github.com/apache/beam/sdks/v2@v2.48.2/python/apache_beam/examples/inference/pytorch_image_segmentation.py

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"""A pipeline that uses RunInference API to perform image segmentation."""

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 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

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))


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)
  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 PostProcessor(beam.DoFn):
  def process(self, element: Tuple[str, PredictionResult]) -> Iterable[str]:
    filename, prediction_result = element
    prediction_labels = prediction_result.inference['labels']
    classes = [CLASS_ID_TO_NAME[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',
      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_path',
      dest='model_state_dict_path',
      required=True,
      help="Path to the model's state_dict. "
      "Default state_dict would be maskrcnn_resnet50_fpn.")
  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)


def run(
    argv=None,
    model_class=None,
    model_params=None,
    save_main_session=True,
    test_pipeline=None) -> PipelineResult:
  """
  Args:
    argv: Command line arguments defined for this example.
    model_class: Reference to the class definition of the model.
                If None, maskrcnn_resnet50_fpn will be used as default .
    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

  if not model_class:
    model_class = maskrcnn_resnet50_fpn
    model_params = {'num_classes': 91}

  model_handler = PytorchModelHandlerTensor(
      state_dict_path=known_args.model_state_dict_path,
      model_class=model_class,
      model_params=model_params)

  pipeline = test_pipeline
  if not test_pipeline:
    pipeline = beam.Pipeline(options=pipeline_options)

  filename_value_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: (file_name, preprocess_image(data))))
  predictions = (
      filename_value_pair
      | 'PyTorchRunInference' >> RunInference(KeyedModelHandler(model_handler))
      | 'ProcessOutput' >> beam.ParDo(PostProcessor()))

  _ = predictions | "WriteOutput" >> beam.io.WriteToText(
      known_args.output, shard_name_template='', append_trailing_newlines=True)

  result = pipeline.run()
  result.wait_until_finish()
  return result


if __name__ == '__main__':
  logging.getLogger().setLevel(logging.INFO)
  run()