yeandy commented on code in PR #21766:
URL: https://github.com/apache/beam/pull/21766#discussion_r893541839
##########
sdks/python/apache_beam/ml/inference/pytorch_inference_it_test.py:
##########
@@ -89,6 +90,42 @@ def test_torch_run_inference_imagenet_mobilenetv2(self):
filename, prediction = prediction.split(',')
self.assertEqual(_EXPECTED_OUTPUTS[filename], prediction)
+ @pytest.mark.uses_pytorch
+ @pytest.mark.it_postcommit
+ def test_torch_run_inference_coco_maskrcnn_resnet50_fpn(self):
+ test_pipeline = TestPipeline(is_integration_test=True)
+ # text files containing absolute path to the coco validation data on GCS
+ file_of_image_names =
'gs://apache-beam-ml/testing/inputs/it_maskrcnn_resnet50_fpn_coco_validation_inputs.txt'
# disable: line-too-long
+ output_file_dir = 'gs://apache-beam-ml/testing/predictions'
+ output_file = '/'.join([output_file_dir, str(uuid.uuid4()), 'result.txt'])
+
+ model_state_dict_path =
'gs://apache-beam-ml/models/torchvision.models.detection.maskrcnn_resnet50_fpn.pth'
+ images_dir = 'gs://apache-beam-ml/datasets/coco/raw-data/val2017'
+ extra_opts = {
+ 'input': file_of_image_names,
+ 'output': output_file,
+ 'model_state_dict_path': model_state_dict_path,
+ 'images_dir': images_dir,
+ }
+ pytorch_image_segmentation.run(
+ test_pipeline.get_full_options_as_args(**extra_opts),
+ save_main_session=False)
+
+ self.assertEqual(FileSystems().exists(output_file), True)
+ predictions = process_outputs(filepath=output_file)
+ actuals_file =
'gs://apache-beam-ml/testing/expected_outputs/test_torch_run_inference_coco_maskrcnn_resnet50_fpn_actuals.txt'
+ actuals = process_outputs(filepath=actuals_file)
+
+ predictions_dict = {}
+ for prediction in predictions:
+ filename, prediction_labels = prediction.split(';')
+ predictions_dict[filename] = prediction_labels
+
+ for actual in actuals:
+ filename, actual_labels = actual.split(';')
+ prediction_labels = predictions_dict[filename]
+ self.assertEqual(actual_labels, prediction_labels)
+
Review Comment:
@AnandInguva I've uploaded my expected outputs to GCS, but yours is hard
coded. We should standardize?
##########
sdks/python/apache_beam/examples/inference/pytorch_image_segmentation.py:
##########
@@ -0,0 +1,249 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You 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.
+#
+
+""""A pipeline that uses RunInference API to perform image segmentation."""
+
+import argparse
+import io
+import os
+from typing import Iterable
+from typing import Optional
+from typing import Tuple
+
+import apache_beam as beam
+import torch
+from apache_beam.io.filesystems import FileSystems
+from apache_beam.ml.inference.api import PredictionResult
+from apache_beam.ml.inference.api import RunInference
+from apache_beam.ml.inference.pytorch_inference import PytorchModelLoader
+from apache_beam.options.pipeline_options import PipelineOptions
+from apache_beam.options.pipeline_options import SetupOptions
+from PIL import Image
+from torchvision import transforms
+from torchvision.models.detection import maskrcnn_resnet50_fpn
+
+COCO_INSTANCE_CLASSES = [
+ '__background__',
+ 'person',
+ 'bicycle',
+ 'car',
+ 'motorcycle',
+ 'airplane',
+ 'bus',
+ 'train',
+ 'truck',
+ 'boat',
+ 'traffic light',
+ 'fire hydrant',
+ 'N/A',
+ 'stop sign',
+ 'parking meter',
+ 'bench',
+ 'bird',
+ 'cat',
+ 'dog',
+ 'horse',
+ 'sheep',
+ 'cow',
+ 'elephant',
+ 'bear',
+ 'zebra',
+ 'giraffe',
+ 'N/A',
+ 'backpack',
+ 'umbrella',
+ 'N/A',
+ 'N/A',
+ 'handbag',
+ 'tie',
+ 'suitcase',
+ 'frisbee',
+ 'skis',
+ 'snowboard',
+ 'sports ball',
+ 'kite',
+ 'baseball bat',
+ 'baseball glove',
+ 'skateboard',
+ 'surfboard',
+ 'tennis racket',
+ 'bottle',
+ 'N/A',
+ 'wine glass',
+ 'cup',
+ 'fork',
+ 'knife',
+ 'spoon',
+ 'bowl',
+ 'banana',
+ 'apple',
+ 'sandwich',
+ 'orange',
+ 'broccoli',
+ 'carrot',
+ 'hot dog',
+ 'pizza',
+ 'donut',
+ 'cake',
+ 'chair',
+ 'couch',
+ 'potted plant',
+ 'bed',
+ 'N/A',
+ 'dining table',
+ 'N/A',
+ 'N/A',
+ 'toilet',
+ 'N/A',
+ 'tv',
+ 'laptop',
+ 'mouse',
+ 'remote',
+ 'keyboard',
+ 'cell phone',
+ 'microwave',
+ 'oven',
+ 'toaster',
+ 'sink',
+ 'refrigerator',
+ 'N/A',
+ 'book',
+ 'clock',
+ 'vase',
+ 'scissors',
+ 'teddy bear',
+ 'hair drier',
+ 'toothbrush'
+]
+
+COCO_INSTANCE_CLASSES_TO_IDX = {
+ idx: cls
+ for (idx, cls) in enumerate(COCO_INSTANCE_CLASSES)
+}
+
+
+def read_image(image_file_name: str,
+ path_to_dir: Optional[str] = None) -> Tuple[str, Image.Image]:
+ if path_to_dir is not None:
+ image_file_name = os.path.join(path_to_dir, image_file_name)
+ with FileSystems().open(image_file_name, 'r') as file:
+ data = Image.open(io.BytesIO(file.read())).convert('RGB')
+ return image_file_name, data
+
+
+def preprocess_image(data: Image.Image) -> torch.Tensor:
+ image_size = (224, 224)
+ # Pre-trained PyTorch models expect input images normalized with the
+ # below values (see: https://pytorch.org/vision/stable/models.html)
+ normalize = transforms.Normalize(
+ mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+ transform = transforms.Compose([
+ transforms.Resize(image_size),
+ transforms.ToTensor(),
+ normalize,
+ ])
+ return transform(data)
+
+
+class PostProcessor(beam.DoFn):
+ def process(self, element: Tuple[str, PredictionResult]) -> Iterable[str]:
+ filename, prediction_result = element
+ prediction_labels = prediction_result.inference['labels']
+ classes = [
+ COCO_INSTANCE_CLASSES_TO_IDX[label.item()]
+ for label in prediction_labels
+ ]
+ yield filename + ';' + str(classes)
+
+
+def parse_known_args(argv):
+ """Parses args for the workflow."""
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ '--input',
+ dest='input',
+ default='gs://apache-beam-ml/testing/inputs/'
+ 'it_maskrcnn_resnet50_fpn_coco_validation_inputs.txt',
+ help='Path to the text file containing image names.')
+ parser.add_argument(
+ '--output',
+ dest='output',
+ help='Path where to save output predictions.'
+ ' text file.')
+ parser.add_argument(
+ '--model_state_dict_path',
+ dest='model_state_dict_path',
+ default='gs://apache-beam-ml/'
+ 'models/torchvision.models.detection.maskrcnn_resnet50_fpn.pth',
Review Comment:
@AnandInguva your mobilenetv2 model is from torchvision too, right? I also
uploaded a state_dict from the pre-trained model, but with this naming pattern
`torchvision.models.mobilenet_v2.pth`
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