#!/usr/bin/env python
# coding: utf-8

# In[1]:


from __future__ import print_function
from __future__ import division
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 
import random
import shutil, sys
import pandas as pd
from torch.utils.tensorboard import SummaryWriter


# In[2]:


# Top level data directory. Here we assume the format of the directory conforms
#   to the ImageFolder structure
data_dir = '/home/jimi/dissertacao/covid19/datasets/80-20/'

# Models to choose from [resnet, alexnet, vgg, squeezenet, densenet, inception]
#model_name = "densenet"

# Number of classes in the dataset
#num_classes = 3

# Batch size for training (change depending on how much memory you have)
batch_size = 8

# Number of epochs to train for


# Flag for feature extracting. When False, we finetune the whole model,
#   when True we only update the reshaped layer params
feature_extract = True

#num_epochs = 500


# In[ ]:





# In[3]:


# Data augmentation and normalization for training
# Just normalization for validation
#transforms.RandomRotation(degrees=(-5, 5)),
#transforms.ColorJitter(brightness=.02),
    
data_transforms = {
    'train': transforms.Compose([
        transforms.ToTensor()
    ]),
    'val': transforms.Compose([
        transforms.ToTensor()
    ]),
}

print("Initializing Datasets and Dataloaders...")

# Create training and validation datasets
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
                                          data_transforms[x]) for x in ['train', 'val']}

# Create training and validation dataloaders
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], 
                    batch_size=batch_size, shuffle=True, num_workers=4) for x in ['train', 'val']}

class_names = image_datasets['train'].classes

# Detect if we have a GPU available
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")


# In[5]:


# Path to data
data_dir = '/home/jimi/dissertacao/covid19/datasets/80-20/'

train_dir = data_dir+'train/'
test_dir = data_dir+'test/'
val_dir = data_dir+'val/'

normal_dir = data_dir+'normal/'
pneumonia_dir = data_dir+'pneumonia/'
covid_dir = data_dir+'covid/'

len_covid = len([iq for iq in os.scandir(normal_dir)])
len_normal = len([iq for iq in os.scandir(pneumonia_dir)])
len_pneumonia = len([iq for iq in os.scandir(covid_dir)])

len_train_dir = len([iq for iq in os.scandir(train_dir+'covid/')]) + len([iq for iq in os.scandir(train_dir+'normal/')]) + len([iq for iq in os.scandir(train_dir+'pneumonia/')])
len_test_dir = len([iq for iq in os.scandir(test_dir+'covid/')]) + len([iq for iq in os.scandir(test_dir+'normal/')]) + len([iq for iq in os.scandir(test_dir+'pneumonia/')])
len_val_dir = len([iq for iq in os.scandir(val_dir+'covid/')]) + len([iq for iq in os.scandir(val_dir+'normal/')]) + len([iq for iq in os.scandir(val_dir+'pneumonia/')])
print("<br>")
print('Files per classes')
print("<br>")
print("----"*10)
print("<br>")
print("normal : ", len_covid)
print("<br>")
print("pneumonia : ", len_normal)
print("<br>")
print("covid : ", len_pneumonia)
print("<br>")
print("-"*20)
print("<br>")
print('Train, test, validation')
print("<br>")
print("-"*20)
print("<br>")
print("len_train_dir : ", len_train_dir)
print("<br>")
print("len_test_dir : ", len_test_dir)
print("<br>")
print("len_val_dir : ", len_val_dir)
print("<br>")


# In[6]:


def train_model(model, dataloaders, criterion, optimizer, num_epochs, model_name, lr, batch_size):
    since = time.time()
    is_inception = False
    
    #tensorboard
    writer = SummaryWriter(f'runs/dg_{model_name}_lr={lr}_epoch={num_epochs}_batch_size={batch_size}')
    step = 0

    val_acc_history = []
    val_loss_history = []

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0

    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print("<br>")
        print('-' * 10)
        print("<br>")

        # Each epoch has a training and validation phase
        for phase in ['train', 'val']:
            if phase == 'train':
                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode

            running_loss = 0.0
            running_corrects = 0

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

                # zero the parameter gradients
                optimizer.zero_grad()

                # forward
                # track history if only in train
                with torch.set_grad_enabled(phase == 'train'):
                    # Get model outputs and calculate loss
                    # Special case for inception because in training it has an auxiliary output. In train
                    #   mode we calculate the loss by summing the final output and the auxiliary output
                    #   but in testing we only consider the final output.
                    if is_inception and phase == 'train':
                        # From https://discuss.pytorch.org/t/how-to-optimize-inception-model-with-auxiliary-classifiers/7958
                        outputs, aux_outputs = model(inputs)
                        loss1 = criterion(outputs, labels)
                        loss2 = criterion(aux_outputs, labels)
                        loss = loss1 + 0.4*loss2
                    else:
                        outputs = model(inputs)
                        loss = criterion(outputs, labels)

                    _, preds = torch.max(outputs, 1)

                    # backward + optimize only if in training phase
                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                # statistics
                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)

            epoch_loss = running_loss / len(dataloaders[phase].dataset)
            epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))
            print("<br>")

            writer.add_scalar('training loss', loss, global_step=step)
            writer.add_scalar('training accuracy', epoch_acc, global_step=step)
            step += 1
            
            # deep copy the model
            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())
            if phase == 'val':
                val_acc_history.append(epoch_acc)

        print()
        
        
    time_elapsed = time.time() - since
    print()
    print("<br>")
    print('#'*30)
    print("<br>")
    print('------ Summary ------')
    print("<br>")
    print(f'model -> {_model}')
    print("<br>")
    print(f'epochs -> {_epochs}')
    print("<br>")
    print(f'lr -> {_lrs}')
    print("<br>")
    print(f'batch size -> {_batch}')
    print("<br>")
    print()
    print("<br>")
    print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
    print("<br>")
    print('Best val Acc: {:4f}'.format(best_acc))
    print("<br>")
    print('#'*30)
    print("<br>")
    print()
    print("<br>")
    print('==== END ====')
    print("<br>")

    # load best model weights
    model.load_state_dict(best_model_wts)
    return model, val_acc_history


# In[7]:


def visualize_model(model, num_images=4):
    was_training = model.training
    model.eval()
    images_so_far = 0
    fig = plt.figure()

    with torch.no_grad():
        for i, (inputs, labels) in enumerate(dataloaders['val']):
            inputs = inputs.to(device)
            labels = labels.to(device)

            outputs = model(inputs)
            _, preds = torch.max(outputs, 1)

            for j in range(inputs.size()[0]):
                images_so_far += 1
                ax = plt.subplot(num_images//2, 3, images_so_far)
                ax.axis('off')
                ax.set_title('predicted: {}'.format(class_names[preds[j]]))
                imshow(inputs.cpu().data[j])

                if images_so_far == num_images:
                    model.train(mode=was_training)
                    return
        model.train(mode=was_training)


# In[8]:


def set_parameter_requires_grad(model, feature_extracting):
    if feature_extracting:
        for param in model.parameters():
            param.requires_grad = False


# In[9]:


def initialize_model(model_name, num_classes, feature_extract, use_pretrained=True):
    # Initialize these variables which will be set in this if statement. Each of these
    #   variables is model specific.
    model_ft = None
    input_size = 0

    if model_name == "resnet":
        """ Resnet18
        """
        model_ft = models.resnet18(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        num_ftrs = model_ft.fc.in_features
        model_ft.fc = nn.Linear(num_ftrs, num_classes)
        input_size = 224

    elif model_name == "alexnet":
        """ Alexnet
        """
        model_ft = models.alexnet(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        num_ftrs = model_ft.classifier[6].in_features
        model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
        input_size = 224

    elif model_name == "vgg":
        """ VGG11_bn
        """
        model_ft = models.vgg11_bn(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        num_ftrs = model_ft.classifier[6].in_features
        model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
        input_size = 224

    elif model_name == "squeezenet":
        """ Squeezenet
        """
        model_ft = models.squeezenet1_0(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        model_ft.classifier[1] = nn.Conv2d(512, num_classes, kernel_size=(1,1), stride=(1,1))
        model_ft.num_classes = num_classes
        input_size = 224

    elif model_name == "densenet":
        """ Densenet
        """
        model_ft = models.densenet121(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        num_ftrs = model_ft.classifier.in_features
        model_ft.classifier = nn.Linear(num_ftrs, num_classes)
        input_size = 224

    elif model_name == "inception":
        """ Inception v3
        Be careful, expects (299,299) sized images and has auxiliary output
        """
        model_ft = models.inception_v3(pretrained=use_pretrained)
        set_parameter_requires_grad(model_ft, feature_extract)
        # Handle the auxilary net
        num_ftrs = model_ft.AuxLogits.fc.in_features
        model_ft.AuxLogits.fc = nn.Linear(num_ftrs, num_classes)
        # Handle the primary net
        num_ftrs = model_ft.fc.in_features
        model_ft.fc = nn.Linear(num_ftrs,num_classes)
        input_size = 299

    else:
        print("Invalid model name, exiting...")
        exit()

    return model_ft, input_size


# In[ ]:


# Models to choose from [resnet, alexnet, vgg, squeezenet, densenet, inception]
num_classes = 3

_models = ['squeezenet', 'densenet', 'resnet', 'alexnet', 'vgg']
lrs = [1e-2, 1e-3, 1e-4, 1e-5]
_epoch = [500]
batch_sizes = [8, 16, 32]

for _model in _models:
    for _epochs in _epoch:
        for _lrs in lrs:
            for _batch in batch_sizes:
                               
                print()
                print()
                print()
                print("<br>")
                print('='*60)
                print("<br>")
                print('==== INITIALIZING WITH PARAMETERS: ====')
                print("<br>")
                print()
                print("<br>")
                print(f'model -> {_model}')
                print("<br>")
                print(f'epochs -> {_epochs}')
                print("<br>")
                print(f'lr -> {_lrs}')
                print("<br>")
                print(f'batch size -> {_batch}')
                print("<br>")
                print()
                print("<br>")

                feature_extract = True
                
                model_ft, input_size = initialize_model(_model, num_classes, 
                                                        feature_extract, use_pretrained=True)
                
                # Send the model to GPU
                model_ft = model_ft.to(device)

                print('-'*20)
                params_to_update = model_ft.parameters()
                print("Params to learn:")
                if feature_extract:
                    params_to_update = []
                    for name,param in model_ft.named_parameters():
                        if param.requires_grad == True:
                            params_to_update.append(param)
                            print("\t",name)
                            print("<br>")
                else:
                    for name,param in model_ft.named_parameters():
                        if param.requires_grad == True:
                            print("\t",name)
                            print("<br>")

                print()
                print("<br>")
                print('-'*20)
                print("<br>")
                
                print()
                print('== Epochs ==')
                print("<br>")
                
                optimizer_ft = optim.SGD(params_to_update, _lrs, momentum=0.9)
                exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=10, gamma=0.1)

                criterion = nn.CrossEntropyLoss()

                model_ft, hist = train_model(model_ft, dataloaders, criterion, optimizer_ft,
                                             num_epochs=_epochs, model_name=_model, lr=_lrs, batch_size=_batch)


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