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Add Pytorch image segmentation example (apache#21766)
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sdks/python/apache_beam/examples/inference/pytorch_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. | ||
# | ||
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"""A pipeline that uses RunInference API to perform image segmentation.""" | ||
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import argparse | ||
import io | ||
import os | ||
from typing import Iterable | ||
from typing import Optional | ||
from typing import Tuple | ||
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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 PIL import Image | ||
from torchvision import transforms | ||
from torchvision.models.detection import maskrcnn_resnet50_fpn | ||
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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' | ||
] | ||
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CLASS_ID_TO_NAME = dict(enumerate(COCO_INSTANCE_CLASSES)) | ||
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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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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) | ||
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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) | ||
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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_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) | ||
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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 | ||
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if not model_class: | ||
model_class = maskrcnn_resnet50_fpn | ||
model_params = {'num_classes': 91} | ||
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model_handler = PytorchModelHandlerTensor( | ||
state_dict_path=known_args.model_state_dict_path, | ||
model_class=model_class, | ||
model_params=model_params) | ||
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with beam.Pipeline(options=pipeline_options) as p: | ||
filename_value_pair = ( | ||
p | ||
| 'ReadImageNames' >> beam.io.ReadFromText( | ||
known_args.input, skip_header_lines=1) | ||
| '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 | ||
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'PyTorchRunInference' >> RunInference(KeyedModelHandler(model_handler)) | ||
| 'ProcessOutput' >> beam.ParDo(PostProcessor())) | ||
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_ = predictions | "WriteOutput" >> beam.io.WriteToText( | ||
known_args.output, | ||
shard_name_template='', | ||
append_trailing_newlines=True) | ||
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if __name__ == '__main__': | ||
run() |
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