resolved batch_explainations

yolov5.py

@@ -232,15 +232,12 @@ def dff_nmf(image, target_lyr, n_components):
         ax.text(x1, y1, f"{predicted_label}: {top_score:.2f}",
             color='r', fontsize=12, verticalalignment='top')
         plt.show()
-        print("#################################")
-        print("saving the test files........")
         plt.savefig("test_" + str(indx) + ".png" )
         plt.clf()
-        return rgb_img_float,  explanations
+        return rgb_img_float, batch_explanations
 
 
 def visualize_batch_explanations(rgb_img_float, batch_explanations, image_weight=0.7):
-    print('shape_batch', batch_explanations.shape)
     for i, explanation in enumerate(batch_explanations):
         # Create visualization for each explanation
         visualization = show_factorization_on_image(rgb_img_float, explanation, image_weight=image_weight)