def load_model(model_name, model_id, GPU='0'):
from utils import label_map_util
path_to_frozen_graph = r'/hdd6/Models/transmission_line/' \
r'export_model/{}/{}/frozen_inference_graph.pb'.format(model_name, model_id)
path_to_labels = r'/home/lab/Documents/bohao/data/transmission_line/data/label_map_t.pbtxt'
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = str(GPU)
nn_utils.tf_warn_level(3)
# load frozen tf model into memory
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(path_to_frozen_graph, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
# load label map
category_index = label_map_util.create_category_index_from_labelmap(
path_to_labels, use_display_name=True)
return detection_graph, category_index
import tensorflow as tf
import uab_collectionFunctions
from nn import nn_utils
from bohaoCustom import uabMakeNetwork_UNet
if __name__ == '__main__':
# settings
nn_utils.tf_warn_level(3)
model_dir = r'/hdd6/Models/UNET_rand_gird/UnetCrop_inria_aug_grid_0_PS(572, 572)_BS5_' \
r'EP100_LR0.0001_DS60_DR0.1_SFN32'
gpu = 0
batch_size = 1
input_size = [572, 572]
#for city_name in ['Arlington', 'Atlanta', 'Austin', 'DC', 'NewHaven', 'NewYork', 'SanFrancisco', 'Seekonk']:
for city_name in ['Norfolk']:
tf.reset_default_graph()
blCol = uab_collectionFunctions.uabCollection(city_name)
blCol.readMetadata()
file_list, parent_dir = blCol.getAllTileByDirAndExt([0, 1, 2])
file_list_truth, parent_dir_truth = blCol.getAllTileByDirAndExt(3)
img_mean = blCol.getChannelMeans([0, 1, 2])
# make the model
# define place holder
X = tf.placeholder(tf.float32,
shape=[None, input_size[0], input_size[1], 3],
name='X')
Z = tf.placeholder(tf.float32,
shape=[None, input_size[0], input_size[1], 3],
name='Z')