Why tensorflow1.1 gets slower and slower when training? Is it memory leak or queue starvation? - memory-leaks

Question

I trained a ESPCN in tensorflow1.1, the costed time per patch increase nearly linearly when training. The first 100 epoch takes only 4-5 seconds, but the 70th epoch takes about half a minute. See the training result below:
I've searched the same question on Google and Stack-overflow, and tried the solutions below, but seemed no work:
1.add tf.reset_default_graph() after every sess.run();
2.add time.sleep(5) to prevent queue starvation;
I know the general idea, that is to reduce the operations in Session(). But how? Anyone have the solution?
Here's part of my code:
L3, var_w_list, var_b_list = model_train(IN, FLAGS)
cost = tf.reduce_mean(tf.reduce_sum(tf.square(OUT - L3), reduction_indices=0))
global_step = tf.Variable(0, trainable=False)
learning_rate = tf.train.exponential_decay(FLAGS.base_lr, global_step * FLAGS.batch_size, FLAGS.decay_step, 0.96, staircase=True)
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost, global_step = global_step, var_list = var_w_list + var_b_list)
# optimizer = tf.train.MomentumOptimizer(learning_rate, 0.9).minimize(cost, var_list = var_w_list + var_b_list)
cnt = 0
with tf.Session() as sess:
init_op = tf.initialize_all_variables()
sess.run(init_op)
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
print('\n\n\n =========== All initialization finished, now training begins ===========\n\n\n')
t_start = time.time()
t1 = t_start
for i in range(1, FLAGS.max_Epoch + 1):
LR_batch, HR_batch = batch.__next__()
global_step += 1
[_, cost1] = sess.run([optimizer, cost], feed_dict = {IN: LR_batch, OUT: HR_batch})
# tf.reset_default_graph()
if i % 100 == 0 or i == 1:
print_step = i
print_loss = cost1 / FLAGS.batch_size
test_LR_batch, test_HR_batch = test_batch.__next__()
test_SR_batch = test_HR_batch.copy()
test_SR_batch[:,:,:,0:3] = sess.run(L3, feed_dict = {IN: test_LR_batch[:,:,:,0:3]})
# tf.reset_default_graph()
psnr_tmp = 0.0
ssim_tmp = 0.0
for k in range(test_SR_batch.shape[0]):
com1 = test_SR_batch[k, :, :, 0]
com2 = test_HR_batch[k, :, :, 0]
psnr_tmp += get_psnr(com1, com2, FLAGS.HR_size, FLAGS.HR_size)
ssim_tmp += get_ssim(com1, com2, FLAGS.HR_size, FLAGS.HR_size)
psnr[cnt] = psnr_tmp / test_SR_batch.shape[0]
ssim[cnt] = ssim_tmp / test_SR_batch.shape[0]
ep[cnt] = print_step
t2 = time.time()
print_time = t2 - t1
t1 = t2
print(("[Epoch] : {0:d} [Current cost] : {1:5.8f} \t [Validation PSNR] : {2:5.8f} \t [Duration time] : {3:10.8f} s \n").format(print_step, print_loss, psnr[cnt], print_time))
# tf.reset_default_graph()
cnt += 1
if i % 1000 == 0:
L3_test = model_test(IN_TEST, var_w_list, var_b_list, FLAGS)
output_img = single_HR.copy()
output_img[:,:,:,0:3] = sess.run(L3_test, feed_dict = {IN_TEST:single_LR[:,:,:,0:3]})
tf.reset_default_graph()
subname = FLAGS.img_save_dir + '/' + str(i) + ".jpg"
img_gen(output_img[0,:,:,:], subname)
print(('================= Saving model to {}/model.ckpt ================= \n').format(FLAGS.checkpoint_dir))
time.sleep(5)
# saver.save(sess, FLAGS.checkpoint_dir + '/model.ckpt', print_step)
t_tmp = time.time() - t_start
My configuration is: windows10 + tf1.1 + python3.5 + cuda8.0 + cudnn5.1
================================================================
Besides, I used pixel-shuffle(PS) layer instead of deconvolution in the last layer. I copied the PS code from others, which is shown below:
def _phase_shift(I, r):
bsize, a, b, c = I.get_shape().as_list()
bsize = tf.shape(I)[0] # Handling Dimension(None) type for undefined batch dim
X = tf.reshape(I, (bsize, a, b, r, r))
X = tf.transpose(X, (0, 1, 2, 4, 3)) # bsize, a, b, 1, 1
X = tf.split(X, a, 1) # a, [bsize, b, r, r]
X = tf.concat([tf.squeeze(x, axis=1) for x in X], 2) # bsize, b, a*r, r
X = tf.split(X, b, 1) # b, [bsize, a*r, r]
X = tf.concat([tf.squeeze(x, axis=1) for x in X], 2) # bsize, a*r, b*r
return tf.reshape(X, (bsize, a*r, b*r, 1))
def PS(X, r, color=False):
if color:
Xc = tf.split(X, 3, 3)
X = tf.concat([_phase_shift(x, r) for x in Xc], 3)
else:
X = _phase_shift(X, r)
return X
Which X is the 4-dimensional image tensor, r means the up-scaling factor, color determine whether the channel of images is 3(Ycbcr format) or 1(Grayscale format).
To use the layer is very simple, just like the tf.nn.relu() does:
L3_ps = PS(L3, scale, True)
Now I'm wondering whether this layer caused the slowing-down, because the program goes well when using deconvolution layer. Using deconvolution layer may be a solution, but I have to use PS layer for some reason.

Answer 1

I suspect this line is causing a memory leak (although without seeing the code, I can't say for certain):
L3_test = model_test(IN_TEST, var_w_list, var_b_list, FLAGS)
L3_test seems to be a tf.Tensor (because you later pass it to sess.run(), so it seems likely that model_test() is adding new nodes to the graph each time it is called (every 1000 steps), which causes more work to be done over time.
The solution is quite simple though: since model_test() does not depend on anything calculated in the training loop, you can move the call to outside the training loop, so it is only called once.