310 lines
12 KiB
Python
310 lines
12 KiB
Python
# Copyright 2020 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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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.common.api import jit
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from mindspore.common.initializer import initializer
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from mindspore.common.parameter import Parameter
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from mindspore.ops import operations as P
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from mindspore.ops.operations import _inner_ops as inner
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context.set_context(device_target='GPU')
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class Transpose(nn.Cell):
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def __init__(self, nptype):
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super(Transpose, self).__init__()
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self.transpose = P.Transpose()
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self.x_2D = Parameter(initializer(Tensor(np.arange(5 * 6).reshape(5, 6).astype(nptype)), [5, 6]),
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name='x_2D')
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self.perm_2D = (1, 0)
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self.x_3D = Parameter(initializer(Tensor(np.arange(2 * 2 * 4).reshape(2, 2, 4).astype(nptype)), [2, 2, 4]),
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name='x_3D')
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self.perm_3D = (1, 0, 2)
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self.x_4D = Parameter(
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initializer(Tensor(np.arange(2 * 3 * 4 * 5).reshape(2, 3, 4, 5).astype(nptype)), [2, 3, 4, 5]),
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name='x_4D')
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self.perm_4D = (0, 1, 2, 3)
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self.x_5D = Parameter(
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initializer(Tensor(np.arange(1 * 2 * 3 * 4 * 5).reshape(1, 2, 3, 4, 5).astype(nptype)),
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[1, 2, 3, 4, 5]), name='x_5D')
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self.perm_5D = (1, 0, 3, 4, 2)
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@jit
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def construct(self):
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return (self.transpose(self.x_2D, self.perm_2D), self.transpose(self.x_3D, self.perm_3D),
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self.transpose(self.x_4D, self.perm_4D), self.transpose(self.x_5D, self.perm_5D))
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class Transpose_dynamic(nn.Cell):
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def __init__(self, nptype):
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super(Transpose_dynamic, self).__init__()
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self.transpose = P.Transpose()
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self.test_dynamic = inner.GpuConvertToDynamicShape()
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self.x = Parameter(
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initializer(Tensor(np.arange(1 * 2 * 3 * 4 * 5).reshape(1, 2, 3, 4, 5).astype(nptype)),
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[1, 2, 3, 4, 5]), name='5D')
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self.perm = (1, 0, 3, 4, 2)
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@jit
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def construct(self):
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out = self.test_dynamic(self.x)
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return self.transpose(out, self.perm)
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class Transpose_dynamic2(nn.Cell):
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def __init__(self, input_1, input_2, perm_1, perm_2):
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super(Transpose_dynamic2, self).__init__()
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self.transpose = P.Transpose()
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self.test_dynamic = inner.GpuConvertToDynamicShape()
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self.x_1 = input_1
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self.x_2 = input_2
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self.perm_1 = perm_1
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self.perm_2 = perm_2
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@jit
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def construct(self):
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out_1 = self.test_dynamic(self.x_1)
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out_1 = self.transpose(out_1, self.perm_1)
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out_2 = self.test_dynamic(self.x_2)
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out_2 = self.transpose(out_2, self.perm_2)
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return (out_1, out_2)
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def transpose1(nptype):
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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transpose = Transpose(nptype)
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output = transpose()
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expect0 = np.array([[[0, 6, 12, 18, 24],
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[1, 7, 13, 19, 25],
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[2, 8, 14, 20, 26],
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[3, 9, 15, 21, 27],
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[4, 10, 16, 22, 28],
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[5, 11, 17, 23, 29]]]).astype(nptype)
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expect1 = np.array([[[[0, 1, 2, 3],
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[8, 9, 10, 11]],
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[[4, 5, 6, 7],
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[12, 13, 14, 15]]]]).astype(nptype)
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expect2 = np.array([[[[[0, 1, 2, 3, 4],
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[5, 6, 7, 8, 9],
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[10, 11, 12, 13, 14],
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[15, 16, 17, 18, 19]],
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[[20, 21, 22, 23, 24],
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[25, 26, 27, 28, 29],
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[30, 31, 32, 33, 34],
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[35, 36, 37, 38, 39]],
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[[40, 41, 42, 43, 44],
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[45, 46, 47, 48, 49],
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[50, 51, 52, 53, 54],
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[55, 56, 57, 58, 59]]],
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[[[60, 61, 62, 63, 64],
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[65, 66, 67, 68, 69],
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[70, 71, 72, 73, 74],
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[75, 76, 77, 78, 79]],
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[[80, 81, 82, 83, 84],
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[85, 86, 87, 88, 89],
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[90, 91, 92, 93, 94],
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[95, 96, 97, 98, 99]],
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[[100, 101, 102, 103, 104],
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[105, 106, 107, 108, 109],
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[110, 111, 112, 113, 114],
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[115, 116, 117, 118, 119]]]]]).astype(nptype)
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expect3 = np.array([[[[[[0, 20, 40],
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[1, 21, 41],
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[2, 22, 42],
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[3, 23, 43],
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[4, 24, 44]],
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[[5, 25, 45],
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[6, 26, 46],
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[7, 27, 47],
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[8, 28, 48],
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[9, 29, 49]],
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[[10, 30, 50],
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[11, 31, 51],
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[12, 32, 52],
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[13, 33, 53],
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[14, 34, 54]],
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[[15, 35, 55],
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[16, 36, 56],
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[17, 37, 57],
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[18, 38, 58],
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[19, 39, 59]]]],
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[[[[60, 80, 100],
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[61, 81, 101],
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[62, 82, 102],
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[63, 83, 103],
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[64, 84, 104]],
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[[65, 85, 105],
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[66, 86, 106],
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[67, 87, 107],
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[68, 88, 108],
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[69, 89, 109]],
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[[70, 90, 110],
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[71, 91, 111],
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[72, 92, 112],
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[73, 93, 113],
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[74, 94, 114]],
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[[75, 95, 115],
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[76, 96, 116],
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[77, 97, 117],
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[78, 98, 118],
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[79, 99, 119]]]]]]).astype(nptype)
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assert (output[0].asnumpy() == expect0).all()
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assert (output[1].asnumpy() == expect1).all()
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assert (output[2].asnumpy() == expect2).all()
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assert (output[3].asnumpy() == expect3).all()
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def transpose_d(nptype):
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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transpose = Transpose_dynamic(nptype)
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output = transpose()
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expect = np.array([[[[[[0, 20, 40],
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[1, 21, 41],
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[2, 22, 42],
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[3, 23, 43],
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[4, 24, 44]],
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[[5, 25, 45],
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[6, 26, 46],
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[7, 27, 47],
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[8, 28, 48],
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[9, 29, 49]],
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[[10, 30, 50],
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[11, 31, 51],
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[12, 32, 52],
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[13, 33, 53],
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[14, 34, 54]],
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[[15, 35, 55],
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[16, 36, 56],
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[17, 37, 57],
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[18, 38, 58],
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[19, 39, 59]]]],
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[[[[60, 80, 100],
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[61, 81, 101],
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[62, 82, 102],
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[63, 83, 103],
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[64, 84, 104]],
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[[65, 85, 105],
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[66, 86, 106],
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[67, 87, 107],
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[68, 88, 108],
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[69, 89, 109]],
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[[70, 90, 110],
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[71, 91, 111],
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[72, 92, 112],
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[73, 93, 113],
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[74, 94, 114]],
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[[75, 95, 115],
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[76, 96, 116],
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[77, 97, 117],
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[78, 98, 118],
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[79, 99, 119]]]]]]).astype(nptype)
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assert (output.asnumpy() == expect).all()
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def transpose_d2(nptype):
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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input_1 = Parameter(Tensor(np.arange(5 * 6).reshape(5, 6).astype(nptype)),
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name="input_1")
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input_2 = Parameter(Tensor(np.arange(2 * 2 * 4).reshape(2, 2, 4).astype(nptype)),
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name="input_2")
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perm_1 = (1, 0)
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perm_2 = (1, 0, 2)
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expect_1 = np.array([[[0, 6, 12, 18, 24],
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[1, 7, 13, 19, 25],
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[2, 8, 14, 20, 26],
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[3, 9, 15, 21, 27],
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[4, 10, 16, 22, 28],
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[5, 11, 17, 23, 29]]]).astype(nptype)
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expect_2 = np.array([[[[0, 1, 2, 3],
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[8, 9, 10, 11]],
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[[4, 5, 6, 7],
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[12, 13, 14, 15]]]]).astype(nptype)
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net = Transpose_dynamic2(input_1, input_2, perm_1, perm_2)
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output_1, output_2 = net()
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assert (output_1.asnumpy() == expect_1).all()
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assert (output_2.asnumpy() == expect_2).all()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_transpose_float32():
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transpose1(np.float32)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_transpose_float16():
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transpose1(np.float16)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_transpose_int32():
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transpose1(np.int32)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_transpose_int64():
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transpose1(np.int64)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_transpose_dynamic_int64():
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transpose_d(np.int64)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_transpose_dynamic_two_inputs_int64():
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transpose_d2(np.int64)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_transpose_dynamic_float32():
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transpose_d(np.float32)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_transpose_dynamic_float16():
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transpose_d(np.float16)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_transpose_dynamic_int32():
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transpose_d(np.int32)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_transpose_dynamic_two_inputs_float32():
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transpose_d2(np.float32)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_transpose_dynamic_two_inputs_float16():
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transpose_d2(np.float16)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_transpose_dynamic_two_inputs_int32():
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transpose_d2(np.int32)
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