import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init


class BasicBlock(nn.Module):
    expansion = 1
    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        # self.bn1 = nn.GroupNorm(2,planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        # self.bn2 = nn.GroupNorm(2,planes)
        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                # nn.GroupNorm(2,self.expansion*planes)
            )
    def forward(self, x):
        # out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.conv1(x))
        # out = self.bn2(self.conv2(out))
        out = self.conv2(out)
        out += self.shortcut(x)
        out = F.relu(out)
        return out
    
class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):
        super(ResNet, self).__init__()
        self.in_planes = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        # self.bn1 = nn.GroupNorm(2,64)
        self.relu = nn.ReLU()
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.flatten = nn.Flatten()
        self.fc = nn.Linear(512*block.expansion, num_classes)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)
    
    def forward(self, x):
        # out = F.relu(self.bn1(self.conv1(x)))
        out = self.relu(self.conv1(x))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.avgpool(out)
        out = self.flatten(out)
        out = self.fc(out)
        return out

def resnetii10(num_classes=10):
    return ResNet(BasicBlock, [1,1,1,1], num_classes=num_classes)

def resnetii18(num_classes=10):
    return ResNet(BasicBlock, [2,2,2,2], num_classes=num_classes)

def resnetii34(num_classes=10):
    return ResNet(BasicBlock, [3,4,6,3], num_classes=num_classes)




def test(net):
    import numpy as np
    total_params = 0

    for x in filter(lambda p: p.requires_grad, net.parameters()):
        total_params += np.prod(x.data.numpy().shape)
    print("Total number of params", total_params)
    print("Total layers", len(list(filter(lambda p: p.requires_grad and len(p.data.size())>1, net.parameters()))))


if __name__ == '__main__':
    from pytorch_model_summary import summary
    from model_utils import count_parameters
    model = resnetii18()
    #assert len(dict(model.named_parameters()).keys()) == len(model.state_dict().keys()), 'More BN layers are there...'
    count_parameters(model)
    print(summary(model, torch.zeros((1, 3, 32, 32)), show_input=True)) # 1, 3, 32, 32

