Ivan Tikhonovandcecilia peng
a329611349
* Support for dynamic shapes in ReadValue/Assign ops and MakeStateful transformation * fix unit tests * codestyle * fix warning * fix visitors test * Fix lowLatency transformation, fix functional tests * fix LowLatency2 * codestyle * Fix CPU/GNA tests * fix shape inference of assign operation * fix python API * fix mypy error * Fix read_value operation version in python API for opsets7-13 * Update python API and fix tests * set the same Varibable obj to Assign/ReadValue in py API, disable fp16 memory tests * Update shape inference methods of Assign, ReadValue; update tests; fix codestyle * fix python codestyle * fix tests * Resolve review comments * codestyle * resolve review comments * codestyle * Update ReadValue op in kaldi frontend, fix GNA tests * fix Assign/ReadValue bindings, fix read_value test * Apply suggestions from code review Co-authored-by: cecilia peng <cecilia.peng@intel.com> * Fix functional tests, add ov::Type support as an argument for read_value py binding * temporary disable test_query_state_write_buffer fp16 tests * Fix backward compatibility with the IRs with ReadValue/Assign ops generated via prev versions of OV; fix func tests * fix python test, support type as str in read_value/assign bindings * fix model binding * fix pre-commit tests: variables vector cleanup * fix integration with cpu changes * codestyle * fix warning * Fix type/shape validation for read_value op * fix a type_prop test for assign op --------- Co-authored-by: cecilia peng <cecilia.peng@intel.com>
85 lines
2.6 KiB
Python
85 lines
2.6 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import logging as log
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import numpy as np
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import sys
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import openvino as ov
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from openvino.runtime import opset12 as ops
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def main():
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# 1. Load inference engine
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log.info("Loading OpenVINO")
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core = ov.Core()
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#! [ov:model_create]
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# 2. Creating ov.Model
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input = ops.parameter([1, 1], dtype=np.float32, name="data")
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init_const = ops.constant([[0]], dtype=np.float32)
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read = ops.read_value(init_const, "variable0", "f32", [1, 1])
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add = ops.add(input, read)
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assign = ops.assign(add, "variable0")
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add2 = ops.add(add, read)
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result = ops.result(add2)
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model = ov.Model(results=[result], sinks=[assign], parameters=[input], name="model")
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#! [ov:model_create]
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log.info("Loading network files")
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# 3. Load network to CPU
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compiled_model = core.compile_model(model, "CPU")
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# 4. Create Infer Request
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infer_request = compiled_model.create_infer_request()
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# 5. Prepare inputs
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input_tensors = []
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for input in compiled_model.inputs:
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input_tensors.append(infer_request.get_tensor(input))
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# 6. Prepare outputs
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output_tensors = []
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for output in compiled_model.outputs:
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output_tensors.append(infer_request.get_tensor(output))
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# 7. Initialize memory state before starting
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for state in infer_request.query_state():
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state.reset()
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#! [ov:part1]
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# input data
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input_data = np.arange(start=1, stop=7, dtype=np.float32)
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log.info("Infer the first utterance")
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for next_input in range(int(len(input_data)/2)):
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infer_request.infer({"data" : np.asarray([input_data[next_input]]).reshape([1,1])})
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# check states
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states = infer_request.query_state()
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if len(states) == 0:
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log.error("Queried states are empty")
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return -1
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mstate = states[0].state
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if not mstate:
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log.error("Can't cast state to MemoryBlob")
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return -1
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state_buf = mstate.data
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log.info(state_buf[0])
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log.info("\nReset state between utterances...\n")
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for state in infer_request.query_state():
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state.reset()
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log.info("Infer the second utterance")
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for next_input in range(int(len(input_data)/2), len(input_data)):
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infer_request.infer({0 : np.asarray([input_data[next_input]]).reshape([1,1])})
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# check states
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states = infer_request.query_state()
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mstate = states[0].state
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state_buf = mstate.data
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log.info(state_buf[0])
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#! [ov:part1]
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log.info("Execution successful")
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return 0
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