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Posted to github@beam.apache.org by GitBox <gi...@apache.org> on 2022/07/08 16:51:55 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_r916989262
##########
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:
That makes sense, but to me this looks like a bulleted list defining the four inputs to the script, not a list of directions/actions. For each input it identifies if they are Required/Optional, and explains how to populate them. It's odd to me to then have one separate bullet that represents an instruction.
##########
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:
That makes sense, but to me this looks like a bulleted list defining the four inputs to the script, not a list of directions/actions. For each input it identifies if they are Required/Optional, and explains how to populate them. It's odd to me to then have one separate bullet that represents an action.
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