def build_masking_graph(model_name, var_init=None):
with tf.Graph().as_default() as graph:
with tf.name_scope('input'):
input = tf.placeholder(tf.float32, [1, img_size, img_size, 3],
name='m_input')
if var_init == None:
mask_var = tf.Variable(tf.truncated_normal(
(1, img_size, img_size, 1), 4, 0.1),
name='mask_var')
else:
mask_var = tf.Variable(tf.constant(var_init,
dtype=tf.float32,
shape=(1, img_size,
img_size, 1)),
name='mask_var')
noise_background = tf.random_uniform([1, img_size, img_size, 3], 0,
255)
sig_mask_op = tf.sigmoid(mask_var, name='mask_sigmoid')
masked_input = tf.multiply(input, sig_mask_op) + tf.multiply(
noise_background, 1 - sig_mask_op)
cfg = tf.ConfigProto()
cfg.gpu_options.allow_growth = True
sess = tf.InteractiveSession(graph=graph, config=cfg)
load_model(model_name, masked_input)
return sess, graph, mask_var, sig_mask_op, masked_input
def build_masking_graph(model_name, var_init=None):
with tf.Graph().as_default() as graph:
with tf.name_scope('input'):
input = tf.placeholder(tf.float32, [1, img_size, img_size, 3],
name='m_input')
if var_init == None:
mask_var = tf.Variable(tf.truncated_normal(
(1, img_size, img_size, 1), 4, 0.1),
name='mask_var')
else:
mask_var = tf.Variable(tf.constant(var_init,
dtype=tf.float32,
shape=(1, img_size,
img_size, 1)),
name='mask_var')
# noise_r = tf.truncated_normal([1, img_size, img_size, 1], 122.46, 70.63)
# noise_g = tf.truncated_normal([1, img_size, img_size, 1], 114.26, 68.61)
# noise_b = tf.truncated_normal([1, img_size, img_size, 1], 101.37, 71.93)
# noise_background = tf.clip_by_value(tf.concat([noise_r, noise_g, noise_b], axis=3), 0, 255)
noise_background = tf.random_uniform([1, img_size, img_size, 3], 0,
255)
sig_mask_op = tf.sigmoid(mask_var, name='mask_sigmoid')
pool_octave = tf.placeholder(tf.int32,
shape=[],
name='pool_octave')
# mask_pooled_ops = tf.stack([make_shrink_and_expand(2 ** s, sig_mask_op, img_size) for s in range(5, -1, -1)])
# sig_mask_op = mask_pooled_ops[pool_octave]
masked_input = tf.multiply(input, sig_mask_op) + tf.multiply(
noise_background, 1 - sig_mask_op)
cfg = tf.ConfigProto()
cfg.gpu_options.allow_growth = True
sess = tf.InteractiveSession(graph=graph, config=cfg)
load_model(model_name, masked_input)
return sess, graph, mask_var, sig_mask_op, masked_input, noise_background
dir = 'test_data_3'
# imgs, img_filenames = prepare_imgs('test_data_final_2_large', dir)
imgs, img_filenames = prepare_imgs(dir, dir)
# s=50
# imgs = imgs[2*s:3*s]
# img_filenames = img_filenames[2*s:3*s]
imgs = (2 * imgs / 255) - 1
tf.reset_default_graph()
cfg = tf.ConfigProto()
cfg.gpu_options.allow_growth = True
sess = tf.Session(config=cfg)
with DeepExplain(session=sess, graph=sess.graph) as de:
load_model(model_name,
tf.placeholder(tf.float32, [None, 224, 224, 3], 'input'), True)
labels, ids = top_labels_and_ids(sess, imgs)
# print(labels, ids)
input_n = input_node()
output_n = presoftmax()
target_n = tf.reduce_max(output_n, 1)
attributions = {}
BATCH_SIZE = 10
for i in range(0, len(imgs), BATCH_SIZE):
print('iter %s' % i)
batch = imgs[i:i + BATCH_SIZE]
batch_filenames = img_filenames[i:i + BATCH_SIZE]
for method_name, curve in curve_records.items():
aopc = np.mean(curve)
print('%s: %s' % (method_name, aopc))
root_dir = 'result/methods_final_2_large'
input_img_dir = 'test_data_final_2_large'
input_imgs, _ = prepare_imgs(input_img_dir, 'lerf')
cfg = tf.ConfigProto()
cfg.gpu_options.allow_growth = True
sess = tf.Session(config=cfg)
input_ph = tf.placeholder(tf.float32, [None, 299, 299, 3], name='m_input')
load_model('inception_v3', input_ph)
_, ids = top_labels_and_ids(sess, input_imgs[:100])
_, ids2 = top_labels_and_ids(sess, input_imgs[100:])
ids = np.append(ids, ids2)
average_score_drop_of_methods = {}
for sub_dir in glob.glob(root_dir + '/*'):
method_name = os.path.basename(sub_dir)
if method_name != 'occlusion':
continue
maps = load_saliency_maps(sub_dir)
record_morfs(method_name, input_imgs, ids, maps, average_score_drop_of_methods)