From 0d5b67a35518c9f61cdd4fccd19089c0fc54642f Mon Sep 17 00:00:00 2001 From: jh Date: Tue, 16 Mar 2021 17:11:31 -0700 Subject: [PATCH] test --- models/Model_SAEHD/Model.py | 34 ++++++++++++++++------------------ 1 file changed, 16 insertions(+), 18 deletions(-) diff --git a/models/Model_SAEHD/Model.py b/models/Model_SAEHD/Model.py index 5d48762..216f861 100644 --- a/models/Model_SAEHD/Model.py +++ b/models/Model_SAEHD/Model.py @@ -497,32 +497,30 @@ Examples: df, liae, df-d, df-ud, liae-ud, ... print("Shape (gpu_pred_src_src_d): ", gpu_pred_src_src_d.shape) print("Shape (gpu_pred_src_src_d2): ", gpu_pred_src_src_d2.shape) - for i in gpu_pred_src_src_d2.shape: - print("Shape (gpu_pred_src_src_d2) i: ", i) - # def get_smooth_noisy_labels_like(label, tensor, smoothing=0.2, noise=0.05): - # new_labels = [] - # for label in labels: - # if np.random.random() < noise: - # label = 0 if label == 1 else 1 - # if label == 0: - # new_labels.append(np.random.uniform(0, 0+smoothing/2)) - # else: - # new_labels.append(np.random.uniform(1-smoothing/2, 1.0)) - # return new_labels + def get_smooth_noisy_label(label, smoothing=0.2, noise=0.05): + if np.random.random() < noise: + label = 0 if label == 1 else 1 + if label == 0: + return lambda x: np.random.uniform(0, 0+smoothing/2) + else: + return lambda x: np.random.uniform(1-smoothing/2, 1.0) - gpu_pred_src_src_d_ones = tf.ones_like (gpu_pred_src_src_d) - gpu_pred_src_src_d_zeros = tf.zeros_like(gpu_pred_src_src_d) + gpu_pred_src_src_d_ones = tf.map_fn (get_smooth_noisy_label(1, smoothing=0.2, noise=0.05), gpu_pred_src_src_d) + 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_d2_ones = tf.ones_like (gpu_pred_src_src_d2) - gpu_pred_src_src_d2_zeros = tf.zeros_like(gpu_pred_src_src_d2) + print("Shape (gpu_pred_src_src_d_ones): ", gpu_pred_src_src_d.shape) + print("Shape (gpu_pred_src_src_d_zeros): ", gpu_pred_src_src_d2.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_zeros = tf.map_fn (get_smooth_noisy_label(0, smoothing=0.2, noise=0.05), gpu_pred_src_src_d2) gpu_target_src_d, \ gpu_target_src_d2 = self.D_src(gpu_target_src_masked_opt) - gpu_target_src_d_ones = tf.ones_like(gpu_target_src_d) - gpu_target_src_d2_ones = tf.ones_like(gpu_target_src_d2) + 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_d2_ones = tf.map_fn (get_smooth_noisy_label(1, smoothing=0.2, noise=0.05), gpu_target_src_d2) 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 + \