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import tensorflow as tf
import os
import time
import numpy as np
import pickle
from models import generator
from utils import DataLoader, load, save, psnr_error
from constant import const
import evaluate
from PIL import Image
slim = tf.contrib.slim
os.environ['CUDA_DEVICES_ORDER'] = "PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES'] = const.GPU
dataset_name = const.DATASET
test_folder = const.TEST_FOLDER
num_his = const.NUM_HIS
height, width = 256, 256
snapshot_dir = const.SNAPSHOT_DIR
psnr_dir = const.PSNR_DIR
evaluate_name = const.EVALUATE
print(const)
# define dataset
with tf.name_scope('dataset'):
test_video_clips_tensor = tf.placeholder(shape=[1, height, width, 3 * (num_his + 1)],
dtype=tf.float32)
test_inputs = test_video_clips_tensor[..., 0:num_his*3]
test_gt = test_video_clips_tensor[..., -3:]
print('test inputs = {}'.format(test_inputs))
print('test prediction gt = {}'.format(test_gt))
# define testing generator function and
# in testing, only generator networks, there is no discriminator networks and flownet.
with tf.variable_scope('generator', reuse=None):
print('testing = {}'.format(tf.get_variable_scope().name))
test_outputs = generator(test_inputs, layers=4, output_channel=3)
test_psnr_error = psnr_error(gen_frames=test_outputs, gt_frames=test_gt)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
# dataset
data_loader = DataLoader(test_folder, height, width)
# initialize weights
sess.run(tf.global_variables_initializer())
print('Init global successfully!')
# tf saver
saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=None)
restore_var = [v for v in tf.global_variables()]
loader = tf.train.Saver(var_list=restore_var)
def inference_func(ckpt, dataset_name, evaluate_name):
load(loader, sess, ckpt)
output_records = []
outputs = []
videos_info = data_loader.videos
num_videos = len(videos_info.keys())
total = 0
timestamp = time.time()
for video_name, video in videos_info.items():
length = video['length']
total += length
psnrs = np.empty(shape=(length,), dtype=np.float32)
for i in range(num_his, length):
video_clip = data_loader.get_video_clips(video_name, i - num_his, i + 1)
output, psnr = sess.run([test_outputs, test_psnr_error],
feed_dict={test_video_clips_tensor: video_clip[np.newaxis, ...]})
outputs.append(output)
#tf.image.encode_png(
# output,
# compression=-1,
# name=None
#)
ohhh = (output[0,...] + 1.0) * 127.5
print(ohhh.shape, np.amin(ohhh), np.amax(ohhh))
out = Image.fromarray(ohhh, 'RGB')
out.save(os.path.join(os.getcwd(), '{}_{:05d}.png'.format(video_name, i)))
print('video = {} / {}, i = {} / {}, psnr = {:.6f}'.format(
video_name, num_videos, i, length, psnr))
outputs[0:num_his] = outputs[num_his]
output_records.append(outputs)
result_dict = {'dataset': dataset_name, 'output': output_records, 'flow': [], 'names': [], 'diff_mask': []}
used_time = time.time() - timestamp
print('total time = {}, fps = {}'.format(used_time, total / used_time))
# TODO specify what's the actual name of ckpt.
pickle_path = os.path.join(output_dir, os.path.split(ckpt)[-1])
with open(pickle_path, 'wb') as writer:
pickle.dump(result_dict, writer, pickle.HIGHEST_PROTOCOL)
# results = evaluate.evaluate(evaluate_name, pickle_path)
# print(results)
print(os.path.isdir(snapshot_dir))
if os.path.isdir(snapshot_dir):
def check_ckpt_valid(ckpt_name):
is_valid = False
ckpt = ''
if ckpt_name.startswith('model.ckpt-'):
ckpt_name_splits = ckpt_name.split('.')
ckpt = str(ckpt_name_splits[0]) + '.' + str(ckpt_name_splits[1])
ckpt_path = os.path.join(snapshot_dir, ckpt)
if os.path.exists(ckpt_path + '.index') and os.path.exists(ckpt_path + '.meta') and \
os.path.exists(ckpt_path + '.data-00000-of-00001'):
is_valid = True
return is_valid, ckpt
def scan_psnr_folder():
tested_ckpt_in_psnr_sets = set()
for test_psnr in os.listdir(psnr_dir):
tested_ckpt_in_psnr_sets.add(test_psnr)
return tested_ckpt_in_psnr_sets
def scan_model_folder():
saved_models = set()
for ckpt_name in os.listdir(snapshot_dir):
is_valid, ckpt = check_ckpt_valid(ckpt_name)
if is_valid:
saved_models.add(ckpt)
return saved_models
tested_ckpt_sets = scan_psnr_folder()
while True:
all_model_ckpts = scan_model_folder()
new_model_ckpts = all_model_ckpts - tested_ckpt_sets
for ckpt_name in new_model_ckpts:
# inference
ckpt = os.path.join(snapshot_dir, ckpt_name)
inference_func(ckpt, dataset_name, evaluate_name)
tested_ckpt_sets.add(ckpt_name)
print('waiting for models...')
# evaluate.evaluate('compute_auc', psnr_dir)
time.sleep(60)
else:
inference_func(snapshot_dir, dataset_name, evaluate_name)
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