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])