import torch
import torch.nn as nn
#from .utils import load_state_dict_from_url


"""
1. removed inplace relu to make it inline with pipedream 
2. changed torch.flatten to nn.flatten to make it inline with pipedream
"""

__all__ = [
    'VGG', 'vgg_small',
]


class VGG(nn.Module):
    def __init__(self, features, num_classes=10, init_weights=True):
        super(VGG, self).__init__()
        self.features = features
        self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
        self.flatten = nn.Flatten()
        self.classifier = nn.Sequential(
            nn.Linear(6272, 128),
            nn.ReLU(), # modified here
            nn.Dropout(),
            nn.Linear(128, 10),
        )
        if init_weights:
            self._initialize_weights()

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

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.constant_(m.bias, 0)


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()] #modification made here
            else:
                layers += [conv2d, nn.ReLU()] #modification made here
            in_channels = v
    return nn.Sequential(*layers)


cfgs = {
    'A': [32, 32, 'M', 64, 64, 'M', 128, 128, 'M'],
}

def _vgg(arch, cfg, batch_norm, pretrained, progress, **kwargs):
    if pretrained:
        kwargs['init_weights'] = False
    model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **kwargs)
    if pretrained:
        state_dict = load_state_dict_from_url(model_urls[arch],
                                              progress=progress)
        model.load_state_dict(state_dict)
    return model

def vgg_small(**kwargs):
    r"""VGG 16- like layer model (configuration "A")
    small vgg model`_
    """
    return _vgg('vgg16', 'A', False, False, False, **kwargs)




