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train.py
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train.py
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from __future__ import print_function
from __future__ import division
import torch
import torchvision
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from torchvision import models, transforms
from torch.optim.lr_scheduler import OneCycleLR
from sklearn.utils import class_weight
from sklearn.metrics import precision_recall_fscore_support, accuracy_score, ConfusionMatrixDisplay
import numpy as np
import os
import time
import argparse
import matplotlib.pyplot as plt
from tqdm import tqdm
from PIL import Image, ImageFile
from pathlib import Path
import utils
print(f"PyTorch Version: {torch.__version__}")
print(f"Torchvision Version: {torchvision.__version__}\n")
parser = argparse.ArgumentParser('Arguments for model training and validation')
parser.add_argument('--tr_data_folder', type=str, default="./data/train/",
help='path to training data with faulty images')
parser.add_argument('--val_data_folder', type=str, default="./data/validation/",
help='path to validation data with faulty images')
parser.add_argument('--results_folder', type=str, default="./results",
help='Folder for saving training results.')
parser.add_argument('--save_model_path', type=str, default="./models",
help='Path for saving model file.')
parser.add_argument('--batch_size', type=int, default=16,
help='Batch size used for model training. ')
parser.add_argument('--lr', type=float, default=0.0001,
help='Base learning rate.')
parser.add_argument('--device', type=str, default='cpu',
help='Defines whether the model is trained using cpu or gpu.')
parser.add_argument('--num_classes', type=int, default=5,
help='Number of classes used in classification.')
parser.add_argument('--num_epochs', type=int, default=5,
help='number of training epochs for the classification head')
parser.add_argument('--unfreeze_epochs', type=int, default=5,
help='number of training epochs for the entire model')
parser.add_argument('--random_seed', type=int, default=8765,
help='Number used for initializing random number generation.')
parser.add_argument('--early_stop_threshold', type=int, default=2,
help='Threshold value of epochs after which training stops if validation accuracy does not improve.')
parser.add_argument('--date', type=str, default=time.strftime("%d%m%Y"),
help='Current date.')
args = parser.parse_args()
# PIL settings to avoid errors caused by truncated and large images
ImageFile.LOAD_TRUNCATED_IMAGES = True
Image.MAX_IMAGE_PIXELS = None
# List for saving the names of damaged images
damaged_images = []
def get_datapaths():
data_tr = []
labels_tr = []
data_val = []
labels_val = []
for i in range(1, args.num_classes + 1):
tr_data = list(Path(args.tr_data_folder + str(i)).glob('*'))
val_data = list(Path(args.val_data_folder + str(i)).glob('*'))
tr_labels = [i-1] * len(tr_data)
val_labels = [i-1] * len(val_data)
data_tr += tr_data
labels_tr += tr_labels
data_val += val_data
labels_val += val_labels
print(f'Training samples in class {i}: {len(tr_data)}')
print(f'Validation samples in class {i}: {len(val_data)}\n')
data_dict = {'tr_data': data_tr, 'tr_labels': labels_tr,
'val_data': data_val, 'val_labels': labels_val}
return data_dict
class ImageDataset(Dataset):
"""PyTorch Dataset class is used for generating training and validation datasets."""
def __init__(self, img_paths, img_labels, transform):
self.img_paths = img_paths
self.img_labels = img_labels
self.transform = transform
def __len__(self):
return len(self.img_labels)
def __getitem__(self, idx):
img_path = self.img_paths[idx]
try:
image = Image.open(img_path).convert('RGB')
label = self.img_labels[idx]
except:
# Image is considered damaged if reading the image fails
damaged_images.append(img_path)
return None
image = self.transform(image)
return image, label
def initialize_model():
"""Function for initializing pretrained neural network model (DenseNet121)."""
model_ft = models.densenet121(weights=torchvision.models.DenseNet121_Weights.IMAGENET1K_V1)
num_ftrs = model_ft.classifier.in_features
model_ft.classifier = nn.Linear(num_ftrs, args.num_classes)
return model_ft
def collate_fn(batch):
"""Helper function for creating data batches."""
batch = list(filter(lambda x: x is not None, batch))
return torch.utils.data.dataloader.default_collate(batch)
data_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Resize((224, 224)),
# uses imagenet_stats for mean and std
# https://discuss.pytorch.org/t/discussion-why-normalise-according-to-imagenet-mean-and-std-dev-for-transfer-learning/115670/2
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
def get_conf_matrix(y_true, y_pred, epoch, stage):
"""Create confusion matrix from classification results."""
conf_matrix = ConfusionMatrixDisplay.from_predictions(y_true, y_pred, normalize='true', display_labels=np.arange(1, args.num_classes + 1))
plt.savefig(args.results_folder + '/' + args.date + '_' + stage + '_epoch_' + str(epoch) + '.jpg', bbox_inches='tight')
plt.close()
def initialize_dataloaders(data_dict):
"""Function for initializing datasets and dataloaders."""
# Train and validation datasets
train_dataset = ImageDataset(img_paths=data_dict['tr_data'], img_labels=data_dict['tr_labels'], transform=data_transforms)
validation_dataset = ImageDataset(img_paths=data_dict['val_data'], img_labels=data_dict['val_labels'], transform=data_transforms)
# Train and validation dataloaders
train_dataloader = DataLoader(train_dataset, collate_fn=collate_fn, batch_size=args.batch_size, shuffle=True, num_workers=4)
validation_dataloader = DataLoader(validation_dataset, collate_fn=collate_fn, batch_size=args.batch_size, shuffle=True, num_workers=4)
return {'train': train_dataloader, 'val': validation_dataloader}
def get_criterion(data_dict):
"""Function for generating class weights and initializing the loss function."""
y = np.asarray(data_dict['tr_labels'])
# Class weights are used for compensating the unbalance
# in the number of training data from the different classes
class_weights=class_weight.compute_class_weight(class_weight='balanced', classes=np.unique(y), y=y)
class_weights=torch.tensor(class_weights, dtype=torch.float).to(args.device)
print('\nClass weights: ', class_weights.tolist())
# Cross Entropy Loss function
criterion = nn.CrossEntropyLoss(weight=class_weights, reduction='mean')
return criterion
def get_optimizer(model, lr, epochs, n_steps):
"""Function for initializing the optimizer."""
optimizer = torch.optim.SGD(model.parameters(), lr, momentum=0.9)
scheduler = OneCycleLR(optimizer, max_lr=0.01, steps_per_epoch=n_steps, epochs=epochs)
return optimizer, scheduler
def train_model(model, dataloaders, criterion, optimizer, scheduler, stage):
"""Function for model training and validation."""
since = time.time()
# Lists for saving train and validation metrics for each epoch
tr_loss_history = []
tr_acc_history = []
tr_f1_history = []
val_loss_history = []
val_acc_history = []
val_f1_history = []
# Best F1 value and best epoch are saved in variables
best_f1 = 0
best_epoch = 0
early_stop = False
# Train / validation loop
for epoch in tqdm(range(args.num_epochs)):
all_preds = []
all_labels = []
print('Epoch {}/{}'.format(epoch+1, args.num_epochs))
print('-' * 10)
# 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_acc = 0.0
running_f1 = 0.0
# Iterate over data in batch
for inputs, labels in dataloaders[phase]:
if dataloaders[phase] is None:
continue
else:
inputs = inputs.to(args.device)
labels = labels.long().to(args.device)
# Zero the parameter gradients
optimizer.zero_grad()
# Track history only in training phase
with torch.set_grad_enabled(phase == 'train'):
# Get model outputs and calculate loss
outputs = model(inputs)
loss = criterion(outputs, labels)
# Model predictions of the image labels for the batch
_, preds = torch.max(outputs, 1)
# Backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
scheduler.step()
all_preds += preds.tolist()
all_labels += labels.tolist()
# Get weighted F1 score for the results
precision_recall_fscore = precision_recall_fscore_support(labels.cpu().numpy(), preds.cpu().numpy(), average='weighted', zero_division=0)
f1_score = precision_recall_fscore[2]
# update statistics
running_loss += loss.item() * inputs.size(0)
running_acc += accuracy_score(labels.tolist(), preds.tolist())
running_f1 += f1_score
# Calculate loss, accuracy and F1 score for the epoch
epoch_loss = running_loss / len(dataloaders[phase].dataset)
epoch_acc = running_acc / len(dataloaders[phase])
epoch_f1 = running_f1 / len(dataloaders[phase])
print('\nEpoch {} - {} - Loss: {:.4f} Acc: {:.4f} F1: {:.4f}\n'.format(epoch+1, phase, epoch_loss, epoch_acc, epoch_f1))
# Validation step
if phase == 'val':
val_acc_history.append(epoch_acc)
val_loss_history.append(epoch_loss)
val_f1_history.append(epoch_f1)
if epoch_f1 > best_f1:
print('\nF1 score {:.4f} improved from {:.4f}. Saving the model.\n'.format(epoch_f1, best_f1))
# Model with best F1 score is saved
pytorch_model_path = os.path.join(args.save_model_path, 'densenet_' + args.date + '.pth')
torch.save(model, pytorch_model_path)
print('Model saved to ', pytorch_model_path)
model = model.to(args.device)
best_f1 = epoch_f1
best_epoch = epoch
elif epoch - best_epoch > args.early_stop_threshold:
# terminates the training loop if validation accuracy has not improved
print("Early stopped training at epoch ", str(epoch +1))
# Set early stopping condition
early_stop = True
break
elif phase == 'train':
tr_acc_history.append(epoch_acc)
tr_loss_history.append(epoch_loss)
tr_f1_history.append(epoch_f1)
# Creates confusion matrix after each epoch
get_conf_matrix(all_labels, all_preds, epoch, stage)
# Break outer loop if early stopping condition is activated
if early_stop:
break
# Take scheduler step
if scheduler:
scheduler.step(val_acc_history[-1])
time_elapsed = time.time() - since
print('\nTraining complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
print('Best validation F1 score: {:.4f}'.format(best_f1))
# Returns model with the weights from the best epoch (based on validation accuracy)
hist_dict = {'tr_acc': tr_acc_history,
'val_acc': val_acc_history,
'val_loss': val_loss_history,
'val_f1': val_f1_history,
'tr_loss': tr_loss_history,
'tr_f1': tr_f1_history}
return hist_dict
def main():
# Set random seed(s)
utils.set_seed(args.random_seed)
# Load image paths and labels
data_dict = get_datapaths()
# Initialize the model
model = initialize_model()
# Print the model architecture
#print(model)
# Send the model to GPU (if available)
model = model.to(args.device)
print("\nInitializing Datasets and Dataloaders...")
dataloaders_dict = initialize_dataloaders(data_dict)
criterion = get_criterion(data_dict)
n_steps = len(dataloaders_dict['train'])
if not os.path.exists(args.results_folder):
os.makedirs(args.results_folder)
if args.num_epochs > 0:
for name, param in model.named_parameters():
if name not in ["classifier.weight", "classifier.bias"]:
param.requires_grad = False
optimizer, scheduler = get_optimizer(model, args.lr*10, args.num_epochs, n_steps)
# Train and evaluate model
hist_dict = train_model(model, dataloaders_dict, criterion, optimizer, scheduler, stage='part_1')
utils.plot_metrics(hist_dict, args.results_folder, args.date, 'part_1')
if args.unfreeze_epochs > 0:
pytorch_model_path = os.path.join(args.save_model_path, 'densenet_' + args.date + '.pth')
model = torch.load(pytorch_model_path)
model = model.to(args.device)
for param in model.parameters():
param.requires_grad = True
optimizer, scheduler = get_optimizer(model, args.lr, args.unfreeze_epochs, n_steps)
# Train and evaluate model
hist_dict = train_model(model, dataloaders_dict, criterion, optimizer, scheduler, stage='part_2')
utils.plot_metrics(hist_dict, args.results_folder, args.date, 'part_2')
print('Damaged images: ', damaged_images)
if __name__ == '__main__':
main()