import argparse import logging import torch import torch.nn as nn from torch import cuda from torch.autograd import Variable from torch.utils.data import DataLoader, Dataset import torchvision import torchvision.datasets as dset import torchvision.transforms as transforms import torchvision.utils from PIL import Image import torch.nn.functional as F import matplotlib.pyplot as plt import matplotlib.ticker as ticker import numpy as np import random from collections import Counter from custom_transform2D import CustomResize from custom_transform2D import CustomToTensor from AD_Dataset import AD_Dataset from AD_Standard_2DSlicesData import AD_Standard_2DSlicesData from AD_Standard_2DRandomSlicesData import AD_Standard_2DRandomSlicesData from AD_Standard_2DTestingSlices import AD_Standard_2DTestingSlices from AlexNet2D import alexnet logging.basicConfig( format='%(asctime)s %(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S', level=logging.INFO) parser = argparse.ArgumentParser(description="Starter code for JHU CS661 Computer Vision HW3.") parser.add_argument("--load", help="Load saved network weights.") parser.add_argument("--save", default="AlexNet", help="Save network weights.") parser.add_argument("--augmentation", default=True, type=bool, help="Save network weights.") parser.add_argument("--epochs", default=20, type=int, help="Epochs through the data. (default=20)") parser.add_argument("--learning_rate", "-lr", default=1e-3, type=float, help="Learning rate of the optimization. (default=0.01)") parser.add_argument('--momentum', default=0.9, type=float, metavar='M', help='momentum') parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float, metavar='W', help='weight decay (default: 1e-4)') parser.add_argument("--estop", default=1e-2, type=float, help="Early stopping criteria on the development set. (default=1e-2)") parser.add_argument("--batch_size", default=1, type=int, help="Batch size for training. (default=1)") parser.add_argument("--optimizer", default="Adam", choices=["SGD", "Adadelta", "Adam"], help="Optimizer of choice for training. (default=Adam)") parser.add_argument("--gpuid", default=[0], nargs='+', type=int, help="ID of gpu device to use. Empty implies cpu usage.") # feel free to add more arguments as you need def main(options): # Path configuration TRAINING_PATH = 'train_2classes.txt' TESTING_PATH = 'test_2classes.txt' IMG_PATH = './Image' trg_size = (224, 224) transformations = transforms.Compose([CustomResize(trg_size), CustomToTensor() ]) dset_train = AD_Standard_2DRandomSlicesData(IMG_PATH, TRAINING_PATH, transformations) dset_test = AD_Standard_2DSlicesData(IMG_PATH, TESTING_PATH, transformations) # Use argument load to distinguish training and testing if options.load is None: train_loader = DataLoader(dset_train, batch_size=options.batch_size, shuffle=True, num_workers=4, drop_last=True ) else: # Only shuffle the data when doing training train_loader = DataLoader(dset_train, batch_size=options.batch_size, shuffle=False, num_workers=4, drop_last=True ) test_loader = DataLoader(dset_test, batch_size=options.batch_size, shuffle=False, num_workers=4, drop_last=True ) use_cuda = (len(options.gpuid) >= 1) if options.gpuid: cuda.set_device(options.gpuid[0]) # Initial the model model = alexnet(pretrained=True) # model.load_state_dict(torch.load(options.load)) if use_cuda > 0: model.cuda() else: model.cpu() # Binary cross-entropy loss # criterion = torch.nn.CrossEntropyLoss() criterion = torch.nn.NLLLoss() lr = options.learning_rate optimizer = eval("torch.optim." + options.optimizer)(filter(lambda x: x.requires_grad, model.parameters()), lr) best_accuracy = float("-inf") train_loss_f = open("train_loss.txt", "w") test_acu_f = open("test_accuracy.txt", "w") for epoch_i in range(options.epochs): logging.info("At {0}-th epoch.".format(epoch_i)) train_loss, correct_cnt = train(model, train_loader, use_cuda, criterion, optimizer, train_loss_f) # each instance in one batch has 3 views train_avg_loss = train_loss / (len(dset_train) * 3 / options.batch_size) train_avg_acu = float(correct_cnt) / (len(dset_train) * 3) logging.info( "Average training loss is {0:.5f} at the end of epoch {1}".format(train_avg_loss.data[0], epoch_i)) logging.info("Average training accuracy is {0:.5f} at the end of epoch {1}".format(train_avg_acu, epoch_i)) correct_cnt = validate(model, test_loader, use_cuda, criterion) dev_avg_acu = float(correct_cnt) / len(dset_test) logging.info("Average validation accuracy is {0:.5f} at the end of epoch {1}".format(dev_avg_acu, epoch_i)) # write validation accuracy to file test_acu_f.write("{0:.5f}\n".format(dev_avg_acu)) if dev_avg_acu > best_accuracy: best_accuracy = dev_avg_acu torch.save(model.state_dict(), open(options.save, 'wb')) train_loss_f.close() test_acu_f.close() def train(model, train_loader, use_cuda, criterion, optimizer, train_loss_f): # main training loop train_loss = 0.0 correct_cnt = 0.0 model.train() for it, train_data in enumerate(train_loader): for data_dic in train_data: if use_cuda: imgs, labels = Variable(data_dic['image']).cuda(), Variable(data_dic['label']).cuda() else: imgs, labels = Variable(data_dic['image']), Variable(data_dic['label']) integer_encoded = labels.data.cpu().numpy() # target should be LongTensor in loss function ground_truth = Variable(torch.from_numpy(integer_encoded)).long() if use_cuda: ground_truth = ground_truth.cuda() train_output = model(imgs) _, predict = train_output.topk(1) loss = criterion(train_output, ground_truth) train_loss += loss correct_this_batch = (predict.squeeze(1) == ground_truth).sum().float() correct_cnt += correct_this_batch accuracy = float(correct_this_batch) / len(ground_truth) logging.info("batch {0} training loss is : {1:.5f}".format(it, loss.data[0])) logging.info("batch {0} training accuracy is : {1:.5f}".format(it, accuracy)) # write the training loss to file train_loss_f.write("{0:.5f}\n".format(loss.data[0])) optimizer.zero_grad() loss.backward() optimizer.step() return train_loss, correct_cnt def validate(model, test_loader, use_cuda, criterion): # validation -- this is a crude estimation because there might be some paddings at the end correct_cnt = 0.0 model.eval() for it, test_data in enumerate(test_loader): vote = [] for data_dic in test_data: if use_cuda: imgs, labels = Variable(data_dic['image'], volatile=True).cuda(), Variable(data_dic['label'], volatile=True).cuda() else: imgs, labels = Variable(data_dic['image'], volatile=True), Variable(data_dic['label'], volatile=True) test_output = model(imgs) _, predict = test_output.topk(1) vote.append(predict) vote = torch.cat(vote, 1) final_vote, _ = torch.mode(vote, 1) ground_truth = test_data[0]['label'] correct_this_batch = (final_vote.cpu().data == ground_truth).sum() correct_cnt += correct_this_batch accuracy = float(correct_this_batch) / len(ground_truth) logging.info("batch {0} dev accuracy is : {1:.5f}".format(it, accuracy)) return correct_cnt def show_plot(points): plt.figure() fig, ax = plt.subplots() loc = ticker.MultipleLocator(base=0.2) # put ticks at regular intervals ax.yaxis.set_major_locator(loc) plt.plot(points) if __name__ == "__main__": ret = parser.parse_known_args() options = ret[0] if ret[1]: logging.warning("unknown arguments: {0}".format(parser.parse_known_args()[1])) main(options)