def main():
images = tf.placeholder(tf.float32, [batch_size, 224, 224, 3])
poses_x = tf.placeholder(tf.float32, [batch_size, 3])
poses_q = tf.placeholder(tf.float32, [batch_size, 4])
datasource = get_data()
net = PoseNet({'data': images})
p1_x = net.layers['cls1_fc_pose_xyz']
p1_q = net.layers['cls1_fc_pose_wpqr']
p2_x = net.layers['cls2_fc_pose_xyz']
p2_q = net.layers['cls2_fc_pose_wpqr']
p3_x = net.layers['cls3_fc_pose_xyz']
p3_q = net.layers['cls3_fc_pose_wpqr']
l1_x = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p1_x, poses_x)))) * 0.3
l1_q = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p1_q, poses_q)))) * 150
l2_x = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p2_x, poses_x)))) * 0.3
l2_q = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p2_q, poses_q)))) * 150
l3_x = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p3_x, poses_x)))) * 1
l3_q = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p3_q, poses_q)))) * 500
loss = l1_x + l1_q + l2_x + l2_q + l3_x + l3_q
opt = tf.train.AdamOptimizer(learning_rate=0.0001, beta1=0.9, beta2=0.999, epsilon=0.00000001, use_locking=False, name='Adam').minimize(loss)
# Set GPU options
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6833)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
outputFile = "/home/prasenjit/posenet/trainingresult/PoseNet.ckpt"
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
# Load the data
sess.run(init)
net.load('posenet.npy', sess)
data_gen = gen_data_batch(datasource)
for i in range(max_iterations):
np_images, np_poses_x, np_poses_q = next(data_gen)
feed = {images: np_images, poses_x: np_poses_x, poses_q: np_poses_q}
sess.run(opt, feed_dict=feed)
np_loss = sess.run(loss, feed_dict=feed)
if i % 20 == 0:
print("iteration: " + str(i) + "\n\t" + "Loss is: " + str(np_loss))
if i % 5000 == 0:
saver.save(sess, outputFile)
print("Intermediate file saved at: " + outputFile)
saver.save(sess, outputFile)
print("Intermediate file saved at: " + outputFile)
def main():
today = datetime.date.today()
formatted_today = today.strftime('%y%m%d')
outputFoldPath = path + formatted_today + '/'
folder = os.path.exists(outputFoldPath)
if not folder:
os.makedirs(outputFoldPath)
curFoldPath = os.getcwd()
formatted_today_1 = today.strftime('%m%d')
resFile = curFoldPath + '/' + formatted_today_1
# print curFoldPath
images = tf.placeholder(tf.float32, [batch_size, 224, 224, 3])
poses_x = tf.placeholder(tf.float32, [batch_size, 3])
poses_q = tf.placeholder(tf.float32, [batch_size, 4])
datasource = get_data()
net = PoseNet({'data': images})
# p1_x = net.layers['cls1_fc_pose_xyz']
p1_q = net.layers['cls1_fc_pose_wpqr']
# p2_x = net.layers['cls2_fc_pose_xyz']
p2_q = net.layers['cls2_fc_pose_wpqr']
# p3_x = net.layers['cls3_fc_pose_xyz']
p3_q = net.layers['cls3_fc_pose_wpqr']
# l1_x = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p1_x, poses_x)))) * 0.3
l1_q = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p1_q, poses_q)))) * 0.3
# l2_x = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p2_x, poses_x)))) * 0.3
l2_q = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p2_q, poses_q)))) * 0.3
# l3_x = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p3_x, poses_x)))) * 1
l3_q = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p3_q, poses_q))))
# loss = l1_x + l1_q + l2_x + l2_q + l3_x + l3_q
loss = l1_q + l2_q + l3_q
opt = tf.train.AdamOptimizer(learning_rate=0.0001,
beta1=0.9,
beta2=0.999,
epsilon=0.00000001,
use_locking=False,
name='Adam').minimize(loss)
# Set GPU options
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6833)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
#outputFile = path+'PoseNet.ckpt'
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
# Load the data
sess.run(init)
net.load(path + 'posenet.npy', sess)
resfo = open(resFile, 'w')
resfo.write('load initial weight file:' + path + 'posenet.npy\n')
resfo.close()
data_gen = gen_data_batch(datasource)
for i in range(max_iterations):
np_images, np_poses_x, np_poses_q = next(data_gen)
feed = {
images: np_images,
poses_x: np_poses_x,
poses_q: np_poses_q
}
sess.run(opt, feed_dict=feed)
np_loss = sess.run(loss, feed_dict=feed)
# outputFile = outputFoldPath + 'PoseNet_' + str(i) + '.ckpt'
time_str = time.strftime('%m-%d %H:%M:%S',
time.localtime(time.time()))
if i % 20 == 0:
resfo = open(resFile, 'a')
print(time_str + "\titeration: " + str(i) + "\n\t" +
"Loss is: " + str(np_loss))
resfo.write(time_str + "\titeration: " + str(i) + "\n\t" +
"Loss is: " + str(np_loss) + '\n')
resfo.close()
if i > 0 and i % 5000 == 0:
resfo = open(resFile, 'a')
outputFile = outputFoldPath + 'PoseNet_' + str(i) + '.ckpt'
saver.save(sess, outputFile)
print("Intermediate file saved at: " + outputFile)
resfo.write("Intermediate file saved at: " + outputFile + '\n')
resfo.close()
saver.save(sess, outputFile)
print("Intermediate file saved at: " + outputFile)
def train():
batch_size = 75
max_iterations = 3000
images = tf.placeholder(tf.float32, [batch_size, 224, 224, 3])
poses_x = tf.placeholder(tf.float32, [batch_size, 3])
poses_q = tf.placeholder(tf.float32, [batch_size, 4])
datasource = utils.get_data("train")
net = PoseNet({'data': images})
p1_x = net.layers['cls1_fc_pose_xyz']
p1_q = net.layers['cls1_fc_pose_wpqr']
p2_x = net.layers['cls2_fc_pose_xyz']
p2_q = net.layers['cls2_fc_pose_wpqr']
p3_x = net.layers['cls3_fc_pose_xyz']
p3_q = net.layers['cls3_fc_pose_wpqr']
l1_x = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p1_x, poses_x)))) * 0.3
l1_q = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p1_q, poses_q)))) * 150
l2_x = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p2_x, poses_x)))) * 0.3
l2_q = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p2_q, poses_q)))) * 150
l3_x = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p3_x, poses_x)))) * 1
l3_q = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p3_q, poses_q)))) * 500
loss = l1_x + l1_q + l2_x + l2_q + l3_x + l3_q
opt = tf.train.AdamOptimizer(learning_rate=0.0001,
beta1=0.9,
beta2=0.999,
epsilon=0.00000001,
use_locking=False,
name='Adam').minimize(loss)
# ---- create a summary to monitor cost tensor
tf.summary.scalar("loss", loss)
merged_summary_op = tf.summary.merge_all(
) # merge all summaries into a single op
logs_path = './logs' # op to write logs to Tensorboard
summary_writer = tf.summary.FileWriter(logs_path,
graph=tf.get_default_graph())
print("Run the command line: --> tensorboard --logdir=./logs " \
"\nThen open http://0.0.0.0:6006/ into your web browser")
# ---- Set GPU options
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6833)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
# Load the data
sess.run(init)
net.load('posenet.npy', sess)
data_gen = utils.gen_data_batch(datasource, batch_size)
for i in range(max_iterations):
np_images, np_poses_x, np_poses_q = next(data_gen)
feed = {
images: np_images,
poses_x: np_poses_x,
poses_q: np_poses_q
}
sess.run(opt, feed_dict=feed) # run the optimizer
np_loss = sess.run(loss, feed_dict=feed) #get the loss
# ---- print the logs
if i % 20 == 0:
print("iteration: " + str(i) + "\n\t" + "Loss is: " +
str(np_loss))
if i % 100 == 0:
saver.save(sess, path_ckpt)
print("Intermediate file saved at: " + path_ckpt)
# ---- write logs at every iteration
summary = merged_summary_op.eval(feed_dict=feed)
summary_writer.add_summary(summary, i)
saver.save(sess, path_ckpt)
print("Intermediate file saved at: " + path_ckpt)
def main():
random.seed(27)
images = tf.placeholder(tf.float32, [batch_size, 224, 224, 3])
poses_x = tf.placeholder(tf.float32, [batch_size, 3])
poses_q = tf.placeholder(tf.float32, [batch_size, 4])
net = PoseNet({'data': images})
p1_x = net.layers['cls1_fc_pose_xyz']
p1_q = net.layers['cls1_fc_pose_wpqr']
p2_x = net.layers['cls2_fc_pose_xyz']
p2_q = net.layers['cls2_fc_pose_wpqr']
p3_x = net.layers['cls3_fc_pose_xyz']
p3_q = net.layers['cls3_fc_pose_wpqr']
l1_x = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p1_x, poses_x)))) * 0.3
l1_q = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p1_q, poses_q)))) * 225
l2_x = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p2_x, poses_x)))) * 0.3
l2_q = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p2_q, poses_q)))) * 225
l3_x = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p3_x, poses_x)))) * 1
l3_q = tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(p3_q, poses_q)))) * 500
WEIGHT_DECAY_FACTOR = 0.005
# Create your variables
#weights = tf.get_variable('weights', collections=['variables'])
with tf.variable_scope('weights_norm') as scope:
weights_norm = tf.reduce_sum(
input_tensor=WEIGHT_DECAY_FACTOR *
tf.stack([tf.nn.l2_loss(i) for i in tf.get_collection('weights')]),
name='weights_norm')
# Add the weight decay loss to another collection called losses
#tf.add_to_collection('losses', weights_norm)
# To calculate your total loss
#tf.add_n(tf.get_collection('losses'), name='total_loss')
loss = l1_x + l1_q + l2_x + l2_q + l3_x + l3_q
losses = loss + weights_norm
opt = tf.train.AdamOptimizer(learning_rate=0.0001,
beta1=0.9,
beta2=0.999,
epsilon=0.00000001,
use_locking=False,
name='Adam').minimize(losses)
# Set GPU options
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6833)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
outputFile = directory + "PoseNet.ckpt"
#f = open('D:/PythonWorkSpace/posenet/PoseNet_AllDatasets_Closer/practiceCurves.txt','w')
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
# Load the data
sess.run(init)
net.load('.../weights/posenet.npy', sess)
#saver.restore(sess, directory + "PoseNet.ckpt")
datasource = get_data(directory + dataset)
datasource_test = get_data(directory + dataset_test)
data_gen = gen_data_batch(datasource)
data_gen_test = gen_data_batch(datasource_test)
for i in range(max_iterations):
np_images, np_poses_x, np_poses_q = next(data_gen)
feed = {
images: np_images,
poses_x: np_poses_x,
poses_q: np_poses_q
}
sess.run(opt, feed_dict=feed)
if i % 500 == 0:
np_loss = sess.run(loss, feed_dict=feed)
np_images_test, np_poses_x_test, np_poses_q_test = next(
data_gen_test)
feed_test = {
images: np_images_test,
poses_x: np_poses_x_test,
poses_q: np_poses_q_test
}
np_loss_test = sess.run(loss, feed_dict=feed_test)
print("iteration: " + str(i) + "\n\t" + "Loss is: " +
str(np_loss) + "\n\t" + "test Loss is: " +
str(np_loss_test))
f = open(directory + 'practiceCurves.txt', 'a')
f.write(
str(i) + " " + str(np_loss) + " " + str(np_loss_test) +
"\n")
f.close()
if i % 2000 == 0:
saver.save(sess, outputFile, global_step=i)
print("Intermediate file saved at: " + outputFile)
saver.save(sess, outputFile)
print("Intermediate file saved at: " + outputFile)