gradcam.gradcam¶
References
| [1] | Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, Dhruv Batra “Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization” ICCV2017 |
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class
Gradcam(x_placeholder, num_classes, featuremap_op_name, predict_op_name=None, graph=None)[source]¶ Gradcam Class
Parameters: - x_placeholder (tf.Tensor) – Data place holder Tensor from tf.placeholder()
- num_classes (int) – number of classes
- featuremap_op_name (str) – Operation name of CNN feature map layer
To get the list of candidate names, use
Gradcam.candidate_featuremap_op_names() - predict_op_name (str) – Operation name of prediction layer (decision output)
To get the list of candidate names, use
Gradcam.candidate_predict_op_names() - graph (tf.Graph) – Tensorflow graph
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static
candidate_featuremap_op_names(sess, graph=None, feed_options=None)[source]¶ Returns the list of candidates for operation names of CNN feature map layer
Parameters: - sess (tf.Session) – Tensorflow session
- graph (tf.Graph) – Tensorflow graph
- feed_options (dict) – Optional parameters to graph
Returns: list – String list of candidates
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static
candidate_predict_op_names(sess, num_classes, graph=None, feed_options=None)[source]¶ Returns the list of candidate for operation names of prediction layer
Parameters: Returns: list – String list of candidates
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gradcam(sess, input_data, target_index=None, feed_options={})[source]¶ Calculate Grad-CAM (class activation map) and Guided Grad-CAM for given input on target class
Parameters: - sess (tf.Session) – Tensorflow session
- input_data (numpy.ndarray) – A single input instance
- target_index (int) – Target class index If None, predicted class index is used
- feed_options (dict) – Optional parameters to graph
Returns: dict
Note
- Keys in return:
- gradcam_img: Heatmap overlayed on input
- guided_gradcam_img: Guided Grad-CAM result
- heatmap: Heatmap of input on the target class
- guided_backprop: Guided backprop result
