forked from mindspore-Ecosystem/mindspore
199 lines
7.7 KiB
Python
199 lines
7.7 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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import mindspore.context as context
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from mindspore import Tensor
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from mindspore.nn import Cell
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import mindspore.ops.operations as P
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import mindspore.ops as ops
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import mindspore.common.dtype as mstype
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from mindspore.ops.functional import vmap
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class OneHotNet(Cell):
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def __init__(self):
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super(OneHotNet, self).__init__()
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self.onehot = P.OneHot()
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def construct(self, indices, depth, on_value, off_value):
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res = self.onehot(indices, depth, on_value, off_value)
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return res
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def one_hot_static_shape_test_case(in_type, value_type, out_type):
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depth = 5
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indices = Tensor(np.array([1, 3, 2, 4, 0]).astype(in_type))
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on_value = Tensor(1.0, value_type)
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off_value = Tensor(0.0, value_type)
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net = OneHotNet()
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output = net(indices, depth, on_value, off_value)
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expect = np.array([[0.0, 1.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 1.0, 0.0],
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[0.0, 0.0, 1.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 0.0, 1.0],
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[1.0, 0.0, 0.0, 0.0, 0.0]]).astype(out_type)
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assert np.allclose(expect, output.asnumpy(), 1.e-4, 1.e-7)
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class OneHotTensorNet(Cell):
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def construct(self, indices, depth, on_value, off_value):
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return indices.one_hot(depth, on_value, off_value)
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def one_hot_tensor_graph(in_type, value_type, out_type):
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depth = 5
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indices = Tensor(np.array([1, 3, 2, 4, 0]).astype(in_type))
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on_value = Tensor(1.0, value_type)
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off_value = Tensor(0.0, value_type)
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net = OneHotTensorNet()
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output = net(indices, depth, on_value, off_value)
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expect = np.array([[0.0, 1.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 1.0, 0.0],
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[0.0, 0.0, 1.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 0.0, 1.0],
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[1.0, 0.0, 0.0, 0.0, 0.0]]).astype(out_type)
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assert np.allclose(expect, output.asnumpy(), 1.e-4, 1.e-7)
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def one_hot_tensor_pynative(in_type, value_type, out_type):
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depth = 5
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indices = Tensor(np.array([1, 3, 2, 4, 0]).astype(in_type))
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on_value = Tensor(1.0, value_type)
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off_value = Tensor(0.0, value_type)
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output = indices.one_hot(depth, on_value, off_value)
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expect = np.array([[0.0, 1.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 1.0, 0.0],
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[0.0, 0.0, 1.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 0.0, 1.0],
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[1.0, 0.0, 0.0, 0.0, 0.0]]).astype(out_type)
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assert np.allclose(expect, output.asnumpy(), 1.e-4, 1.e-7)
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def one_hot_functional(in_type, value_type, out_type):
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depth = 5
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indices = Tensor(np.array([1, 3, 2, 4, 0]).astype(in_type))
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on_value = Tensor(1.0, value_type)
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off_value = Tensor(0.0, value_type)
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output = ops.one_hot(indices, depth, on_value, off_value)
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expect = np.array([[0.0, 1.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 1.0, 0.0],
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[0.0, 0.0, 1.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 0.0, 1.0],
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[1.0, 0.0, 0.0, 0.0, 0.0]]).astype(out_type)
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assert np.allclose(expect, output.asnumpy(), 1.e-4, 1.e-7)
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class OneHotDynamicShapeNet(Cell):
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def __init__(self):
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super(OneHotDynamicShapeNet, self).__init__()
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self.onehot = P.OneHot()
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self.unique = P.Unique()
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def construct(self, indices, depth, on_value, off_value):
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real_input, _ = self.unique(indices)
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res = self.onehot(real_input, depth, on_value, off_value)
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return res
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def one_hot_dynamic_shape_test_case(in_type, value_type, out_type):
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depth = 5
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indices = Tensor(np.array([1, 3, 1, 2, 2, 4, 0, 4]).astype(in_type))
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on_value = Tensor(1.0, value_type)
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off_value = Tensor(0.0, value_type)
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net = OneHotDynamicShapeNet()
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output = net(indices, depth, on_value, off_value)
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expect = np.array([[0.0, 1.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 1.0, 0.0],
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[0.0, 0.0, 1.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 0.0, 1.0],
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[1.0, 0.0, 0.0, 0.0, 0.0]]).astype(out_type)
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assert np.allclose(expect, output.asnumpy(), 1.e-4, 1.e-7)
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def one_hot_vmap(in_type, value_type):
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def cal_onehot(ind, dep, on_v, off_v):
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return P.OneHot()(ind, dep, on_v, off_v)
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depth = 5
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on_value = Tensor(1.0, value_type)
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off_value = Tensor(0.0, value_type)
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indices = Tensor(np.array([[1, 3, 2, 4, 0], [1, 3, 2, 4, 0]]).astype(in_type))
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outputs = vmap(cal_onehot, in_axes=(0, None, None, None), out_axes=0)(indices, depth, on_value, off_value)
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expect = np.array([[[0.0, 1.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 1.0, 0.0],
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[0.0, 0.0, 1.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 0.0, 1.0],
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[1.0, 0.0, 0.0, 0.0, 0.0]],
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[[0.0, 1.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 1.0, 0.0],
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[0.0, 0.0, 1.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 0.0, 1.0],
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[1.0, 0.0, 0.0, 0.0, 0.0]]]).astype(in_type)
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assert np.allclose(expect, outputs.asnumpy(), 1.e-4, 1.e-7)
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def one_hot_static_shape_all_types():
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one_hot_static_shape_test_case(np.uint8, mstype.int8, np.int8)
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one_hot_static_shape_test_case(np.uint8, mstype.uint8, np.uint8)
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one_hot_static_shape_test_case(np.uint8, mstype.int32, np.int32)
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one_hot_static_shape_test_case(np.uint8, mstype.float16, np.float16)
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one_hot_static_shape_test_case(np.uint8, mstype.float32, np.float32)
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one_hot_static_shape_test_case(np.int32, mstype.int8, np.int8)
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one_hot_static_shape_test_case(np.int32, mstype.uint8, np.uint8)
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one_hot_static_shape_test_case(np.int32, mstype.int32, np.int32)
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one_hot_static_shape_test_case(np.int32, mstype.float16, np.float16)
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one_hot_static_shape_test_case(np.int32, mstype.float32, np.float32)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_ascend_graph_mode():
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"""
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Feature: test one_hot static shape on Ascend in graph mode
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Description: test interface
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Expectation: result match numpy result
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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one_hot_static_shape_all_types()
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one_hot_dynamic_shape_test_case(np.int32, mstype.float32, np.float32)
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one_hot_tensor_graph(np.int32, mstype.float32, np.float32)
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_ascend_pynative_mode():
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"""
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Feature: test one_hot static shape on Ascend in pynative mode
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Description: test interface
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Expectation: result match numpy result
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"""
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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one_hot_static_shape_all_types()
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one_hot_dynamic_shape_test_case(np.int32, mstype.float32, np.float32)
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one_hot_tensor_pynative(np.int32, mstype.float32, np.float32)
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one_hot_functional(np.int32, mstype.float32, np.float32)
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one_hot_vmap(np.int32, mstype.float32)
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