def main(args): """Get dataset hyperparameters.""" assert len(args) == 2 and isinstance(args[1], str) dataset_name = args[1] coord_add = get_coord_add(dataset_name) dataset_size_train = get_dataset_size_train(dataset_name) dataset_size_test = get_dataset_size_test(dataset_name) num_classes = get_num_classes(dataset_name) create_inputs = get_create_inputs(dataset_name, is_train=False, epochs=cfg.epoch) """Set reproduciable random seed""" tf.set_random_seed(1234) with tf.Graph().as_default(): num_batches_per_epoch_train = int(dataset_size_train / cfg.batch_size) num_batches_test = int(dataset_size_test / cfg.batch_size) batch_x, batch_labels = create_inputs() output = net.build_arch(batch_x, coord_add, is_train=False, num_classes=num_classes) batch_acc = net.test_accuracy(output, batch_labels) saver = tf.train.Saver() step = 0 summaries = [] summaries.append(tf.summary.scalar('accuracy', batch_acc)) summary_op = tf.summary.merge(summaries) with tf.Session() as sess: tf.train.start_queue_runners(sess=sess) summary_writer = tf.summary.FileWriter( cfg.test_logdir, graph=None) # graph=sess.graph, huge! for epoch in range(cfg.epoch): # requires a regex to adapt the loss value in the file name here ckpt_re = re.compile() ckpt = os.path.join( cfg.logdir, 'model.ckpt-%d' % (num_batches_per_epoch_train * epoch)) saver.restore(sess, ckpt) accuracy_sum = 0 for i in range(num_batches_test): batch_acc_v, summary_str = sess.run( [batch_acc, summary_op]) print('%d batches are tested.' % step) summary_writer.add_summary(summary_str, step) accuracy_sum += batch_acc_v step += 1 ave_acc = accuracy_sum / num_batches_test print('the average accuracy is %f' % ave_acc)
def main(args): tf.set_random_seed(1234) coord_add = get_coord_add(dataset_name) dataset_size_train = get_dataset_size_train(dataset_name) dataset_size_test = get_dataset_size_test(dataset_name) num_classes = get_num_classes(dataset_name) create_inputs = get_create_inputs(dataset_name, is_train=False, epochs=cfg.epoch) with tf.Graph().as_default(): num_batches_test = int(dataset_size_test / cfg.batch_size * 0.5) batch_x, batch_labels = create_inputs() output, pose_out = net.build_arch(batch_x, coord_add, is_train=False, num_classes=num_classes) tf.logging.debug(pose_out.get_shape()) batch_acc = net.test_accuracy(output, batch_labels) saver = tf.train.Saver() session_config = tf.ConfigProto( device_count={'GPU': 0}, gpu_options={ 'allow_growth': 1, # 'per_process_gpu_memory_fraction': 0.1, 'visible_device_list': '0' }, allow_soft_placement=True) with tf.Session(config=session_config) as sess: sess.run(tf.local_variables_initializer()) sess.run(tf.global_variables_initializer()) coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess=sess, coord=coord) mode_file = tf.train.latest_checkpoint(ckpt) saver.restore(sess, mode_file) accuracy_sum = 0 for i in range(num_batches_test): batch_acc_v = sess.run([batch_acc]) accuracy_sum += batch_acc_v[0] print(accuracy_sum) ave_acc = accuracy_sum / num_batches_test print('the average accuracy is %f' % ave_acc)
def main(args): # Set reproduciable random seed tf.set_random_seed(1234) # Directories # Get name split = FLAGS.load_dir.split('/') if split[-1]: name = split[-1] else: name = split[-2] # Get parent directory split = FLAGS.load_dir.split("/" + name) parent_dir = split[0] test_dir = '{}/{}/test'.format(parent_dir, name) test_summary_dir = test_dir + '/summary' # Clear the test log directory if (FLAGS.reset is True) and os.path.exists(test_dir): shutil.rmtree(test_dir) if not os.path.exists(test_summary_dir): os.makedirs(test_summary_dir) # Logger conf.setup_logger(logger_dir=test_dir, name="logger_test.txt") logger.info("name: " + name) logger.info("parent_dir: " + parent_dir) logger.info("test_dir: " + test_dir) if FLAGS.patch_path: logger.info("patch_path: " + FLAGS.patch_path) # Load hyperparameters from train run conf.load_or_save_hyperparams() # Get dataset hyperparameters logger.info('Using dataset: {}'.format(FLAGS.dataset)) # Dataset dataset_size_test = conf.get_dataset_size_test( FLAGS.dataset ) if FLAGS.partition == "test" else conf.get_dataset_size_train( FLAGS.dataset) num_classes = conf.get_num_classes(FLAGS.dataset) create_inputs_test = conf.get_create_inputs(FLAGS.dataset, mode=FLAGS.partition) #---------------------------------------------------------------------------- # GRAPH - TEST #---------------------------------------------------------------------------- logger.info('BUILD TEST GRAPH') g_test = tf.Graph() with g_test.as_default(): # Get global_step global_step = tf.train.get_or_create_global_step() num_batches_test = int(dataset_size_test / FLAGS.batch_size) # Get data input_dict = create_inputs_test() batch_x = input_dict['image'] batch_labels = input_dict['label'] # AG 10/12/2018: Split batch for multi gpu implementation # Each split is of size FLAGS.batch_size / FLAGS.num_gpus # See: https://github.com/naturomics/CapsNet- # Tensorflow/blob/master/dist_version/distributed_train.py splits_x = tf.split(axis=0, num_or_size_splits=FLAGS.num_gpus, value=batch_x) splits_labels = tf.split(axis=0, num_or_size_splits=FLAGS.num_gpus, value=batch_labels) # Build architecture build_arch = conf.get_dataset_architecture(FLAGS.dataset) # for baseline #build_arch = conf.get_dataset_architecture('baseline') #-------------------------------------------------------------------------- # MULTI GPU - TEST #-------------------------------------------------------------------------- # Calculate the logits for each model tower tower_logits = [] tower_recon_losses = [] reuse_variables = None with tf.device("/cpu:0"): scale_min_feed = tf.placeholder(tf.float32, shape=[], name="scale_min_feed") scale_max_feed = tf.placeholder(tf.float32, shape=[], name="scale_max_feed") patch_feed = None if FLAGS.patch_path: patch_feed = tf.placeholder( tf.float32, shape=batch_x.get_shape().as_list()[-3:], name="patch_feed") for i in range(FLAGS.num_gpus): with tf.device('/gpu:%d' % i): with tf.name_scope('tower_%d' % i) as scope: with slim.arg_scope([slim.variable], device='/cpu:0'): logits, recon_losses, patch_node = tower_fn( build_arch, splits_x[i], scale_min_feed, scale_max_feed, patch_feed, scope, num_classes, reuse_variables=reuse_variables, is_train=False) # Don't reuse variable for first GPU, but do reuse for others reuse_variables = True # Keep track of losses and logits across for each tower tower_logits.append(logits) tower_recon_losses.append(recon_losses) # Combine logits from all towers test_metrics = {} if not FLAGS.save_patch: test_logits = tf.concat(tower_logits, axis=0) test_preds = tf.argmax(test_logits, axis=-1) test_recon_losses = tf.concat(tower_recon_losses, axis=0) test_metrics = { 'preds': test_preds, 'labels': batch_labels, 'recon_losses': test_recon_losses } if FLAGS.adv_patch: test_metrics['patch'] = patch_node # Reset and read operations for streaming metrics go here test_reset = {} test_read = {} # Saver saver = tf.train.Saver(max_to_keep=None) # Set summary op #-------------------------------------------------------------------------- # SESSION - TEST #-------------------------------------------------------------------------- #sess_test = tf.Session( # config=tf.ConfigProto(allow_soft_placement=True, # log_device_placement=False), # graph=g_test) # Perry: added in for RTX 2070 incompatibility workaround config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False) config.gpu_options.allow_growth = True sess_test = tf.Session(config=config, graph=g_test) #sess_test.run(tf.local_variables_initializer()) #sess_test.run(tf.global_variables_initializer()) summary_writer = tf.summary.FileWriter(test_summary_dir, graph=sess_test.graph) ckpts_to_test = [] load_dir_chechpoint = os.path.join(FLAGS.load_dir, "train", "checkpoint") # Evaluate the latest ckpt in dir if FLAGS.ckpt_name is None: latest_ckpt = tf.train.latest_checkpoint(load_dir_chechpoint) ckpts_to_test.append(latest_ckpt) # Evaluate all ckpts in dir elif FLAGS.ckpt_name == "all": # Get list of files in firectory and sort by date created filenames = os.listdir(load_dir_chechpoint) regex = re.compile(r'.*.index') filenames = filter(regex.search, filenames) data_ckpts = (os.path.join(load_dir_chechpoint, fn) for fn in filenames) data_ckpts = ((os.stat(path), path) for path in data_ckpts) # regular files, insert creation date data_ckpts = ((stat[ST_CTIME], path) for stat, path in data_ckpts if S_ISREG(stat[ST_MODE])) data_ckpts = sorted(data_ckpts) # remove ".index" ckpts_to_test = [path[:-6] for ctime, path in data_ckpts] # Evaluate ckpt specified by name else: ckpt_name = os.path.join(load_dir_chechpoint, FLAGS.ckpt_name) ckpts_to_test.append(ckpt_name) #-------------------------------------------------------------------------- # MAIN LOOP #-------------------------------------------------------------------------- # Run testing on checkpoints for ckpt in ckpts_to_test: saver.restore(sess_test, ckpt) if FLAGS.save_patch: out = sess_test.run(test_metrics['patch']) patch = out if patch.shape[-1] == 1: patch = np.squeeze(patch, axis=-1) formatted = (patch * 255).astype('uint8') img = Image.fromarray(formatted) save_dir = os.path.join(FLAGS.storage, 'logs/', FLAGS.dataset, FLAGS.logdir) img.save( os.path.join(FLAGS.load_dir, "test", "saved_patch.png")) return # Reset accumulators sess_test.run(test_reset) test_preds_vals = [] test_labels_vals = [] test_recon_losses_vals = [] test_scales = [] interval = 0.1 if FLAGS.adv_patch else 1 for scale in np.arange(0, 1, interval): for i in range(num_batches_test): feed_dict = {scale_min_feed: scale, scale_max_feed: scale} if FLAGS.patch_path: patch_dims = patch_feed.get_shape() patch = np.asarray(Image.open(FLAGS.patch_path), dtype=np.float32) if len(patch.shape) < 3: patch = np.expand_dims(patch, axis=-1) if patch_dims[-1] == 1: patch = np.mean(patch, axis=-1, keepdims=True) patch = patch / 255 feed_dict[patch_feed] = patch out = sess_test.run([test_metrics], feed_dict=feed_dict) test_metrics_v = out[0] #ckpt_num = re.split('-', ckpt)[-1] #logger.info('TEST ckpt-{}'.format(ckpt_num) # + ' bch-{:d}'.format(i) # ) test_preds_vals.append(test_metrics_v['preds']) test_labels_vals.append(test_metrics_v['labels']) test_recon_losses_vals.append( test_metrics_v['recon_losses']) test_scales.append( np.full(test_metrics_v['preds'].shape, fill_value=scale)) logger.info('writing to csv') test_preds_vals = np.concatenate(test_preds_vals) test_labels_vals = np.concatenate(test_labels_vals) test_recon_losses_vals = np.concatenate(test_recon_losses_vals) test_scales = np.concatenate(test_scales) data = { 'predictions': test_preds_vals, 'labels': test_labels_vals, 'reconstruction_losses': test_recon_losses_vals, 'scales': test_scales } filename = "recon_losses.csv" if FLAGS.patch_path: filename = re.sub( '[^\w\-_]', '_', FLAGS.patch_path) + "_" + FLAGS.partition + ".csv" csv_save_path = os.path.join(FLAGS.load_dir, FLAGS.partition, filename) pd.DataFrame(data).to_csv(csv_save_path, index=False) logger.info('csv saved at ' + csv_save_path)
def main(args): """Get dataset hyperparameters.""" assert len(args) == 3 and isinstance(args[1], str) and isinstance( args[2], str) dataset_name = args[1] model_name = args[2] """Set reproduciable random seed""" tf.set_random_seed(1234) coord_add = get_coord_add(dataset_name) dataset_size_train = get_dataset_size_train(dataset_name) dataset_size_test = get_dataset_size_test(dataset_name) num_classes = get_num_classes(dataset_name) create_inputs = get_create_inputs(dataset_name, is_train=False, epochs=cfg.epoch) with tf.Graph().as_default(): num_batches_per_epoch_train = int(dataset_size_train / cfg.batch_size) num_batches_test = 2 # int(dataset_size_test / cfg.batch_size * 0.1) batch_x, batch_labels = create_inputs() batch_squash = tf.divide(batch_x, 255.) batch_x_norm = slim.batch_norm(batch_x, center=False, is_training=False, trainable=False) output, pose_out = net.build_arch(batch_x_norm, coord_add, is_train=False, num_classes=num_classes) tf.logging.debug(pose_out.get_shape()) batch_acc = net.test_accuracy(output, batch_labels) m_op = tf.constant(0.9) loss, spread_loss, mse, recon_img_squash = net.spread_loss( output, pose_out, batch_squash, batch_labels, m_op) tf.summary.scalar('spread_loss', spread_loss) tf.summary.scalar('reconstruction_loss', mse) tf.summary.scalar('all_loss', loss) data_size = int(batch_x.get_shape()[1]) recon_img = tf.multiply( tf.reshape(recon_img_squash, shape=[cfg.batch_size, data_size, data_size, 1]), 255.) orig_img = tf.reshape(batch_x, shape=[cfg.batch_size, data_size, data_size, 1]) tf.summary.image('orig_image', orig_img) tf.summary.image('recon_image', recon_img) saver = tf.train.Saver() step = 0 tf.summary.scalar('accuracy', batch_acc) summary_op = tf.summary.merge_all() with tf.Session( config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess: sess.run(tf.local_variables_initializer()) sess.run(tf.global_variables_initializer()) coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess=sess, coord=coord) if not os.path.exists(cfg.test_logdir + '/{}/{}/'.format(model_name, dataset_name)): os.makedirs(cfg.test_logdir + '/{}/{}/'.format(model_name, dataset_name)) summary_writer = tf.summary.FileWriter( cfg.test_logdir + '/{}/{}/'.format(model_name, dataset_name), graph=sess.graph) # graph=sess.graph, huge! files = os.listdir(cfg.logdir + '/{}/{}/'.format(model_name, dataset_name)) for epoch in range(45, 46): # requires a regex to adapt the loss value in the file name here ckpt_re = ".ckpt-%d" % (num_batches_per_epoch_train * epoch) for __file in files: if __file.endswith(ckpt_re + ".index"): ckpt = os.path.join( cfg.logdir + '/{}/{}/'.format(model_name, dataset_name), __file[:-6]) #ckpt = os.path.join(cfg.logdir, "model.ckpt-%d" % (num_batches_per_epoch_train * epoch)) ############Comentar linea de abajo #ckpt = os.path.join(cfg.logdir, "caps/mnist/model-0.3764.ckpt-1718") saver.restore(sess, ckpt) accuracy_sum = 0 for i in range(num_batches_test): batch_acc_v, summary_str, orig_image, recon_image = sess.run( [batch_acc, summary_op, orig_img, recon_img]) print('%d batches are tested.' % step) summary_writer.add_summary(summary_str, step) accuracy_sum += batch_acc_v step += 1 # display original/reconstructed images in matplotlib plot_imgs(orig_image, i, 'ori') plot_imgs(recon_image, i, 'rec') ave_acc = accuracy_sum / num_batches_test print('the average accuracy is %f' % ave_acc)
def main(args): """Get dataset hyperparameters.""" assert len(args) == 3 and isinstance(args[1], str) and isinstance(args[2], str) dataset_name = args[1] model_name = args[2] coord_add = get_coord_add(dataset_name) dataset_size_train = get_dataset_size_train(dataset_name) dataset_size_test = get_dataset_size_test(dataset_name) num_classes = get_num_classes(dataset_name) create_inputs = get_create_inputs( dataset_name, is_train=False, epochs=cfg.epoch) """Set reproduciable random seed""" tf.set_random_seed(1234) with tf.Graph().as_default(): num_batches_per_epoch_train = int(dataset_size_train / cfg.batch_size) num_batches_test = int(dataset_size_test / cfg.batch_size * 0.1) batch_x, batch_labels = create_inputs() batch_x = slim.batch_norm(batch_x, center=False, is_training=False, trainable=False) if model_name == "caps": output, _ = net.build_arch(batch_x, coord_add, is_train=False, num_classes=num_classes) elif model_name == "cnn_baseline": output = net.build_arch_baseline(batch_x, is_train=False, num_classes=num_classes) else: raise "Please select model from 'caps' or 'cnn_baseline' as the secondary argument of eval.py!" batch_acc = net.test_accuracy(output, batch_labels) saver = tf.train.Saver() step = 0 summaries = [] summaries.append(tf.summary.scalar('accuracy', batch_acc)) summary_op = tf.summary.merge(summaries) with tf.Session(config=tf.ConfigProto( allow_soft_placement=True, log_device_placement=False)) as sess: sess.run(tf.local_variables_initializer()) sess.run(tf.global_variables_initializer()) coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess=sess, coord=coord) if not os.path.exists(cfg.test_logdir + '/{}/{}/'.format(model_name, dataset_name)): os.makedirs(cfg.test_logdir + '/{}/{}/'.format(model_name, dataset_name)) summary_writer = tf.summary.FileWriter( cfg.test_logdir + '/{}/{}/'.format(model_name, dataset_name), graph=sess.graph) # graph=sess.graph, huge! files = os.listdir(cfg.logdir + '/{}/{}/'.format(model_name, dataset_name)) for epoch in range(1, cfg.epoch): # requires a regex to adapt the loss value in the file name here ckpt_re = ".ckpt-%d" % (num_batches_per_epoch_train * epoch) for __file in files: if __file.endswith(ckpt_re + ".index"): ckpt = os.path.join(cfg.logdir + '/{}/{}/'.format(model_name, dataset_name), __file[:-6]) # ckpt = os.path.join(cfg.logdir, "model.ckpt-%d" % (num_batches_per_epoch_train * epoch)) saver.restore(sess, ckpt) accuracy_sum = 0 for i in range(num_batches_test): batch_acc_v, summary_str = sess.run([batch_acc, summary_op]) print('%d batches are tested.' % step) summary_writer.add_summary(summary_str, step) accuracy_sum += batch_acc_v step += 1 ave_acc = accuracy_sum / num_batches_test print('the average accuracy is %f' % ave_acc) coord.join(threads)
def main(args): """Get dataset hyperparameters.""" assert len(args) == 2 and isinstance(args[1], str) dataset_name = args[1] logger.info('Using dataset: {}'.format(dataset_name)) coord_add = get_coord_add(dataset_name) num_classes = get_num_classes(dataset_name) dataset_size = get_dataset_size_train(dataset_name) dataset_size_test = get_dataset_size_test(dataset_name) create_inputs = get_create_inputs(dataset_name, is_train=True, epochs=cfg.epoch) test_inputs = get_create_inputs(dataset_name, is_train=False, epochs=cfg.epoch) """Set reproduciable random seed""" tf.set_random_seed(1234) with tf.Graph().as_default(), tf.device('/cpu:0'): """Get global_step.""" global_step = tf.get_variable('global_step', [], initializer=tf.constant_initializer(0), trainable=False) """Get batches per epoch.""" num_batches_per_epoch = int(dataset_size / cfg.batch_size) num_batches_test = int(dataset_size_test / cfg.batch_size) """Set tf summaries.""" summaries = [] valid_sum = [] """Use exponential decay leanring rate?""" # lrn_rate = tf.maximum(tf.train.exponential_decay( # 1e-2, global_step, num_batches_per_epoch, 0.8), 1e-5) # summaries.append(tf.summary.scalar('learning_rate', lrn_rate)) opt = tf.train.AdamOptimizer(learning_rate=0.001) """Get batch from data queue.""" train_q = create_inputs() test_q = test_inputs() use_train_data = tf.placeholder(dtype=tf.bool, shape=()) batch_x, batch_labels = tf.cond(use_train_data, true_fn=lambda: train_q, false_fn=lambda: test_q) # batch_y = tf.one_hot(batch_labels, depth=10, axis=1, dtype=tf.float32) """Define the dataflow graph.""" m_op = tf.placeholder(dtype=tf.float32, shape=()) with tf.device('/gpu:0'): with slim.arg_scope([slim.variable], device='/cpu:0'): norm_batch_x = tf.contrib.layers.batch_norm(batch_x, is_training=True) # Select network architecture. if cfg.network == 'conv': import capsnet_em as net output = net.build_arch(norm_batch_x, coord_add, is_train=True, num_classes=num_classes) elif cfg.network == 'fc': import capsnet_fc as net output = net.build_arch(norm_batch_x, is_train=True, num_classes=num_classes) else: raise ValueError('Invalid network architecture: ' % cfg.network) # Select loss function. if cfg.loss_fn == 'spread': loss = net.spread_loss(output, batch_labels, m_op) elif cfg.loss_fn == 'margin': loss = net.margin_loss(output, batch_labels) elif cfg.loss_fn == 'cross_en': loss = net.cross_entropy_loss(output, batch_labels) else: raise ValueError('Invalid loss function: ' % cfg.loss_fn) acc = net.accuracy(output, batch_labels) """Compute gradient.""" grad = opt.compute_gradients(loss) # See: https://stackoverflow.com/questions/40701712/how-to-check-nan-in-gradients-in-tensorflow-when-updating grad_check = [tf.check_numerics(g, message='Gradient NaN Found!') for g, _ in grad] + \ [tf.check_numerics(loss, message='Loss NaN Found')] """Add to summary.""" summaries.append(tf.summary.scalar('loss', loss)) summaries.append(tf.summary.scalar('acc', acc)) valid_sum.append(tf.summary.scalar('val_loss', loss)) valid_sum.append(tf.summary.scalar('val_acc', acc)) """Apply graident.""" with tf.control_dependencies(grad_check): update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): train_op = opt.apply_gradients(grad, global_step=global_step) """Set Session settings.""" gpu_options = tf.GPUOptions( per_process_gpu_memory_fraction=cfg.gpu_frac) sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False, gpu_options=gpu_options)) sess.run(tf.local_variables_initializer()) sess.run(tf.global_variables_initializer()) """Set Saver.""" var_to_save = [ v for v in tf.global_variables() if 'Adam' not in v.name ] # Don't save redundant Adam beta/gamma saver = tf.train.Saver(var_list=var_to_save, max_to_keep=cfg.epoch) """Display parameters""" total_p = np.sum([ np.prod(v.get_shape().as_list()) for v in var_to_save ]).astype(np.int32) train_p = np.sum([ np.prod(v.get_shape().as_list()) for v in tf.trainable_variables() ]).astype(np.int32) logger.info('Total Parameters: {}'.format(total_p)) logger.info('Trainable Parameters: {}'.format(train_p)) # read snapshot # latest = os.path.join(cfg.logdir, 'model.ckpt-4680') # saver.restore(sess, latest) """Set summary op.""" summary_op = tf.summary.merge(summaries) valid_sum_op = tf.summary.merge(valid_sum) """Start coord & queue.""" coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess=sess, coord=coord) """Set summary writer""" summary_writer = tf.summary.FileWriter( cfg.logdir, graph=None) # graph = sess.graph, huge! """Main loop.""" m_min = 0.2 m_max = 0.9 m = m_min for step in range(cfg.epoch * num_batches_per_epoch): if (step % num_batches_per_epoch) == 0: tic = time.time() progbar = tf.keras.utils.Progbar( num_batches_per_epoch, verbose=(1 if cfg.progbar else 0)) """"TF queue would pop batch until no file""" try: _, loss_value, acc_value = sess.run([train_op, loss, acc], feed_dict={ use_train_data: True, m_op: m }) progbar.update((step % num_batches_per_epoch), values=[('loss', loss_value), ('acc', acc_value)]) except KeyboardInterrupt: sess.close() sys.exit() except tf.errors.InvalidArgumentError: logger.warning( '%d iteration contains NaN gradients. Discard.' % step) continue """Write to summary.""" if step % 10 == 0: summary_str = sess.run(summary_op, feed_dict={ use_train_data: True, m_op: m }) summary_writer.add_summary(summary_str, step) """Epoch wise linear annealling.""" if (step % num_batches_per_epoch) == 0: if step > 0: m += (m_max - m_min) / (cfg.epoch * 0.6) if m > m_max: m = m_max """Save model periodically""" ckpt_path = os.path.join( cfg.logdir, 'model-{0:.4f}.ckpt'.format(loss_value)) saver.save(sess, ckpt_path, global_step=step) # Add a new progress bar if ((step + 1) % num_batches_per_epoch) == 0: toc = time.time() val_loss_value, val_acc_value = (0.0, 0.0) for i in range(num_batches_test): val_batch = sess.run([loss, acc], feed_dict={ use_train_data: False, m_op: m }) val_loss_batch, val_acc_batch = val_batch val_loss_value += val_loss_batch / num_batches_test val_acc_value += val_acc_batch / num_batches_test valid_sum_str = sess.run(valid_sum_op, feed_dict={ use_train_data: False, m_op: m }) summary_writer.add_summary(valid_sum_str, step) print('\nEpoch %d/%d in ' % (step // num_batches_per_epoch + 1, cfg.epoch) + '%.1fs' % (toc - tic) + ' - loss: %f' % val_loss_value + ' - acc: %f' % val_acc_value) """Join threads""" coord.join(threads)
def main(args): # Set reproduciable random seed tf.set_random_seed(1234) # Directories # Get name split = FLAGS.load_dir.split('/') if split[-1]: name = split[-1] else: name = split[-2] # Get parent directory split = FLAGS.load_dir.split("/" + name) parent_dir = split[0] test_dir = '{}/{}/test'.format(parent_dir, name) test_summary_dir = test_dir + '/summary' # Clear the test log directory if (FLAGS.reset is True) and os.path.exists(test_dir): shutil.rmtree(test_dir) if not os.path.exists(test_summary_dir): os.makedirs(test_summary_dir) # Logger conf.setup_logger(logger_dir=test_dir, name="logger_test.txt") logger.info("name: " + name) logger.info("parent_dir: " + parent_dir) logger.info("test_dir: " + test_dir) # Load hyperparameters from train run conf.load_or_save_hyperparams() # Get dataset hyperparameters logger.info('Using dataset: {}'.format(FLAGS.dataset)) # Dataset dataset_size_test = conf.get_dataset_size_test(FLAGS.dataset) num_classes = conf.get_num_classes(FLAGS.dataset) create_inputs_test = conf.get_create_inputs(FLAGS.dataset, mode="test") #---------------------------------------------------------------------------- # GRAPH - TEST #---------------------------------------------------------------------------- logger.info('BUILD TEST GRAPH') g_test = tf.Graph() with g_test.as_default(): # Get global_step global_step = tf.train.get_or_create_global_step() num_batches_test = int(dataset_size_test / FLAGS.batch_size) # Get data input_dict = create_inputs_test() batch_x = input_dict['image'] batch_labels = input_dict['label'] # AG 10/12/2018: Split batch for multi gpu implementation # Each split is of size FLAGS.batch_size / FLAGS.num_gpus # See: https://github.com/naturomics/CapsNet- # Tensorflow/blob/master/dist_version/distributed_train.py splits_x = tf.split( axis=0, num_or_size_splits=FLAGS.num_gpus, value=batch_x) splits_labels = tf.split( axis=0, num_or_size_splits=FLAGS.num_gpus, value=batch_labels) # Build architecture build_arch = conf.get_dataset_architecture(FLAGS.dataset) # for baseline #build_arch = conf.get_dataset_architecture('baseline') #-------------------------------------------------------------------------- # MULTI GPU - TEST #-------------------------------------------------------------------------- # Calculate the logits for each model tower tower_logits = [] reuse_variables = None for i in range(FLAGS.num_gpus): with tf.device('/gpu:%d' % i): with tf.name_scope('tower_%d' % i) as scope: with slim.arg_scope([slim.variable], device='/cpu:0'): loss, logits = tower_fn( build_arch, splits_x[i], splits_labels[i], scope, num_classes, reuse_variables=reuse_variables, is_train=False) # Don't reuse variable for first GPU, but do reuse for others reuse_variables = True # Keep track of losses and logits across for each tower tower_logits.append(logits) # Loss for each tower tf.summary.histogram("test_logits", logits) # Combine logits from all towers logits = tf.concat(tower_logits, axis=0) # Calculate metrics test_loss = mod.spread_loss(logits, batch_labels) test_acc = met.accuracy(logits, batch_labels) # Prepare predictions and one-hot labels test_probs = tf.nn.softmax(logits=logits) test_labels_oh = tf.one_hot(batch_labels, num_classes) # Group metrics together # See: https://cs230-stanford.github.io/tensorflow-model.html test_metrics = {'loss' : test_loss, 'labels' : batch_labels, 'labels_oh' : test_labels_oh, 'logits' : logits, 'probs' : test_probs, 'acc' : test_acc, } # Reset and read operations for streaming metrics go here test_reset = {} test_read = {} tf.summary.scalar("test_loss", test_loss) tf.summary.scalar("test_acc", test_acc) # Saver saver = tf.train.Saver(max_to_keep=None) # Set summary op test_summary = tf.summary.merge_all() #-------------------------------------------------------------------------- # SESSION - TEST #-------------------------------------------------------------------------- #sess_test = tf.Session( # config=tf.ConfigProto(allow_soft_placement=True, # log_device_placement=False), # graph=g_test) # Perry: added in for RTX 2070 incompatibility workaround config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False) config.gpu_options.allow_growth = True sess_test = tf.Session(config=config, graph=g_test) #sess_test.run(tf.local_variables_initializer()) #sess_test.run(tf.global_variables_initializer()) summary_writer = tf.summary.FileWriter( test_summary_dir, graph=sess_test.graph) ckpts_to_test = [] load_dir_chechpoint = os.path.join(FLAGS.load_dir, "train", "checkpoint") # Evaluate the latest ckpt in dir if FLAGS.ckpt_name is None: latest_ckpt = tf.train.latest_checkpoint(load_dir_chechpoint) ckpts_to_test.append(latest_ckpt) # Evaluate all ckpts in dir elif FLAGS.ckpt_name == "all": # Get list of files in firectory and sort by date created filenames = os.listdir(load_dir_chechpoint) regex = re.compile(r'.*.index') filenames = filter(regex.search, filenames) data_ckpts = (os.path.join(load_dir_chechpoint, fn) for fn in filenames) data_ckpts = ((os.stat(path), path) for path in data_ckpts) # regular files, insert creation date data_ckpts = ((stat[ST_CTIME], path) for stat, path in data_ckpts if S_ISREG(stat[ST_MODE])) data_ckpts= sorted(data_ckpts) # remove ".index" ckpts_to_test = [path[:-6] for ctime, path in data_ckpts] # Evaluate ckpt specified by name else: ckpt_name = os.path.join(load_dir_chechpoint, FLAGS.ckpt_name) ckpts_to_test.append(ckpt_name) #-------------------------------------------------------------------------- # MAIN LOOP #-------------------------------------------------------------------------- # Run testing on checkpoints for ckpt in ckpts_to_test: saver.restore(sess_test, ckpt) # Reset accumulators accuracy_sum = 0 loss_sum = 0 sess_test.run(test_reset) for i in range(num_batches_test): test_metrics_v, test_summary_str_v = sess_test.run( [test_metrics, test_summary]) # Update accuracy_sum += test_metrics_v['acc'] loss_sum += test_metrics_v['loss'] ckpt_num = re.split('-', ckpt)[-1] logger.info('TEST ckpt-{}'.format(ckpt_num) + ' bch-{:d}'.format(i) + ' cum_acc: {:.2f}%'.format(accuracy_sum/(i+1)*100) + ' cum_loss: {:.4f}'.format(loss_sum/(i+1)) ) ave_acc = accuracy_sum / num_batches_test ave_loss = loss_sum / num_batches_test logger.info('TEST ckpt-{}'.format(ckpt_num) + ' avg_acc: {:.2f}%'.format(ave_acc*100) + ' avg_loss: {:.4f}'.format(ave_loss)) logger.info("Write Test Summary") summary_test = tf.Summary() summary_test.value.add(tag="test_acc", simple_value=ave_acc) summary_test.value.add(tag="test_loss", simple_value=ave_loss) summary_writer.add_summary(summary_test, ckpt_num)
def main(args): """Get dataset hyperparameters.""" assert len(args) == 2 and isinstance(args[1], str) dataset_name = args[1] coord_add = get_coord_add(dataset_name) dataset_size_train = get_dataset_size_train(dataset_name) dataset_size_test = get_dataset_size_test(dataset_name) num_classes = get_num_classes(dataset_name) create_inputs = get_create_inputs(dataset_name, is_train=False, epochs=cfg.epoch) """Set reproduciable random seed""" tf.set_random_seed(1234) with tf.Graph().as_default(): num_batches_per_epoch_train = int(dataset_size_train / cfg.batch_size) num_batches_test = int(dataset_size_test / cfg.batch_size * 0.1) batch_x, batch_labels = create_inputs() batch_x = slim.batch_norm(batch_x, center=False, is_training=False, trainable=False) output, _ = net.build_arch(batch_x, coord_add, is_train=False, num_classes=num_classes) batch_acc = net.test_accuracy(output, batch_labels) saver = tf.train.Saver() step = 0 summaries = [] summaries.append(tf.summary.scalar('accuracy', batch_acc)) summary_op = tf.summary.merge(summaries) with tf.Session( config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess: sess.run(tf.local_variables_initializer()) sess.run(tf.global_variables_initializer()) coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess=sess, coord=coord) summary_writer = tf.summary.FileWriter( cfg.test_logdir, graph=sess.graph) # graph=sess.graph, huge! files = os.listdir(cfg.logdir) for epoch in range(1, cfg.epoch): # requires a regex to adapt the loss value in the file name here ckpt_re = ".ckpt-%d" % (num_batches_per_epoch_train * epoch) for __file in files: if __file.endswith(ckpt_re + ".index"): ckpt = os.path.join(cfg.logdir, __file[:-6]) # ckpt = os.path.join(cfg.logdir, "model.ckpt-%d" % (num_batches_per_epoch_train * epoch)) saver.restore(sess, ckpt) accuracy_sum = 0 for i in range(num_batches_test): batch_acc_v, summary_str = sess.run( [batch_acc, summary_op]) print('%d batches are tested.' % step) summary_writer.add_summary(summary_str, step) print('%d batch accuracy.' % batch_acc_v) accuracy_sum += batch_acc_v step += 1 ave_acc = accuracy_sum / num_batches_test print('the average accuracy is %f' % ave_acc) coord.join(threads)
def main(args): """Get dataset hyperparameters.""" assert len(args) == 3 and isinstance(args[1], str) and isinstance( args[2], str) dataset_name = args[1] model_name = args[2] coord_add = get_coord_add(dataset_name) dataset_size_train = get_dataset_size_train(dataset_name) dataset_size_test = get_dataset_size_test(dataset_name) num_classes = get_num_classes(dataset_name) create_inputs = get_create_inputs(dataset_name, is_train=False, epochs=cfg.epoch) """Set reproduciable random seed""" tf.set_random_seed(1234) with tf.Graph().as_default(): num_batches_per_epoch_train = int(dataset_size_train / cfg.batch_size) num_batches_test = int(dataset_size_test / cfg.batch_size * 0.1) batch_x, batch_labels = create_inputs() batch_x = slim.batch_norm(batch_x, center=False, is_training=False, trainable=False) if model_name == "caps": output, _ = net.build_arch(batch_x, coord_add, is_train=False, num_classes=num_classes) elif model_name == "cnn_baseline": output = net.build_arch_baseline(batch_x, is_train=False, num_classes=num_classes) else: raise "Please select model from 'caps' or 'cnn_baseline' as the secondary argument of eval.py!" batch_acc = net.test_accuracy(output, batch_labels) saver = tf.train.Saver() step = 0 summaries = [] summaries.append(tf.summary.scalar('accuracy', batch_acc)) summary_op = tf.summary.merge(summaries) session_config = tf.ConfigProto( device_count={'GPU': 0}, gpu_options={ 'allow_growth': 1, # 'per_process_gpu_memory_fraction': 0.1, 'visible_device_list': '0' }, allow_soft_placement=True) with tf.Session(config=session_config) as sess: sess.run(tf.local_variables_initializer()) sess.run(tf.global_variables_initializer()) coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess=sess, coord=coord) if not os.path.exists(cfg.test_logdir + '/{}/{}/'.format(model_name, dataset_name)): os.makedirs(cfg.test_logdir + '/{}/{}/'.format(model_name, dataset_name)) summary_writer = tf.summary.FileWriter( cfg.test_logdir + '/{}/{}/'.format(model_name, dataset_name), graph=sess.graph) # graph=sess.graph, huge! files = os.listdir(cfg.logdir + '/{}/{}/'.format(model_name, dataset_name)) for epoch in range(1, cfg.epoch): # requires a regex to adapt the loss value in the file name here ckpt_re = ".ckpt-%d" % (num_batches_per_epoch_train * epoch) for __file in files: if __file.endswith(ckpt_re + ".index"): ckpt = os.path.join( cfg.logdir + '/{}/{}/'.format(model_name, dataset_name), __file[:-6]) # ckpt = os.path.join(cfg.logdir, "model.ckpt-%d" % (num_batches_per_epoch_train * epoch)) saver.restore(sess, ckpt) accuracy_sum = 0 for i in range(num_batches_test): batch_acc_v, summary_str = sess.run( [batch_acc, summary_op]) print('%d batches are tested.' % step) summary_writer.add_summary(summary_str, step) accuracy_sum += batch_acc_v step += 1 ave_acc = accuracy_sum / num_batches_test print('the average accuracy is %f' % ave_acc) coord.join(threads)
def main(args): """Get dataset hyperparameters.""" assert len(args) == 3 and isinstance(args[1], str) and isinstance(args[2], str) dataset_name = args[1] model_name = args[2] """Set reproduciable random seed""" tf.set_random_seed(1234) coord_add = get_coord_add(dataset_name) dataset_size_train = get_dataset_size_train(dataset_name) dataset_size_test = get_dataset_size_test(dataset_name) num_classes = get_num_classes(dataset_name) create_inputs = get_create_inputs( dataset_name, is_train=False, epochs=cfg.epoch) with tf.Graph().as_default(): num_batches_per_epoch_train = int(dataset_size_train / cfg.batch_size) num_batches_test = 2 # int(dataset_size_test / cfg.batch_size * 0.1) batch_x, batch_labels = create_inputs() batch_squash = tf.divide(batch_x, 255.) batch_x_norm = slim.batch_norm(batch_x, center=False, is_training=False, trainable=False) output, pose_out = net.build_arch(batch_x_norm, coord_add, is_train=False, num_classes=num_classes) tf.logging.debug(pose_out.get_shape()) batch_acc = net.test_accuracy(output, batch_labels) m_op = tf.constant(0.9) loss, spread_loss, mse, recon_img_squash = net.spread_loss( output, pose_out, batch_squash, batch_labels, m_op) tf.summary.scalar('spread_loss', spread_loss) tf.summary.scalar('reconstruction_loss', mse) tf.summary.scalar('all_loss', loss) data_size = int(batch_x.get_shape()[1]) recon_img = tf.multiply(tf.reshape(recon_img_squash, shape=[ cfg.batch_size, data_size, data_size, 1]), 255.) orig_img = tf.reshape(batch_x, shape=[ cfg.batch_size, data_size, data_size, 1]) tf.summary.image('orig_image', orig_img) tf.summary.image('recon_image', recon_img) saver = tf.train.Saver() step = 0 tf.summary.scalar('accuracy', batch_acc) summary_op = tf.summary.merge_all() with tf.Session(config=tf.ConfigProto( allow_soft_placement=True, log_device_placement=False)) as sess: sess.run(tf.local_variables_initializer()) sess.run(tf.global_variables_initializer()) coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess=sess, coord=coord) if not os.path.exists(cfg.test_logdir + '/{}/{}/'.format(model_name, dataset_name)): os.makedirs(cfg.test_logdir + '/{}/{}/'.format(model_name, dataset_name)) summary_writer = tf.summary.FileWriter( cfg.test_logdir + '/{}/{}/'.format(model_name, dataset_name), graph=sess.graph) # graph=sess.graph, huge! files = os.listdir(cfg.logdir + '/{}/{}/'.format(model_name, dataset_name)) for epoch in range(14, 15): # requires a regex to adapt the loss value in the file name here ckpt_re = ".ckpt-%d" % (num_batches_per_epoch_train * epoch) for __file in files: if __file.endswith(ckpt_re + ".index"): ckpt = os.path.join( cfg.logdir + '/{}/{}/'.format(model_name, dataset_name), __file[:-6]) # ckpt = os.path.join(cfg.logdir, "model.ckpt-%d" % (num_batches_per_epoch_train * epoch)) saver.restore(sess, ckpt) accuracy_sum = 0 for i in range(num_batches_test): batch_acc_v, summary_str, orig_image, recon_image = sess.run( [batch_acc, summary_op, orig_img, recon_img]) print('%d batches are tested.' % step) summary_writer.add_summary(summary_str, step) accuracy_sum += batch_acc_v step += 1 # display original/reconstructed images in matplotlib plot_imgs(orig_image, i, 'ori') plot_imgs(recon_image, i, 'rec') ave_acc = accuracy_sum / num_batches_test print('the average accuracy is %f' % ave_acc)
# print(C) import logging import daiquiri daiquiri.setup(level=logging.DEBUG) logger = daiquiri.getLogger(__name__) import capsnet_em as net from confusion_matrix_API import plot_confusion import matplotlib.pyplot as plt #################### 改这里 ########################################## import capsnet_em as net ckpt = 'logdir/caps/asl/' dataset_name = 'asl' #想要测试多少个batch test_dataset_size = cfg.get_dataset_size_test('asl') batch_size =cfg.batch_size num_batches_test = 10 * (test_dataset_size // batch_size) # num_batches_test = 10 #数量太少会报错,因为画图有的行是NaN值 recognize_data_dir ='../data/asl_tf' recognize_labels_txt_keywords = 'labels.txt' #################### end ######################################## def conver_number_to_label_name(the_list,labels_maps): for idx,each in enumerate(the_list): the_list[idx] = labels_maps[str(each)] return the_list def main(args): # 1、设置GPU模式 session_config = cfg.set_gpu()
def main(args): """Run training and validation. 1. Build graphs 1.1 Training graph to run on multiple GPUs 1.2 Validation graph to run on multiple GPUs 2. Configure sessions 2.1 Train 2.2 Validate 3. Main loop 3.1 Train 3.2 Write summary 3.3 Save model 3.4 Validate model Author: Perry Deng """ # Set reproduciable random seed tf.set_random_seed(1234) # Directories train_dir, train_summary_dir = conf.setup_train_directories() # Logger conf.setup_logger(logger_dir=train_dir, name="logger_train.txt") # Hyperparameters conf.load_or_save_hyperparams(train_dir) # Get dataset hyperparameters logger.info('Using dataset: {}'.format(FLAGS.dataset)) dataset_size_train = conf.get_dataset_size_train(FLAGS.dataset)\ if not FLAGS.train_on_test else conf.get_dataset_size_test(FLAGS.dataset) dataset_size_val = conf.get_dataset_size_validate(FLAGS.dataset) build_arch = conf.get_dataset_architecture(FLAGS.dataset) num_classes = conf.get_num_classes(FLAGS.dataset) create_inputs_train = conf.get_create_inputs(FLAGS.dataset, mode="train_whole")\ if not FLAGS.train_on_test else conf.get_create_inputs(FLAGS.dataset, mode="train_on_test") create_inputs_train_wholeset = conf.get_create_inputs(FLAGS.dataset, mode="train_whole") if dataset_size_val > 0: create_inputs_val = conf.get_create_inputs(FLAGS.dataset, mode="validate") #***************************************************************************** # 1. BUILD GRAPHS #***************************************************************************** #---------------------------------------------------------------------------- # GRAPH - TRAIN #---------------------------------------------------------------------------- logger.info('BUILD TRAIN GRAPH') g_train = tf.Graph() with g_train.as_default(), tf.device('/cpu:0'): # Get global_step global_step = tf.train.get_or_create_global_step() # Get batches per epoch num_batches_per_epoch = int(dataset_size_train / FLAGS.batch_size) # In response to a question on OpenReview, Hinton et al. wrote the # following: # "We use an exponential decay with learning rate: 3e-3, decay_steps: 20000, # decay rate: 0.96." # https://openreview.net/forum?id=HJWLfGWRb¬eId=ryxTPFDe2X lrn_rate = tf.train.exponential_decay(learning_rate = FLAGS.lrn_rate, global_step = global_step, decay_steps = 20000, decay_rate = 0.96) tf.summary.scalar('learning_rate', lrn_rate) opt = tf.train.AdamOptimizer(learning_rate=lrn_rate) # Get batch from data queue. Batch size is FLAGS.batch_size, which is then # divided across multiple GPUs input_dict = create_inputs_train() batch_x = input_dict['image'] batch_labels = input_dict['label'] # AG 03/10/2018: Split batch for multi gpu implementation # Each split is of size FLAGS.batch_size / FLAGS.num_gpus # See: https://github.com/naturomics/CapsNet-Tensorflow/blob/master/ # dist_version/distributed_train.py splits_x = tf.split( axis=0, num_or_size_splits=FLAGS.num_gpus, value=batch_x) splits_labels = tf.split( axis=0, num_or_size_splits=FLAGS.num_gpus, value=batch_labels) #-------------------------------------------------------------------------- # MULTI GPU - TRAIN #-------------------------------------------------------------------------- # Calculate the gradients for each model tower tower_grads = [] tower_losses = [] tower_logits = [] tower_target_labels = [] reuse_variables = None for i in range(FLAGS.num_gpus): with tf.device('/gpu:%d' % i): with tf.name_scope('tower_%d' % i) as scope: logger.info('TOWER %d' % i) #with slim.arg_scope([slim.model_variable, slim.variable], # device='/cpu:0'): with slim.arg_scope([slim.variable], device='/cpu:0'): loss, logits, x, patch, target_labels = tower_fn( build_arch, splits_x[i], splits_labels[i], scope, num_classes, reuse_variables=reuse_variables, is_train=True) # Don't reuse variable for first GPU, but do reuse for others reuse_variables = True # Compute gradients for one GPU patch_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "patch_params") grads = opt.compute_gradients(loss, var_list=patch_params) # Keep track of the gradients across all towers. tower_grads.append(grads) tower_target_labels.append(target_labels) # Keep track of losses and logits across for each tower tower_logits.append(logits) tower_losses.append(loss) # Loss for each tower tf.summary.scalar("loss", loss) # We must calculate the mean of each gradient. Note that this is the # synchronization point across all towers. grads = average_gradients(tower_grads) # See: https://stackoverflow.com/questions/40701712/how-to-check-nan-in- # gradients-in-tensorflow-when-updating grad_check = ([tf.check_numerics(g, message='Gradient NaN Found!') for g, _ in grads if g is not None] + [tf.check_numerics(loss, message='Loss NaN Found')]) # Apply the gradients to adjust the shared variables with tf.control_dependencies(grad_check): update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): train_op = opt.apply_gradients(grads, global_step=global_step) # Calculate mean loss loss = tf.reduce_mean(tower_losses) # Calculate accuracy logits = tf.concat(tower_logits, axis=0) target_labels = tf.concat(tower_target_labels, axis=0) acc = met.accuracy(logits, target_labels) # Prepare predictions and one-hot labels probs = tf.nn.softmax(logits=logits) labels_oh = tf.one_hot(batch_labels, num_classes) # Group metrics together # See: https://cs230-stanford.github.io/tensorflow-model.html trn_metrics = {'loss' : loss, 'labels' : batch_labels, 'labels_oh' : labels_oh, 'logits' : logits, 'probs' : probs, 'acc' : acc, } # Reset and read operations for streaming metrics go here trn_reset = {} trn_read = {} # Logging tf.summary.scalar('batch_loss', loss) tf.summary.scalar('batch_success_rate', acc) # Set Saver # AG 26/09/2018: Save all variables including Adam so that we can continue # training from where we left off # max_to_keep=None should keep all checkpoints saver = tf.train.Saver(tf.global_variables(), max_to_keep=None) # Display number of parameters train_params = np.sum([np.prod(v.get_shape().as_list()) for v in tf.trainable_variables()]).astype(np.int32) logger.info('Trainable Parameters: {}'.format(train_params)) # Set summary op trn_summary = tf.summary.merge_all() #---------------------------------------------------------------------------- # GRAPH - TRAINING SET ACCURACY #---------------------------------------------------------------------------- logger.info('BUILD TRAINING SET ACCURACY GRAPH') g_trn_acc = tf.Graph() with g_trn_acc.as_default(): # Get global_step global_step = tf.train.get_or_create_global_step() # Get data input_dict = create_inputs_train_wholeset() batch_x = input_dict['image'] batch_labels = input_dict['label'] # AG 10/12/2018: Split batch for multi gpu implementation # Each split is of size FLAGS.batch_size / FLAGS.num_gpus # See: https://github.com/naturomics/CapsNet- # Tensorflow/blob/master/dist_version/distributed_train.py splits_x = tf.split( axis=0, num_or_size_splits=FLAGS.num_gpus, value=batch_x) splits_labels = tf.split( axis=0, num_or_size_splits=FLAGS.num_gpus, value=batch_labels) #-------------------------------------------------------------------------- # MULTI GPU - TRAINING SET ACCURACY #-------------------------------------------------------------------------- # Calculate the logits for each model tower tower_logits = [] tower_target_labels = [] reuse_variables = None for i in range(FLAGS.num_gpus): with tf.device('/gpu:%d' % i): with tf.name_scope('tower_%d' % i) as scope: with slim.arg_scope([slim.variable], device='/cpu:0'): loss, logits, x, patch, target_labels = tower_fn( build_arch, splits_x[i], splits_labels[i], scope, num_classes, reuse_variables=reuse_variables, is_train=False) # Don't reuse variable for first GPU, but do reuse for others reuse_variables = True # Keep track of losses and logits across for each tower tower_logits.append(logits) tower_target_labels.append(target_labels) # Loss for each tower tf.summary.histogram("train_set_logits", logits) # Combine logits from all towers logits = tf.concat(tower_logits, axis=0) target_labels = tf.concat(tower_target_labels, axis=0) # Calculate metrics train_set_loss = mod.spread_loss(logits, target_labels) train_set_acc = met.accuracy(logits, target_labels) # Prepare predictions and one-hot labels train_set_probs = tf.nn.softmax(logits=logits) train_set_labels_oh = tf.one_hot(batch_labels, num_classes) # Group metrics together # See: https://cs230-stanford.github.io/tensorflow-model.html train_set_metrics = {'loss' : train_set_loss, 'labels' : batch_labels, 'labels_oh' : train_set_labels_oh, 'logits' : logits, 'probs' : train_set_probs, 'acc' : train_set_acc, } # Reset and read operations for streaming metrics go here train_set_reset = {} train_set_read = {} saver = tf.train.Saver(max_to_keep=None) tf.summary.scalar("train_set_loss", train_set_loss) tf.summary.scalar("train_set_success_rate", train_set_acc) trn_acc_summary = tf.summary.merge_all() if dataset_size_val > 0: #---------------------------------------------------------------------------- # GRAPH - VALIDATION #---------------------------------------------------------------------------- logger.info('BUILD VALIDATION GRAPH') g_val = tf.Graph() with g_val.as_default(): # Get global_step global_step = tf.train.get_or_create_global_step() num_batches_val = int(dataset_size_val / FLAGS.batch_size) # Get data input_dict = create_inputs_val() batch_x = input_dict['image'] batch_labels = input_dict['label'] # AG 10/12/2018: Split batch for multi gpu implementation # Each split is of size FLAGS.batch_size / FLAGS.num_gpus # See: https://github.com/naturomics/CapsNet- # Tensorflow/blob/master/dist_version/distributed_train.py splits_x = tf.split( axis=0, num_or_size_splits=FLAGS.num_gpus, value=batch_x) splits_labels = tf.split( axis=0, num_or_size_splits=FLAGS.num_gpus, value=batch_labels) #-------------------------------------------------------------------------- # MULTI GPU - VALIDATE #-------------------------------------------------------------------------- # Calculate the logits for each model tower tower_logits = [] tower_target_labels = [] reuse_variables = None for i in range(FLAGS.num_gpus): with tf.device('/gpu:%d' % i): with tf.name_scope('tower_%d' % i) as scope: with slim.arg_scope([slim.variable], device='/cpu:0'): loss, logits, x, patch, target_labels = tower_fn( build_arch, splits_x[i], splits_labels[i], scope, num_classes, reuse_variables=reuse_variables, is_train=False) # Don't reuse variable for first GPU, but do reuse for others reuse_variables = True # Keep track of losses and logits across for each tower tower_logits.append(logits) tower_target_labels.append(target_labels) # Loss for each tower tf.summary.histogram("val_logits", logits) # take patch and patched images from last tower val_patch = patch val_x = x # Combine logits from all towers logits = tf.concat(tower_logits, axis=0) target_labels = tf.concat(tower_target_labels, axis=0) # Calculate metrics val_loss = mod.spread_loss(logits, target_labels) val_acc = met.accuracy(logits, target_labels) # Prepare predictions and one-hot labels val_probs = tf.nn.softmax(logits=logits) val_labels_oh = tf.one_hot(batch_labels, num_classes) # Group metrics together # See: https://cs230-stanford.github.io/tensorflow-model.html val_metrics = {'loss' : val_loss, 'labels' : batch_labels, 'labels_oh' : val_labels_oh, 'logits' : logits, 'probs' : val_probs, 'acc' : val_acc, } val_images = {'patch' : val_patch, 'x' : val_x} # Reset and read operations for streaming metrics go here val_reset = {} val_read = {} tf.summary.scalar("val_loss", val_loss) tf.summary.scalar("val_success_rate", val_acc) # Saver saver = tf.train.Saver(max_to_keep=1) # Set summary op val_summary = tf.summary.merge_all() #**************************************************************************** # 2. SESSIONS #**************************************************************************** #----- SESSION TRAIN -----# # Session settings #sess_train = tf.Session(config=tf.ConfigProto(allow_soft_placement=True, # log_device_placement=False), # graph=g_train) # Perry: added in for RTX 2070 incompatibility workaround config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False) config.gpu_options.allow_growth = True sess_train = tf.Session(config=config, graph=g_train) # Debugger # AG 05/06/2018: Debugging using either command line or TensorBoard if FLAGS.debugger is not None: # sess = tf_debug.LocalCLIDebugWrapperSession(sess) sess_train = tf_debug.TensorBoardDebugWrapperSession(sess_train, FLAGS.debugger) with g_train.as_default(): sess_train.run([tf.global_variables_initializer(), tf.local_variables_initializer()]) # Restore previous checkpoint # AG 26/09/2018: where should this go??? if FLAGS.load_dir is not None: prev_step = load_training(saver, sess_train, FLAGS.load_dir, opt) else: prev_step = 0 # Create summary writer, and write the train graph summary_writer = tf.summary.FileWriter(train_summary_dir, graph=sess_train.graph) #----- SESSION TRAIN SET ACCURACY -----# #sess_val = tf.Session(config=tf.ConfigProto(allow_soft_placement=True, # log_device_placement=False), # graph=g_val) # Perry: added in for RTX 2070 incompatibility workaround config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False) config.gpu_options.allow_growth = True sess_train_acc = tf.Session(config=config, graph=g_trn_acc) with g_trn_acc.as_default(): sess_train_acc.run([tf.local_variables_initializer(), tf.global_variables_initializer()]) if dataset_size_val > 0: #----- SESSION VALIDATION -----# #sess_val = tf.Session(config=tf.ConfigProto(allow_soft_placement=True, # log_device_placement=False), # graph=g_val) # Perry: added in for RTX 2070 incompatibility workaround config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False) config.gpu_options.allow_growth = True sess_val = tf.Session(config=config, graph=g_val) with g_val.as_default(): sess_val.run([tf.local_variables_initializer(), tf.global_variables_initializer()]) #**************************************************************************** # 3. MAIN LOOP #**************************************************************************** SUMMARY_FREQ = 100 SAVE_MODEL_FREQ = num_batches_per_epoch # 500 VAL_FREQ = num_batches_per_epoch # 500 PROFILE_FREQ = 5 #print("starting main loop") for step in range(prev_step, FLAGS.epoch * num_batches_per_epoch + 1): #print("looping") #for step in range(0,3): # AG 23/05/2018: limit number of iterations for testing # for step in range(100): epoch_decimal = step/num_batches_per_epoch epoch = int(np.floor(epoch_decimal)) # TF queue would pop batch until no file try: # TRAIN with g_train.as_default(): # With profiling if (FLAGS.profile is True) and ((step % PROFILE_FREQ) == 0): logger.info("Train with Profiling") run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) run_metadata = tf.RunMetadata() # Without profiling else: run_options = None run_metadata = None # Reset streaming metrics if step % (num_batches_per_epoch/4) == 1: logger.info("Reset streaming metrics") sess_train.run([trn_reset]) # MAIN RUN tic = time.time() train_op_v, trn_metrics_v, trn_summary_v = sess_train.run( [train_op, trn_metrics, trn_summary], options=run_options, run_metadata=run_metadata) toc = time.time() # Read streaming metrics trn_read_v = sess_train.run(trn_read) # Write summary for profiling if run_options is not None: summary_writer.add_run_metadata( run_metadata, 'epoch{:f}'.format(epoch_decimal)) # Logging #logger.info('TRN' # + ' e-{:d}'.format(epoch) # + ' stp-{:d}'.format(step) # ) # + ' {:.2f}s'.format(toc - tic) # + ' loss: {:.4f}'.format(trn_metrics_v['loss']) # + ' acc: {:.2f}%'.format(trn_metrics_v['acc']*100) # ) except KeyboardInterrupt: sess_train.close() sess_val.close() sys.exit() except tf.errors.InvalidArgumentError as e: logger.warning('%d iteration contains NaN gradients. Discard.' % step) logger.error(str(e)) continue else: # WRITE SUMMARY if (step % SUMMARY_FREQ) == 0: logger.info("Write Train Summary") with g_train.as_default(): # Summaries from graph summary_writer.add_summary(trn_summary_v, step) # SAVE MODEL if (step % SAVE_MODEL_FREQ) == 0: logger.info("Save Model") with g_train.as_default(): train_checkpoint_dir = train_dir + '/checkpoint' if not os.path.exists(train_checkpoint_dir): os.makedirs(train_checkpoint_dir) # Save ckpt from train session ckpt_path = os.path.join(train_checkpoint_dir, 'model.ckpt' + str(epoch)) saver.save(sess_train, ckpt_path, global_step=step) if (step % VAL_FREQ) == 0: # calculate metrics every epoch with g_trn_acc.as_default(): logger.info("Start Train Set Accuracy") # Restore ckpt to val session latest_ckpt = tf.train.latest_checkpoint(train_checkpoint_dir) saver.restore(sess_train_acc, latest_ckpt) # Reset accumulators accuracy_sum = 0 loss_sum = 0 sess_train_acc.run(train_set_reset) for i in range(num_batches_per_epoch): train_set_metrics_v, train_set_summary_str_v = sess_train_acc.run( [train_set_metrics, trn_acc_summary]) # Update accuracy_sum += train_set_metrics_v['acc'] loss_sum += train_set_metrics_v['loss'] # Read trn_read_v = sess_train_acc.run(val_read) # Get checkpoint number ckpt_num = re.split('-', latest_ckpt)[-1] # Average across batches ave_acc = accuracy_sum / num_batches_per_epoch ave_loss = loss_sum / num_batches_per_epoch logger.info('TRN ckpt-{}'.format(ckpt_num) + ' avg_success: {:.2f}%'.format(ave_acc*100) + ' avg_loss: {:.4f}'.format(ave_loss) ) logger.info("Write Train Summary") summary_train = tf.Summary() summary_train.value.add(tag="trn_success", simple_value=ave_acc) summary_train.value.add(tag="trn_loss", simple_value=ave_loss) summary_writer.add_summary(summary_train, epoch) if dataset_size_val > 0: #----- Validation -----# with g_val.as_default(): logger.info("Start Validation") # Restore ckpt to val session latest_ckpt = tf.train.latest_checkpoint(train_checkpoint_dir) saver.restore(sess_val, latest_ckpt) # Reset accumulators accuracy_sum = 0 loss_sum = 0 sess_val.run(val_reset) for i in range(num_batches_val): if i == num_batches_val - 1: # take a sample of patched images on the last validation batch val_metrics_v, val_summary_str_v, val_images_v = sess_val.run( [val_metrics, val_summary, val_images]) x = val_images_v['x'] patch = val_images_v['patch'] else: val_metrics_v, val_summary_str_v = sess_val.run( [val_metrics, val_summary]) # Update accuracy_sum += val_metrics_v['acc'] loss_sum += val_metrics_v['loss'] # Read val_read_v = sess_val.run(val_read) # Get checkpoint number ckpt_num = re.split('-', latest_ckpt)[-1] # Logging #logger.info('VAL ckpt-{}'.format(ckpt_num) # + ' bch-{:d}'.format(i) # + ' cum_acc: {:.2f}%'.format(accuracy_sum/(i+1)*100) # + ' cum_loss: {:.4f}'.format(loss_sum/(i+1)) # ) # Average across batches ave_acc = accuracy_sum / num_batches_val ave_loss = loss_sum / num_batches_val logger.info('VAL ckpt-{}'.format(ckpt_num) + ' avg_success: {:.2f}%'.format(ave_acc*100) + ' avg_loss: {:.4f}'.format(ave_loss) ) logger.info("Write Val Summary") summary_val = tf.Summary() summary_val.value.add(tag="val_success", simple_value=ave_acc) summary_val.value.add(tag="val_loss", simple_value=ave_loss) summary_writer.add_summary(summary_val, epoch) log_images(summary_writer, "patch", [patch], epoch) log_images(summary_writer, "patched_input", x, epoch) if patch.shape[-1] == 1: patch = np.squeeze(patch, axis=-1) formatted = (patch * 255).astype('uint8') img = Image.fromarray(formatted) img.save(os.path.join(train_dir, "saved_patch.png")) # Close (main loop) sess_train.close() sess_val.close() sys.exit()
def main(args): # Set reproduciable random seed tf.set_random_seed(1234) # Directories # Get name split = FLAGS.load_dir.split('/') if split[-1]: name = split[-1] else: name = split[-2] # Get parent directory split = FLAGS.load_dir.split("/" + name) parent_dir = split[0] test_dir = '{}/{}/reconstructions'.format(parent_dir, name) test_summary_dir = test_dir + '/summary' # Clear the test log directory if (FLAGS.reset is True) and os.path.exists(test_dir): shutil.rmtree(test_dir) if not os.path.exists(test_summary_dir): os.makedirs(test_summary_dir) # Logger conf.setup_logger(logger_dir=test_dir, name="logger_test.txt") logger.info("name: " + name) logger.info("parent_dir: " + parent_dir) logger.info("test_dir: " + test_dir) # Load hyperparameters from train run conf.load_or_save_hyperparams() # Get dataset hyperparameters logger.info('Using dataset: {}'.format(FLAGS.dataset)) # Dataset dataset_size_test = conf.get_dataset_size_test(FLAGS.dataset) num_classes = conf.get_num_classes(FLAGS.dataset) # train mode for random sampling create_inputs_test = conf.get_create_inputs(FLAGS.dataset, mode="train") # ---------------------------------------------------------------------------- # GRAPH - TEST # ---------------------------------------------------------------------------- logger.info('BUILD TEST GRAPH') g_test = tf.Graph() with g_test.as_default(): tf.train.get_or_create_global_step() # Get data input_dict = create_inputs_test() batch_x = input_dict['image'] batch_labels = input_dict['label'] # Build architecture build_arch = conf.get_dataset_architecture(FLAGS.dataset) # for baseline # build_arch = conf.get_dataset_architecture('baseline') # -------------------------------------------------------------------------- # MULTI GPU - TEST # -------------------------------------------------------------------------- # Calculate the logits for each model tower with tf.device('/gpu:0'): with tf.name_scope('tower_0') as scope: with slim.arg_scope([slim.variable], device='/cpu:0'): loss, logits, recon, cf_recon = tower_fn( build_arch, batch_x, batch_labels, scope, num_classes, reuse_variables=tf.AUTO_REUSE, is_train=False) # Keep track of losses and logits across for each tower recon_images = tf.reshape(recon, batch_x.get_shape()) cf_recon_images = tf.reshape(cf_recon, batch_x.get_shape()) images = { "reconstructed_images": recon_images, "reconstructed_images_zeroed_background": cf_recon_images, "input": batch_x } saver = tf.train.Saver(max_to_keep=None) # -------------------------------------------------------------------------- # SESSION - TEST # -------------------------------------------------------------------------- # sess_test = tf.Session( # config=tf.ConfigProto(allow_soft_placement=True, # log_device_placement=False), # graph=g_test) # Perry: added in for RTX 2070 incompatibility workaround config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False) config.gpu_options.allow_growth = True sess_test = tf.Session(config=config, graph=g_test) # sess_test.run(tf.local_variables_initializer()) # sess_test.run(tf.global_variables_initializer()) summary_writer = tf.summary.FileWriter(test_summary_dir, graph=sess_test.graph) ckpts_to_test = [] load_dir_chechpoint = os.path.join(FLAGS.load_dir, "train", "checkpoint") # Evaluate the latest ckpt in dir if FLAGS.ckpt_name is None: latest_ckpt = tf.train.latest_checkpoint(load_dir_chechpoint) ckpts_to_test.append(latest_ckpt) # Evaluate all ckpts in dir else: ckpt_name = os.path.join(load_dir_chechpoint, FLAGS.ckpt_name) ckpts_to_test.append(ckpt_name) # -------------------------------------------------------------------------- # MAIN LOOP # -------------------------------------------------------------------------- # Run testing on checkpoints for ckpt in ckpts_to_test: saver.restore(sess_test, ckpt) for i in range(dataset_size_test): out = sess_test.run([images]) reconstructed_image, reconstructed_image_zeroed_background, input_img =\ out[0]["reconstructed_images"], out[0]["reconstructed_images_zeroed_background"], out[0]["input"] if reconstructed_image.shape[0] == 1: reconstructed_image = np.squeeze(reconstructed_image, axis=0) reconstructed_image_zeroed_background = np.squeeze( reconstructed_image_zeroed_background, axis=0) input_img = np.squeeze(input_img, axis=0) if reconstructed_image.shape[-1] == 1: reconstructed_image = np.squeeze(reconstructed_image, axis=-1) reconstructed_image_zeroed_background = np.squeeze( reconstructed_image_zeroed_background, axis=-1) input_img = np.squeeze(input_img, axis=-1) reconstructed_image = Image.fromarray( (reconstructed_image * 255).astype('uint8')) reconstructed_image_zeroed_background = Image.fromarray( (reconstructed_image_zeroed_background * 255).astype('uint8')) input_img = Image.fromarray((input_img * 255).astype('uint8')) fig = plt.figure(figsize=(1, 3)) fig.add_subplot(1, 3, 1) plt.imshow(input_img) fig.add_subplot(1, 3, 2) plt.imshow(reconstructed_image) fig.add_subplot(1, 3, 3) plt.imshow(reconstructed_image_zeroed_background) plt.show()