def train_CAE(path_boxes_np, args):
epoch_len = len(np.load(path_boxes_np))
f_imgs, g_imgs, b_imgs = util.CAE_dataset_feed_dict(
prefix, path_boxes_np, dataset_name=args.dataset)
#former_batch,gray_batch,back_batch=util.CAE_dataset(path_boxes_np,args.dataset,epochs,batch_size)
former_batch = tf.placeholder(dtype=tf.float32,
shape=[batch_size, 64, 64, 1],
name='former_batch')
gray_batch = tf.placeholder(dtype=tf.float32,
shape=[batch_size, 64, 64, 1],
name='gray_batch')
back_batch = tf.placeholder(dtype=tf.float32,
shape=[batch_size, 64, 64, 1],
name='back_batch')
grad1_x, grad1_y = tf.image.image_gradients(former_batch)
# grad2_x,grad2_y=tf.image.image_gradients(gray_batch)
grad3_x, grad3_y = tf.image.image_gradients(back_batch)
grad_dis_1 = tf.sqrt(tf.square(grad1_x) + tf.square(grad1_y))
grad_dis_2 = tf.sqrt(tf.square(grad3_x) + tf.square(grad3_y))
former_outputs = CAE.CAE(grad_dis_1, 'former', bn=args.bn, training=True)
gray_outputs = CAE.CAE(gray_batch, 'gray', bn=args.bn, training=True)
back_outputs = CAE.CAE(grad_dis_2, 'back', bn=args.bn, training=True)
former_loss = CAE.pixel_wise_L2_loss(former_outputs, grad_dis_1)
gray_loss = CAE.pixel_wise_L2_loss(gray_outputs, gray_batch)
back_loss = CAE.pixel_wise_L2_loss(back_outputs, grad_dis_2)
global_step = tf.Variable(0, dtype=tf.int32, trainable=False)
global_step_a = tf.Variable(0, dtype=tf.int32, trainable=False)
global_step_b = tf.Variable(0, dtype=tf.int32, trainable=False)
lr_decay_epochs[0] = int(epoch_len // batch_size) * lr_decay_epochs[0]
lr = tf.train.piecewise_constant(global_step,
boundaries=lr_decay_epochs,
values=learning_rate)
former_vars = tf.get_collection(key=tf.GraphKeys.TRAINABLE_VARIABLES,
scope='former_')
gray_vars = tf.get_collection(key=tf.GraphKeys.TRAINABLE_VARIABLES,
scope='gray_')
back_vars = tf.get_collection(key=tf.GraphKeys.TRAINABLE_VARIABLES,
scope='back_')
# print(former_vars)
if args.weight_reg != 0:
former_loss = former_loss + args.weight_reg * weiht_regualized_loss(
former_vars)
gray_loss = gray_loss + args.weight_reg * weiht_regualized_loss(
gray_vars)
back_loss = back_loss + args.weight_reg * weiht_regualized_loss(
back_vars)
former_op = tf.train.AdamOptimizer(learning_rate=lr).minimize(
former_loss, var_list=former_vars, global_step=global_step)
gray_op = tf.train.AdamOptimizer(learning_rate=lr).minimize(
gray_loss, var_list=gray_vars, global_step=global_step_a)
back_op = tf.train.AdamOptimizer(learning_rate=lr).minimize(
back_loss, var_list=back_vars, global_step=global_step_b)
step = 0
if not args.bn:
writer = tf.summary.FileWriter(logdir=summary_save_path_pre +
args.dataset)
else:
writer = tf.summary.FileWriter(logdir=summary_save_path_pre +
args.dataset + '_bn')
tf.summary.scalar('loss/former_loss', former_loss)
tf.summary.scalar('loss/gray_loss', gray_loss)
tf.summary.scalar('loss/back_loss', back_loss)
tf.summary.image('inputs/former', grad_dis_1)
tf.summary.image('inputs/gray', gray_batch)
tf.summary.image('inputs/back', grad_dis_2)
tf.summary.image('outputs/former', former_outputs)
tf.summary.image('outputs/gray', gray_outputs)
tf.summary.image('outputs/back', back_outputs)
summary_op = tf.summary.merge_all()
saver = tf.train.Saver(var_list=tf.global_variables())
indices = list(range(epoch_len))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(epochs):
random.shuffle(indices)
for i in range(epoch_len // batch_size):
feed_dict = {
former_batch: [
f_imgs[d]
for d in indices[i * batch_size:(i + 1) * batch_size]
],
gray_batch: [
g_imgs[d]
for d in indices[i * batch_size:(i + 1) * batch_size]
],
back_batch: [
b_imgs[d]
for d in indices[i * batch_size:(i + 1) * batch_size]
]
}
step, _lr, _, _, _, _former_loss, _gray_loss, _back_loss = sess.run(
[
global_step, lr, former_op, gray_op, back_op,
former_loss, gray_loss, back_loss
],
feed_dict=feed_dict)
if step % 10 == 0:
print('At step {}'.format(step))
print('\tLearning Rate {:.4f}'.format(_lr))
print('\tFormer Loss {:.4f}'.format(_former_loss))
print('\tGray Loss {:.4f}'.format(_gray_loss))
print('\tBack Loss {:.4f}'.format(_back_loss))
if step % 50 == 0:
_summary = sess.run(summary_op, feed_dict=feed_dict)
writer.add_summary(_summary, global_step=step)
if not args.bn:
saver.save(sess, model_save_path_pre + args.dataset)
else:
saver.save(sess, model_save_path_pre + args.dataset + '_bn')
print('train finished!')
sess.close()
def train_CAE(path_boxes_np, args):
epoch_len = len(np.load(path_boxes_np))
f_imgs, g_imgs, b_imgs, class_indexs = util.CAE_dataset_feed_dict(
prefix, path_boxes_np, dataset_name=args.dataset)
#former_batch,gray_batch,back_batch=util.CAE_dataset(path_boxes_np,args.dataset,epochs,batch_size)
former_batch = tf.placeholder(dtype=tf.float32,
shape=[batch_size, 64, 64, 1],
name='former_batch')
gray_batch = tf.placeholder(dtype=tf.float32,
shape=[batch_size, 64, 64, 1],
name='gray_batch')
back_batch = tf.placeholder(dtype=tf.float32,
shape=[batch_size, 64, 64, 1],
name='back_batch')
# * tf.image.image_gradients() 计算单张图片的x和y方向的梯度，与论文意思不一致
# * 应修改为计算frame_{t} 和 frame_{t-3}及 frame_{t+3}的 帧差（absdiff）
# grad1_x, grad1_y = tf.image.image_gradients(former_batch)
# grad1=tf.concat([grad1_x,grad1_y],axis=-1)
grad1 = tf.math.abs(tf.math.subtract(former_batch, gray_batch))
# grad2_x,grad2_y=tf.image.image_gradients(gray_batch)
# grad3_x, grad3_y = tf.image.image_gradients(back_batch)
# grad3=tf.concat([grad3_x,grad3_y],axis=-1)
grad3 = tf.math.abs(tf.math.subtract(back_batch, gray_batch))
#grad_dis_1 = tf.sqrt(tf.square(grad1_x) + tf.square(grad1_y))
#grad_dis_2 = tf.sqrt(tf.square(grad3_x) + tf.square(grad3_y))
former_outputs = CAE.CAE(grad1, 'former', bn=args.bn, training=True)
gray_outputs = CAE.CAE(gray_batch, 'gray', bn=args.bn, training=True)
back_outputs = CAE.CAE(grad3, 'back', bn=args.bn, training=True)
former_loss = CAE.pixel_wise_L2_loss(former_outputs, grad1)
gray_loss = CAE.pixel_wise_L2_loss(gray_outputs, gray_batch)
back_loss = CAE.pixel_wise_L2_loss(back_outputs, grad3)
global_step = tf.Variable(0, dtype=tf.int32, trainable=False)
global_step_a = tf.Variable(0, dtype=tf.int32, trainable=False)
global_step_b = tf.Variable(0, dtype=tf.int32, trainable=False)
lr_decay_epochs[0] = int(epoch_len // batch_size) * lr_decay_epochs[0]
lr = tf.train.piecewise_constant(global_step,
boundaries=lr_decay_epochs,
values=learning_rate)
former_vars = tf.get_collection(key=tf.GraphKeys.TRAINABLE_VARIABLES,
scope='former_')
gray_vars = tf.get_collection(key=tf.GraphKeys.TRAINABLE_VARIABLES,
scope='gray_')
back_vars = tf.get_collection(key=tf.GraphKeys.TRAINABLE_VARIABLES,
scope='back_')
# print(former_vars)
if args.weight_reg != 0:
former_loss = former_loss + args.weight_reg * weiht_regualized_loss(
former_vars)
gray_loss = gray_loss + args.weight_reg * weiht_regualized_loss(
gray_vars)
back_loss = back_loss + args.weight_reg * weiht_regualized_loss(
back_vars)
former_op = tf.train.AdamOptimizer(learning_rate=lr).minimize(
former_loss, var_list=former_vars, global_step=global_step)
gray_op = tf.train.AdamOptimizer(learning_rate=lr).minimize(
gray_loss, var_list=gray_vars, global_step=global_step_a)
back_op = tf.train.AdamOptimizer(learning_rate=lr).minimize(
back_loss, var_list=back_vars, global_step=global_step_b)
step = 0
if not args.bn:
logdir = f'{summary_save_path_pre}/{args.dataset}'
else:
logdir = f'{summary_save_path_pre}/{args.dataset}_bn'
writer = tf.summary.FileWriter(logdir=logdir)
tf.summary.scalar('loss/former_loss', former_loss)
tf.summary.scalar('loss/gray_loss', gray_loss)
tf.summary.scalar('loss/back_loss', back_loss)
#tf.summary.image('inputs/former',grad_dis_1)
tf.summary.image('inputs/gray', gray_batch)
#tf.summary.image('inputs/back',grad_dis_2)
#tf.summary.image('outputs/former',former_outputs)
tf.summary.image('outputs/gray', gray_outputs)
#tf.summary.image('outputs/back',back_outputs)
summary_op = tf.summary.merge_all()
saver = tf.train.Saver(var_list=tf.global_variables())
indices = list(range(epoch_len))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(epochs):
random.shuffle(indices)
for i in range(epoch_len // batch_size):
feed_dict = {
former_batch: [
f_imgs[d]
for d in indices[i * batch_size:(i + 1) * batch_size]
],
gray_batch: [
g_imgs[d]
for d in indices[i * batch_size:(i + 1) * batch_size]
],
back_batch: [
b_imgs[d]
for d in indices[i * batch_size:(i + 1) * batch_size]
]
}
step, _lr, _, _, _, _former_loss, _gray_loss, _back_loss = sess.run(
[
global_step, lr, former_op, gray_op, back_op,
former_loss, gray_loss, back_loss
],
feed_dict=feed_dict)
step_result = f'step{step}: lr={_lr:.4f}, fl={_former_loss:.4f}, gl={_gray_loss:.4f}, bl={_back_loss:.4f}'
if step % 10 == 0:
print(step_result)
if step % 50 == 0:
_summary = sess.run(summary_op, feed_dict=feed_dict)
writer.add_summary(_summary, global_step=step)
if not args.bn:
ckpt_path = f'{model_save_path_pre}{args.dataset}/{args.dataset}.ckpt'
else:
ckpt_path = f'{model_save_path_pre}{args.dataset}_bn/{args.dataset}.ckpt'
saver.save(sess, ckpt_path)
print('train finished!')
sess.close()