AnandInguva commented on code in PR #17462: URL: https://github.com/apache/beam/pull/17462#discussion_r864409165
########## sdks/python/apache_beam/ml/inference/examples/pytorch_image_classification.py: ########## @@ -0,0 +1,123 @@ +# +# 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. +# +# pylint: skip-file + +import argparse +import io +import os +from functools import partial + +import apache_beam as beam +import torch +import torchvision +import torchvision.transforms as transforms +from apache_beam.io.filesystems import FileSystems +from apache_beam.ml.inference.api import RunInference +from apache_beam.ml.inference.pytorch import PytorchModelLoader +from apache_beam.options.pipeline_options import PipelineOptions +from apache_beam.options.pipeline_options import SetupOptions +from PIL import Image + +_IMG_SIZE = (224, 224) +normalize = transforms.Normalize( + mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) + +transform = transforms.Compose([ + transforms.Resize(_IMG_SIZE), + transforms.ToTensor(), + normalize, +]) + + +def read_image(path_to_file: str, path_to_dir: str): + path_to_file = os.path.join(path_to_dir, path_to_file) + with FileSystems().open(path_to_file, 'r') as file: + data = Image.open(io.BytesIO(file.read())).convert('RGB') + return path_to_file, data + + +def preprocess_data(data): + return transform(data) + + +class PostProcessor(beam.DoFn): + """Post process PredictionResult to output filename and + prediction using torch.""" + def process(self, element): + filename, prediction_result = element + prediction = torch.argmax(prediction_result.inference, dim=0) + yield filename + ',' + str(int(prediction)) + + +def setup_pipeline(options: PipelineOptions, args=None): Review Comment: Yes, it should be run_pipeline(). Started with setup_pipeline(). Will edit once I put it to review -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
