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Posted to github@beam.apache.org by GitBox <gi...@apache.org> on 2022/06/27 23:36:08 UTC
[GitHub] [beam] TheNeuralBit commented on a diff in pull request #22069: Reviewing the RunInference ReadMe file for clarity.
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.
```
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