# Source code for torchreid.models.inceptionv4

    from __future__ import absolute_import
    from __future__ import division
    __all__ = ['inceptionv4']

    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 = {
        'inceptionv4': {
            'imagenet': {
                'url': 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.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/inceptionv4-8e4777a0.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=True)

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

    class Mixed_3a(nn.Module):

        def __init__(self):
            super(Mixed_3a, self).__init__()
            self.maxpool = nn.MaxPool2d(3, stride=2)
            self.conv = BasicConv2d(64, 96, kernel_size=3, stride=2)

        def forward(self, x):
            x0 = self.maxpool(x)
            x1 = self.conv(x)
            out = torch.cat((x0, x1), 1)
            return out

    class Mixed_4a(nn.Module):

        def __init__(self):
            super(Mixed_4a, self).__init__()

            self.branch0 = nn.Sequential(
                BasicConv2d(160, 64, kernel_size=1, stride=1),
                BasicConv2d(64, 96, kernel_size=3, stride=1)
            )

            self.branch1 = nn.Sequential(
                BasicConv2d(160, 64, kernel_size=1, stride=1),
                BasicConv2d(64, 64, kernel_size=(1,7), stride=1, padding=(0,3)),
                BasicConv2d(64, 64, kernel_size=(7,1), stride=1, padding=(3,0)),
                BasicConv2d(64, 96, kernel_size=(3,3), stride=1)
            )

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

    class Mixed_5a(nn.Module):

        def __init__(self):
            super(Mixed_5a, self).__init__()
            self.conv = BasicConv2d(192, 192, kernel_size=3, stride=2)
            self.maxpool = nn.MaxPool2d(3, stride=2)

        def forward(self, x):
            x0 = self.conv(x)
            x1 = self.maxpool(x)
            out = torch.cat((x0, x1), 1)
            return out

    class Inception_A(nn.Module):

        def __init__(self):
            super(Inception_A, self).__init__()
            self.branch0 = BasicConv2d(384, 96, kernel_size=1, stride=1)

            self.branch1 = nn.Sequential(
                BasicConv2d(384, 64, kernel_size=1, stride=1),
                BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1)
            )

            self.branch2 = nn.Sequential(
                BasicConv2d(384, 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(384, 96, 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 Reduction_A(nn.Module):

        def __init__(self):
            super(Reduction_A, self).__init__()
            self.branch0 = BasicConv2d(384, 384, kernel_size=3, stride=2)

            self.branch1 = nn.Sequential(
                BasicConv2d(384, 192, kernel_size=1, stride=1),
                BasicConv2d(192, 224, kernel_size=3, stride=1, padding=1),
                BasicConv2d(224, 256, 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 Inception_B(nn.Module):

        def __init__(self):
            super(Inception_B, self).__init__()
            self.branch0 = BasicConv2d(1024, 384, kernel_size=1, stride=1)

            self.branch1 = nn.Sequential(
                BasicConv2d(1024, 192, kernel_size=1, stride=1),
                BasicConv2d(192, 224, kernel_size=(1,7), stride=1, padding=(0,3)),
                BasicConv2d(224, 256, kernel_size=(7,1), stride=1, padding=(3,0))
            )

            self.branch2 = nn.Sequential(
                BasicConv2d(1024, 192, kernel_size=1, stride=1),
                BasicConv2d(192, 192, kernel_size=(7,1), stride=1, padding=(3,0)),
                BasicConv2d(192, 224, kernel_size=(1,7), stride=1, padding=(0,3)),
                BasicConv2d(224, 224, kernel_size=(7,1), stride=1, padding=(3,0)),
                BasicConv2d(224, 256, kernel_size=(1,7), stride=1, padding=(0,3))
            )

            self.branch3 = nn.Sequential(
                nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
                BasicConv2d(1024, 128, 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 Reduction_B(nn.Module):

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

            self.branch0 = nn.Sequential(
                BasicConv2d(1024, 192, kernel_size=1, stride=1),
                BasicConv2d(192, 192, kernel_size=3, stride=2)
            )

            self.branch1 = nn.Sequential(
                BasicConv2d(1024, 256, kernel_size=1, stride=1),
                BasicConv2d(256, 256, kernel_size=(1,7), stride=1, padding=(0,3)),
                BasicConv2d(256, 320, kernel_size=(7,1), stride=1, padding=(3,0)),
                BasicConv2d(320, 320, 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 Inception_C(nn.Module):

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

            self.branch0 = BasicConv2d(1536, 256, kernel_size=1, stride=1)

            self.branch1_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
            self.branch1_1a = BasicConv2d(384, 256, kernel_size=(1,3), stride=1, padding=(0,1))
            self.branch1_1b = BasicConv2d(384, 256, kernel_size=(3,1), stride=1, padding=(1,0))

            self.branch2_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
            self.branch2_1 = BasicConv2d(384, 448, kernel_size=(3,1), stride=1, padding=(1,0))
            self.branch2_2 = BasicConv2d(448, 512, kernel_size=(1,3), stride=1, padding=(0,1))
            self.branch2_3a = BasicConv2d(512, 256, kernel_size=(1,3), stride=1, padding=(0,1))
            self.branch2_3b = BasicConv2d(512, 256, kernel_size=(3,1), stride=1, padding=(1,0))

            self.branch3 = nn.Sequential(
                nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
                BasicConv2d(1536, 256, kernel_size=1, stride=1)
            )

        def forward(self, x):
            x0 = self.branch0(x)

            x1_0 = self.branch1_0(x)
            x1_1a = self.branch1_1a(x1_0)
            x1_1b = self.branch1_1b(x1_0)
            x1 = torch.cat((x1_1a, x1_1b), 1)

            x2_0 = self.branch2_0(x)
            x2_1 = self.branch2_1(x2_0)
            x2_2 = self.branch2_2(x2_1)
            x2_3a = self.branch2_3a(x2_2)
            x2_3b = self.branch2_3b(x2_2)
            x2 = torch.cat((x2_3a, x2_3b), 1)

            x3 = self.branch3(x)

            out = torch.cat((x0, x1, x2, x3), 1)
            return out

    class InceptionV4(nn.Module):
        """Inception-v4.

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

        Public keys:
            - ``inceptionv4``: InceptionV4.
        """

        def __init__(self, num_classes, loss, **kwargs):
            super(InceptionV4, self).__init__()
            self.loss = loss

            self.features = nn.Sequential(
                BasicConv2d(3, 32, kernel_size=3, stride=2),
                BasicConv2d(32, 32, kernel_size=3, stride=1),
                BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1),
                Mixed_3a(),
                Mixed_4a(),
                Mixed_5a(),
                Inception_A(),
                Inception_A(),
                Inception_A(),
                Inception_A(),
                Reduction_A(), # Mixed_6a
                Inception_B(),
                Inception_B(),
                Inception_B(),
                Inception_B(),
                Inception_B(),
                Inception_B(),
                Inception_B(),
                Reduction_B(), # Mixed_7a
                Inception_C(),
                Inception_C(),
                Inception_C()
            )
            self.global_avgpool = nn.AdaptiveAvgPool2d(1)
            self.last_linear = nn.Linear(1536, num_classes)

        def forward(self, x):
            f = self.features(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 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 inceptionv4(num_classes, loss='softmax', pretrained=True, **kwargs):
        model = InceptionV4(num_classes, loss, **kwargs)
        if pretrained:
            model_url = pretrained_settings['inceptionv4']['imagenet']['url']
            init_pretrained_weights(model, model_url)
        return model