[
https://issues.apache.org/jira/browse/BEAM-14068?focusedWorklogId=778233&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-778233
]
ASF GitHub Bot logged work on BEAM-14068:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 03/Jun/22 17:47
Start Date: 03/Jun/22 17:47
Worklog Time Spent: 10m
Work Description: tvalentyn commented on code in PR #17462:
URL: https://github.com/apache/beam/pull/17462#discussion_r889198878
##########
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]]
Review Comment:
I think this is because RunInference API can produce predictions with and
without original examples. I think there are two ways to resolve this:
1) Clarify in RunInference transform that we will have only examples and
predictions in the output
```
| 'PyTorch RunInference' >>
RunInference(model_loader).with_output_types(Tuple[str, PredictionResult])
```
2) We generalize the postprocessor to handle both cases: when there are
examples + predictions and when there are no examples, only predictions. In
that case, the current type hint would make sense.
If we go with 2) we can also consider adding an additional pipeline option
whether or not to include examples or just predictions in the final result.
Issue Time Tracking
-------------------
Worklog Id: (was: 778233)
Time Spent: 9h (was: 8h 50m)
> 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
> 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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