def main(_):
config = process_config(FLAGS.config_path)
print(config)
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
deploy_config = deploy.DeploymentConfig(num_clones=1)
global_step = tf.Variable(0, trainable=False, name='global_step')
# select model and build net
net = tdr2n2.Unet(config)
# create batch dataset
with tf.device(deploy_config.inputs_device()):
data = DataGenerator(config.input)
x_test, y_test = data.get_eval_data()
x_test = tf.expand_dims(x_test, -1)
x_test.set_shape([
None, config.input.img_out_shape[0],
config.input.img_out_shape[1], config.input.img_out_shape[2]
])
y_test.set_shape([
None, config.input.mask_out_shape[0],
config.input.mask_out_shape[1]
])
y_test = tf.cast(y_test, tf.int32)
y_test_hot = tf.one_hot(y_test,
depth=config.network.num_classes,
axis=-1)
f_score, end_points = net.net(x_test)
f_score_img = tf.expand_dims(
tf.cast(tf.argmax(f_score, axis=-1), tf.float32) * 50., -1)
y_test_img = tf.expand_dims(
tf.cast(tf.argmax(y_test_hot, axis=-1), tf.float32) * 50., -1)
## add precision and recall
f_score = tf.cast(tf.argmax(f_score, -1), tf.int32)
#f_score = tf.image.resize_bilinear(f_score, (config.input.img_out_shape[0]))
f_score = tf.one_hot(f_score,
depth=config.network.num_classes,
axis=-1)
pred = tf.reduce_sum(f_score * y_test_hot, axis=(0, 1, 2))
all_pred = tf.reduce_sum(f_score, axis=(0, 1, 2)) + 1e-5
all_true = tf.reduce_sum(y_test_hot, axis=(0, 1, 2)) + 1e-5
# Variables to restore: moving avg. or normal weights.
if config.train.moving_average_decay:
variable_averages = tf.train.ExponentialMovingAverage(
config.train.moving_average_decay, global_step)
variables_to_restore = variable_averages.variables_to_restore(
slim.get_model_variables())
variables_to_restore[global_step.op.name] = global_step
else:
variables_to_restore = slim.get_variables_to_restore()
saver = None
if variables_to_restore is not None:
saver = tf_saver.Saver(variables_to_restore)
# =================================================================== #
# Evaluation loop.
# =================================================================== #
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=config.deploy.gpu_memory_fraction)
configproto = tf.ConfigProto(
gpu_options=gpu_options,
log_device_placement=False,
allow_soft_placement=True,
)
merged = tf.summary.merge_all()
sum_writer = tf.summary.FileWriter(logdir=config.summary.test_dir)
for checkpoint_path in evaluation.checkpoints_iterator(
config.finetune.eval_checkpoint_dir):
with tf.Session(config=configproto) as session:
session.run(tf.global_variables_initializer())
session.run(data.get_iterator(is_train=False).initializer)
saver.restore(session, checkpoint_path)
logging.info('Starting evaluation at ' +
time.strftime('%Y-%m-%d-%H:%M:%S', time.gmtime()))
k = 1
tp = []
tp_fp = []
tp_fn = []
imgs = []
while True:
try:
pred_, all_pred_, all_true_, pred_img, true_img, g_step = session.run(
[
pred, all_pred, all_true, f_score_img,
y_test_img, global_step
])
tp.append(np.expand_dims(pred_, 0))
tp_fp.append(np.expand_dims(all_true_, 0))
tp_fn.append(np.expand_dims(all_pred_, 0))
#img = util.merge_pics(pred_img, true_img)
print("deal with {} images".format(
k * config.input.batch_size))
k += 1
except tf.errors.OutOfRangeError:
tp_ = np.sum(np.concatenate(tp, 0), 0)
tp_fn_ = np.sum(np.concatenate(tp_fn, 0), 0)
tp_fp_ = np.sum(np.concatenate(tp_fp, 0), 0)
precison = tp_ / tp_fp_
recall = tp_ / tp_fn_
dice = 2 * tp_ / (tp_fp_ + tp_fn_)
print(precison)
print(recall)
print(dice)
summary = tf.Summary()
for i in range(recall.shape[0]):
summary.value.add(
tag='evaluation/{}th_class_precision'.format(
i),
simple_value=precison[i])
summary.value.add(
tag='evaluation/{}th_class_recall'.format(i),
simple_value=recall[i])
summary.value.add(
tag='evaluation/{}th_class_dice'.format(i),
simple_value=dice[i])
sum_writer.add_summary(summary, g_step)
break
logging.info('Finished evaluation at ' +
time.strftime('%Y-%m-%d-%H:%M:%S', time.gmtime()))
align_corners=align_corners)
# out = out.set_shape([None, h, w, c])
return out
dataset = DataGenerator(config.input)
x_train, y_train = dataset.get_train_data()
x_train.set_shape([
None, config.input.img_out_shape[0], config.input.img_out_shape[1],
config.input.img_out_shape[2]
])
y_train.set_shape(
[None, config.input.mask_out_shape[0], config.input.mask_out_shape[1]])
x_test, y_test = dataset.get_eval_data()
x_test.set_shape([
None, config.input.img_out_shape[0], config.input.img_out_shape[1],
config.input.img_out_shape[2]
])
y_test.set_shape(
[None, config.input.mask_out_shape[0], config.input.mask_out_shape[1]])
print(x_train)
#print(y_test)
scaffold = tf.train.Scaffold(
init_op=None,
init_feed_dict=None,
init_fn=None,
ready_op=None,