# -*- coding: utf-8 -*-
# @Time    : 2019/7/21
# @Author  : Godder
# @Github  : https://github.com/WangGodder
import torch
import torch.nn as nn
from torchcmh.models import BasicModule


__all__ = ['AlexNet', 'alexnet']


model_urls = {
    'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',
}


class AlexNet(BasicModule):
    def __init__(self, bit = 64, num_classes=1000):
        super(AlexNet, self).__init__()
        self.module_name = "Alexnet"
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.LocalResponseNorm(size=2, k=1),
            nn.Conv2d(64, 192, kernel_size=5, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.LocalResponseNorm(size=2, k=1),
            nn.Conv2d(192, 384, kernel_size=3, padding=1, groups=2),
            nn.ReLU(inplace=True),
            nn.Conv2d(384, 256, kernel_size=3, padding=1, groups=2),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1, groups=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
        )
        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(256 * 6 * 6, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, num_classes),

        )
        self.hash = nn.Sequential(
            nn.ReLU(inplace = True),
            nn.Linear(num_classes, bit)
        )

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


def get_alexnet(pretrained=True, **kwargs):
    r"""AlexNet model architecture from the
    `"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    model = AlexNet(**kwargs)
    if pretrained:
        model.init_pretrained_weights(model_urls['alexnet'])
    return model

# -*- coding: utf-8 -*-
# @Time    : 2019/7/21
# @Author  : Godder
# @Github  : https://github.com/WangGodder
import torch
import torch.nn as nn
from torchcmh.models import BasicModule


__all__ = ['AlexNet', 'alexnet']


model_urls = {
    'alexnet': 'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth',
}


class AlexNet(BasicModule):
    def __init__(self, bit = 64, num_classes=1000):
        super(AlexNet, self).__init__()
        self.module_name = "Alexnet"
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.LocalResponseNorm(size=2, k=1),
            nn.Conv2d(64, 192, kernel_size=5, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.LocalResponseNorm(size=2, k=1),
            nn.Conv2d(192, 384, kernel_size=3, padding=1, groups=2),
            nn.ReLU(inplace=True),
            nn.Conv2d(384, 256, kernel_size=3, padding=1, groups=2),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1, groups=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
        )
        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(256 * 6 * 6, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, num_classes),

        )
        self.hash = nn.Sequential(
            nn.ReLU(inplace = True),
            nn.Linear(num_classes, bit)
        )

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


def get_alexnet(pretrained=True, **kwargs):
    r"""AlexNet model architecture from the
    `"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    model = AlexNet(**kwargs)
    if pretrained:
        model.init_pretrained_weights(model_urls['alexnet'])
    return model

# -*- coding: utf-8 -*- # @Time    : 2019/7/21 # @Author  : Godder # @Github  : https://github.com/WangGodder AlexNet model architecture from the     `"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.     Args:         pretrained (bool): If True, returns a model pre-trained on ImageNet         progress (bool): If True, displays a progress bar of the download to stderr