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Hint from TensorLayer

  • This implementation is from https://github.com/deepsense-ai/roi-pooling, date: 31 Aug 2017.
  • To install this, you have to clone TensorLayer from Github instead of pip install.
  • Remember to modify the CUDA_LIB in Makefile before running python setup.py install in this folder.
  • Make sure roi_pooling_example.py and test_roi_layer.py is runable.

RoI pooling in TensorFlow

This repo contains the implementation of Region of Interest pooling as a custom TensorFlow operation. The CUDA code responsible for the computations was largely taken from the original Caffe implementation by Ross Girshick.

For more information about RoI pooling you can check out Region of interest pooling explained at our deepsense.io blog.

Region of Interest Pooling animation

Requirements

To compile and use roi_pooling layer you need to have:

Only official TensorFlow releases are currently supported. If you're using a custom built TensorFlow compiled with a different GCC version (e.g. 5.X) you may need to modify the makefile to enable the new ABI version.

Install

Since it uses compilation

$ git clone git@github.com:deepsense-io/roi-pooling.git
$ cd roi-pooling
$ python setup.py install

Right now we provide only GPU implementation (no CPU at this time).

Usage

After successful installation you can use the operation like this:

from roi_pooling.roi_pooling_ops import roi_pooling

# here obtain feature map and regions of interest
rpooling = roi_pooling(feature_map, rois, 7, 7)
# continue the model

Working example in Jupyter Notebook: examples/roi_pooling_minimal_example.ipynb