# new:

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])

data_transforms = {
    'train': transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize(mean, std)
    ]),
    'val': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean, std)
    ])
}

#import data
data_dir = 'data/hymenoptera_data'
sets = ['train', 'val']
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in sets}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, shuffle=True, num_workers=0) for x in sets}
dataset_sizes = {x: len(image_datasets[x]) for x in sets}
class_names = image_datasets['train'].classes
print(f'Classes: {class_names}')


# # show sample images
# def imshow(inp, title):
#     """Imshow for Tensor."""
#     inp = inp.numpy().transpose((1, 2, 0))
#     inp = std * inp + mean
#     inp = np.clip(inp, 0, 1)
#     plt.imshow(inp)
#     plt.title(title)
#     plt.show()
# inputs, classes = next(iter(dataloaders['train']))
# out = torchvision.utils.make_grid(inputs)
# imshow(out, title=[class_names[x] for x in classes])


def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
    since = time.time()
    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0

    for epoch in range(num_epochs):
        print(F'Epoch {epoch+1}/{num_epochs}')
        print('-'*11)

        for phase in sets:
            if phase == 'train':
                model.train()
            else:
                model.eval()
            
            running_loss = 0.0
            running_corrects = 0

            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)

                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

                    if phase == 'train':
                        optimizer.zero_grad()
                        loss.backward()
                        optimizer.step()
                
                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)
            
            if phase == 'train':
                scheduler.step()
            
            epoch_loss = running_loss/dataset_sizes[phase]
            epoch_acc = running_corrects.double()/dataset_sizes[phase]

            print(f'{phase} Loss: {epoch_loss:.4f}, Acc: {epoch_acc:.4f}')

            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())
            
        print()
    
    time_elapsed = time.time() - since
    print(f'Training complete in {time_elapsed//(60*60):02.0f}:{(time_elapsed%(60*60)//60):02.0f}:{time_elapsed%60:02.0f}')
    print(f'Best val Acc: {best_acc:.4f}')

    model.load_state_dict(best_model_wts)
    return model

## transfer learning - finetuning ##
model = torchvision.models.resnet18(weights=torchvision.models.ResNet18_Weights.DEFAULT) # import pretrained model

#change last layer
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, 2)
model.to(device)

criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)

# scheduler
# updates the learning rate
step_lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1) # every seven epochs: lr = lr*gamma

model = train_model(model, criterion, optimizer, step_lr_scheduler, num_epochs=50)

print('-'*50)
print()

## transfer learning - feature extractor ##
model = torchvision.models.resnet18(weights=torchvision.models.ResNet18_Weights.DEFAULT) # import pretrained model # import pretrained model
for param in model.parameters():
    param.requires_grad = False
    # freezes all layers in beginning

#change last layer
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, 2)
model.to(device)

criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)

# scheduler
# updates the learning rate
step_lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1) # every seven epochs: lr = lr*gamma

model = train_model(model, criterion, optimizer, step_lr_scheduler, num_epochs=50)