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import tensorflow as tf
from pca_utility import PCAUtility
import numpy as np
class ASMLoss:
def __init__(self, dataset_name, accuracy):
self.dataset_name = dataset_name
self.accuracy = accuracy
def calculate_pose_loss(self, x_pr, x_gt):
return tf.reduce_mean(tf.square(x_gt - x_pr))
def calculate_landmark_ASM_assisted_loss(self, landmark_pr, landmark_gt, current_epoch, total_steps):
"""
:param landmark_pr:
:param landmark_gt:
:param current_epoch:
:param total_steps:
:return:
"""
# calculating ASMLoss weight:
asm_weight = 0.5
if current_epoch < total_steps//3: asm_weight = 2.0
elif total_steps//3 <= current_epoch < 2*total_steps//3: asm_weight = 1.0
# creating the ASM-ground truth
landmark_gt_asm = self._calculate_asm(input_tensor=landmark_gt)
# calculating ASMLoss
asm_loss = tf.reduce_mean(tf.square(landmark_gt_asm - landmark_pr))
# calculating MSELoss
mse_loss = tf.reduce_mean(tf.square(landmark_gt - landmark_pr))
# calculating total loss
return mse_loss + asm_weight * asm_loss
def _calculate_asm(self, input_tensor):
pca_utility = PCAUtility()
eigenvalues, eigenvectors, meanvector = pca_utility.load_pca_obj(self.dataset_name, pca_percentages=self.accuracy)
input_vector = np.array(input_tensor)
out_asm_vector = []
batch_size = input_vector.shape[0]
for i in range(batch_size):
b_vector_p = self._calculate_b_vector(input_vector[i], eigenvalues, eigenvectors, meanvector)
out_asm_vector.append(meanvector + np.dot(eigenvectors, b_vector_p))
out_asm_vector = np.array(out_asm_vector)
return out_asm_vector
def _calculate_b_vector(self, predicted_vector, eigenvalues, eigenvectors, meanvector):
b_vector = np.dot(eigenvectors.T, predicted_vector - meanvector)
# revised b to be in -3lambda =>
i = 0
for b_item in b_vector:
lambda_i_sqr = 3 * np.sqrt(eigenvalues[i])
if b_item > 0:
b_item = min(b_item, lambda_i_sqr)
else:
b_item = max(b_item, -1 * lambda_i_sqr)
b_vector[i] = b_item
i += 1
return b_vector
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