def load_model(self): core = IECore() if self.extensions != None: core.add_extension(self.extensions, self.device) self.net = core.load_network(network=self.model, device_name=self.device, num_requests=1)
def load_model(self): ''' TODO: You will need to complete this method. This method is for loading the model to the device specified by the user. If your model requires any Plugins, this is where you can load them. ''' self.plugin = IECore() if self.extension and self.device == 'CPU': self.plugin.add_extension(self.extension, self.device) # Check for supported layers ### supported_layers = self.plugin.query_network( network=self.model, device_name=self.device) unsupported_layers = [ l for l in self.model.layers.keys() if l not in supported_layers ] if len(unsupported_layers) != 0: logger.error( "Unsupported layers found: {}".format(unsupported_layers)) logger.error( "Check whether extensions are available to add to IECore.") exit(1) self.net = self.plugin.load_network(network=self.model, device_name=self.device, num_requests=1) return self.net
def __init__(self, model_name, device='CPU', extensions=None): """ Initialize :param model_name: model path :param device: device to use :param extensions: extensions """ # model_weights self.model_bin = model_name + ".bin" # model_structure self.model_xml = model_name + ".xml" # device to use self.device = device self.plugin = IECore() self.network = None self.net_input = None # extensions to use self.extensions = extensions # Get the input layer self.input_blob = None self.input_shape = None self.output_blob = None self.exec_network = None self.infer_request = None # name of the model extended self.model_name = None
def __init__(self, model_name, device='CPU', extensions=None): ''' TODO: Use this to set your instance variables. ''' self.model_weights = model_name + '.bin' self.model_structure = model_name + '.xml' self.device = device self.extension = extensions core = IECore() self.model = core.read_network(self.model_structure, self.model_weights) self.input_name = next(iter(self.model.inputs)) self.input_shape = self.model.inputs[self.input_name].shape self.output_name = next(iter(self.model.outputs)) self.output_shape = self.model.outputs[self.output_name].shape
import yaml import cv2 import numpy as np import pandas as pd from PIL import Image from numpy import count_nonzero, vstack, newaxis, argmax, where from openvino.inference_engine.ie_api import IECore from sklearn.metrics.pairwise import cosine_similarity from tqdm import tqdm from dl_src.dnn import get_data_frame_from_folder, DNN opj = os.path.join image_extensions = ['.png', '.jpg'] del_label = 'DELETE' ie = IECore() # TODO Make size output def make_list_of_files_by_extension(source, extensions=None, escape_copies=True): if extensions is None: extensions = ('.jpg', '.png') ck = set() q = Queue() q.put(source) paths = [] file_names = [] while not q.empty():