})
inference[inference > 1.0] = 1.0
inference[inference < 0.0] = 0.0
inference = inference * 255.0
metric = tool.psnr(inference, test_data[j][2])
format_time = str(
time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
log_info = format_time + ' ' + 'iters:%d, img:%d, loss:%.6f, psnr:%.6f' % (
i, j, loss, metric)
print(log_info)
val_log.write(log_info + '\n')
writer.add_summary(train_summary, i)
writer.close()
if __name__ == '__main__':
cfg = Config('SRCNN')
tool = Tools()
batch_size = 64
# train data
datasets_path = './datasets/training_91_image_patches.h5'
data, label = tool.read_h5_file(datasets_path)
data_loder = tool.data_iterator(data, label, batch_size)
# val data
path = './datasets/Test/Set5'
test_data = tool.read_test_data(path, cfg)
train(cfg, data_loder, test_data)
