# Copyright 2019 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== r"""Constructs model, inputs, and training environment.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import copy import os import time import tensorflow.compat.v1 as tf import tensorflow.compat.v2 as tf2 from object_detection import eval_util from object_detection import inputs from object_detection import model_lib from object_detection.builders import optimizer_builder from object_detection.core import standard_fields as fields from object_detection.protos import train_pb2 from object_detection.utils import config_util from object_detection.utils import label_map_util from object_detection.utils import ops from object_detection.utils import visualization_utils as vutils MODEL_BUILD_UTIL_MAP = model_lib.MODEL_BUILD_UTIL_MAP RESTORE_MAP_ERROR_TEMPLATE = ( 'Since we are restoring a v2 style checkpoint' ' restore_map was expected to return a (str -> Model) mapping,' ' but we received a ({} -> {}) mapping instead.' ) def _compute_losses_and_predictions_dicts( model, features, labels, add_regularization_loss=True): """Computes the losses dict and predictions dict for a model on inputs. Args: model: a DetectionModel (based on Keras). features: Dictionary of feature tensors from the input dataset. Should be in the format output by `inputs.train_input` and `inputs.eval_input`. features[fields.InputDataFields.image] is a [batch_size, H, W, C] float32 tensor with preprocessed images. features[HASH_KEY] is a [batch_size] int32 tensor representing unique identifiers for the images. features[fields.InputDataFields.true_image_shape] is a [batch_size, 3] int32 tensor representing the true image shapes, as preprocessed images could be padded. features[fields.InputDataFields.original_image] (optional) is a [batch_size, H, W, C] float32 tensor with original images. labels: A dictionary of groundtruth tensors post-unstacking. The original labels are of the form returned by `inputs.train_input` and `inputs.eval_input`. The shapes may have been modified by unstacking with `model_lib.unstack_batch`. However, the dictionary includes the following fields. labels[fields.InputDataFields.num_groundtruth_boxes] is a int32 tensor indicating the number of valid groundtruth boxes per image. labels[fields.InputDataFields.groundtruth_boxes] is a float32 tensor containing the corners of the groundtruth boxes. labels[fields.InputDataFields.groundtruth_classes] is a float32 one-hot tensor of classes. labels[fields.InputDataFields.groundtruth_weights] is a float32 tensor containing groundtruth weights for the boxes. -- Optional -- labels[fields.InputDataFields.groundtruth_instance_masks] is a float32 tensor containing only binary values, which represent instance masks for objects. labels[fields.InputDataFields.groundtruth_keypoints] is a float32 tensor containing keypoints for each box. labels[fields.InputDataFields.groundtruth_dp_num_points] is an int32 tensor with the number of sampled DensePose points per object. labels[fields.InputDataFields.groundtruth_dp_part_ids] is an int32 tensor with the DensePose part ids (0-indexed) per object. labels[fields.InputDataFields.groundtruth_dp_surface_coords] is a float32 tensor with the DensePose surface coordinates. labels[fields.InputDataFields.groundtruth_group_of] is a tf.bool tensor containing group_of annotations. labels[fields.InputDataFields.groundtruth_labeled_classes] is a float32 k-hot tensor of classes. labels[fields.InputDataFields.groundtruth_track_ids] is a int32 tensor of track IDs. add_regularization_loss: Whether or not to include the model's regularization loss in the losses dictionary. Returns: A tuple containing the losses dictionary (with the total loss under the key 'Loss/total_loss'), and the predictions dictionary produced by `model.predict`. """ model_lib.provide_groundtruth(model, labels) preprocessed_images = features[fields.InputDataFields.image] prediction_dict = model.predict( preprocessed_images, features[fields.InputDataFields.true_image_shape], **model.get_side_inputs(features)) prediction_dict = ops.bfloat16_to_float32_nested(prediction_dict) losses_dict = model.loss( prediction_dict, features[fields.InputDataFields.true_image_shape]) losses = [loss_tensor for loss_tensor in losses_dict.values()] if add_regularization_loss: # TODO(kaftan): As we figure out mixed precision & bfloat 16, we may ## need to convert these regularization losses from bfloat16 to float32 ## as well. regularization_losses = model.regularization_losses() if regularization_losses: regularization_losses = ops.bfloat16_to_float32_nested( regularization_losses) regularization_loss = tf.add_n( regularization_losses, name='regularization_loss') losses.append(regularization_loss) losses_dict['Loss/regularization_loss'] = regularization_loss total_loss = tf.add_n(losses, name='total_loss') losses_dict['Loss/total_loss'] = total_loss return losses_dict, prediction_dict # TODO(kaftan): Explore removing learning_rate from this method & returning ## The full losses dict instead of just total_loss, then doing all summaries ## saving in a utility method called by the outer training loop. # TODO(kaftan): Explore adding gradient summaries def eager_train_step(detection_model, features, labels, unpad_groundtruth_tensors, optimizer, learning_rate, add_regularization_loss=True, clip_gradients_value=None, global_step=None, num_replicas=1.0): """Process a single training batch. This method computes the loss for the model on a single training batch, while tracking the gradients with a gradient tape. It then updates the model variables with the optimizer, clipping the gradients if clip_gradients_value is present. This method can run eagerly or inside a tf.function. Args: detection_model: A DetectionModel (based on Keras) to train. features: Dictionary of feature tensors from the input dataset. Should be in the format output by `inputs.train_input. features[fields.InputDataFields.image] is a [batch_size, H, W, C] float32 tensor with preprocessed images. features[HASH_KEY] is a [batch_size] int32 tensor representing unique identifiers for the images. features[fields.InputDataFields.true_image_shape] is a [batch_size, 3] int32 tensor representing the true image shapes, as preprocessed images could be padded. features[fields.InputDataFields.original_image] (optional, not used during training) is a [batch_size, H, W, C] float32 tensor with original images. labels: A dictionary of groundtruth tensors. This method unstacks these labels using model_lib.unstack_batch. The stacked labels are of the form returned by `inputs.train_input` and `inputs.eval_input`. labels[fields.InputDataFields.num_groundtruth_boxes] is a [batch_size] int32 tensor indicating the number of valid groundtruth boxes per image. labels[fields.InputDataFields.groundtruth_boxes] is a [batch_size, num_boxes, 4] float32 tensor containing the corners of the groundtruth boxes. labels[fields.InputDataFields.groundtruth_classes] is a [batch_size, num_boxes, num_classes] float32 one-hot tensor of classes. num_classes includes the background class. labels[fields.InputDataFields.groundtruth_weights] is a [batch_size, num_boxes] float32 tensor containing groundtruth weights for the boxes. -- Optional -- labels[fields.InputDataFields.groundtruth_instance_masks] is a [batch_size, num_boxes, H, W] float32 tensor containing only binary values, which represent instance masks for objects. labels[fields.InputDataFields.groundtruth_keypoints] is a [batch_size, num_boxes, num_keypoints, 2] float32 tensor containing keypoints for each box. labels[fields.InputDataFields.groundtruth_dp_num_points] is a [batch_size, num_boxes] int32 tensor with the number of DensePose sampled points per instance. labels[fields.InputDataFields.groundtruth_dp_part_ids] is a [batch_size, num_boxes, max_sampled_points] int32 tensor with the part ids (0-indexed) for each instance. labels[fields.InputDataFields.groundtruth_dp_surface_coords] is a [batch_size, num_boxes, max_sampled_points, 4] float32 tensor with the surface coordinates for each point. Each surface coordinate is of the form (y, x, v, u) where (y, x) are normalized image locations and (v, u) are part-relative normalized surface coordinates. labels[fields.InputDataFields.groundtruth_labeled_classes] is a float32 k-hot tensor of classes. labels[fields.InputDataFields.groundtruth_track_ids] is a int32 tensor of track IDs. unpad_groundtruth_tensors: A parameter passed to unstack_batch. optimizer: The training optimizer that will update the variables. learning_rate: The learning rate tensor for the current training step. This is used only for TensorBoard logging purposes, it does not affect model training. add_regularization_loss: Whether or not to include the model's regularization loss in the losses dictionary. clip_gradients_value: If this is present, clip the gradients global norm at this value using `tf.clip_by_global_norm`. global_step: The current training step. Used for TensorBoard logging purposes. This step is not updated by this function and must be incremented separately. num_replicas: The number of replicas in the current distribution strategy. This is used to scale the total loss so that training in a distribution strategy works correctly. Returns: The total loss observed at this training step """ # """Execute a single training step in the TF v2 style loop.""" is_training = True detection_model._is_training = is_training # pylint: disable=protected-access tf.keras.backend.set_learning_phase(is_training) labels = model_lib.unstack_batch( labels, unpad_groundtruth_tensors=unpad_groundtruth_tensors) with tf.GradientTape() as tape: losses_dict, _ = _compute_losses_and_predictions_dicts( detection_model, features, labels, add_regularization_loss) total_loss = losses_dict['Loss/total_loss'] # Normalize loss for num replicas total_loss = tf.math.divide(total_loss, tf.constant(num_replicas, dtype=tf.float32)) losses_dict['Loss/normalized_total_loss'] = total_loss for loss_type in losses_dict: tf.compat.v2.summary.scalar( loss_type, losses_dict[loss_type], step=global_step) trainable_variables = detection_model.trainable_variables gradients = tape.gradient(total_loss, trainable_variables) if clip_gradients_value: gradients, _ = tf.clip_by_global_norm(gradients, clip_gradients_value) optimizer.apply_gradients(zip(gradients, trainable_variables)) tf.compat.v2.summary.scalar('learning_rate', learning_rate, step=global_step) tf.compat.v2.summary.image( name='train_input_images', step=global_step, data=features[fields.InputDataFields.image], max_outputs=3) return total_loss def validate_tf_v2_checkpoint_restore_map(checkpoint_restore_map): """Ensure that given dict is a valid TF v2 style restore map. Args: checkpoint_restore_map: A nested dict mapping strings to tf.keras.Model objects. Raises: ValueError: If they keys in checkpoint_restore_map are not strings or if the values are not keras Model objects. """ for key, value in checkpoint_restore_map.items(): if not (isinstance(key, str) and (isinstance(value, tf.Module) or isinstance(value, tf.train.Checkpoint))): if isinstance(key, str) and isinstance(value, dict): validate_tf_v2_checkpoint_restore_map(value) else: raise TypeError( RESTORE_MAP_ERROR_TEMPLATE.format(key.__class__.__name__, value.__class__.__name__)) def is_object_based_checkpoint(checkpoint_path): """Returns true if `checkpoint_path` points to an object-based checkpoint.""" var_names = [var[0] for var in tf.train.list_variables(checkpoint_path)] return '_CHECKPOINTABLE_OBJECT_GRAPH' in var_names def load_fine_tune_checkpoint( model, checkpoint_path, checkpoint_type, checkpoint_version, input_dataset, unpad_groundtruth_tensors): """Load a fine tuning classification or detection checkpoint. To make sure the model variables are all built, this method first executes the model by computing a dummy loss. (Models might not have built their variables before their first execution) It then loads an object-based classification or detection checkpoint. This method updates the model in-place and does not return a value. Args: model: A DetectionModel (based on Keras) to load a fine-tuning checkpoint for. checkpoint_path: Directory with checkpoints file or path to checkpoint. checkpoint_type: Whether to restore from a full detection checkpoint (with compatible variable names) or to restore from a classification checkpoint for initialization prior to training. Valid values: `detection`, `classification`. checkpoint_version: train_pb2.CheckpointVersion.V1 or V2 enum indicating whether to load checkpoints in V1 style or V2 style. In this binary we only support V2 style (object-based) checkpoints. input_dataset: The tf.data Dataset the model is being trained on. Needed to get the shapes for the dummy loss computation. unpad_groundtruth_tensors: A parameter passed to unstack_batch. Raises: IOError: if `checkpoint_path` does not point at a valid object-based checkpoint ValueError: if `checkpoint_version` is not train_pb2.CheckpointVersion.V2 """ if not is_object_based_checkpoint(checkpoint_path): raise IOError('Checkpoint is expected to be an object-based checkpoint.') if checkpoint_version == train_pb2.CheckpointVersion.V1: raise ValueError('Checkpoint version should be V2') features, labels = iter(input_dataset).next() @tf.function def _dummy_computation_fn(features, labels): model._is_training = False # pylint: disable=protected-access tf.keras.backend.set_learning_phase(False) labels = model_lib.unstack_batch( labels, unpad_groundtruth_tensors=unpad_groundtruth_tensors) return _compute_losses_and_predictions_dicts( model, features, labels) strategy = tf.compat.v2.distribute.get_strategy() if hasattr(tf.distribute.Strategy, 'run'): strategy.run( _dummy_computation_fn, args=( features, labels, )) else: strategy.experimental_run_v2( _dummy_computation_fn, args=( features, labels, )) restore_from_objects_dict = model.restore_from_objects( fine_tune_checkpoint_type=checkpoint_type) validate_tf_v2_checkpoint_restore_map(restore_from_objects_dict) ckpt = tf.train.Checkpoint(**restore_from_objects_dict) ckpt.restore(checkpoint_path).assert_existing_objects_matched() def get_filepath(strategy, filepath): """Get appropriate filepath for worker. Args: strategy: A tf.distribute.Strategy object. filepath: A path to where the Checkpoint object is stored. Returns: A temporary filepath for non-chief workers to use or the original filepath for the chief. """ if strategy.extended.should_checkpoint: return filepath else: # TODO(vighneshb) Replace with the public API when TF exposes it. task_id = strategy.extended._task_id # pylint:disable=protected-access return os.path.join(filepath, 'temp_worker_{:03d}'.format(task_id)) def clean_temporary_directories(strategy, filepath): """Temporary directory clean up for MultiWorker Mirrored Strategy. This is needed for all non-chief workers. Args: strategy: A tf.distribute.Strategy object. filepath: The filepath for the temporary directory. """ if not strategy.extended.should_checkpoint: if tf.io.gfile.exists(filepath) and tf.io.gfile.isdir(filepath): tf.io.gfile.rmtree(filepath) def train_loop( pipeline_config_path, model_dir, config_override=None, train_steps=None, use_tpu=False, save_final_config=False, checkpoint_every_n=1000, checkpoint_max_to_keep=7, record_summaries=True, **kwargs): """Trains a model using eager + functions. This method: 1. Processes the pipeline configs 2. (Optionally) saves the as-run config 3. Builds the model & optimizer 4. Gets the training input data 5. Loads a fine-tuning detection or classification checkpoint if requested 6. Loops over the train data, executing distributed training steps inside tf.functions. 7. Checkpoints the model every `checkpoint_every_n` training steps. 8. Logs the training metrics as TensorBoard summaries. Args: pipeline_config_path: A path to a pipeline config file. model_dir: The directory to save checkpoints and summaries to. config_override: A pipeline_pb2.TrainEvalPipelineConfig text proto to override the config from `pipeline_config_path`. train_steps: Number of training steps. If None, the number of training steps is set from the `TrainConfig` proto. use_tpu: Boolean, whether training and evaluation should run on TPU. save_final_config: Whether to save final config (obtained after applying overrides) to `model_dir`. checkpoint_every_n: Checkpoint every n training steps. checkpoint_max_to_keep: int, the number of most recent checkpoints to keep in the model directory. record_summaries: Boolean, whether or not to record summaries. **kwargs: Additional keyword arguments for configuration override. """ ## Parse the configs get_configs_from_pipeline_file = MODEL_BUILD_UTIL_MAP[ 'get_configs_from_pipeline_file'] merge_external_params_with_configs = MODEL_BUILD_UTIL_MAP[ 'merge_external_params_with_configs'] create_pipeline_proto_from_configs = MODEL_BUILD_UTIL_MAP[ 'create_pipeline_proto_from_configs'] configs = get_configs_from_pipeline_file( pipeline_config_path, config_override=config_override) kwargs.update({ 'train_steps': train_steps, 'use_bfloat16': configs['train_config'].use_bfloat16 and use_tpu }) configs = merge_external_params_with_configs( configs, None, kwargs_dict=kwargs) model_config = configs['model'] train_config = configs['train_config'] train_input_config = configs['train_input_config'] unpad_groundtruth_tensors = train_config.unpad_groundtruth_tensors add_regularization_loss = train_config.add_regularization_loss clip_gradients_value = None if train_config.gradient_clipping_by_norm > 0: clip_gradients_value = train_config.gradient_clipping_by_norm # update train_steps from config but only when non-zero value is provided if train_steps is None and train_config.num_steps != 0: train_steps = train_config.num_steps if kwargs['use_bfloat16']: tf.compat.v2.keras.mixed_precision.experimental.set_policy('mixed_bfloat16') if train_config.load_all_detection_checkpoint_vars: raise ValueError('train_pb2.load_all_detection_checkpoint_vars ' 'unsupported in TF2') config_util.update_fine_tune_checkpoint_type(train_config) fine_tune_checkpoint_type = train_config.fine_tune_checkpoint_type fine_tune_checkpoint_version = train_config.fine_tune_checkpoint_version # Write the as-run pipeline config to disk. if save_final_config: pipeline_config_final = create_pipeline_proto_from_configs(configs) config_util.save_pipeline_config(pipeline_config_final, model_dir) # Build the model, optimizer, and training input strategy = tf.compat.v2.distribute.get_strategy() with strategy.scope(): detection_model = MODEL_BUILD_UTIL_MAP['detection_model_fn_base']( model_config=model_config, is_training=True) def train_dataset_fn(input_context): """Callable to create train input.""" # Create the inputs. train_input = inputs.train_input( train_config=train_config, train_input_config=train_input_config, model_config=model_config, model=detection_model, input_context=input_context) train_input = train_input.repeat() return train_input train_input = strategy.experimental_distribute_datasets_from_function( train_dataset_fn) global_step = tf.Variable( 0, trainable=False, dtype=tf.compat.v2.dtypes.int64, name='global_step', aggregation=tf.compat.v2.VariableAggregation.ONLY_FIRST_REPLICA) optimizer, (learning_rate,) = optimizer_builder.build( train_config.optimizer, global_step=global_step) if callable(learning_rate): learning_rate_fn = learning_rate else: learning_rate_fn = lambda: learning_rate ## Train the model # Get the appropriate filepath (temporary or not) based on whether the worker # is the chief. summary_writer_filepath = get_filepath(strategy, os.path.join(model_dir, 'train')) if record_summaries: summary_writer = tf.compat.v2.summary.create_file_writer( summary_writer_filepath) else: summary_writer = tf2.summary.create_noop_writer() if use_tpu: num_steps_per_iteration = 100 else: # TODO(b/135933080) Explore setting to 100 when GPU performance issues # are fixed. num_steps_per_iteration = 1 with summary_writer.as_default(): with strategy.scope(): with tf.compat.v2.summary.record_if( lambda: global_step % num_steps_per_iteration == 0): # Load a fine-tuning checkpoint. if train_config.fine_tune_checkpoint: load_fine_tune_checkpoint(detection_model, train_config.fine_tune_checkpoint, fine_tune_checkpoint_type, fine_tune_checkpoint_version, train_input, unpad_groundtruth_tensors) ckpt = tf.compat.v2.train.Checkpoint( step=global_step, model=detection_model, optimizer=optimizer) manager_dir = get_filepath(strategy, model_dir) if not strategy.extended.should_checkpoint: checkpoint_max_to_keep = 1 manager = tf.compat.v2.train.CheckpointManager( ckpt, manager_dir, max_to_keep=checkpoint_max_to_keep) # We use the following instead of manager.latest_checkpoint because # manager_dir does not point to the model directory when we are running # in a worker. latest_checkpoint = tf.train.latest_checkpoint(model_dir) ckpt.restore(latest_checkpoint) def train_step_fn(features, labels): """Single train step.""" loss = eager_train_step( detection_model, features, labels, unpad_groundtruth_tensors, optimizer, learning_rate=learning_rate_fn(), add_regularization_loss=add_regularization_loss, clip_gradients_value=clip_gradients_value, global_step=global_step, num_replicas=strategy.num_replicas_in_sync) global_step.assign_add(1) return loss def _sample_and_train(strategy, train_step_fn, data_iterator): features, labels = data_iterator.next() if hasattr(tf.distribute.Strategy, 'run'): per_replica_losses = strategy.run( train_step_fn, args=(features, labels)) else: per_replica_losses = strategy.experimental_run_v2( train_step_fn, args=(features, labels)) # TODO(anjalisridhar): explore if it is safe to remove the ## num_replicas scaling of the loss and switch this to a ReduceOp.Mean return strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_losses, axis=None) @tf.function def _dist_train_step(data_iterator): """A distributed train step.""" if num_steps_per_iteration > 1: for _ in tf.range(num_steps_per_iteration - 1): # Following suggestion on yaqs/5402607292645376 with tf.name_scope(''): _sample_and_train(strategy, train_step_fn, data_iterator) return _sample_and_train(strategy, train_step_fn, data_iterator) train_input_iter = iter(train_input) if int(global_step.value()) == 0: manager.save() checkpointed_step = int(global_step.value()) logged_step = global_step.value() last_step_time = time.time() for _ in range(global_step.value(), train_steps, num_steps_per_iteration): loss = _dist_train_step(train_input_iter) time_taken = time.time() - last_step_time last_step_time = time.time() tf.compat.v2.summary.scalar( 'steps_per_sec', num_steps_per_iteration * 1.0 / time_taken, step=global_step) if global_step.value() - logged_step >= 100: tf.logging.info( 'Step {} per-step time {:.3f}s loss={:.3f}'.format( global_step.value(), time_taken / num_steps_per_iteration, loss)) logged_step = global_step.value() if ((int(global_step.value()) - checkpointed_step) >= checkpoint_every_n): manager.save() checkpointed_step = int(global_step.value()) # Remove the checkpoint directories of the non-chief workers that # MultiWorkerMirroredStrategy forces us to save during sync distributed # training. clean_temporary_directories(strategy, manager_dir) clean_temporary_directories(strategy, summary_writer_filepath) def prepare_eval_dict(detections, groundtruth, features): """Prepares eval dictionary containing detections and groundtruth. Takes in `detections` from the model, `groundtruth` and `features` returned from the eval tf.data.dataset and creates a dictionary of tensors suitable for detection eval modules. Args: detections: A dictionary of tensors returned by `model.postprocess`. groundtruth: `inputs.eval_input` returns an eval dataset of (features, labels) tuple. `groundtruth` must be set to `labels`. Please note that: * fields.InputDataFields.groundtruth_classes must be 0-indexed and in its 1-hot representation. * fields.InputDataFields.groundtruth_verified_neg_classes must be 0-indexed and in its multi-hot repesentation. * fields.InputDataFields.groundtruth_not_exhaustive_classes must be 0-indexed and in its multi-hot repesentation. * fields.InputDataFields.groundtruth_labeled_classes must be 0-indexed and in its multi-hot repesentation. features: `inputs.eval_input` returns an eval dataset of (features, labels) tuple. This argument must be set to a dictionary containing the following keys and their corresponding values from `features` -- * fields.InputDataFields.image * fields.InputDataFields.original_image * fields.InputDataFields.original_image_spatial_shape * fields.InputDataFields.true_image_shape * inputs.HASH_KEY Returns: eval_dict: A dictionary of tensors to pass to eval module. class_agnostic: Whether to evaluate detection in class agnostic mode. """ groundtruth_boxes = groundtruth[fields.InputDataFields.groundtruth_boxes] groundtruth_boxes_shape = tf.shape(groundtruth_boxes) # For class-agnostic models, groundtruth one-hot encodings collapse to all # ones. class_agnostic = ( fields.DetectionResultFields.detection_classes not in detections) if class_agnostic: groundtruth_classes_one_hot = tf.ones( [groundtruth_boxes_shape[0], groundtruth_boxes_shape[1], 1]) else: groundtruth_classes_one_hot = groundtruth[ fields.InputDataFields.groundtruth_classes] label_id_offset = 1 # Applying label id offset (b/63711816) groundtruth_classes = ( tf.argmax(groundtruth_classes_one_hot, axis=2) + label_id_offset) groundtruth[fields.InputDataFields.groundtruth_classes] = groundtruth_classes label_id_offset_paddings = tf.constant([[0, 0], [1, 0]]) if fields.InputDataFields.groundtruth_verified_neg_classes in groundtruth: groundtruth[ fields.InputDataFields.groundtruth_verified_neg_classes] = tf.pad( groundtruth[ fields.InputDataFields.groundtruth_verified_neg_classes], label_id_offset_paddings) if fields.InputDataFields.groundtruth_not_exhaustive_classes in groundtruth: groundtruth[ fields.InputDataFields.groundtruth_not_exhaustive_classes] = tf.pad( groundtruth[ fields.InputDataFields.groundtruth_not_exhaustive_classes], label_id_offset_paddings) if fields.InputDataFields.groundtruth_labeled_classes in groundtruth: groundtruth[fields.InputDataFields.groundtruth_labeled_classes] = tf.pad( groundtruth[fields.InputDataFields.groundtruth_labeled_classes], label_id_offset_paddings) use_original_images = fields.InputDataFields.original_image in features if use_original_images: eval_images = features[fields.InputDataFields.original_image] true_image_shapes = features[fields.InputDataFields.true_image_shape][:, :3] original_image_spatial_shapes = features[ fields.InputDataFields.original_image_spatial_shape] else: eval_images = features[fields.InputDataFields.image] true_image_shapes = None original_image_spatial_shapes = None eval_dict = eval_util.result_dict_for_batched_example( eval_images, features[inputs.HASH_KEY], detections, groundtruth, class_agnostic=class_agnostic, scale_to_absolute=True, original_image_spatial_shapes=original_image_spatial_shapes, true_image_shapes=true_image_shapes) return eval_dict, class_agnostic def concat_replica_results(tensor_dict): new_tensor_dict = {} for key, values in tensor_dict.items(): new_tensor_dict[key] = tf.concat(values, axis=0) return new_tensor_dict def eager_eval_loop( detection_model, configs, eval_dataset, use_tpu=False, postprocess_on_cpu=False, global_step=None): """Evaluate the model eagerly on the evaluation dataset. This method will compute the evaluation metrics specified in the configs on the entire evaluation dataset, then return the metrics. It will also log the metrics to TensorBoard. Args: detection_model: A DetectionModel (based on Keras) to evaluate. configs: Object detection configs that specify the evaluators that should be used, as well as whether regularization loss should be included and if bfloat16 should be used on TPUs. eval_dataset: Dataset containing evaluation data. use_tpu: Whether a TPU is being used to execute the model for evaluation. postprocess_on_cpu: Whether model postprocessing should happen on the CPU when using a TPU to execute the model. global_step: A variable containing the training step this model was trained to. Used for logging purposes. Returns: A dict of evaluation metrics representing the results of this evaluation. """ del postprocess_on_cpu train_config = configs['train_config'] eval_input_config = configs['eval_input_config'] eval_config = configs['eval_config'] add_regularization_loss = train_config.add_regularization_loss is_training = False detection_model._is_training = is_training # pylint: disable=protected-access tf.keras.backend.set_learning_phase(is_training) evaluator_options = eval_util.evaluator_options_from_eval_config( eval_config) batch_size = eval_config.batch_size class_agnostic_category_index = ( label_map_util.create_class_agnostic_category_index()) class_agnostic_evaluators = eval_util.get_evaluators( eval_config, list(class_agnostic_category_index.values()), evaluator_options) class_aware_evaluators = None if eval_input_config.label_map_path: class_aware_category_index = ( label_map_util.create_category_index_from_labelmap( eval_input_config.label_map_path)) class_aware_evaluators = eval_util.get_evaluators( eval_config, list(class_aware_category_index.values()), evaluator_options) evaluators = None loss_metrics = {} @tf.function def compute_eval_dict(features, labels): """Compute the evaluation result on an image.""" # For evaling on train data, it is necessary to check whether groundtruth # must be unpadded. boxes_shape = ( labels[fields.InputDataFields.groundtruth_boxes].get_shape().as_list()) unpad_groundtruth_tensors = (boxes_shape[1] is not None and not use_tpu and batch_size == 1) groundtruth_dict = labels labels = model_lib.unstack_batch( labels, unpad_groundtruth_tensors=unpad_groundtruth_tensors) losses_dict, prediction_dict = _compute_losses_and_predictions_dicts( detection_model, features, labels, add_regularization_loss) prediction_dict = detection_model.postprocess( prediction_dict, features[fields.InputDataFields.true_image_shape]) eval_features = { fields.InputDataFields.image: features[fields.InputDataFields.image], fields.InputDataFields.original_image: features[fields.InputDataFields.original_image], fields.InputDataFields.original_image_spatial_shape: features[fields.InputDataFields.original_image_spatial_shape], fields.InputDataFields.true_image_shape: features[fields.InputDataFields.true_image_shape], inputs.HASH_KEY: features[inputs.HASH_KEY], } return losses_dict, prediction_dict, groundtruth_dict, eval_features agnostic_categories = label_map_util.create_class_agnostic_category_index() per_class_categories = label_map_util.create_category_index_from_labelmap( eval_input_config.label_map_path) keypoint_edges = [ (kp.start, kp.end) for kp in eval_config.keypoint_edge] strategy = tf.compat.v2.distribute.get_strategy() for i, (features, labels) in enumerate(eval_dataset): try: (losses_dict, prediction_dict, groundtruth_dict, eval_features) = strategy.run( compute_eval_dict, args=(features, labels)) except: # pylint:disable=bare-except tf.logging.info('A replica probably exhausted all examples. Skipping ' 'pending examples on other replicas.') break (local_prediction_dict, local_groundtruth_dict, local_eval_features) = tf.nest.map_structure( strategy.experimental_local_results, [prediction_dict, groundtruth_dict, eval_features]) local_prediction_dict = concat_replica_results(local_prediction_dict) local_groundtruth_dict = concat_replica_results(local_groundtruth_dict) local_eval_features = concat_replica_results(local_eval_features) eval_dict, class_agnostic = prepare_eval_dict(local_prediction_dict, local_groundtruth_dict, local_eval_features) for loss_key, loss_tensor in iter(losses_dict.items()): losses_dict[loss_key] = strategy.reduce(tf.distribute.ReduceOp.MEAN, loss_tensor, None) if class_agnostic: category_index = agnostic_categories else: category_index = per_class_categories if i % 100 == 0: tf.logging.info('Finished eval step %d', i) use_original_images = fields.InputDataFields.original_image in features if (use_original_images and i < eval_config.num_visualizations and batch_size == 1): sbys_image_list = vutils.draw_side_by_side_evaluation_image( eval_dict, category_index=category_index, max_boxes_to_draw=eval_config.max_num_boxes_to_visualize, min_score_thresh=eval_config.min_score_threshold, use_normalized_coordinates=False, keypoint_edges=keypoint_edges or None) sbys_images = tf.concat(sbys_image_list, axis=0) tf.compat.v2.summary.image( name='eval_side_by_side_' + str(i), step=global_step, data=sbys_images, max_outputs=eval_config.num_visualizations) if eval_util.has_densepose(eval_dict): dp_image_list = vutils.draw_densepose_visualizations( eval_dict) dp_images = tf.concat(dp_image_list, axis=0) tf.compat.v2.summary.image( name='densepose_detections_' + str(i), step=global_step, data=dp_images, max_outputs=eval_config.num_visualizations) if evaluators is None: if class_agnostic: evaluators = class_agnostic_evaluators else: evaluators = class_aware_evaluators for evaluator in evaluators: evaluator.add_eval_dict(eval_dict) for loss_key, loss_tensor in iter(losses_dict.items()): if loss_key not in loss_metrics: loss_metrics[loss_key] = [] loss_metrics[loss_key].append(loss_tensor) eval_metrics = {} for evaluator in evaluators: eval_metrics.update(evaluator.evaluate()) for loss_key in loss_metrics: eval_metrics[loss_key] = tf.reduce_mean(loss_metrics[loss_key]) eval_metrics = {str(k): v for k, v in eval_metrics.items()} tf.logging.info('Eval metrics at step %d', global_step) for k in eval_metrics: tf.compat.v2.summary.scalar(k, eval_metrics[k], step=global_step) tf.logging.info('\t+ %s: %f', k, eval_metrics[k]) return eval_metrics def eval_continuously( pipeline_config_path, config_override=None, train_steps=None, sample_1_of_n_eval_examples=1, sample_1_of_n_eval_on_train_examples=1, use_tpu=False, override_eval_num_epochs=True, postprocess_on_cpu=False, model_dir=None, checkpoint_dir=None, wait_interval=180, timeout=3600, eval_index=0, **kwargs): """Run continuous evaluation of a detection model eagerly. This method builds the model, and continously restores it from the most recent training checkpoint in the checkpoint directory & evaluates it on the evaluation data. Args: pipeline_config_path: A path to a pipeline config file. config_override: A pipeline_pb2.TrainEvalPipelineConfig text proto to override the config from `pipeline_config_path`. train_steps: Number of training steps. If None, the number of training steps is set from the `TrainConfig` proto. sample_1_of_n_eval_examples: Integer representing how often an eval example should be sampled. If 1, will sample all examples. sample_1_of_n_eval_on_train_examples: Similar to `sample_1_of_n_eval_examples`, except controls the sampling of training data for evaluation. use_tpu: Boolean, whether training and evaluation should run on TPU. override_eval_num_epochs: Whether to overwrite the number of epochs to 1 for eval_input. postprocess_on_cpu: When use_tpu and postprocess_on_cpu are true, postprocess is scheduled on the host cpu. model_dir: Directory to output resulting evaluation summaries to. checkpoint_dir: Directory that contains the training checkpoints. wait_interval: The mimmum number of seconds to wait before checking for a new checkpoint. timeout: The maximum number of seconds to wait for a checkpoint. Execution will terminate if no new checkpoints are found after these many seconds. eval_index: int, If given, only evaluate the dataset at the given index. By default, evaluates dataset at 0'th index. **kwargs: Additional keyword arguments for configuration override. """ get_configs_from_pipeline_file = MODEL_BUILD_UTIL_MAP[ 'get_configs_from_pipeline_file'] merge_external_params_with_configs = MODEL_BUILD_UTIL_MAP[ 'merge_external_params_with_configs'] configs = get_configs_from_pipeline_file( pipeline_config_path, config_override=config_override) kwargs.update({ 'sample_1_of_n_eval_examples': sample_1_of_n_eval_examples, 'use_bfloat16': configs['train_config'].use_bfloat16 and use_tpu }) if train_steps is not None: kwargs['train_steps'] = train_steps if override_eval_num_epochs: kwargs.update({'eval_num_epochs': 1}) tf.logging.warning( 'Forced number of epochs for all eval validations to be 1.') configs = merge_external_params_with_configs( configs, None, kwargs_dict=kwargs) model_config = configs['model'] train_input_config = configs['train_input_config'] eval_config = configs['eval_config'] eval_input_configs = configs['eval_input_configs'] eval_on_train_input_config = copy.deepcopy(train_input_config) eval_on_train_input_config.sample_1_of_n_examples = ( sample_1_of_n_eval_on_train_examples) if override_eval_num_epochs and eval_on_train_input_config.num_epochs != 1: tf.logging.warning('Expected number of evaluation epochs is 1, but ' 'instead encountered `eval_on_train_input_config' '.num_epochs` = ' '{}. Overwriting `num_epochs` to 1.'.format( eval_on_train_input_config.num_epochs)) eval_on_train_input_config.num_epochs = 1 if kwargs['use_bfloat16']: tf.compat.v2.keras.mixed_precision.experimental.set_policy('mixed_bfloat16') eval_input_config = eval_input_configs[eval_index] strategy = tf.compat.v2.distribute.get_strategy() with strategy.scope(): detection_model = MODEL_BUILD_UTIL_MAP['detection_model_fn_base']( model_config=model_config, is_training=True) eval_input = strategy.experimental_distribute_dataset( inputs.eval_input( eval_config=eval_config, eval_input_config=eval_input_config, model_config=model_config, model=detection_model)) global_step = tf.compat.v2.Variable( 0, trainable=False, dtype=tf.compat.v2.dtypes.int64) for latest_checkpoint in tf.train.checkpoints_iterator( checkpoint_dir, timeout=timeout, min_interval_secs=wait_interval): ckpt = tf.compat.v2.train.Checkpoint( step=global_step, model=detection_model) ckpt.restore(latest_checkpoint).expect_partial() summary_writer = tf.compat.v2.summary.create_file_writer( os.path.join(model_dir, 'eval', eval_input_config.name)) with summary_writer.as_default(): eager_eval_loop( detection_model, configs, eval_input, use_tpu=use_tpu, postprocess_on_cpu=postprocess_on_cpu, global_step=global_step)