Internal change

PiperOrigin-RevId: 387878897

official/vision/beta/configs/maskrcnn.py

@@ -15,11 +15,10 @@
 # Lint as: python3
 """Mask R-CNN configuration definition."""
 
+import dataclasses
 import os
 from typing import List, Optional
 
-import dataclasses
-
 from official.core import config_definitions as cfg
 from official.core import exp_factory
 from official.modeling import hyperparams
@@ -79,6 +78,8 @@ class DataConfig(cfg.DataConfig):
   shuffle_buffer_size: int = 10000
   file_type: str = 'tfrecord'
   drop_remainder: bool = True
+  # Number of examples in the data set, it's used to create the annotation file.
+  num_examples: int = -1
 
 
 @dataclasses.dataclass

official/vision/beta/evaluation/coco_utils.py

@@ -18,6 +18,7 @@ import copy
 import json
 
 # Import libraries
+
 from absl import logging
 import numpy as np
 from PIL import Image
@@ -26,6 +27,7 @@ from pycocotools import mask as mask_api
 import six
 import tensorflow as tf
 
+from official.common import dataset_fn
 from official.vision.beta.dataloaders import tf_example_decoder
 from official.vision.beta.ops import box_ops
 from official.vision.beta.ops import mask_ops
@@ -240,10 +242,20 @@ def convert_groundtruths_to_coco_dataset(groundtruths, label_map=None):
               (boxes[j, k, 3] - boxes[j, k, 1]) *
               (boxes[j, k, 2] - boxes[j, k, 0]))
         if 'masks' in groundtruths:
-          mask = Image.open(six.BytesIO(groundtruths['masks'][i][j, k]))
+          if isinstance(groundtruths['masks'][i][j, k], tf.Tensor):
+            mask = Image.open(
+                six.BytesIO(groundtruths['masks'][i][j, k].numpy()))
+            width, height = mask.size
+            np_mask = (
+                np.array(mask.getdata()).reshape(height,
+                                                 width).astype(np.uint8))
+          else:
+            mask = Image.open(
+                six.BytesIO(groundtruths['masks'][i][j, k]))
             width, height = mask.size
             np_mask = (
-              np.array(mask.getdata()).reshape(height, width).astype(np.uint8))
+                np.array(mask.getdata()).reshape(height,
+                                                 width).astype(np.uint8))
           np_mask[np_mask > 0] = 255
           encoded_mask = mask_api.encode(np.asfortranarray(np_mask))
           ann['segmentation'] = encoded_mask
@@ -271,11 +283,11 @@ def convert_groundtruths_to_coco_dataset(groundtruths, label_map=None):
 class COCOGroundtruthGenerator:
   """Generates the groundtruth annotations from a single example."""
 
-  def __init__(self, file_pattern, num_examples, include_mask):
+  def __init__(self, file_pattern, file_type, num_examples, include_mask):
     self._file_pattern = file_pattern
     self._num_examples = num_examples
     self._include_mask = include_mask
-    self._dataset_fn = tf.data.TFRecordDataset
+    self._dataset_fn = dataset_fn.pick_dataset_fn(file_type)
 
   def _parse_single_example(self, example):
     """Parses a single serialized tf.Example proto.
@@ -308,7 +320,7 @@ class COCOGroundtruthGenerator:
     boxes = box_ops.denormalize_boxes(
         decoded_tensors['groundtruth_boxes'], image_size)
     groundtruths = {
-        'source_id': tf.string_to_number(
+        'source_id': tf.strings.to_number(
             decoded_tensors['source_id'], out_type=tf.int64),
         'height': decoded_tensors['height'],
         'width': decoded_tensors['width'],
@@ -344,12 +356,13 @@ class COCOGroundtruthGenerator:
 
 
 def scan_and_generator_annotation_file(file_pattern: str,
+                                       file_type: str,
                                        num_samples: int,
                                        include_mask: bool,
                                        annotation_file: str):
   """Scans and generate the COCO-style annotation JSON file given a dataset."""
   groundtruth_generator = COCOGroundtruthGenerator(
-      file_pattern, num_samples, include_mask)
+      file_pattern, file_type, num_samples, include_mask)
   generate_annotation_file(groundtruth_generator, annotation_file)
 
 

official/vision/beta/tasks/maskrcnn.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 """RetinaNet task definition."""
+import os
 from typing import Any, Optional, List, Tuple, Mapping
 
 from absl import logging
@@ -26,6 +27,7 @@ from official.vision.beta.dataloaders import maskrcnn_input
 from official.vision.beta.dataloaders import tf_example_decoder
 from official.vision.beta.dataloaders import tf_example_label_map_decoder
 from official.vision.beta.evaluation import coco_evaluator
+from official.vision.beta.evaluation import coco_utils
 from official.vision.beta.losses import maskrcnn_losses
 from official.vision.beta.modeling import factory
 
@@ -259,10 +261,33 @@ class MaskRCNNTask(base_task.Task):
         metrics.append(tf.keras.metrics.Mean(name, dtype=tf.float32))
 
     else:
+      if self._task_config.annotation_file:
         self.coco_metric = coco_evaluator.COCOEvaluator(
             annotation_file=self._task_config.annotation_file,
             include_mask=self._task_config.model.include_mask,
             per_category_metrics=self._task_config.per_category_metrics)
+      else:
+        annotation_path = os.path.join(self._logging_dir, 'annotation.json')
+        if tf.io.gfile.exists(annotation_path):
+          logging.info(
+              'annotation.json file exists, skipping creating the annotation'
+              ' file.')
+        else:
+          if self._task_config.validation_data.num_examples <= 0:
+            logging.info('validation_data.num_examples needs to be > 0')
+          if not self._task_config.validation_data.input_path:
+            logging.info('Can not create annotation file for tfds.')
+          logging.info(
+              'Creating coco-style annotation file: %s', annotation_path)
+          coco_utils.scan_and_generator_annotation_file(
+              self._task_config.validation_data.input_path,
+              self._task_config.validation_data.file_type,
+              self._task_config.validation_data.num_examples,
+              self.task_config.model.include_mask, annotation_path)
+        self.coco_metric = coco_evaluator.COCOEvaluator(
+            annotation_file=annotation_path,
+            include_mask=self._task_config.model.include_mask,
+            per_category_metrics=self._task_config.per_category_metrics)
 
     return metrics