forked from mindspore-Ecosystem/mindspore
390 lines
17 KiB
Python
390 lines
17 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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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.initializer import initializer
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from mindspore.common.parameter import Parameter
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from mindspore.common.parameter import ParameterTuple
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from mindspore.ops import 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 NetConv2d(nn.Cell):
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def __init__(self):
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super(NetConv2d, self).__init__()
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out_channel = 2
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kernel_size = 1
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self.conv = P.Conv2D(out_channel,
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kernel_size,
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mode=1,
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pad_mode="valid",
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pad=0,
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stride=1,
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dilation=1,
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group=1)
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self.w = Parameter(initializer(
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Tensor(np.arange(2 * 3 * 1 * 1).reshape(2, 3, 1, 1).astype(np.float32)), [2, 3, 1, 1]), name='w')
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self.x = Parameter(initializer(
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Tensor(np.arange(1 * 3 * 3 * 3).reshape(1, 3, 3, 3).astype(np.float32)), [1, 3, 3, 3]), name='x')
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def construct(self):
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return self.conv(self.x, self.w)
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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_conv2d():
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conv2d = NetConv2d()
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output = conv2d()
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expect = np.array([[[[45, 48, 51],
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[54, 57, 60],
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[63, 66, 69]],
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[[126, 138, 150],
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[162, 174, 186],
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[198, 210, 222]]]]).astype(np.float32)
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assert (output.asnumpy() == expect).all()
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class NetConv(nn.Cell):
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def __init__(self, weight, x):
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super(NetConv, self).__init__()
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self.conv = nn.Conv2d(in_channels=3,
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out_channels=3,
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kernel_size=(5, 3),
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stride=2,
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pad_mode='same',
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padding=(0, 0, 0, 0),
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dilation=(1, 1),
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group=1,
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has_bias=False,
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weight_init=Tensor(weight)
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)
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self.x = Parameter(initializer(Tensor(x), [1, 3, 4, 2]), name="x")
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def construct(self):
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return self.conv(self.x)
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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_conv():
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weight = np.array([[[[0.38968208, 0.14398979, 0.7962463],
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[-2.1836321, -0.63823014, -0.50588065],
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[0.6660469, 0.64673275, -0.13160042],
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[1.3683757, 1.4005762, -0.37235805],
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[-0.22638111, 0.45427424, -0.10293389]],
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[[1.4985064, -0.29318333, -0.92694616],
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[1.539068, 0.8937254, -1.2598171],
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[0.9658142, -0.63945454, -0.23185322],
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[1.363089, -0.41694695, -2.2750475],
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[-0.4865508, -1.6938025, 0.609849]],
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[[1.1844803, 0.99874926, -1.9475793],
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[0.4987858, 0.5307887, -0.04226681],
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[0.4529779, -1.1960793, 0.9456575],
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[3.133675, 0.2309789, -0.29201075],
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[-0.59632736, -0.0789804, -0.69486314]]],
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[[[-0.5606142, 0.6420862, 0.2478745],
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[0.02717604, 1.5483379, -0.9373383],
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[-1.1017276, -0.259478, 1.0311872],
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[1.8387799, 0.16468556, 0.33392152],
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[-1.8781787, 1.0158662, 1.6527579]],
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[[0.45696944, -0.5652523, -1.5618048],
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[-0.30304828, 0.1331878, -0.36955845],
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[0.91655576, 0.66612357, 0.3068175],
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[-0.45732066, 0.8923335, 1.0542952],
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[-0.73519516, 1.0518405, -1.0273266]],
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[[-0.79712886, -0.26814285, 0.12779616],
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[1.0367643, -1.6180774, 0.42999932],
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[-0.81818223, -0.81502074, 0.882194],
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[0.53640485, 0.4178927, 1.6037121],
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[0.9256354, -1.1006796, 0.16614541]]],
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[[[-1.5216796, -1.2473261, 0.6549515],
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[0.63627815, 0.7221449, 0.02977821],
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[-0.61331123, -0.49451825, 0.33852202],
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[1.4510741, -1.3818305, -0.791747],
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[0.6989747, 0.49558765, 1.0813237]],
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[[-0.03969796, 0.71586496, 0.8326594],
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[-0.15443641, 1.0389746, -0.59301984],
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[0.7197836, 0.03257621, 1.8398637],
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[0.6111736, -0.16166899, -2.4869773],
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[1.3066711, -1.8003578, 0.17412892]],
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[[-0.31470737, -0.5938182, -1.1311078],
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[-0.99081016, 0.4005125, 0.44154453],
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[1.0876914, -2.5958562, -0.5914863],
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[1.3759689, -0.7741513, 0.19928917],
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[1.6792973, 2.2744863, -0.04308867]]]]).astype(np.float32)
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x = np.array([[[[-1.4311737, 1.015344],
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[0.04431088, -2.2886624],
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[1.4832113, 1.240908],
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[0.67040104, 0.15266363]],
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[[0.44226435, 1.1461105],
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[1.194218, 1.5547837],
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[0.23152256, 1.5911953],
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[0.11206784, 0.17978816]],
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[[-0.57803905, 0.8039611],
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[0.0823025, -0.6134477],
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[-1.4171146, 1.6269946],
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[0.48878875, 0.9117505]]]]).astype(np.float32)
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conv2d = NetConv(weight, x)
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output = conv2d()
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expected = np.array([[[[2.3498724],
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[-1.9199573]],
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[[5.376562],
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[-5.425745]],
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[[5.9105043],
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[7.469034]]]]).astype(np.float32)
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loss = np.abs(expected - output.asnumpy())
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error = 1e-4 * np.ones(loss.shape)
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assert (loss < error).all()
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class NetConv3d(nn.Cell):
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def __init__(self, mode, pad_mode, pad):
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super(NetConv3d, self).__init__()
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out_channel = 4
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kernel_size = 2
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self.conv = P.Conv3D(out_channel,
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kernel_size,
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mode=mode,
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pad_mode=pad_mode,
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pad=pad,
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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(x, w)
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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_conv3d():
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x = Tensor(np.arange(1 * 3 * 3 * 3 * 3).reshape(1, 3, 3, 3, 3).astype(np.float32))
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w = Tensor(np.arange(4 * 3 * 2 * 2 * 2).reshape(4, 3, 2, 2, 2).astype(np.float32))
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expect = np.array([[[[[12960., 13236.],
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[13788., 14064.]],
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[[15444., 15720.],
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[16272., 16548.]]],
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[[[32256., 33108.],
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[34812., 35664.]],
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[[39924., 40776.],
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[42480., 43332.]]],
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[[[51552., 52980.],
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[55836., 57264.]],
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[[64404., 65832.],
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[68688., 70116.]]],
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[[[70848., 72852.],
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[76860., 78864.]],
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[[88884., 90888.],
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[94896., 96900.]]]]]).astype(np.float32)
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mode = 1
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pad_mode = "valid"
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pad = 0
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net = NetConv3d(mode, pad_mode, pad)
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output = net(x, w)
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assert (output.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_conv3d_2():
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x = Tensor(np.arange(1 * 3 * 3 * 3 * 3).reshape(1, 3, 3, 3, 3).astype(np.float32))
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w = Tensor(np.arange(4 * 3 * 2 * 2 * 2).reshape(4, 3, 2, 2, 2).astype(np.float32))
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expect = np.array([[[[[1647, 3258, 3345, 1650],
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[3267, 6447, 6609, 3252],
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[3519, 6933, 7095, 3486],
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[1719, 3378, 3453, 1692]],
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[[3375, 6639, 6789, 3330],
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[6606, 12960, 13236, 6474],
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[7038, 13788, 14064, 6870],
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[3393, 6627, 6753, 3288]],
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[[4077, 7989, 8139, 3978],
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[7902, 15444, 15720, 7662],
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[8334, 16272, 16548, 8058],
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[3987, 7761, 7887, 3828]],
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[[1917, 3732, 3795, 1842],
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[3663, 7107, 7221, 3492],
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[3843, 7449, 7563, 3654],
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[1809, 3492, 3543, 1704]]],
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[[[3591, 7218, 7449, 3738],
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[7371, 14799, 15249, 7644],
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[8055, 16149, 16599, 8310],
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[4095, 8202, 8421, 4212]],
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[[7911, 15855, 16293, 8154],
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[16110, 32256, 33108, 16554],
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[17406, 34812, 35664, 17814],
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[8793, 17571, 17985, 8976]],
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[[9909, 19797, 20235, 10098],
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[19998, 39924, 40776, 20334],
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[21294, 42480, 43332, 21594],
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[10683, 21297, 21711, 10812]],
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[[5157, 10284, 10491, 5226],
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[10359, 20643, 21045, 10476],
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[10971, 21849, 22251, 11070],
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[5481, 10908, 11103, 5520]]],
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[[[5535, 11178, 11553, 5826],
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[11475, 23151, 23889, 12036],
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[12591, 25365, 26103, 13134],
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[6471, 13026, 13389, 6732]],
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[[12447, 25071, 25797, 12978],
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[25614, 51552, 52980, 26634],
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[27774, 55836, 57264, 28758],
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[14193, 28515, 29217, 14664]],
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[[15741, 31605, 32331, 16218],
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[32094, 64404, 65832, 33006],
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[34254, 68688, 70116, 35130],
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[17379, 34833, 35535, 17796]],
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[[8397, 16836, 17187, 8610],
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[17055, 34179, 34869, 17460],
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[18099, 36249, 36939, 18486],
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[9153, 18324, 18663, 9336]]],
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[[[7479, 15138, 15657, 7914],
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[15579, 31503, 32529, 16428],
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[17127, 34581, 35607, 17958],
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[8847, 17850, 18357, 9252]],
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[[16983, 34287, 35301, 17802],
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[35118, 70848, 72852, 36714],
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[38142, 76860, 78864, 39702],
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[19593, 39459, 40449, 20352]],
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[[21573, 43413, 44427, 22338],
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[44190, 88884, 90888, 45678],
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[47214, 94896, 96900, 48666],
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[24075, 48369, 49359, 24780]],
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[[11637, 23388, 23883, 11994],
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[23751, 47715, 48693, 24444],
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[25227, 50649, 51627, 25902],
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[12825, 25740, 26223, 13152]]]]]).astype(np.float32)
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mode = 1
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pad_mode = "pad"
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pad = (1, 1, 1, 1, 1, 1)
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net = NetConv3d(mode, pad_mode, pad)
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output = net(x, w)
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assert (output.asnumpy() == expect).all()
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class Conv3dNet(nn.Cell):
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def __init__(self, in_channels, out_channels, kernel_size, pad_mode='pad', padding=0, stride=1, dilation=1,
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has_bias=False, weight_init='normal'):
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super(Conv3dNet, self).__init__()
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self.cv1 = nn.Conv3d(in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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pad_mode=pad_mode,
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padding=padding,
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stride=stride,
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dilation=dilation,
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group=1,
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has_bias=has_bias,
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weight_init=weight_init,
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data_format='NCDHW')
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def construct(self, x):
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x = self.cv1(x)
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return x
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class GradNet(nn.Cell):
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def __init__(self, network):
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super(GradNet, self).__init__()
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self.grad = C.GradOperation(get_all=True, sens_param=True, get_by_list=True)
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self.network = network
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self.params = ParameterTuple(network.trainable_params())
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def construct(self, x, dy):
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grad_op = self.grad(self.network, self.params)
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output = grad_op(x, dy)
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return output
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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_conv3d_with_grad():
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"""
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Feature: test conv3d 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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np_type = np.float32
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x = Tensor(np.array([[[[[1.6924546, 0.05080776, -0.6369957],
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[0.19091548, 2.1002553, 0.12015896],
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[0.6172031, 0.30017033, -0.35224986]],
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[[-1.1425182, -0.34934273, -0.20889424],
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[0.5866232, 0.8389834, 0.9311021],
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[0.2855873, 0.8851412, -0.7543979]],
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[[1.2528682, 0.5129298, -0.29809284],
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[0.48851815, -0.07557172, 1.1316293],
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[1.5198169, 2.1855755, -1.3964963]]]]]).astype(np_type))
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dy = Tensor(np.array([[[[[-1.4441139, -0.5044659],
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[0.16003707, 0.8761689]],
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[[0.31563494, -2.0222013],
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[-0.30620402, 0.8279746]]],
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[[[0.23009473, 0.7620112],
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[-0.22232814, -0.20075807]],
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[[0.18656139, 0.41005164],
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[0.19829972, 0.11900865]]]]]).astype(np_type))
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w = Tensor(np.array([[[[[-0.9358, -0.2679],
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[0.5304, -0.6917]],
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[[-0.3968, -0.6872],
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[-0.8452, -0.6712]]]],
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[[[[-0.0127, -1.1173],
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[0.2344, 1.6598]],
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[[0.7420, -0.1918],
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[-0.8876, -0.7472]]]]]).astype(np_type))
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net = Conv3dNet(in_channels=x.shape[1], out_channels=2, kernel_size=(2, 2, 2), weight_init=w)
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actual_out = net(x)
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expect_out = np.array([[[[[-3.3144155, 0.10207337],
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[-2.266387, -2.8092794]],
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[[-0.31821766, -0.51052636],
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[-4.127921, -1.700856]]],
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[[[1.524173, -0.2567379],
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[-2.346652, -0.4532562]],
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[[2.3889866, 1.6392273],
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[-2.0138235, -3.2652235]]]]]).astype(np_type)
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assert np.allclose(actual_out.asnumpy(), expect_out)
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grad_net = GradNet(net)
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actual_grads = grad_net(x, dy)
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expect_dx = np.array([[[[[1.3484796, 0.5921949, -0.71624875],
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[-0.8589629, 0.68001556, 1.6033065],
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[0.03276994, -0.06205562, -0.9392643]],
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[[0.44601417, 3.308012, 0.2841122],
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[1.2830329, -1.8175733, 0.93020254],
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[-0.05385402, 0.5043542, -0.81325763]],
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[[0.01318461, 0.85398096, 1.3110089],
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[-0.16372639, 0.9261035, 0.45910096],
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[0.0827928, -0.7480816, -0.6446598]]]]]).astype(np_type)
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expect_dw = np.array([[[[[0.26185727, 1.515559],
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[-1.8394437, -5.867935]],
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[[1.8011744, 3.2847447],
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[1.2020903, -6.338352]]]],
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[[[[-0.17617291, -0.8384279],
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[2.0623026, 1.2028661]],
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[[-0.29600215, -0.5198703],
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[1.1547322, 1.5743471]]]]]).astype(np_type)
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assert np.allclose(actual_grads[0][0].asnumpy(), expect_dx)
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assert np.allclose(actual_grads[1][0].asnumpy(), expect_dw)
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