# Source code for torchreid.models.inceptionresnetv2

    from __future__ import absolute_import
    from __future__ import division

    __all__ = ['inceptionresnetv2']

    import torch
    import torch.nn as nn
    from torch.nn import functional as F
    import torch.utils.model_zoo as model_zoo
    import os
    import sys

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

    pretrained_settings = {
        'inceptionresnetv2': {
            'imagenet': {
                'url': 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionresnetv2-520b38e4.pth',
                'input_space': 'RGB',
                'input_size': [3, 299, 299],
                '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/inceptionresnetv2-520b38e4.pth',
                'input_space': 'RGB',
                'input_size': [3, 299, 299],
                'input_range': [0, 1],
                'mean': [0.5, 0.5, 0.5],
                'std': [0.5, 0.5, 0.5],
                'num_classes': 1001
            }
        }
    }

    class BasicConv2d(nn.Module):

        def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
            super(BasicConv2d, self).__init__()
            self.conv = nn.Conv2d(in_planes, out_planes,
                                  kernel_size=kernel_size, stride=stride,
                                  padding=padding, bias=False) # verify bias false
            self.bn = nn.BatchNorm2d(out_planes,
                                     eps=0.001, # value found in tensorflow
                                     momentum=0.1, # default pytorch value
                                     affine=True)
            self.relu = nn.ReLU(inplace=False)

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

    class Mixed_5b(nn.Module):

        def __init__(self):
            super(Mixed_5b, self).__init__()

            self.branch0 = BasicConv2d(192, 96, kernel_size=1, stride=1)

            self.branch1 = nn.Sequential(
                BasicConv2d(192, 48, kernel_size=1, stride=1),
                BasicConv2d(48, 64, kernel_size=5, stride=1, padding=2)
            )

            self.branch2 = nn.Sequential(
                BasicConv2d(192, 64, kernel_size=1, stride=1),
                BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1),
                BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1)
            )

            self.branch3 = nn.Sequential(
                nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
                BasicConv2d(192, 64, kernel_size=1, stride=1)
            )

        def forward(self, x):
            x0 = self.branch0(x)
            x1 = self.branch1(x)
            x2 = self.branch2(x)
            x3 = self.branch3(x)
            out = torch.cat((x0, x1, x2, x3), 1)
            return out

    class Block35(nn.Module):

        def __init__(self, scale=1.0):
            super(Block35, self).__init__()

            self.scale = scale

            self.branch0 = BasicConv2d(320, 32, kernel_size=1, stride=1)

            self.branch1 = nn.Sequential(
                BasicConv2d(320, 32, kernel_size=1, stride=1),
                BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
            )

            self.branch2 = nn.Sequential(
                BasicConv2d(320, 32, kernel_size=1, stride=1),
                BasicConv2d(32, 48, kernel_size=3, stride=1, padding=1),
                BasicConv2d(48, 64, kernel_size=3, stride=1, padding=1)
            )

            self.conv2d = nn.Conv2d(128, 320, kernel_size=1, stride=1)
            self.relu = nn.ReLU(inplace=False)

        def forward(self, x):
            x0 = self.branch0(x)
            x1 = self.branch1(x)
            x2 = self.branch2(x)
            out = torch.cat((x0, x1, x2), 1)
            out = self.conv2d(out)
            out = out * self.scale + x
            out = self.relu(out)
            return out

    class Mixed_6a(nn.Module):

        def __init__(self):
            super(Mixed_6a, self).__init__()

            self.branch0 = BasicConv2d(320, 384, kernel_size=3, stride=2)

            self.branch1 = nn.Sequential(
                BasicConv2d(320, 256, kernel_size=1, stride=1),
                BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1),
                BasicConv2d(256, 384, kernel_size=3, stride=2)
            )

            self.branch2 = nn.MaxPool2d(3, stride=2)

        def forward(self, x):
            x0 = self.branch0(x)
            x1 = self.branch1(x)
            x2 = self.branch2(x)
            out = torch.cat((x0, x1, x2), 1)
            return out

    class Block17(nn.Module):

        def __init__(self, scale=1.0):
            super(Block17, self).__init__()

            self.scale = scale

            self.branch0 = BasicConv2d(1088, 192, kernel_size=1, stride=1)

            self.branch1 = nn.Sequential(
                BasicConv2d(1088, 128, kernel_size=1, stride=1),
                BasicConv2d(128, 160, kernel_size=(1,7), stride=1, padding=(0,3)),
                BasicConv2d(160, 192, kernel_size=(7,1), stride=1, padding=(3,0))
            )

            self.conv2d = nn.Conv2d(384, 1088, kernel_size=1, stride=1)
            self.relu = nn.ReLU(inplace=False)

        def forward(self, x):
            x0 = self.branch0(x)
            x1 = self.branch1(x)
            out = torch.cat((x0, x1), 1)
            out = self.conv2d(out)
            out = out * self.scale + x
            out = self.relu(out)
            return out

    class Mixed_7a(nn.Module):

        def __init__(self):
            super(Mixed_7a, self).__init__()

            self.branch0 = nn.Sequential(
                BasicConv2d(1088, 256, kernel_size=1, stride=1),
                BasicConv2d(256, 384, kernel_size=3, stride=2)
            )

            self.branch1 = nn.Sequential(
                BasicConv2d(1088, 256, kernel_size=1, stride=1),
                BasicConv2d(256, 288, kernel_size=3, stride=2)
            )

            self.branch2 = nn.Sequential(
                BasicConv2d(1088, 256, kernel_size=1, stride=1),
                BasicConv2d(256, 288, kernel_size=3, stride=1, padding=1),
                BasicConv2d(288, 320, kernel_size=3, stride=2)
            )

            self.branch3 = nn.MaxPool2d(3, stride=2)

        def forward(self, x):
            x0 = self.branch0(x)
            x1 = self.branch1(x)
            x2 = self.branch2(x)
            x3 = self.branch3(x)
            out = torch.cat((x0, x1, x2, x3), 1)
            return out

    class Block8(nn.Module):

        def __init__(self, scale=1.0, noReLU=False):
            super(Block8, self).__init__()

            self.scale = scale
            self.noReLU = noReLU

            self.branch0 = BasicConv2d(2080, 192, kernel_size=1, stride=1)

            self.branch1 = nn.Sequential(
                BasicConv2d(2080, 192, kernel_size=1, stride=1),
                BasicConv2d(192, 224, kernel_size=(1,3), stride=1, padding=(0,1)),
                BasicConv2d(224, 256, kernel_size=(3,1), stride=1, padding=(1,0))
            )

            self.conv2d = nn.Conv2d(448, 2080, kernel_size=1, stride=1)
            if not self.noReLU:
                self.relu = nn.ReLU(inplace=False)

        def forward(self, x):
            x0 = self.branch0(x)
            x1 = self.branch1(x)
            out = torch.cat((x0, x1), 1)
            out = self.conv2d(out)
            out = out * self.scale + x
            if not self.noReLU:
                out = self.relu(out)
            return out

    def inceptionresnetv2(num_classes=1000, pretrained='imagenet'):
        r"""InceptionResNetV2 model architecture from the
        `"InceptionV4, Inception-ResNet..." <https://arxiv.org/abs/1602.07261>`_ paper.
        """
        if pretrained:
            settings = pretrained_settings['inceptionresnetv2'][pretrained]
            assert num_classes == settings['num_classes'], \
                'num_classes should be {}, but is {}'.format(settings['num_classes'], num_classes)

            # both 'imagenet'&'imagenet+background' are loaded from same parameters
            model = InceptionResNetV2(num_classes=1001)
            model.load_state_dict(model_zoo.load_url(settings['url']))

            if pretrained == 'imagenet':
                new_last_linear = nn.Linear(1536, 1000)
                new_last_linear.weight.data = model.last_linear.weight.data[1:]
                new_last_linear.bias.data = model.last_linear.bias.data[1:]
                model.last_linear = new_last_linear

            model.input_space = settings['input_space']
            model.input_size = settings['input_size']
            model.input_range = settings['input_range']

            model.mean = settings['mean']
            model.std = settings['std']
        else:
            model = InceptionResNetV2(num_classes=num_classes)
        return model

    ##################### Model Definition #########################

    class InceptionResNetV2(nn.Module):
        """Inception-ResNet-V2.

        Reference:
            Szegedy et al. Inception-v4, Inception-ResNet and the Impact of Residual
            Connections on Learning. AAAI 2017.

        Public keys:
            - ``inceptionresnetv2``: Inception-ResNet-V2.
        """

        def __init__(self, num_classes, loss='softmax', **kwargs):
            super(InceptionResNetV2, self).__init__()
            self.loss = loss

            # Modules
            self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2)
            self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1)
            self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1)
            self.maxpool_3a = nn.MaxPool2d(3, stride=2)
            self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1)
            self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1)
            self.maxpool_5a = nn.MaxPool2d(3, stride=2)
            self.mixed_5b = Mixed_5b()
            self.repeat = nn.Sequential(
                Block35(scale=0.17),
                Block35(scale=0.17),
                Block35(scale=0.17),
                Block35(scale=0.17),
                Block35(scale=0.17),
                Block35(scale=0.17),
                Block35(scale=0.17),
                Block35(scale=0.17),
                Block35(scale=0.17),
                Block35(scale=0.17)
            )
            self.mixed_6a = Mixed_6a()
            self.repeat_1 = nn.Sequential(
                Block17(scale=0.10),
                Block17(scale=0.10),
                Block17(scale=0.10),
                Block17(scale=0.10),
                Block17(scale=0.10),
                Block17(scale=0.10),
                Block17(scale=0.10),
                Block17(scale=0.10),
                Block17(scale=0.10),
                Block17(scale=0.10),
                Block17(scale=0.10),
                Block17(scale=0.10),
                Block17(scale=0.10),
                Block17(scale=0.10),
                Block17(scale=0.10),
                Block17(scale=0.10),
                Block17(scale=0.10),
                Block17(scale=0.10),
                Block17(scale=0.10),
                Block17(scale=0.10)
            )
            self.mixed_7a = Mixed_7a()
            self.repeat_2 = nn.Sequential(
                Block8(scale=0.20),
                Block8(scale=0.20),
                Block8(scale=0.20),
                Block8(scale=0.20),
                Block8(scale=0.20),
                Block8(scale=0.20),
                Block8(scale=0.20),
                Block8(scale=0.20),
                Block8(scale=0.20)
            )

            self.block8 = Block8(noReLU=True)
            self.conv2d_7b = BasicConv2d(2080, 1536, kernel_size=1, stride=1)
            self.global_avgpool = nn.AdaptiveAvgPool2d(1)
            self.classifier = nn.Linear(1536, num_classes)

        def load_imagenet_weights(self):
            settings = pretrained_settings['inceptionresnetv2']['imagenet']
            pretrain_dict = model_zoo.load_url(settings['url'])
            model_dict = self.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)
            self.load_state_dict(model_dict)
            print('Initialized model with pretrained weights from {}'.format(settings['url']))

        def featuremaps(self, x):
            x = self.conv2d_1a(x)
            x = self.conv2d_2a(x)
            x = self.conv2d_2b(x)
            x = self.maxpool_3a(x)
            x = self.conv2d_3b(x)
            x = self.conv2d_4a(x)
            x = self.maxpool_5a(x)
            x = self.mixed_5b(x)
            x = self.repeat(x)
            x = self.mixed_6a(x)
            x = self.repeat_1(x)
            x = self.mixed_7a(x)
            x = self.repeat_2(x)
            x = self.block8(x)
            x = self.conv2d_7b(x)
            return x

        def forward(self, x):
            f = self.featuremaps(x)
            v = self.global_avgpool(f)
            v = v.view(v.size(0), -1)

            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 inceptionresnetv2(num_classes, loss='softmax', pretrained=True, **kwargs):
        model = InceptionResNetV2(
            num_classes=num_classes,
            loss=loss,
            **kwargs
        )
        if pretrained:
            model.load_imagenet_weights()
        return model