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test_sparse_norm_op.py
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test_sparse_norm_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.incubate.sparse import nn
import paddle.fluid as fluid
from paddle.fluid.framework import _test_eager_guard
import copy
class TestSparseBatchNorm(unittest.TestCase):
def test(self):
fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
with _test_eager_guard():
paddle.seed(0)
channels = 4
shape = [2, 3, 6, 6, channels]
#there is no zero in dense_x
dense_x = paddle.randn(shape)
dense_x.stop_gradient = False
batch_norm = paddle.nn.BatchNorm3D(channels, data_format="NDHWC")
dense_y = batch_norm(dense_x)
dense_y.backward(dense_y)
sparse_dim = 4
dense_x2 = copy.deepcopy(dense_x)
dense_x2.stop_gradient = False
sparse_x = dense_x2.to_sparse_coo(sparse_dim)
sparse_batch_norm = paddle.incubate.sparse.nn.BatchNorm(channels)
# set same params
sparse_batch_norm._mean.set_value(batch_norm._mean)
sparse_batch_norm._variance.set_value(batch_norm._variance)
sparse_batch_norm.weight.set_value(batch_norm.weight)
sparse_y = sparse_batch_norm(sparse_x)
# compare the result with dense batch_norm
assert np.allclose(dense_y.flatten().numpy(),
sparse_y.values().flatten().numpy(),
atol=1e-5,
rtol=1e-5)
# test backward
sparse_y.backward(sparse_y)
assert np.allclose(dense_x.grad.flatten().numpy(),
sparse_x.grad.values().flatten().numpy(),
atol=1e-5,
rtol=1e-5)
fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
def test_error_layout(self):
with _test_eager_guard():
with self.assertRaises(ValueError):
shape = [2, 3, 6, 6, 3]
x = paddle.randn(shape)
sparse_x = x.to_sparse_coo(4)
sparse_batch_norm = paddle.incubate.sparse.nn.BatchNorm(
3, data_format='NCDHW')
sparse_batch_norm(sparse_x)
def test2(self):
with _test_eager_guard():
paddle.seed(123)
channels = 3
x_data = paddle.randn((1, 6, 6, 6, channels)).astype('float32')
dense_x = paddle.to_tensor(x_data)
sparse_x = dense_x.to_sparse_coo(4)
batch_norm = paddle.incubate.sparse.nn.BatchNorm(channels)
batch_norm_out = batch_norm(sparse_x)
print(batch_norm_out.shape)
# [1, 6, 6, 6, 3]
class TestConvertSyncBatchNorm(unittest.TestCase):
def test_convert(self):
base_model = paddle.nn.Sequential(nn.Conv3D(3, 5, 3), nn.BatchNorm(5),
nn.BatchNorm(5))
model = paddle.nn.Sequential(
nn.Conv3D(3, 5, 3), nn.BatchNorm(5),
nn.BatchNorm(5,
weight_attr=fluid.ParamAttr(name='bn.scale'),
bias_attr=fluid.ParamAttr(name='bn.bias')))
model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
for idx, sublayer in enumerate(base_model.sublayers()):
if isinstance(sublayer, nn.BatchNorm):
self.assertEqual(isinstance(model[idx], nn.SyncBatchNorm), True)
if __name__ == "__main__":
unittest.main()