import torch.nn as nn
import torch.nn.functional as F
import numpy as np

def conv_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        #nn.init.xavier_uniform(m.weight, gain=np.sqrt(2))
        nn.init.normal_(m.weight, mean=0, std=1)
        nn.init.constant(m.bias, 0)

class AlexNet(nn.Module):

    def __init__(self, num_classes, inputs=3):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(inputs, 64, kernel_size=11, stride=4, padding=5),
            nn.ReLU(inplace=True),
            nn.Dropout(p=0.5),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 192, kernel_size=5, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(192, 384, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Dropout(p=0.5),
            nn.Conv2d(384, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Dropout(p=0.5),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.classifier = nn.Linear(256, num_classes)

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x