}
# get dataset
dataset = BinaryDbReader(mode='training',
batch_size=8,
shuffle=True,
hue_aug=True,
random_crop_to_size=True)
# build network graph
data = dataset.get()
# build network
evaluation = tf.placeholder_with_default(True, shape=())
net = PoseEstimationNetwork()
hand_mask_pred = net.HandSegNet(data['image'], train=True)
# 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)
# Loss
loss = 0.0
s = data['hand_mask'].get_shape().as_list()
for i, pred_item in enumerate(hand_mask_pred):
gt = tf.reshape(data['hand_mask'], [s[0] * s[1] * s[2], -1])
pred = tf.reshape(hand_mask_pred, [s[0] * s[1] * s[2], -1])
loss += tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=gt))
image_list = list()
image_list.append('./data/1.png')
image_list.append('./data/2.png')
image_list.append('./data/3.png')
image_list.append('./data/4.png')
image_list.append('./data/5.png')
# network input
image_tf = tf.placeholder(tf.float32, shape=(1, 240, 320, 3))
hand_side_tf = tf.constant([[1.0, 0.0]
]) # left hand (true for all samples provided)
evaluation = tf.placeholder_with_default(True, shape=())
# build network
net = PoseEstimationNetwork()
hand_scoremap_tf = net.HandSegNet(image_tf)
hand_scoremap_tf = hand_scoremap_tf[-1]
# Start TF
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
# initialize network
# net.init(sess)
# 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'])