def tfreloader(bs, cls, ct):
filename = data_dir + '/test.tfrecords'
datasets = data_input.DataSet(bs,
ct,
ep=1,
cls=cls,
mode='test',
filename=filename)
return datasets
def tfreloader(mode, ep, bs, cls, ctr, cte, cva):
filename = data_dir + '/' + mode + '.tfrecords'
if mode == 'train':
ct = ctr
elif mode == 'test':
ct = cte
else:
ct = cva
datasets = data_input3.DataSet(bs, ct, ep=ep, cls=cls, mode=mode, filename=filename)
return datasets
tes = test_tiles
tecc = len(tes['label'])
if not os.path.isfile(data_dir + '/test.tfrecords'):
loaders.loaderX(data_dir, 'test')
m = cnn5.INCEPTION(INPUT_DIM,
HYPERPARAMS,
meta_graph=opt.modeltoload,
log_dir=LOG_DIR,
meta_dir=METAGRAPH_DIR,
model=opt.mode)
print("Loaded! Ready for test!")
if tecc >= bs:
datasets = data_input_fusion.DataSet(bs,
tecc,
ep=1,
cls=2,
mode='test',
filename=data_dir +
'/test.tfrecords')
m.inference(datasets, opt.dirr, testset=tes, pmd=opt.pdmd)
else:
print("Not enough testing images!")
else:
bs = 64
# input image dimension
INPUT_DIM = [bs, 299, 299, 3]
# hyper parameters
HYPERPARAMS = {
"batch_size": bs,
"dropout": 0.3,