64 lines
2.4 KiB
Python
Executable File
64 lines
2.4 KiB
Python
Executable File
import numpy as np
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import tensorflow as tf
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from tensorflow.keras.applications import densenet
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from base import SaliencyMap
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class IntegratedGradients(SaliencyMap):
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def get_mask(self, image, baseline=None, num_steps=2):
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"""Computes Integrated Gradients for a predicted label.
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Args:
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image (ndarray): Original image
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top_pred_idx: Predicted label for the input image
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baseline (ndarray): The baseline image to start with for interpolation
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num_steps: Number of interpolation steps between the baseline
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and the input used in the computation of integrated gradients. These
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steps along determine the integral approximation error. By default,
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num_steps is set to 50.
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Returns:
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Integrated gradients w.r.t input image
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"""
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# If baseline is not provided, start with a black image
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# having same size as the input image.
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if baseline is None:
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img_size = image.shape
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baseline = np.zeros(img_size).astype(np.float32)
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else:
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baseline = baseline.astype(np.float32)
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print(">>>> step ONE completed")
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img_input = image
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top_pred_idx = self.get_top_predicted_idx(image)
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interpolated_image = [
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baseline + (i / num_steps) * (img_input - baseline)
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for i in range(num_steps + 1)
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]
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interpolated_image = np.vstack(interpolated_image).astype(np.float32)
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print(">>>> step TWO completed")
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grads = []
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for i, img in enumerate(interpolated_image):
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print("number of image:",i)
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print("size of image:",np.shape(img))
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img = tf.expand_dims(img, axis=0)
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grad = self.get_gradients(img)
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print("size of grad is:",np.shape(grad))
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grads.append(grad[0])
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grads = tf.convert_to_tensor(grads, dtype=tf.float32)
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print(">>>> step THREE completed")
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# 4. Approximate the integral using the trapezoidal rule
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grads = (grads[:-1] + grads[1:]) / 2.0
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avg_grads = tf.reduce_mean(grads, axis=0)
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# tf.reduce_mean(grads, axis=(0, 1, 2, 3))
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print(">>>> step FOUR completed")
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# 5. Calculate integrated gradients and return
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integrated_grads = (img_input - baseline) * avg_grads
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print(">>>> step FIVE completed")
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return integrated_grads
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