forked from mindspore-Ecosystem/mindspore
259 lines
10 KiB
Python
259 lines
10 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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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 import Tensor
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class Net_Pool(nn.Cell):
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def __init__(self):
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super(Net_Pool, self).__init__()
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self.maxpool_fun = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode="VALID")
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def construct(self, x):
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return self.maxpool_fun(x)
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class Net_Pool2(nn.Cell):
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def __init__(self):
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super(Net_Pool2, self).__init__()
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self.maxpool_fun = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="SAME")
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def construct(self, x):
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return self.maxpool_fun(x)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_maxpool2d():
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x = Tensor(np.array([[[
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[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]
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]]]).astype(np.float32))
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expect_result = (np.array([[[
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[7, 9, -4],
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[19, 21, 23],
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[31, 33, 35]
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]]]))
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expect_result2 = (np.array([[[
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[14, 14, -4],
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[26, 28, 29],
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[32, 34, 35]
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]]]))
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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maxpool2d = Net_Pool()
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maxpool2d2 = Net_Pool2()
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output2 = maxpool2d2(x)
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output = maxpool2d(x)
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assert (output.asnumpy() == expect_result).all()
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assert (output2.asnumpy() == expect_result2).all()
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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maxpool2d = Net_Pool()
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maxpool2d2 = Net_Pool2()
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output2 = maxpool2d2(x)
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output = maxpool2d(x)
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assert (output.asnumpy() == expect_result).all()
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assert (output2.asnumpy() == expect_result2).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_max_pool3d_1():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x_shape = (2, 3, 2, 3, 4)
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kernel_size = (2, 2, 3)
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strides = 1
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pad_mode = 'VALID'
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x_val = np.arange(reduce(lambda x, y: x * y, x_shape))
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x_ms = Tensor(x_val).reshape(x_shape).astype(np.float32)
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output_ms = P.MaxPool3D(kernel_size=kernel_size, strides=strides, pad_mode=pad_mode)(x_ms)
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expert_result = (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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[[[[90, 91],
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[94, 95]]],
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[[[114, 115],
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[118, 119]]],
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[[[138, 139],
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[142, 143]]]]]))
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assert (output_ms.asnumpy() == expert_result).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_max_pool3d_2():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x_shape = (2, 3, 2, 3, 4)
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kernel_size = 2
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strides = 1
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pad_mode = 'VALID'
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x_val = np.arange(reduce(lambda x, y: x * y, x_shape))
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x_ms = Tensor(x_val).reshape(x_shape).astype(np.float32)
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output_ms = P.MaxPool3D(kernel_size=kernel_size, strides=strides, pad_mode=pad_mode)(x_ms)
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expert_result = (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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[[[[89, 90, 91],
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[93, 94, 95]]],
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[[[113, 114, 115],
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[117, 118, 119]]],
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[[[137, 138, 139],
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[141, 142, 143]]]]]))
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assert (output_ms.asnumpy() == expert_result).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_max_pool3d_3():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x_shape = (2, 3, 2, 3, 4)
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kernel_size = 2
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strides = 3
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pad_mode = 'VALID'
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x_val = np.arange(reduce(lambda x, y: x * y, x_shape))
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x_ms = Tensor(x_val).reshape(x_shape).astype(np.float32)
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output_ms = P.MaxPool3D(kernel_size=kernel_size, strides=strides, pad_mode=pad_mode)(x_ms)
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expert_result = (np.array([[[[[17]]],
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[[[41]]],
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[[[65]]]],
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[[[[89]]],
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[[[113]]],
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[[[137]]]]]))
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assert (output_ms.asnumpy() == expert_result).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_max_pool3d_4():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x_shape = (2, 3, 2, 3, 4)
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kernel_size = (2, 2, 3)
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strides = 1
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pad_mode = 'SAME'
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x_val = np.arange(reduce(lambda x, y: x * y, x_shape))
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x_ms = Tensor(x_val).reshape(x_shape).astype(np.float32)
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output_ms = P.MaxPool3D(kernel_size=kernel_size, strides=strides, pad_mode=pad_mode)(x_ms)
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expert_result = (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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[[[[89, 90, 91, 91],
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[93, 94, 95, 95],
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[93, 94, 95, 95]],
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[[89, 90, 91, 91],
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[93, 94, 95, 95],
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[93, 94, 95, 95]]],
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[[[113, 114, 115, 115],
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[117, 118, 119, 119],
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[117, 118, 119, 119]],
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[[113, 114, 115, 115],
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[117, 118, 119, 119],
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[117, 118, 119, 119]]],
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[[[137, 138, 139, 139],
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[141, 142, 143, 143],
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[141, 142, 143, 143]],
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[[137, 138, 139, 139],
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[141, 142, 143, 143],
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[141, 142, 143, 143]]]]]))
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assert (output_ms.asnumpy() == expert_result).all()
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_max_pool3d_5():
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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x_shape = (2, 3, 2, 3, 4)
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kernel_size = (2, 2, 3)
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strides = 1
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pad_mode = 'SAME'
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x_val = np.arange(reduce(lambda x, y: x * y, x_shape))
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x_ms = Tensor(x_val).reshape(x_shape).astype(np.float32)
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output_ms = P.MaxPool3D(kernel_size=kernel_size, strides=strides, pad_mode=pad_mode)(x_ms)
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expert_result = (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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[[[[89, 90, 91, 91],
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[93, 94, 95, 95],
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[93, 94, 95, 95]],
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[[89, 90, 91, 91],
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[93, 94, 95, 95],
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[93, 94, 95, 95]]],
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[[[113, 114, 115, 115],
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[117, 118, 119, 119],
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[117, 118, 119, 119]],
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[[113, 114, 115, 115],
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[117, 118, 119, 119],
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[117, 118, 119, 119]]],
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[[[137, 138, 139, 139],
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[141, 142, 143, 143],
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[141, 142, 143, 143]],
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[[137, 138, 139, 139],
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[141, 142, 143, 143],
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[141, 142, 143, 143]]]]]))
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assert (output_ms.asnumpy() == expert_result).all()
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