try:
saver.restore(sess, opt.load_from_checkpoint)
print('--> load from checkpoint ' + opt.load_from_checkpoint)
except:
print('unable to load checkpoint ...')
sys.exit(0)
dice_score = 0
for it in range(0, test_samples):
x_batch, y_batch = next(test_generator)
# tensorflow wants a different tensor order
print(x_batch.shape)
feed_dict = {img: x_batch, label: y_batch}
loss, pred_logits = sess.run([cross_entropy_loss, pred],
feed_dict=feed_dict)
# pred_logits.shape: (1, 256, 256, 4)
pred_map = np.argmax(pred_logits[0], axis=2)
# pred_map.shape: (256, 256)
mean_iou, pixel_acc, dice = vis.add_sample(pred_map, y_batch[0])
im, gt, gt_pred = deprocess(x_batch[0], y_batch[0], pred_map,
label_classes, dataset_mean, dataset_std)
vis.save_seg(gt_pred,
name='{0:}_{1:.3f}.png'.format(it, mean_iou),
im=im,
gt=gt)
print('[iter %f]: loss=%f, meanIU=%f, PixelAccuracy=%f, Dice=%f' %
(it, loss, mean_iou, pixel_acc, dice))
vis.compute_scores()
saver.restore(sess,
tf.train.latest_checkpoint(opt.load_from_checkpoint))
print('--> load from checkpoint ' + opt.load_from_checkpoint)
except Exception as ex:
print('Unable to load checkpoint ...', ex)
sys.exit(0)
dice_score = 0
for it in range(0, test_samples):
x_batch, y_batch = next(test_generator)
# tensorflow wants a different tensor order
feed_dict = {img: x_batch, label: y_batch}
loss, pred_logits = sess.run([cross_entropy_loss, pred],
feed_dict=feed_dict)
pred_map = np.argmax(pred_logits[0], axis=2)
score = vis.add_sample(pred_map, y_batch[0])
im, gt = deprocess(x_batch[0], dataset_mean, dataset_std, y_batch[0])
# im = Image.fromarray(im)
# im.save(os.path.join(opt.load_from_checkpoint, '{0:}.png'.format(it)))
# gt = Image.fromarray(gt, 'L')
# gt.save(os.path.join(opt.load_from_checkpoint, '{0:}_{1:.3f}.png'.format(it, score)))
vis.save_seg(pred_map,
name='{0:}_{1:.3f}.png'.format(it, score),
im=im,
gt=gt)
print('[iter %f]: loss=%f, meanIU=%f' % (it, loss, score))
vis.compute_scores()
def get_roi(dir_path):
'''
Extract brain ROI from mri scans of fetuses.
:param dir_path: directory contains npz files which are inputs to the NN.
:return: list of tuples of numpy arrays: (img, label, gt)
'''
vis = VIS(save_path=opt.load_from_checkpoint)
# configuration session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
# define data loader
img_shape = [opt.imSize, opt.imSize, opt.num_channels]
test_generator, test_samples = dataLoader(dir_path,
1,
img_shape,
train_mode=False)
# define model, the last dimension is the channel
label = tf.placeholder(tf.int32, shape=[None] + img_shape[:-1])
with tf.name_scope('unet'):
# model = UNet().create_model(img_shape=img_shape+[3], num_class=opt.num_class)
model = UNet().create_model(img_shape=img_shape,
num_class=opt.num_class)
img = model.input
pred = model.output
# define loss
# with tf.name_scope('cross_entropy'):
# cross_entropy_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=label, logits=pred))
with tf.name_scope('dice_loss'):
dice_loss = dice_coef_loss(label, pred)
saver = tf.train.Saver() # must be added in the end
''' Main '''
init_op = tf.global_variables_initializer()
sess.run(init_op)
with sess.as_default():
# restore from a checkpoint if exists
try:
last_checkpoint = tf.train.latest_checkpoint(opt.checkpoint_path)
# saver.restore(sess, opt.load_from_checkpoint)
print('--> load from checkpoint ' + last_checkpoint)
saver.restore(sess, last_checkpoint)
except Exception as ex:
print('unable to load checkpoint ... {}'.format(ex))
print('tried loading from: {}'.format(opt.checkpoint_path))
sys.exit(0)
dice_score = 0
# tuples of (img, label, gt)
results = []
for it in range(0, test_samples):
x_batch, y_batch = next(test_generator)
# tensorflow wants a different tensor order
feed_dict = {img: x_batch, label: y_batch}
# loss, pred_logits = sess.run([cross_entropy_loss, pred], feed_dict=feed_dict)
# pred_map = np.argmax(pred_logits[0], axis=2)
loss, pred_logits = sess.run([dice_loss, pred],
feed_dict=feed_dict)
pred_map_batch = pred_logits > 0.5
pred_map = pred_map_batch.squeeze()
score = vis.add_sample(pred_map, y_batch[0])
im, gt = deprocess(x_batch[0], dataset_mean, dataset_std,
y_batch[0])
results.append((im, pred_map, gt))
# vis.save_seg(pred_map, name='{0:04d}_{1:.3f}.png'.format(it, score), im=im, gt=gt)
# print ('[iter %f]: loss=%f, meanIU=%f' % (it, loss, score))
vis.compute_scores()
return results