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test.py
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test.py
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import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from utils import pp, visualize, to_json, show_all_variables
from models import ALOCC_Model
import matplotlib.pyplot as plt
from kh_tools import *
import numpy as np
import scipy.misc
from utils import *
import time
import os
flags = tf.app.flags
flags.DEFINE_integer("epoch", 1, "Epoch to train [25]")
flags.DEFINE_float("learning_rate", 0, "Learning rate of for adam [0.0002]")
flags.DEFINE_float("beta1", 0.5, "Momentum term of adam [0.5]")
flags.DEFINE_integer("attention_label", 1, "Conditioned label that growth attention of training label [1]")
flags.DEFINE_float("r_alpha", 0.2, "Refinement parameter [0.2]")
flags.DEFINE_integer("train_size", np.inf, "The size of train images [np.inf]")
flags.DEFINE_integer("batch_size", 128, "The size of batch images [64]")
flags.DEFINE_integer("input_height", 45, "The size of image to use. [45]")
flags.DEFINE_integer("input_width", None, "The size of image to use. If None, same value as input_height [None]")
flags.DEFINE_integer("output_height", 45, "The size of the output images to produce [45]")
flags.DEFINE_integer("output_width", None, "The size of the output images to produce. If None, same value as output_height [None]")
flags.DEFINE_string("dataset", "UCSD", "The name of dataset [UCSD, mnist]")
flags.DEFINE_string("dataset_address", "./dataset/UCSD_Anomaly_Dataset.v1p2/UCSDped2/Test", "The path of dataset")
flags.DEFINE_string("input_fname_pattern", "*", "Glob pattern of filename of input images [*]")
flags.DEFINE_string("checkpoint_dir", "./checkpoint/UCSD_128_45_45/", "Directory name to save the checkpoints [checkpoint]")
flags.DEFINE_string("log_dir", "log", "Directory name to save the log [log]")
flags.DEFINE_string("sample_dir", "samples", "Directory name to save the image samples [samples]")
flags.DEFINE_boolean("train", False, "True for training, False for testing [False]")
FLAGS = flags.FLAGS
def check_some_assertions():
"""
to check some assertions in inputs and also check sth else.
"""
if FLAGS.input_width is None:
FLAGS.input_width = FLAGS.input_height
if FLAGS.output_width is None:
FLAGS.output_width = FLAGS.output_height
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
def main(_):
print('Program is started at', time.clock())
pp.pprint(flags.FLAGS.__flags)
n_per_itr_print_results = 100
n_fetch_data = 10
kb_work_on_patch= False
nd_input_frame_size = (240, 360)
#nd_patch_size = (45, 45)
n_stride = 10
#FLAGS.checkpoint_dir = "./checkpoint/UCSD_128_45_45/"
#FLAGS.dataset = 'UCSD'
#FLAGS.dataset_address = './dataset/UCSD_Anomaly_Dataset.v1p2/UCSDped2/Test'
lst_test_dirs = ['Test004','Test005','Test006']
#DATASET PARAMETER : MNIST
#FLAGS.dataset = 'mnist'
#FLAGS.dataset_address = './dataset/mnist'
#nd_input_frame_size = (28, 28)
#nd_patch_size = (28, 28)
#FLAGS.checkpoint_dir = "./checkpoint/mnist_128_28_28/"
#FLAGS.input_width = nd_patch_size[0]
#FLAGS.input_height = nd_patch_size[1]
#FLAGS.output_width = nd_patch_size[0]
#FLAGS.output_height = nd_patch_size[1]
check_some_assertions()
nd_patch_size = (FLAGS.input_width, FLAGS.input_height)
nd_patch_step = (n_stride, n_stride)
FLAGS.nStride = n_stride
#FLAGS.input_fname_pattern = '*'
FLAGS.train = False
FLAGS.epoch = 1
FLAGS.batch_size = 504
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.1)
run_config = tf.ConfigProto(gpu_options=gpu_options)
run_config.gpu_options.allow_growth=True
with tf.Session(config=run_config) as sess:
tmp_ALOCC_model = ALOCC_Model(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
attention_label=FLAGS.attention_label,
r_alpha=FLAGS.r_alpha,
is_training=FLAGS.train,
dataset_name=FLAGS.dataset,
dataset_address=FLAGS.dataset_address,
input_fname_pattern=FLAGS.input_fname_pattern,
checkpoint_dir=FLAGS.checkpoint_dir,
sample_dir=FLAGS.sample_dir,
nd_patch_size=nd_patch_size,
n_stride=n_stride,
n_per_itr_print_results=n_per_itr_print_results,
kb_work_on_patch=kb_work_on_patch,
nd_input_frame_size = nd_input_frame_size,
n_fetch_data=n_fetch_data)
show_all_variables()
print('--------------------------------------------------')
print('Load Pretrained Model...')
tmp_ALOCC_model.f_check_checkpoint()
if FLAGS.dataset=='mnist':
mnist = input_data.read_data_sets(FLAGS.dataset_address)
specific_idx_anomaly = np.where(mnist.train.labels != 6)[0]
specific_idx = np.where(mnist.train.labels == 6)[0]
ten_precent_anomaly = [specific_idx_anomaly[x] for x in
random.sample(range(0, len(specific_idx_anomaly)), len(specific_idx) // 40)]
data = mnist.train.images[specific_idx].reshape(-1, 28, 28, 1)
tmp_data = mnist.train.images[ten_precent_anomaly].reshape(-1, 28, 28, 1)
data = np.append(data, tmp_data).reshape(-1, 28, 28, 1)
lst_prob = tmp_ALOCC_model.f_test_frozen_model(data[0:FLAGS.batch_size])
print('check is ok')
exit()
#generated_data = tmp_ALOCC_model.feed2generator(data[0:FLAGS.batch_size])
# else in UCDS (depends on infrustructure)
for s_image_dirs in sorted(glob(os.path.join(FLAGS.dataset_address, 'Test[0-9][0-9][0-9]'))):
tmp_lst_image_paths = []
if os.path.basename(s_image_dirs) not in ['Test004']:
print('Skip ',os.path.basename(s_image_dirs))
continue
for s_image_dir_files in sorted(glob(os.path.join(s_image_dirs + '/*'))):
if os.path.basename(s_image_dir_files) not in ['068.tif']:
print('Skip ', os.path.basename(s_image_dir_files))
continue
tmp_lst_image_paths.append(s_image_dir_files)
#random
#lst_image_paths = [tmp_lst_image_paths[x] for x in random.sample(range(0, len(tmp_lst_image_paths)), n_fetch_data)]
lst_image_paths = tmp_lst_image_paths
#images =read_lst_images(lst_image_paths,nd_patch_size,nd_patch_step,b_work_on_patch=False)
images = read_lst_images_w_noise2(lst_image_paths, nd_patch_size, nd_patch_step)
lst_prob = process_frame(os.path.basename(s_image_dirs),images,tmp_ALOCC_model)
print('pseudocode test is finished')
# This code for just check output for readers
# ...
def process_frame(s_name,frames_src,sess):
nd_patch,nd_location = get_image_patches(frames_src,sess.patch_size,sess.patch_step)
frame_patches = nd_patch.transpose([1,0,2,3])
print('frame patches :{}\npatches size:{}'.format(len(frame_patches),(frame_patches.shape[1],frame_patches.shape[2])))
lst_prob = sess.f_test_frozen_model(frame_patches)
# This code for just check output for readers
# ...
# ---------------------------------------------------------------------------------------
# ---------------------------------------------------------------------------------------
if __name__ == '__main__':
tf.app.run()