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Add README for image classification example (apache#21758)
* Add README for image classification example * Fix typos and input name changes * Fix typos and clarify inputs text * Add link to GCP console; Add clarifying comment
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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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# Example RunInference API Pipelines | ||
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This module contains example pipelines that use the Beam RunInference | ||
API. <!---TODO: Add link to full documentation on Beam website when it's published.--> | ||
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## Pre-requisites | ||
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You must have `apache-beam>=2.40.0` installed in order to run these pipelines, | ||
because the `apache_beam.examples.inference` module was added in that release. | ||
``` | ||
pip install apache-beam==2.40.0 | ||
``` | ||
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### Pytorch dependencies | ||
The RunInference API has support for the Pytorch framework. To use Pytorch locally, first install `torch`. | ||
``` | ||
pip install torch==1.11.0 | ||
``` | ||
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For installation of the `torch` dependency on a distributed runner, like Dataflow, refer to these | ||
[instructions](https://beam.apache.org/documentation/sdks/python-pipeline-dependencies/#pypi-dependencies). | ||
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<!--- | ||
TODO: Add link to full documentation on Beam website when it's published. | ||
i.e. "See the | ||
[documentation](https://beam.apache.org/documentation/dsls/dataframes/overview/#pre-requisites) | ||
for details." | ||
--> | ||
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### Datasets and Models for RunInference | ||
Data related to RunInference has been staged in | ||
`gs://apache-beam-ml/` for use with these example pipelines. You can view the data [here](https://console.cloud.google.com/storage/browser/apache-beam-ml). You also can see this by using the [gsutil tool](https://cloud.google.com/storage/docs/gsutil#gettingstarted). | ||
``` | ||
gsutil ls gs://apache-beam-ml | ||
``` | ||
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--- | ||
## Image Classification with ImageNet dataset | ||
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[`pytorch_image_classification.py`](./pytorch_image_classification.py) contains | ||
an implementation for a RunInference pipeline thatpeforms image classification | ||
on [ImageNet dataset](https://www.image-net.org/) using the MobileNetV2 | ||
architecture. | ||
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The pipeline reads the images, performs basic preprocessing, passes them to the | ||
PyTorch implementation of RunInference, and then writes the predictions | ||
to a text file in GCS. | ||
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### Dataset and model for Image Classification | ||
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<!--- | ||
TODO: Add once benchmark test is released | ||
- `gs://apache-beam-ml/testing/inputs/imagenet_validation_inputs.txt`: | ||
text file containing the GCS paths of the images of all 5000 imagenet validation data | ||
- gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00000001.JPEG | ||
- ... | ||
- gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00050000.JPEG | ||
--> | ||
- `gs://apache-beam-ml/testing/inputs/it_imagenet_validation_inputs.txt`: | ||
text file containing the GCS paths of the images of a subset of 15 imagenet | ||
validation data. See the following example command to view contents of the file: | ||
``` | ||
$ gsutil cat gs://apache-beam-ml/testing/inputs/it_imagenet_validation_inputs.txt | ||
gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00000001.JPEG | ||
... | ||
gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_00000015.JPEG | ||
``` | ||
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- `gs://apache-beam-ml/datasets/imagenet/raw-data/validation/ILSVRC2012_val_*.JPEG`: | ||
JPEG images for the entire validation dataset. | ||
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- `gs://apache-beam-ml/models/torchvision.models.mobilenet_v2.pth`: Path to | ||
the location of the saved state_dict of the pretrained mobilenet_v2 model | ||
from the `torchvision.models` subdirectory. | ||
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### Running `pytorch_image_classification.py` | ||
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To run the image classification pipeline locally, use the following command: | ||
```sh | ||
python -m apache_beam.examples.inference.pytorch_image_classification \ | ||
--input gs://apache-beam-ml/testing/inputs/it_imagenet_validation_inputs.txt \ | ||
--output predictions.csv \ | ||
--model_state_dict_path gs://apache-beam-ml/models/torchvision.models.mobilenet_v2.pth | ||
``` | ||
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This will write the output to the `predictions.csv` with contents like: | ||
``` | ||
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 | ||
... | ||
``` | ||
where the second item in each line is the integer representing the predicted class of the | ||
image. |