forked from mindspore-Ecosystem/mindspore
306 lines
12 KiB
Python
306 lines
12 KiB
Python
# Copyright 2019 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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from functools import reduce
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import numpy as np
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import pytest
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from mindspore import Tensor
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import mindspore.context as context
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import mindspore.nn as nn
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import mindspore.ops.operations as P
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from mindspore.ops import composite as C
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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class MaxPool(nn.Cell):
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def __init__(self, dim, kernel_size, strides, pad_mode):
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super(MaxPool, self).__init__()
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if dim == 2:
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self.maxpool = P.MaxPool(kernel_size=kernel_size, strides=strides, pad_mode=pad_mode)
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else:
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self.maxpool = P.MaxPool3D(kernel_size=kernel_size, strides=strides, pad_mode=pad_mode)
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def construct(self, x):
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return self.maxpool(x)
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class MaxPoolGrad(nn.Cell):
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def __init__(self, forward):
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super(MaxPoolGrad, self).__init__()
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self.forward = forward
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self.grad = C.GradOperation(get_all=True, sens_param=True)
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def construct(self, x, sens):
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return self.grad(self.forward)(x, sens)
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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_maxpool2d_valid():
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"""
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Feature: test maxpool2d op.
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Description: including forward and backward.
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Expectation: expect correct forward and backward result.
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"""
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x = Tensor(np.array([[[[10, 1, 2, 3, -4, -5],
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[6, 7, 8, 9, -10, -11],
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[12, 13, 24, -15, -16, -17],
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[18, 19, 20, 21, 22, 23],
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[32, 25, 26, 27, 28, 40],
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[30, 31, 35, 33, 34, 35]]]]).astype(np.float32))
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maxpool = MaxPool(dim=2, kernel_size=2, strides=2, pad_mode="VALID")
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actual_output = maxpool(x)
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expect_output = np.array([[[[10, 9, -4],
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[19, 24, 23],
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[32, 35, 40]]]]).astype(np.float32)
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assert (actual_output.asnumpy() == expect_output).all()
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maxpool_grad = MaxPoolGrad(maxpool)
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sens = Tensor(np.arange(1, 10).reshape(actual_output.shape).astype(np.float32))
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actual_grad = maxpool_grad(x, sens)
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expect_dx = np.array([[[[1, 0, 0, 0, 3, 0],
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[0, 0, 0, 2, 0, 0],
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[0, 0, 5, 0, 0, 0],
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[0, 4, 0, 0, 0, 6],
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[7, 0, 0, 0, 0, 9],
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[0, 0, 8, 0, 0, 0]]]]).astype(np.float32)
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assert (actual_grad[0].asnumpy() == expect_dx).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_maxpool2d_same():
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"""
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Feature: test maxpool2d op.
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Description: including forward and backward.
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Expectation: expect correct forward and backward result.
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"""
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x = Tensor(np.array([[[[0, 1, 2, 3, -4, -5],
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[6, 7, 8, 9, -10, -11],
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[12, 13, 14, -15, -16, -17],
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[18, 19, 20, 21, 22, 23],
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[24, 25, 26, 27, 28, 29],
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[30, 31, 32, 33, 34, 35]]]]).astype(np.float32))
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maxpool = MaxPool(dim=2, kernel_size=3, strides=2, pad_mode="SAME")
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actual_output = maxpool(x)
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expect_output = np.array([[[[14, 14, -4],
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[26, 28, 29],
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[32, 34, 35]]]])
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assert (actual_output.asnumpy() == expect_output).all()
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maxpool_grad = MaxPoolGrad(maxpool)
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sens = Tensor(np.arange(1, 10).reshape(actual_output.shape).astype(np.float32))
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actual_grad = maxpool_grad(x, sens)
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expect_dx = np.array([[[[0, 0, 0, 0, 3, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 3, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 4, 0, 5, 6],
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[0, 0, 7, 0, 8, 9]]]]).astype(np.float32)
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assert (actual_grad[0].asnumpy() == expect_dx).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_maxpool3d_1():
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"""
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Feature: test maxpool3d op.
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Description: including forward and backward.
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Expectation: expect correct forward and backward result.
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"""
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x_shape = (1, 3, 2, 3, 4)
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x = Tensor(np.arange(reduce(lambda x, y: x * y, x_shape))).reshape(x_shape).astype(np.float32)
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maxpool = MaxPool(dim=3, kernel_size=(2, 2, 3), strides=1, pad_mode='VALID')
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actual_output = maxpool(x)
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expect_output = np.array([[[[[18, 19],
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[22, 23]]],
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[[[42, 43],
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[46, 47]]],
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[[[66, 67],
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[70, 71]]]]])
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assert (actual_output.asnumpy() == expect_output).all()
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maxpool_grad = MaxPoolGrad(maxpool)
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sens = actual_output + 1
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actual_grad = maxpool_grad(x, sens)
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expect_dx = np.array([[[[[0, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 0, 0]],
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[[0, 0, 0, 0],
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[0, 0, 19, 20],
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[0, 0, 23, 24]]],
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[[[0, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 0, 0]],
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[[0, 0, 0, 0],
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[0, 0, 43, 44],
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[0, 0, 47, 48]]],
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[[[0, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 0, 0]],
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[[0, 0, 0, 0],
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[0, 0, 67, 68],
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[0, 0, 71, 72]]]]]).astype(np.float32)
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assert (actual_grad[0].asnumpy() == expect_dx).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_maxpool3d_2():
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"""
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Feature: test maxpool3d op.
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Description: including forward and backward.
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Expectation: expect correct forward and backward result.
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"""
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x_shape = (1, 3, 2, 3, 4)
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x = Tensor(np.arange(reduce(lambda x, y: x * y, x_shape))).reshape(x_shape).astype(np.float32)
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maxpool = MaxPool(dim=3, kernel_size=2, strides=1, pad_mode='VALID')
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actual_output = maxpool(x)
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expect_output = np.array([[[[[17, 18, 19],
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[21, 22, 23]]],
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[[[41, 42, 43],
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[45, 46, 47]]],
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[[[65, 66, 67],
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[69, 70, 71]]]]])
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assert (actual_output.asnumpy() == expect_output).all()
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maxpool_grad = MaxPoolGrad(maxpool)
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sens = actual_output + 1
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actual_grad = maxpool_grad(x, sens)
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expect_dx = np.array([[[[[0, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 0, 0]],
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[[0, 0, 0, 0],
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[0, 18, 19, 20],
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[0, 22, 23, 24]]],
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[[[0, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 0, 0]],
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[[0, 0, 0, 0],
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[0, 42, 43, 44],
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[0, 46, 47, 48]]],
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[[[0, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 0, 0]],
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[[0, 0, 0, 0],
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[0, 66, 67, 68],
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[0, 70, 71, 72]]]]]).astype(np.float32)
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assert (actual_grad[0].asnumpy() == expect_dx).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_maxpool3d_3():
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"""
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Feature: test maxpool3d op.
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Description: including forward and backward.
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Expectation: expect correct forward and backward result.
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"""
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x_shape = (1, 3, 2, 3, 4)
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x = Tensor(np.arange(reduce(lambda x, y: x * y, x_shape))).reshape(x_shape).astype(np.float32)
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maxpool = MaxPool(dim=3, kernel_size=2, strides=3, pad_mode='VALID')
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actual_output = maxpool(x)
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expect_output = np.array([[[[[17]]],
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[[[41]]],
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[[[65]]]]])
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assert (actual_output.asnumpy() == expect_output).all()
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maxpool_grad = MaxPoolGrad(maxpool)
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sens = actual_output + 1
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actual_grad = maxpool_grad(x, sens)
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expect_dx = np.array([[[[[0, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 0, 0]],
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[[0, 0, 0, 0],
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[0, 18, 0, 0],
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[0, 0, 0, 0]]],
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[[[0, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 0, 0]],
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[[0, 0, 0, 0],
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[0, 42, 0, 0],
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[0, 0, 0, 0]]],
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[[[0, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 0, 0]],
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[[0, 0, 0, 0],
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[0, 66, 0, 0],
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[0, 0, 0, 0]]]]]).astype(np.float32)
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assert (actual_grad[0].asnumpy() == expect_dx).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_maxpool3d_4():
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"""
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Feature: test maxpool3d op.
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Description: including forward and backward.
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Expectation: expect correct forward and backward result.
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"""
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x_shape = (1, 3, 2, 3, 4)
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x = Tensor(np.arange(reduce(lambda x, y: x * y, x_shape))).reshape(x_shape).astype(np.float32)
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maxpool = MaxPool(dim=3, kernel_size=(2, 2, 3), strides=1, pad_mode='SAME')
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actual_output = maxpool(x)
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expect_output = np.array([[[[[17, 18, 19, 19],
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[21, 22, 23, 23],
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[21, 22, 23, 23]],
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[[17, 18, 19, 19],
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[21, 22, 23, 23],
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[21, 22, 23, 23]]],
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[[[41, 42, 43, 43],
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[45, 46, 47, 47],
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[45, 46, 47, 47]],
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[[41, 42, 43, 43],
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[45, 46, 47, 47],
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[45, 46, 47, 47]]],
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[[[65, 66, 67, 67],
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[69, 70, 71, 71],
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[69, 70, 71, 71]],
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[[65, 66, 67, 67],
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[69, 70, 71, 71],
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[69, 70, 71, 71]]]]])
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assert (actual_output.asnumpy() == expect_output).all()
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maxpool_grad = MaxPoolGrad(maxpool)
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sens = actual_output + 1
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actual_grad = maxpool_grad(x, sens)
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expect_dx = np.array([[[[[0, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 0, 0]],
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[[0, 0, 0, 0],
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[0, 36, 38, 80],
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[0, 88, 92, 192]]],
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[[[0, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 0, 0]],
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[[0, 0, 0, 0],
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[0, 84, 86, 176],
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[0, 184, 188, 384]]],
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[[[0, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 0, 0]],
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[[0, 0, 0, 0],
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[0, 132, 134, 272],
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[0, 280, 284, 576]]]]]).astype(np.float32)
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assert (actual_grad[0].asnumpy() == expect_dx).all()
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