tf.reshape(inputs[1],
[-1, REGION_SIZES[1], REGION_SIZES[1], REGION_SIZES[1], 1]),
tf.reshape(inputs[2],
[-1, REGION_SIZES[2], REGION_SIZES[2], REGION_SIZES[2], 1])
]
if "bn_files" in dir():
bn_params = [
np.load(bn_files[0]),
np.load(bn_files[1]),
np.load(bn_files[2])
]
else:
bn_params = [None, None, None]
outputs0, variables0, _ = tft.volume_bndo_flbias_l5_20(
inputs_reshape[0],
dropout_rate=0.0,
batch_normalization_statistic=False,
bn_params=bn_params[0])
outputs1, variables1, _ = tft.volume_bndo_flbias_l5_30(
inputs_reshape[1],
dropout_rate=0.0,
batch_normalization_statistic=False,
bn_params=bn_params[1])
outputs2, variables2, _ = tft.volume_bndo_flbias_l6_40(
inputs_reshape[2],
dropout_rate=0.0,
batch_normalization_statistic=False,
bn_params=bn_params[2])
if FUSION_MODE == 'vote':
predictions = [
outputs0['sm_out'], outputs1['sm_out'], outputs2['sm_out']
]
real_label = tf.placeholder(tf.float32, [None, 2])
#bn_params = np.load(net_init_path + "/batch_normalization_statistic.npy")
positive_confidence = aug_proportion / float(aug_proportion + np_proportion)
if "bn_files" in dir():
bn_params = [
np.load(bn_files[0]),
np.load(bn_files[1]),
np.load(bn_files[2])
]
else:
bn_params = [None, None, None]
net_outs0, variables0, _ = tft.volume_bndo_flbias_l5_20(
volumes_reshape[0],
False,
positive_confidence,
dropout_rate=0.0,
batch_normalization_statistic=False,
bn_params=bn_params[0])
net_outs1, variables1, _ = tft.volume_bndo_flbias_l5_30(
volumes_reshape[1],
False,
positive_confidence,
dropout_rate=0.0,
batch_normalization_statistic=False,
bn_params=bn_params[1])
net_outs2, variables2, _ = tft.volume_bndo_flbias_l6_40(
volumes_reshape[2],
False,
positive_confidence,
dropout_rate=0.0,
