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https://issues.apache.org/jira/browse/BEAM-14068?focusedWorklogId=778242&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-778242
]
ASF GitHub Bot logged work on BEAM-14068:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 03/Jun/22 18:06
Start Date: 03/Jun/22 18:06
Worklog Time Spent: 10m
Work Description: AnandInguva commented on code in PR #17462:
URL: https://github.com/apache/beam/pull/17462#discussion_r889212195
##########
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',
+ required=True,
+ help='Path to the CSV file containing image names')
+ parser.add_argument(
+ '--output',
+ dest='output',
+ help='Predictions are saved to the output'
+ ' text file.')
+ parser.add_argument(
+ '--model_state_dict_path',
+ dest='model_state_dict_path',
+ required=True,
+ help='Path to load the model.')
Review Comment:
Adde model_class and model_params as input argument to the run() method. In
this way, this example can be used to run different models that can predict on
Imagenet Data
##########
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',
+ required=True,
+ help='Path to the CSV file containing image names')
+ parser.add_argument(
+ '--output',
+ dest='output',
+ help='Predictions are saved to the output'
+ ' text file.')
+ parser.add_argument(
+ '--model_state_dict_path',
+ dest='model_state_dict_path',
+ required=True,
+ help='Path to load the model.')
Review Comment:
Added model_class and model_params as input argument to the run() method. In
this way, this example can be used to run different models that can predict on
Imagenet Data
Issue Time Tracking
-------------------
Worklog Id: (was: 778242)
Time Spent: 9h 20m (was: 9h 10m)
> RunInference Benchmarking tests
> -------------------------------
>
> Key: BEAM-14068
> URL: https://issues.apache.org/jira/browse/BEAM-14068
> Project: Beam
> Issue Type: Sub-task
> Components: sdk-py-core
> Reporter: Anand Inguva
> Assignee: Anand Inguva
> Priority: P2
> Time Spent: 9h 20m
> Remaining Estimate: 0h
>
> RunInference benchmarks will evaluate performance of Pipelines, which
> represent common use cases of Beam + Dataflow in Pytorch, sklearn and
> possibly TFX. These benchmarks would be the integration tests that exercise
> several software components using Beam, PyTorch, Scikit learn and TensorFlow
> extended.
> we would use the datasets that's available publicly (Eg; Kaggle).
> Size: small / 10 GB / 1 TB etc
> The default execution runner would be Dataflow unless specified otherwise.
> These tests would be run very less frequently(every release cycle).
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