# -*- coding: utf-8 -*-
"""
Created on Wed Mar 7 16:42:15 2018
This file is for selecting the most uncertain regions in each acquisition step based on different
acquisition functions given the updated model
@author: s161488
"""
import tensorflow as tf
from data_utils.prepare_data import padding_training_data, prepare_train_data
from models.inference import ResNet_V2_DMNN
from models.acquisition_region import select_most_uncertain_patch
import numpy as np
import os
def selection(test_data_statistics_dir, ckpt_dir, acqu_method, already_selected_imindex,
already_selected_binarymask, kernel_window, stride_size, num_most_uncert_patch, data_path,
check_overconfident=False):
"""Now the selection method has changed to be region-specific selection and most certain images selection
test_data_statistics_dir: the test_data_statistics_dir for saving data
ckpt_dir: already trained model
acqu_method: ["B"]
already_selected_imindex: the image index that have been already selected
already_selected_binarymask: the binary mask that defines the regions that have already been selected
in the previous acquisition steps
kernel_window: the size of the anchor
stride_size: the stride while searching for the most uncertain regions
num_most_uncert_patch: the maximum number of patches that are selected in per step
data_path: the directory that saves the pool dataset, default '/home/blia/Exp_Data/Data/glanddata.npy
check_overconfident: bool variable, it's set to be True when I use this function for analyzing whether the selected
regions are indeed uncertain
"""
# --------Here lots of parameters need to be set------Or maybe we could set it in the configuration file-----#
if check_overconfident is True:
if not os.path.exists(test_data_statistics_dir):
os.makedirs(test_data_statistics_dir)
batch_size = 1
targ_height_npy = 528 # this is for padding images
targ_width_npy = 784 # this is for padding images
ckpt_dir = ckpt_dir
image_c = 3
MOVING_AVERAGE_DECAY = 0.999
num_sample = 1
num_sample_drop = 30
Dropout_State = True
selec_training_index = np.zeros([2, 5])
selec_training_index[0, :] = [0, 1, 2, 3, 4] # this is the index for the initial benign images
selec_training_index[1, :] = [2, 4, 5, 6, 7] # this is the index for the initial malignant images
selec_training_index = selec_training_index.astype('int64')
with tf.Graph().as_default():
# The placeholder below is for extracting the input for the network #####
images_train = tf.placeholder(tf.float32, [batch_size, targ_height_npy, targ_width_npy, image_c])
instance_labels_train = tf.placeholder(tf.int64, [batch_size, targ_height_npy, targ_width_npy, 1])
edges_labels_train = tf.placeholder(tf.int64, [batch_size, targ_height_npy, targ_width_npy, 1])
phase_train = tf.placeholder(tf.bool, shape=None, name="training_state")
dropout_phase = tf.placeholder(tf.bool, shape=None, name="dropout_state")
data_train, data_pool, data_val = prepare_train_data(data_path, selec_training_index[0, :],
selec_training_index[1, :])
x_image_pl, y_label_pl, y_edge_pl = padding_training_data(data_pool[0], data_pool[1], data_pool[2],
targ_height_npy, targ_width_npy)
print("The pooling data size %d" % np.shape(x_image_pl)[0])
# Here is for build up the network-----------------------------------------------------------------###
fb_logits, ed_logits = ResNet_V2_DMNN(images=images_train, training_state=phase_train,
dropout_state=dropout_phase, Num_Classes=2)
edge_prob = tf.nn.softmax(tf.add_n(ed_logits))
fb_prob = tf.nn.softmax(tf.add_n(fb_logits))
var_train = tf.trainable_variables()
variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY)
variable_averages.apply(var_train)
variables_to_restore = variable_averages.variables_to_restore(tf.moving_average_variables())
saver = tf.train.Saver(variables_to_restore)
print(" =====================================================")
print("Dropout Phase", Dropout_State)
print("The acquire method", acqu_method)
print("The number of repeat times", num_sample)
print("The number of dropout times", num_sample_drop)
print("The images which include most uncertain patches", already_selected_imindex)
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(ckpt_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print("restore parameter from ", ckpt.model_checkpoint_path)
for Repeat in range(num_sample):
ed_prob_tot = []
fb_prob_tot = []
fb_prob_var_tot = []
ed_prob_var_tot = []
fb_bald_mean_tot = []
ed_bald_mean_tot = []
num_image = np.shape(x_image_pl)[0]
for single_image in range(num_image):
feed_dict_op = {images_train: np.expand_dims(x_image_pl[single_image], 0),
instance_labels_train: np.expand_dims(y_label_pl[single_image], 0),
edges_labels_train: np.expand_dims(y_edge_pl[single_image], 0),
phase_train: False,
dropout_phase: Dropout_State}
fb_prob_per_image = []
ed_prob_per_image = []
fb_bald_per_image = []
ed_bald_per_image = []
fetches_pool = [fb_prob, edge_prob]
for single_sample in range(num_sample_drop):
_fb_prob, _ed_prob = sess.run(fetches=fetches_pool, feed_dict=feed_dict_op)
single_fb_bald = _fb_prob * np.log(_fb_prob + 1e-08)
# single_ed_bald = _ed_prob*np.log(_ed_prob+1e-08)
fb_bald_per_image.append(single_fb_bald)
# ed_bald_per_image.append(single_ed_bald)
fb_prob_per_image.append(_fb_prob[0])
ed_prob_per_image.append(_ed_prob[0])
fb_pred = np.mean(fb_prob_per_image, axis=0)
ed_pred = np.mean(ed_prob_per_image, axis=0)
fb_prob_tot.append(fb_pred)
ed_prob_tot.append(ed_pred)
fb_prob_var_tot.append(np.std(fb_prob_per_image, axis=0))
ed_prob_var_tot.append(np.std(ed_prob_per_image, axis=0))
fb_bald_mean_tot.append(np.mean(fb_bald_per_image, axis=0))
# ed_bald_mean_tot.append(np.mean(ed_bald_per_image, axis = 0))
fb_bald_mean_tot = np.squeeze(np.array(fb_bald_mean_tot), axis=1)
# ed_bald_mean_tot = np.squeeze(np.array(ed_bald_mean_tot), axis = 1)
print("Using seletion method", acqu_method)
most_uncertain_data = select_most_uncertain_patch(x_image_pl, y_label_pl,
np.array(fb_prob_tot),
np.array(ed_prob_tot),
fb_bald_mean_tot,
kernel_window, stride_size,
already_selected_imindex,
already_selected_binarymask,
num_most_uncert_patch,
acqu_method)
if check_overconfident is True:
np.save(os.path.join(test_data_statistics_dir, 'fbprob'), np.array(fb_prob_tot))
if acqu_method is "D":
np.save(os.path.join(test_data_statistics_dir, 'fbbald'), fb_bald_mean_tot)
return most_uncertain_data