forked from mindspore-Ecosystem/mindspore
552 lines
14 KiB
Python
552 lines
14 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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import pytest
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import numpy as np
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from mindspore import Tensor
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from mindspore.ops import operations as P
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import mindspore.nn as nn
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import mindspore.ops as ops
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import mindspore.context as context
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from mindspore.ops.functional import vmap
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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class ConcatV10(nn.Cell):
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def __init__(self, nptype):
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super(ConcatV10, self).__init__()
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self.cat = P.Concat(axis=2)
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self.x1 = Tensor(np.array([[[0., 0., 1.],
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[1., 2., 3.]],
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[[2., 4., 5.],
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[3., 6., 7.]]]).astype(nptype))
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def construct(self):
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return self.cat((self.x1,))
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def axis10(nptype):
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cat = ConcatV10(nptype)
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output = cat()
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expect = np.array([[[0., 0., 1.],
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[1., 2., 3.]],
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[[2., 4., 5.],
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[3., 6., 7.]]]).astype(nptype)
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print(output)
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assert (output.asnumpy() == expect).all()
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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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def test_axis10_float32():
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"""
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Feature: concat with one input
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Description: Concat with one input of float32 dtype
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Expectation: success
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"""
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axis10(np.float32)
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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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def test_axis10_int32():
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"""
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Feature: concat with one input
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Description: Concat with one input of int32 dtype
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Expectation: success
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"""
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axis10(np.int32)
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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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def test_axis10_bool():
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"""
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Feature: concat with one input
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Description: Concat with one input of bool dtype
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Expectation: success
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"""
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axis10(np.bool)
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class ConcatV32(nn.Cell):
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def __init__(self, nptype):
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super(ConcatV32, self).__init__()
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self.cat = P.Concat(axis=2)
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self.x1 = Tensor(np.arange(2 * 2 * 1).reshape(2, 2, 1).astype(nptype))
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self.x2 = Tensor(np.arange(2 * 2 * 2).reshape(2, 2, 2).astype(nptype))
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def construct(self):
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return self.cat((self.x1, self.x2))
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def axis32(nptype):
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cat = ConcatV32(nptype)
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output = cat()
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expect = np.array([[[0., 0., 1.],
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[1., 2., 3.]],
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[[2., 4., 5.],
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[3., 6., 7.]]]).astype(nptype)
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print(output)
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assert (output.asnumpy() == expect).all()
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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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def test_axis32_float32():
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"""
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Feature: concat in axis-2
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Description: Concat in axis 2 and float32 dtype inputs
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Expectation: success
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"""
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axis32(np.float32)
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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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def test_axis32_int32():
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"""
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Feature: concat in axis-2
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Description: Concat in axis 2 and int32 dtype inputs
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Expectation: success
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"""
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axis32(np.int32)
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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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def test_axis32_bool():
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"""
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Feature: concat in axis-2
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Description: Concat in axis 2 and bool dtype inputs
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Expectation: success
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"""
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axis32(np.bool)
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class ConcatWithList(nn.Cell):
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def __init__(self):
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super(ConcatWithList, self).__init__()
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self.concat = P.Concat(axis=2)
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def construct(self, x, y):
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input_list = [x, y]
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return self.concat(input_list)
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class ConcatWithTuple(nn.Cell):
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def __init__(self):
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super(ConcatWithTuple, self).__init__()
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self.concat = P.Concat(axis=2)
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def construct(self, x, y):
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input_list = (x, y)
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return self.concat(input_list)
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class GradConcat(nn.Cell):
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def __init__(self, network):
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super(GradConcat, self).__init__()
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self.grad = ops.GradOperation()
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self.network = network
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def construct(self, x, y):
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gout = self.grad(self.network)(x, y)
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return gout
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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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def test_concat_list_grad():
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"""
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Feature: concat grad
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Description: ConcatGrad with list input
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Expectation: success
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"""
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x1 = Tensor(np.arange(2 * 2 * 1).reshape(2, 2, 1).astype(np.float32))
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x2 = Tensor(np.arange(2 * 2 * 2).reshape(2, 2, 2).astype(np.float32))
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concat = ConcatWithList()
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output = GradConcat(concat)(x1, x2)
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expect = np.array([[[1.],
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[1.]],
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[[1.],
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[1.]]]).astype(np.float32)
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print(output)
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assert (output.asnumpy() == expect).all()
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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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def test_concat_tuple_grad():
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"""
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Feature: concat grad
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Description: ConcatGrad with tuple input
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Expectation: success
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"""
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x1 = Tensor(np.arange(2 * 2 * 1).reshape(2, 2, 1).astype(np.float32))
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x2 = Tensor(np.arange(2 * 2 * 2).reshape(2, 2, 2).astype(np.float32))
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concat = ConcatWithTuple()
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output = GradConcat(concat)(x1, x2)
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expect = np.array([[[1.],
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[1.]],
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[[1.],
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[1.]]]).astype(np.float32)
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print(output)
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assert (output.asnumpy() == expect).all()
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class ConcatV43(nn.Cell):
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def __init__(self, nptype):
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super(ConcatV43, self).__init__()
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self.cat = P.Concat(axis=3)
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self.x1 = Tensor(np.arange(2 * 2 * 2 * 2).reshape(2, 2, 2, 2).astype(nptype))
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self.x2 = Tensor(np.arange(2 * 2 * 2 * 3).reshape(2, 2, 2, 3).astype(nptype))
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def construct(self):
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return self.cat((self.x1, self.x2))
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def axis43(nptype):
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cat = ConcatV43(nptype)
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output = cat()
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expect = np.array([[[[0., 1., 0., 1., 2.],
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[2., 3., 3., 4., 5.]],
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[[4., 5., 6., 7., 8.],
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[6., 7., 9., 10., 11.]]],
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[[[8., 9., 12., 13., 14.],
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[10., 11., 15., 16., 17.]],
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[[12., 13., 18., 19., 20.],
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[14., 15., 21., 22., 23.]]]]).astype(nptype)
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assert (output.asnumpy() == expect).all()
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print(output)
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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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def test_axis43_float32():
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"""
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Feature: concat in axis-3
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Description: Concat in axis 3 and float32 dtype inputs
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Expectation: success
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"""
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axis43(np.float32)
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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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def test_axis43_int32():
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"""
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Feature: concat in axis-3
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Description: Concat in axis 3 and int32 dtype inputs
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Expectation: success
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"""
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axis43(np.int32)
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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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def test_axis43_bool():
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"""
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Feature: concat in axis-3
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Description: Concat in axis 3 and bool dtype inputs
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Expectation: success
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"""
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axis43(np.bool)
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class ConcatV21(nn.Cell):
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def __init__(self, nptype):
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super(ConcatV21, self).__init__()
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self.cat = P.Concat(axis=1)
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self.x1 = Tensor(np.arange(2 * 2).reshape(2, 2).astype(nptype))
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self.x2 = Tensor(np.arange(2 * 3).reshape(2, 3).astype(nptype))
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def construct(self):
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return self.cat((self.x1, self.x2))
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def axis21(nptype):
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cat = ConcatV21(nptype)
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output = cat()
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expect = np.array([[0., 1., 0., 1., 2.],
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[2., 3., 3., 4., 5.]]).astype(nptype)
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assert (output.asnumpy() == expect).all()
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print(output)
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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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def test_axis21_float32():
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"""
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Feature: concat with 2 inputs
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Description: Concat with 2 inputs of float32 dtype, asix = 1
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Expectation: success
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"""
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axis21(np.float32)
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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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def test_axis21_int32():
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"""
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Feature: concat with 2 inputs
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Description: Concat with 2 inputs of int32 dtype, asix = 1
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Expectation: success
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"""
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axis21(np.int32)
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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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def test_axis21_bool():
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"""
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Feature: concat with 2 inputs
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Description: Concat with 2 inputs of bool dtype, asix = 1
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Expectation: success
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"""
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axis21(np.bool)
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class Concat3INet(nn.Cell):
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def __init__(self):
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super(Concat3INet, self).__init__()
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self.cat = P.Concat(axis=1)
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def construct(self, x1, x2, x3):
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return self.cat((x1, x2, x3))
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def concat_3i(nptype):
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cat = Concat3INet()
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x1_np = np.random.randn(32, 4, 224, 224).astype(nptype)
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x2_np = np.random.randn(32, 8, 224, 224).astype(nptype)
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x3_np = np.random.randn(32, 10, 224, 224).astype(nptype)
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output_np = np.concatenate((x1_np, x2_np, x3_np), axis=1)
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x1_ms = Tensor(x1_np)
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x2_ms = Tensor(x2_np)
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x3_ms = Tensor(x3_np)
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output_ms = cat(x1_ms, x2_ms, x3_ms)
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error = np.ones(shape=output_np.shape) * 10e-6
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diff = output_ms.asnumpy() - output_np
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assert np.all(diff < error)
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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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def test_concat_3i_float32():
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"""
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Feature: concat with 3 inputs
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Description: Concat with 3 inputs of float32 dtype
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Expectation: success
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"""
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concat_3i(np.float32)
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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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def test_concat_3i_int32():
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"""
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Feature: concat with 3 inputs
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Description: Concat with 3 inputs of int32 dtype
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Expectation: success
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"""
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concat_3i(np.int32)
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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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def test_concat_3i_bool():
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"""
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Feature: concat with 3 inputs
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Description: Concat with 3 inputs of bool dtype
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Expectation: success
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"""
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cat = Concat3INet()
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x1_np = np.random.choice([True, False], (32, 4, 224, 224)).astype(np.bool)
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x2_np = np.random.choice([True, False], (32, 8, 224, 224)).astype(np.bool)
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x3_np = np.random.choice([True, False], (32, 10, 224, 224)).astype(np.bool)
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output_np = np.concatenate((x1_np, x2_np, x3_np), axis=1)
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x1_ms = Tensor(x1_np)
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x2_ms = Tensor(x2_np)
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x3_ms = Tensor(x3_np)
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output_ms = cat(x1_ms, x2_ms, x3_ms)
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assert (output_ms.asnumpy() == output_np).all()
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class Concat4INet(nn.Cell):
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def __init__(self):
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super(Concat4INet, self).__init__()
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self.cat = P.Concat(axis=1)
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def construct(self, x1, x2, x3, x4):
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return self.cat((x1, x2, x3, x4))
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def concat_4i(nptype):
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cat = Concat4INet()
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x1_np = np.random.randn(32, 4, 224, 224).astype(nptype)
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x2_np = np.random.randn(32, 8, 224, 224).astype(nptype)
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x3_np = np.random.randn(32, 10, 224, 224).astype(nptype)
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x4_np = np.random.randn(32, 5, 224, 224).astype(nptype)
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output_np = np.concatenate((x1_np, x2_np, x3_np, x4_np), axis=1)
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x1_ms = Tensor(x1_np)
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x2_ms = Tensor(x2_np)
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x3_ms = Tensor(x3_np)
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x4_ms = Tensor(x4_np)
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output_ms = cat(x1_ms, x2_ms, x3_ms, x4_ms)
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error = np.ones(shape=output_np.shape) * 10e-6
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diff = output_ms.asnumpy() - output_np
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assert np.all(diff < error)
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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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def test_concat_4i_float32():
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"""
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Feature: concat with 4 inputs
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Description: Concat with 4 inputs of float32 dtype
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Expectation: success
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"""
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concat_4i(np.float32)
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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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def test_concat_4i_int32():
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"""
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Feature: concat with 4 inputs
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Description: Concat with 4 inputs of int32 dtype
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Expectation: success
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"""
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concat_4i(np.int32)
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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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def test_concat_4i_int8():
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"""
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Feature: concat with 4 inputs
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Description: Concat with 4 inputs of int8 dtype
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Expectation: success
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"""
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concat_4i(np.int8)
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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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def test_concat_4i_uint64():
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"""
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Feature: concat with 4 inputs
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Description: Concat with 4 inputs of uint64 dtype
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Expectation: success
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"""
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concat_4i(np.uint64)
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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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def test_concat_4i_bool():
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"""
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Feature: concat with 4 inputs
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Description: Concat with 4 inputs of bool dtype
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Expectation: success
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"""
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cat = Concat4INet()
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x1_np = np.random.choice([True, False], (32, 4, 224, 224)).astype(np.bool)
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x2_np = np.random.choice([True, False], (32, 8, 224, 224)).astype(np.bool)
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x3_np = np.random.choice([True, False], (32, 10, 224, 224)).astype(np.bool)
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x4_np = np.random.choice([True, False], (32, 5, 224, 224)).astype(np.bool)
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output_np = np.concatenate((x1_np, x2_np, x3_np, x4_np), axis=1)
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x1_ms = Tensor(x1_np)
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x2_ms = Tensor(x2_np)
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|
x3_ms = Tensor(x3_np)
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|
x4_ms = Tensor(x4_np)
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|
output_ms = cat(x1_ms, x2_ms, x3_ms, x4_ms)
|
|
|
|
assert (output_ms.asnumpy() == output_np).all()
|
|
|
|
|
|
def vmap_basic():
|
|
def cal(a, b, axis):
|
|
return P.Concat(axis)((a, b))
|
|
|
|
def vmap_cal(a, b, axis):
|
|
result = vmap(cal, in_axes=(0, 0, None))(a, b, axis)
|
|
return result
|
|
|
|
def naive_cal(a, b, axis):
|
|
result = []
|
|
for i in range(a.shape[0]):
|
|
result.append(np.concatenate((a[i], b[i]), axis=axis))
|
|
return np.stack(result)
|
|
|
|
input1 = np.arange(2*3*4).reshape(2, 3, 4).astype(np.float32)
|
|
input2 = np.arange(2*3*4).reshape(2, 3, 4).astype(np.float32)
|
|
axis = 0
|
|
output = vmap_cal(Tensor(input1), Tensor(input2), axis).asnumpy()
|
|
expect = naive_cal(input1, input2, axis)
|
|
assert np.allclose(output, expect)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_concat_vmap():
|
|
"""
|
|
Feature: vmap for concat
|
|
Description: vmap rule for Concat
|
|
Expectation: success
|
|
"""
|
|
context.set_context(mode=context.PYNATIVE_MODE)
|
|
vmap_basic()
|