# Name: Peng Cheng
# UIN: 674792652
#
# Code adapted from:
#  https://github.com/jameschengpeng/PyTorch-CNN-on-CIFAR10
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F

transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])

transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])

class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=48, kernel_size=(3,3), padding=(1,1))
        self.conv2 = nn.Conv2d(in_channels=48, out_channels=96, kernel_size=(3,3), padding=(1,1))
        self.conv3 = nn.Conv2d(in_channels=96, out_channels=192, kernel_size=(3,3), padding=(1,1))
        self.conv4 = nn.Conv2d(in_channels=192, out_channels=256, kernel_size=(3,3), padding=(1,1))
        self.pool = nn.MaxPool2d(2,2)
        self.fc1 = nn.Linear(in_features=8*8*256, out_features=512)
        self.fc2 = nn.Linear(in_features=512, out_features=64)
        self.Dropout = nn.Dropout(0.25)
        self.fc3 = nn.Linear(in_features=64, out_features=10)

    def forward(self, x):
        x = F.relu(self.conv1(x)) #32*32*48
        x = F.relu(self.conv2(x)) #32*32*96
        x = self.pool(x) #16*16*96
        x = self.Dropout(x)
        x = F.relu(self.conv3(x)) #16*16*192
        x = F.relu(self.conv4(x)) #16*16*256
        x = self.pool(x) # 8*8*256
        x = self.Dropout(x)
        x = x.view(-1, 8*8*256) # reshape x
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.Dropout(x)
        x = self.fc3(x)
        return x