NUM_CLASSES = 1
else:
NUM_CLASSES = 90
def __init__(self):
super(TestConfig, self).__init__(type)
return TestConfig()
# Read sequentail Models or Gather all Models from models/
config = Config('od')
if config.SEQ_MODELS:
model_names = config.SEQ_MODELS
else:
model_names = get_model_list(MODELS_DIR)
# Sequential testing
for model_name in model_names:
print("> testing model: {}".format(model_name))
# conditionals
optimized = False
single_class = False
# Test Model
if 'hands' in model_name or 'person' in model_name:
single_class = True
if 'deeplab' in model_name:
config = create_test_config('dl', model_name, optimized, single_class)
model = DeepLabModel(config).prepare_model(INPUT_TYPE)
else:
config = create_test_config('od', model_name, optimized, single_class)
if optimized:
model_path=ROOT_DIR+'/models/{}/optimized_inference_graph.pb'.format(model)
log_dir=ROOT_DIR+'/models/{}/log_opt/'.format(model)
else:
model_path=ROOT_DIR+'/models/{}/frozen_inference_graph.pb'.format(model)
log_dir=ROOT_DIR+'/models/{}/log/'.format(model)
with session.Session(graph=ops.Graph()) as sess:
with gfile.FastGFile(model_path, "rb") as f:
graph_def = graph_pb2.GraphDef()
graph_def.ParseFromString(f.read())
importer.import_graph_def(graph_def)
pb_visual_writer = summary.FileWriter(log_dir)
pb_visual_writer.add_graph(sess.graph)
print("> Model {} Imported. \nVisualize by running: \
tensorboard --logdir={}".format(model_path, log_dir))
# Gather all Model Names in models/
MODELS_DIR = os.path.join(ROOT_DIR,'models')
models = get_model_list(MODELS_DIR)
# Create Tensorboard readable tfevent files in models/{}/log
for model in models:
optimized=False
create_tfevent_from_pb(model,optimized)
# Check if there is an optimized graph
model_dir = os.path.join(MODELS_DIR,model)
optimized = check_if_optimized_model(model_dir)
if optimized:
create_tfevent_from_pb(model,optimized)