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SAE: revert back CA weights option

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iperov 2019-04-24 13:44:12 +04:00
commit 9535a657d2
6 changed files with 8 additions and 5 deletions
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11
facelib/PoseEstimator.py
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@ -23,7 +23,6 @@ class PoseEstimator(object):
self.angles = [90, 45, 30, 10, 2]
self.alpha_cat_losses = [7,5,3,1,1]
self.class_nums = [ angle+1 for angle in self.angles ]
self.model = PoseEstimator.BuildModel(resolution, class_nums=self.class_nums)
@ -57,7 +56,7 @@ class PoseEstimator(object):
loss_pitch = []
loss_yaw = []
loss_roll = []
for i,class_num in enumerate(self.class_nums):
a = self.alpha_cat_losses[i]
loss_pitch += [ a*K.categorical_crossentropy( inp_bins_t[i*3+0], bins_t[i*3+0] ) ]
@ -65,7 +64,12 @@ class PoseEstimator(object):
loss_roll += [ a*K.categorical_crossentropy( inp_bins_t[i*3+2], bins_t[i*3+2] ) ]
idx_tensor = K.constant( np.array([idx for idx in range(self.class_nums[0])], dtype=K.floatx() ) )
pitch_t, yaw_t, roll_t = K.sum ( bins_t[0] * idx_tensor, 1), K.sum ( bins_t[1] * idx_tensor, 1), K.sum ( bins_t[2] * idx_tensor, 1)
#pitch_t, yaw_t, roll_t = K.sum ( bins_t[0] * idx_tensor, 1), K.sum ( bins_t[1] * idx_tensor, 1), K.sum ( bins_t[2] * idx_tensor, 1)
pitch_t, yaw_t, roll_t = nnlib.tf.reduce_sum ( bins_t[0] * idx_tensor, 1), nnlib.tf.reduce_sum ( bins_t[1] * idx_tensor, 1), nnlib.tf.reduce_sum ( bins_t[2] * idx_tensor, 1)
reg_alpha = 2
reg_pitch_loss = reg_alpha * K.mean(K.square( inp_pitch_t - pitch_t), -1)
@ -75,7 +79,6 @@ class PoseEstimator(object):
pitch_loss = reg_pitch_loss + sum(loss_pitch)
yaw_loss = reg_yaw_loss + sum(loss_yaw)
roll_loss = reg_roll_loss + sum(loss_roll)
opt = Adam(lr=0.000001)
if training: