Submit only path to .xml file to read_model()

Author: anzhella-pankratova

demos/3d_segmentation_demo/python/3d_segmentation_demo.py

@@ -262,7 +262,7 @@ def main():
         raise AttributeError("Device {} do not support of 3D convolution. "
                              "Please use CPU, GPU or HETERO:*CPU*, HETERO:*GPU*")
 
-    # --------------------- 2. Read IR Generated by ModelOptimizer (.xml and .bin files) ---------------------
+    # --------------------- 2. Read IR Generated by ModelOptimizer (.xml file) ---------------------
     log.info('Reading model {}'.format(args.path_to_model))
     model = core.read_model(args.path_to_model)
 

demos/colorization_demo/python/colorization_demo.py

@@ -67,7 +67,7 @@ def main(args):
     core = Core()
 
     log.info('Reading model {}'.format(args.model))
-    model = core.read_model(args.model, args.model.with_suffix(".bin"))
+    model = core.read_model(args.model)
 
     input_tensor_name = 'data_l'
     input_shape = model.input(input_tensor_name).shape

demos/face_recognition_demo/python/ie_module.py

@@ -23,7 +23,7 @@ def __init__(self, core, model, model_type):
         self.core = core
         self.model_type = model_type
         log.info('Reading {} model {}'.format(model_type, model))
-        self.model = core.read_model(model, model.with_suffix('.bin'))
+        self.model = core.read_model(model)
         self.model_path = model
         self.active_requests = 0
         self.clear()

demos/formula_recognition_demo/python/utils.py

@@ -121,10 +121,8 @@ def prerocess_crop(crop, tgt_shape, preprocess_type='crop'):
 
 
 def read_net(model_xml, ie, model_type):
-    model_bin = os.path.splitext(model_xml)[0] + ".bin"
-
     log.info('Reading {} model {}'.format(model_type, model_xml))
-    return ie.read_model(model_xml, model_bin)
+    return ie.read_model(model_xml)
 
 
 def change_layout(model_input):

demos/human_pose_estimation_3d_demo/python/modules/inference_engine.py

@@ -18,16 +18,16 @@
 
 
 class InferenceEngine:
-    def __init__(self, net_model_xml_path, device, stride):
+    def __init__(self, model_path, device, stride):
         self.device = device
         self.stride = stride
 
         log.info('OpenVINO Inference Engine')
         log.info('\tbuild: {}'.format(get_version()))
         self.core = Core()
 
-        log.info('Reading model {}'.format(net_model_xml_path))
-        self.model = self.core.read_model(net_model_xml_path)
+        log.info('Reading model {}'.format(model_path))
+        self.model = self.core.read_model(model_path)
 
         required_output_keys = {'features', 'heatmaps', 'pafs'}
         for output_tensor_name in required_output_keys:
@@ -40,7 +40,7 @@ def __init__(self, net_model_xml_path, device, stride):
         self.input_tensor_name = self.model.inputs[0].get_any_name()
         compiled_model = self.core.compile_model(self.model, self.device)
         self.infer_request = compiled_model.create_infer_request()
-        log.info('The model {} is loaded to {}'.format(net_model_xml_path, self.device))
+        log.info('The model {} is loaded to {}'.format(model_path, self.device))
 
     def infer(self, img):
         img = img[0:img.shape[0] - (img.shape[0] % self.stride),

demos/image_inpainting_demo/python/inpainting.py

@@ -16,7 +16,7 @@
 
 class ImageInpainting:
     def __init__(self, core, model_path, device='CPU'):
-        model = core.read_model(model_path, model_path.with_suffix('.bin'))
+        model = core.read_model(model_path)
 
         if len(model.inputs) != 2:
             raise RuntimeError("The model expects 2 input layers")

demos/image_retrieval_demo/python/image_retrieval_demo/image_retrieval.py

@@ -36,9 +36,8 @@ def __init__(self, model_path, device, cpu_extension):
         if cpu_extension and device == 'CPU':
             core.add_extension(cpu_extension, 'CPU')
 
-        path = '.'.join(model_path.split('.')[:-1])
         log.info('Reading model {}'.format(model_path))
-        self.model = core.read_model(path + '.xml', path + '.bin')
+        self.model = core.read_model(model_path)
         self.input_tensor_name = "Placeholder"
         compiled_model = core.compile_model(self.model, device)
         self.infer_request = compiled_model.create_infer_request()

demos/instance_segmentation_demo/python/instance_segmentation_demo.py

@@ -115,7 +115,7 @@ def main():
 
     # Read IR
     log.info('Reading model {}'.format(args.model))
-    model = core.read_model(args.model, args.model.with_suffix('.bin'))
+    model = core.read_model(args.model)
     image_input, image_info_input, (n, c, h, w), model_type, output_names, postprocessor = check_model(model)
     args.no_keep_aspect_ratio = model_type == 'yolact' or args.no_keep_aspect_ratio
 

demos/machine_translation_demo/python/machine_translation_demo.py

@@ -35,8 +35,8 @@ class Translator:
         tokenizer_src (str): path to src tokenizer.
         tokenizer_tgt (str): path to tgt tokenizer.
     """
-    def __init__(self, model_xml, model_bin, device, tokenizer_src, tokenizer_tgt, output_name):
-        self.engine = TranslationEngine(model_xml, model_bin, device, output_name)
+    def __init__(self, model_xml, device, tokenizer_src, tokenizer_tgt, output_name):
+        self.engine = TranslationEngine(model_xml, device, output_name)
         self.max_tokens = self.engine.get_max_tokens()
         self.tokenizer_src = Tokenizer(tokenizer_src, self.max_tokens)
         log.debug('Loaded src tokenizer, max tokens: {}'.format(self.max_tokens))
@@ -66,16 +66,15 @@ class TranslationEngine:
 
     Arguments:
         model_xml (str): path to model's .xml file.
-        model_bin (str): path to model's .bin file.
         output_name (str): name of output blob of model.
     """
-    def __init__(self, model_xml, model_bin, device, output_name):
+    def __init__(self, model_xml, device, output_name):
         log.info('OpenVINO Inference Engine')
         log.info('\tbuild: {}'.format(get_version()))
         core = Core()
 
         log.info('Reading model {}'.format(model_xml))
-        self.model = core.read_model(model_xml, model_bin)
+        self.model = core.read_model(model_xml)
         compiled_model = core.compile_model(self.model, args.device)
         self.infer_request = compiled_model.create_infer_request()
         log.info('The model {} is loaded to {}'.format(model_xml, device))
@@ -218,7 +217,6 @@ def parse_input(input):
 def main(args):
     model = Translator(
         model_xml=args.model,
-        model_bin=args.model.with_suffix(".bin"),
         device=args.device,
         tokenizer_src=args.tokenizer_src,
         tokenizer_tgt=args.tokenizer_tgt,

demos/place_recognition_demo/python/place_recognition_demo/place_recognition.py

@@ -36,7 +36,7 @@ def __init__(self, model_path, device, cpu_extension):
             core.add_extension(cpu_extension, 'CPU')
 
         log.info('Reading model {}'.format(model_path))
-        self.model = core.read_model(model_path, model_path.with_suffix('.bin'))
+        self.model = core.read_model(model_path)
         self.input_tensor_name = self.model.inputs[0].get_any_name()
         self.input_size = self.model.input(self.input_tensor_name).shape
         self.nchw_layout = self.input_size[1] == 3