def main(unused_argv): tf.logging.set_verbosity(tf.logging.INFO) dataset = data_generator.Dataset( dataset_name=FLAGS.dataset, split_name=FLAGS.eval_split, dataset_dir=FLAGS.dataset_dir, batch_size=FLAGS.eval_batch_size, crop_size=[int(sz) for sz in FLAGS.eval_crop_size], min_resize_value=FLAGS.min_resize_value, max_resize_value=FLAGS.max_resize_value, resize_factor=FLAGS.resize_factor, model_variant=FLAGS.model_variant, num_readers=2, is_training=False, should_shuffle=False, should_repeat=False, with_cls=True, cls_only=False, output_valid=True) tf.gfile.MakeDirs(FLAGS.eval_logdir) tf.logging.info('Evaluating on %s set', FLAGS.eval_split) with tf.Graph().as_default(): samples = dataset.get_one_shot_iterator().get_next() model_options = common.ModelOptions( outputs_to_num_classes={ common.OUTPUT_TYPE: dataset.num_of_classes }, crop_size=[int(sz) for sz in FLAGS.eval_crop_size], atrous_rates=FLAGS.atrous_rates, output_stride=FLAGS.output_stride) # Set shape in order for tf.contrib.tfprof.model_analyzer to work properly. samples[common.IMAGE].set_shape([ FLAGS.eval_batch_size, int(FLAGS.eval_crop_size[0]), int(FLAGS.eval_crop_size[1]), 3 ]) if tuple(FLAGS.eval_scales) == (1.0, ): tf.logging.info('Performing single-scale test.') predictions = model.predict_labels( samples[common.IMAGE], model_options, image_pyramid=FLAGS.image_pyramid) else: tf.logging.info('Performing multi-scale test.') raise NotImplementedError('Multi-scale is not supported yet!') metric_map = {} ## Extract cls logits if FLAGS.weakly: _, end_points = feature_extractor.extract_features( samples[common.IMAGE], output_stride=model_options.output_stride, multi_grid=model_options.multi_grid, model_variant=model_options.model_variant, depth_multiplier=model_options.depth_multiplier, divisible_by=model_options.divisible_by, reuse=tf.AUTO_REUSE, is_training=False, preprocessed_images_dtype=model_options. preprocessed_images_dtype, global_pool=True, num_classes=dataset.num_of_classes - 1) # ResNet beta version has an additional suffix in FLAGS.model_variant, but # it shares the same variable names with original version. Add a special # handling here for beta version ResNet. logits = end_points['{}/logits'.format( FLAGS.model_variant).replace('_beta', '')] logits = tf.reshape(logits, [-1, dataset.num_of_classes - 1]) cls_pred = tf.sigmoid(logits) # Multi-label classification evaluation cls_label = samples['cls_label'] cls_pred = tf.cast(tf.greater_equal(cls_pred, 0.5), tf.int32) ## For classification metric_map['eval/cls_overall'] = tf.metrics.accuracy( labels=cls_label, predictions=cls_pred) metric_map['eval/cls_precision'] = tf.metrics.precision( labels=cls_label, predictions=cls_pred) metric_map['eval/cls_recall'] = tf.metrics.recall( labels=cls_label, predictions=cls_pred) ## For segmentation branch eval predictions = predictions[common.OUTPUT_TYPE] predictions = tf.reshape(predictions, shape=[-1]) labels = tf.reshape(samples[common.LABEL], shape=[-1]) weights = tf.to_float(tf.not_equal(labels, dataset.ignore_label)) # Set ignore_label regions to label 0, because metrics.mean_iou requires # range of labels = [0, dataset.num_classes). Note the ignore_label regions # are not evaluated since the corresponding regions contain weights = 0. labels = tf.where(tf.equal(labels, dataset.ignore_label), tf.zeros_like(labels), labels) predictions_tag = 'miou' # Define the evaluation metric. num_classes = dataset.num_of_classes ## For segmentation metric_map['eval/%s_overall' % predictions_tag] = tf.metrics.mean_iou( labels=labels, predictions=predictions, num_classes=num_classes, weights=weights) # IoU for each class. one_hot_predictions = tf.one_hot(predictions, num_classes) one_hot_predictions = tf.reshape(one_hot_predictions, [-1, num_classes]) one_hot_labels = tf.one_hot(labels, num_classes) one_hot_labels = tf.reshape(one_hot_labels, [-1, num_classes]) for c in range(num_classes): predictions_tag_c = '%s_class_%d' % (predictions_tag, c) tp, tp_op = tf.metrics.true_positives( labels=one_hot_labels[:, c], predictions=one_hot_predictions[:, c], weights=weights) fp, fp_op = tf.metrics.false_positives( labels=one_hot_labels[:, c], predictions=one_hot_predictions[:, c], weights=weights) fn, fn_op = tf.metrics.false_negatives( labels=one_hot_labels[:, c], predictions=one_hot_predictions[:, c], weights=weights) tp_fp_fn_op = tf.group(tp_op, fp_op, fn_op) iou = tf.where(tf.greater(tp + fn, 0.0), tp / (tp + fn + fp), tf.constant(np.NaN)) metric_map['eval/%s' % predictions_tag_c] = (iou, tp_fp_fn_op) (metrics_to_values, metrics_to_updates) = contrib_metrics.aggregate_metric_map(metric_map) summary_ops = [] for metric_name, metric_value in six.iteritems(metrics_to_values): op = tf.summary.scalar(metric_name, metric_value) op = tf.Print(op, [metric_value], metric_name) summary_ops.append(op) summary_op = tf.summary.merge(summary_ops) summary_hook = contrib_training.SummaryAtEndHook( log_dir=FLAGS.eval_logdir, summary_op=summary_op) hooks = [summary_hook] num_eval_iters = None if FLAGS.max_number_of_evaluations > 0: num_eval_iters = FLAGS.max_number_of_evaluations if FLAGS.quantize_delay_step >= 0: contrib_quantize.create_eval_graph() contrib_tfprof.model_analyzer.print_model_analysis( tf.get_default_graph(), tfprof_options=contrib_tfprof.model_analyzer. TRAINABLE_VARS_PARAMS_STAT_OPTIONS) contrib_tfprof.model_analyzer.print_model_analysis( tf.get_default_graph(), tfprof_options=contrib_tfprof.model_analyzer.FLOAT_OPS_OPTIONS) contrib_training.evaluate_repeatedly( checkpoint_dir=FLAGS.checkpoint_dir, master=FLAGS.master, eval_ops=list(metrics_to_updates.values()), max_number_of_evaluations=num_eval_iters, hooks=hooks, eval_interval_secs=FLAGS.eval_interval_secs)
def main(unused_argv): tf.logging.set_verbosity(tf.logging.INFO) tf.set_random_seed(FLAGS.seed) # Set up deployment (i.e., multi-GPUs and/or multi-replicas). config = model_deploy.DeploymentConfig(num_clones=FLAGS.num_clones, clone_on_cpu=FLAGS.clone_on_cpu, replica_id=FLAGS.task, num_replicas=FLAGS.num_replicas, num_ps_tasks=FLAGS.num_ps_tasks) # Split the batch across GPUs. assert FLAGS.train_batch_size % config.num_clones == 0, ( 'Training batch size not divisble by number of clones (GPUs).') clone_batch_size = FLAGS.train_batch_size // config.num_clones tf.gfile.MakeDirs(FLAGS.train_logdir) tf.logging.info('Training segmentation and self-attention on %s set', FLAGS.train_split) if FLAGS.weakly: tf.logging.info('Training classification on %s set', FLAGS.train_split_cls) else: tf.logging.info('Training classification on %s set', FLAGS.train_split) tf.logging.info('Enforcing consistency constraint on %s set', FLAGS.train_split_cls) with tf.Graph().as_default() as graph: with tf.device(config.inputs_device()): dataset = data_generator.Dataset( dataset_name=FLAGS.dataset, split_name=FLAGS.train_split, dataset_dir=FLAGS.dataset_dir, batch_size=clone_batch_size, crop_size=[int(sz) for sz in FLAGS.train_crop_size], min_resize_value=FLAGS.min_resize_value, max_resize_value=FLAGS.max_resize_value, resize_factor=FLAGS.resize_factor, min_scale_factor=FLAGS.min_scale_factor, max_scale_factor=FLAGS.max_scale_factor, scale_factor_step_size=FLAGS.scale_factor_step_size, model_variant=FLAGS.model_variant, num_readers=4, is_training=True, should_shuffle=True, should_repeat=True, output_valid=True, with_cls=True, cls_only=False) dataset_cls = data_generator.Dataset( dataset_name=FLAGS.dataset, split_name=FLAGS.train_split_cls, dataset_dir=FLAGS.dataset_dir, batch_size=clone_batch_size, crop_size=[int(sz) for sz in FLAGS.train_crop_size], min_resize_value=FLAGS.min_resize_value, max_resize_value=FLAGS.max_resize_value, resize_factor=FLAGS.resize_factor, min_scale_factor=FLAGS.min_scale_factor, max_scale_factor=FLAGS.max_scale_factor, scale_factor_step_size=FLAGS.scale_factor_step_size, model_variant=FLAGS.model_variant, num_readers=4, is_training=True, should_shuffle=True, should_repeat=True, with_cls=FLAGS.weakly, cls_only=False, strong_weak=True) # Create the global step on the device storing the variables. with tf.device(config.variables_device()): global_step = tf.train.get_or_create_global_step() # Define the model and create clones. model_fn = _build_pseudo_seg model_args = (dataset.get_one_shot_iterator(), dataset_cls.get_one_shot_iterator(), { common.OUTPUT_TYPE: dataset.num_of_classes }, dataset.ignore_label, clone_batch_size) clones = model_deploy.create_clones(config, model_fn, args=model_args) # Gather update_ops from the first clone. These contain, for example, # the updates for the batch_norm variables created by model_fn. first_clone_scope = config.clone_scope(0) update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, first_clone_scope) # Gather initial summaries. summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES)) # Add summaries for model variables. for model_var in tf.model_variables(): summaries.add(tf.summary.histogram(model_var.op.name, model_var)) if FLAGS.use_attention: summary = graph.get_tensor_by_name( ('%s/%s:0' % (first_clone_scope, 'max_prob_weak')).strip('/')) summaries.add(tf.summary.histogram('max_prob_weak', summary)) summary = graph.get_tensor_by_name( ('%s/%s:0' % (first_clone_scope, 'max_att_prob_weak')).strip('/')) summaries.add(tf.summary.histogram('max_att_prob_weak', summary)) summary = graph.get_tensor_by_name( ('%s/%s:0' % (first_clone_scope, 'max_prob_avg')).strip('/')) summaries.add(tf.summary.histogram('max_prob_avg', summary)) if FLAGS.soft_pseudo_label and FLAGS.temperature != 1.0: summary = graph.get_tensor_by_name( ('%s/%s:0' % (first_clone_scope, 'max_prob_avg_t')).strip('/')) summaries.add(tf.summary.histogram('max_prob_avg_t', summary)) # Add summaries for images, labels, semantic predictions # Visualize seg image and predictions if FLAGS.save_summaries_images: summary_image = graph.get_tensor_by_name( ('%s/%s:0' % (first_clone_scope, common.IMAGE + '_seg')).strip('/')) summaries.add( tf.summary.image('samples/%s' % common.IMAGE + '_seg', summary_image)) first_clone_label = graph.get_tensor_by_name( ('%s/%s:0' % (first_clone_scope, common.LABEL + '_seg')).strip('/')) # Scale up summary image pixel values for better visualization. pixel_scaling = max(1, 255 // dataset.num_of_classes) summary_label = tf.cast(first_clone_label * pixel_scaling, tf.uint8) summaries.add( tf.summary.image('samples/%s' % common.LABEL + '_seg', summary_label)) first_clone_output = graph.get_tensor_by_name( ('%s/%s:0' % (first_clone_scope, common.OUTPUT_TYPE + '_seg')).strip('/')) predictions = tf.expand_dims(tf.argmax(first_clone_output, 3), -1) summary_predictions = tf.cast(predictions * pixel_scaling, tf.uint8) summaries.add( tf.summary.image('samples/%s' % common.OUTPUT_TYPE + '_seg', summary_predictions)) # For unlabeled image summary_image = graph.get_tensor_by_name( ('%s/%s:0' % (first_clone_scope, 'valid_mask')).strip('/')) summaries.add( tf.summary.image('sanity_check/valid_mask', summary_image)) summary_image = graph.get_tensor_by_name( ('%s/%s:0' % (first_clone_scope, 'weak')).strip('/')) summaries.add(tf.summary.image('unlabeled/weak', summary_image)) summary_image = graph.get_tensor_by_name( ('%s/%s:0' % (first_clone_scope, 'strong')).strip('/')) summaries.add(tf.summary.image('unlabeled/strong', summary_image)) first_clone_label = graph.get_tensor_by_name( ('%s/%s:0' % (first_clone_scope, 'unlabeled')).strip('/')) pixel_scaling = max(1, 255 // dataset.num_of_classes) summary_label = tf.cast(first_clone_label * pixel_scaling, tf.uint8) summaries.add( tf.summary.image('unlabeled/%s' % common.LABEL, summary_label)) first_clone_output = graph.get_tensor_by_name( ('%s/%s:0' % (first_clone_scope, 'logits_weak')).strip('/')) predictions = tf.expand_dims(tf.argmax(first_clone_output, 3), -1) summary_predictions = tf.cast(predictions * pixel_scaling, tf.uint8) summaries.add( tf.summary.image('unlabeled/logits_weak', summary_predictions)) first_clone_output = graph.get_tensor_by_name( ('%s/%s:0' % (first_clone_scope, 'logits_strong')).strip('/')) predictions = tf.expand_dims(tf.argmax(first_clone_output, 3), -1) summary_predictions = tf.cast(predictions * pixel_scaling, tf.uint8) summaries.add( tf.summary.image('unlabeled/logits_strong', summary_predictions)) if FLAGS.use_attention: first_clone_output = graph.get_tensor_by_name( ('%s/%s:0' % (first_clone_scope, 'att_logits_weak')).strip('/')) predictions = tf.expand_dims(tf.argmax(first_clone_output, 3), -1) predictions = tf.compat.v1.image.resize_bilinear( predictions, [int(sz) for sz in FLAGS.train_crop_size], align_corners=True) summary_predictions = tf.cast(predictions * pixel_scaling, tf.uint8) summaries.add( tf.summary.image('att/att_logits_weak', summary_predictions)) first_clone_output = graph.get_tensor_by_name( ('%s/%s:0' % (first_clone_scope, 'cam_weak')).strip('/')) predictions = tf.expand_dims(tf.argmax(first_clone_output, 3), -1) predictions = tf.compat.v1.image.resize_bilinear( predictions, [int(sz) for sz in FLAGS.train_crop_size], align_corners=True) summary_predictions = tf.cast(predictions * pixel_scaling, tf.uint8) summaries.add( tf.summary.image('att/cam_weak', summary_predictions)) first_clone_output = graph.get_tensor_by_name( ('%s/%s:0' % (first_clone_scope, 'merged_logits')).strip('/')) predictions = tf.expand_dims(tf.argmax(first_clone_output, 3), -1) predictions = tf.compat.v1.image.resize_bilinear( predictions, [int(sz) for sz in FLAGS.train_crop_size], align_corners=True) summary_predictions = tf.cast(predictions * pixel_scaling, tf.uint8) summaries.add( tf.summary.image('att/merged_logits', summary_predictions)) first_clone_output = graph.get_tensor_by_name( ('%s/%s:0' % (first_clone_scope, 'att_logits_labeled')).strip('/')) predictions = tf.expand_dims(tf.argmax(first_clone_output, 3), -1) predictions = tf.compat.v1.image.resize_bilinear( predictions, [int(sz) for sz in FLAGS.train_crop_size], align_corners=True) summary_predictions = tf.cast(predictions * pixel_scaling, tf.uint8) summaries.add( tf.summary.image('att/att_logits_labeled', summary_predictions)) # Add summaries for losses. for loss in tf.get_collection(tf.GraphKeys.LOSSES, first_clone_scope): summaries.add(tf.summary.scalar('losses/%s' % loss.op.name, loss)) # Monitor pseudo label quality summary = graph.get_tensor_by_name( ('%s/%s:0' % (first_clone_scope, 'acc_seg')).strip('/')) summaries.add(tf.summary.scalar('sanity_check/acc_seg', summary)) summary = graph.get_tensor_by_name( ('%s/%s:0' % (first_clone_scope, 'acc_weak')).strip('/')) summaries.add(tf.summary.scalar('sanity_check/acc_weak', summary)) summary = graph.get_tensor_by_name( ('%s/%s:0' % (first_clone_scope, 'acc_strong')).strip('/')) summaries.add(tf.summary.scalar('sanity_check/acc_strong', summary)) if FLAGS.pseudo_label_threshold > 0: summary = graph.get_tensor_by_name( ('%s/%s:0' % (first_clone_scope, 'acc_pseudo')).strip('/')) summaries.add(tf.summary.scalar('sanity_check/acc_pseudo', summary)) summary = graph.get_tensor_by_name( ('%s/%s:0' % (first_clone_scope, 'acc_strong_confident')).strip('/')) summaries.add( tf.summary.scalar('sanity_check/acc_strong_confident', summary)) summary = graph.get_tensor_by_name( ('%s/%s:0' % (first_clone_scope, 'valid_ratio')).strip('/')) summaries.add( tf.summary.scalar('sanity_check/valid_ratio', summary)) # Build the optimizer based on the device specification. with tf.device(config.optimizer_device()): learning_rate = train_utils.get_model_learning_rate( FLAGS.learning_policy, FLAGS.base_learning_rate, FLAGS.learning_rate_decay_step, FLAGS.learning_rate_decay_factor, FLAGS.training_number_of_steps, FLAGS.learning_power, FLAGS.slow_start_step, FLAGS.slow_start_learning_rate, decay_steps=FLAGS.decay_steps, end_learning_rate=FLAGS.end_learning_rate) summaries.add(tf.summary.scalar('learning_rate', learning_rate)) if FLAGS.optimizer == 'momentum': optimizer = tf.train.MomentumOptimizer(learning_rate, FLAGS.momentum) elif FLAGS.optimizer == 'adam': optimizer = tf.train.AdamOptimizer( learning_rate=FLAGS.adam_learning_rate, epsilon=FLAGS.adam_epsilon) else: raise ValueError('Unknown optimizer') startup_delay_steps = FLAGS.task * FLAGS.startup_delay_steps with tf.device(config.variables_device()): total_loss, grads_and_vars = model_deploy.optimize_clones( clones, optimizer) total_loss = tf.check_numerics(total_loss, 'Loss is inf or nan.') summaries.add(tf.summary.scalar('total_loss', total_loss)) # Modify the gradients for biases and last layer variables. last_layers = model.get_extra_layer_scopes( FLAGS.last_layers_contain_logits_only) grad_mult = train_utils.get_model_gradient_multipliers( last_layers, FLAGS.last_layer_gradient_multiplier) if grad_mult: grads_and_vars = slim.learning.multiply_gradients( grads_and_vars, grad_mult) # NOTE: Neither last cls nor last seg layer loads pre-trained weights last_layers += [ '{}/logits'.format(FLAGS.model_variant).replace('_beta', '') ] # Create gradient update op. grad_updates = optimizer.apply_gradients(grads_and_vars, global_step=global_step) update_ops.append(grad_updates) update_op = tf.group(*update_ops) with tf.control_dependencies([update_op]): train_tensor = tf.identity(total_loss, name='train_op') # Add the summaries from the first clone. These contain the summaries # created by model_fn and either optimize_clones() or _gather_clone_loss(). summaries |= set( tf.get_collection(tf.GraphKeys.SUMMARIES, first_clone_scope)) # Merge all summaries together. summary_op = tf.summary.merge(list(summaries)) # Soft placement allows placing on CPU ops without GPU implementation. session_config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False) session_config.gpu_options.allow_growth = True # Start the training. profile_dir = FLAGS.profile_logdir if profile_dir is not None: tf.gfile.MakeDirs(profile_dir) with contrib_tfprof.ProfileContext(enabled=profile_dir is not None, profile_dir=profile_dir): init_fn = None if FLAGS.tf_initial_checkpoint: init_fn = train_utils.get_model_init_fn( FLAGS.train_logdir, FLAGS.tf_initial_checkpoint, FLAGS.initialize_last_layer, last_layers, ignore_missing_vars=True) slim.learning.train(train_tensor, logdir=FLAGS.train_logdir, log_every_n_steps=FLAGS.log_steps, master=FLAGS.master, number_of_steps=FLAGS.training_number_of_steps, is_chief=(FLAGS.task == 0), session_config=session_config, startup_delay_steps=startup_delay_steps, init_fn=init_fn, summary_op=summary_op, save_summaries_secs=FLAGS.save_summaries_secs, save_interval_secs=FLAGS.save_interval_secs)
def main(unused_argv): tf.logging.set_verbosity(tf.logging.INFO) # Get dataset-dependent information. dataset = data_generator.Dataset( dataset_name=FLAGS.dataset, split_name=FLAGS.vis_split, dataset_dir=FLAGS.dataset_dir, batch_size=FLAGS.vis_batch_size, crop_size=[int(sz) for sz in FLAGS.vis_crop_size], min_resize_value=FLAGS.min_resize_value, max_resize_value=FLAGS.max_resize_value, resize_factor=FLAGS.resize_factor, model_variant=FLAGS.model_variant, is_training=False, should_shuffle=False, should_repeat=False) train_id_to_eval_id = None if dataset.dataset_name == data_generator.get_cityscapes_dataset_name(): tf.logging.info('Cityscapes requires converting train_id to eval_id.') train_id_to_eval_id = _CITYSCAPES_TRAIN_ID_TO_EVAL_ID # Prepare for visualization. tf.gfile.MakeDirs(FLAGS.vis_logdir) save_dir = os.path.join(FLAGS.vis_logdir, _SEMANTIC_PREDICTION_SAVE_FOLDER) tf.gfile.MakeDirs(save_dir) raw_save_dir = os.path.join(FLAGS.vis_logdir, _RAW_SEMANTIC_PREDICTION_SAVE_FOLDER) tf.gfile.MakeDirs(raw_save_dir) tf.logging.info('Visualizing on %s set', FLAGS.vis_split) with tf.Graph().as_default(): samples = dataset.get_one_shot_iterator().get_next() model_options = common.ModelOptions( outputs_to_num_classes={ common.OUTPUT_TYPE: dataset.num_of_classes }, crop_size=[int(sz) for sz in FLAGS.vis_crop_size], atrous_rates=FLAGS.atrous_rates, output_stride=FLAGS.output_stride) if tuple(FLAGS.eval_scales) == (1.0, ): tf.logging.info('Performing single-scale test.') predictions = model.predict_labels( samples[common.IMAGE], model_options=model_options, image_pyramid=FLAGS.image_pyramid) else: tf.logging.info('Performing multi-scale test.') if FLAGS.quantize_delay_step >= 0: raise ValueError( 'Quantize mode is not supported with multi-scale test.') predictions = model.predict_labels_multi_scale( samples[common.IMAGE], model_options=model_options, eval_scales=FLAGS.eval_scales, add_flipped_images=FLAGS.add_flipped_images) predictions = predictions[common.OUTPUT_TYPE] if FLAGS.min_resize_value and FLAGS.max_resize_value: # Only support batch_size = 1, since we assume the dimensions of original # image after tf.squeeze is [height, width, 3]. assert FLAGS.vis_batch_size == 1 # Reverse the resizing and padding operations performed in preprocessing. # First, we slice the valid regions (i.e., remove padded region) and then # we resize the predictions back. original_image = tf.squeeze(samples[common.ORIGINAL_IMAGE]) original_image_shape = tf.shape(original_image) predictions = tf.slice( predictions, [0, 0, 0], [1, original_image_shape[0], original_image_shape[1]]) resized_shape = tf.to_int32([ tf.squeeze(samples[common.HEIGHT]), tf.squeeze(samples[common.WIDTH]) ]) predictions = tf.squeeze( tf.image.resize_images( tf.expand_dims(predictions, 3), resized_shape, method=tf.image.ResizeMethod.NEAREST_NEIGHBOR, align_corners=True), 3) tf.train.get_or_create_global_step() if FLAGS.quantize_delay_step >= 0: contrib_quantize.create_eval_graph() num_iteration = 0 max_num_iteration = FLAGS.max_number_of_iterations if True: checkpoint_path = FLAGS.checkpoint_dir num_iteration += 1 tf.logging.info('Starting visualization at ' + time.strftime('%Y-%m-%d-%H:%M:%S', time.gmtime())) tf.logging.info('Visualizing with model %s', checkpoint_path) scaffold = tf.train.Scaffold( init_op=tf.global_variables_initializer()) session_creator = tf.train.ChiefSessionCreator( scaffold=scaffold, master=FLAGS.master, checkpoint_filename_with_path=checkpoint_path) with tf.train.MonitoredSession(session_creator=session_creator, hooks=None) as sess: batch = 0 image_id_offset = 0 while not sess.should_stop(): tf.logging.info('Visualizing batch %d', batch + 1) _process_batch( sess=sess, original_images=samples[common.ORIGINAL_IMAGE], semantic_predictions=predictions, image_names=samples[common.IMAGE_NAME], image_heights=samples[common.HEIGHT], image_widths=samples[common.WIDTH], image_id_offset=image_id_offset, save_dir=save_dir, raw_save_dir=raw_save_dir, train_id_to_eval_id=train_id_to_eval_id) image_id_offset += FLAGS.vis_batch_size batch += 1 tf.logging.info('Finished visualization at ' + time.strftime('%Y-%m-%d-%H:%M:%S', time.gmtime()))