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

__all__ = ['vgg19']
model_urls = {
    'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth',
}

class VGG(nn.Module):
    def __init__(self, features):
        super(VGG, self).__init__()
        self.features = features
        self.reg_layer = nn.Sequential(
            nn.Conv2d(512, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 1, 1)
        )
        # left_header
        # self.left_header = nn.Sequential(
        #     nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),
        #     nn.MaxPool2d(kernel_size=2, stride=2),
        #     nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),
        #     nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),
        #     nn.AvgPool2d(kernel_size=2, stride=2),
        #     nn.Flatten(),
        #     nn.Linear(18432, 1000),
        #     nn.Linear(1000, 5)
        # )
        # # right_header
        # self.right_header = nn.Sequential(
        #     nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),
        #     nn.MaxPool2d(kernel_size=2, stride=2),
        #     nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),
        #     nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1),
        #     nn.AvgPool2d(kernel_size=2, stride=2),
        #     nn.Flatten(),
        #     nn.Linear(18432, 1000),  #512 - 32768
        #     nn.Linear(1000, 13)
        # )

    def forward(self, x):
        outputs = []
        # VGG19 outputs 0
        x = self.features(x)
        outputs.append(x)
        #with torch.no_grad():
            # left_header 1 posture
        # x_left = self.left_header(x)
        # outputs.append(x_left)
        # # right_header 2
        # x_right = self.right_header(x)
        # outputs.append(x_right)
        # density outputs 3
        x = F.upsample_bilinear(x, scale_factor=2)
        x = self.reg_layer(x)
        outputs.append(torch.abs(x))
        return torch.abs(x)

def make_layers(cfg, batch_norm=False):
    layers = []
    in_channels = 3
    for v in cfg:
        if v == 'M':
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
        else:
            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
            if batch_norm:
                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
            else:
                layers += [conv2d, nn.ReLU(inplace=True)]
            in_channels = v
    return nn.Sequential(*layers)

cfg = {
    'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512]
}

def vgg19():
    """VGG 19-layer model (configuration "E")
        model pre-trained on ImageNet
    """
    #make_layers(cfg['E'])
    model = VGG(make_layers(cfg['E']))
    model.load_state_dict(model_zoo.load_url(model_urls['vgg19']), strict=False)
    # checkpoint = torch.load('./Net_model_bird/0121-110701/115_ckpt.tar')
    # model.load_state_dict(checkpoint['model_state_dict'])
    # model.load_state_dict(torch.load(os.path.join('./Net_model_bird', '84_best_model.pth')))

    return model