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update labels

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jh 2021-03-16 19:16:48 -07:00
commit 91301d0516
1 changed files with 23 additions and 16 deletions
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models/Model_SAEHD/Model.py
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@ -498,31 +498,38 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
print("gpu_pred_src_src_d.shape: ", gpu_pred_src_src_d.shape) print("gpu_pred_src_src_d.shape: ", gpu_pred_src_src_d.shape)
print("gpu_pred_src_src_d2.shape: ", gpu_pred_src_src_d2.shape) print("gpu_pred_src_src_d2.shape: ", gpu_pred_src_src_d2.shape)
print("tf.reshape(gpu_pred_src_src_d, [-1]).shape: ", tf.reshape(gpu_pred_src_src_d, [-1]).shape) # print("tf.reshape(gpu_pred_src_src_d, [-1]).shape: ", tf.reshape(gpu_pred_src_src_d, [-1]).shape)
def get_smooth_noisy_label(label, smoothing=0.2, noise=0.05): def get_smooth_noisy_labels(label, tensor, smoothing=0.2, noise=0.05):
if np.random.random() < noise: labels = []
label = 0 if label == 1 else 1 num_labels = self.batch_size
if label == 0: for d in tensor.shape[1:]:
return lambda x: np.random.uniform(0, 0+smoothing/2) num_labels *= d
else: for _ in num_labels:
return lambda x: np.random.uniform(1-smoothing/2, 1.0) if np.random.random() < noise:
label = 0 if label == 1 else 1
if label == 0:
label = np.random.uniform(0, 0+smoothing/2)
else:
label = np.random.uniform(1-smoothing/2, 1.0)
labels.append(label)
return tf.reshape(labels, tensor.shape)
gpu_pred_src_src_d_ones = tf.reshape(tf.map_fn (get_smooth_noisy_label(1, smoothing=0.2, noise=0.05), tf.reshape(gpu_pred_src_src_d, [-1])), gpu_pred_src_src_d.shape) gpu_pred_src_src_d_ones = get_smooth_noisy_labels(1, gpu_pred_src_src_d, smoothing=0.2, noise=0.05)
gpu_pred_src_src_d_zeros = tf.map_fn (get_smooth_noisy_label(0, smoothing=0.2, noise=0.05), gpu_pred_src_src_d) gpu_pred_src_src_d_zeros = get_smooth_noisy_labels(0, gpu_pred_src_src_d, smoothing=0.2, noise=0.05)
print("Shape (gpu_pred_src_src_d_ones): ", gpu_pred_src_src_d.shape) print("gpu_pred_src_src_d_ones.shape: ", gpu_pred_src_src_d_ones.shape)
print("Shape (gpu_pred_src_src_d_zeros): ", gpu_pred_src_src_d2.shape) print("gpu_pred_src_src_d_zeros.shape: ", gpu_pred_src_src_d_zeros.shape)
gpu_pred_src_src_d2_ones = tf.map_fn (get_smooth_noisy_label(1, smoothing=0.2, noise=0.05), gpu_pred_src_src_d2) gpu_pred_src_src_d2_ones = get_smooth_noisy_labels(1, gpu_pred_src_src_d2, smoothing=0.2, noise=0.05)
gpu_pred_src_src_d2_zeros = tf.map_fn (get_smooth_noisy_label(0, smoothing=0.2, noise=0.05), gpu_pred_src_src_d2) gpu_pred_src_src_d2_zeros = get_smooth_noisy_labels(0, gpu_pred_src_src_d2, smoothing=0.2, noise=0.05)
gpu_target_src_d, \ gpu_target_src_d, \
gpu_target_src_d2 = self.D_src(gpu_target_src_masked_opt) gpu_target_src_d2 = self.D_src(gpu_target_src_masked_opt)
gpu_target_src_d_ones = tf.map_fn (get_smooth_noisy_label(1, smoothing=0.2, noise=0.05), gpu_target_src_d) gpu_target_src_d_ones = get_smooth_noisy_labels(1, gpu_target_src_d, smoothing=0.2, noise=0.05)
gpu_target_src_d2_ones = tf.map_fn (get_smooth_noisy_label(1, smoothing=0.2, noise=0.05), gpu_target_src_d2) gpu_target_src_d2_ones = get_smooth_noisy_labels(1, gpu_target_src_d2, smoothing=0.2, noise=0.05)
gpu_D_src_dst_loss = (DLoss(gpu_target_src_d_ones , gpu_target_src_d) + \ gpu_D_src_dst_loss = (DLoss(gpu_target_src_d_ones , gpu_target_src_d) + \
DLoss(gpu_pred_src_src_d_zeros , gpu_pred_src_src_d) ) * 0.5 + \ DLoss(gpu_pred_src_src_d_zeros , gpu_pred_src_src_d) ) * 0.5 + \