TheNeuralBit commented on code in PR #22069: URL: https://github.com/apache/beam/pull/22069#discussion_r907877286
########## sdks/python/apache_beam/examples/inference/README.md: ########## @@ -60,27 +61,28 @@ for details." --- ## Image classification -[`pytorch_image_classification.py`](./pytorch_image_classification.py) contains an implementation for a RunInference pipeline that performs image classification using the mobilenet_v2 architecture. +[`pytorch_image_classification.py`](./pytorch_image_classification.py) contains an implementation for a RunInference pipeline that performs image classification using the `mobilenet_v2` architecture. -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. +The pipeline reads the images, performs basic preprocessing, passes the images to the PyTorch implementation of RunInference, and then writes the predictions to a text file. ### Dataset and model for image classification -You will need to create or download images, and place them into your `IMAGES_DIR` directory. One popular dataset is from [ImageNet](https://www.image-net.org/). Please follow their instructions to download the images. -- **Required**: A path to a file called `IMAGE_FILE_NAMES` that contains the absolute paths of each of the images in `IMAGES_DIR` on which you want to run image segmentation. Paths can be different types of URIs such as your local file system, a AWS S3 bucket or GCP Cloud Storage bucket. For example: +Create a directory named `IMAGES_DIR`. Create or download images and put them in this directory. The directory is not required if image names in the input file `IMAGE_FILE_NAMES` (see below) have absolute paths. +One popular dataset is from [ImageNet](https://www.image-net.org/). Follow their instructions to download the images. +- **Required**: A path to a file named `IMAGE_FILE_NAMES` that contains the absolute paths of each of the images in `IMAGES_DIR` that you want to use to run image classification. Paths can be different types of URIs such as your local file system, an AWS S3 bucket, or a GCP Cloud Storage bucket. For example: ``` /absolute/path/to/image1.jpg /absolute/path/to/image2.jpg ``` -- **Required**: A path to a file called `MODEL_STATE_DICT` that contains the saved parameters of the maskrcnn_resnet50_fpn model. You will need to download the [mobilenet_v2](https://pytorch.org/vision/stable/_modules/torchvision/models/mobilenetv2.html) model from Pytorch's repository of pretrained models. Note that this requires `torchvision` library. +- **Required**: Download the [mobilenet_v2](https://pytorch.org/vision/stable/_modules/torchvision/models/mobilenetv2.html) model from Pytorch's repository of pretrained models. This model requires the torchvision library. To download this model, run the following commands: Review Comment: nit: this is a script, not commands ```suggestion - **Required**: Download the [mobilenet_v2](https://pytorch.org/vision/stable/_modules/torchvision/models/mobilenetv2.html) model from Pytorch's repository of pretrained models. This model requires the torchvision library. To download this model, run the following script: ########## sdks/python/apache_beam/examples/inference/README.md: ########## @@ -60,27 +61,28 @@ for details." --- ## Image classification -[`pytorch_image_classification.py`](./pytorch_image_classification.py) contains an implementation for a RunInference pipeline that performs image classification using the mobilenet_v2 architecture. +[`pytorch_image_classification.py`](./pytorch_image_classification.py) contains an implementation for a RunInference pipeline that performs image classification using the `mobilenet_v2` architecture. -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. +The pipeline reads the images, performs basic preprocessing, passes the images to the PyTorch implementation of RunInference, and then writes the predictions to a text file. ### Dataset and model for image classification -You will need to create or download images, and place them into your `IMAGES_DIR` directory. One popular dataset is from [ImageNet](https://www.image-net.org/). Please follow their instructions to download the images. -- **Required**: A path to a file called `IMAGE_FILE_NAMES` that contains the absolute paths of each of the images in `IMAGES_DIR` on which you want to run image segmentation. Paths can be different types of URIs such as your local file system, a AWS S3 bucket or GCP Cloud Storage bucket. For example: +Create a directory named `IMAGES_DIR`. Create or download images and put them in this directory. The directory is not required if image names in the input file `IMAGE_FILE_NAMES` (see below) have absolute paths. +One popular dataset is from [ImageNet](https://www.image-net.org/). Follow their instructions to download the images. +- **Required**: A path to a file named `IMAGE_FILE_NAMES` that contains the absolute paths of each of the images in `IMAGES_DIR` that you want to use to run image classification. Paths can be different types of URIs such as your local file system, an AWS S3 bucket, or a GCP Cloud Storage bucket. For example: ``` /absolute/path/to/image1.jpg /absolute/path/to/image2.jpg ``` -- **Required**: A path to a file called `MODEL_STATE_DICT` that contains the saved parameters of the maskrcnn_resnet50_fpn model. You will need to download the [mobilenet_v2](https://pytorch.org/vision/stable/_modules/torchvision/models/mobilenetv2.html) model from Pytorch's repository of pretrained models. Note that this requires `torchvision` library. +- **Required**: Download the [mobilenet_v2](https://pytorch.org/vision/stable/_modules/torchvision/models/mobilenetv2.html) model from Pytorch's repository of pretrained models. This model requires the torchvision library. To download this model, run the following commands: ``` import torch from torchvision.models.detection import mobilenet_v2 model = mobilenet_v2(pretrained=True) torch.save(model.state_dict(), 'mobilenet_v2.pth') ``` -- **Required**: A path to a file called `OUTPUT`, to which the pipeline will write the predictions. -- **Optional**: `IMAGES_DIR`, which is the path to the directory where images are stored. Not required if image names in the input file `IMAGE_FILE_NAMES` have absolute paths. +- **Required**: A path to a file named `MODEL_STATE_DICT` that contains the saved parameters of the `mobilenet_v2` model. Review Comment: Could we instead consolidate the above bullet with this one? That way each bullet corresponds to one of the inputs in "Running `pytorch_image_classification.py`." Something like this: ```suggestion - **Required**: A path to a file named `MODEL_STATE_DICT` that contains the saved parameters of the `mobilenet_v2` model. You can download the mobilenet_v2 model from ... use the following script: ``` ########## sdks/python/apache_beam/examples/inference/README.md: ########## @@ -157,21 +159,22 @@ Each line has data separated by a semicolon ";". The first item is the file name --- ## Language modeling -[`pytorch_language_modeling.py`](./pytorch_language_modeling.py) contains an implementation for a RunInference pipeline that performs masked language modeling (i.e. decoding a masked token in a sentence) using the BertForMaskedLM architecture from Hugging Face. +[`pytorch_language_modeling.py`](./pytorch_language_modeling.py) contains an implementation for a RunInference pipeline that performs masked language modeling (that is, decoding a masked token in a sentence) using the `BertForMaskedLM` architecture from Hugging Face. The pipeline reads sentences, performs basic preprocessing to convert the last word into a `[MASK]` token, passes the masked sentence to the PyTorch implementation of RunInference, and then writes the predictions to a text file. ### Dataset and model for language modeling -- **Required**: A path to a file called `MODEL_STATE_DICT` that contains the saved parameters of the BertForMaskedLM model. You will need to download the [BertForMaskedLM](https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertForMaskedLM) model from Hugging Face's repository of pretrained models. Make sure you have installed `transformers` too. +- **Required**: Download the [BertForMaskedLM](https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertForMaskedLM) model from Hugging Face's repository of pretrained models. You must already have `transformers` installed. To download this model, run the following commands: ``` import torch from transformers import BertForMaskedLM model = BertForMaskedLM.from_pretrained('bert-base-uncased', return_dict=True) torch.save(model.state_dict(), 'BertForMaskedLM.pth') ``` -- **Required**: A path to a file called `OUTPUT`, to which the pipeline will write the predictions. -- **Optional**: A path to a file called `SENTENCES` that contains sentences to feed into the model. It should look something like this: +- **Required**: A path to a file namedd `MODEL_STATE_DICT` that contains the saved parameters of the `BertForMaskedLM` model. Review Comment: Similarly here ########## sdks/python/apache_beam/examples/inference/README.md: ########## @@ -37,17 +37,18 @@ The RunInference API supports the PyTorch framework. To use PyTorch locally, fir pip install torch==1.11.0 ``` -If you are using pretrained models from Pytorch's `torchvision.models` [subpackage](https://pytorch.org/vision/0.12/models.html#models-and-pre-trained-weights), you may also need to install `torchvision`. +If you are using pretrained models from Pytorch's `torchvision.models` [subpackage](https://pytorch.org/vision/0.12/models.html#models-and-pre-trained-weights), you might also need to install `torchvision`. Review Comment: @yeandy shouldn't these be "will" or "you must install"? Are there cases where users can use this without installing them? ########## sdks/python/apache_beam/examples/inference/README.md: ########## @@ -60,27 +61,28 @@ for details." --- ## Image classification -[`pytorch_image_classification.py`](./pytorch_image_classification.py) contains an implementation for a RunInference pipeline that performs image classification using the mobilenet_v2 architecture. +[`pytorch_image_classification.py`](./pytorch_image_classification.py) contains an implementation for a RunInference pipeline that performs image classification using the `mobilenet_v2` architecture. -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. +The pipeline reads the images, performs basic preprocessing, passes the images to the PyTorch implementation of RunInference, and then writes the predictions to a text file. ### Dataset and model for image classification -You will need to create or download images, and place them into your `IMAGES_DIR` directory. One popular dataset is from [ImageNet](https://www.image-net.org/). Please follow their instructions to download the images. -- **Required**: A path to a file called `IMAGE_FILE_NAMES` that contains the absolute paths of each of the images in `IMAGES_DIR` on which you want to run image segmentation. Paths can be different types of URIs such as your local file system, a AWS S3 bucket or GCP Cloud Storage bucket. For example: +Create a directory named `IMAGES_DIR`. Create or download images and put them in this directory. The directory is not required if image names in the input file `IMAGE_FILE_NAMES` (see below) have absolute paths. +One popular dataset is from [ImageNet](https://www.image-net.org/). Follow their instructions to download the images. +- **Required**: A path to a file named `IMAGE_FILE_NAMES` that contains the absolute paths of each of the images in `IMAGES_DIR` that you want to use to run image classification. Paths can be different types of URIs such as your local file system, an AWS S3 bucket, or a GCP Cloud Storage bucket. For example: ``` /absolute/path/to/image1.jpg /absolute/path/to/image2.jpg ``` -- **Required**: A path to a file called `MODEL_STATE_DICT` that contains the saved parameters of the maskrcnn_resnet50_fpn model. You will need to download the [mobilenet_v2](https://pytorch.org/vision/stable/_modules/torchvision/models/mobilenetv2.html) model from Pytorch's repository of pretrained models. Note that this requires `torchvision` library. +- **Required**: Download the [mobilenet_v2](https://pytorch.org/vision/stable/_modules/torchvision/models/mobilenetv2.html) model from Pytorch's repository of pretrained models. This model requires the torchvision library. To download this model, run the following commands: ``` import torch from torchvision.models.detection import mobilenet_v2 model = mobilenet_v2(pretrained=True) torch.save(model.state_dict(), 'mobilenet_v2.pth') ``` -- **Required**: A path to a file called `OUTPUT`, to which the pipeline will write the predictions. -- **Optional**: `IMAGES_DIR`, which is the path to the directory where images are stored. Not required if image names in the input file `IMAGE_FILE_NAMES` have absolute paths. Review Comment: Was removing this intentional? It's still referenced below ########## sdks/python/apache_beam/examples/inference/README.md: ########## @@ -157,21 +159,22 @@ Each line has data separated by a semicolon ";". The first item is the file name --- ## Language modeling -[`pytorch_language_modeling.py`](./pytorch_language_modeling.py) contains an implementation for a RunInference pipeline that performs masked language modeling (i.e. decoding a masked token in a sentence) using the BertForMaskedLM architecture from Hugging Face. +[`pytorch_language_modeling.py`](./pytorch_language_modeling.py) contains an implementation for a RunInference pipeline that performs masked language modeling (that is, decoding a masked token in a sentence) using the `BertForMaskedLM` architecture from Hugging Face. The pipeline reads sentences, performs basic preprocessing to convert the last word into a `[MASK]` token, passes the masked sentence to the PyTorch implementation of RunInference, and then writes the predictions to a text file. ### Dataset and model for language modeling -- **Required**: A path to a file called `MODEL_STATE_DICT` that contains the saved parameters of the BertForMaskedLM model. You will need to download the [BertForMaskedLM](https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertForMaskedLM) model from Hugging Face's repository of pretrained models. Make sure you have installed `transformers` too. +- **Required**: Download the [BertForMaskedLM](https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertForMaskedLM) model from Hugging Face's repository of pretrained models. You must already have `transformers` installed. To download this model, run the following commands: ``` import torch from transformers import BertForMaskedLM model = BertForMaskedLM.from_pretrained('bert-base-uncased', return_dict=True) torch.save(model.state_dict(), 'BertForMaskedLM.pth') ``` -- **Required**: A path to a file called `OUTPUT`, to which the pipeline will write the predictions. -- **Optional**: A path to a file called `SENTENCES` that contains sentences to feed into the model. It should look something like this: +- **Required**: A path to a file namedd `MODEL_STATE_DICT` that contains the saved parameters of the `BertForMaskedLM` model. Review Comment: Also there's a small typo ```suggestion - **Required**: A path to a file named `MODEL_STATE_DICT` that contains the saved parameters of the `BertForMaskedLM` model. ``` -- 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]
