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jblitzar
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github-python-corpus

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seq_ftr_B_frames = seq_ftr_B
seq_ftr_B = tf.reduce_mean(seq_ftr_B, axis=1)
seq_ftr_B = tf.reshape(seq_ftr_B, [batch_size, -1])
with tf.name_scope("Hi_MPC"), tf.variable_scope("Hi_MPC", reuse=tf.AUTO_REUSE):
def Hi_MPC_hard(t, pseudo_lab, all_ftr, cluster_ftr, pseudo_lab_P, all_ftr_P, cluster_ftr_P, pseudo_lab_B,
all_ftr_B, cluster_ftr_B):
global imp_val, imp_val_P, imp_val_B
M = int(FLAGS.M)
concat_heads = tf.zeros_like(seq_ftr)
concat_heads_clu = tf.zeros_like(cluster_ftr)
W_head = lambda: tf.Variable(tf.random_normal([H, H]))
all_ftr_mean = tf.reduce_mean(all_ftr, axis=1)
all_ftr_P_mean = tf.reduce_mean(all_ftr_P, axis=1)
all_ftr_B_mean = tf.reduce_mean(all_ftr_B, axis=1)
for i in range(M):
W_q_head = W_k_head = tf.Variable(initial_value=W_head)
W_q_head_P = W_k_head_P = tf.Variable(initial_value=W_head)
W_q_head_B = W_k_head_B = tf.Variable(initial_value=W_head)
all_ftr_trans = tf.matmul(all_ftr, W_q_head)
all_ftr_trans_mean = tf.matmul(all_ftr_mean, W_q_head)
cluster_ftr_trans = tf.matmul(cluster_ftr, W_k_head)
all_ftr_trans_P = tf.matmul(all_ftr_P, W_q_head_P)
all_ftr_trans_P_mean = tf.matmul(all_ftr_P_mean, W_q_head_P)
cluster_ftr_trans_P = tf.matmul(cluster_ftr_P, W_k_head_P)
all_ftr_trans_B = tf.matmul(all_ftr_B, W_q_head_B)
all_ftr_trans_B_mean = tf.matmul(all_ftr_B_mean, W_q_head_B)
cluster_ftr_trans_B = tf.matmul(cluster_ftr_B, W_k_head_B)
pred_lbl = tf.argmax(tf.matmul(all_ftr_trans_mean, tf.transpose(cluster_ftr_trans)) / np.sqrt(H),
-1)
pred_lbl_P = tf.argmax(
tf.matmul(all_ftr_trans_P_mean, tf.transpose(cluster_ftr_trans_P)) / np.sqrt(H), -1)
pred_lbl_B = tf.argmax(
tf.matmul(all_ftr_trans_B_mean, tf.transpose(cluster_ftr_trans_B)) / np.sqrt(H), -1)
# importance inference
logits = tf.matmul(all_ftr_trans, tf.transpose(cluster_ftr_trans)) / np.sqrt(H)
logits_P = tf.matmul(all_ftr_trans_P, tf.transpose(cluster_ftr_trans_P)) / np.sqrt(H)
logits_B = tf.matmul(all_ftr_trans_B, tf.transpose(cluster_ftr_trans_B)) / np.sqrt(H)
pred_lbl_frames = tf.reshape(tf.tile(tf.reshape(pred_lbl, [-1, 1]), [1, time_step]), [-1])
pred_lbl_P_frames = tf.reshape(tf.tile(tf.reshape(pred_lbl_P, [-1, 1]), [1, time_step]), [-1])
pred_lbl_B_frames = tf.reshape(tf.tile(tf.reshape(pred_lbl_B, [-1, 1]), [1, time_step]), [-1])
pred_lbl_frames = tf.cast(pred_lbl_frames, tf.int32)
pred_lbl_P_frames = tf.cast(pred_lbl_P_frames, tf.int32)
pred_lbl_B_frames = tf.cast(pred_lbl_B_frames, tf.int32)
# [batch_size, time_step]
pseudo_lab_frames = tf.reshape(tf.tile(tf.reshape(pseudo_lab, [-1, 1]), [1, time_step]), [-1])
pseudo_lab_P_frames = tf.reshape(tf.tile(tf.reshape(pseudo_lab_P, [-1, 1]), [1, time_step]), [-1])
pseudo_lab_B_frames = tf.reshape(tf.tile(tf.reshape(pseudo_lab_B, [-1, 1]), [1, time_step]), [-1])
# [batch_size, time_step]
# If pred. is true, focus on less-score frames, otherwise focus on high-score (wrong label) frames
indices = tf.concat([tf.reshape(tf.range(0, batch_size * time_step), [-1, 1]),
tf.reshape(pred_lbl_frames, [-1, 1])], axis=-1)
indices_P = tf.concat([tf.reshape(tf.range(0, batch_size * time_step), [-1, 1]),
tf.reshape(pred_lbl_P_frames, [-1, 1])], axis=-1)
indices_B = tf.concat([tf.reshape(tf.range(0, batch_size * time_step), [-1, 1]),
tf.reshape(pred_lbl_B_frames, [-1, 1])], axis=-1)
imp_frames_unorm = tf.gather_nd(tf.reshape(logits, [batch_size * time_step, -1]), indices)
imp_P_frames_unorm = tf.gather_nd(tf.reshape(logits_P, [batch_size * time_step, -1]), indices_P)
imp_B_frames_unorm = tf.gather_nd(tf.reshape(logits_B, [batch_size * time_step, -1]), indices_B)
imp_frames_unorm = tf.reshape(imp_frames_unorm, [-1, time_step])
imp_P_frames_unorm = tf.reshape(imp_P_frames_unorm, [-1, time_step])
imp_B_frames_unorm = tf.reshape(imp_B_frames_unorm, [-1, time_step])
ones = tf.ones_like(pseudo_lab_frames, dtype=tf.float32)
if FLAGS.focus == '1':
imp_frames = tf.nn.softmax(tf.reshape(
tf.reshape(imp_frames_unorm, [-1]) * tf.where(tf.equal(pred_lbl_frames, pseudo_lab_frames),
-ones, ones), [batch_size, time_step]),
axis=-1)
imp_P_frames = tf.nn.softmax(tf.reshape(tf.reshape(imp_P_frames_unorm, [-1]) * tf.where(
tf.equal(pred_lbl_P_frames, pseudo_lab_P_frames),
-ones, ones), [batch_size, time_step]), axis=-1)
imp_B_frames = tf.nn.softmax(tf.reshape(tf.reshape(imp_B_frames_unorm, [-1]) * tf.where(
tf.equal(pred_lbl_B_frames, pseudo_lab_B_frames),
-ones, ones), [batch_size, time_step]), axis=-1)
elif FLAGS.focus == '-1':
imp_frames = tf.nn.softmax(tf.reshape(tf.reshape(imp_frames_unorm, [-1]) * tf.where(
tf.equal(pred_lbl_frames, pseudo_lab_frames),
ones, -ones), [batch_size, time_step]), axis=-1)
imp_P_frames = tf.nn.softmax(tf.reshape(tf.reshape(imp_P_frames_unorm, [-1]) * tf.where(