def main(_): """3.print configurations""" print('tf version:', tf.__version__) print('tf setup:') for k, v in FLAGS.flag_values_dict().items(): print(k, v) FLAGS.TB_dir += '_' + str(FLAGS.c_dim) """4.check/create folders""" print("check dirs...") if not os.path.exists(FLAGS.checkpoint_dir): os.makedirs(FLAGS.checkpoint_dir) if not os.path.exists(FLAGS.TB_dir): os.makedirs(FLAGS.TB_dir) """5.begin tf session""" config = tf.ConfigProto() config.gpu_options.allow_growth = True with tf.Session(config=config) as sess: print("building model...") """6.init srcnn model""" srcnn = SRCNN(sess, FLAGS) """7.start to train/test""" if (FLAGS.is_train): srcnn.train() elif FLAGS.patch_test: srcnn.test() else: srcnn.test_whole_img()
def main(_): t0 = time.time() pp.pprint(FLAGS.build_model) if not FLAGS.build_model: FLAGS.test_img = validate(FLAGS.test_img) print("Image path = ", FLAGS.test_img) if not os.path.isfile(FLAGS.test_img): print("File does not exist ", FLAGS.test_img) sys.exit() create_required_directories(FLAGS) with tf.compat.v1.Session() as sess: srcnn = SRCNN(sess, image_size=FLAGS.image_size, label_size=FLAGS.label_size, batch_size=FLAGS.batch_size, c_dim=FLAGS.c_dim, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=FLAGS.sample_dir) if FLAGS.build_model: srcnn.train(FLAGS) else: srcnn.test(FLAGS) print("\n\nTime taken %4.2f\n\n" % (time.time() - t0))
def main(): dataloaders = myDataloader() train_loader = dataloaders.getTrainLoader(batch_size) model = SRCNN().cuda() model.train() optimizer = optim.Adam(model.parameters(), lr=lr) mse_loss = nn.MSELoss() for ep in range(epoch): running_loss = 0.0 for i, (pic, blurPic, _) in enumerate(train_loader): pic = pic.cuda() blurPic = blurPic.cuda() optimizer.zero_grad() out = model(blurPic) loss = mse_loss(out, pic) loss.backward() optimizer.step() running_loss += loss if i % 10 == 9: print('[%d %d] loss: %.4f' % (ep + 1, i + 1, running_loss / 20)) running_loss = 0.0 if ep % 10 == 9: torch.save(model.state_dict(), f="./result/train/" + str(ep + 1) + "srcnnParms.pth") print("finish training")
def main(_): #? with tf.Session() as sess: srcnn = SRCNN(sess, image_size = FLAGS.image_size, label_size = FLAGS.label_size, c_dim = FLAGS.c_dim) srcnn.train(FLAGS)
def main(_): with tf.Session() as sess: srcnn = SRCNN(sess, image_dim=FLAGS.image_dim, label_dim=FLAGS.label_dim, channel=FLAGS.channel) srcnn.train(FLAGS)
def main(args): srcnn = SRCNN( image_size=args.image_size, c_dim=args.c_dim, is_training=True, learning_rate=args.learning_rate, batch_size=args.batch_size, epochs=args.epochs) X_train, Y_train = load_train(image_size=args.image_size, stride=args.stride, scale=args.scale) srcnn.train(X_train, Y_train)
def main(_): pp.pprint(flags.FLAGS.__flags) if not os.path.exists(FLAGS.checkpoint_dir): os.makedirs(FLAGS.checkpoint_dir) if not os.path.exists(FLAGS.sample_dir): os.makedirs(FLAGS.sample_dir) if not os.path.exists(FLAGS.log_dir): os.makedirs(FLAGS.log_dir) with tf.Session() as sess: srcnn = SRCNN(sess, FLAGS) srcnn.train()
def main(_): #? with tf.Session() as sess: #print("Calling init") srcnn = SRCNN(sess, image_size = FLAGS.image_size, label_size = FLAGS.label_size, c_dim = FLAGS.c_dim) #print("Calling train") srcnn.train(FLAGS)
def main(_): """3.print configurations""" print('tf version:',tf.__version__) print('tf setup:') #os.makedirs(FLAGS.checkpoint_dir) """5.begin tf session""" with tf.Session() as sess: """6.init srcnn model""" srcnn = SRCNN(sess, FLAGS) """7.start to train/test""" if(FLAGS.is_train): srcnn.train() else: srcnn.test()
def main(_): if not os.path.exists(FLAGS.checkpoint_dir): os.makedirs(FLAGS.checkpoint_dir) if not os.path.exists(FLAGS.sample_dir): os.makedirs(FLAGS.sample_dir) with tf.Session() as sess: srcnn = SRCNN(sess, image_size=FLAGS.image_size, label_size=FLAGS.label_size, batch_size=FLAGS.batch_size, c_dim=FLAGS.c_dim, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=FLAGS.sample_dir) srcnn.train(FLAGS)
def main(): if not os.path.exists(Config.checkpoint_dir): os.makedirs(Config.checkpoint_dir) with tf.Session() as sess: trysr = SRCNN(sess, image_size=Config.image_size, label_size=Config.label_size, batch_size=Config.batch_size, c_dim=Config.c_dim, checkpoint_dir=Config.checkpoint_dir, scale=Config.scale) trysr.train(Config)
def train(): print("process the image to h5file.....") data_dir = flags.data_dir h5_dir = flags.h5_dir stride = flags.train_stride data_helper.gen_input_image(data_dir, h5_dir, stride) print("reading data......") h5_path = os.path.join(h5_dir, "data.h5") data, label = data_helper.load_data(h5_path) print("initialize the model......") model = SRCNN(flags) model.build_graph() model.train(data, label)
def main(_): """3.print configurations""" print('tf version:', tf.__version__) print('tf setup:') for k, v in FLAGS.flag_values_dict().items(): print(k, v) """4.check/create folders""" if not os.path.exists(FLAGS.checkpoint_dir): os.makedirs(FLAGS.checkpoint_dir) """5.begin tf session""" with tf.Session() as sess: """6.init srcnn model""" srcnn = SRCNN(sess, FLAGS) """7.start to train/test""" if (FLAGS.is_train): srcnn.train() else: srcnn.test()
def main(_): pp.pprint(flags.FLAGS.__flags) if not os.path.exists(FLAGS.checkpoint_dir): os.makedirs(FLAGS.checkpoint_dir) if not os.path.exists(FLAGS.sample_dir): os.makedirs(FLAGS.sample_dir) with tf.Session() as sess: srcnn = SRCNN(sess, image_size=FLAGS.image_size, label_size=FLAGS.label_size, batch_size=FLAGS.batch_size, is_grayscale=FLAGS.is_grayscale, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=FLAGS.sample_dir) srcnn.train(FLAGS)
def main(_): pp.pprint(flags.FLAGS.__flags) if not os.path.exists(FLAGS.checkpoint_dir): os.makedirs(FLAGS.checkpoint_dir) if not os.path.exists(FLAGS.sample_dir): os.makedirs(FLAGS.sample_dir) with tf.Session() as sess: srcnn = SRCNN(sess, image_size=FLAGS.image_size, label_size=FLAGS.label_size, batch_size=FLAGS.batch_size, c_dim=FLAGS.c_dim, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=FLAGS.sample_dir) #srcnn.test('Test/Set5/baby_GT.bmp', FLAGS) srcnn.train(FLAGS)
def main(_): pp.pprint(flags.FLAGS.__flags) if not os.path.exists(FLAGS.checkpoint_dir): os.makedirs(FLAGS.checkpoint_dir) if not os.path.exists(FLAGS.sample_dir): os.makedirs(FLAGS.sample_dir) config = tf.ConfigProto() config.gpu_options.allow_growth = True with tf.Session(config=config) as sess: srcnn = SRCNN(sess, image_size=FLAGS.image_size, label_size=FLAGS.label_size, batch_size=FLAGS.batch_size, c_dim=FLAGS.c_dim, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=FLAGS.sample_dir) srcnn.train(FLAGS)
def main(_): pp.pprint(flags.FLAGS.__flags) if not os.path.exists(FLAGS.checkpoint_dir): os.makedirs(FLAGS.checkpoint_dir) if not os.path.exists(FLAGS.sample_dir): os.makedirs(FLAGS.sample_dir) srcnn = SRCNN(image_size=FLAGS.image_size, label_size=FLAGS.label_size, batch_size=FLAGS.batch_size, c_dim=FLAGS.c_dim, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=FLAGS.sample_dir, FLAGS=FLAGS) srcnn.call() if FLAGS.is_train == True: srcnn.train() else: srcnn.inference()
def main(_): pp.pprint(flags.FLAGS.__flags) if not os.path.exists(FLAGS.checkpoint_dir): os.makedirs(FLAGS.checkpoint_dir) if not os.path.exists(FLAGS.sample_dir): os.makedirs(FLAGS.sample_dir) os.environ["CUDA_VISIBLE_DEVICES"] = "0" config = tf.ConfigProto(allow_soft_placement=True) with tf.device('/gpu:0'): with tf.Session(config=config) as sess: srcnn = SRCNN(sess, image_size=FLAGS.image_size, label_size=FLAGS.label_size, batch_size=FLAGS.batch_size, c_dim=FLAGS.c_dim, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=FLAGS.sample_dir, config=FLAGS) srcnn.train(FLAGS)
def main(_): pp.pprint(flags.FLAGS.__flags) if not os.path.exists(FLAGS.checkpoint_dir): os.makedirs(FLAGS.checkpoint_dir) if not os.path.exists(FLAGS.sample_dir): os.makedirs(FLAGS.sample_dir) config = tf.ConfigProto(allow_soft_placement=True) with tf.device('/device:GPU:0'): with tf.Session(config=config) as sess: srcnn = SRCNN(sess, image_size=FLAGS.image_size, label_size=FLAGS.label_size, batch_size=FLAGS.batch_size, ci_dim=FLAGS.ci_dim, co_dim=FLAGS.co_dim, scale_factor=FLAGS.scale_factor, checkpoint_dir=FLAGS.checkpoint_dir, sample_dir=FLAGS.sample_dir, model_carac=FLAGS.model_carac, train_dir=FLAGS.train_dir, test_dir=FLAGS.test_dir) srcnn.train(FLAGS)
train_dataloader = DataLoader(dataset=train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True, drop_last=True) eval_dataset = EvalDataset(args.eval_file) eval_dataloader = DataLoader(dataset=eval_dataset, batch_size=1) best_weights = copy.deepcopy(model.state_dict()) best_epoch = 0 best_psnr = 0.0 for epoch in range(args.num_epochs): model.train() epoch_losses = AverageMeter() with tqdm(total=(len(train_dataset) - len(train_dataset) % args.batch_size)) as t: t.set_description('epoch:{}/{}'.format(epoch, args.num_epochs - 1)) for data in train_dataloader: inputs, labels = data inputs = inputs.to(device) labels = labels.to(device) preds = model(inputs) loss = criterion(preds, labels)
self.learning_rate = 1e-4 self.batch_size = 128 self.result_dir = 'result' self.test_img = '' # Do not change this. arg = this_config() print( "Hello TA! We are group 7. Thank you for your work for us. Hope you have a happy day!" ) with tf.Session() as sess: FLAGS = arg srcnn = SRCNN(sess, image_size=FLAGS.image_size, label_size=FLAGS.label_size, c_dim=FLAGS.c_dim) srcnn.train(FLAGS) # Testing files = glob.glob(os.path.join(os.getcwd(), 'train_set', 'LR0', '*.jpg')) test_files = random.sample(files, len(files) // 5) FLAGS.is_train = False count = 1 for f in test_files: FLAGS.test_img = f print('Saving ', count, '/', len(test_files), ': ', FLAGS.test_img, '\n') count += 1 srcnn.test(FLAGS)