244 lines
10 KiB
Python
244 lines
10 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.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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from mindspore.ops.functional import vmap
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class NetConv3d(nn.Cell):
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def __init__(self):
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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=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.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_dshape_1():
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"""
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Feature: Test conv3d dynamic shape.
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Description: Test conv3d 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 = NetConv3d()
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input_x_dyn = Tensor(shape=[1, 3, 3, 3, None], dtype=ms.float32)
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input_w_dyn = Tensor(shape=[4, 3, 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 * 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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output = net(x, w)
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expect_shape = (1, 4, 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_dshape_2():
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"""
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Feature: Test conv3d dynamic shape.
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Description: Test conv3d 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 = NetConv3d()
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input_x_dyn = Tensor(shape=[None, 3, 3, 3, 3], dtype=ms.float32)
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input_w_dyn = Tensor(shape=[None, 3, 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 * 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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output = net(x, w)
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expect_shape = (1, 4, 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():
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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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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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net = NetConv3d()
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output = net(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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net = NetConv3d()
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output = net(x, w)
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assert (output.asnumpy() == expect).all()
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class MSConv3dNet(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(MSConv3dNet, 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 MSGradNet(nn.Cell):
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def __init__(self, network):
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super(MSGradNet, 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.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_grad():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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dtype = np.float32
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out_c = 2
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kernel_size = (2, 2, 2)
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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(dtype))
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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(dtype))
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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(dtype))
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w_exp = np.array([[[[[-0.9384, -0.2830],
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[0.5487, -0.6330]],
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[[-0.4148, -0.7200],
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[-0.8572, -0.6079]]]],
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[[[[-0.0109, -1.1089],
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[0.2138, 1.6478]],
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[[0.7450, -0.1866],
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[-0.8992, -0.7629]]]]]).astype(dtype)
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net = MSConv3dNet(x.shape[1], out_c, kernel_size, weight_init=w)
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grad_net = MSGradNet(net)
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optimizer = nn.SGD(net.trainable_params(), learning_rate=0.01, momentum=0.9)
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grad_net.set_train(True)
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output = grad_net(x, dy)
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optimizer(output[1])
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assert np.allclose(net.cv1.weight.asnumpy(), w_exp, atol=1.0e-4)
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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_vmap():
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"""
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Feature: Conv3D op
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Description: Test vmap rule for Conv3D 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 = NetConv3d()
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batch_x = Tensor(np.arange(2 * 1 * 1 * 3 * 3 * 3).reshape(2, 1, 1, 3, 3, 3).astype(np.float32))
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w = Tensor(np.ones([4, 1, 2, 2, 2]).astype(np.float32))
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expected1 = np.array([[[[[[52., 60.], [76., 84.]], [[124., 132.], [148., 156.]]],
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[[[52., 60.], [76., 84.]], [[124., 132.], [148., 156.]]],
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[[[52., 60.], [76., 84.]], [[124., 132.], [148., 156.]]],
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[[[52., 60.], [76., 84.]], [[124., 132.], [148., 156.]]]]],
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[[[[[268., 276.], [292., 300.]], [[340., 348.], [364., 372.]]],
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[[[268., 276.], [292., 300.]], [[340., 348.], [364., 372.]]],
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[[[268., 276.], [292., 300.]], [[340., 348.], [364., 372.]]],
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[[[268., 276.], [292., 300.]], [[340., 348.], [364., 372.]]]]]]).astype(np.float32)
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output1 = vmap(conv3d, (0, None))(batch_x, w)
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assert np.allclose(output1.asnumpy(), expected1, 0.0001, 0.0001)
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x = Tensor(np.arange(1 * 1 * 3 * 3 * 3).reshape(1, 1, 3, 3, 3).astype(np.float32))
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batch_w = Tensor(np.arange(2 * 4 * 1 * 2 * 2 * 2).reshape(2, 4, 1, 2, 2, 2).astype(np.float32))
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expected2 = np.array([[[[[[268., 296.], [352., 380.]], [[520., 548.], [604., 632.]]],
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[[[684., 776.], [960., 1052.]], [[1512., 1604.], [1788., 1880.]]],
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[[[1100., 1256.], [1568., 1724.]], [[2504., 2660.], [2972., 3128.]]],
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[[[1516., 1736.], [2176., 2396.]], [[3496., 3716.], [4156., 4376.]]]]],
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[[[[[1932., 2216.], [2784., 3068.]], [[4488., 4772.], [5340., 5624.]]],
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[[[2348., 2696.], [3392., 3740.]], [[5480., 5828.], [6524., 6872.]]],
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[[[2764., 3176.], [4000., 4412.]], [[6472., 6884.], [7708., 8120.]]],
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[[[3180., 3656.], [4608., 5084.]], [[7464., 7940.], [8892., 9368.]]]]]]
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).astype(np.float32)
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output2 = vmap(conv3d, (None, 0))(x, batch_w)
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assert np.allclose(output2.asnumpy(), expected2, 0.0001, 0.0001)
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