def test_load_tf_openvino(self):
local_path = self.create_temp_dir()
url = data_url + "/models/object_detection/faster_rcnn_resnet101_coco_2018_01_28.tar.gz"
file_abs_path = maybe_download(
"faster_rcnn_resnet101_coco_2018_01_28.tar.gz", local_path, url)
tar = tarfile.open(file_abs_path, "r:gz")
extracted_to = os.path.join(local_path,
"faster_rcnn_resnet101_coco_2018_01_28")
if not os.path.exists(extracted_to):
print("Extracting %s to %s" % (file_abs_path, extracted_to))
tar.extractall(local_path)
tar.close()
model = InferenceModel(3)
model.load_tf(model_path=extracted_to + "/frozen_inference_graph.pb",
backend="openvino",
model_type="faster_rcnn_resnet101_coco",
ov_pipeline_config_path=extracted_to +
"/pipeline.config",
ov_extensions_config_path=None)
input_data = np.random.random([4, 1, 3, 600, 600])
output_data = model.predict(input_data)
model2 = InferenceModel(3)
model2.load_tf_object_detection_as_openvino(
model_path=extracted_to + "/frozen_inference_graph.pb",
object_detection_model_type="faster_rcnn_resnet101_coco",
pipeline_config_path=extracted_to + "/pipeline.config",
extensions_config_path=None)
model2.predict(input_data)
def test_load_tf_openvino(self):
local_path = self.create_temp_dir()
url = data_url + "/TF_faster_rcnn_resnet101_coco_2018_01_28"
maybe_download("frozen_inference_graph.pb", local_path,
url + "/frozen_inference_graph.pb")
maybe_download("pipeline.config", local_path, url + "/pipeline.config")
maybe_download("faster_rcnn_support.json", local_path,
url + "/faster_rcnn_support.json")
model = InferenceModel(3)
model.load_tf(local_path + "/frozen_inference_graph.pb",
backend="openvino",
ov_pipeline_config_path=local_path + "/pipeline.config",
ov_extensions_config_path=local_path +
"/faster_rcnn_support.json")
input_data = np.random.random([4, 1, 3, 600, 600])
output_data = model.predict(input_data)
model2 = InferenceModel(5)
model2.load_tf(local_path + "/frozen_inference_graph.pb",
backend="openvino",
model_type="faster_rcnn_resnet101_coco")
output_data2 = model2.predict(input_data)
help="The path where the images are stored, "
"can be either a folder or an image path")
parser.add_option("--model",
type=str,
dest="model_path",
help="Path to the TensorFlow model file")
parser.add_option("--model_type",
type=str,
dest="model_type",
help="The type of the TensorFlow model",
default="faster_rcnn_resnet101_coco")
(options, args) = parser.parse_args(sys.argv)
sc = init_nncontext("OpenVINO Object Detection Inference Example")
images = ImageSet.read(options.img_path,
sc,
resize_height=600,
resize_width=600).get_image().collect()
input_data = np.concatenate(
[image.reshape((1, 1) + image.shape) for image in images], axis=0)
model = InferenceModel()
model.load_tf(join(options.model_path, "frozen_inference_graph.pb"),
backend="openvino",
model_type=options.model_type,
ov_pipeline_config_path=join(options.model_path,
"pipeline.config"))
predictions = model.predict(input_data)
# Print the detection result of the first image.
print(predictions[0])
import numpy as np
from optparse import OptionParser
from zoo.common.nncontext import init_nncontext
from zoo.feature.image import ImageSet
from zoo.pipeline.inference import InferenceModel
if __name__ == "__main__":
parser = OptionParser()
parser.add_option("--image", type=str, dest="img_path",
help="The path where the images are stored, "
"can be either a folder or an image path")
parser.add_option("--model", type=str, dest="model_path",
help="Path to the TensorFlow model file")
parser.add_option("--model_type", type=str, dest="model_type",
help="The type of the TensorFlow model",
default="faster_rcnn_resnet101_coco")
(options, args) = parser.parse_args(sys.argv)
sc = init_nncontext("OpenVINO Object Detection Inference Example")
images = ImageSet.read(options.img_path, sc,
resize_height=600, resize_width=600).get_image().collect()
input_data = np.concatenate([image.reshape((1, 1) + image.shape) for image in images], axis=0)
model = InferenceModel()
model.load_tf(options.model_path, backend="openvino", model_type=options.model_type)
predictions = model.predict(input_data)
# Print the detection result of the first image.
print(predictions[0])