ee31b648d1
* save work * Add common snipp * update ie pipeline with python snippets * ov_common_snippet * Python snippets for graph construction * Fix docs * Add missed old api snippets * Fix names * Fix markers * Fix methods call
91 lines
2.5 KiB
Python
91 lines
2.5 KiB
Python
# Copyright (C) 2022 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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#
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#! [ie:create_core]
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import numpy as np
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import openvino.inference_engine as ie
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core = ie.IECore()
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#! [ie:create_core]
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#! [ie:read_model]
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network = core.read_network("model.xml")
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#! [ie:read_model]
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#! [ie:compile_model]
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# Load network to the device and create infer requests
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exec_network = core.load_network(network, "CPU", num_requests=4)
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#! [ie:compile_model]
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#! [ie:create_infer_request]
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# Done in the previous step
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#! [ie:create_infer_request]
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#! [ie:get_input_tensor]
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infer_request = exec_network.requests[0]
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# Get input blobs mapped to input layers names
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input_blobs = infer_request.input_blobs
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data = input_blobs["data1"].buffer
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# Original I64 precision was converted to I32
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assert data.dtype == np.int32
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# Fill the first blob ...
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#! [ie:get_input_tensor]
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#! [ie:inference]
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results = infer_request.infer()
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#! [ie:inference]
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input_data = iter(list())
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def process_results(results, frame_id):
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pass
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#! [ie:start_async_and_wait]
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# Start async inference on a single infer request
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infer_request.async_infer()
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# Wait for 1 milisecond
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infer_request.wait(1)
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# Wait for inference completion
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infer_request.wait()
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# Demonstrates async pipeline using ExecutableNetwork
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results = []
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# Callback to process inference results
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def callback(output_blobs, _):
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# Copy the data from output blobs to numpy array
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results_copy = {out_name: out_blob.buffer[:] for out_name, out_blob in output_blobs.items()}
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results.append(process_results(results_copy))
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# Setting callback for each infer requests
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for infer_request in exec_network.requests:
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infer_request.set_completion_callback(callback, py_data=infer_request.output_blobs)
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# Async pipline is managed by ExecutableNetwork
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total_frames = 100
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for _ in range(total_frames):
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# Wait for at least one free request
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exec_network.wait(num_request=1)
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# Get idle id
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idle_id = exec_network.get_idle_request_id()
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# Start asynchronous inference on idle request
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exec_network.start_async(request_id=idle_id, inputs=next(input_data))
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# Wait for all requests to complete
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exec_network.wait()
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#! [ie:start_async_and_wait]
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#! [ie:get_output_tensor]
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# Get output blobs mapped to output layers names
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output_blobs = infer_request.output_blobs
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data = output_blobs["out1"].buffer
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# Original I64 precision was converted to I32
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assert data.dtype == np.int32
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# Process output data
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#! [ie:get_output_tensor]
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#! [ie:load_old_extension]
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core.add_extension("path_to_extension_library.so", "CPU")
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#! [ie:load_old_extension]
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