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Add per key inference integration test (#28026)
* Add per key inference integration test * Lint
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sdks/python/apache_beam/examples/inference/pytorch_model_per_key_image_segmentation.py
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| # | ||
| # 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 using | ||
| multiple different models. | ||
| """ | ||
|
|
||
| import argparse | ||
| import io | ||
| import logging | ||
| import os | ||
| from typing import Iterable | ||
| from typing import Iterator | ||
| 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.base import KeyedModelHandler | ||
| from apache_beam.ml.inference.base import KeyMhMapping | ||
| from apache_beam.ml.inference.base import PredictionResult | ||
| from apache_beam.ml.inference.base import RunInference | ||
| from apache_beam.ml.inference.pytorch_inference import PytorchModelHandlerTensor | ||
| from apache_beam.options.pipeline_options import PipelineOptions | ||
| from apache_beam.options.pipeline_options import SetupOptions | ||
| from apache_beam.runners.runner import PipelineResult | ||
| from PIL import Image | ||
| from torchvision import transforms | ||
| from torchvision.models.detection import maskrcnn_resnet50_fpn | ||
| from torchvision.models.detection import maskrcnn_resnet50_fpn_v2 | ||
|
|
||
| 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' | ||
| ] | ||
|
|
||
| CLASS_ID_TO_NAME = dict(enumerate(COCO_INSTANCE_CLASSES)) | ||
|
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|
|
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| 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 | ||
|
|
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|
|
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| def preprocess_image(data: Image.Image) -> torch.Tensor: | ||
| image_size = (224, 224) | ||
| transform = transforms.Compose([ | ||
| transforms.Resize(image_size), | ||
| transforms.ToTensor(), | ||
| ]) | ||
| return transform(data) | ||
|
|
||
|
|
||
| def filter_empty_lines(text: str) -> Iterator[str]: | ||
| if len(text.strip()) > 0: | ||
| yield text | ||
|
|
||
|
|
||
| class KeyExamplesForEachModelType(beam.DoFn): | ||
| """Duplicate data to run against each model type""" | ||
| def process( | ||
| self, element: Tuple[torch.Tensor, | ||
| str]) -> Iterable[Tuple[str, torch.Tensor]]: | ||
| yield 'v1', element[0] | ||
| yield 'v2', element[0] | ||
|
|
||
|
|
||
| class PostProcessor(beam.DoFn): | ||
| def process( | ||
| self, element: Tuple[str, PredictionResult]) -> Tuple[torch.Tensor, str]: | ||
| model, prediction_result = element | ||
| prediction_labels = prediction_result.inference['labels'] | ||
| classes = [CLASS_ID_TO_NAME[label.item()] for label in prediction_labels] | ||
| yield prediction_result.example, f'{model} predictions: {str(classes)}' | ||
|
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|
|
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| class FormatResults(beam.DoFn): | ||
| def process(self, element): | ||
| _, filename_prediction = element | ||
| predictions = filename_prediction['predictions'] | ||
| v1_predictions = next(p for p in predictions if 'v1 predictions' in p) | ||
| v2_predictions = next(p for p in predictions if 'v2 predictions' in p) | ||
| yield ( | ||
| f"{filename_prediction['image_names'][0]} --- " | ||
| f"{v1_predictions} --- " | ||
| f"{v2_predictions}") | ||
|
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||
|
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||
| def parse_known_args(argv): | ||
| """Parses args for the workflow.""" | ||
| parser = argparse.ArgumentParser() | ||
| parser.add_argument( | ||
| '--input', | ||
| dest='input', | ||
| required=True, | ||
| help='Path to the text file containing image names.') | ||
| parser.add_argument( | ||
| '--output', | ||
| dest='output', | ||
| required=True, | ||
| help='Path where to save output predictions.' | ||
| ' text file.') | ||
| parser.add_argument( | ||
| '--model_state_dict_paths', | ||
| dest='model_state_dict_paths', | ||
| required=True, | ||
| help="Comma separated paths to the models' state_dicts. " | ||
| "For this example, should include exactly 2 state_dicts corresponding " | ||
| "to maskrcnn_resnet50_fpn and maskrcnn_resnet50_fpn_v2 classes.") | ||
| parser.add_argument( | ||
| '--images_dir', | ||
| help='Path to the directory where images are stored.' | ||
| 'Not required if image names in the input file have absolute path.') | ||
| return parser.parse_known_args(argv) | ||
|
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||
|
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||
| def run( | ||
| argv=None, save_main_session=True, test_pipeline=None) -> PipelineResult: | ||
| """ | ||
| Args: | ||
| argv: Command line arguments defined for this example. | ||
| model_params: Parameters passed to the constructor of the model_class. | ||
| These will be used to instantiate the model object in the | ||
| RunInference API. | ||
| save_main_session: Used for internal testing. | ||
| test_pipeline: Used for internal testing. | ||
| """ | ||
| known_args, pipeline_args = parse_known_args(argv) | ||
| pipeline_options = PipelineOptions(pipeline_args) | ||
| pipeline_options.view_as(SetupOptions).save_main_session = save_main_session | ||
|
|
||
| state_dicts = known_args.model_state_dict_paths.split(',') | ||
| if len(state_dicts) != 2: | ||
| raise AssertionError( | ||
| f'Expected exactly 2 state_dicts to be supplied, got {len(state_dicts)}' | ||
| ) | ||
|
|
||
| mh1 = PytorchModelHandlerTensor( | ||
| state_dict_path=state_dicts[0], | ||
| model_class=maskrcnn_resnet50_fpn, | ||
| model_params={'num_classes': 91}) | ||
| mh2 = PytorchModelHandlerTensor( | ||
| state_dict_path=state_dicts[1], | ||
| model_class=maskrcnn_resnet50_fpn_v2, | ||
| model_params={'num_classes': 91}) | ||
|
|
||
| # We'll use v1 and v2 as our keys to point to our model handlers. | ||
| # Note that multiple keys can also point to a single model handler, | ||
| # unlike this example. | ||
| model_handler = KeyedModelHandler( | ||
| [KeyMhMapping(['v1'], mh1), KeyMhMapping(['v2'], mh2)]) | ||
|
|
||
| pipeline = test_pipeline | ||
| if not test_pipeline: | ||
| pipeline = beam.Pipeline(options=pipeline_options) | ||
|
|
||
| value_filename_pair = ( | ||
| pipeline | ||
| | 'ReadImageNames' >> beam.io.ReadFromText(known_args.input) | ||
| | 'FilterEmptyLines' >> beam.ParDo(filter_empty_lines) | ||
| | 'ReadImageData' >> beam.Map( | ||
| lambda image_name: read_image( | ||
| image_file_name=image_name, path_to_dir=known_args.images_dir)) | ||
| | 'PreprocessImages' >> beam.MapTuple( | ||
| lambda file_name, data: (preprocess_image(data), file_name))) | ||
|
|
||
| predictions = ( | ||
| value_filename_pair | ||
| | 'DuplicateData' >> beam.ParDo(KeyExamplesForEachModelType()) | ||
| | 'PyTorchRunInference' >> RunInference(model_handler) | ||
| | 'ProcessOutput' >> beam.ParDo(PostProcessor())) | ||
|
|
||
| # We now have our set of (example, prediction) and (example, original | ||
| # filename) tuples. We can use CoGroupByKey to join them by original example, | ||
| # converting the Tensors to lists first so that Beam can compare them without | ||
| # a custom coder. If all you care about is the model used for inference, you | ||
| # can also get that from the PredictionResult returned from RunInference. | ||
| results = ({ | ||
| 'image_names': ( | ||
| value_filename_pair | beam.MapTuple( | ||
| lambda example, filename: (example.tolist(), filename))), | ||
| 'predictions': ( | ||
| predictions | beam.MapTuple( | ||
| lambda example, prediction: (example.tolist(), prediction))) | ||
| } | beam.CoGroupByKey()) | ||
|
|
||
| _ = ( | ||
| results | ||
| | 'FormatResults' >> beam.ParDo(FormatResults()) | ||
| | "WriteOutput" >> beam.io.WriteToText( | ||
| known_args.output, | ||
| shard_name_template='', | ||
| append_trailing_newlines=True)) | ||
|
|
||
| result = pipeline.run() | ||
| result.wait_until_finish() | ||
| return result | ||
|
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|
|
||
| if __name__ == '__main__': | ||
| logging.getLogger().setLevel(logging.INFO) | ||
| run() |
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