forked from mindspore-Ecosystem/mindspore
141 lines
5.8 KiB
Python
141 lines
5.8 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 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 as ms
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from mindspore import Tensor
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from mindspore.ops import operations as P
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from mindspore.ops.functional import vmap
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class NetConv3dTranspose(nn.Cell):
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def __init__(self):
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super(NetConv3dTranspose, self).__init__()
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in_channel = 2
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out_channel = 2
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kernel_size = 2
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self.conv_trans = P.Conv3DTranspose(in_channel, out_channel,
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kernel_size,
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pad_mode="pad",
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pad=1,
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stride=1,
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dilation=1,
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group=1)
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def construct(self, x, w):
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return self.conv_trans(x, w)
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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_conv3dtranspose_dshape_1():
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"""
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Feature: Test conv3dtranspose dynamic shape.
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Description: Test conv3dtranspose dynamic shape.
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Expectation: Success.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = NetConv3dTranspose()
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input_x_dyn = Tensor(shape=[1, 2, 3, 3, None], dtype=ms.float32)
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input_w_dyn = Tensor(shape=[2, 2, 2, 2, None], dtype=ms.float32)
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net.set_inputs(input_x_dyn, input_w_dyn)
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x = Tensor(np.arange(1 * 2 * 3 * 3 * 3).reshape(1, 2, 3, 3, 3).astype(np.float32))
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w = Tensor(np.arange(2 * 2 * 2 * 2 * 2).reshape(2, 2, 2, 2, 2).astype(np.float32))
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output = net(x, w)
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expect_shape = (1, 2, 2, 2, 2)
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assert output.asnumpy().shape == expect_shape
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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_conv3dtranspose_dshape_2():
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"""
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Feature: Test conv3dtranspose dynamic shape.
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Description: Test conv3dtranspose dynamic shape.
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Expectation: Success.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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net = NetConv3dTranspose()
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input_x_dyn = Tensor(shape=[None, 2, 3, 3, 3], dtype=ms.float32)
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input_w_dyn = Tensor(shape=[None, 2, 2, 2, 2], dtype=ms.float32)
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net.set_inputs(input_x_dyn, input_w_dyn)
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x = Tensor(np.arange(1 * 2 * 3 * 3 * 3).reshape(1, 2, 3, 3, 3).astype(np.float32))
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w = Tensor(np.arange(2 * 2 * 2 * 2 * 2).reshape(2, 2, 2, 2, 2).astype(np.float32))
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output = net(x, w)
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expect_shape = (1, 2, 2, 2, 2)
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assert output.asnumpy().shape == expect_shape
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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_conv3d_transpose():
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x = Tensor(np.arange(1 * 2 * 3 * 3 * 3).reshape(1, 2, 3, 3, 3).astype(np.float32))
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w = Tensor(np.ones((2, 2, 2, 2, 2)).astype(np.float32))
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expect = np.array([[[[[320., 336.],
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[368., 384.]],
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[[464., 480.],
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[512., 528.]]],
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[[[320., 336.],
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[368., 384.]],
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[[464., 480.],
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[512., 528.]]]]]).astype(np.float32)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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conv3dtranspose = NetConv3dTranspose()
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output = conv3dtranspose(x, w)
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assert (output.asnumpy() == expect).all()
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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conv3dtranspose = NetConv3dTranspose()
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output = conv3dtranspose(x, w)
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assert (output.asnumpy() == expect).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_conv3d_transpose_vmap():
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"""
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Feature: Conv3DTranspose op
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Description: Test vmap rule for Conv3DTranspose op
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Expectation: The dataset is processed as expected
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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conv3d_trans = NetConv3dTranspose()
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batch_dout = Tensor(np.arange(2 * 1 * 2 * 3 * 3 * 3).reshape(2, 1, 2, 3, 3, 3).astype(np.float32))
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weight = Tensor(np.ones([2, 2, 2, 2, 2]).astype(np.float32))
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expected1 = np.array([[[[[[320., 336.], [368., 384.]], [[464., 480.], [512., 528.]]],
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[[[320., 336.], [368., 384.]], [[464., 480.], [512., 528.]]]]],
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[[[[[1184., 1200.], [1232., 1248.]], [[1328., 1344.], [1376., 1392.]]],
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[[[1184., 1200.], [1232., 1248.]], [[1328., 1344.], [1376., 1392.]]]]]]).astype(np.float32)
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output1 = vmap(conv3d_trans, (0, None))(batch_dout, weight)
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assert np.allclose(output1.asnumpy(), expected1, 0.0001, 0.0001)
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dout = Tensor(np.arange(1 * 2 * 3 * 3 * 3).reshape(1, 2, 3, 3, 3).astype(np.float32))
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batch_weight = Tensor(np.ones([2, 2, 2, 2, 2, 2]).astype(np.float32))
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expected2 = np.array([[[[[[320., 336.], [368., 384.]], [[464., 480.], [512., 528.]]],
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[[[320., 336.], [368., 384.]], [[464., 480.], [512., 528.]]]]],
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[[[[[320., 336.], [368., 384.]], [[464., 480.], [512., 528.]]],
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[[[320., 336.], [368., 384.]], [[464., 480.], [512., 528.]]]]]]).astype(np.float32)
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output2 = vmap(conv3d_trans, (None, 0))(dout, batch_weight)
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assert np.allclose(output2.asnumpy(), expected2, 0.0001, 0.0001)
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