# Source code for torchreid.models.nasnet

    from __future__ import absolute_import
    from __future__ import division

    __all__ = ['nasnetamobile']

    import torch
    import torch.nn as nn
    import torch.nn.functional as F
    import torch.utils.model_zoo as model_zoo
    import numpy as np

    """
    NASNet Mobile
    Thanks to Anastasiia (https://github.com/DagnyT) for the great help, support and motivation!

    ------------------------------------------------------------------------------------
          Architecture       | Top-1 Acc | Top-5 Acc |  Multiply-Adds |  Params (M)
    ------------------------------------------------------------------------------------
    |   NASNet-A (4 @ 1056)  |   74.08%  |   91.74%  |       564 M    |     5.3        |
    ------------------------------------------------------------------------------------
    # References:
     - [Learning Transferable Architectures for Scalable Image Recognition]
        (https://arxiv.org/abs/1707.07012)
    """

    """
    Code imported from https://github.com/Cadene/pretrained-models.pytorch
    """

    pretrained_settings = {
        'nasnetamobile': {
            'imagenet': {
                #'url': 'https://github.com/veronikayurchuk/pretrained-models.pytorch/releases/download/v1.0/nasnetmobile-7e03cead.pth.tar',
                'url': 'http://data.lip6.fr/cadene/pretrainedmodels/nasnetamobile-7e03cead.pth',
                'input_space': 'RGB',
                'input_size': [3, 224, 224], # resize 256
                'input_range': [0, 1],
                'mean': [0.5, 0.5, 0.5],
                'std': [0.5, 0.5, 0.5],
                'num_classes': 1000
            },
            # 'imagenet+background': {
            #     # 'url': 'http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth',
            #     'input_space': 'RGB',
            #     'input_size': [3, 224, 224], # resize 256
            #     'input_range': [0, 1],
            #     'mean': [0.5, 0.5, 0.5],
            #     'std': [0.5, 0.5, 0.5],
            #     'num_classes': 1001
            # }
        }
    }

    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:].contiguous()
            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:].contiguous()
            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, name=None, 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)
            self.name = name

        def forward(self, x):
            x = self.relu(x)
            if self.name == 'specific':
                x = nn.ZeroPad2d((1, 0, 1, 0))(x)
            x = self.separable_1(x)
            if self.name == 'specific':
                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 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_filters, num_filters=42):
            super(CellStem0, self).__init__()
            self.num_filters = num_filters
            self.stem_filters = stem_filters
            self.conv_1x1 = nn.Sequential()
            self.conv_1x1.add_module('relu', nn.ReLU())
            self.conv_1x1.add_module('conv', nn.Conv2d(self.stem_filters, self.num_filters, 1, stride=1, bias=False))
            self.conv_1x1.add_module('bn', nn.BatchNorm2d(self.num_filters, eps=0.001, momentum=0.1, affine=True))

            self.comb_iter_0_left = BranchSeparables(self.num_filters, self.num_filters, 5, 2, 2)
            self.comb_iter_0_right = BranchSeparablesStem(self.stem_filters, self.num_filters, 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_filters, self.num_filters, 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_filters, self.num_filters, 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_filters, self.num_filters, 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_filters, num_filters):
            super(CellStem1, self).__init__()
            self.num_filters = num_filters
            self.stem_filters = stem_filters
            self.conv_1x1 = nn.Sequential()
            self.conv_1x1.add_module('relu', nn.ReLU())
            self.conv_1x1.add_module('conv', nn.Conv2d(2*self.num_filters, self.num_filters, 1, stride=1, bias=False))
            self.conv_1x1.add_module('bn', nn.BatchNorm2d(self.num_filters, 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_filters, self.num_filters//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_filters, self.num_filters//2, 1, stride=1, bias=False))

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

            self.comb_iter_0_left = BranchSeparables(self.num_filters, self.num_filters, 5, 2, 2, name='specific', bias=False)
            self.comb_iter_0_right = BranchSeparables(self.num_filters, self.num_filters, 7, 2, 3, name='specific', bias=False)

            # self.comb_iter_1_left = nn.MaxPool2d(3, stride=2, padding=1)
            self.comb_iter_1_left = MaxPoolPad()
            self.comb_iter_1_right = BranchSeparables(self.num_filters, self.num_filters, 7, 2, 3, name='specific', bias=False)

            # self.comb_iter_2_left = nn.AvgPool2d(3, stride=2, padding=1, count_include_pad=False)
            self.comb_iter_2_left = AvgPoolPad()
            self.comb_iter_2_right = BranchSeparables(self.num_filters, self.num_filters, 5, 2, 2, name='specific', 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_filters, self.num_filters, 3, 1, 1, name='specific', bias=False)
            # self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, padding=1)
            self.comb_iter_4_right = MaxPoolPad()

        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, name='specific', bias=False)
            self.comb_iter_0_right = BranchSeparables(out_channels_right, out_channels_right, 7, 2, 3, name='specific', bias=False)

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

            # self.comb_iter_2_left = nn.AvgPool2d(3, stride=2, padding=1, count_include_pad=False)
            self.comb_iter_2_left = AvgPoolPad()
            self.comb_iter_2_right = BranchSeparables(out_channels_right, out_channels_right, 5, 2, 2, name='specific', 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, name='specific', bias=False)
            # self.comb_iter_4_right = nn.MaxPool2d(3, stride=2, padding=1)
            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 NASNetAMobile(nn.Module):
        """Neural Architecture Search (NAS).

        Reference:
            Zoph et al. Learning Transferable Architectures
            for Scalable Image Recognition. CVPR 2018.

        Public keys:
            - ``nasnetamobile``: NASNet-A Mobile.
        """

        def __init__(self, num_classes, loss, stem_filters=32, penultimate_filters=1056, filters_multiplier=2, **kwargs):
            super(NASNetAMobile, self).__init__()
            self.stem_filters = stem_filters
            self.penultimate_filters = penultimate_filters
            self.filters_multiplier = filters_multiplier
            self.loss = loss

            filters = self.penultimate_filters // 24
            # 24 is default value for the architecture

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

            self.cell_stem_0 = CellStem0(self.stem_filters, num_filters=filters // (filters_multiplier ** 2))
            self.cell_stem_1 = CellStem1(self.stem_filters, num_filters=filters // filters_multiplier)

            self.cell_0 = FirstCell(in_channels_left=filters, out_channels_left=filters//2, # 1, 0.5
                                    in_channels_right=2*filters, out_channels_right=filters) # 2, 1
            self.cell_1 = NormalCell(in_channels_left=2*filters, out_channels_left=filters, # 2, 1
                                     in_channels_right=6*filters, out_channels_right=filters) # 6, 1
            self.cell_2 = NormalCell(in_channels_left=6*filters, out_channels_left=filters, # 6, 1
                                     in_channels_right=6*filters, out_channels_right=filters) # 6, 1
            self.cell_3 = NormalCell(in_channels_left=6*filters, out_channels_left=filters, # 6, 1
                                     in_channels_right=6*filters, out_channels_right=filters) # 6, 1

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

            self.cell_6 = FirstCell(in_channels_left=6*filters, out_channels_left=filters, # 6, 1
                                    in_channels_right=8*filters, out_channels_right=2*filters) # 8, 2
            self.cell_7 = NormalCell(in_channels_left=8*filters, out_channels_left=2*filters, # 8, 2
                                     in_channels_right=12*filters, out_channels_right=2*filters) # 12, 2
            self.cell_8 = NormalCell(in_channels_left=12*filters, out_channels_left=2*filters, # 12, 2
                                     in_channels_right=12*filters, out_channels_right=2*filters) # 12, 2
            self.cell_9 = NormalCell(in_channels_left=12*filters, out_channels_left=2*filters, # 12, 2
                                     in_channels_right=12*filters, out_channels_right=2*filters) # 12, 2

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

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

            self.relu = nn.ReLU()
            self.dropout = nn.Dropout()
            self.classifier = nn.Linear(24 * filters, num_classes)

            self._init_params()

        def _init_params(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.BatchNorm1d):
                    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)
                    if m.bias is not None:
                        nn.init.constant_(m.bias, 0)

        def features(self, input):
            x_conv0 = self.conv0(input)
            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_reduction_cell_0 = self.reduction_cell_0(x_cell_3, x_cell_2)

            x_cell_6 = self.cell_6(x_reduction_cell_0, x_cell_3)
            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_reduction_cell_1 = self.reduction_cell_1(x_cell_9, x_cell_8)

            x_cell_12 = self.cell_12(x_reduction_cell_1, x_cell_9)
            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_15 = self.relu(x_cell_15)
            x_cell_15 = F.avg_pool2d(x_cell_15, x_cell_15.size()[2:]) # global average pool
            x_cell_15 = x_cell_15.view(x_cell_15.size(0), -1)
            x_cell_15 = self.dropout(x_cell_15)

            return x_cell_15

        def forward(self, input):
            v = self.features(input)

            if not self.training:
                return v

            y = self.classifier(v)

            if self.loss == 'softmax':
                return y
            elif self.loss == 'triplet':
                return y, v
            else:
                raise KeyError('Unsupported loss: {}'.format(self.loss))

    def init_pretrained_weights(model, model_url):
        """Initializes model with pretrained weights.

        Layers that don't match with pretrained layers in name or size are kept unchanged.
        """
        pretrain_dict = model_zoo.load_url(model_url)
        model_dict = model.state_dict()
        pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size()}
        model_dict.update(pretrain_dict)
        model.load_state_dict(model_dict)
        print('Initialized model with pretrained weights from {}'.format(model_url))

    def nasnetamobile(num_classes, loss='softmax', pretrained=True, **kwargs):
        model = NASNetAMobile(num_classes, loss, **kwargs)
        if pretrained:
            model_url = pretrained_settings['nasnetamobile']['imagenet']['url']
            init_pretrained_weights(model, model_url)
        return model