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test_sparse_conv_op.py
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test_sparse_conv_op.py
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import print_function
import unittest
import numpy as np
import paddle
from paddle import _C_ops
from paddle.fluid import core
from paddle.fluid.framework import _test_eager_guard
class TestSparseConv(unittest.TestCase):
def test_conv3d(self):
with _test_eager_guard():
kernel = [[[[[1], [1], [1]], [[1], [1], [1]], [[1], [1], [1]]]]]
dense_kernel = paddle.to_tensor(
kernel, dtype='float32', stop_gradient=False)
dense_kernel = paddle.reshape(dense_kernel, [1, 3, 3, 1, 1])
paddings = [0, 0, 0]
strides = [1, 1, 1]
dilations = [1, 1, 1]
bias = [1]
indices = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
values = [1, 2, 3, 4]
indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32')
dense_shape = [1, 1, 3, 4, 1]
correct_out_values = [[5], [11]]
sparse_input = core.eager.sparse_coo_tensor(indices, values,
dense_shape, False)
out = paddle.sparse.functional.conv3d(
sparse_input,
dense_kernel,
bias=paddle.to_tensor(
bias, dtype='float32'),
stride=strides,
padding=paddings,
dilation=dilations,
groups=1,
data_format="NDHWC")
out.backward(out)
assert np.array_equal(correct_out_values, out.values().numpy())
def test_subm_conv3d(self):
with _test_eager_guard():
indices = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
values = [[1], [2], [3], [4]]
indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32')
dense_shape = [1, 1, 3, 4, 1]
sparse_x = paddle.sparse.sparse_coo_tensor(
indices, values, dense_shape, stop_gradient=True)
weight = paddle.randn((1, 3, 3, 1, 1), dtype='float32')
y = paddle.sparse.functional.subm_conv3d(sparse_x, weight)
assert np.array_equal(sparse_x.indices().numpy(),
y.indices().numpy())
def test_Conv3D(self):
with _test_eager_guard():
#(4, non_zero_num), 4-D:(N, D, H, W)
indices = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
#(non_zero_num, C)
values = [[1], [2], [3], [4]]
indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32')
dense_shape = [1, 1, 3, 4, 1]
correct_out_values = [[4], [10]]
sparse_input = paddle.sparse.sparse_coo_tensor(indices, values,
dense_shape, False)
sparse_conv3d = paddle.sparse.Conv3D(
1, 1, (1, 3, 3), data_format='NDHWC')
sparse_out = sparse_conv3d(sparse_input)
#test errors
with self.assertRaises(ValueError):
#Currently, only support data_format='NDHWC'
conv3d = paddle.sparse.SubmConv3D(
1, 1, (1, 3, 3), data_format='NCDHW')
def test_SubmConv3D(self):
with _test_eager_guard():
indices = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
values = [[1], [2], [3], [4]]
indices = paddle.to_tensor(indices, dtype='int32')
values = paddle.to_tensor(values, dtype='float32')
dense_shape = [1, 1, 3, 4, 1]
correct_out_values = [[4], [10]]
sparse_input = paddle.sparse.sparse_coo_tensor(indices, values,
dense_shape, False)
subm_conv3d = paddle.sparse.SubmConv3D(
1, 1, (1, 3, 3), data_format='NDHWC')
# test extra_repr
print(subm_conv3d.extra_repr())
sparse_out = subm_conv3d(sparse_input)
# the output shape of subm_conv is same as input shape
assert np.array_equal(indices, sparse_out.indices().numpy())
#test errors
with self.assertRaises(ValueError):
#Currently, only support data_format='NDHWC'
conv3d = paddle.sparse.SubmConv3D(
1, 1, (1, 3, 3), data_format='NCDHW')