wehub-resource-sync
785 lines
28 KiB
Python
785 lines
28 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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import logging
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import unittest
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import numpy as np
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from utils import compare_legacy_with_pt
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import paddle
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import paddle.device
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from paddle import sparse
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from paddle.base import core
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from paddle.base.framework import in_pir_mode
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logging.basicConfig(
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format='%(asctime)s - %(levelname)s - %(message)s', level=logging.INFO
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)
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logger = logging.getLogger(__name__)
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class TestSparseConv(unittest.TestCase):
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def test_conv2d(self):
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kernel = [[[[1], [1], [1]], [[1], [1], [1]], [[1], [1], [1]]]]
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dense_kernel = paddle.to_tensor(
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kernel, dtype='float32', stop_gradient=False
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)
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dense_kernel = paddle.reshape(dense_kernel, [3, 3, 1, 1])
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paddings = [0, 0]
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strides = [1, 1]
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dilations = [1, 1]
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bias = [1]
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indices = [[0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
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values = [1, 2, 3, 4]
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indices = paddle.to_tensor(indices, dtype='int32')
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values = paddle.to_tensor(values, dtype='float32')
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dense_shape = [1, 3, 4, 1]
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correct_out_values = [[5], [11]]
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sparse_input = core.eager.sparse_coo_tensor(
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indices, values, dense_shape, False
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)
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out = paddle.sparse.nn.functional.conv2d(
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sparse_input,
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dense_kernel,
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bias=paddle.to_tensor(bias, dtype='float32'),
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stride=strides,
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padding=paddings,
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dilation=dilations,
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groups=1,
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data_format="NHWC",
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)
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out.backward(out)
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out = paddle.sparse.coalesce(out)
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np.testing.assert_array_equal(correct_out_values, out.values().numpy())
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def test_conv3d(self):
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kernel = [[[[[1], [1], [1]], [[1], [1], [1]], [[1], [1], [1]]]]]
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dense_kernel = paddle.to_tensor(
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kernel, dtype='float32', stop_gradient=False
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)
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dense_kernel = paddle.reshape(dense_kernel, [1, 3, 3, 1, 1])
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paddings = [0, 0, 0]
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strides = [1, 1, 1]
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dilations = [1, 1, 1]
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bias = [1]
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indices = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
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values = [1, 2, 3, 4]
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indices = paddle.to_tensor(indices, dtype='int32')
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values = paddle.to_tensor(values, dtype='float32')
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dense_shape = [1, 1, 3, 4, 1]
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correct_out_values = [[5], [11]]
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sparse_input = core.eager.sparse_coo_tensor(
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indices, values, dense_shape, False
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)
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out = paddle.sparse.nn.functional.conv3d(
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sparse_input,
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dense_kernel,
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bias=paddle.to_tensor(bias, dtype='float32'),
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stride=strides,
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padding=paddings,
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dilation=dilations,
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groups=1,
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data_format="NDHWC",
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)
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out.backward(out)
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out = paddle.sparse.coalesce(out)
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np.testing.assert_array_equal(correct_out_values, out.values().numpy())
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def test_subm_conv2d(self):
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indices = [[0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
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values = [[1], [2], [3], [4]]
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indices = paddle.to_tensor(indices, dtype='int32')
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values = paddle.to_tensor(values, dtype='float32')
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dense_shape = [1, 3, 4, 1]
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sparse_x = paddle.sparse.sparse_coo_tensor(
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indices, values, dense_shape, stop_gradient=True
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)
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weight = paddle.randn((1, 3, 3, 1), dtype='float32')
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y = paddle.sparse.nn.functional.subm_conv2d(
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sparse_x, weight, key='subm_conv'
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)
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np.testing.assert_array_equal(
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sparse_x.indices().numpy(), y.indices().numpy()
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)
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def test_subm_conv3d(self):
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indices = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
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values = [[1], [2], [3], [4]]
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indices = paddle.to_tensor(indices, dtype='int32')
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values = paddle.to_tensor(values, dtype='float32')
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dense_shape = [1, 1, 3, 4, 1]
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sparse_x = paddle.sparse.sparse_coo_tensor(
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indices, values, dense_shape, stop_gradient=True
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)
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weight = paddle.randn((1, 3, 3, 1, 1), dtype='float32')
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y = paddle.sparse.nn.functional.subm_conv3d(
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sparse_x, weight, key='subm_conv'
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)
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np.testing.assert_array_equal(
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sparse_x.indices().numpy(), y.indices().numpy()
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)
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def test_Conv2D(self):
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# (3, non_zero_num), 3-D:(N, H, W)
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indices = [[0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
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# (non_zero_num, C)
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values = [[1], [2], [3], [4]]
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indices = paddle.to_tensor(indices, dtype='int32')
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values = paddle.to_tensor(values, dtype='float32')
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dense_shape = [1, 3, 4, 1]
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correct_out_values = [[4], [10]]
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sparse_input = paddle.sparse.sparse_coo_tensor(
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indices, values, dense_shape, False
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)
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sparse_conv2d = paddle.sparse.nn.Conv2D(
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1, 1, (3, 3), data_format='NHWC'
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)
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sparse_out = sparse_conv2d(sparse_input)
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# test errors
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with self.assertRaises(ValueError):
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# Currently, only support data_format='NDHWC'
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conv2d = paddle.sparse.nn.SubmConv2D(
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1, 1, (3, 3), data_format='NCHW', key='subm_conv'
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)
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def test_Conv3D(self):
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# (4, non_zero_num), 4-D:(N, D, H, W)
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indices = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
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# (non_zero_num, C)
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values = [[1], [2], [3], [4]]
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indices = paddle.to_tensor(indices, dtype='int32')
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values = paddle.to_tensor(values, dtype='float32')
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dense_shape = [1, 1, 3, 4, 1]
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correct_out_values = [[4], [10]]
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sparse_input = paddle.sparse.sparse_coo_tensor(
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indices, values, dense_shape, False
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)
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sparse_conv3d = paddle.sparse.nn.Conv3D(
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1, 1, (1, 3, 3), data_format='NDHWC'
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)
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sparse_out = sparse_conv3d(sparse_input)
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# test errors
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with self.assertRaises(ValueError):
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# Currently, only support data_format='NDHWC'
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conv3d = paddle.sparse.nn.SubmConv3D(
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1, 1, (1, 3, 3), data_format='NCDHW', key='subm_conv'
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)
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def test_SubmConv2D(self):
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indices = [[0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
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values = [[1], [2], [3], [4]]
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indices = paddle.to_tensor(indices, dtype='int32')
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values = paddle.to_tensor(values, dtype='float32')
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dense_shape = [1, 3, 4, 1]
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correct_out_values = [[4], [10]]
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sparse_input = paddle.sparse.sparse_coo_tensor(
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indices, values, dense_shape, False
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)
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subm_conv2d = paddle.sparse.nn.SubmConv2D(
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1, 1, (3, 3), data_format='NHWC', key='subm_conv'
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)
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# test extra_repr
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logger.info(subm_conv2d.extra_repr())
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sparse_out = subm_conv2d(sparse_input)
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# the output shape of subm_conv is same as input shape
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np.testing.assert_array_equal(indices, sparse_out.indices().numpy())
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# test errors
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with self.assertRaises(ValueError):
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# Currently, only support data_format='NHWC'
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conv2d = paddle.sparse.nn.SubmConv2D(
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1, 1, (3, 3), data_format='NCHW', key='subm_conv'
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)
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def test_SubmConv3D(self):
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indices = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
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values = [[1], [2], [3], [4]]
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indices = paddle.to_tensor(indices, dtype='int32')
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values = paddle.to_tensor(values, dtype='float32')
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dense_shape = [1, 1, 3, 4, 1]
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correct_out_values = [[4], [10]]
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sparse_input = paddle.sparse.sparse_coo_tensor(
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indices, values, dense_shape, False
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)
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subm_conv3d = paddle.sparse.nn.SubmConv3D(
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1, 1, (1, 3, 3), data_format='NDHWC', key='subm_conv'
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)
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# test extra_repr
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print(subm_conv3d.extra_repr())
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sparse_out = subm_conv3d(sparse_input)
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# the output shape of subm_conv is same as input shape
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np.testing.assert_array_equal(indices, sparse_out.indices().numpy())
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# test errors
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with self.assertRaises(ValueError):
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# Currently, only support data_format='NDHWC'
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conv3d = paddle.sparse.nn.SubmConv3D(
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1, 1, (1, 3, 3), data_format='NCDHW', key='subm_conv'
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)
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def test_Conv2D_bias(self):
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paddle.seed(0)
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shape = [1, 4, 4, 3]
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x = paddle.randn(shape)
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sp_x = x.to_sparse_coo(3)
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conv2d = paddle.nn.Conv2D(3, 2, 3, data_format='NHWC')
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sp_conv2d = paddle.sparse.nn.Conv2D(3, 2, 3, data_format='NHWC')
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sp_conv2d.weight.set_value(
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paddle.to_tensor(conv2d.weight.numpy().transpose(2, 3, 1, 0))
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)
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sp_conv2d.bias.set_value(paddle.to_tensor(conv2d.bias.numpy()))
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x.stop_gradient = False
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out = conv2d(x)
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loss = out.mean()
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loss.backward()
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sp_x.stop_gradient = False
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sp_out = sp_conv2d(sp_x)
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dense_out = sp_out.to_dense()
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sp_loss = dense_out.mean()
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sp_loss.backward()
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np.testing.assert_allclose(
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out.numpy(), dense_out.numpy(), atol=1e-3, rtol=1e-3
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)
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np.testing.assert_allclose(
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conv2d.weight.grad.numpy().transpose(2, 3, 1, 0),
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sp_conv2d.weight.grad.numpy(),
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atol=1e-3,
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rtol=1e-3,
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)
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np.testing.assert_allclose(
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conv2d.bias.grad.numpy(),
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sp_conv2d.bias.grad.numpy(),
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atol=1e-5,
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rtol=1e-5,
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)
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def test_Conv3D_bias(self):
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paddle.seed(0)
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shape = [1, 4, 4, 4, 3]
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x = paddle.randn(shape)
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sp_x = x.to_sparse_coo(4)
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conv3d = paddle.nn.Conv3D(3, 2, 3, data_format='NDHWC')
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sp_conv3d = paddle.sparse.nn.Conv3D(3, 2, 3, data_format='NDHWC')
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sp_conv3d.weight.set_value(
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paddle.to_tensor(conv3d.weight.numpy().transpose(2, 3, 4, 1, 0))
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)
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sp_conv3d.bias.set_value(paddle.to_tensor(conv3d.bias.numpy()))
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x.stop_gradient = False
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out = conv3d(x)
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loss = out.mean()
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loss.backward()
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sp_x.stop_gradient = False
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sp_out = sp_conv3d(sp_x)
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dense_out = sp_out.to_dense()
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sp_loss = dense_out.mean()
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sp_loss.backward()
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np.testing.assert_allclose(
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out.numpy(), dense_out.numpy(), atol=1e-3, rtol=1e-3
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)
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np.testing.assert_allclose(
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conv3d.weight.grad.numpy().transpose(2, 3, 4, 1, 0),
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sp_conv3d.weight.grad.numpy(),
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atol=1e-3,
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rtol=1e-3,
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)
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np.testing.assert_allclose(
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conv3d.bias.grad.numpy(),
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sp_conv3d.bias.grad.numpy(),
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atol=1e-5,
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rtol=1e-5,
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)
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class TestStatic(unittest.TestCase):
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@compare_legacy_with_pt
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def test(self):
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paddle.enable_static()
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main = paddle.static.Program()
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with paddle.static.program_guard(main):
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indices = paddle.static.data(
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name='indices', shape=[4, 4], dtype='int32'
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)
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values = paddle.static.data(
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name='values', shape=[4, 1], dtype='float32'
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)
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dense_shape = [1, 1, 3, 4, 1]
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sp_x = sparse.sparse_coo_tensor(indices, values, dense_shape)
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weight_shape = [1, 3, 3, 1, 1]
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weight = paddle.static.data(
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name='weight', shape=weight_shape, dtype='float32'
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)
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bias_shape = [1]
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bias = paddle.static.data(
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name='bias', shape=bias_shape, dtype='float32'
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)
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out = sparse.nn.functional.conv3d(
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sp_x,
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weight,
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bias,
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stride=1,
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padding=0,
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dilation=1,
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groups=1,
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data_format="NDHWC",
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)
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sp_out = sparse.nn.functional.relu(out)
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out_indices = sp_out.indices()
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out_values = sp_out.values()
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out = sp_out.to_dense()
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exe = paddle.static.Executor()
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indices_data = [
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[0, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 1, 2],
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[1, 3, 2, 3],
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]
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values_data = [[1.0], [2.0], [3.0], [4.0]]
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weight_data = np.array(
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[[[[[1], [1], [1]], [[1], [1], [1]], [[1], [1], [1]]]]]
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).astype('float32')
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weight_data = weight_data.reshape(weight_shape)
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bias_data = np.array([1]).astype('float32')
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fetch = exe.run(
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feed={
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'indices': indices_data,
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'values': values_data,
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'weight': weight_data,
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'bias': bias_data,
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},
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fetch_list=[out, out_indices, out_values],
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return_numpy=True,
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)
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correct_out = np.array([[[[[5.0], [11.0]]]]]).astype('float64')
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correct_out_values = [[5.0], [11.0]]
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np.testing.assert_array_equal(correct_out, fetch[0])
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np.testing.assert_array_equal(correct_out_values, fetch[2])
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self.assertTrue(out_indices.dtype == paddle.int32)
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paddle.disable_static()
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@compare_legacy_with_pt
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def test_cpu(self):
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paddle.enable_static()
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main = paddle.static.Program()
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with paddle.static.program_guard(main):
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indices = paddle.static.data(
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name='indices', shape=[4, 4], dtype='int32'
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)
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values = paddle.static.data(
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name='values', shape=[4, 1], dtype='float32'
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)
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dense_shape = [1, 1, 3, 4, 1]
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sp_x = sparse.sparse_coo_tensor(indices, values, dense_shape)
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weight_shape = [1, 3, 3, 1, 1]
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weight = paddle.static.data(
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name='weight', shape=weight_shape, dtype='float32'
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)
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bias_shape = [1]
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bias = paddle.static.data(
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name='bias', shape=bias_shape, dtype='float32'
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)
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out = sparse.nn.functional.conv3d(
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sp_x,
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weight,
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bias,
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stride=1,
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padding=0,
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dilation=1,
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groups=1,
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data_format="NDHWC",
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)
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sp_out = sparse.nn.functional.relu(out)
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out_indices = sp_out.indices()
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out_values = sp_out.values()
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out = sp_out.to_dense()
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place = paddle.CPUPlace()
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exe = paddle.static.Executor()
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indices_data = [
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[0, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 1, 2],
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[1, 3, 2, 3],
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]
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values_data = [[1.0], [2.0], [3.0], [4.0]]
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weight_data = np.array(
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[[[[[1], [1], [1]], [[1], [1], [1]], [[1], [1], [1]]]]]
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).astype('float32')
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weight_data = weight_data.reshape(weight_shape)
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bias_data = np.array([1]).astype('float32')
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fetch = exe.run(
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feed={
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'indices': indices_data,
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'values': values_data,
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'weight': weight_data,
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'bias': bias_data,
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},
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fetch_list=[out, out_indices, out_values],
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return_numpy=True,
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)
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correct_out = np.array([[[[[5.0], [11.0]]]]]).astype('float64')
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correct_out_values = [[5.0], [11.0]]
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np.testing.assert_array_equal(correct_out, fetch[0])
|
|
np.testing.assert_array_equal(correct_out_values, fetch[2])
|
|
self.assertTrue(out_indices.dtype == paddle.int32)
|
|
paddle.disable_static()
|
|
|
|
@compare_legacy_with_pt
|
|
def test2D(self):
|
|
paddle.enable_static()
|
|
main = paddle.static.Program()
|
|
with paddle.static.program_guard(main):
|
|
indices = paddle.static.data(
|
|
name='indices', shape=[3, 4], dtype='int32'
|
|
)
|
|
values = paddle.static.data(
|
|
name='values', shape=[4, 1], dtype='float32'
|
|
)
|
|
dense_shape = [1, 3, 4, 1]
|
|
sp_x = sparse.sparse_coo_tensor(indices, values, dense_shape)
|
|
|
|
weight_shape = [3, 3, 1, 1]
|
|
weight = paddle.static.data(
|
|
name='weight', shape=weight_shape, dtype='float32'
|
|
)
|
|
bias_shape = [1]
|
|
bias = paddle.static.data(
|
|
name='bias', shape=bias_shape, dtype='float32'
|
|
)
|
|
out = sparse.nn.functional.conv2d(
|
|
sp_x,
|
|
weight,
|
|
bias,
|
|
stride=1,
|
|
padding=0,
|
|
dilation=1,
|
|
groups=1,
|
|
data_format="NHWC",
|
|
)
|
|
sp_out = sparse.nn.functional.relu(out)
|
|
out_indices = sp_out.indices()
|
|
out_values = sp_out.values()
|
|
out = sp_out.to_dense()
|
|
|
|
exe = paddle.static.Executor()
|
|
|
|
indices_data = [[0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
|
|
values_data = [[1.0], [2.0], [3.0], [4.0]]
|
|
weight_data = np.array(
|
|
[[[[[1], [1], [1]], [[1], [1], [1]], [[1], [1], [1]]]]]
|
|
).astype('float32')
|
|
weight_data = weight_data.reshape(weight_shape)
|
|
bias_data = np.array([1]).astype('float32')
|
|
|
|
fetch = exe.run(
|
|
feed={
|
|
'indices': indices_data,
|
|
'values': values_data,
|
|
'weight': weight_data,
|
|
'bias': bias_data,
|
|
},
|
|
fetch_list=[out, out_indices, out_values],
|
|
return_numpy=True,
|
|
)
|
|
correct_out = np.array([[[[5.0], [11.0]]]]).astype('float64')
|
|
correct_out_values = [[5.0], [11.0]]
|
|
np.testing.assert_array_equal(correct_out, fetch[0])
|
|
np.testing.assert_array_equal(correct_out_values, fetch[2])
|
|
self.assertTrue(out_indices.dtype == paddle.int32)
|
|
paddle.disable_static()
|
|
|
|
@compare_legacy_with_pt
|
|
def test2D_cpu(self):
|
|
paddle.enable_static()
|
|
main = paddle.static.Program()
|
|
with paddle.static.program_guard(main):
|
|
indices = paddle.static.data(
|
|
name='indices', shape=[3, 4], dtype='int32'
|
|
)
|
|
values = paddle.static.data(
|
|
name='values', shape=[4, 1], dtype='float32'
|
|
)
|
|
dense_shape = [1, 3, 4, 1]
|
|
sp_x = sparse.sparse_coo_tensor(indices, values, dense_shape)
|
|
|
|
weight_shape = [3, 3, 1, 1]
|
|
weight = paddle.static.data(
|
|
name='weight', shape=weight_shape, dtype='float32'
|
|
)
|
|
bias_shape = [1]
|
|
bias = paddle.static.data(
|
|
name='bias', shape=bias_shape, dtype='float32'
|
|
)
|
|
out = sparse.nn.functional.conv2d(
|
|
sp_x,
|
|
weight,
|
|
bias,
|
|
stride=1,
|
|
padding=0,
|
|
dilation=1,
|
|
groups=1,
|
|
data_format="NHWC",
|
|
)
|
|
sp_out = sparse.nn.functional.relu(out)
|
|
out_indices = sp_out.indices()
|
|
out_values = sp_out.values()
|
|
out = sp_out.to_dense()
|
|
|
|
place = paddle.CPUPlace()
|
|
exe = paddle.static.Executor()
|
|
|
|
indices_data = [[0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
|
|
values_data = [[1.0], [2.0], [3.0], [4.0]]
|
|
weight_data = np.array(
|
|
[[[[[1], [1], [1]], [[1], [1], [1]], [[1], [1], [1]]]]]
|
|
).astype('float32')
|
|
weight_data = weight_data.reshape(weight_shape)
|
|
bias_data = np.array([1]).astype('float32')
|
|
|
|
fetch = exe.run(
|
|
feed={
|
|
'indices': indices_data,
|
|
'values': values_data,
|
|
'weight': weight_data,
|
|
'bias': bias_data,
|
|
},
|
|
fetch_list=[out, out_indices, out_values],
|
|
return_numpy=True,
|
|
)
|
|
correct_out = np.array([[[[5.0], [11.0]]]]).astype('float64')
|
|
correct_out_values = [[5.0], [11.0]]
|
|
np.testing.assert_array_equal(correct_out, fetch[0])
|
|
np.testing.assert_array_equal(correct_out_values, fetch[2])
|
|
self.assertTrue(out_indices.dtype == paddle.int32)
|
|
paddle.disable_static()
|
|
|
|
|
|
devices = []
|
|
if paddle.device.get_device() != "cpu":
|
|
devices.append(paddle.device.get_device())
|
|
else:
|
|
devices.append('cpu')
|
|
|
|
|
|
class TestSparseSubmConvStatic(unittest.TestCase):
|
|
'''
|
|
test subm_conv2d and subm_conv3d in static graph in pir mode.
|
|
compare the results of subm_conv2d in static graph and dynamic graph, use the result in dynamic graph as the correct answer.
|
|
'''
|
|
|
|
def check_result_subm_conv2d(self, x_shape, weight_shape):
|
|
'''
|
|
x_shape: the shape of input tensor x, [N, H, W, C]
|
|
weight_shape: the shape of conv kernel, [kH, kW, C/g, M]
|
|
compare the output of paddle.sparse.nn.functional.subm_conv2d in static graph and dynamic graph.
|
|
'''
|
|
for device in devices:
|
|
paddle.device.set_device(device)
|
|
x = paddle.rand(x_shape, dtype='float32')
|
|
weight = paddle.randn(weight_shape, dtype='float32')
|
|
x_indices_data, x_values_data = (
|
|
x.detach().to_sparse_coo(sparse_dim=len(x_shape)).indices(),
|
|
x.detach().to_sparse_coo(sparse_dim=len(x_shape)).values(),
|
|
)
|
|
w_indices_data, w_values_data = (
|
|
weight.detach()
|
|
.to_sparse_coo(sparse_dim=len(weight_shape))
|
|
.indices(),
|
|
weight.detach()
|
|
.to_sparse_coo(sparse_dim=len(weight_shape))
|
|
.values(),
|
|
)
|
|
x.stop_gradient = False
|
|
weight.stop_gradient = False
|
|
|
|
dynamic_out = paddle.sparse.nn.functional.subm_conv2d(x, weight)
|
|
dynamic_out_dense = dynamic_out.to_dense()
|
|
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
x_indices = paddle.static.data(
|
|
name="x_indices",
|
|
shape=x_indices_data.shape,
|
|
dtype=x_indices_data.dtype,
|
|
)
|
|
x_values = paddle.static.data(
|
|
name="x_values",
|
|
shape=x_values_data.shape,
|
|
dtype=x_values_data.dtype,
|
|
)
|
|
w_indices = paddle.static.data(
|
|
name="w_indices",
|
|
shape=w_indices_data.shape,
|
|
dtype=w_indices_data.dtype,
|
|
)
|
|
w_values = paddle.static.data(
|
|
name="w_values",
|
|
shape=w_values_data.shape,
|
|
dtype=w_values_data.dtype,
|
|
)
|
|
|
|
static_x = paddle.sparse.sparse_coo_tensor(
|
|
x_indices,
|
|
x_values,
|
|
shape=x_shape,
|
|
dtype=x.dtype,
|
|
)
|
|
static_w = paddle.sparse.sparse_coo_tensor(
|
|
w_indices,
|
|
w_values,
|
|
shape=weight_shape,
|
|
dtype=weight.dtype,
|
|
)
|
|
static_out = paddle.sparse.nn.functional.subm_conv2d(
|
|
static_x, static_w
|
|
)
|
|
static_dense_out = static_out.to_dense()
|
|
|
|
st_exe = paddle.static.Executor()
|
|
st_fetch = st_exe.run(
|
|
feed={
|
|
"x_indices": x_indices_data.numpy(),
|
|
"x_values": x_values_data.numpy(),
|
|
"w_indices": w_indices_data.numpy(),
|
|
"w_values": w_values_data.numpy(),
|
|
},
|
|
fetch_list=[static_dense_out],
|
|
return_numpy=True,
|
|
)
|
|
np.testing.assert_allclose(
|
|
dynamic_out_dense.numpy(), st_fetch[0], rtol=1e-05
|
|
)
|
|
paddle.disable_static()
|
|
|
|
def check_result_subm_conv3d(self, x_shape, weight_shape):
|
|
'''
|
|
x_shape: the shape of input tensor x, [N, D, H, W, C]
|
|
weight_shape: the shape of conv kernel, [kD, kH, kW, C/g, M]
|
|
compare the output of paddle.sparse.nn.functional.subm_conv3d in static graph and dynamic graph.
|
|
'''
|
|
for device in devices:
|
|
paddle.device.set_device(device)
|
|
x = paddle.rand(x_shape, dtype='float32')
|
|
weight = paddle.randn(weight_shape, dtype='float32')
|
|
x_indices_data, x_values_data = (
|
|
x.detach().to_sparse_coo(sparse_dim=len(x_shape)).indices(),
|
|
x.detach().to_sparse_coo(sparse_dim=len(x_shape)).values(),
|
|
)
|
|
w_indices_data, w_values_data = (
|
|
weight.detach()
|
|
.to_sparse_coo(sparse_dim=len(weight_shape))
|
|
.indices(),
|
|
weight.detach()
|
|
.to_sparse_coo(sparse_dim=len(weight_shape))
|
|
.values(),
|
|
)
|
|
x.stop_gradient = False
|
|
weight.stop_gradient = False
|
|
|
|
dynamic_out = paddle.sparse.nn.functional.subm_conv3d(x, weight)
|
|
dynamic_out_dense = dynamic_out.to_dense()
|
|
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
x_indices = paddle.static.data(
|
|
name="x_indices",
|
|
shape=x_indices_data.shape,
|
|
dtype=x_indices_data.dtype,
|
|
)
|
|
x_values = paddle.static.data(
|
|
name="x_values",
|
|
shape=x_values_data.shape,
|
|
dtype=x_values_data.dtype,
|
|
)
|
|
w_indices = paddle.static.data(
|
|
name="w_indices",
|
|
shape=w_indices_data.shape,
|
|
dtype=w_indices_data.dtype,
|
|
)
|
|
w_values = paddle.static.data(
|
|
name="w_values",
|
|
shape=w_values_data.shape,
|
|
dtype=w_values_data.dtype,
|
|
)
|
|
|
|
static_x = paddle.sparse.sparse_coo_tensor(
|
|
x_indices,
|
|
x_values,
|
|
shape=x_shape,
|
|
dtype=x.dtype,
|
|
)
|
|
static_w = paddle.sparse.sparse_coo_tensor(
|
|
w_indices,
|
|
w_values,
|
|
shape=weight_shape,
|
|
dtype=weight.dtype,
|
|
)
|
|
static_out = paddle.sparse.nn.functional.subm_conv3d(
|
|
static_x, static_w
|
|
)
|
|
static_dense_out = static_out.to_dense()
|
|
|
|
st_exe = paddle.static.Executor()
|
|
st_fetch = st_exe.run(
|
|
feed={
|
|
"x_indices": x_indices_data.numpy(),
|
|
"x_values": x_values_data.numpy(),
|
|
"w_indices": w_indices_data.numpy(),
|
|
"w_values": w_values_data.numpy(),
|
|
},
|
|
fetch_list=[static_dense_out],
|
|
return_numpy=True,
|
|
)
|
|
np.testing.assert_allclose(
|
|
dynamic_out_dense.numpy(), st_fetch[0], rtol=1e-05
|
|
)
|
|
paddle.disable_static()
|
|
|
|
def test_subm_conv2d(self):
|
|
if in_pir_mode():
|
|
self.check_result_subm_conv2d([1, 3, 4, 1], [3, 3, 1, 1])
|
|
|
|
def test_subm_conv3d(self):
|
|
if in_pir_mode():
|
|
self.check_result_subm_conv3d([1, 1, 3, 4, 1], [1, 3, 3, 1, 1])
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|