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evaluate.py
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evaluate.py
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"""Code for testing SIFA."""
import json
import numpy as np
import os
import medpy.metric.binary as mmb
import tensorflow as tf
import model
from stats_func import *
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
CHECKPOINT_PATH = '' # model path
BASE_FID = '' # folder path of test files
TESTFILE_FID = '' # path of the .txt file storing the test filenames
TEST_MODALITY = 'CT'
USE_newstat = True
KEEP_RATE = 1.0
IS_TRAINING = False
BATCH_SIZE = 128
data_size = [256, 256, 1]
label_size = [256, 256, 1]
contour_map = {
"bg": 0,
"la_myo": 1,
"la_blood": 2,
"lv_blood": 3,
"aa": 4,
}
class SIFA:
"""The SIFA module."""
def __init__(self, config):
self.keep_rate = KEEP_RATE
self.is_training = IS_TRAINING
self.checkpoint_pth = CHECKPOINT_PATH
self.batch_size = BATCH_SIZE
self._pool_size = int(config['pool_size'])
self._skip = bool(config['skip'])
self._num_cls = int(config['num_cls'])
self.base_fd = BASE_FID
self.test_fid = BASE_FID + '/' + TESTFILE_FID
def model_setup(self):
self.input_a = tf.placeholder(
tf.float32, [
self.batch_size,
model.IMG_WIDTH,
model.IMG_HEIGHT,
1
], name="input_A")
self.input_b = tf.placeholder(
tf.float32, [
self.batch_size,
model.IMG_WIDTH,
model.IMG_HEIGHT,
1
], name="input_B")
self.fake_pool_A = tf.placeholder(
tf.float32, [
None,
model.IMG_WIDTH,
model.IMG_HEIGHT,
1
], name="fake_pool_A")
self.fake_pool_B = tf.placeholder(
tf.float32, [
None,
model.IMG_WIDTH,
model.IMG_HEIGHT,
1
], name="fake_pool_B")
self.gt_a = tf.placeholder(
tf.float32, [
self.batch_size,
model.IMG_WIDTH,
model.IMG_HEIGHT,
self._num_cls
], name="gt_A")
self.gt_b = tf.placeholder(
tf.float32, [
self.batch_size,
model.IMG_WIDTH,
model.IMG_HEIGHT,
self._num_cls
], name="gt_B")
inputs = {
'images_a': self.input_a,
'images_b': self.input_b,
'fake_pool_a': self.fake_pool_A,
'fake_pool_b': self.fake_pool_B,
}
outputs = model.get_outputs(inputs, skip=self._skip, is_training=self.is_training, keep_rate=self.keep_rate)
self.pred_mask_b = outputs['pred_mask_b']
self.predicter_b = tf.nn.softmax(self.pred_mask_b)
self.compact_pred_b = tf.argmax(self.predicter_b, 3)
self.compact_y_b = tf.argmax(self.gt_b, 3)
def read_lists(self, fid):
"""read test file list """
with open(fid, 'r') as fd:
_list = fd.readlines()
my_list = []
for _item in _list:
my_list.append(self.base_fd + '/' + _item.split('\n')[0])
return my_list
def label_decomp(self, label_batch):
"""decompose label for one-hot encoding """
_batch_shape = list(label_batch.shape)
_vol = np.zeros(_batch_shape)
_vol[label_batch == 0] = 1
_vol = _vol[..., np.newaxis]
for i in range(self._num_cls):
if i == 0:
continue
_n_slice = np.zeros(label_batch.shape)
_n_slice[label_batch == i] = 1
_vol = np.concatenate( (_vol, _n_slice[..., np.newaxis]), axis = 3 )
return np.float32(_vol)
def test(self):
"""Test Function."""
self.model_setup()
saver = tf.train.Saver()
init = tf.global_variables_initializer()
test_list = self.read_lists(self.test_fid)
with tf.Session() as sess:
sess.run(init)
saver.restore(sess, self.checkpoint_pth)
dice_list = []
assd_list = []
for idx_file, fid in enumerate(test_list):
_npz_dict = np.load(fid)
data = _npz_dict['arr_0']
label = _npz_dict['arr_1']
# This is to make the orientation of test data match with the training data
# Set to False if the orientation of test data has already been aligned with the training data
if True:
data = np.flip(data, axis=0)
data = np.flip(data, axis=1)
label = np.flip(label, axis=0)
label = np.flip(label, axis=1)
tmp_pred = np.zeros(label.shape)
frame_list = [kk for kk in range(data.shape[2])]
for ii in range(int(np.floor(data.shape[2] // self.batch_size))):
data_batch = np.zeros([self.batch_size, data_size[0], data_size[1], data_size[2]])
label_batch = np.zeros([self.batch_size, label_size[0], label_size[1]])
for idx, jj in enumerate(frame_list[ii * self.batch_size: (ii + 1) * self.batch_size]):
data_batch[idx, ...] = np.expand_dims(data[..., jj].copy(), 3)
label_batch[idx, ...] = label[..., jj].copy()
label_batch = self.label_decomp(label_batch)
if TEST_MODALITY=='CT':
if USE_newstat:
data_batch = np.subtract(np.multiply(np.divide(np.subtract(data_batch, -2.8), np.subtract(3.2, -2.8)), 2.0),1) # {-2.8, 3.2} need to be changed according to the data statistics
else:
data_batch = np.subtract(np.multiply(np.divide(np.subtract(data_batch, -1.9), np.subtract(3.0, -1.9)), 2.0),1) # {-1.9, 3.0} need to be changed according to the data statistics
elif TEST_MODALITY=='MR':
data_batch = np.subtract(np.multiply(np.divide(np.subtract(data_batch, -1.8), np.subtract(4.4, -1.8)), 2.0),1) # {-1.8, 4.4} need to be changed according to the data statistics
compact_pred_b_val = sess.run(self.compact_pred_b, feed_dict={self.input_b: data_batch, self.gt_b: label_batch})
for idx, jj in enumerate(frame_list[ii * self.batch_size: (ii + 1) * self.batch_size]):
tmp_pred[..., jj] = compact_pred_b_val[idx, ...].copy()
for c in range(1, self._num_cls):
pred_test_data_tr = tmp_pred.copy()
pred_test_data_tr[pred_test_data_tr != c] = 0
pred_gt_data_tr = label.copy()
pred_gt_data_tr[pred_gt_data_tr != c] = 0
dice_list.append(mmb.dc(pred_test_data_tr, pred_gt_data_tr))
assd_list.append(mmb.assd(pred_test_data_tr, pred_gt_data_tr))
dice_arr = 100 * np.reshape(dice_list, [4, -1]).transpose()
dice_mean = np.mean(dice_arr, axis=1)
dice_std = np.std(dice_arr, axis=1)
print 'Dice:'
print 'AA :%.1f(%.1f)' % (dice_mean[3], dice_std[3])
print 'LAC:%.1f(%.1f)' % (dice_mean[1], dice_std[1])
print 'LVC:%.1f(%.1f)' % (dice_mean[2], dice_std[2])
print 'Myo:%.1f(%.1f)' % (dice_mean[0], dice_std[0])
print 'Mean:%.1f' % np.mean(dice_mean)
assd_arr = np.reshape(assd_list, [4, -1]).transpose()
assd_mean = np.mean(assd_arr, axis=1)
assd_std = np.std(assd_arr, axis=1)
print 'ASSD:'
print 'AA :%.1f(%.1f)' % (assd_mean[3], assd_std[3])
print 'LAC:%.1f(%.1f)' % (assd_mean[1], assd_std[1])
print 'LVC:%.1f(%.1f)' % (assd_mean[2], assd_std[2])
print 'Myo:%.1f(%.1f)' % (assd_mean[0], assd_std[0])
print 'Mean:%.1f' % np.mean(assd_mean)
def main(config_filename):
with open(config_filename) as config_file:
config = json.load(config_file)
sifa_model = SIFA(config)
sifa_model.test()
if __name__ == '__main__':
main(config_filename='./config_param.json')