forked from huawei/mindspore2022
74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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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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"""test function grad in pynative mode"""
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import numpy as np
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import mindspore.nn as nn
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import mindspore.context as context
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from mindspore import Tensor
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from mindspore.ops.functional import grad
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context.set_context(mode=context.PYNATIVE_MODE)
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class SingleInputSingleOutputNet(nn.Cell):
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def construct(self, x):
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return x**3
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class MultipleInputsMultipleOutputsNet(nn.Cell):
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def construct(self, x, y, z):
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return x**2 + y**2 + z**2, x*y*z
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def function(x, y, z):
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return x**2 + y**2 + z**2, x*y*z
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def test_grad_single_input_single_output_cell_pynative():
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"""
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Features: Function grad.
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Description: Test F.grad with single input and single output net in pynative mode.
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Expectation: No exception.
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"""
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x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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net = SingleInputSingleOutputNet()
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grad(net)(x)
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def test_grad_multiple_inputs_multiple_outputs_cell_pynative():
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"""
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Features: Function grad.
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Description: Test F.grad with multiple inputs and multiple outputs net in pynative mode.
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Expectation: No exception.
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"""
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x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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y = Tensor(np.array([[-2, 3], [-1, 2]]).astype(np.float32))
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z = Tensor(np.array([[0, 3], [5, -1]]).astype(np.float32))
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net = MultipleInputsMultipleOutputsNet()
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grad(net, grad_position=(1, 2))(x, y, z)
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def test_grad_function_with_sens_pynative():
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"""
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Features: Function grad.
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Description: Test F.grad with function setting sens_param in pynative mode.
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Expectation: No exception.
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"""
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x = Tensor(np.array([[1, 2], [3, 4]]).astype(np.float32))
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y = Tensor(np.array([[-2, 3], [-1, 2]]).astype(np.float32))
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z = Tensor(np.array([[0, 3], [5, -1]]).astype(np.float32))
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v = Tensor(np.array([[-1, 3], [2, 1]]).astype(np.float32))
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grad(function, grad_position=(1, 2), sens_param=True)(x, y, z, (v, v))
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