AnandInguva commented on code in PR #17462: URL: https://github.com/apache/beam/pull/17462#discussion_r889185574
########## sdks/python/apache_beam/examples/inference/pytorch_image_classification.py: ########## @@ -0,0 +1,146 @@ +# +# 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. +# + +"""Pipeline that uses RunInference API to perform classification task on imagenet dataset""" # pylint: disable=line-too-long + +import argparse +import io +import os +from functools import partial +from typing import Any +from typing import Iterable +from typing import Tuple +from typing import Union + +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 PytorchModelLoader +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.mobilenetv2 import MobileNetV2 + + +def read_image(image_file_name: str, + path_to_dir: 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) -> torch.Tensor: Review Comment: Thanks for catching ########## sdks/python/apache_beam/examples/inference/pytorch_image_classification.py: ########## @@ -0,0 +1,146 @@ +# +# 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. +# + +"""Pipeline that uses RunInference API to perform classification task on imagenet dataset""" # pylint: disable=line-too-long + +import argparse +import io +import os +from functools import partial +from typing import Any +from typing import Iterable +from typing import Tuple +from typing import Union + +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 PytorchModelLoader +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.mobilenetv2 import MobileNetV2 + + +def read_image(image_file_name: str, + path_to_dir: 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) -> torch.Tensor: + image_size = (224, 224) + # to use models in torch with imagenet weights, Review Comment: Done ########## sdks/python/apache_beam/examples/inference/pytorch_image_classification.py: ########## @@ -0,0 +1,146 @@ +# +# 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. +# + +"""Pipeline that uses RunInference API to perform classification task on imagenet dataset""" # pylint: disable=line-too-long + +import argparse +import io +import os +from functools import partial +from typing import Any +from typing import Iterable +from typing import Tuple +from typing import Union + +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 PytorchModelLoader +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.mobilenetv2 import MobileNetV2 + + +def read_image(image_file_name: str, + path_to_dir: 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) -> torch.Tensor: + image_size = (224, 224) + # to use models in torch with imagenet weights, + # normalize the images using the below values. + # ref: 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: Union[PredictionResult, Tuple[Any, PredictionResult]] + ) -> Iterable[str]: + filename, prediction_result = element + prediction = torch.argmax(prediction_result.inference, dim=0) + yield filename + ',' + str(prediction.item()) + + +def run_pipeline(options: PipelineOptions, args=None): + """Sets up PyTorch RunInference pipeline""" + # reference to the class definition of the model. + model_class = MobileNetV2 + # params for model class constructor. These values will be used in + # RunInference API to instantiate the model object. + model_params = {'num_classes': 1000} # imagenet has 1000 classes. + # for this example, the pretrained weights are downloaded from + # "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth"; + # and saved on GCS bucket gs://apache-beam-ml/models/imagenet_classification_mobilenet_v2.pt, + # which will be used to load the model state_dict in the RunInference API. + model_loader = PytorchModelLoader( + state_dict_path=args.model_state_dict_path, + model_class=model_class, + model_params=model_params) + with beam.Pipeline(options=options) as p: + filename_value_pair = ( + p + | 'Read from csv file' >> beam.io.ReadFromText( + args.input, skip_header_lines=1) + | 'Parse and read files from the input_file' >> beam.Map( Review Comment: Done ########## sdks/python/apache_beam/ml/inference/pytorch_inference_it_test.py: ########## @@ -0,0 +1,95 @@ +# +# 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 + +"""End-to-End test for Pytorch Inference""" + +import logging +import os +import unittest +import uuid + +import pytest + +from apache_beam.io.filesystems import FileSystems +from apache_beam.testing.test_pipeline import TestPipeline + +try: + import torch + from apache_beam.examples.inference import pytorch_image_classification +except ImportError as e: + torch = None + +_EXPECTED_OUTPUTS = { + 'gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005001.JPEG': '681', + 'gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005002.JPEG': '333', + 'gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005003.JPEG': '711', + 'gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005004.JPEG': '286', + 'gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005005.JPEG': '433', + 'gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005006.JPEG': '290', + 'gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005007.JPEG': '890', + 'gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005008.JPEG': '592', + 'gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005009.JPEG': '406', + 'gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005010.JPEG': '996', + 'gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005011.JPEG': '327', + 'gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005012.JPEG': '573' +} + + +def process_outputs(filepath): + with FileSystems().open(filepath) as f: + lines = f.readlines() + lines = [l.decode('utf-8').strip('\n') for l in lines] + return lines + + [email protected]( + os.getenv('FORCE_TORCH_IT') is None and torch is None, + 'Missing dependencies. ' + 'Test depends on torch, torchvision and pillow') +class PyTorchInference(unittest.TestCase): + @pytest.mark.uses_pytorch + @pytest.mark.it_postcommit + def test_torch_run_inference_imagenet_mobilenetv2(self): + test_pipeline = TestPipeline(is_integration_test=True) + # text files containing absolute path to the imagenet validation data on GCS + file_of_image_names = 'gs://apache-beam-ml/testing/inputs/it_mobilenetv2_imagenet_validation_inputs.txt' # disable: line-too-long + output_file_dir = 'gs://apache-beam-ml/testing/predictions' + output_file = '/'.join([output_file_dir, str(uuid.uuid4()), 'result.txt']) + + model_state_dict_path = 'gs://apache-beam-ml/models/imagenet_classification_mobilenet_v2.pt' Review Comment: Yes thats true but for the sake of illustration I think its better to define here as well ########## sdks/python/apache_beam/examples/inference/pytorch_image_classification.py: ########## @@ -0,0 +1,146 @@ +# +# 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. +# + +"""Pipeline that uses RunInference API to perform classification task on imagenet dataset""" # pylint: disable=line-too-long + +import argparse +import io +import os +from functools import partial +from typing import Any +from typing import Iterable +from typing import Tuple +from typing import Union + +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 PytorchModelLoader +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.mobilenetv2 import MobileNetV2 + + +def read_image(image_file_name: str, + path_to_dir: 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) -> torch.Tensor: + image_size = (224, 224) + # to use models in torch with imagenet weights, + # normalize the images using the below values. + # ref: 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: Union[PredictionResult, Tuple[Any, PredictionResult]] + ) -> Iterable[str]: + filename, prediction_result = element + prediction = torch.argmax(prediction_result.inference, dim=0) + yield filename + ',' + str(prediction.item()) + + +def run_pipeline(options: PipelineOptions, args=None): + """Sets up PyTorch RunInference pipeline""" + # reference to the class definition of the model. + model_class = MobileNetV2 + # params for model class constructor. These values will be used in + # RunInference API to instantiate the model object. + model_params = {'num_classes': 1000} # imagenet has 1000 classes. + # for this example, the pretrained weights are downloaded from + # "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth"; + # and saved on GCS bucket gs://apache-beam-ml/models/imagenet_classification_mobilenet_v2.pt, + # which will be used to load the model state_dict in the RunInference API. + model_loader = PytorchModelLoader( + state_dict_path=args.model_state_dict_path, + model_class=model_class, + model_params=model_params) + with beam.Pipeline(options=options) as p: + filename_value_pair = ( + p + | 'Read from csv file' >> beam.io.ReadFromText( + args.input, skip_header_lines=1) + | 'Parse and read files from the input_file' >> beam.Map( + partial(read_image, path_to_dir=args.images_dir)) + | 'Preprocess images' >> beam.MapTuple( + lambda file_name, data: (file_name, preprocess_image(data)))) + predictions = ( + filename_value_pair + | 'PyTorch RunInference' >> RunInference(model_loader) + | 'Process output' >> beam.ParDo(PostProcessor())) + + if args.output: + predictions | "Write output to GCS" >> beam.io.WriteToText( # pylint: disable=expression-not-assigned + args.output, + shard_name_template='', + append_trailing_newlines=True) + + +def parse_known_args(argv): + """Parses args for the workflow.""" + parser = argparse.ArgumentParser() + parser.add_argument( + '--input', + dest='input', Review Comment: Done ########## sdks/python/apache_beam/ml/inference/pytorch_inference_it_test.py: ########## @@ -0,0 +1,95 @@ +# +# 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 + +"""End-to-End test for Pytorch Inference""" + +import logging +import os +import unittest +import uuid + +import pytest + +from apache_beam.io.filesystems import FileSystems +from apache_beam.testing.test_pipeline import TestPipeline + +try: + import torch + from apache_beam.examples.inference import pytorch_image_classification +except ImportError as e: + torch = None + +_EXPECTED_OUTPUTS = { + 'gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00005001.JPEG': '681', Review Comment: Yes, I will change this when I add benchmarking tests. -- This is an automated message from the Apache Git Service. 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