# 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. import unittest from functools import partial from typing import Any, Dict, List import numpy as np from program_config import ProgramConfig, TensorConfig from trt_layer_auto_scan_test import TrtLayerAutoScanTest import paddle.inference as paddle_infer class TrtConvertStridedSliceTest(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)) ] return True def sample_program_configs(self): def generate_input1(attrs: List[Dict[str, Any]]): return np.random.random([1, 56, 56, 192]).astype(np.float32) for axes in [[1, 2]]: for starts in [[1, 1]]: for ends in [[10000000, 10000000]]: for decrease_axis in [[]]: for infer_flags in [[1, 1]]: for strides in [[2, 2]]: dics = [ { "axes": axes, "starts": starts, "ends": ends, "decrease_axis": decrease_axis, "infer_flags": infer_flags, "strides": strides, } ] ops_config = [ { "op_type": "strided_slice", "op_inputs": {"Input": ["input_data"]}, "op_outputs": { "Out": ["slice_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=["slice_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, 56, 56, 192] } self.dynamic_shape.max_input_shape = { "input_data": [8, 56, 56, 192] } self.dynamic_shape.opt_input_shape = { "input_data": [4, 56, 56, 192] } 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): inputs = program_config.inputs if dynamic_shape: for i in range(len(attrs[0]["starts"])): if attrs[0]["starts"][i] < 0 or attrs[0]["ends"][i] < 0: return 0, 3 if not dynamic_shape: for x in attrs[0]["axes"]: if x == 0: return 0, 3 ver = paddle_infer.get_trt_compile_version() if ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 7000: return 0, 3 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 program_config.set_input_type(np.float32) yield self.create_inference_config(), generate_trt_nodes_num( attrs, False ), 1e-5 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 program_config.set_input_type(np.float32) yield self.create_inference_config(), generate_trt_nodes_num( attrs, True ), 1e-5 def test(self): self.run_test() class TrtConvertStridedSliceTest2(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input1(attrs: List[Dict[str, Any]]): return np.random.random([1, 56, 56, 192]).astype(np.float32) for axes in [[1, 2], [2, 3], [1, 3]]: for starts in [ [-10, 1], [-10, 20], [-10, 15], [-10, 16], [-10, 20], ]: for ends in [[-9, 10000], [-9, -1], [-9, 40]]: for decrease_axis in [[]]: for infer_flags in [[1, 1]]: for strides in [[2, 2]]: dics = [ { "axes": axes, "starts": starts, "ends": ends, "decrease_axis": [axes[0]], "infer_flags": infer_flags, "strides": strides, } ] ops_config = [ { "op_type": "strided_slice", "op_inputs": {"Input": ["input_data"]}, "op_outputs": { "Out": ["slice_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=["slice_output_data"], ) yield program_config def sample_predictor_configs( self, program_config ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(): self.dynamic_shape.min_input_shape = { "input_data": [1, 56, 56, 192] } self.dynamic_shape.max_input_shape = { "input_data": [8, 100, 100, 200] } self.dynamic_shape.opt_input_shape = { "input_data": [4, 56, 56, 192] } def clear_dynamic_shape(): self.dynamic_shape.min_input_shape = {} self.dynamic_shape.max_input_shape = {} self.dynamic_shape.opt_input_shape = {} # for static_shape clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 program_config.set_input_type(np.float32) yield self.create_inference_config(), (1, 2), 1e-5 # for dynamic_shape generate_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 program_config.set_input_type(np.float32) yield self.create_inference_config(), (1, 2), 1e-5 def test(self): self.run_test() if __name__ == "__main__": unittest.main()