<filename>paddle/infrt/tests/models/abs_model.py
# 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.

import paddle
from paddle.nn import Layer
from paddle.static import InputSpec
from paddle.jit import to_static
import sys


class AbsNet(paddle.nn.Layer):
    def __init__(self):
        super(AbsNet, self).__init__()

    def forward(self, x):
        x = paddle.abs(x)
        return x


if __name__ == '__main__':
    # build network
    model = AbsNet()
    # save inferencing format model
    net = to_static(
        model, input_spec=[InputSpec(
            shape=[None, 1, 28, 28], name='x')])
    paddle.jit.save(net, sys.argv[1])

<filename>paddle/infrt/tests/models/abs_model.py
# 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.

import paddle
from paddle.nn import Layer
from paddle.static import InputSpec
from paddle.jit import to_static
import sys


class AbsNet(paddle.nn.Layer):
    def __init__(self):
        super(AbsNet, self).__init__()

    def forward(self, x):
        x = paddle.abs(x)
        return x


if __name__ == '__main__':
    # build network
    model = AbsNet()
    # save inferencing format model
    net = to_static(
        model, input_spec=[InputSpec(
            shape=[None, 1, 28, 28], name='x')])
    paddle.jit.save(net, sys.argv[1])

# 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. # build network # save inferencing format model