test_trt_convert_hard_swish.py

# Copyright (c) 2021 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 trt_layer_auto_scan_test import TrtLayerAutoScanTest
from program_config import TensorConfig, ProgramConfig
import numpy as np
import paddle.inference as paddle_infer
from functools import partial
from typing import Any, Dict, List
import unittest


class TrtConvertHardSwishTest(TrtLayerAutoScanTest):

    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        inputs = program_config.inputs
        weights = program_config.weights
        attrs = [
            program_config.ops[i].attrs for i in range(len(program_config.ops))
        ]

        if attrs[0]['threshold'] <= 0 or attrs[0]['scale'] <= 0:
            return False

        return True

    def sample_program_configs(self):

        def generate_input1(attrs: List[Dict[str, Any]]):
            return np.ones([1, 3, 32, 32]).astype(np.float32)

        for threshold in [6.0, 7.0, 100.0, 0.0, -1.0]:
            for scale in [5.0, 7.0, -1.0, 0.0, 100.0]:
                for offset in [3.0, 5.0, -1.0, 0.0, 100.0]:
                    dics = [{
                        "threshold": threshold,
                        "scale": scale,
                        "offset": offset
                    }]

                    ops_config = [{
                        "op_type": "hard_swish",
                        "op_inputs": {
                            "X": ["input_data"]
                        },
                        "op_outputs": {
                            "Out": ["hard_swish_output_data"]
                        },
                        "op_attrs": dics[0]
                    }]
                    ops = self.generate_op_config(ops_config)

                    program_config = ProgramConfig(
                        ops=ops,
                        weights={},
                        inputs={
                            "input_data":
                            TensorConfig(
                                data_gen=partial(generate_input1, dics))
                        },
                        outputs=["hard_swish_output_data"])

                    yield program_config

    def sample_predictor_configs(
            self, program_config) -> (paddle_infer.Config, List[int], float):

        def generate_dynamic_shape(attrs):
            self.dynamic_shape.min_input_shape = {"input_data": [1, 3, 16, 16]}
            self.dynamic_shape.max_input_shape = {"input_data": [2, 3, 32, 32]}
            self.dynamic_shape.opt_input_shape = {"input_data": [1, 3, 32, 32]}

        def clear_dynamic_shape():
            self.dynamic_shape.min_input_shape = {}
            self.dynamic_shape.max_input_shape = {}
            self.dynamic_shape.opt_input_shape = {}

        def generate_trt_nodes_num(attrs, dynamic_shape):
            return 1, 2

        attrs = [
            program_config.ops[i].attrs for i in range(len(program_config.ops))
        ]

        # for static_shape
        clear_dynamic_shape()
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        yield self.create_inference_config(), generate_trt_nodes_num(
            attrs, False), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), generate_trt_nodes_num(
            attrs, False), (1e-3, 1e-3)

        # for dynamic_shape
        generate_dynamic_shape(attrs)
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        yield self.create_inference_config(), generate_trt_nodes_num(
            attrs, True), 1e-5
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        yield self.create_inference_config(), generate_trt_nodes_num(
            attrs, True), (1e-3, 1e-3)

    def test(self):
        self.run_test()


if __name__ == "__main__":
    unittest.main()