import re
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from collections import OrderedDict


class _DenseLayer(nn.Sequential):
    def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
        super(_DenseLayer, self).__init__()
        self.add_module('norm1', nn.BatchNorm2d(num_input_features)),
        self.add_module('relu1', nn.ReLU(inplace=True)),
        self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *
                        growth_rate, kernel_size=1, stride=1, bias=False)),
        self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),
        self.add_module('relu2', nn.ReLU(inplace=True)),
        self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,
                        kernel_size=3, stride=1, padding=1, bias=False)),
        self.drop_rate = drop_rate

    def forward(self, x):
        new_features = super(_DenseLayer, self).forward(x)
        if self.drop_rate > 0:
            new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
        return torch.cat([x, new_features], 1)


class _DenseBlock(nn.Sequential):
    def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate):
        super(_DenseBlock, self).__init__()
        for i in range(num_layers):
            layer = _DenseLayer(num_input_features + i * growth_rate, growth_rate, bn_size, drop_rate)
            self.add_module('denselayer%d' % (i + 1), layer)


class _Transition(nn.Sequential):
    def __init__(self, num_input_features, num_output_features):
        super(_Transition, self).__init__()
        self.add_module('norm', nn.BatchNorm2d(num_input_features))
        self.add_module('relu', nn.ReLU(inplace=True))
        self.add_module('conv', nn.Conv2d(num_input_features, num_output_features,
                                        kernel_size=1, stride=1, bias=False))
        self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))


class DenseNet(nn.Module):
    r"""Densenet-BC model class, based on
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_

    Args:
        growth_rate (int) - how many filters to add each layer (`k` in paper)
        block_config (list of 4 ints) - how many layers in each pooling block
        num_init_features (int) - the number of filters to learn in the first convolution layer
        bn_size (int) - multiplicative factor for number of bottle neck layers
        (i.e. bn_size * k features in the bottleneck layer)
        drop_rate (float) - dropout rate after each dense layer
        num_classes (int) - number of classification classes
    """

    def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),
                num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000, input_shapes=(3, 224, 224)):

        super(DenseNet, self).__init__()
        self.num_classes = num_classes
        # First convolution
        self.features_first = nn.Sequential(OrderedDict([
            ('conv0', nn.Conv2d(input_shapes[0], num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
            ('norm0', nn.BatchNorm2d(num_init_features)),
            ('relu0', nn.ReLU(inplace=True)),
            ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
        ]))

        # Each denseblock
        num_features = num_init_features

        self.features = nn.ModuleList()

        for i, num_layers in enumerate(block_config):
            _denseblock = nn.Sequential()

            block = _DenseBlock(num_layers=num_layers, num_input_features=num_features,
                                bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate)
            _denseblock.add_module('denseblock%d' % (i + 1), block)
            num_features = num_features + num_layers * growth_rate
            if i != len(block_config) - 1:
                trans = _Transition(num_input_features=num_features, num_output_features=num_features // 2)
                _denseblock.add_module('transition%d' % (i + 1), trans)
                num_features = num_features // 2
            
            else:
                # Final batch norm
                _denseblock.add_module('norm5', nn.BatchNorm2d(num_features))
            
            self.features.append(_denseblock)
            



        # Linear layer

        self.classifier = nn.Linear(num_features, num_classes)

        # Official init from torch repo.
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.constant_(m.bias, 0)

    def save_gradient(self, grad):
        alpha_c = grad.mean(dim=(-2,-1)) # mean over space slice, batch_size x channels
        self.alpha.append(alpha_c.unsqueeze(-1))  

    def clear_gradient(self):
        """
        清除gradient
        """
        self.alpha = []

    # 注册梯度
    def sethook(self):
        for dense_feature in self.dense_features:
            dense_feature.register_hook(self.save_gradient)

    def get_cam(self):
        features = F.relu(self.dense_features[-1])
        batch_size, channels, height, width = features.shape
        
        features = features.view((batch_size, channels, height*width)).transpose(1,2).view((batch_size*height*width, channels))
        cam = self.classifier(features).transpose(0,1).view((batch_size, self.num_classes, height, width))-self.classifier.bias
        return cam

    def get_gradcam(self, cls_score):
        grad_cam_blocks = []

        mean_class_y = cls_score.sum(dim=0) # classes_num

        for y in mean_class_y: # 单个class梯度回传
            self.clear_gradient() # 清空存储梯度
            grad_cam_block = [] # 存储该class下的grad-cam
            self.sethook() # 注册梯度回调函数
            y.backward(retain_graph=True) # 梯度回传，获取梯度
            # 
            for b, alpha in enumerate(self.alpha, 1): # 回传顺序反过来了
                grad_cam_block.append(F.relu(torch.sum(alpha.unsqueeze(-1)*self.dense_features[-b], dim=1, keepdim=True)))

            grad_cam_blocks.append(grad_cam_block)

            # 顺序与self.dense_features一样
            grad_cam_blocks = [torch.cat([grad_cam_blocks[c][b] for c in range(len(mean_class_y))], 1) for b in range(len(grad_cam_blocks[0])-1, -1, -1)]
        
        return grad_cam_blocks



    def forward(self, x, block=-1):

        self.dense_features = []

        x = self.features_first(x)
        # densenet classification branch
        for i, l in enumerate(self.features):
            x = l(x)
            self.dense_features.append(x)

        out = F.relu(x, inplace=True)

        out = F.adaptive_avg_pool2d(out, (1, 1)).view(x.size(0), -1)
        cls_score = F.sigmoid(self.classifier(out))

        cam = self.get_cam()

        grad_cams = self.get_gradcam(cls_score)
        

        return cls_score, cam, grad_cams


def densenet169(pretrained=False, input_shapes=(3, 224, 224), **kwargs):
    r"""Densenet-169 model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """

    
    model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 32, 32), input_shapes=input_shapes,
                    **kwargs)
    # if pretrained:
    #     # '.'s are no longer allowed in module names, but pervious _DenseLayer
    #     # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
    #     # They are also in the checkpoints in model_urls. This pattern is used
    #     # to find such keys.
    #     pattern = re.compile(
    #         r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
    #     state_dict = model_zoo.load_url(model_urls['densenet169'])
    #     for key in list(state_dict.keys()):
    #         res = pattern.match(key)
    #         if res:
    #             new_key = res.group(1) + res.group(2)
    #             state_dict[new_key] = state_dict[key]
    #             del state_dict[key]
    #     model.load_state_dict(state_dict)
    return model