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IP_test_multi.py
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# Copyright (c) 2021-2022 Alibaba Group Holding Limited.
import os
import sys
import glob
import argparse
import numpy as np
from time import time
from PIL import Image
import tensorflow as tf
from datetime import datetime
from IP_config import PConfig
from networks.IP_model import PModel
from utils.dataloader import Dataloader
from utils.utils import load_single_image
def main():
#built the model
PMod=PModel(is_train=False)
graph, sess = PMod.build()
if not os.path.isdir(PConfig.info_dir):
os.makedirs(PConfig.info_dir)
print("imgaeGenerator--%d"%IPPar.gpu)
with graph.as_default():
print("the test seq_name is %s" % seq_name)
file = open(PConfig.datatext_dir, 'a+')
file.write("\n#######################################################\n")
file.write("Below is %s sequence test results (1I%dP)\n"%(seq_name, PConfig.test_GOP-1))
file.write("#######################################################\n")
file.close()
ori_seq_dir = os.path.join(PConfig.test_set_dir, seq_name)
print(ori_seq_dir)
test_loader = Dataloader(ori_seq_dir, 8, batch_size, model_type="test") # pipeline的方式
test_len = test_loader.file_len if PConfig.frames==9999 else PConfig.frames
test_filenames = test_loader.test_filenames
init_test = test_loader.initializer
test_next = test_loader.test_image_batch
index_random = PConfig.idx_test
alpha_rand = PConfig.alpha_test
# alpha_rand = 0.7
if PConfig.is_multi:
l_onehot = alpha_rand*PConfig.lambda_onehot[index_random] + (1-alpha_rand)*PConfig.lambda_onehot[index_random+1]
lambda_test = alpha_rand*PConfig.lambda_list[index_random] + (1-alpha_rand)*PConfig.lambda_list[index_random+1]
PConfig.test_comment = "lambda_%d"%(lambda_test)
print("\n\n =====> %s ======================" % PConfig.test_comment )
else:
lambda_test = PConfig.train_lambda
PConfig.test_comment = "lambda_%d"%(lambda_test)
sess.run(init_test) # 每次都重新开始初始化test iteration迭代器，重头开始
average_psnr = 0.
average_bpp = 0.
average_estbpp = 0.
average_estbpp_res = 0.
average_estbpp_min = 0.
average_msssim = 0.
os.makedirs(PConfig.bin_dir, exist_ok=True)
os.makedirs(os.path.join(PConfig.rescon_dir, seq_name), exist_ok=True)
#######################################################################
for i in range(test_len):
tic = time()
if i%PConfig.test_GOP == 0: # I帧直接读取已经完成的编解码信息
frame_flag = "I"
cur_img_batch = sess.run(test_next)
image_name = os.path.basename(test_filenames[i])
input_image_batch = np.concatenate((cur_img_batch, cur_img_batch, cur_img_batch), axis=2)
if PConfig.is_multi:
feed_dict={PMod.input_image_in:input_image_batch, PMod.lambda_onehot:l_onehot}
else:
feed_dict={PMod.input_image_in:input_image_batch}
psnr, ms_ssim, bpp, bpp_y = sess.run([PMod.psnr, PMod.ms_ssim, PMod.bpp, PMod.bpp_y], feed_dict=feed_dict)
bpp_res = bpp
bpp_min = np.minimum(bpp, bpp_res)
else:
frame_flag = "P"
cur_img_batch = sess.run(test_next)
# pre_img_recon_batch = clipped_recon_image
image_shape = np.shape(cur_img_batch)
image_name = os.path.basename(test_filenames[i])
image_recon_path = os.path.join(PConfig.rescon_dir, seq_name, image_name)
input_image_batch = np.concatenate((pre_img_batch, pre_img_batch, cur_img_batch), axis=2)
if PConfig.is_multi:
feed_dict={PMod.input_image_in:input_image_batch, PMod.lambda_onehot:l_onehot}
else:
feed_dict={PMod.input_image_in:input_image_batch}
psnr, ms_ssim, bpp, bpp_res, bpp_y_res, recon_image = sess.run([PMod.psnr, PMod.ms_ssim, PMod.bpp, PMod.bpp_res, PMod.bpp_y_res, PMod.clip_recon_image], feed_dict=feed_dict)
tic1 = time()
bpp_min = np.minimum(bpp, bpp_res)
# print(bpp_res, bpp_y_res, bpp_res-bpp_y_res)
#########################调用最新的编码文件###################################################
# bin_path = PConfig.bin_dir + image_name.replace(".png",".bin")
actual_total_bits = 0 # 这里还没有开始写实际编解码函数-entropy_encoding
actual_bpp = actual_total_bits / (batch_size * image_shape[1] * image_shape[2])
tic2 = time()
# 将训练后的图像保存到data-recon-mini512中
# print('The shape is Recon', clipped_recon_image.shape)
# clipped_recon_image = (np.round(recon_image*255)).astype(np.uint8)
# Image.fromarray(clipped_recon_image[0]).save(image_recon_path)
# nowtime = datetime.now().strftime('%Y-%m-%d %H:%M:%2S')
# print("%s %s : the nn time is %.2f s, the entropy time is %.2f s "%(nowtime, image_name, tic1-tic, tic2-tic1), image_shape)
pre_img_batch = cur_img_batch
average_psnr += psnr
# average_bpp += actual_bpp
average_estbpp += bpp
average_estbpp_res += bpp_res
average_estbpp_min += bpp_min
average_msssim += ms_ssim
print("%dth frame (%s) in %20s result is %.4f, %.4f, %.4f, %.4f, %.4f (bpp, bpp_res, bpp_min, psnr, msssim)"%(i, frame_flag, seq_name, bpp, bpp_res, bpp_min, psnr, ms_ssim))
# txt_write[image_name] = [psnr_val, ms_ssim_np, 0, bpp_val]
if i < 108:
file = open(PConfig.datatext_dir, 'a+')
file.write("%dth frame (%s) result is %.4f, %.4f, %.4f, %.4f, %.4f (bpp, bpp_res, bpp_min, psnr, msssim)\n"%(i, frame_flag, bpp, bpp_res, bpp_min, psnr, ms_ssim))
file.close()
print("lambda:%d, %s sequence test result, PSNR:%.3f, msssim:%.4f, Ibpp:%.4f, Resbpp:%.4f, Minbpp:%.4f\n\n" % (
lambda_test, seq_name,
average_psnr / test_len,
average_msssim / test_len,
average_estbpp / test_len,
average_estbpp_res / test_len,
average_estbpp_min / test_len))
file = open(PConfig.datatext_dir, 'a+')
file.write("the number of PMod is %s %s\n" % (PMod.module_file, PConfig.test_comment))
file.write("%20s sequence test result is PSNR:%.6f, msssim:%.6f, Ibpp:%.6f, Resbpp:%.6f, Minbpp:%.6f\n\n" % \
(seq_name,
average_psnr / test_len,
average_msssim / test_len,
average_estbpp / test_len,
average_estbpp_res / test_len,
average_estbpp_min / test_len))
file.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="IP_test")
# 节点及GPU使用
parser.add_argument("--data", type=str, default="UVG", choices=["UVG", "HB", "HC", "HD", "HE", "MCL"],
help="test different data")
parser.add_argument("--seq_name", type=str, default="Beauty",
help="test different data")
parser.add_argument("--test_set_dir", type=str, default=PConfig.test_set_dir,
help="test different data")
parser.add_argument("--gpu", type=int, default=0,
help="gpu")
parser.add_argument("--gop", type=int, default=108, choices=[2, 5, 6, 10, 12, 108],
help="test gop in video") # UVG=12/100, HEVC=10/100
parser.add_argument("--frames", type=int, default=9999, choices=[100, 108, 9999],
help="numbers of test frames in video") # UVG=9999, HEVC=100
parser.add_argument("--idx_test", type=int, default=0, choices=range(10),
help="idx")
parser.add_argument("--alpha_test", type=float, default=0.5,
help="alpha range 0-1")
IPPar = parser.parse_args()
#model and test config
os.environ['CUDA_VISIBLE_DEVICES']='%d'%(IPPar.gpu)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
batch_size = 1
if IPPar.data == "UVG":
PConfig.fix_size =[1, 1080, 1920*3, 3]
elif IPPar.data == "HB":
PConfig.fix_size =[1, 1080, 1920*3, 3]
elif IPPar.data == "HC":
PConfig.fix_size =[1, 480, 832*3, 3]
elif IPPar.data == "HD":
PConfig.fix_size =[1, 240, 416*3, 3]
elif IPPar.data == "HE":
PConfig.fix_size =[1, 720, 1280*3, 3]
elif IPPar.data == "MCL":
PConfig.fix_size =[1, 1080, 1920*3, 3]
PConfig.is_post = True
PConfig.test_set_dir = IPPar.test_set_dir
PConfig.test_GOP = IPPar.gop
PConfig.idx_test = IPPar.idx_test
PConfig.alpha_test = IPPar.alpha_test
PConfig.frames = IPPar.frames
seq_name = IPPar.seq_name
PConfig.datatext_dir = os.path.join(PConfig.info_dir, IPPar.data, \
"test_GOP%d_%s.txt"%(PConfig.test_GOP, seq_name))
os.makedirs(os.path.dirname(PConfig.datatext_dir), exist_ok=True)
main()