# Copyright 2022 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. """The COCO-style evaluator. The following snippet demonstrates the use of interfaces: evaluator = COCOEvaluator(...) for _ in range(num_evals): for _ in range(num_batches_per_eval): predictions, groundtruth = predictor.predict(...) # pop a batch. evaluator.update_state(groundtruths, predictions) evaluator.result() # finish one full eval and reset states. See also: https://github.com/cocodataset/cocoapi/ """ import atexit import tempfile # Import libraries from absl import logging import numpy as np from pycocotools import cocoeval import six import tensorflow as tf from official.vision.evaluation import coco_utils class COCOEvaluator(object): """COCO evaluation metric class.""" def __init__(self, annotation_file, include_mask, need_rescale_bboxes=True, per_category_metrics=False): """Constructs COCO evaluation class. The class provides the interface to COCO metrics_fn. The _update_op() takes detections from each image and push them to self.detections. The _evaluate() loads a JSON file in COCO annotation format as the groundtruths and runs COCO evaluation. Args: annotation_file: a JSON file that stores annotations of the eval dataset. If `annotation_file` is None, groundtruth annotations will be loaded from the dataloader. include_mask: a boolean to indicate whether or not to include the mask eval. need_rescale_bboxes: If true bboxes in `predictions` will be rescaled back to absolute values (`image_info` is needed in this case). per_category_metrics: Whether to return per category metrics. """ if annotation_file: if annotation_file.startswith('gs://'): _, local_val_json = tempfile.mkstemp(suffix='.json') tf.io.gfile.remove(local_val_json) tf.io.gfile.copy(annotation_file, local_val_json) atexit.register(tf.io.gfile.remove, local_val_json) else: local_val_json = annotation_file self._coco_gt = coco_utils.COCOWrapper( eval_type=('mask' if include_mask else 'box'), annotation_file=local_val_json) self._annotation_file = annotation_file self._include_mask = include_mask self._per_category_metrics = per_category_metrics self._metric_names = [ 'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'ARmax1', 'ARmax10', 'ARmax100', 'ARs', 'ARm', 'ARl' ] self._required_prediction_fields = [ 'source_id', 'num_detections', 'detection_classes', 'detection_scores', 'detection_boxes' ] self._need_rescale_bboxes = need_rescale_bboxes if self._need_rescale_bboxes: self._required_prediction_fields.append('image_info') self._required_groundtruth_fields = [ 'source_id', 'height', 'width', 'classes', 'boxes' ] if self._include_mask: mask_metric_names = ['mask_' + x for x in self._metric_names] self._metric_names.extend(mask_metric_names) self._required_prediction_fields.extend(['detection_masks']) self._required_groundtruth_fields.extend(['masks']) self.reset_states() @property def name(self): return 'coco_metric' def reset_states(self): """Resets internal states for a fresh run.""" self._predictions = {} if not self._annotation_file: self._groundtruths = {} def result(self): """Evaluates detection results, and reset_states.""" metric_dict = self.evaluate() # Cleans up the internal variables in order for a fresh eval next time. self.reset_states() return metric_dict def evaluate(self): """Evaluates with detections from all images with COCO API. Returns: coco_metric: float numpy array with shape [24] representing the coco-style evaluation metrics (box and mask). """ if not self._annotation_file: logging.info('There is no annotation_file in COCOEvaluator.') gt_dataset = coco_utils.convert_groundtruths_to_coco_dataset( self._groundtruths) coco_gt = coco_utils.COCOWrapper( eval_type=('mask' if self._include_mask else 'box'), gt_dataset=gt_dataset) else: logging.info('Using annotation file: %s', self._annotation_file) coco_gt = self._coco_gt coco_predictions = coco_utils.convert_predictions_to_coco_annotations( self._predictions) coco_dt = coco_gt.loadRes(predictions=coco_predictions) image_ids = [ann['image_id'] for ann in coco_predictions] coco_eval = cocoeval.COCOeval(coco_gt, coco_dt, iouType='bbox') coco_eval.params.imgIds = image_ids coco_eval.evaluate() coco_eval.accumulate() coco_eval.summarize() coco_metrics = coco_eval.stats if self._include_mask: mcoco_eval = cocoeval.COCOeval(coco_gt, coco_dt, iouType='segm') mcoco_eval.params.imgIds = image_ids mcoco_eval.evaluate() mcoco_eval.accumulate() mcoco_eval.summarize() mask_coco_metrics = mcoco_eval.stats if self._include_mask: metrics = np.hstack((coco_metrics, mask_coco_metrics)) else: metrics = coco_metrics metrics_dict = {} for i, name in enumerate(self._metric_names): metrics_dict[name] = metrics[i].astype(np.float32) # Adds metrics per category. if self._per_category_metrics: metrics_dict.update(self._retrieve_per_category_metrics(coco_eval)) if self._include_mask: metrics_dict.update(self._retrieve_per_category_metrics( mcoco_eval, prefix='mask')) return metrics_dict def _retrieve_per_category_metrics(self, coco_eval, prefix=''): """Retrieves and per-category metrics and retuns them in a dict. Args: coco_eval: a cocoeval.COCOeval object containing evaluation data. prefix: str, A string used to prefix metric names. Returns: metrics_dict: A dictionary with per category metrics. """ metrics_dict = {} if prefix: prefix = prefix + ' ' if hasattr(coco_eval, 'category_stats'): for category_index, category_id in enumerate(coco_eval.params.catIds): if self._annotation_file: coco_category = self._coco_gt.cats[category_id] # if 'name' is available use it, otherwise use `id` category_display_name = coco_category.get('name', category_id) else: category_display_name = category_id metrics_dict[prefix + 'Precision mAP ByCategory/{}'.format( category_display_name )] = coco_eval.category_stats[0][category_index].astype(np.float32) metrics_dict[prefix + 'Precision mAP ByCategory@50IoU/{}'.format( category_display_name )] = coco_eval.category_stats[1][category_index].astype(np.float32) metrics_dict[prefix + 'Precision mAP ByCategory@75IoU/{}'.format( category_display_name )] = coco_eval.category_stats[2][category_index].astype(np.float32) metrics_dict[prefix + 'Precision mAP ByCategory (small) /{}'.format( category_display_name )] = coco_eval.category_stats[3][category_index].astype(np.float32) metrics_dict[prefix + 'Precision mAP ByCategory (medium) /{}'.format( category_display_name )] = coco_eval.category_stats[4][category_index].astype(np.float32) metrics_dict[prefix + 'Precision mAP ByCategory (large) /{}'.format( category_display_name )] = coco_eval.category_stats[5][category_index].astype(np.float32) metrics_dict[prefix + 'Recall AR@1 ByCategory/{}'.format( category_display_name )] = coco_eval.category_stats[6][category_index].astype(np.float32) metrics_dict[prefix + 'Recall AR@10 ByCategory/{}'.format( category_display_name )] = coco_eval.category_stats[7][category_index].astype(np.float32) metrics_dict[prefix + 'Recall AR@100 ByCategory/{}'.format( category_display_name )] = coco_eval.category_stats[8][category_index].astype(np.float32) metrics_dict[prefix + 'Recall AR (small) ByCategory/{}'.format( category_display_name )] = coco_eval.category_stats[9][category_index].astype(np.float32) metrics_dict[prefix + 'Recall AR (medium) ByCategory/{}'.format( category_display_name )] = coco_eval.category_stats[10][category_index].astype(np.float32) metrics_dict[prefix + 'Recall AR (large) ByCategory/{}'.format( category_display_name )] = coco_eval.category_stats[11][category_index].astype(np.float32) return metrics_dict def _process_predictions(self, predictions): image_scale = np.tile(predictions['image_info'][:, 2:3, :], (1, 1, 2)) predictions['detection_boxes'] = ( predictions['detection_boxes'].astype(np.float32)) predictions['detection_boxes'] /= image_scale if 'detection_outer_boxes' in predictions: predictions['detection_outer_boxes'] = ( predictions['detection_outer_boxes'].astype(np.float32)) predictions['detection_outer_boxes'] /= image_scale def _convert_to_numpy(self, groundtruths, predictions): """Converts tesnors to numpy arrays.""" if groundtruths: labels = tf.nest.map_structure(lambda x: x.numpy(), groundtruths) numpy_groundtruths = {} for key, val in labels.items(): if isinstance(val, tuple): val = np.concatenate(val) numpy_groundtruths[key] = val else: numpy_groundtruths = groundtruths if predictions: outputs = tf.nest.map_structure(lambda x: x.numpy(), predictions) numpy_predictions = {} for key, val in outputs.items(): if isinstance(val, tuple): val = np.concatenate(val) numpy_predictions[key] = val else: numpy_predictions = predictions return numpy_groundtruths, numpy_predictions def update_state(self, groundtruths, predictions): """Update and aggregate detection results and groundtruth data. Args: groundtruths: a dictionary of Tensors including the fields below. See also different parsers under `../dataloader` for more details. Required fields: - source_id: a numpy array of int or string of shape [batch_size]. - height: a numpy array of int of shape [batch_size]. - width: a numpy array of int of shape [batch_size]. - num_detections: a numpy array of int of shape [batch_size]. - boxes: a numpy array of float of shape [batch_size, K, 4]. - classes: a numpy array of int of shape [batch_size, K]. Optional fields: - is_crowds: a numpy array of int of shape [batch_size, K]. If the field is absent, it is assumed that this instance is not crowd. - areas: a numy array of float of shape [batch_size, K]. If the field is absent, the area is calculated using either boxes or masks depending on which one is available. - masks: a numpy array of float of shape [batch_size, K, mask_height, mask_width], predictions: a dictionary of tensors including the fields below. See different parsers under `../dataloader` for more details. Required fields: - source_id: a numpy array of int or string of shape [batch_size]. - image_info [if `need_rescale_bboxes` is True]: a numpy array of float of shape [batch_size, 4, 2]. - num_detections: a numpy array of int of shape [batch_size]. - detection_boxes: a numpy array of float of shape [batch_size, K, 4]. - detection_classes: a numpy array of int of shape [batch_size, K]. - detection_scores: a numpy array of float of shape [batch_size, K]. Optional fields: - detection_masks: a numpy array of float of shape [batch_size, K, mask_height, mask_width]. Raises: ValueError: if the required prediction or groundtruth fields are not present in the incoming `predictions` or `groundtruths`. """ groundtruths, predictions = self._convert_to_numpy(groundtruths, predictions) for k in self._required_prediction_fields: if k not in predictions: raise ValueError( 'Missing the required key `{}` in predictions!'.format(k)) if self._need_rescale_bboxes: self._process_predictions(predictions) for k, v in six.iteritems(predictions): if k not in self._predictions: self._predictions[k] = [v] else: self._predictions[k].append(v) if not self._annotation_file: assert groundtruths for k in self._required_groundtruth_fields: if k not in groundtruths: raise ValueError( 'Missing the required key `{}` in groundtruths!'.format(k)) for k, v in six.iteritems(groundtruths): if k not in self._groundtruths: self._groundtruths[k] = [v] else: self._groundtruths[k].append(v)