def test(self):
"""Run evaluation of the model
Specify LOG_DIR to point to the saved checkpoint directory
"""
if self.restore_check is None:
raise RuntimeError(
'restore did not succeed, pleas check if you set config.LOG_DIR correctly'
)
self.restore_check.assert_existing_objects_matched(
).assert_nontrivial_match()
# Place tensors on the CPU
with tf.device('/CPU:0'):
dataset = Dataset()
ds_test = dataset.get_test()
start = time.time()
print('Start of Testing')
mpjpe, mpjpe_aligned, sequences = [], [], []
total = int(self.config.NUM_TEST_SAMPLES / self.config.BATCH_SIZE)
for image_data in tqdm(ds_test,
total=total,
position=0,
desc='testing'):
image, kp3d, sequence = image_data[0], image_data[1], image_data[2]
kp3d_mpjpe, kp3d_mpjpe_aligned = self._test_step(image, kp3d)
mpjpe.append(kp3d_mpjpe)
mpjpe_aligned.append(kp3d_mpjpe_aligned)
sequences.append(sequence)
print('Time taken for testing {} sec\n'.format(time.time() - start))
def convert(tensor, num=None):
if num is None:
num = self.config.NUM_KP3D
return tf.squeeze(tf.reshape(tf.stack(tensor), [-1, num]))
mpjpe, mpjpe_aligned, sequences = convert(mpjpe), convert(
mpjpe_aligned), convert(sequences, 1)
result_dict = {
"kp3d_mpjpe": mpjpe,
"kp3d_mpjpe_aligned": mpjpe_aligned,
"seq": sequences,
}
return result_dict
def test(self, return_kps=False):
"""Run evaluation of the model
Specify LOG_DIR to point to the saved checkpoint directory
Args:
return_kps: set to return keypoints - default = False
"""
if self.restore_check is None:
raise RuntimeError(
'restore did not succeed, pleas check if you set config.LOG_DIR correctly'
)
if self.config.INITIALIZE_CUSTOM_REGRESSOR:
self.restore_check.assert_nontrivial_match()
else:
self.restore_check.assert_existing_objects_matched(
).assert_nontrivial_match()
# Place tensors on the CPU
with tf.device('/CPU:0'):
dataset = Dataset()
ds_test = dataset.get_test()
start = time.time()
print('Start of Testing')
mpjpe, mpjpe_aligned, sequences, kps3d_pred, kps3d_real = [], [], [], [], []
total = int(self.config.NUM_TEST_SAMPLES / self.config.BATCH_SIZE)
for image_data in tqdm(ds_test,
total=total,
position=0,
desc='testing'):
image, kp3d, sequence = image_data[0], image_data[1], image_data[2]
kp3d_mpjpe, kp3d_mpjpe_aligned, predict_kp3d, real_kp3d = self._test_step(
image, kp3d, return_kps=return_kps)
if return_kps:
kps3d_pred.append(predict_kp3d)
kps3d_real.append(real_kp3d)
mpjpe.append(kp3d_mpjpe)
mpjpe_aligned.append(kp3d_mpjpe_aligned)
sequences.append(sequence)
print('Time taken for testing {} sec\n'.format(time.time() - start))
def convert(tensor, num=None, is_kp=False):
if num is None:
num = self.config.NUM_KP3D
if is_kp:
return tf.squeeze(tf.reshape(tf.stack(tensor), [-1, num, 3]))
return tf.squeeze(tf.reshape(tf.stack(tensor), [-1, num]))
mpjpe, mpjpe_aligned, sequences = convert(mpjpe), convert(
mpjpe_aligned), convert(sequences, 1)
result_dict = {
"kp3d_mpjpe": mpjpe,
"kp3d_mpjpe_aligned": mpjpe_aligned,
"seq": sequences,
}
if return_kps:
kps3d_pred, kps3d_real = convert(kps3d_pred,
is_kp=True), convert(kps3d_real,
is_kp=True)
result_dict.update({
'kps3d_pred': kps3d_pred,
'kps3d_real': kps3d_real
})
return result_dict
