ResNet18_train.py

#### Shervin Minaee
#### March 2020
#### Training code for covid detection by finetuning ResNet18

from __future__ import print_function, 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 copy, pickle, os, time
import argparse



parser = argparse.ArgumentParser(description='COVID-19 Detection from X-ray Images')
parser.add_argument('--batch_size', type=int, default=20, 
                    help='input batch size for training (default: 20)')
parser.add_argument('--epochs', type=int, default=100, 
                    help='number of epochs to train (default: 100)')
parser.add_argument('--num_workers', type=int, default=0, 
                    help='number of workers to train (default: 0)')
parser.add_argument('--learning_rate', type=float, default=0.0001, 
                    help='learning rate (default: 0.0001)')
parser.add_argument('--momentum', type=float, default=0.9, 
                    help='momentum (default: 0.9)')
parser.add_argument('--dataset_path', type=str, default='./data/',
                      help='training and validation dataset')

args = parser.parse_args()



start_time= time.time()


data_transforms = {
    'train': transforms.Compose([
        transforms.Resize(224),
        transforms.RandomResizedCrop(224),
        #transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Resize(224),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])
}


data_dir = args.dataset_path

image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
                                          data_transforms[x])
                  for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size= args.batch_size,
                                             shuffle=True, num_workers= args.num_workers)
              for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes  ## 0: child, and 1: nonchild


device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")



def imshow(inp, title= None):
    """Imshow for Tensor."""
    inp = inp.numpy().transpose((1, 2, 0))
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    inp = std * inp + mean
    inp = np.clip(inp, 0, 1)
    plt.imshow(inp)
    if title is not None:
        plt.title(title)
    plt.pause(0.001)  # pause a bit so that plots are updated



def train_model(model, criterion, optimizer, scheduler, batch_szie, num_epochs= 20):
    since = time.time()
    
    
    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0
    train_acc= list()
    valid_acc= list()

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

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

            running_loss = 0.0
            running_corrects = 0
            running_prec= 0.0
            running_rec = 0.0
            running_f1  = 0.0

            # Iterate over data.
            cur_batch_ind= 0
            for inputs, labels in dataloaders[phase]:
                #print(cur_batch_ind,"batch inputs shape:", inputs.shape)
                #print(cur_batch_ind,"batch label shape:", labels.shape)
                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'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

                    # 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)

                cur_acc= torch.sum(preds == labels.data).double()/batch_szie
                cur_batch_ind +=1
                print("\npreds:", preds)
                print("label:", labels.data)
                print("%d-th epoch, %d-th batch (size=%d), %s acc= %.3f \n" %(epoch+1, cur_batch_ind, len(labels), phase, cur_acc ))
                
                if phase=='train':
                    train_acc.append(cur_acc)
                else:
                    valid_acc.append(cur_acc)
                
            epoch_loss= running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects.double() / dataset_sizes[phase]
            
            print('{} Loss: {:.4f} Acc: {:.4f} \n\n'.format(
                phase, epoch_loss, epoch_acc))

            # deep copy the model
            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_epoch= epoch
                best_model_wts = copy.deepcopy(model.state_dict())



    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(
        time_elapsed // 60, time_elapsed % 60))
    print('Best val Acc= %.3f at Epoch: %d' %(best_acc,best_epoch) )

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



def visualize_model(model, num_images= 64):
    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/8, 8, 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)


#### load model
model_conv = torchvision.models.resnet18(pretrained=True)
for param in model_conv.parameters():
    param.requires_grad = False

# Parameters of newly constructed modules have requires_grad=True by default   
num_ftrs = model_conv.fc.in_features        
model_conv.fc = nn.Linear(num_ftrs, 2)


model_conv = model_conv.to(device)  
criterion = nn.CrossEntropyLoss()

# Observe that only parameters of final layer are being optimized as
# opoosed to before.
optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr= args.learning_rate, momentum= args.momentum)  

# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)


if __name__ == "__main__":
    model_conv, train_acc, valid_acc = train_model(model_conv, criterion, optimizer_conv,
                         exp_lr_scheduler, args.batch_size, num_epochs= args.epochs)
    model_conv.eval()
    torch.save(model_conv, './covid_resnet18_epoch%d.pt' %args.epochs )




end_time= time.time()
print("total_time tranfer learning=", end_time - start_time)