# 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 unittest from functools import partial from typing import 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 TrtConvertPad3dTensorPadding(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: valid_version = (8, 2, 0) compile_version = paddle_infer.get_trt_compile_version() runtime_version = paddle_infer.get_trt_runtime_version() self.assertTrue(compile_version == runtime_version) if compile_version < valid_version: return False return True def sample_program_configs(self): def generate_input1(): shape = [6, 6, 6, 64, 64] return np.random.uniform(low=0.1, high=1.0, size=shape).astype( np.float32 ) def generate_paddings(p): return np.array(p).astype(np.int32) for value in [0, 1.5, 2, 2.5, 3]: for paddings in [ [0, 0, 0, 0, 1, 1], [0, 0, 1, 2, 1, 2], [1, 1, 1, 1, 1, 1], [0, 0, -1, -1, 1, 1], ]: for pad_mode in ['constant', 'reflect', 'replicate']: dics = [ { "value": value, "data_format": "NCDHW", "mode": pad_mode, "paddings": [], }, {}, ] ops_config = [ { "op_type": "pad3d", "op_inputs": { "X": ["input_data"], "Paddings": ["input_paddings"], }, "op_outputs": {"Out": ["output_data"]}, "op_attrs": dics[0], } ] ops = self.generate_op_config(ops_config) inputs = { "input_data": TensorConfig( data_gen=partial(generate_input1) ) } program_config = ProgramConfig( ops=ops, weights={ "input_paddings": TensorConfig( data_gen=partial(generate_paddings, paddings) ) }, inputs=inputs, outputs=["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": [6, 6, 6, 64, 64], } self.dynamic_shape.max_input_shape = { "input_data": [8, 8, 8, 66, 66], } self.dynamic_shape.opt_input_shape = { "input_data": [6, 6, 6, 64, 64], } def clear_dynamic_shape(): self.dynamic_shape.max_input_shape = {} self.dynamic_shape.min_input_shape = {} self.dynamic_shape.opt_input_shape = {} def generate_trt_nodes_num(attrs, dynamic_shape): if dynamic_shape: return 1, 2 return 0, 3 attrs = [ program_config.ops[i].attrs for i in range(len(program_config.ops)) ] 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 self.trt_param.precision = paddle_infer.PrecisionType.Half program_config.set_input_type(np.float16) yield self.create_inference_config(), generate_trt_nodes_num( attrs, False ), 1e-3 # 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 self.trt_param.precision = paddle_infer.PrecisionType.Half program_config.set_input_type(np.float16) yield self.create_inference_config(), generate_trt_nodes_num( attrs, True ), 1e-3 def test(self): self.run_test() class TrtConvertPad3dListPadding(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: valid_version = (8, 2, 0) compile_version = paddle_infer.get_trt_compile_version() runtime_version = paddle_infer.get_trt_runtime_version() self.assertTrue(compile_version == runtime_version) if compile_version < valid_version: return False return True def sample_program_configs(self): def generate_input1(): shape = [6, 6, 6, 64, 64] return np.random.uniform(low=0.1, high=1.0, size=shape).astype( np.float32 ) for value in [0, 1.1, 2.3, 3]: for paddings in [ [0, 0, 0, 0, 1, 1], [0, 0, 1, 2, 1, 2], [1, 1, 1, 1, 1, 1], [0, 0, -1, -1, 1, 1], ]: for pad_mode in ['constant', 'reflect', 'replicate']: dics = [ { "value": value, "data_format": "NCDHW", "mode": pad_mode, "paddings": paddings, }, {}, ] ops_config = [ { "op_type": "pad3d", "op_inputs": {"X": ["input_data"]}, "op_outputs": {"Out": ["output_data"]}, "op_attrs": dics[0], } ] ops = self.generate_op_config(ops_config) inputs = { "input_data": TensorConfig( data_gen=partial(generate_input1) ) } program_config = ProgramConfig( ops=ops, weights={}, inputs=inputs, outputs=["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": [6, 6, 6, 64, 64], } self.dynamic_shape.max_input_shape = { "input_data": [8, 8, 8, 66, 66], } self.dynamic_shape.opt_input_shape = { "input_data": [6, 6, 6, 64, 64], } def clear_dynamic_shape(): self.dynamic_shape.max_input_shape = {} self.dynamic_shape.min_input_shape = {} self.dynamic_shape.opt_input_shape = {} def generate_trt_nodes_num(attrs, dynamic_shape): if dynamic_shape: return 1, 2 return 0, 3 attrs = [ program_config.ops[i].attrs for i in range(len(program_config.ops)) ] 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 self.trt_param.precision = paddle_infer.PrecisionType.Half program_config.set_input_type(np.float16) yield self.create_inference_config(), generate_trt_nodes_num( attrs, False ), 1e-3 # 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 self.trt_param.precision = paddle_infer.PrecisionType.Half program_config.set_input_type(np.float16) yield self.create_inference_config(), generate_trt_nodes_num( attrs, True ), 1e-3 def test(self): self.run_test() if __name__ == "__main__": unittest.main()