val_files = pfiles[-validation_num:]
else:
print("No test data")
exit()
if 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_l6_40(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)
#ktb.set_session(mt.get_session(0.5))
start_time = time.time()
#patient_evaluations = open(evaluation_path + "/patient_evaluations.log", "w")
results = []
CPMs = []
CPMs2 = []
test_patients = all_patients[:1]
bt.filelist_store(all_patients, evaluation_path + "/patientfilelist.log")
]
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 = [
outputs0['sm_out'], outputs1['sm_out'], outputs2['sm_out']
]
combined_prediction = tft.committe_fusion(predictions)
elif FUSION_MODE == 'late':
features = [
#negative_importances = 1000*np.ones(shape=[num_negative], dtype=float)
#net construct
volume_input = tf.placeholder(tf.float32,
[None, REGION_SIZE, REGION_SIZE, REGION_SIZE])
volume_reshape = tf.reshape(volume_input,
[-1, REGION_SIZE, REGION_SIZE, REGION_SIZE, 1])
real_label = tf.placeholder(tf.float32, [None, 2])
#r_bn1, b_bn1, w_conv1, w_conv2, out_conv1, out_bn1, hidden_conv1, hidden_conv2, hidden_conv3, out_fc1, out_fc2, softmax_out = tft.volume_bnnet2_l6_56(volume_reshape)
#bn_params = np.load(net_init_path + "/batch_normalization_statistic.npy")
positive_confidence = num_positive_aug / float(num_positive_aug +
num_positive * np_proportion)
net_outs, _, bn_params = tft.volume_bndo_flbias_l6_40(
volume_reshape,
True,
positive_confidence,
dropout_rate=0.0,
batch_normalization_statistic=True,
bn_params=None)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
logits=net_outs['last_out'], labels=real_label)
balancing = real_label[:, 0] + tf.pow(tf.constant(-1, dtype=tf.float32),
real_label[:, 0]) * tf.constant(
positive_confidence, tf.float32)
modulation = tf.pow(
real_label[:, 0] +
tf.pow(tf.constant(-1, dtype=tf.float32), real_label[:, 0]) *
net_outs['sm_out'][:, 0], tf.constant(2, dtype=tf.float32))
focal_loss = modulation * balancing * cross_entropy
batch_loss = tf.reduce_mean(focal_loss)
correct_prediction = tf.equal(tf.argmax(net_outs['sm_out'], 1),
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,
positive_confidence)
loss = tf.nn.softmax_cross_entropy_with_logits(logits=net_out,
labels=real_label)
if type(LOSS_BALANCING) == float:
balancing_factor = LOSS_BALANCING
else:
balancing_factor = positive_confidence
