forked from mindspore-Ecosystem/mindspore
336 lines
10 KiB
Python
336 lines
10 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 sys
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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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import mindspore.common.dtype as mstype
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from mindspore.ops import operations as P
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from mindspore.ops.operations import _grad_ops as G
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from mindspore.ops.composite import GradOperation
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context.set_context(mode=context.GRAPH_MODE,
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device_target="CPU")
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class TileNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.tile = P.Tile()
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def construct(self, x, multiples):
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out = self.tile(x, multiples)
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return out
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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_tile_multiple_tensor_cpu():
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"""
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/// Feature: Tile op dynamic shape
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/// Description: Tile forward with dynamic shape
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/// Expectation: Euqal to expected value
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"""
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if sys.platform != 'linux':
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return
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multiples_1 = Tensor(np.array([2, 1]), mstype.int64)
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multiples_2 = Tensor(np.array([4, 1]), mstype.int64)
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x = Tensor(np.array([[1, 2, 3, 4]]), mstype.float32)
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tile_net = TileNet()
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expect_1 = np.array([[1., 2., 3., 4.],
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[1., 2., 3., 4.]])
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expect_2 = np.array([[1., 2., 3., 4.],
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[1., 2., 3., 4.],
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[1., 2., 3., 4.],
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[1., 2., 3., 4.]])
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expect = [expect_1, expect_2]
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for i, multiples in enumerate([multiples_1, multiples_2]):
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output = tile_net(x, multiples)
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assert (output.asnumpy() == expect[i]).all()
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class GradTile(nn.Cell):
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def __init__(self, network):
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super().__init__()
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self.grad = GradOperation(sens_param=True)
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self.network = network
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self.unique = P.Unique()
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self.reshape = P.Reshape()
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def construct(self, input_x, multiples, grad):
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dy = self.unique(grad)[0]
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dy = self.reshape(dy, (2, 4))
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return self.grad(self.network)(input_x, multiples, dy)
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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_tile_multiple_tensor_grad_cpu():
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"""
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/// Feature: Tile op dynamic shape
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/// Description: Tile backward with dynamic shape
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/// Expectation: Euqal to expected value
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"""
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if sys.platform != 'linux':
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return
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multiples = Tensor(np.array([2, 1]), mstype.int64)
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x0 = Tensor(np.array([[1, 2, 3, 4]]), mstype.float32)
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tile_net = GradTile(TileNet())
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dout = Tensor(np.arange(1, 9), mstype.float32)
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output = tile_net(x0, multiples, dout)
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expect = np.array([[6., 8., 10., 12.]])
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assert (output.asnumpy() == expect).all()
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class ConcatOffsetNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.unique = P.Unique()
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self.concat_offset = G.ConcatOffset(3, 0)
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self.reshape = P.Reshape()
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def construct(self, x, y, z):
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x = self.reshape(self.unique(x)[0], (-1, 1, 2, 1))
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y = self.reshape(self.unique(y)[0], (-1, 1, 2, 1))
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z = self.reshape(self.unique(z)[0], (-1, 1, 2, 1))
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out = self.concat_offset((x, y, z))
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return out
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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_offset_dynamic_cpu():
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"""
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/// Feature: Concatoffset op dynamic shape
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/// Description: Concatoffset forward with dynamic shape
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/// Expectation: Euqal to expected value
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"""
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if sys.platform != 'linux':
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return
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x = Tensor(np.array([1, 2, 3, 4, 5, 6]), mstype.float32)
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x2 = Tensor(np.array([1, 2, 3, 4, 5, 6]), mstype.float32)
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x3 = Tensor(np.array([1, 2, 3, 4, 5, 6]), mstype.float32)
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net = ConcatOffsetNet()
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out = net(x, x2, x3)
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expect = np.array([[0, 0, 0, 0],
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[3, 0, 0, 0],
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[6, 0, 0, 0]])
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if isinstance(out, tuple):
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assert (np.array(out) == expect).all()
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else:
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assert (out.asnumpy() == expect).all()
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class ConcatNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.unique = P.Unique()
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self.concat = P.Concat()
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self.reshape = P.Reshape()
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def construct(self, x, y, z, shape_tensor):
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x = self.reshape(x, shape_tensor)
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y = self.reshape(y, shape_tensor)
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z = self.reshape(z, shape_tensor)
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out = self.concat((x, y, z))
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return out
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class GradConcat(nn.Cell):
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def __init__(self, network):
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super().__init__()
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self.grad = GradOperation(sens_param=True)
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self.network = network
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self.unique = P.Unique()
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self.reshape = P.Reshape()
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def construct(self, x, y, z, shape, grad):
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# grad = self.reshape(grad, (-1,))
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dy = self.reshape(self.unique(grad)[0], (-1, 1, 2, 1))
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return self.grad(self.network)(x, y, z, shape, dy)
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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_dynamic_grad_cpu():
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"""
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/// Feature: Concat op dynamic shape
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/// Description: Concat backward with dynamic shape
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/// Expectation: Euqal to expected value
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"""
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if sys.platform != 'linux':
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return
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x = Tensor(np.array([1, 2, 3, 4, 5, 6]), mstype.float32)
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x2 = Tensor(np.array([1, 2, 3, 4, 5, 6]), mstype.float32)
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x3 = Tensor(np.array([1, 2, 3, 4, 5, 6]), mstype.float32)
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shape = Tensor(np.array([3, 1, 2, 1]), mstype.int64)
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dout = Tensor(np.arange(1, 19), mstype.float32)
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net = GradConcat(ConcatNet())
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output = net(x, x2, x3, shape, dout)
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expect = np.array([1., 2., 3., 4., 5., 6.])
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assert (output.asnumpy() == expect).all()
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class SliceNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.slice = P.Slice()
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def construct(self, x, begin, size):
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return self.slice(x, begin, size)
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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_slice_begin_size_tensor_cpu():
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"""
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/// Feature: Slice op dynamic shape
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/// Description: Slice forward with dynamic shape
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/// Expectation: Euqal to expected value
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"""
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if sys.platform != 'linux':
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return
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x = Tensor(
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np.array([[[1, -1, 1], [2, -2, 2]], [[3, -3, 3], [4, -4, 4]], [[5, -5, 5], [6, -6, 6]]]), mstype.float32)
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begin = Tensor(
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np.array([0, 1, 0]), mstype.int64)
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size = Tensor(
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np.array([2, 1, 2]), mstype.int64)
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slice_net = SliceNet()
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output = slice_net(x, begin, size)
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expect = np.array([[[2., -2.]],
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[[4., -4.]]])
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assert (output.asnumpy() == expect).all()
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class GradSlice(nn.Cell):
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def __init__(self, network):
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super().__init__()
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self.grad = GradOperation(sens_param=True)
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self.network = network
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self.unique = P.Unique()
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self.reshape = P.Reshape()
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def construct(self, input_x, begin, size, grad):
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# grad = self.reshape(grad, (-1,))
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dy = self.unique(grad)[0]
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dy = self.reshape(dy, size)
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return self.grad(self.network)(input_x, begin, size, dy)
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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_slice_begin_size_tensor_grad():
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"""
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/// Feature: Slice op dynamic shape
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/// Description: Slice backward with dynamic shape
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/// Expectation: Euqal to expected value
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"""
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if sys.platform != 'linux':
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return
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dy = Tensor(np.array([1, 2, 3, 4]), mstype.float32)
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x = Tensor(
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np.array([[[1, -1, 1], [2, -2, 2]], [[3, -3, 3], [4, -4, 4]], [[5, -5, 5], [6, -6, 6]]]), mstype.float32)
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begin = Tensor(
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np.array([0, 1, 0]), mstype.int64)
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size = Tensor(
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np.array([2, 1, 2]), mstype.int64)
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net = GradSlice(SliceNet())
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output = net(x, begin, size, dy)
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expect = np.array([[[0., 0., 0.],
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[1., 2., 0.]],
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[[0., 0., 0.],
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[3., 4., 0.]],
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[[0., 0., 0.],
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[0., 0., 0.]]])
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assert (output.asnumpy() == expect).all()
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class ReduceMeanNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.reduce_mean = P.ReduceMean(keep_dims=True)
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self.reshape = P.Reshape()
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self.tile = P.Tile()
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def construct(self, x, shape):
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y = self.reshape(x, shape)
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return self.reduce_mean(y, 0)
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class GradReduceMean(nn.Cell):
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def __init__(self, network):
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super().__init__()
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self.grad = GradOperation(get_all=True, sens_param=True)
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self.network = network
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self.reshape = P.Reshape()
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self.unique = P.Unique()
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def construct(self, input_x, shape, grad):
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grad = self.reshape(self.unique(grad)[0], (1, 2))
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return self.grad(self.network)(input_x, shape, grad)
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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_reducemean_dynamic_cpu():
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"""
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/// Feature: ReduceMean op dynamic shape
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/// Description: ReduceMean forward with dynamic shape
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/// Expectation: Euqal to expected value
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"""
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if sys.platform != 'linux':
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return
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x = Tensor(np.array([10, 10, 2, 2]), mstype.float32)
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x2 = Tensor(np.array([2, 2]), mstype.int64)
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reduce_mean = ReduceMeanNet()
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out = reduce_mean(x, x2)
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expect = np.array([[6., 6.]])
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assert (out.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_reducemean_dynamic_grad_cpu():
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"""
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/// Feature: ReduceMean op dynamic shape
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/// Description: ReduceMean backward with dynamic shape
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/// Expectation: Euqal to expected value
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"""
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if sys.platform != 'linux':
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return
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x = Tensor(np.array([10, 10, 2, 2]), mstype.float32)
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x2 = Tensor(np.array([2, 2]), mstype.int64)
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dout = Tensor(np.array([1, 3]), mstype.float32)
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reduce_mean = GradReduceMean(ReduceMeanNet())
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out = reduce_mean(x, x2, dout)
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expect = np.array([[0.5, 1.5, 0.5, 1.5]])
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assert (out[0].asnumpy() == expect).all()
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