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268 lines
11 KiB
268 lines
11 KiB
# 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.ops import operations as P
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from mindspore.ops.operations import _inner_ops as inner
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from mindspore.common.parameter import Parameter
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from mindspore.common.initializer import initializer
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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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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_gpu_training
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@pytest.mark.env_onecard
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def test_conv2d():
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x = Tensor(np.arange(1 * 3 * 3 * 3).reshape(1, 3, 3, 3).astype(np.float32))
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w = Tensor(np.arange(2 * 3 * 1 * 1).reshape(2, 3, 1, 1).astype(np.float32))
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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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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU", max_device_memory="0.2GB")
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conv2d = NetConv2d()
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output = conv2d(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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conv2d = NetConv2d()
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output = conv2d(x, w)
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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_gpu_training
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@pytest.mark.env_onecard
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def test_conv():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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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 NetConv2dDynamic(nn.Cell):
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def __init__(self, axis=0, out_nums=1):
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super(NetConv2dDynamic, self).__init__()
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self.dynshape = inner.GpuConvertToDynamicShape()
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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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def construct(self, x, w):
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x_dyn = self.dynshape(x)
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w_dyn = self.dynshape(w)
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x_conv = self.conv(x_dyn, w_dyn)
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return x_conv
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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_conv2d_dynamic():
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x1 = Tensor(np.arange(1 * 3 * 3 * 3).reshape(1, 3, 3, 3).astype(np.float32))
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w1 = Tensor(np.arange(2 * 3 * 1 * 1).reshape(2, 3, 1, 1).astype(np.float32))
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expect1 = 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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x2 = Tensor(np.arange(5 * 1 * 2 * 2).reshape(5, 1, 2, 2).astype(np.float32))
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w2 = Tensor(np.arange(2 * 1 * 1 * 1).reshape(2, 1, 1, 1).astype(np.float32))
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expect2 = np.array([[[[0., 0.],
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[0., 0.]],
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[[0., 1.],
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[2., 3.]]],
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[[[0., 0.],
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[0., 0.]],
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[[4., 5.],
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[6., 7.]]],
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[[[0., 0.],
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[0., 0.]],
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[[8., 9.],
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[10., 11.]]],
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[[[0., 0.],
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[0., 0.]],
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[[12., 13.],
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[14., 15.]]],
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[[[0., 0.],
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[0., 0.]],
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[[16., 17.],
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[18., 19.]]]]).astype(np.float32)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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conv2d = NetConv2dDynamic()
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output1 = conv2d(x1, w1)
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assert (output1.asnumpy() == expect1).all()
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output2 = conv2d(x2, w2)
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assert (output2.asnumpy() == expect2).all()
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class NetConvNHWC(nn.Cell):
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def __init__(self, weight, x):
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super(NetConvNHWC, self).__init__()
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self.conv = nn.Conv2d(in_channels=1,
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out_channels=3,
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kernel_size=2,
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stride=2,
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pad_mode="valid",
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weight_init=Tensor(weight),
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data_format='NHWC'
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)
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self.x = Parameter(initializer(Tensor(x), [1, 4, 4, 1]), 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_gpu_training
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@pytest.mark.env_onecard
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def test_conv_NHWC():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x1 = Tensor(np.arange(1 * 4 * 4 * 1).reshape(1, 4, 4, 1).astype(np.float32))
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w1 = Tensor(np.arange(3 * 2 * 2 * 1).reshape(3, 2, 2, 1).astype(np.float32))
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expected = np.array([[[[24., 64., 104.],
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[36., 108., 180.]],
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[[72., 240., 408.],
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[84., 284., 484.]]]]).astype(np.float32)
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conv2d = NetConvNHWC(w1, x1)
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output = conv2d()
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assert (output.asnumpy() == expected).all()
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