def __init__(self, batch_size, rgbd_input_batch, output_dim, normalization_epsilon):
self.network_input = rgbd_input_batch
self.output_dim = output_dim
self.logger = get_logger()
self.batch_size = batch_size
self.total_weights = 0
self.initial_params = np.load('resources/VGG_16_4ch.npy').item()
self.initial_params = {key.encode('utf-8'):self.initial_params[key] for key in self.initial_params}
self.logger.info('Weight keys:{}'.format(self.initial_params.keys()))
self.epsilon = normalization_epsilon
def create_association_data(base_dir):
logger = get_logger()
filenames = [f for f in os.listdir(base_dir)]
for filename in filenames:
logger.info('Associating data set :{}'.format(filename))
rgb_file = os.path.join(base_dir, filename, 'rgb.txt')
depth_file = os.path.join(base_dir, filename, 'depth.txt')
association_file = os.path.join(base_dir, filename, 'associate.txt')
association_data = get_association(rgb_file, depth_file)
with open(association_file, 'w') as fw:
fw.write(association_data)
logger.info('Wrote association data to :{}'.format(association_file))
import os
import tensorflow as tf
from slam.network.model_input import PoseNetInputProvider
from slam.utils.logging_utils import get_logger
from slam.network.google_net_noLRN import GoogleNet
"""
Posenet impl.
"""
if __name__ == '__main__':
img_h = 224
img_w = 224
input_provider = PoseNetInputProvider()
logger = get_logger()
base_dir = '/usr/prakt/s085/posenet/'
LOG_DIR = os.path.join(base_dir, 'logs/')
LEARNED_WEIGHTS_FILENAME = os.path.join(
base_dir, 'checkpoints/learned_weights.ckpt')
epoch = 1000
batch_size = 1
rgb_input_batch = tf.placeholder(tf.float32, [batch_size, img_h, img_w, 3],
name='rgbd_input')
groundtruth_batch = tf.placeholder(tf.float32, [batch_size, 7],
name='groundtruth')
google_net = GoogleNet({'data': rgb_input_batch}, 7)
loss = google_net.add_l2_loss(groundtruth_batch)
def __init__(self, sequence_info, batch_size):
self.sequence_info = sequence_info
self.index = 0
self.batch_size = batch_size
self.logger = get_logger()
def __init__(self, input_provider, seqdir_vs_offset, sequence_length):
self.counter = 0
self.seqdir_vs_offset = seqdir_vs_offset
self.input_provider = input_provider
self.sequence_length = sequence_length
self.logger = get_logger()
def __init__(self):
self.config_provider = get_config_provider()
training_filenames = self.config_provider.training_filenames()
self.training_filenames = [os.path.join(self.BASE_DATA_DIR, filename) for filename in training_filenames]
self.logger = get_logger()
self.batch_size = len(self.training_filenames)