# feed trough network
evaluation = tf.placeholder_with_default(True, shape=())
# _, coord3d_pred, R = net.inference(data['scoremap'], data['hand_side'], evaluation)
#inference = net.inference(data["image"], evaluation, train=False)
gesture_pred = net.inference(data['image'], evaluation, train=False)
# Start TF
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
tf.train.start_queue_runners(sess=sess)
if USE_RETRAINED:
# retrained version: HandSegNet
last_cpt = tf.train.latest_checkpoint(PATH_TO_HANDSEGNET_SNAPSHOTS)
assert last_cpt is not None, "Could not locate snapshot to load. Did you already train the network and set the path accordingly?"
load_weights_from_snapshot(sess,
last_cpt,
discard_list=['Adam', 'global_step', 'beta'])
# retrained version: PoseNet
last_cpt = tf.train.latest_checkpoint(PATH_TO_POSENET_SNAPSHOTS)
assert last_cpt is not None, "Could not locate snapshot to load. Did you already train the network and set the path accordingly?"
load_weights_from_snapshot(sess,
last_cpt,
discard_list=['Adam', 'global_step', 'beta'])
last_cpt = tf.train.latest_checkpoint(PATH_TO_GESTURE_SNAPSHOTS)
assert last_cpt is not None, "Could not locate snapshot to load. Did you already train the network and set the path accordingly?"
load_weights_from_snapshot(sess,
last_cpt,
discard_list=['Adam', 'global_step', 'beta'])
# Solver
global_step = tf.Variable(0, trainable=False, name="global_step")
lr_scheduler = LearningRateScheduler(values=train_para['lr'],
steps=train_para['lr_iter'])
lr = lr_scheduler.get_lr(global_step)
opt = tf.train.AdamOptimizer(lr)
train_op = opt.minimize(loss)
# init weights
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(max_to_keep=1, keep_checkpoint_every_n_hours=4.0)
rename_dict = {'CPM/PoseNet': 'PoseNet2D', '_CPM': ''}
load_weights_from_snapshot(sess, './weights/cpm-model-mpii',
['PersonNet', 'PoseNet/Mconv', 'conv5_2_CPM'],
rename_dict)
# snapshot dir
if not os.path.exists(train_para['snapshot_dir']):
os.mkdir(train_para['snapshot_dir'])
print('Created snapshot dir:', train_para['snapshot_dir'])
# Training loop
print('Starting to train ...')
for i in range(train_para['max_iter']):
_, loss_v = sess.run([train_op, loss])
if (i % train_para['show_loss_freq']) == 0:
print('Iteration %d\t Loss %.1e' % (i, loss_v))
sys.stdout.flush()
tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=gt))
# Solver
global_step = tf.Variable(0, trainable=False, name="global_step")
lr_scheduler = LearningRateScheduler(values=train_para['lr'],
steps=train_para['lr_iter'])
lr = lr_scheduler.get_lr(global_step)
opt = tf.train.AdamOptimizer(lr)
train_op = opt.minimize(loss)
# init weights
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(max_to_keep=1, keep_checkpoint_every_n_hours=4.0)
rename_dict = {'CPM/PersonNet': 'HandSegNet', '_CPM': ''}
load_weights_from_snapshot(sess, './weights/cpm-model-mpii',
['PoseNet', 'Mconv', 'conv6'], rename_dict)
# snapshot dir
if not os.path.exists(train_para['snapshot_dir']):
os.mkdir(train_para['snapshot_dir'])
print('Created snapshot dir:', train_para['snapshot_dir'])
# Training loop
for i in tqdm(range(train_para['max_iter'])):
_, loss_v = sess.run([train_op, loss])
if (i % train_para['show_loss_freq']) == 0:
print('Iteration %d\t Loss %.1e' % (i, loss_v))
sys.stdout.flush()
if (i % train_para['snapshot_freq']) == 0:
# Solver
global_step = tf.Variable(0, trainable=False, name="global_step")
lr_scheduler = LearningRateScheduler(values=train_para['lr'], steps=train_para['lr_iter'])
lr = lr_scheduler.get_lr(global_step)
opt = tf.train.AdamOptimizer(lr)
train_op = opt.minimize(loss)
# init weights
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(max_to_keep=1, keep_checkpoint_every_n_hours=1.0)
# use weights from previous snapshots to train the network
PATH_TO_POSENET_SNAPSHOTS = './snapshots_posenet/'
last_cpt = tf.train.latest_checkpoint(PATH_TO_POSENET_SNAPSHOTS)
assert last_cpt is not None, "Could not locate snapshot to load. Did you already train the network and set the path accordingly?"
load_weights_from_snapshot(sess, last_cpt, discard_list=['Adam', 'global_step', 'beta'])
# use initial weights to train the network
'''rename_dict = {'CPM/PoseNet': 'PoseNet2D',
'_CPM': ''}
load_weights_from_snapshot(sess, './weights/cpm-model-mpii', ['PersonNet', 'PoseNet/Mconv', 'conv5_2_CPM'], rename_dict)
'''
# snapshot dir
if not os.path.exists(train_para['snapshot_dir']):
os.mkdir(train_para['snapshot_dir'])
print('Created snapshot dir:', train_para['snapshot_dir'])
# Training loop
print('Starting to train ...')
for i in range(train_para['max_iter']):
