forked from mindspore-Ecosystem/mindspore
92 lines
3.2 KiB
Python
92 lines
3.2 KiB
Python
# Copyright 2022 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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import numpy as np
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import pytest
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from mindspore import Tensor
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from mindspore import context
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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from mindspore.ops.functional import vmap
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from mindspore.common.api import jit
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from mindspore.common.api import _pynative_executor
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def np_all_close_with_loss(out, expect):
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"""np_all_close_with_loss"""
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return np.allclose(out, expect, 0.0005, 0.0005, equal_nan=True)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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@pytest.mark.parametrize("data_type", [np.int32, np.int64])
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def test_data_formata_dim_map(data_type):
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"""
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Feature: DataFormatDimMapNet cpu kernel.
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Description: test the rightness of DataFormatDimMapNet cpu kernel.
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Expectation: Success.
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"""
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x_np_1 = np.array([-4, -3, -2, -1, 0, 1, 2, 3]).astype(data_type)
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output_1 = P.DataFormatDimMap()(Tensor(x_np_1))
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output_1_expect = np.array([0, 3, 1, 2, 0, 3, 1, 2]).astype(data_type)
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assert np.allclose(output_1.asnumpy(), output_1_expect)
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output_2 = P.DataFormatDimMap(src_format="NHWC", dst_format="NHWC")(Tensor(x_np_1))
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output_2_expect = np.array([0, 1, 2, 3, 0, 1, 2, 3]).astype(data_type)
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assert np.allclose(output_2.asnumpy(), output_2_expect)
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output_3 = P.DataFormatDimMap(src_format="NCHW", dst_format="NHWC")(Tensor(x_np_1))
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output_3_expect = np.array([0, 2, 3, 1, 0, 2, 3, 1]).astype(data_type)
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assert np.allclose(output_3.asnumpy(), output_3_expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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@pytest.mark.parametrize("data_type", [np.int32, np.int64])
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def test_data_formata_dim_map_vmap(data_type):
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"""
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Feature: DataFormatDimMapNet cpu kernel
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Description: test the rightness of DataFormatDimMapNet cpu kernel vmap feature.
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Expectation: Success.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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def data_formata_dim_map_fun(x):
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"""data_formata_dim_map_fun"""
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return P.DataFormatDimMap()(x)
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x_np = np.random.randint(low=-4, high=4, size=(100, 100)).astype(data_type)
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x = Tensor(x_np)
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x = F.sub(x, 0)
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output_vmap = vmap(data_formata_dim_map_fun, in_axes=(0,))(x)
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_pynative_executor.sync()
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@jit
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def manually_batched(xs):
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"""manually_batched"""
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output = []
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for i in range(xs.shape[0]):
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output.append(data_formata_dim_map_fun(xs[i]))
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return F.stack(output)
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output_manually = manually_batched(x)
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_pynative_executor.sync()
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assert np_all_close_with_loss(output_vmap.asnumpy(), output_manually.asnumpy())
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