wehub-resource-sync
122 lines
3.8 KiB
Python
122 lines
3.8 KiB
Python
# Copyright (c) 2025 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.
|
|
|
|
import unittest
|
|
|
|
import numpy as np
|
|
from inference_pass_test import InferencePassTest
|
|
|
|
import paddle
|
|
from paddle.framework import core
|
|
from paddle.inference import Config, create_predictor
|
|
|
|
|
|
class TestNet(paddle.nn.Layer):
|
|
def __init__(self, in_planes, out_planes):
|
|
super().__init__()
|
|
self.sp_conv = paddle.sparse.nn.Conv2D(
|
|
in_planes,
|
|
out_planes,
|
|
kernel_size=3,
|
|
stride=2,
|
|
padding=1,
|
|
bias_attr=True,
|
|
)
|
|
self.sp_bn = paddle.sparse.nn.BatchNorm(
|
|
out_planes, epsilon=1e-3, momentum=1 - 0.01, data_format='NHWC'
|
|
)
|
|
|
|
def forward(self, indices, values):
|
|
x = paddle.sparse.sparse_coo_tensor(
|
|
indices=indices,
|
|
values=values,
|
|
shape=[1, 32, 32, 3],
|
|
dtype='float32',
|
|
)
|
|
x = self.sp_conv(x)
|
|
x = self.sp_bn(x)
|
|
return x.to_dense()
|
|
|
|
|
|
class SparseConvUsingBuffer(InferencePassTest):
|
|
def setUp(self):
|
|
paddle.disable_static()
|
|
self.test_model = TestNet(3, 3)
|
|
self.test_values = np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]]).astype(
|
|
'float32'
|
|
)
|
|
self.test_indices = np.array(
|
|
[[0, 0, 0], [0, 16, 16], [0, 20, 8]]
|
|
).astype('int32')
|
|
self.out_baseline = self.test_model(
|
|
paddle.to_tensor(self.test_indices, stop_gradient=False),
|
|
paddle.to_tensor(self.test_values, stop_gradient=False),
|
|
).flatten()
|
|
|
|
self.path_prefix = "inference_test_models/sparse_conv_using_buffer"
|
|
self.cache_dir = "inference_test_models/cache"
|
|
paddle.jit.save(
|
|
self.test_model,
|
|
self.path_prefix,
|
|
input_spec=[
|
|
paddle.static.InputSpec(
|
|
shape=[3, -1], dtype='int32', name="indices"
|
|
),
|
|
paddle.static.InputSpec(
|
|
shape=[-1, 3], dtype='float32', name="values"
|
|
),
|
|
],
|
|
)
|
|
|
|
def test_check_output(self):
|
|
if core.is_compiled_with_cuda():
|
|
out_check = self.inference()
|
|
np.testing.assert_allclose(
|
|
self.out_baseline, out_check, rtol=1e-5, atol=1e-2
|
|
)
|
|
|
|
def inference(self):
|
|
# Config
|
|
config = Config(
|
|
self.path_prefix + ".json", self.path_prefix + ".pdiparams"
|
|
)
|
|
config.enable_use_gpu(100, 0)
|
|
config.set_optim_cache_dir(self.cache_dir)
|
|
config.exp_sparse_conv_using_buffer([[3, 3]], [[2, 2]])
|
|
|
|
# predictor
|
|
predictor = create_predictor(config)
|
|
|
|
# inference
|
|
values_tensor = predictor.get_input_handle("values")
|
|
indices_tensor = predictor.get_input_handle("indices")
|
|
|
|
values_tensor.reshape(self.test_values.shape)
|
|
indices_tensor.reshape(self.test_indices.shape)
|
|
|
|
values_tensor.copy_from_cpu(self.test_values.copy())
|
|
indices_tensor.copy_from_cpu(self.test_indices.copy())
|
|
|
|
predictor.run()
|
|
output_tensor = predictor.get_output_handle(
|
|
predictor.get_output_names()[0]
|
|
)
|
|
out = output_tensor.copy_to_cpu()
|
|
out = np.array(out).flatten()
|
|
return out
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|