print("No test data")
exit()
if 'vision_path' in dir() and 'vision_path' is not None and not os.access(
vision_path, os.F_OK):
os.makedirs(vision_path)
if os.access(evaluation_path, os.F_OK):
shutil.rmtree(evaluation_path)
os.makedirs(evaluation_path)
x = tf.placeholder(tf.float32,
[None, REGION_SIZE, REGION_SIZE, REGION_SIZE])
x_image = tf.reshape(x, [-1, REGION_SIZE, REGION_SIZE, REGION_SIZE, 1])
bn_params = np.load(bn_file)
outputs, _, _ = tft.volume_bndo_flbias_l5_30(
x_image,
dropout_rate=0.0,
batch_normalization_statistic=False,
bn_params=bn_params)
prediction_out = outputs['sm_out']
saver = tf.train.Saver()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
saver.restore(sess, net_file)
start_time = time.time()
#patient_evaluations = open(evaluation_path + "/patient_evaluations.log", "w")
results = []
CPMs = []
CPMs2 = []
test_patients = all_patients[4:5]
#bn_params = np.load(net_init_path + "/batch_normalization_statistic.npy")
aug_proportion = (1 + 2 * int(SCALE_AUGMENTATION)) * (
1 + 26 * int(TRANSLATION_AUGMENTATION) *
(1 + 2 * int(CENTERING))) * (1 + 9 * int(ROTATION_AUGMENTATION) +
3 * int(FLIP_AUGMENTATION))
if ALL_NEGATIVE:
np_proportion = negative_train_num / float(positive_train_num)
else:
np_proportion = aug_proportion
num_positive_aug = positive_train_num * aug_proportion
positive_confidence = num_positive_aug / float(
num_positive_aug + positive_train_num * np_proportion)
net_outs, _, bn_params = tft.volume_bndo_flbias_l5_30(
volume_reshape,
True,
positive_confidence,
dropout_rate=0.3,
batch_normalization_statistic=True,
bn_params=None)
loss = tf.nn.softmax_cross_entropy_with_logits(logits=net_outs['last_out'],
labels=real_label)
if type(LOSS_BALANCING) == float:
balancing_factor = LOSS_BALANCING
else:
balancing_factor = positive_confidence
balancing = real_label[:, 0] + tf.pow(tf.constant(-1, dtype=tf.float32),
real_label[:, 0]) * tf.constant(
balancing_factor, tf.float32)
modulation = tf.pow(
real_label[:, 0] +
tf.pow(tf.constant(-1, dtype=tf.float32), real_label[:, 0]) *
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']
]
combined_prediction = tft.vote_fusion(predictions)
combined_prediction = tf.reshape(combined_prediction, [-1, 1])
elif FUSION_MODE == 'committe':
predictions = [
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,
batch_normalization_statistic=False,
bn_params=bn_params[2])
features = [
net_outs0['flattened_out'], net_outs1['flattened_out'],
net_outs2['fc1_out']
]
net_out, sm_out, fusion_variables = tft.late_fusion(features, True,
