# 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 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 TrtConvertBilinearInterpV2Test(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)) ] ver = paddle_infer.get_trt_compile_version() # here is consistent with op_teller.cc if ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 7100: return False return True def sample_program_configs(self): def generate_input1(attrs: List[Dict[str, Any]]): return np.random.uniform( low=0.0, high=1.0, size=[1, 3, 64, 64] ).astype(np.float32) def generate_input2(attrs: List[Dict[str, Any]]): return np.random.uniform(low=0.5, high=6.0, size=(2)).astype( "float32" ) for data_layout in ["NCHW", "NHWC"]: for align_corners in [False, True]: for scale_y in [2.0, 1.0]: for scale_x in [2.0]: scale = [scale_y, scale_x] dics = [ { "data_layout": data_layout, "interp_method": "bilinear", "align_corners": align_corners, "align_mode": 0, "scale": scale, "out_h": -1, "out_w": -1, } ] ops_config = [ { "op_type": "bilinear_interp_v2", "op_inputs": { "X": ["input_data"], "Scale": ["input_scale"], }, "op_outputs": { "Out": ["bilinear_interp_v2_output_data"] }, "op_attrs": dics[0], } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={ "input_scale": TensorConfig( data_gen=partial(generate_input2, dics) ) }, inputs={ "input_data": TensorConfig( data_gen=partial(generate_input1, dics) ) }, outputs=["bilinear_interp_v2_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, 64, 64]} self.dynamic_shape.max_input_shape = {"input_data": [4, 3, 64, 64]} self.dynamic_shape.opt_input_shape = {"input_data": [1, 3, 64, 64]} 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 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-2 # 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, 1e-5) self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( attrs, True ), 1e-2 def test(self): self.run_test() class TrtConvertBilinearInterpV2Test1(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)) ] ver = paddle_infer.get_trt_compile_version() # here is consistent with op_teller.cc if ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 7100: return False return True def sample_program_configs(self): self.workspace_size = 1 << 32 def generate_input1(attrs: List[Dict[str, Any]]): return np.random.uniform( low=0.0, high=1.0, size=[1, 18, 144, 144] ).astype(np.float32) for data_layout in ["NCHW", "NHWC"]: for align_corners in [False, True]: for out_h in [128, 288]: for out_w in [288]: dics = [ { "data_layout": data_layout, "interp_method": "bilinear", "align_corners": align_corners, "align_mode": 0, "scale": [], "out_h": out_h, "out_w": out_w, } ] ops_config = [ { "op_type": "bilinear_interp_v2", "op_inputs": { "X": ["input_data"], }, "op_outputs": { "Out": ["bilinear_interp_v2_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=["bilinear_interp_v2_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, 18, 144, 144] } self.dynamic_shape.max_input_shape = { "input_data": [8, 18, 144, 144] } self.dynamic_shape.opt_input_shape = { "input_data": [4, 18, 144, 144] } 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 program_config.set_input_type(np.float16) yield self.create_inference_config(), generate_trt_nodes_num( attrs, False ), 1e-2 # 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, 1e-5) self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( attrs, True ), 1e-2 def test(self): self.run_test() if __name__ == "__main__": unittest.main()