right(x_left)
        x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right

        x_comb_iter_1_left = self.comb_iter_1_left(x_right)
        x_comb_iter_1_right = self.comb_iter_1_right(x_left)
        x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right

        x_comb_iter_2_left = self.comb_iter_2_left(x_right)
        x_comb_iter_2_right = self.comb_iter_2_right(x_left)
        x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right

        x_comb_iter_3_right = self.comb_iter_3_right(x_comb_iter_0)
        x_comb_iter_3 = x_comb_iter_3_right + x_comb_iter_1

        x_comb_iter_4_left = self.comb_iter_4_left(x_comb_iter_0)
        x_comb_iter_4_right = self.comb_iter_4_right(x_right)
        x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right

        x_out = torch.cat([x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1)
        return x_out


class NASNetALarge(nn.Module):
    """NASNetALarge (6 @ 4032) """

    def __init__(self, num_classes=1000, in_chans=1, stem_size=96, num_features=4032, channel_multiplier=2,
                 drop_rate=0., global_pool='avg'):
        super(NASNetALarge, self).__init__()
        self.num_classes = num_classes
        self.stem_size = stem_size
        self.num_features = num_features
        self.channel_multiplier = channel_multiplier
        self.drop_rate = drop_rate

        channels = self.num_features // 24
        # 24 is default value for the architecture

        self.conv0 = nn.Sequential()
        self.conv0.add_module('conv', nn.Conv2d(
            in_channels=in_chans, out_channels=self.stem_size, kernel_size=3, padding=0, stride=2, bias=False))
        self.conv0.add_module('bn', nn.BatchNorm2d(self.stem_size, eps=0.001, momentum=0.1, affine=True))

        self.cell_stem_0 = CellStem0(self.stem_size, num_channels=channels // (channel_multiplier ** 2))
        self.cell_stem_1 = CellStem1(self.stem_size, num_channels=channels // channel_multiplier)

        self.cell_0 = FirstCell(in_channels_left=channels, out_channels_left=channels // 2,
                                in_channels_right=2 * channels, out_channels_right=channels)
        self.cell_1 = NormalCell(in_channels_left=2 * channels, out_channels_left=channels,
                                 in_channels_right=6 * channels, out_channels_right=channels)
        self.cell_2 = NormalCell(in_channels_left=6 * channels, out_channels_left=channels,
                                 in_channels_right=6 * channels, out_channels_right=channels)
        self.cell_3 = NormalCell(in_channels_left=6 * channels, out_channels_left=channels,
                                 in_channels_right=6 * channels, out_channels_right=channels)
        self.cell_4 = NormalCell(in_channels_left=6 * channels, out_channels_left=channels,
                                 in_channels_right=6 * channels, out_channels_right=channels)
        self.cell_5 = NormalCell(in_channels_left=6 * channels, out_channels_left=channels,
                                 in_channels_right=6 * channels, out_channels_right=channels)

        self.reduction_cell_0 = ReductionCell0(in_channels_left=6 * channels, out_channels_left=2 * channels,
                                               in_channels_right=6 * channels, out_channels_right=2 * channels)

        self.cell_6 = FirstCell(in_channels_left=6 * channels, out_channels_left=channels,
                                in_channels_right=8 * channels, out_channels_right=2 * channels)
        self.cell_7 = NormalCell(in_channels_left=8 * channels, out_channels_left=2 * channels,
                                 in_channels_right=12 * channels, out_channels_right=2 * channels)
        self.cell_8 = NormalCell(in_channels_left=12 * channels, out_channels_left=2 * channels,
                                 in_channels_right=12 * channels, out_channels_right=2 * channels)
        self.cell_9 = NormalCell(in_channels_left=12 * channels, out_channels_left=2 * channels,
                                 in_channels_right=12 * channels, out_channels_right=2 * channels)
        self.cell_10 = NormalCell(in_channels_left=12 * channels, out_channels_left=2 * channels,
                                  in_channels_right=12 * channels, out_channels_right=2 * channels)
        self.cell_11 = NormalCell(in_channels_left=12 * channels, out_channels_left=2 * channels,
                                  in_channels_right=12 * channels, out_channels_right=2 * channels)

        self.reduction_cell_1 = ReductionCell1(in_channels_left=12 * channels, out_channels_left=4 * channels,
                                               in_channels_right=12 * channels, out_channels_right=4 * channels)

        self.cell_12 = FirstCell(in_channels_left=12 * channels, out_channels_left=2 * channels,
                                 in_channels_right=16 * channels, out_channels_right=4 * channels)
        self.cell_13 = NormalCell(in_channels_left=16 * channels, out_channels_left=4 * channels,
                                  in_channels_right=24 * channels, out_channels_right=4 * channels)
        self.cell_14 = NormalCell(in_channels_left=24 * channels, out_channels_left=4 * channels,
                                  in_channels_right=24 * channels, out_channels_right=4 * channels)
        self.cell_15 = NormalCell(in_channels_left=24 * channels, out_channels_left=4 * channels,
                                  in_channels_right=24 * channels, out_channels_right=4 * channels)
        self.cell_16 = NormalCell(in_channels_left=24 * channels, out_channels_left=4 * channels,
                                  in_channels_right=24 * channels, out_channels_right=4 * channels)
        self.cell_17 = NormalCell(in_channels_left=24 * channels, out_channels_left=4 * channels,
                                  in_channels_right=24 * channels, out_channels_right=4 * channels)

        self.relu = nn.ReLU()
        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
        self.last_linear = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)

    def get_classifier(self):
        return self.last_linear

    def reset_classifier(self, num_classes, global_pool='avg'):
        self.num_classes = num_classes
        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
        if num_classes:
            num_features = self.num_features * self.global_pool.feat_mult()
            self.last_linear = nn.Linear(num_features, num_classes)
        else:
            self.last_linear = nn.Identity()

    def forward_features(self, x):
        x_conv0 = self.conv0(x)
        x_stem_0 = self.cell_stem_0(x_conv0)
        x_stem_1 = self.cell_stem_1(x_conv0, x_stem_0)

        x_cell_0 = self.cell_0(x_stem_1, x_stem_0)
        x_cell_1 = self.cell_1(x_cell_0, x_stem_1)
        x_cell_2 = self.cell_2(x_cell_1, x_cell_0)
        x_cell_3 = self.cell_3(x_cell_2, x_cell_1)
        x_cell_4 = self.cell_4(x_cell_3, x_cell_2)
        x_cell_5 = self.cell_5(x_cell_4, x_cell_3)

        x_reduction_cell_0 = self.reduction_cell_0(x_cell_5, x_cell_4)

        x_cell_6 = self.cell_6(x_reduction_cell_0, x_cell_4)
        x_cell_7 = self.cell_7(x_cell_6, x_reduction_cell_0)
        x_cell_8 = self.cell_8(x_cell_7, x_cell_6)
        x_cell_9 = self.cell_9(x_cell_8, x_cell_7)
        x_cell_10 = self.cell_10(x_cell_9, x_cell_8)
        x_cell_11 = self.cell_11(x_cell_10, x_cell_9)

        x_reduction_cell_1 = self.reduction_cell_1(x_cell_11, x_cell_10)

        x_cell_12 = self.cell_12(x_reduction_cell_1, x_cell_10)
        x_cell_13 = self.cell_13(x_cell_12, x_reduction_cell_1)
        x_cell_14 = self.cell_14(x_cell_13, x_cell_12)
        x_cell_15 = self.cell_15(x_cell_14, x_cell_13)
        x_cell_16 = self.cell_16(x_cell_15, x_cell_14)
        x_cell_17 = self.cell_17(x_cell_16, x_cell_15)
        x = self.relu(x_cell_17)
        return x

    def forward(self, x):
        x = self.forward_features(x)
        x = self.global_pool(x).flatten(1)
        if self.drop_rate > 0:
            x = F.dropout(x, self.drop_rate, training=self.training)
        x = self.last_linear(x)
        return x


@register_model
def nasnetalarge(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """NASNet-A large model architecture.
    """
    default_cfg = default_cfgs['nasnetalarge']
    model = NASNetALarge(num_classes=num_classes, in_chans=in_chans, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)

    return model