Merge pull request #1052 from tensorflow/sync

Sync (0-30 of 206)

Author: danjan1234

models/official/detection/dataloader/tf_example_decoder.py

@@ -38,8 +38,11 @@ def __init__(
       # copypara:strip_end
       regenerate_source_id=False,
       label_key='image/object/class/label',
-      label_dtype=tf.int64):
+      label_dtype=tf.int64,
+      include_keypoint=False,
+      num_keypoints_per_instance=0):
     self._include_mask = include_mask
+    self._include_keypoint = include_keypoint
     # copypara:strip_begin
     self._include_polygon = include_polygon
     # copypara:strip_end
@@ -62,6 +65,13 @@ def __init__(
         self._label_dtype)
     if include_mask:
       self._keys_to_features['image/object/mask'] = tf.VarLenFeature(tf.string)
+    if include_keypoint:
+      self._num_keypoints_per_instance = num_keypoints_per_instance
+      self._keys_to_features.update({
+          'image/object/keypoint/visibility': tf.io.VarLenFeature(tf.int64),
+          'image/object/keypoint/x': tf.io.VarLenFeature(tf.float32),
+          'image/object/keypoint/y': tf.io.VarLenFeature(tf.float32),
+      })
 
   def _decode_image(self, parsed_tensors):
     """Decodes the image and set its static shape."""
@@ -106,6 +116,17 @@ def _decode_areas(self, parsed_tensors):
         lambda: parsed_tensors['image/object/area'],
         lambda: (xmax - xmin) * (ymax - ymin) * height * width)
 
+  def _decode_keypoints(self, parsed_tensors):
+    """Decode keypoint coordinates and visibilities."""
+    keypoint_x = parsed_tensors['image/object/keypoint/x']
+    keypoint_y = parsed_tensors['image/object/keypoint/y']
+    keypoints = tf.stack([keypoint_y, keypoint_x], axis=-1)
+    keypoints = tf.reshape(keypoints, [-1, self._num_keypoints_per_instance, 2])
+    keypoint_visibilities = parsed_tensors['image/object/keypoint/visibility']
+    keypoint_visibilities = tf.reshape(keypoint_visibilities,
+                                       [-1, self._num_keypoints_per_instance])
+    return keypoints, keypoint_visibilities
+
   def decode(self, serialized_example):
     """Decode the serialized example.
 
@@ -165,6 +186,8 @@ def decode(self, serialized_example):
           lambda: _get_source_id_from_encoded_image(parsed_tensors))
     if self._include_mask:
       masks = self._decode_masks(parsed_tensors)
+    if self._include_keypoint:
+      keypoints, keypoint_visibilities = self._decode_keypoints(parsed_tensors)
 
     groundtruth_classes = parsed_tensors[self._label_key]
     decoded_tensors = {
@@ -182,4 +205,9 @@ def decode(self, serialized_example):
           'groundtruth_instance_masks': masks,
           'groundtruth_instance_masks_png': parsed_tensors['image/object/mask'],
       })
+    if self._include_keypoint:
+      decoded_tensors.update({
+          'groundtruth_keypoints': keypoints,
+          'groundtruth_keypoint_visibilities': keypoint_visibilities,
+      })
     return decoded_tensors

models/official/detection/evaluation/coco_evaluator.py

@@ -55,6 +55,9 @@ def __init__(
       need_rescale_bboxes=True,
       per_category_metrics=False,
       remove_invalid_boxes=False,
+      include_keypoint=False,
+      need_rescale_keypoints=False,
+      kpt_oks_sigmas=None
   ):
     """Constructs COCO evaluation class.
 
@@ -80,6 +83,13 @@ def __init__(
       per_category_metrics: Whether to return per category metrics.
       remove_invalid_boxes: A boolean indicating whether to remove invalid box
         during evaluation.
+      include_keypoint: a boolean to indicate whether or not to include the
+        keypoint eval.
+      need_rescale_keypoints: If true keypoints in `predictions` will be
+        rescaled back to absolute values (`image_info` is needed in this case).
+      kpt_oks_sigmas: The sigmas used to calculate keypoint OKS. See
+        http://cocodataset.org/#keypoints-eval. When None, it will use the
+        defaults in COCO.
     """
     if annotation_file:
       if annotation_file.startswith('gs://'):
@@ -105,7 +115,8 @@ def __init__(
         'detection_boxes'
     ]
     self._need_rescale_bboxes = need_rescale_bboxes
-    if self._need_rescale_bboxes:
+    self._need_rescale_keypoints = need_rescale_keypoints
+    if self._need_rescale_bboxes or self._need_rescale_keypoints:
       self._required_prediction_fields.append('image_info')
     self._required_groundtruth_fields = [
         'source_id', 'height', 'width', 'classes', 'boxes'
@@ -116,6 +127,18 @@ def __init__(
       self._required_prediction_fields.extend(['detection_masks'])
       self._required_groundtruth_fields.extend(['masks'])
     self.remove_invalid_boxes = remove_invalid_boxes
+    self._include_keypoint = include_keypoint
+    self._kpt_oks_sigmas = kpt_oks_sigmas
+    if self._include_keypoint:
+      keypoint_metric_names = [
+          'AP', 'AP50', 'AP75', 'APm', 'APl', 'ARmax1', 'ARmax10', 'ARmax100',
+          'ARm', 'ARl'
+      ]
+      keypoint_metric_names = ['keypoint_' + x for x in keypoint_metric_names]
+      self._metric_names.extend(keypoint_metric_names)
+      self._required_prediction_fields.extend(['detection_keypoints'])
+      self._required_groundtruth_fields.extend(['keypoints'])
+
     self.reset()
 
   def reset(self):
@@ -168,7 +191,7 @@ def evaluate(self):
     coco_eval.evaluate()
     coco_eval.accumulate()
     coco_eval.summarize()
-    coco_metrics = coco_eval.stats
+    metrics = coco_eval.stats
 
     if self._include_mask:
       mcoco_eval = cocoeval.COCOeval(coco_gt, coco_dt, iouType='segm')
@@ -177,11 +200,17 @@ def evaluate(self):
       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 = np.hstack((metrics, mask_coco_metrics))
+
+    if self._include_keypoint:
+      kcoco_eval = cocoeval.COCOeval(coco_gt, coco_dt, iouType='keypoints',
+                                     kpt_oks_sigmas=self._kpt_oks_sigmas)
+      kcoco_eval.params.imgIds = image_ids
+      kcoco_eval.evaluate()
+      kcoco_eval.accumulate()
+      kcoco_eval.summarize()
+      keypoint_coco_metrics = kcoco_eval.stats
+      metrics = np.hstack((metrics, keypoint_coco_metrics))
 
     # Cleans up the internal variables in order for a fresh eval next time.
     self.reset()
@@ -192,46 +221,64 @@ def evaluate(self):
 
     # Adds metrics per category.
     if self._per_category_metrics and hasattr(coco_eval, 'category_stats'):
-      for category_index, category_id in enumerate(coco_eval.params.catIds):
-        metrics_dict['Precision mAP ByCategory/{}'.format(
-            category_id)] = coco_eval.category_stats[0][category_index].astype(
-                np.float32)
-        metrics_dict['Precision mAP ByCategory@50IoU/{}'.format(
-            category_id)] = coco_eval.category_stats[1][category_index].astype(
-                np.float32)
-        metrics_dict['Precision mAP ByCategory@75IoU/{}'.format(
-            category_id)] = coco_eval.category_stats[2][category_index].astype(
-                np.float32)
-        metrics_dict['Precision mAP ByCategory (small) /{}'.format(
-            category_id)] = coco_eval.category_stats[3][category_index].astype(
-                np.float32)
-        metrics_dict['Precision mAP ByCategory (medium) /{}'.format(
-            category_id)] = coco_eval.category_stats[4][category_index].astype(
-                np.float32)
-        metrics_dict['Precision mAP ByCategory (large) /{}'.format(
-            category_id)] = coco_eval.category_stats[5][category_index].astype(
-                np.float32)
-        metrics_dict['Recall AR@1 ByCategory/{}'.format(
-            category_id)] = coco_eval.category_stats[6][category_index].astype(
-                np.float32)
-        metrics_dict['Recall AR@10 ByCategory/{}'.format(
-            category_id)] = coco_eval.category_stats[7][category_index].astype(
-                np.float32)
-        metrics_dict['Recall AR@100 ByCategory/{}'.format(
-            category_id)] = coco_eval.category_stats[8][category_index].astype(
-                np.float32)
-        metrics_dict['Recall AR (small) ByCategory/{}'.format(
-            category_id)] = coco_eval.category_stats[9][category_index].astype(
-                np.float32)
-        metrics_dict['Recall AR (medium) ByCategory/{}'.format(
-            category_id)] = coco_eval.category_stats[10][category_index].astype(
-                np.float32)
-        metrics_dict['Recall AR (large) ByCategory/{}'.format(
-            category_id)] = coco_eval.category_stats[11][category_index].astype(
-                np.float32)
+      metrics_dict.update(self._retrieve_per_category_metrics(coco_eval))
+
+      if self._include_keypoint:
+        metrics_dict.update(self._retrieve_per_category_metrics(
+            kcoco_eval, prefix='keypoints'))
     return metrics_dict
 
-  def _process_predictions(self, predictions):
+  def _retrieve_per_category_metrics(self, coco_eval, prefix=''):
+    """Retrieves and per-category metrics and returns 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 + ' '
+
+    for category_index, category_id in enumerate(coco_eval.params.catIds):
+      if 'keypoints' in prefix:
+        metrics_dict_keys = [
+            'Precision mAP ByCategory',
+            'Precision mAP ByCategory@50IoU',
+            'Precision mAP ByCategory@75IoU',
+            'Precision mAP ByCategory (medium)',
+            'Precision mAP ByCategory (large)',
+            'Recall AR@1 ByCategory',
+            'Recall AR@10 ByCategory',
+            'Recall AR@100 ByCategory',
+            'Recall AR (medium) ByCategory',
+            'Recall AR (large) ByCategory',
+        ]
+      else:
+        metrics_dict_keys = [
+            'Precision mAP ByCategory',
+            'Precision mAP ByCategory@50IoU',
+            'Precision mAP ByCategory@75IoU',
+            'Precision mAP ByCategory (small)',
+            'Precision mAP ByCategory (medium)',
+            'Precision mAP ByCategory (large)',
+            'Recall AR@1 ByCategory',
+            'Recall AR@10 ByCategory',
+            'Recall AR@100 ByCategory',
+            'Recall AR (small) ByCategory',
+            'Recall AR (medium) ByCategory',
+            'Recall AR (large) ByCategory',
+        ]
+      for idx, key in enumerate(metrics_dict_keys):
+        metrics_dict[prefix + key + '/{}'.format(
+            category_id)] = coco_eval.category_stats[idx][
+                category_index].astype(np.float32)
+    return metrics_dict
+
+  def _process_bboxes_predictions(self, predictions):
     image_scale = np.tile(predictions['image_info'][:, 2:3, :], (1, 1, 2))
     predictions['detection_boxes'] = (
         predictions['detection_boxes'].astype(np.float32))
@@ -241,6 +288,13 @@ def _process_predictions(self, predictions):
           predictions['detection_outer_boxes'].astype(np.float32))
       predictions['detection_outer_boxes'] /= image_scale
 
+  def _process_keypoints_predictions(self, predictions):
+    image_scale = tf.reshape(predictions['image_info'][:, 2:3, :],
+                             [-1, 1, 1, 2])
+    predictions['detection_keypoints'] = (
+        predictions['detection_keypoints'].astype(np.float32))
+    predictions['detection_keypoints'] /= image_scale
+
   def update(self, predictions, groundtruths=None):
     """Update and aggregate detection results and groundtruth data.
 
@@ -286,7 +340,9 @@ def update(self, predictions, groundtruths=None):
         raise ValueError(
             'Missing the required key `{}` in predictions!'.format(k))
     if self._need_rescale_bboxes:
-      self._process_predictions(predictions)
+      self._process_bboxes_predictions(predictions)
+    if self._need_rescale_keypoints:
+      self._process_keypoints_predictions(predictions)
     for k, v in six.iteritems(predictions):
       if k not in self._predictions:
         self._predictions[k] = [v]
@@ -305,6 +361,20 @@ def update(self, predictions, groundtruths=None):
         else:
           self._groundtruths[k].append(v)
 
+  def merge(self, other):
+    """Merges the states from the other CocoEvaluator."""
+    for k, v in other._predictions.items():  # pylint: disable=protected-access
+      if k not in self._predictions:
+        self._predictions[k] = v
+      else:
+        self._predictions[k].extend(v)
+
+    for k, v in other._groundtruths.items():  # pylint: disable=protected-access
+      if k not in self._groundtruths:
+        self._groundtruths[k] = v
+      else:
+        self._groundtruths[k].extend(v)
+
 
 class ShapeMaskCOCOEvaluator(COCOEvaluator):
   """COCO evaluation metric class for ShapeMask."""
@@ -463,6 +533,7 @@ def __init__(
       self._metric_names.extend(mask_metric_names)
       self._required_prediction_fields.extend(['detection_masks'])
       self._required_groundtruth_fields.extend(['masks'])
+    self._need_rescale_keypoints = False
 
     self.reset()
 

models/official/detection/evaluation/coco_utils.py

@@ -138,6 +138,8 @@ def convert_predictions_to_coco_annotations(
       Optional fields:
         - detection_masks: a list of numpy arrays of float of shape
             [batch_size, K, mask_height, mask_width].
+        - detection_keypoints: a list of numpy arrays of float of shape
+            [batch_size, K, num_keypoints, 2]
     remove_invalid_boxes: A boolean indicating whether to remove invalid box
       during evaluation.
 
@@ -160,6 +162,19 @@ def convert_predictions_to_coco_annotations(
 
     # NOTE: Batch size may differ between chunks.
     batch_size = predictions['source_id'][i].shape[0]
+    if 'detection_keypoints' in predictions:
+      # Adds extra ones to indicate the visibility for each keypoint as is
+      # recommended by MSCOCO. Also, convert keypoint from [y, x] to [x, y]
+      # as mandated by COCO.
+      num_keypoints = predictions['detection_keypoints'][i].shape[2]
+      coco_keypoints = np.concatenate(
+          [
+              predictions['detection_keypoints'][i][..., 1:],
+              predictions['detection_keypoints'][i][..., :1],
+              np.ones([batch_size, max_num_detections, num_keypoints, 1]),
+          ],
+          axis=-1,
+      ).astype(int)
     for j in range(batch_size):
       if 'detection_masks' in predictions:
         image_masks = mask_utils.paste_instance_masks(
@@ -185,6 +200,8 @@ def convert_predictions_to_coco_annotations(
         ann['score'] = predictions['detection_scores'][i][j, k]
         if 'detection_masks' in predictions:
           ann['segmentation'] = encoded_masks[k]
+        if 'detection_keypoints' in predictions:
+          ann['keypoints'] = coco_keypoints[j, k].flatten().tolist()
         coco_predictions.append(ann)
 
   for i, ann in enumerate(coco_predictions):
@@ -272,6 +289,25 @@ def convert_groundtruths_to_coco_dataset(groundtruths, label_map=None):
           ann['segmentation'] = encoded_mask
           if 'areas' not in groundtruths:
             ann['area'] = mask_api.area(encoded_mask)
+        if 'keypoints' in groundtruths:
+          keypoints = groundtruths['keypoints'][i]
+          coco_keypoints = []
+          num_valid_keypoints = 0
+          for z in range(len(keypoints[j, k, :, 1])):
+            # Convert from [y, x] to [x, y] as mandated by COCO.
+            x = float(keypoints[j, k, z, 1])
+            y = float(keypoints[j, k, z, 0])
+            coco_keypoints.append(x)
+            coco_keypoints.append(y)
+            if tf.math.is_nan(x) or tf.math.is_nan(y) or (
+                x == 0 and y == 0):
+              visibility = 0
+            else:
+              visibility = 2
+              num_valid_keypoints = num_valid_keypoints + 1
+            coco_keypoints.append(visibility)
+          ann['keypoints'] = coco_keypoints
+          ann['num_keypoints'] = num_valid_keypoints
         gt_annotations.append(ann)
 
   for i, ann in enumerate(gt_annotations):