trial and error with the min_overlap 10% to 2%

src/sfransen/Saliency/__init__.py

@@ -0,0 +1,2 @@
+from .base import *
+from .integrated_gradients import *

src/sfransen/Saliency/integrated_gradients.py

@@ -1,12 +1,11 @@
 import numpy as np
 import tensorflow as tf
 from tensorflow.keras.applications import densenet
-
-from base import SaliencyMap
+from sfransen.Saliency.base import SaliencyMap
 
 class IntegratedGradients(SaliencyMap):
 
-    def get_mask(self, image, baseline=None, num_steps=2):
+    def get_mask(self, image, baseline=None, num_steps=4):
         """Computes Integrated Gradients for a predicted label.
 
         Args:
@@ -28,7 +27,6 @@ class IntegratedGradients(SaliencyMap):
             baseline = np.zeros(img_size).astype(np.float32)
         else:
             baseline = baseline.astype(np.float32)
-        print(">>>> step ONE completed")
 
         img_input = image
         top_pred_idx = self.get_top_predicted_idx(image)
@@ -37,27 +35,20 @@ class IntegratedGradients(SaliencyMap):
             for i in range(num_steps + 1)
             ]
         interpolated_image = np.vstack(interpolated_image).astype(np.float32)
-        print(">>>> step TWO completed")
 
         grads = []
         for i, img in enumerate(interpolated_image):
-            print("number of image:",i)
-            print("size of image:",np.shape(img))
+            print(f"interpolation step:",i," out of {num_steps}")
             img = tf.expand_dims(img, axis=0)
             grad = self.get_gradients(img)
-            print("size of grad is:",np.shape(grad))
             grads.append(grad[0])
         grads = tf.convert_to_tensor(grads, dtype=tf.float32)
-        print(">>>> step THREE completed")
 
         # 4. Approximate the integral using the trapezoidal rule
         grads = (grads[:-1] + grads[1:]) / 2.0
         avg_grads = tf.reduce_mean(grads, axis=0)
-        # tf.reduce_mean(grads, axis=(0, 1, 2, 3))
-        print(">>>> step FOUR completed")
 
         # 5. Calculate integrated gradients and return
         integrated_grads = (img_input - baseline) * avg_grads
-        print(">>>> step FIVE completed")
 
         return integrated_grads