def custom_loss_hm(self, ten_hm_t, ten_hm_p):
# print(ten_hm_t.get_shape()) # [None, 56, 56, 68]
# print(ten_hm_p.get_shape())
tf_utility = TFRecordUtility()
sqr = K.square(ten_hm_t - ten_hm_p) # [None, 56, 56, 68]
mean1 = K.mean(sqr, axis=1)
mean2 = K.mean(mean1, axis=1)
tensor_mean_square_error = K.mean(mean2, axis=1)
# print(tensor_mean_square_error.get_shape().as_list()) # [None, 68]
# vec_mse = K.eval(tensor_mean_square_error)
# print("mse.shape:")
# print(vec_mse.shape) # (50, 68)
# print(vec_mse)
# print("----------->>>")
'''calculate points from generated hm'''
p_points_batch = tf.stack([
tf_utility.from_heatmap_to_point_tensor(ten_hm_p[i], 5, 1)
for i in range(LearningConfig.batch_size)
])
t_points_batch = tf.stack([
tf_utility.from_heatmap_to_point_tensor(ten_hm_t[i], 5, 1)
for i in range(LearningConfig.batch_size)
])
'''p_points_batch is [batch, 2, 68]'''
sqr_2 = K.square(t_points_batch - p_points_batch) # [None, 2, 68]
mean_1 = K.mean(sqr_2, axis=1)
tensor_indices_mean_square_error = K.mean(mean_1, axis=1)
# tensor_total_loss = tf.reduce_mean([tensor_mean_square_error, tensor_indices_mean_square_error])
tensor_total_loss = tf.add(tensor_mean_square_error,
tensor_indices_mean_square_error)
return tensor_total_loss
def convert_hm_to_pts(self, hm):
x_center = InputDataSize.image_input_size // 2
width = InputDataSize.image_input_size
hm_arr = []
tf_util = TFRecordUtility(self.num_landmark // 2)
for i in range(LearningConfig.batch_size):
hm_t = tf_util.from_heatmap_to_point_tensor(heatmaps=hm[i],
number_of_points=5)
hm_t = tf.reshape(tensor=hm_t, shape=self.num_landmark)
'''hm is in [0,224] --> should be in [-0.5,+0.5]'''
hm_t_norm = tf.math.scalar_mul(
scalar=1 / width,
x=tf.math.subtract(hm_t, np.repeat(x_center,
self.num_landmark)))
hm_arr.append(hm_t_norm)
'''reshape hm'''
hm_pts = tf.stack(
[hm_arr[i] for i in range(LearningConfig.batch_size)],
0) # bs * self.num_landmark
return hm_pts
