yeandy commented on code in PR #21766: URL: https://github.com/apache/beam/pull/21766#discussion_r895867087
########## sdks/python/apache_beam/examples/inference/pytorch_image_segmentation.py: ########## @@ -0,0 +1,244 @@ +# +# 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 PytorchModelHandler +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' +] + +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) + # 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 = [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', + default='gs://apache-beam-ml/testing/inputs/' + 'it_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', + help="Path to the model's state_dict. " + "Default state_dict would be maskrcnn_resnet50_fpn.") + parser.add_argument( + '--images_dir', + default='gs://apache-beam-ml/datasets/coco/raw-data/val2017', + 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): + """ + 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. + """ + 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 = PytorchModelHandler( Review Comment: Added. -- This is an automated message from the Apache Git Service. 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