85 lines
3.5 KiB
Python
85 lines
3.5 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import logging
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import os
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import subprocess
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import sys
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import numpy as np
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logger = logging.getLogger(__name__)
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def generate_ir(coverage=False, **kwargs):
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# Get default mo args
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mo = os.path.join(os.environ.get("MO_ROOT"), "mo.py")
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if coverage:
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params = [sys.executable, '-m', 'coverage', 'run', '-p', '--source={}'.format(os.environ.get("MO_ROOT")),
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'--omit=*_test.py', mo]
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else:
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params = [sys.executable, mo]
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for key, value in kwargs.items():
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if key == "batch":
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params.extend(("-b", str(value)))
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elif key == "k":
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params.extend(("-k", str(value)))
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elif isinstance(value, bool) and value:
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params.append("--{}".format(key))
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elif isinstance(value, bool) and not value:
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continue
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elif (isinstance(value, tuple) and value) or (isinstance(value, str)):
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params.extend(("--{}".format(key), str('"{}"'.format(value))))
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elif (key == "mean_values" and (' ' in value or '(' in value)):
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params.extend(("--{}".format(key), str('"{}"'.format(value))))
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else:
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params.extend(("--{}".format(key), str(value)))
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exit_code, stdout, stderr = shell(params)
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logger.info("Model Optimizer out:\n{}".format(stdout))
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logger.error(stderr)
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return exit_code, stderr
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def shell(cmd, env=None, cwd=None, out_format="plain"):
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"""
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Run command execution in specified environment
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:param cmd: list containing command and its parameters
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:param env: set of environment variables to set for this command
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:param cwd: working directory from which execute call
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:param out_format: 'plain' or 'html'. If 'html' all '\n; symbols are replaced by '<br>' tag
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:return:
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"""
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if sys.platform.startswith('linux') or sys.platform == 'darwin':
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cmd = ['/bin/bash', '-c', "unset OMP_NUM_THREADS; " + " ".join(cmd)]
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else:
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cmd = " ".join(cmd)
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sys.stdout.write("Running command:\n" + "".join(cmd) + "\n")
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p = subprocess.Popen(cmd, cwd=cwd, env=env, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
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(stdout, stderr) = p.communicate()
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stdout = str(stdout.decode('utf-8'))
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stderr = str(stderr.decode('utf-8'))
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if out_format == "html":
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stdout = "<br>\n".join(stdout.split('\n'))
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stderr = "<br>\n".join(stderr.split('\n'))
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return p.returncode, stdout, stderr
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def allclose(cur_array, ref_array, atol, rtol):
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"""
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Comparison of abs_diff and rel_diff with tolerances for every values of corresponding elements.
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If (abs_diff < atol) or (rel_diff < rtol) for every element, comparison of elements will pass, else will fail.
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Note: if value is very small, firstly abs_diff will be used. If value is huge, abs_diff may be failed,
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and rel_diff will be used. So if tensor has small and huge values, need to compare every value
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with abs_diff and rel_diff instead of using one of it for the whole array.
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:param cur_array: tensor from IE
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:param ref_array: tensor from FW
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:param atol: absolute tolerance (threshold for absolute difference)
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:param rtol: relative tolerance (threshold for relative difference)
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:return: bool value means that values of tensors are equal with tolerance or not
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"""
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abs_diff = np.absolute(cur_array - ref_array)
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max_val = np.maximum(np.absolute(cur_array), np.absolute(ref_array))
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return ((abs_diff < atol) | (abs_diff < rtol * max_val)).all()
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