apply comments

docs/snippets/ie_common.py

@@ -10,10 +10,10 @@
 core = ie.IECore()
 #! [ie:create_core]
 
-xml_path = get_path_to_model(True)
+model_path = get_path_to_model(True)
 
 #! [ie:read_model]
-network = core.read_network(xml_path)
+network = core.read_network(model_path)
 #! [ie:read_model]
 
 #! [ie:compile_model]

docs/snippets/ov_caching.py

@@ -8,28 +8,28 @@
 import openvino.runtime.properties as props
 
 device_name = 'GNA'
-xml_path = get_path_to_model()
-path_to_cash_dir = get_temp_dir()
+model_path = get_path_to_model()
+path_to_cache_dir = get_temp_dir()
 # ! [ov:caching:part0]
 core = ov.Core()
-core.set_property({props.cache_dir(): path_to_cash_dir})
-model = core.read_model(model=xml_path)
+core.set_property({props.cache_dir(): path_to_cache_dir})
+model = core.read_model(model=model_path)
 compiled_model = core.compile_model(model=model, device_name=device_name)
 # ! [ov:caching:part0]
 
 assert compiled_model
 
 # ! [ov:caching:part1]
 core = ov.Core()
-compiled_model = core.compile_model(model=xml_path, device_name=device_name)
+compiled_model = core.compile_model(model=model_path, device_name=device_name)
 # ! [ov:caching:part1]
 
 assert compiled_model
 
 # ! [ov:caching:part2]
 core = ov.Core()
-core.set_property({props.cache_dir(): path_to_cash_dir})
-compiled_model = core.compile_model(model=xml_path, device_name=device_name)
+core.set_property({props.cache_dir(): path_to_cache_dir})
+compiled_model = core.compile_model(model=model_path, device_name=device_name)
 # ! [ov:caching:part2]
 
 assert compiled_model

docs/snippets/ov_common.py

@@ -11,10 +11,10 @@
 core = ov.Core()
 #! [ov_api_2_0:create_core]
 
-xml_path = get_path_to_model()
+model_path = get_path_to_model()
 
 #! [ov_api_2_0:read_model]
-model = core.read_model(xml_path)
+model = core.read_model(model_path)
 #! [ov_api_2_0:read_model]
 
 #! [ov_api_2_0:compile_model]
@@ -107,7 +107,8 @@ def callback(request, frame_id):
 # process output data ...
 #! [ov_api_2_0:get_output_tensor_v10]
 
-#! [ov_api_2_0:load_old_extension]
 path_to_extension_library = get_path_to_extension_library()
+
+#! [ov_api_2_0:load_old_extension]
 core.add_extension(path_to_extension_library)
 #! [ov_api_2_0:load_old_extension]

docs/snippets/ov_denormals.py

@@ -6,7 +6,6 @@
 from utils import get_model
 
 device_name = 'CPU'
-xml_path = 'modelWithDenormals.xml'
 model = get_model()
 
 # ! [ov:intel_cpu:denormals_optimization:part0]

docs/snippets/ov_dynamic_shapes.py

@@ -2,15 +2,16 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import numpy as np
-from utils import get_model, get_dynamic_model
+from utils import get_dynamic_model
 
 #! [import]
 import openvino as ov
 #! [import]
 
+model = get_dynamic_model()
+
 #! [reshape_undefined]
 core = ov.Core()
-model = get_dynamic_model()
 
 # Set first dimension to be dynamic while keeping others static
 model.reshape([-1, 3, 224, 224])
@@ -37,9 +38,10 @@
 print(model.input().partial_shape)
 #! [print_dynamic]
 
-#! [detect_dynamic]
 model = get_dynamic_model()
 
+#! [detect_dynamic]
+
 if model.input(0).partial_shape.is_dynamic:
     # input is dynamic
     pass
@@ -55,19 +57,20 @@
 
 executable = core.compile_model(model)
 infer_request = executable.create_infer_request()
+input_tensor_name = "input"
 
 #! [set_input_tensor]
 # For first inference call, prepare an input tensor with 1x128 shape and run inference request
 input_data1 = np.ones(shape=[1,128])
-infer_request.infer({"Parameter_319": input_data1})
+infer_request.infer({input_tensor_name: input_data1})
 
 # Get resulting outputs
 output_tensor1 = infer_request.get_output_tensor()
 output_data1 = output_tensor1.data[:]
 
 # For second inference call, prepare a 1x200 input tensor and run inference request
 input_data2 = np.ones(shape=[1,200])
-infer_request.infer({"Parameter_319": input_data2})
+infer_request.infer({input_tensor_name: input_data2})
 
 # Get resulting outputs
 output_tensor2 = infer_request.get_output_tensor()
@@ -98,10 +101,9 @@
 data2 = output_tensor.data[:]
 #! [get_input_tensor]
 
-#! [check_inputs]
-core = ov.Core()
-model = core.read_model("model.xml")
+model = get_dynamic_model()
 
+#! [check_inputs]
 # Print model input layer info
 for input_layer in model.inputs:
     print(input_layer.names, input_layer.partial_shape)

docs/snippets/ov_preprocessing.py

@@ -177,7 +177,7 @@ def custom_abs(output: Output):
 from openvino.runtime.passes import Manager, Serialize
 # ! [ov:preprocess:save_headers]
 
-xml_path = get_path_to_model()
+model_path = get_path_to_model()
 
 # ! [ov:preprocess:save]
 # ========  Step 0: read original model =========
@@ -213,20 +213,14 @@ def custom_abs(output: Output):
 set_batch(model, 2)
 
 # ======== Step 3: Save the model ================
-# First method - using serialize runtime wrapper
-serialize(model, xml_path)
-
-# Second method - using Manager and Serialize pass
-manager = Manager()
-manager.register_pass(Serialize(xml_path))
-manager.run_passes(model)
+serialize(model, model_path)
 # ! [ov:preprocess:save]
 
-path_to_cash_dir = get_temp_dir()
+path_to_cache_dir = get_temp_dir()
 
 # ! [ov:preprocess:save_load]
 core = Core()
-core.set_property({props.cache_dir(): path_to_cash_dir})
+core.set_property({props.cache_dir(): path_to_cache_dir})
 
 # In case that no preprocessing is needed anymore, we can load model on target device directly
 # With cached model available, it will also save some time on reading original model

docs/snippets/ov_python_exclusives.py

@@ -157,7 +157,7 @@ def f(request, userdata):
 assert np.array_equal(unpacked_data , unt8_data)
 #! [unpacking]
 
-xml_path = get_path_to_model()
+model_path = get_path_to_model()
 path_to_image = get_path_to_image()
 
 
@@ -180,7 +180,7 @@ def prepare_data(input, image_path):
     input_data.append(image)
 
 core = ov.Core()
-model = core.read_model(xml_path)
+model = core.read_model(model_path)
 # Create thread with prepare_data function as target and start it
 thread = Thread(target=prepare_data, args=[model.input(), path_to_image])
 thread.start()

docs/snippets/utils.py

@@ -15,7 +15,7 @@
 
 
 def get_dynamic_model():
-    param = ops.parameter(ov.PartialShape([-1, -1]))
+    param = ops.parameter(ov.PartialShape([-1, -1]), name="input")
     return ov.Model(ops.relu(param), [param])