'''
# Load pretrain model & modify it
model = torchvision.models.vgg16(pretrained=True)
model.avgpool = Identity()
model.classifier[0] = nn.Linear(512, 10)
model.to(device)


# model.classifier[0] = nn.Sequential(nn.Linear(512, 100),
#                                   nn.Linear(100, 10))
# print(model)
# for i in range(1,7):
#    model.classifier[i] = Identity()
# sys.exit()
'''

# Imports
import torch
import torchvision
import torch.nn as nn  # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions
import torch.optim as optim  # For all Optimization algorithms, SGD, Adam, etc.
import torch.nn.functional as F  # All functions that don't have any parameters
from torch.utils.data import (
    DataLoader,
)  # Gives easier dataset managment and creates mini batches
import torchvision.datasets as datasets  # Has standard datasets we can import in a nice way
import torchvision.transforms as transforms  # Transformations we can perform on our dataset

# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Hyperparameters
num_classes = 10
learning_rate = 1e-3
batch_size = 1024
num_epochs = 5

# Simple Identity class that let's input pass without changes
class Identity(nn.Module):
    def __init__(self):
        super(Identity, self).__init__()

    def forward(self, x):
        return x


# Load pretrain model & modify it
model = torchvision.models.vgg16(pretrained=True)

# If you want to do finetuning then set requires_grad = False
# Remove these two lines if you want to train entire model,
# and only want to load the pretrain weights.
for param in model.parameters():
    param.requires_grad = False

model.avgpool = Identity()
model.classifier = nn.Sequential(
    nn.Linear(512, 100), nn.ReLU(), nn.Linear(100, num_classes)
)
model.to(device)


# Load Data
train_dataset = datasets.CIFAR10(
    root="dataset/", train=True, transform=transforms.ToTensor(), download=True
)
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)

# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# Train Network
for epoch in range(num_epochs):
    losses = []

    for batch_idx, (data, targets) in enumerate(train_loader):
        # Get data to cuda if possible
        data = data.to(device=device)
        targets = targets.to(device=device)

        # forward
        scores = model(data)
        loss = criterion(scores, targets)

        losses.append(loss.item())
        # backward
        optimizer.zero_grad()
        loss.backward()

        # gradient descent or adam step
        optimizer.step()

    print(f"Cost at epoch {epoch} is {sum(losses)/len(losses):.5f}")

# Check accuracy on training & test to see how good our model


def check_accuracy(loader, model):
    if loader.dataset.train:
        print("Checking accuracy on training data")
    else:
        print("Checking accuracy on test data")

    num_correct = 0
    num_samples = 0
    model.eval()

    with torch.no_grad():
        for x, y in loader:
            x = x.to(device=device)
            y = y.to(device=device)

            scores = model(x)
            _, predictions = scores.max(1)
            num_correct += (predictions == y).sum()
            num_samples += predictions.size(0)

        print(
            f"Got {num_correct} / {num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}"
        )

    model.train()


check_accuracy(train_loader, model)