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# Tensorflow Implementation of Yahoo's Open NSFW Model
This repository contains an implementation of [Yahoo's Open NSFW Classifier](https://github.com/yahoo/open_nsfw) rewritten in tensorflow.
The original caffe weights have been extracted using [Caffe to TensorFlow](https://github.com/ethereon/caffe-tensorflow). You can find them at `data/open_nsfw-weights.npy`.
## Prerequisites
All code should be compatible with `Python 3.6` and `Tensorflow 1.0.0`. The model implementation can be found in `model.py`.
### Usage
```
> python classify_nsfw.py -m data/open_nsfw-weights.npy test.jpg
Results for 'test.jpg'
SFW score: 0.9355766177177429
NSFW score: 0.06442338228225708
```
__Note:__ Currently only jpeg images are supported.
`classify_nsfw.py` accepts some optional parameters you may want to play around with:
```
usage: classify_nsfw.py [-h] -m MODEL_WEIGHTS [-l {yahoo,tensorflow}]
[-t {tensor,base64_jpeg}]
input_jpeg_file
positional arguments:
input_file Path to the input image. Only jpeg images are
supported.
optional arguments:
-h, --help show this help message and exit
-m MODEL_WEIGHTS, --model_weights MODEL_WEIGHTS
Path to trained model weights file
-l {yahoo,tensorflow}, --image_loader {yahoo,tensorflow}
image loading mechanism
-t {tensor,base64_jpeg}, --input_type {tensor,base64_jpeg}
input type
```
__-l/--image-loader__
The classification tool supports two different image loading mechanisms.
* `yahoo` (default) tries to replicate the image loading mechanism used by the original caffe implementation, differs a bit though. See __Caveats__ below.
* `tensorflow` is an image loader which uses tensorflow api's exclusively (no dependencies on `PIL`, `skimage`, etc.).
__Note:__ Classification results may vary depending on the selected image loader!
__-t/--input_type__
Determines if the model internally uses a float tensor (`tensor` - `[None, 224, 224, 3]` - default) or a base64 encoded string tensor (`base64_jpeg` - `[None, ]`) as input. If `base64_jpeg` is used, then the `tensorflow` image loader will be used, regardless of the _-l/--image-loader_ argument.
### Tools
The `tools` folder contains some utility scripts to test the model.
__export_graph.py__
Export the tensorflow graph and checkpoint. Freezes and optimizes the graph per default for improved inference and deployment usage (e.g. Android, iOS, etc.). Import the graph with `tf.import_graph_def`.
__export_savedmodel.py__
Exports the model using the tensorflow serving export api (`SavedModel`). The export can be used to deploy the model on [Google Cloud ML Engine](https://cloud.google.com/ml-engine/docs/concepts/prediction-overview), [Tensorflow Serving]() or on mobile (haven't tried that one yet).
__create_predict_request.py__
Takes an input image and spits out an json file suitable for prediction requests to a Open NSFW Model deployed on [Google Cloud ML Engine](https://cloud.google.com/ml-engine/docs/concepts/prediction-overview) (`gcloud ml-engine predict`).
### Caveats
#### Image loading differences
The classification results sometimes differ more and sometimes less from the original caffe implementation, depending on the image loader and input image. I haven't been able to figure out the cause for this yet. Any help on this would be appreciated.

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