a8e611f90b
* Replace Constant with Parameter in run_op_node. * Pass inputs to function. * Add func to get shape. * Make constant if input is scalar. * Add case for list. * Fix test. * Split tests for run_op_node and run_op_numeric_data. * Split more tests. * Split more and more tests. * Mark tests with xfail. * Mark more tests with xfail. * Replace scalar with parameter. * Code formatting. * Set empty shape for scalar. * Remove check for list.
93 lines
3.2 KiB
Python
93 lines
3.2 KiB
Python
# ******************************************************************************
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# Copyright 2017-2020 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ******************************************************************************
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from typing import Any, Callable, List
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import numpy as np
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import ngraph as ng
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from ngraph.utils.types import NumericData
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from tests.runtime import get_runtime
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from string import ascii_uppercase
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def _get_numpy_dtype(scalar):
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return np.array([scalar]).dtype
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def run_op_node(input_data, op_fun, *args):
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# type: (NumericData, Callable, *Any) -> List[NumericData]
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"""Run computation on node performing `op_fun`.
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`op_fun` has to accept a node as an argument.
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This function converts passed raw input data to nGraph Constant Node and that form is passed
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to `op_fun`.
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:param input_data: The input data for performed computation.
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:param op_fun: The function handler for operation we want to carry out.
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:param args: The arguments passed to operation we want to carry out.
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:return: The result from computations.
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"""
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runtime = get_runtime()
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comp_args = []
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op_fun_args = []
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comp_inputs = []
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for idx, data in enumerate(input_data):
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node = None
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if np.isscalar(data):
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node = ng.parameter([], name=ascii_uppercase[idx], dtype=_get_numpy_dtype(data))
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else:
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node = ng.parameter(data.shape, name=ascii_uppercase[idx], dtype=data.dtype)
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op_fun_args.append(node)
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comp_args.append(node)
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comp_inputs.append(data)
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op_fun_args.extend(args)
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node = op_fun(*op_fun_args)
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computation = runtime.computation(node, *comp_args)
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return computation(*comp_inputs)
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def run_op_numeric_data(input_data, op_fun, *args):
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# type: (NumericData, Callable, *Any) -> List[NumericData]
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"""Run computation on node performing `op_fun`.
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`op_fun` has to accept a scalar or an array.
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This function passess input data AS IS. This mean that in case they're a scalar (integral,
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or floating point value) or a NumPy's ndarray object they will be automatically converted
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to nGraph's Constant Nodes.
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:param input_data: The input data for performed computation.
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:param op_fun: The function handler for operation we want to carry out.
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:param args: The arguments passed to operation we want to carry out.
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:return: The result from computations.
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"""
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runtime = get_runtime()
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node = op_fun(input_data, *args)
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computation = runtime.computation(node)
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return computation()
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def count_ops_of_type(func, op_type):
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count = 0
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for op in func.get_ops():
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if (type(op) is type(op_type)):
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count += 1
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return count
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