train_sota.py

from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
from torch.utils.data import DataLoader
import torch.backends.cudnn as cudnn
import matplotlib.pyplot as plt
import time
import os
import copy
import logging
import warnings

from optparse import OptionParser
from dataset import *
print("PyTorch Version: ",torch.__version__)
print("Torchvision Version: ",torchvision.__version__)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")


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
    print(model_name)
    if model_name == "resnet101":
       
        model_ft = models.resnet101(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 == "resnet50":
        """ Resnet50
        """
        model_ft = models.resnet50(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 == "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 == "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
def main():
    parser = OptionParser()
    parser.add_option('-j', '--workers', dest='workers', default=16, type='int',
                      help='number of data loading workers (default: 16)')
    parser.add_option('-e', '--epochs', dest='epochs', default=20, type='int',
                      help='number of epochs (default: 80)')
    parser.add_option('-b', '--batch-size', dest='batch_size', default=16, type='int',
                      help='batch size (default: 16)')
    parser.add_option('-c', '--ckpt', dest='ckpt', default=False,
                      help='load checkpoint model (default: False)')
    parser.add_option('-v', '--verbose', dest='verbose', default=100, type='int',
                      help='show information for each <verbose> iterations (default: 100)')
    parser.add_option('--lr', '--learning-rate', dest='lr', default=1e-3, type='float',
                      help='learning rate (default: 1e-3)')
    parser.add_option('--sf', '--save-freq', dest='save_freq', default=1, type='int',
                      help='saving frequency of .ckpt models (default: 1)')
    parser.add_option('--sd', '--save-dir', dest='save_dir', default='./models',
                      help='saving directory of .ckpt models (default: ./models)')
    parser.add_option('--init', '--initial-training', dest='initial_training', default=1, type='int',
                      help='train from 1-beginning or 0-resume training (default: 1)')
    parser.add_option('--model', '--model-name', dest='model_name', default='wsdan',
                      help='it can be wsdan,resnet50,resnet100,inception')

    (options, args) = parser.parse_args()

    logging.basicConfig(format='%(asctime)s: %(levelname)s: [%(filename)s:%(lineno)d]: %(message)s', level=logging.INFO)
    
    warnings.filterwarnings("ignore")

    num_classes = 4
    start_epoch = 0

    net, image_size = initialize_model(options.model_name, num_classes, feature_extract=False, use_pretrained=True)
    options.save_dir = os.path.join(options.save_dir,options.model_name)
    
    if options.ckpt:
        ckpt = options.ckpt

        if options.initial_training == 0:
            # Get Name (epoch)
            epoch_name = (ckpt.split('/')[-1]).split('.')[0]
            start_epoch = int(epoch_name)

        # Load ckpt and get state_dict
        checkpoint = torch.load(ckpt)
        state_dict = checkpoint['state_dict']
        # Load weights
        net.load_state_dict(state_dict)
        logging.info('Network loaded from {}'.format(options.ckpt))
    
    save_dir = options.save_dir
    if not os.path.exists(save_dir):
        os.makedirs(save_dir)

    cudnn.benchmark = True
    net.to(torch.device("cuda"))
    net = nn.DataParallel(net)
    # Load dataset
    transform = transforms.Compose([transforms.Resize(size=image_size),
                                    transforms.ToTensor(),
                                    transforms.Normalize(mean=[0.485, 0.456, 0.406], 
                                    std=[0.229, 0.224, 0.225])])
    train_dataset = CustomDataset(data_root='/mnt/HDD/RFW/train/data/',csv_file='data/RFW_Train40k_Images_Metada.csv',transform=transform)
    val_dataset = CustomDataset(data_root='/mnt/HDD/RFW/train/data/',csv_file='data/RFW_Val4k_Images_Metadata.csv',transform=transform)
   

    train_loader = DataLoader(train_dataset, batch_size=options.batch_size, shuffle=True,num_workers=options.workers, pin_memory=True)
    validate_loader = DataLoader(val_dataset, batch_size=options.batch_size * 4, shuffle=False,num_workers=options.workers, pin_memory=True)
    
   # optimizer = torch.optim.SGD(net.parameters(), lr=options.lr, momentum=0.9, weight_decay=0.00001)
    optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
    loss = nn.CrossEntropyLoss()
    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=2, gamma=0.9)

    logging.info('')
    logging.info('Start training: Total epochs: {}, Batch size: {}, Training size: {}, Validation size: {}'.
                 format(options.epochs, options.batch_size, len(train_dataset), len(val_dataset)))
    dataloaders_dict = {'train':train_loader, 'val':validate_loader}
  
    #'model train'
    model,val_acc = train_model(model=net, 
                dataloaders=dataloaders_dict, 
                criterion=loss, 
                optimizer=optimizer, 
                num_epochs=25, 
                is_inception=(options.model_name=="inception"),
                options=options)

   

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

def train_model(model, dataloaders, criterion, optimizer, num_epochs=25, is_inception=False,options=None):
    since = time.time()
    val_acc_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('-' * 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_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))

            # deep copy the model
            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())
                state_dict = model.module.state_dict()
                for key in state_dict.keys():
                    state_dict[key] = state_dict[key].cpu()
                torch.save({
                    'epoch': epoch,
                    'save_dir': options.save_dir,
                    'state_dict': state_dict,
                    'model': model,
                    'optimizer': optimizer,
                    'best_acc': best_acc},
                os.path.join(options.save_dir, '%03d.ckpt' % (epoch + 1)))
            
            
            if phase == 'val':
                val_acc_history.append(epoch_acc)

        
        # save checkpoint model
       
            
    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
    print('Best val Acc: {:4f}'.format(best_acc))

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


if __name__ == '__main__':
    main()