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| -rw-r--r-- | Codes/inference-test.py | 155 | ||||
| -rw-r--r-- | Codes/test.py | 196 |
2 files changed, 351 insertions, 0 deletions
diff --git a/Codes/inference-test.py b/Codes/inference-test.py new file mode 100644 index 0000000..b16a56c --- /dev/null +++ b/Codes/inference-test.py @@ -0,0 +1,155 @@ +import tensorflow as tf +import os +import time +import numpy as np +import pickle + + +from models import generator +from utils import DataLoader, load, save, psnr_error +from constant import const +import evaluate + + +slim = tf.contrib.slim + +os.environ['CUDA_DEVICES_ORDER'] = "PCI_BUS_ID" +os.environ['CUDA_VISIBLE_DEVICES'] = const.GPU + +dataset_name = const.DATASET +test_folder = const.TEST_FOLDER + +num_his = const.NUM_HIS +height, width = 256, 256 + +snapshot_dir = const.SNAPSHOT_DIR +psnr_dir = const.PSNR_DIR +evaluate_name = const.EVALUATE + +print(const) + + +# define dataset +with tf.name_scope('dataset'): + test_video_clips_tensor = tf.placeholder(shape=[1, height, width, 3 * (num_his + 1)], + dtype=tf.float32) + test_inputs = test_video_clips_tensor[..., 0:num_his*3] + test_gt = test_video_clips_tensor[..., -3:] + print('test inputs = {}'.format(test_inputs)) + print('test prediction gt = {}'.format(test_gt)) + +# define testing generator function and +# in testing, only generator networks, there is no discriminator networks and flownet. +with tf.variable_scope('generator', reuse=None): + print('testing = {}'.format(tf.get_variable_scope().name)) + test_outputs = generator(test_inputs, layers=4, output_channel=3) + test_psnr_error = psnr_error(gen_frames=test_outputs, gt_frames=test_gt) + + +config = tf.ConfigProto() +config.gpu_options.allow_growth = True +with tf.Session(config=config) as sess: + # dataset + data_loader = DataLoader(test_folder, height, width) + + # initialize weights + sess.run(tf.global_variables_initializer()) + print('Init global successfully!') + + # tf saver + saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=None) + + restore_var = [v for v in tf.global_variables()] + loader = tf.train.Saver(var_list=restore_var) + + def inference_func(ckpt, dataset_name, evaluate_name): + load(loader, sess, ckpt) + + output_records = [] + videos_info = data_loader.videos + num_videos = len(videos_info.keys()) + total = 0 + timestamp = time.time() + + for video_name, video in videos_info.items(): + length = video['length'] + total += length + psnrs = np.empty(shape=(length,), dtype=np.float32) + + for i in range(num_his, length): + video_clip = data_loader.get_video_clips(video_name, i - num_his, i + 1) + output, psnr = sess.run([test_outputs, test_psnr_error] + feed_dict={test_video_clips_tensor: video_clip[np.newaxis, ...]}) + outputs[i] = output + + tf.image.encode_png( + output, + compression=-1, + name=None + ) + + print('video = {} / {}, i = {} / {}, psnr = {:.6f}'.format( + video_name, num_videos, i, length, psnr)) + + outputs[0:num_his] = outputs[num_his] + output_records.append(outputs) + + result_dict = {'dataset': dataset_name, 'output': output_records, 'flow': [], 'names': [], 'diff_mask': []} + + used_time = time.time() - timestamp + print('total time = {}, fps = {}'.format(used_time, total / used_time)) + + # TODO specify what's the actual name of ckpt. + pickle_path = os.path.join(output_dir, os.path.split(ckpt)[-1]) + with open(pickle_path, 'wb') as writer: + pickle.dump(result_dict, writer, pickle.HIGHEST_PROTOCOL) + + # results = evaluate.evaluate(evaluate_name, pickle_path) + # print(results) + + + if os.path.isdir(snapshot_dir): + def check_ckpt_valid(ckpt_name): + is_valid = False + ckpt = '' + if ckpt_name.startswith('model.ckpt-'): + ckpt_name_splits = ckpt_name.split('.') + ckpt = str(ckpt_name_splits[0]) + '.' + str(ckpt_name_splits[1]) + ckpt_path = os.path.join(snapshot_dir, ckpt) + if os.path.exists(ckpt_path + '.index') and os.path.exists(ckpt_path + '.meta') and \ + os.path.exists(ckpt_path + '.data-00000-of-00001'): + is_valid = True + + return is_valid, ckpt + + def scan_psnr_folder(): + tested_ckpt_in_psnr_sets = set() + for test_psnr in os.listdir(psnr_dir): + tested_ckpt_in_psnr_sets.add(test_psnr) + return tested_ckpt_in_psnr_sets + + def scan_model_folder(): + saved_models = set() + for ckpt_name in os.listdir(snapshot_dir): + is_valid, ckpt = check_ckpt_valid(ckpt_name) + if is_valid: + saved_models.add(ckpt) + return saved_models + + tested_ckpt_sets = scan_psnr_folder() + while True: + all_model_ckpts = scan_model_folder() + new_model_ckpts = all_model_ckpts - tested_ckpt_sets + + for ckpt_name in new_model_ckpts: + # inference + ckpt = os.path.join(snapshot_dir, ckpt_name) + inference_func(ckpt, dataset_name, evaluate_name) + + tested_ckpt_sets.add(ckpt_name) + + print('waiting for models...') + # evaluate.evaluate('compute_auc', psnr_dir) + time.sleep(60) + else: + inference_func(snapshot_dir, dataset_name, evaluate_name) diff --git a/Codes/test.py b/Codes/test.py new file mode 100644 index 0000000..57ee298 --- /dev/null +++ b/Codes/test.py @@ -0,0 +1,196 @@ +import tensorflow as tf +import os + +from models import generator, discriminator, flownet, initialize_flownet +from loss_functions import intensity_loss, gradient_loss +from utils import DataLoader, load, save, psnr_error +from constant import const + + +os.environ['CUDA_DEVICES_ORDER'] = "PCI_BUS_ID" +os.environ['CUDA_VISIBLE_DEVICES'] = const.GPU + +dataset_name = const.DATASET +train_folder = const.TRAIN_FOLDER +test_folder = const.TEST_FOLDER + +batch_size = const.BATCH_SIZE +iterations = const.ITERATIONS +num_his = const.NUM_HIS +height, width = 256, 256 +flow_height, flow_width = const.FLOW_HEIGHT, const.FLOW_WIDTH + +l_num = const.L_NUM +alpha_num = const.ALPHA_NUM +lam_lp = const.LAM_LP +lam_gdl = const.LAM_GDL +lam_adv = const.LAM_ADV +lam_flow = const.LAM_FLOW +adversarial = (lam_adv != 0) + +summary_dir = const.SUMMARY_DIR +snapshot_dir = const.SNAPSHOT_DIR + + +print(const) + +# define dataset +with tf.name_scope('dataset'): + test_loader = DataLoader(test_folder, resize_height=height, resize_width=width) + test_dataset = test_loader(batch_size=batch_size, time_steps=num_his, num_pred=1) + test_it = test_dataset.make_one_shot_iterator() + test_videos_clips_tensor = test_it.get_next() + test_videos_clips_tensor.set_shape([batch_size, height, width, 3*(num_his + 1)]) + + test_inputs = test_videos_clips_tensor[..., 0:num_his*3] + test_gt = test_videos_clips_tensor[..., -3:] + + print('test inputs = {}'.format(test_inputs)) + print('test prediction gt = {}'.format(test_gt)) + +# define testing generator function +with tf.variable_scope('generator', reuse=True): + print('testing = {}'.format(tf.get_variable_scope().name)) + test_outputs = generator(test_inputs, layers=4, output_channel=3) + test_psnr_error = psnr_error(gen_frames=test_outputs, gt_frames=test_gt) + + +# define intensity loss +if lam_lp != 0: + lp_loss = intensity_loss(gen_frames=test_outputs, gt_frames=test_gt, l_num=l_num) +else: + lp_loss = tf.constant(0.0, dtype=tf.float32) + + +# define gdl loss +if lam_gdl != 0: + gdl_loss = gradient_loss(gen_frames=test_outputs, gt_frames=test_gt, alpha=alpha_num) +else: + gdl_loss = tf.constant(0.0, dtype=tf.float32) + + +# define flow loss +if lam_flow != 0: + test_gt_flow = flownet(input_a=train_inputs[..., -3:], input_b=train_gt, + height=flow_height, width=flow_width, reuse=None) + train_pred_flow = flownet(input_a=train_inputs[..., -3:], input_b=train_outputs, + height=flow_height, width=flow_width, reuse=True) + flow_loss = tf.reduce_mean(tf.abs(train_gt_flow - train_pred_flow)) +else: + flow_loss = tf.constant(0.0, dtype=tf.float32) + + +# define adversarial loss +if adversarial: + with tf.variable_scope('discriminator', reuse=None): + real_logits, real_outputs = discriminator(inputs=train_gt) + with tf.variable_scope('discriminator', reuse=True): + fake_logits, fake_outputs = discriminator(inputs=train_outputs) + + print('real_outputs = {}'.format(real_outputs)) + print('fake_outputs = {}'.format(fake_outputs)) + + adv_loss = tf.reduce_mean(tf.square(fake_outputs - 1) / 2) + dis_loss = tf.reduce_mean(tf.square(real_outputs - 1) / 2) + tf.reduce_mean(tf.square(fake_outputs) / 2) +else: + adv_loss = tf.constant(0.0, dtype=tf.float32) + dis_loss = tf.constant(0.0, dtype=tf.float32) + + +with tf.name_scope('training'): + g_loss = tf.add_n([lp_loss * lam_lp, gdl_loss * lam_gdl, adv_loss * lam_adv, flow_loss * lam_flow], name='g_loss') + + g_step = tf.Variable(0, dtype=tf.int32, trainable=False, name='g_step') + g_lrate = tf.train.piecewise_constant(g_step, boundaries=const.LRATE_G_BOUNDARIES, values=const.LRATE_G) + g_optimizer = tf.train.AdamOptimizer(learning_rate=g_lrate, name='g_optimizer') + g_vars = tf.get_collection(key=tf.GraphKeys.TRAINABLE_VARIABLES, scope='generator') + + g_train_op = g_optimizer.minimize(g_loss, global_step=g_step, var_list=g_vars, name='g_train_op') + + if adversarial: + # training discriminator + d_step = tf.Variable(0, dtype=tf.int32, trainable=False, name='d_step') + d_lrate = tf.train.piecewise_constant(d_step, boundaries=const.LRATE_D_BOUNDARIES, values=const.LRATE_D) + d_optimizer = tf.train.AdamOptimizer(learning_rate=d_lrate, name='g_optimizer') + d_vars = tf.get_collection(key=tf.GraphKeys.TRAINABLE_VARIABLES, scope='discriminator') + + d_train_op = d_optimizer.minimize(dis_loss, global_step=d_step, var_list=d_vars, name='d_optimizer') + else: + d_step = None + d_lrate = None + d_train_op = None + +# add all to summaries +tf.summary.scalar(tensor=train_psnr_error, name='train_psnr_error') +tf.summary.scalar(tensor=test_psnr_error, name='test_psnr_error') +tf.summary.scalar(tensor=g_loss, name='g_loss') +tf.summary.scalar(tensor=adv_loss, name='adv_loss') +tf.summary.scalar(tensor=dis_loss, name='dis_loss') +tf.summary.image(tensor=train_outputs, name='train_outputs') +tf.summary.image(tensor=train_gt, name='train_gt') +tf.summary.image(tensor=test_outputs, name='test_outputs') +tf.summary.image(tensor=test_gt, name='test_gt') +summary_op = tf.summary.merge_all() + +config = tf.ConfigProto() +config.gpu_options.allow_growth = True +with tf.Session(config=config) as sess: + # summaries + summary_writer = tf.summary.FileWriter(summary_dir, graph=sess.graph) + + # initialize weights + sess.run(tf.global_variables_initializer()) + print('Init successfully!') + + if lam_flow != 0: + # initialize flownet + initialize_flownet(sess, const.FLOWNET_CHECKPOINT) + + # tf saver + saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=None) + restore_var = [v for v in tf.global_variables()] + loader = tf.train.Saver(var_list=restore_var) + if os.path.isdir(snapshot_dir): + ckpt = tf.train.get_checkpoint_state(snapshot_dir) + if ckpt and ckpt.model_checkpoint_path: + load(loader, sess, ckpt.model_checkpoint_path) + else: + print('No checkpoint file found.') + else: + load(loader, sess, snapshot_dir) + + _step, _loss, _summaries = 0, None, None + while _step < iterations: + try: + if adversarial: + print('Training discriminator...') + _, _d_lr, _d_step, _dis_loss = sess.run([d_train_op, d_lrate, d_step, dis_loss]) + else: + _d_step = 0 + _d_lr = 0 + _dis_loss = 0 + + print('Training generator...') + _, _g_lr, _step, _lp_loss, _gdl_loss, _adv_loss, _flow_loss, _g_loss, _train_psnr, _summaries = sess.run( + [g_train_op, g_lrate, g_step, lp_loss, gdl_loss, adv_loss, flow_loss, g_loss, train_psnr_error, summary_op]) + + if _step % 10 == 0: + print('DiscriminatorModel: Step {} | Global Loss: {:.6f}, lr = {:.6f}'.format(_d_step, _dis_loss, _d_lr)) + print('GeneratorModel : Step {}, lr = {:.6f}'.format(_step, _g_lr)) + print(' Global Loss : ', _g_loss) + print(' intensity Loss : ({:.4f} * {:.4f} = {:.4f})'.format(_lp_loss, lam_lp, _lp_loss * lam_lp)) + print(' gradient Loss : ({:.4f} * {:.4f} = {:.4f})'.format( _gdl_loss, lam_gdl, _gdl_loss * lam_gdl)) + print(' adversarial Loss : ({:.4f} * {:.4f} = {:.4f})'.format(_adv_loss, lam_adv, _adv_loss * lam_adv)) + print(' flownet Loss : ({:.4f} * {:.4f} = {:.4f})'.format(_flow_loss, lam_flow, _flow_loss * lam_flow)) + print(' PSNR Error : ', _train_psnr) + if _step % 100 == 0: + summary_writer.add_summary(_summaries, global_step=_step) + print('Save summaries...') + + if _step % 1000 == 0: + save(saver, sess, snapshot_dir, _step) + + except tf.errors.OutOfRangeError: + print('Finish successfully!') + save(saver, sess, snapshot_dir, _step) + break |