/grad-cam

Grad-CAM (Gradient-weighted Class Activation Mapping)

Primary LanguagePython

grad-cam

Grad-CAM (Gradient-weighted Class Activation Mapping)

The paper: https://arxiv.org/pdf/1610.02391v1.pdf

Input images to imagenet

Grad-CAM output images

Requirement

  • Python (3.6.3)
    • Keras (2.0.9)
    • numpy (1.14.0)
    • tensorflow (1.4.1)
    • opencv-python (3.4.0.12)
    • Pillow (5.0.0)

Usage

  1. Create config yaml file (details about config)
  2. Run grad-cam
> python3 grad_cam path/to/config.yml

Output file name

{model_name}-{layer}-{image_file_name}.{image_file_extension}

Config (yaml)

# <Use Library name>
keras:
  model:
    # <[optional] path to model architecture file (e.g. keras: *.json or *.yml)>
    architecture: path/to/architecture_file.yml
    # <It is also possible to load model architecture from source file.>
    # source:
    #   path: ./example/src/vgg16.py
    #   definition: vgg16
    #   args:
    #     - [224, 224] # image_size
    #     - 3          # channel
    #     - 1000       # classes
    # <path to model params (weight) file (e.g. keras: *.h5)>
    params: path/to/params_file.h5
    # <target layers name for grad-cam>
    layers:
      - block5_conv3
      - block4_conv3
  image:
    # <path to image (*.jpg or *.png)>
    path: path/to/image.png
    # <If you want to target multiple images, specify the image directory.>
    # path: path/to/image_dir
    # <path to output dir>
    output: path/to/output_dir
    # <[optional] image preprocessing>
    source:
      # <path to image preprocessing module (source file)>
      path: path/to/preprocessing.py
      # <definition (defined in image preprocessing source file>)
      definition: definition_name

Image pre-processing

If image pre-processing is necessary, it is also possible to define the pre-processing module.

  • Input
    • path:str: path to image file (Determined by cofig yaml)
    • shape: image shape (determined by model input shape)
  • output
    • image_array:ndarray: ndarray whose shape is input shape

pre-processing module example for imagenet (python/keras)

from keras.applications.vgg16 import preprocess_input
from keras.preprocessing import image
import numpy as np

def image_to_arr(path, shape):
    img = image.load_img(path, target_size=shape[0:2])
    x = image.img_to_array(img)
    x = preprocess_input(x)
    return x

When model architecture is defined in source code

You can load the model architecture from a class or user-defined function (python source code) in which the model is defined.

usage

Specify the class name or user-defined function name in which source file and model are defined in config (yaml).

※ source code and architecture file can not be specified at the same time.

keras:
  model:
    source:
      # path source code
      path: ./example/src/vgg16.py
      # name of the class or user-defined function
      definition: definition_name
      args:
        - arg1 # first argument of definition
        - arg2 # second argument of definition
        - arg3 # third argument of definition
    params: ./example/model/vgg16_weights_tf_dim_ordering_tf_kernels.h5
    layer: block5_conv3

examples of model defined python source code

Example

  1. Download model params file for imagenet 
    # ./grad-cam
wget  https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5 -P ./example/model
  2. Run grad-cam 
    # ./grad-cam
python3 grad_cam.py ./example/config.yml

References