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# -*- coding: utf-8 -*-
"""
Created on Wed Mar 7 16:42:15 2018
This file is used to train the active learning framework with region specific annotation
@author: s161488
"""
import numpy as np
import os
import tensorflow as tf
from data_utils.prepare_data import aug_train_data, generate_batch
from data_utils.update_data import give_init_train_and_val_data, update_training_data, prepare_the_new_uncertain_input
from models.inference import ResNet_V2_DMNN
from optimization.loss_region_specific import Loss, train_op_batchnorm
from sklearn.utils import shuffle
from select_regions import selection as SPR_Region_Im
import pickle
training_data_path = "/home/blia/Region_Active_Learning/DATA/Data/glanddata.npy" # NOTE, NEED TO BE MANUALLY DEFINED
test_data_path = "/home/blia/Region_Active_Learning/DATA/Data/glanddata_testb.npy" # NOTE, NEED TO BE MANUALLY DEFINED
resnet_dir = "/home/blia/Region_Active_Learning/pretrain_model/"
exp_dir = "/scratch/Act_Learn_Desperate_V8/" # USER_DEFINE
ckpt_dir_init = "/home/blia/Exp_Data/initial_model/" # USER_DEFINE
def run_loop_active_learning_region(stage, round_number=[0, 1, 2, 3]):
"""This function is used to train the active learning framework with region specific annotation.
Args:
stage: int, 0--> random selection, 1--> VarRatio, 2--> entropy, 3--> BALD
round_number: [int], repeat experiments in order to get confidence interval
Ops:
1. this script can only be run given the model that is trained with the initial training data (10)!!!
2. in each acquisition step, the experiment is repeated # times to avoid bad local optimal
3. Then after we get the updated model, the function SPR_Region_Im is used to evaluate all the regions
in the pool data. It selects the most #num_most_uncertain_patch from the pool set. And the selections
are added into the training data.
4. Again, a new model will be trained as described in step 2 with the updated data from step 3
5. step 2 to 4 is repeated for #total_active_step times
"""
for single_round_number in round_number:
logs_path = exp_dir
flag_arch_name = "resnet_v2_50"
resnet_ckpt = os.path.join(resnet_dir, flag_arch_name) + '.ckpt'
total_active_step = 10
num_repeat_per_exp = 4
acq_method_total = ["A", "B", "C", "D"]
acq_selec_method = acq_method_total[stage]
kernel_window = np.ones([150, 150])
stride_size = 30
num_most_uncert_patch = 20
logs_path = os.path.join(logs_path,
'Method_%s_Stage_%d_Version_%d' % (acq_selec_method, stage, single_round_number))
most_init_train_data, all_the_time_val_data = give_init_train_and_val_data(training_data_path)
num_of_pixels_need_to_be_annotate = np.zeros([total_active_step])
total_folder_info = []
total_num_im = np.zeros([total_active_step])
for single_acq_step in range(total_active_step):
if single_acq_step == 0:
tds = os.path.join(ckpt_dir_init, 'pool_data')
most_uncertain_data = SPR_Region_Im(tds, ckpt_dir_init, acq_selec_method, None, None, kernel_window,
stride_size, num_most_uncert_patch=20, data_path=training_data_path,
check_overconfident=False)
updated_training_data = update_training_data(most_init_train_data[:4], [], most_uncertain_data[:4])
already_selected_imindex = most_uncertain_data[-1]
already_selected_binary_mask = most_uncertain_data[-2]
most_uncert_old = most_uncertain_data
num_of_pixels_need_to_be_annotate[single_acq_step] = np.sum(np.reshape(most_uncert_old[-2], [
-1])) # this is the binary mask, the number of pixels that needs to be annotate
# equal to the number of pixels which are assigned to be 1
num_im = np.shape(updated_training_data[0])[0]
total_num_im[single_acq_step] = num_im
epsilon_opt = 0.001
batch_size = 5
epoch_size = 1300
model_dir = os.path.join(logs_path, 'FE_step_%d_version_%d' % (single_acq_step, single_round_number))
tot_train_val_stat_for_diff_exp_same_step = np.zeros(
[num_repeat_per_exp, 4]) # fb loss, ed loss, fb f1 score, fb auc score
if single_acq_step == 5:
regu_par = 0.0005
else:
regu_par = 0.001
if single_acq_step >= 10:
decay_steps = np.ceil(num_im / 5) * 600
else:
decay_steps = (num_im // 5) * 600
for repeat_time in range(num_repeat_per_exp):
print("=====================Start Experiment No.%d===========================" % repeat_time)
model_dir_sub = os.path.join(model_dir, 'rep_%d' % repeat_time)
signal = False
while signal is False:
signal_for_bad_optimal = False
while signal_for_bad_optimal is False:
train(resnet_ckpt=resnet_ckpt,
ckpt_dir=None,
model_dir=model_dir_sub,
epoch_size=20,
decay_steps=decay_steps,
epsilon_opt=epsilon_opt,
regu_par=regu_par,
batch_size=batch_size,
training_data=updated_training_data,
val_data=all_the_time_val_data,
FLAG_PRETRAIN=False)
train_stat = np.load(os.path.join(model_dir_sub, 'trainstat.npy'))
val_stat = np.load(os.path.join(model_dir_sub, 'valstat.npy'))
sec_cri = [np.mean(train_stat[-10:, 1]), np.mean(val_stat[-1, 1])] # fb f1 score
thir_cri = [np.mean(train_stat[-10:, 2]), np.mean(val_stat[-1, 2])] # fb auc score
if np.mean(sec_cri) == 0.0 or np.mean(thir_cri) == 0.5:
signal_for_bad_optimal = False
all_the_files = os.listdir(model_dir_sub)
for single_file in all_the_files:
os.remove(os.path.join(model_dir_sub, single_file))
print("--------------------The model start from a really bad optimal----------------")
else:
signal_for_bad_optimal = True
train(resnet_ckpt=resnet_ckpt,
ckpt_dir=model_dir_sub,
model_dir=model_dir_sub,
epoch_size=epoch_size,
decay_steps=decay_steps,
epsilon_opt=epsilon_opt,
regu_par=regu_par,
batch_size=batch_size,
training_data=updated_training_data,
val_data=all_the_time_val_data,
FLAG_PRETRAIN=True)
train_stat = np.load(os.path.join(model_dir_sub, 'trainstat.npy'))
val_stat = np.load(os.path.join(model_dir_sub, 'valstat.npy'))
first_cri = [np.mean(train_stat[-20:, -1]), np.mean(val_stat[-10:, -1])] # ed loss
sec_cri = [np.mean(train_stat[-20:, 1]), np.mean(val_stat[-10:, 1])] # fb f1 score
thir_cri = [np.mean(train_stat[-20:, 2]), np.mean(val_stat[-10:, 2])] # fb auc score
fourth_cri = [np.mean(train_stat[-20:, 0]), np.mean(val_stat[-10:, 0])] # fb loss
if np.mean(first_cri) >= 0.30 or np.mean(sec_cri) <= 0.80 or np.mean(thir_cri) <= 0.80 or np.mean(
fourth_cri) > 0.50:
signal = False
else:
signal = True
if signal is False:
all_the_files = os.listdir(model_dir_sub)
for single_file in all_the_files:
os.remove(os.path.join(model_dir_sub, single_file))
print("mmm The trained model doesn't work, I need to retrain it...")
if signal is True:
tot_train_val_stat_for_diff_exp_same_step[repeat_time, :] = [np.mean(fourth_cri),
np.mean(first_cri),
np.mean(sec_cri),
np.mean(thir_cri)]
print("=============================Finish Experiment No.%d============================" % repeat_time)
# ---------Below is for selecting the best experiment based on the training and validation statistics-----#
fb_loss_index = np.argmin(tot_train_val_stat_for_diff_exp_same_step[:, 0])
ed_loss_index = np.argmin(tot_train_val_stat_for_diff_exp_same_step[:, 1])
fb_f1_index = np.argmax(tot_train_val_stat_for_diff_exp_same_step[:, 2])
fb_auc_index = np.argmax(tot_train_val_stat_for_diff_exp_same_step[:, 3])
perf_comp = [fb_loss_index, ed_loss_index, fb_f1_index, fb_auc_index]
best_per_index = max(set(perf_comp), key=perf_comp.count)
model_dir_goes_into_act_stage = os.path.join(model_dir, 'rep_%d' % best_per_index)
print("The selected folder", model_dir_goes_into_act_stage)
total_folder_info.append(model_dir_goes_into_act_stage)
tds_select = os.path.join(model_dir_goes_into_act_stage, 'pool_data')
most_uncertain = SPR_Region_Im(tds_select, model_dir_goes_into_act_stage, acq_selec_method,
already_selected_imindex,
already_selected_binary_mask,
kernel_window,
stride_size,
num_most_uncert_patch, data_path=training_data_path,
check_overconfident=False)
updated_most_uncertain = prepare_the_new_uncertain_input(most_uncert_old, most_uncertain)
updated_training_data = update_training_data(most_init_train_data[:4], [], updated_most_uncertain[:4])
already_selected_imindex = updated_most_uncertain[-1]
already_selected_binary_mask = updated_most_uncertain[-2]
most_uncert_old = updated_most_uncertain
print("The numeric image index for the most uncertain image:\n", already_selected_imindex)
# np.save(os.path.join(logs_path, 'total_acqu_index'), Already_Selected_Imindex)
np.save(os.path.join(logs_path, 'num_of_pixel'), num_of_pixels_need_to_be_annotate)
np.save(os.path.join(logs_path, 'total_select_folder'), total_folder_info)
np.save(os.path.join(logs_path, 'num_of_image'), total_num_im)
uncertain_data = os.path.join(logs_path, 'updated_uncertain.txt')
with open(uncertain_data, 'wb') as f:
pickle.dump(most_uncert_old, f)
def train(resnet_ckpt, ckpt_dir, model_dir, epoch_size, decay_steps, epsilon_opt, regu_par, batch_size, training_data,
val_data, FLAG_PRETRAIN=False):
# --------Here lots of parameters need to be set------Or maybe we could set it in the configuration file-----#
# batch_size = 5
if not os.path.exists(model_dir):
os.makedirs(model_dir)
image_w, image_h, image_c = [480, 480, 3]
IMAGE_SHAPE = np.array([image_w, image_h, image_c])
targ_height_npy = 528 # this is for padding images
targ_width_npy = 784 # this is for padding images
FLAG_DECAY = True
# if (Acq_Method == "F") and (Acq_Index_Old is None):
# learning_rate = 0.0009
# else:
learning_rate = 0.001
decay_rate = 0.1
save_checkpoint_period = 200
# epsilon_opt = 0.001
FLAG_L2_REGU = True
# FLAG_PRETRAIN = False
ckpt_dir = ckpt_dir
MOVING_AVERAGE_DECAY = 0.999
auxi_weight_num = 1
auxi_decay_step = 300
val_step_size = 10
checkpoint_path = os.path.join(model_dir, 'model.ckpt')
if not os.path.exists(model_dir):
os.makedirs(model_dir)
# ----The part below is for extracting the initial Training Data and Initial Val Data-------------------#
with tf.Graph().as_default():
# This three placeholder is for extracting the augmented training data##
image_aug_placeholder = tf.placeholder(tf.float32, [batch_size, targ_height_npy, targ_width_npy, 3])
label_aug_placeholder = tf.placeholder(tf.int64, [batch_size, targ_height_npy, targ_width_npy, 1])
edge_aug_placeholder = tf.placeholder(tf.int64, [batch_size, targ_height_npy, targ_width_npy, 1])
binary_mask_aug_placeholder = tf.placeholder(tf.int64, [batch_size, targ_height_npy, targ_width_npy, 1])
# The placeholder below is for extracting the input for the network #####
images_train = tf.placeholder(tf.float32, [batch_size, image_w, image_h, image_c])
instance_labels_train = tf.placeholder(tf.int64, [batch_size, image_w, image_h, 1])
edges_labels_train = tf.placeholder(tf.int64, [batch_size, image_w, image_h, 1])
binary_mask_train = tf.placeholder(tf.int64, [batch_size, image_w, image_h, 1])
phase_train = tf.placeholder(tf.bool, shape=None, name="training_state")
dropout_phase = tf.placeholder(tf.bool, shape=None, name="dropout_state")
auxi_weight = tf.placeholder(tf.float32, shape=None, name="auxiliary_weight")
global_step = tf.train.get_or_create_global_step()
# ----------------------Here is for preparing the dataset for training, pooling and validation---#
x_image_tr, y_label_tr, y_edge_tr, y_binary_mask_tr = training_data
x_image_val, y_label_val, y_edge_val, y_binary_mask_val = val_data
print("-----training data shape----")
[print(np.shape(v)) for v in training_data]
print("-----validation data shape---")
[print(np.shape(v)) for v in val_data]
iteration = np.shape(x_image_tr)[0] // batch_size
# ----------Perform data augmentation only on training data------------------------------------------------#
x_image_aug, y_label_aug, y_edge_aug, y_binary_mask_aug = aug_train_data(image_aug_placeholder,
label_aug_placeholder,
edge_aug_placeholder,
binary_mask_aug_placeholder,
batch_size, True, IMAGE_SHAPE)
x_image_aug_val, y_label_aug_val, y_edge_aug_val, \
y_binary_mask_aug_val = aug_train_data(image_aug_placeholder, label_aug_placeholder,
edge_aug_placeholder, binary_mask_aug_placeholder,
batch_size, False, IMAGE_SHAPE)
# ------------------------------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_loss, edge_f1_score, edge_auc_score = Loss(logits=ed_logits, labels=edges_labels_train,
binary_mask=binary_mask_train,
auxi_weight=auxi_weight, loss_name="ed")
fb_loss, fb_f1_score, fb_auc_score = Loss(logits=fb_logits, labels=instance_labels_train,
binary_mask=binary_mask_train,
auxi_weight=auxi_weight, loss_name="fb")
var_train = tf.trainable_variables()
total_loss = edge_loss + fb_loss
if FLAG_L2_REGU is True:
var_l2 = [v for v in var_train if (('kernel' in v.name) or ('weights' in v.name))]
total_loss = tf.add_n(
[total_loss, tf.add_n([tf.nn.l2_loss(v) for v in var_l2 if 'logits' not in v.name]) * regu_par],
name="Total_Loss")
# var_opt = [v for v in var_train if ('resnet' not in v.name)]
# -------------COnduct BackPropagation------------------------------------------------------------#
train = train_op_batchnorm(total_loss=total_loss, global_step=global_step, initial_learning_rate=learning_rate,
lr_decay_rate=decay_rate, decay_steps=decay_steps,
epsilon_opt=epsilon_opt, var_opt=tf.trainable_variables(),
MOVING_AVERAGE_DECAY=MOVING_AVERAGE_DECAY)
# summary_op = tf.summary.merge_all()
if FLAG_PRETRAIN is False:
set_resnet_var = [v for v in var_train if (v.name.startswith('resnet_v2') & ('logits' not in v.name))]
saver_set_resnet = tf.train.Saver(set_resnet_var, max_to_keep=3)
saver_set_all = tf.train.Saver(tf.global_variables(), max_to_keep=1)
else:
saver_set_all = tf.train.Saver(max_to_keep=1)
print("\n =====================================================")
print("The shape of new training data", np.shape(x_image_tr)[0])
print("The final validation data size %d" % np.shape(x_image_val)[0])
print("There are %d iteratioins in each epoch" % iteration)
print("ckpt files are saved to: ", model_dir)
print("Epsilon used in Adam optimizer: ", epsilon_opt)
print("Initial learning rate", learning_rate)
print("Use the Learning rate weight decay", FLAG_DECAY)
print("The learning is decayed every %d steps by %.3f " % (decay_steps, decay_rate))
print("The moving average parameter is ", MOVING_AVERAGE_DECAY)
print("Batch Size:", batch_size)
print("Max epochs: ", epoch_size)
print("Use pretrained model:", FLAG_PRETRAIN)
print("The checkpoing file is saved every %d steps" % save_checkpoint_period)
print("The L2 regularization is turned on:", FLAG_L2_REGU)
print(" =====================================================")
with tf.Session() as sess:
if FLAG_PRETRAIN is False:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
saver_set_resnet.restore(sess, resnet_ckpt)
else:
ckpt = tf.train.get_checkpoint_state(ckpt_dir)
if ckpt and ckpt.model_checkpoint_path:
saver_set_all.restore(sess, ckpt.model_checkpoint_path)
print("restore parameter from ", ckpt.model_checkpoint_path)
all_file = os.listdir(model_dir)
for v in all_file:
os.remove(os.path.join(model_dir, v))
print("-------remove the initial trained model-----")
# train_writer = tf.summary.FileWriter(model_dir, sess.graph)
train_tot_stat = np.zeros([epoch_size, 4])
val_tot_stat = np.zeros([epoch_size // val_step_size, 4])
print(
"Epoch, foreground-background loss, "
"foreground-background accu, contour loss, contour accuracy, total loss")
for single_epoch in range(epoch_size):
if auxi_weight_num > 0.001:
auxi_weight_num = np.power(0.1, np.floor(single_epoch / auxi_decay_step))
else:
auxi_weight_num = 0
x_image_sh, y_label_sh, y_edge_sh, y_binary_mask_sh = shuffle(x_image_tr, y_label_tr, y_edge_tr,
y_binary_mask_tr)
batch_index = 0
train_stat_per_epoch = np.zeros([iteration, 4])
for single_batch in range(iteration):
x_image_batch, y_label_batch, y_edge_batch, y_binary_mask_batch, batch_index = generate_batch(
x_image_sh,
y_label_sh,
y_edge_sh,
y_binary_mask_sh,
batch_index, batch_size)
feed_dict_aug = {image_aug_placeholder: x_image_batch,
label_aug_placeholder: y_label_batch,
edge_aug_placeholder: y_edge_batch,
binary_mask_aug_placeholder: y_binary_mask_batch}
x_image_npy, y_label_npy, y_edge_npy, y_binary_mask_npy = sess.run(
[x_image_aug, y_label_aug, y_edge_aug, y_binary_mask_aug], feed_dict=feed_dict_aug)
feed_dict_op = {images_train: x_image_npy,
instance_labels_train: y_label_npy,
edges_labels_train: y_edge_npy,
binary_mask_train: y_binary_mask_npy,
auxi_weight: auxi_weight_num,
phase_train: True,
dropout_phase: True}
fetches_train = [train, fb_loss, fb_f1_score, fb_auc_score, edge_loss]
# fetches_train = [train, fb_loss, fb_auc_score, edge_loss]
_, _fb_loss, _fb_f1, _fb_auc, _ed_loss = sess.run(fetches=fetches_train, feed_dict=feed_dict_op)
# _, _fb_loss, _fb_auc, _ed_loss = sess.run(fetches = fetches_train, feed_dict = feed_dict_op)
# _fb_f1 = 0.9
# _fb_auc = 0.9
train_stat_per_epoch[single_batch, 0] = _fb_loss
train_stat_per_epoch[single_batch, 1] = _fb_f1
train_stat_per_epoch[single_batch, 2] = _fb_auc
train_stat_per_epoch[single_batch, 3] = _ed_loss
train_tot_stat[single_epoch, :] = np.mean(train_stat_per_epoch, axis=0)
print(single_epoch, train_tot_stat[single_epoch, :])
if single_epoch % val_step_size == 0:
val_iteration = np.shape(x_image_val)[0] // batch_size
print("start validating .......with %d images and %d iterations" % (
np.shape(x_image_val)[0], val_iteration))
val_batch_index = 0
val_stat_per_epoch = np.zeros([val_iteration, 4])
for single_batch_val in range(val_iteration):
x_image_batch_val, y_label_batch_val, y_edge_batch_val, \
y_binary_mask_batch_val, val_batch_index = generate_batch(x_image_val, y_label_val,
y_edge_val, y_binary_mask_val,
val_batch_index, batch_size)
feed_dict_aug_val = {image_aug_placeholder: x_image_batch_val,
label_aug_placeholder: y_label_batch_val,
edge_aug_placeholder: y_edge_batch_val,
binary_mask_aug_placeholder: y_binary_mask_batch_val}
x_image_npy_val, y_label_npy_val, y_edge_npy_val, y_binary_mask_npy_val = sess.run(
[x_image_aug_val,
y_label_aug_val,
y_edge_aug_val,
y_binary_mask_aug_val], feed_dict=feed_dict_aug_val)
fetches_valid = [fb_loss, fb_f1_score, fb_auc_score, edge_loss]
# fetches_valid = [fb_loss, fb_auc_score, edge_loss]
feed_dict_valid = {images_train: x_image_npy_val,
instance_labels_train: y_label_npy_val,
edges_labels_train: y_edge_npy_val,
binary_mask_train: y_binary_mask_npy_val,
auxi_weight: 0,
phase_train: False,
dropout_phase: False}
_fbloss_val, _fb_f1_val, _fb_auc_val, _edloss_val = sess.run(fetches=fetches_valid,
feed_dict=feed_dict_valid)
# _fb_f1_val = 0.9
val_stat_per_epoch[single_batch_val, 0] = _fbloss_val
val_stat_per_epoch[single_batch_val, 1] = _fb_f1_val
val_stat_per_epoch[single_batch_val, 2] = _fb_auc_val
val_stat_per_epoch[single_batch_val, 3] = _edloss_val
val_tot_stat[single_epoch // val_step_size, :] = np.mean(val_stat_per_epoch, axis=0)
print("validation", single_epoch, val_tot_stat[single_epoch // val_step_size, :])
if single_epoch % save_checkpoint_period == 0 or single_epoch == (epoch_size - 1):
saver_set_all.save(sess, checkpoint_path, global_step=single_epoch)
if single_epoch == (epoch_size - 1):
np.save(os.path.join(model_dir, 'trainstat'), train_tot_stat)
np.save(os.path.join(model_dir, 'valstat'), val_tot_stat)
#