The output gradient value is much smaller than the PAIR/saliency implementation

[whyisyoung]

I noticed that the output gradient is pretty small (1e-14\~1e-7) while much bigger (1e-8\~1e-1) in the https://github.com/PAIR-code/saliency implementation.

First, I tried this notebook: https://github.com/PAIR-code/saliency/blob/master/Examples.ipynb and output the min/max of vanilla gradient and the top 20.

np.max(vanilla_mask_3d), np.min(vanilla_mask_3d)

(0.3862362, -0.38338542)

np.sort(vanilla_mask_3d.ravel())[::-1][:20]

array([0.3862362 , 0.36115727, 0.33639386, 0.3209837 , 0.32015106, 0.31286812, 0.3043984 , 0.3020323 , 0.29084682, 0.28999907, 0.28522477, 0.27706975, 0.27350807, 0.27175996, 0.2703794 , 0.2685724 , 0.26725614, 0.26650152, 0.26406616, 0.26123673], dtype=float32)

But when I run it using deep-viz-keras with the same model and same image, the result looks like this:

np.max(mask), np.min(mask)

(2.2573392039149098e-07, -4.179406574422728e-07)

np.sort(mask.ravel())[::-1][:20]

array([2.25733920e-07, 2.23271692e-07, 1.98080411e-07, 1.92421204e-07, 1.90237181e-07, 1.86172134e-07, 1.81749829e-07, 1.76881710e-07, 1.75296812e-07, 1.75214721e-07, 1.74797302e-07, 1.73955064e-07, 1.71986304e-07, 1.71514253e-07, 1.67088409e-07, 1.66170971e-07, 1.66098115e-07, 1.65983923e-07, 1.65190807e-07, 1.61611446e-07]).

Also, the output mask is not exactly the same.

Here's the PAIR/saliency version:

Here's the deep-vis-keras version:

Here's the notebook code to set deep-viz-keras in the same setting as PAIR/saliency. Notice I used the doberman image from the PAIR/saliency repo (https://github.com/PAIR-code/saliency/blob/master/doberman.png).

slim=tf.contrib.slim

if not os.path.exists('models/research/slim'):
  !git clone https://github.com/tensorflow/models/
old_cwd = os.getcwd()
os.chdir('models/research/slim')
from nets import inception_v3
os.chdir(old_cwd)

if not os.path.exists('inception_v3.ckpt'):
  #!wget http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz
  !curl -O http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz
  !tar -xvzf inception_v3_2016_08_28.tar.gz

ckpt_file = './inception_v3.ckpt'

graph = tf.Graph()

with graph.as_default():
  images = tf.placeholder(tf.float32, shape=(None, 299, 299, 3))

  with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
    _, end_points = inception_v3.inception_v3(images, is_training=False, num_classes=1001)

    # Restore the checkpoint
    sess = tf.Session(graph=graph)
    saver = tf.train.Saver()
    saver.restore(sess, ckpt_file)

  # Construct the scalar neuron tensor.
  logits = graph.get_tensor_by_name('InceptionV3/Logits/SpatialSqueeze:0')
  neuron_selector = tf.placeholder(tf.int32)
  y = logits[0][neuron_selector]

  # Construct tensor for predictions.
  prediction = tf.argmax(logits, 1)

from matplotlib import pylab as P
# Boilerplate methods.
def ShowImage(im, title='', ax=None):
  if ax is None:
    P.figure()
  P.axis('off')
  im = ((im + 1) * 127.5).astype(np.uint8)
  P.imshow(im)
  P.title(title)

def LoadImage(file_path):
  im = PIL.Image.open(file_path)
  im = np.asarray(im)
  return im / 127.5 - 1.0

im = LoadImage('fig/doberman.png')

# Show the image
ShowImage(im)

# Make a prediction. 
prediction_class = sess.run(prediction, feed_dict = {images: [im]})[0]

print("Prediction class: " + str(prediction_class))  # Should be a doberman, class idx = 237

from keras.applications import inception_v3

model = inception_v3.InceptionV3(include_top=True, weights='imagenet')

from saliency import GradientSaliency

# TODO: how to create an Inception_v3 model here?

vanilla = GradientSaliency(model)
mask = vanilla.get_mask(im)
show_image(mask, ax=plt.subplot('121'), title='vanilla gradient')

mask = vanilla.get_smoothed_mask(im)
show_image(mask, ax=plt.subplot('122'), title='smoothed vanilla gradient')

print(np.max(mask), np.min(mask))
print(np.sort(mask.ravel())[::-1][:20])

Is this a bug? I'll appreciate if anyone can take a look.