# Copyright 2019 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ import numpy as np import pytest import mindspore.context as context import mindspore.nn as nn from mindspore import Tensor from mindspore.common.initializer import initializer from mindspore.common.parameter import Parameter from mindspore.common.parameter import ParameterTuple from mindspore.ops import operations as P from mindspore.ops import composite as C context.set_context(mode=context.GRAPH_MODE, device_target='CPU') class NetConv2d(nn.Cell): def __init__(self): super(NetConv2d, self).__init__() out_channel = 2 kernel_size = 1 self.conv = P.Conv2D(out_channel, kernel_size, mode=1, pad_mode="valid", pad=0, stride=1, dilation=1, group=1) self.w = Parameter(initializer( Tensor(np.arange(2 * 3 * 1 * 1).reshape(2, 3, 1, 1).astype(np.float32)), [2, 3, 1, 1]), name='w') self.x = Parameter(initializer( Tensor(np.arange(1 * 3 * 3 * 3).reshape(1, 3, 3, 3).astype(np.float32)), [1, 3, 3, 3]), name='x') def construct(self): return self.conv(self.x, self.w) @pytest.mark.level0 @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_conv2d(): conv2d = NetConv2d() output = conv2d() print("================================") # expect output: # [[[[ 45. 48. 51.] # [ 54. 57. 60.] # [ 63. 66. 69.]] # [[126. 138. 150.] # [162. 174. 186.] # [198. 210. 222.]]]] expect = np.array([[[[45, 48, 51], [54, 57, 60], [63, 66, 69]], [[126, 138, 150], [162, 174, 186], [198, 210, 222]]]]).astype(np.float32) print(output) assert (output.asnumpy() == expect).all() class NetConv(nn.Cell): def __init__(self, weight, x): super(NetConv, self).__init__() self.conv = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=(5, 3), stride=2, pad_mode='same', padding=(0, 0, 0, 0), dilation=(1, 1), group=1, has_bias=False, weight_init=Tensor(weight) ) self.x = Parameter(initializer(Tensor(x), [1, 3, 4, 2]), name="x") def construct(self): return self.conv(self.x) def test_conv(): weight = np.array([[[[0.38968208, 0.14398979, 0.7962463], [-2.1836321, -0.63823014, -0.50588065], [0.6660469, 0.64673275, -0.13160042], [1.3683757, 1.4005762, -0.37235805], [-0.22638111, 0.45427424, -0.10293389]], [[1.4985064, -0.29318333, -0.92694616], [1.539068, 0.8937254, -1.2598171], [0.9658142, -0.63945454, -0.23185322], [1.363089, -0.41694695, -2.2750475], [-0.4865508, -1.6938025, 0.609849]], [[1.1844803, 0.99874926, -1.9475793], [0.4987858, 0.5307887, -0.04226681], [0.4529779, -1.1960793, 0.9456575], [3.133675, 0.2309789, -0.29201075], [-0.59632736, -0.0789804, -0.69486314]]], [[[-0.5606142, 0.6420862, 0.2478745], [0.02717604, 1.5483379, -0.9373383], [-1.1017276, -0.259478, 1.0311872], [1.8387799, 0.16468556, 0.33392152], [-1.8781787, 1.0158662, 1.6527579]], [[0.45696944, -0.5652523, -1.5618048], [-0.30304828, 0.1331878, -0.36955845], [0.91655576, 0.66612357, 0.3068175], [-0.45732066, 0.8923335, 1.0542952], [-0.73519516, 1.0518405, -1.0273266]], [[-0.79712886, -0.26814285, 0.12779616], [1.0367643, -1.6180774, 0.42999932], [-0.81818223, -0.81502074, 0.882194], [0.53640485, 0.4178927, 1.6037121], [0.9256354, -1.1006796, 0.16614541]]], [[[-1.5216796, -1.2473261, 0.6549515], [0.63627815, 0.7221449, 0.02977821], [-0.61331123, -0.49451825, 0.33852202], [1.4510741, -1.3818305, -0.791747], [0.6989747, 0.49558765, 1.0813237]], [[-0.03969796, 0.71586496, 0.8326594], [-0.15443641, 1.0389746, -0.59301984], [0.7197836, 0.03257621, 1.8398637], [0.6111736, -0.16166899, -2.4869773], [1.3066711, -1.8003578, 0.17412892]], [[-0.31470737, -0.5938182, -1.1311078], [-0.99081016, 0.4005125, 0.44154453], [1.0876914, -2.5958562, -0.5914863], [1.3759689, -0.7741513, 0.19928917], [1.6792973, 2.2744863, -0.04308867]]]]).astype(np.float32) x = np.array([[[[-1.4311737, 1.015344], [0.04431088, -2.2886624], [1.4832113, 1.240908], [0.67040104, 0.15266363]], [[0.44226435, 1.1461105], [1.194218, 1.5547837], [0.23152256, 1.5911953], [0.11206784, 0.17978816]], [[-0.57803905, 0.8039611], [0.0823025, -0.6134477], [-1.4171146, 1.6269946], [0.48878875, 0.9117505]]]]).astype(np.float32) conv2d = NetConv(weight, x) output = conv2d() expected = np.array([[[[2.3498724], [-1.9199573]], [[5.376562], [-5.425745]], [[5.9105043], [7.469034]]]]).astype(np.float32) loss = np.abs(expected - output.asnumpy()) error = 1e-4 * np.ones(loss.shape) assert (loss < error).all() class NetConv3d(nn.Cell): def __init__(self, mode, pad_mode, pad): super(NetConv3d, self).__init__() out_channel = 4 kernel_size = 2 self.conv = P.Conv3D(out_channel, kernel_size, mode=mode, pad_mode=pad_mode, pad=pad, stride=1, dilation=1, group=1) def construct(self, x, w): return self.conv(x, w) @pytest.mark.level0 @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_conv3d(): x = Tensor(np.arange(1 * 3 * 3 * 3 * 3).reshape(1, 3, 3, 3, 3).astype(np.float32)) w = Tensor(np.arange(4 * 3 * 2 * 2 * 2).reshape(4, 3, 2, 2, 2).astype(np.float32)) expect = np.array([[[[[12960., 13236.], [13788., 14064.]], [[15444., 15720.], [16272., 16548.]]], [[[32256., 33108.], [34812., 35664.]], [[39924., 40776.], [42480., 43332.]]], [[[51552., 52980.], [55836., 57264.]], [[64404., 65832.], [68688., 70116.]]], [[[70848., 72852.], [76860., 78864.]], [[88884., 90888.], [94896., 96900.]]]]]).astype(np.float32) mode = 1 pad_mode = "valid" pad = 0 context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU") net = NetConv3d(mode, pad_mode, pad) output = net(x, w) assert (output.asnumpy() == expect).all() context.set_context(mode=context.GRAPH_MODE, device_target="CPU") net = NetConv3d(mode, pad_mode, pad) output = net(x, w) assert (output.asnumpy() == expect).all() @pytest.mark.level0 @pytest.mark.platform_x86_cpu @pytest.mark.env_onecard def test_conv3d_2(): context.set_context(mode=context.GRAPH_MODE, device_target="CPU") x = Tensor(np.arange(1 * 3 * 3 * 3 * 3).reshape(1, 3, 3, 3, 3).astype(np.float32)) w = Tensor(np.arange(4 * 3 * 2 * 2 * 2).reshape(4, 3, 2, 2, 2).astype(np.float32)) expect = np.array([[[[[1647, 3258, 3345, 1650], [3267, 6447, 6609, 3252], [3519, 6933, 7095, 3486], [1719, 3378, 3453, 1692]], [[3375, 6639, 6789, 3330], [6606, 12960, 13236, 6474], [7038, 13788, 14064, 6870], [3393, 6627, 6753, 3288]], [[4077, 7989, 8139, 3978], [7902, 15444, 15720, 7662], [8334, 16272, 16548, 8058], [3987, 7761, 7887, 3828]], [[1917, 3732, 3795, 1842], [3663, 7107, 7221, 3492], [3843, 7449, 7563, 3654], [1809, 3492, 3543, 1704]]], [[[3591, 7218, 7449, 3738], [7371, 14799, 15249, 7644], [8055, 16149, 16599, 8310], [4095, 8202, 8421, 4212]], [[7911, 15855, 16293, 8154], [16110, 32256, 33108, 16554], [17406, 34812, 35664, 17814], [8793, 17571, 17985, 8976]], [[9909, 19797, 20235, 10098], [19998, 39924, 40776, 20334], [21294, 42480, 43332, 21594], [10683, 21297, 21711, 10812]], [[5157, 10284, 10491, 5226], [10359, 20643, 21045, 10476], [10971, 21849, 22251, 11070], [5481, 10908, 11103, 5520]]], [[[5535, 11178, 11553, 5826], [11475, 23151, 23889, 12036], [12591, 25365, 26103, 13134], [6471, 13026, 13389, 6732]], [[12447, 25071, 25797, 12978], [25614, 51552, 52980, 26634], [27774, 55836, 57264, 28758], [14193, 28515, 29217, 14664]], [[15741, 31605, 32331, 16218], [32094, 64404, 65832, 33006], [34254, 68688, 70116, 35130], [17379, 34833, 35535, 17796]], [[8397, 16836, 17187, 8610], [17055, 34179, 34869, 17460], [18099, 36249, 36939, 18486], [9153, 18324, 18663, 9336]]], [[[7479, 15138, 15657, 7914], [15579, 31503, 32529, 16428], [17127, 34581, 35607, 17958], [8847, 17850, 18357, 9252]], [[16983, 34287, 35301, 17802], [35118, 70848, 72852, 36714], [38142, 76860, 78864, 39702], [19593, 39459, 40449, 20352]], [[21573, 43413, 44427, 22338], [44190, 88884, 90888, 45678], [47214, 94896, 96900, 48666], [24075, 48369, 49359, 24780]], [[11637, 23388, 23883, 11994], [23751, 47715, 48693, 24444], [25227, 50649, 51627, 25902], [12825, 25740, 26223, 13152]]]]]).astype(np.float32) mode = 1 pad_mode = "pad" pad = (1, 1, 1, 1, 1, 1) net = NetConv3d(mode, pad_mode, pad) output = net(x, w) assert (output.asnumpy() == expect).all() class MSConv3dNet(nn.Cell): def __init__(self, in_channels, out_channels, kernel_size, pad_mode='pad', padding=0, stride=1, dilation=1, has_bias=False, weight_init='normal'): super(MSConv3dNet, self).__init__() self.cv1 = nn.Conv3d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, pad_mode=pad_mode, padding=padding, stride=stride, dilation=dilation, group=1, has_bias=has_bias, weight_init=weight_init, data_format='NCDHW') def construct(self, x): x = self.cv1(x) return x class MSGradNet(nn.Cell): def __init__(self, network): super(MSGradNet, self).__init__() self.grad = C.GradOperation(get_all=True, sens_param=True, get_by_list=True) self.network = network self.params = ParameterTuple(network.trainable_params()) def construct(self, x, dy): grad_op = self.grad(self.network, self.params) output = grad_op(x, dy) return output