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():