# 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 TrtConvertPreluTest(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input(attrs: List[Dict[str, Any]], batch): if self.dims == 0: return np.random.random([]).astype(np.float32) elif self.dims == 1: return np.random.random([16]).astype(np.float32) elif self.dims == 2: return np.random.random([1, 3]).astype(np.float32) elif self.dims == 3: if attrs[0]["data_format"] == "NCHW": return np.random.random([batch, 3, 16]).astype(np.float32) elif attrs[0]["data_format"] == "NHWC": return np.random.random([batch, 16, 3]).astype(np.float32) else: raise AssertionError() else: if attrs[0]["data_format"] == "NCHW": return np.random.random([batch, 3, 16, 32]).astype( np.float32 ) else: return np.random.random([batch, 16, 32, 3]).astype( np.float32 ) def generate_alpha(attrs: List[Dict[str, Any]]): if self.dims == 0: return np.random.random([]).astype(np.float32) if attrs[0]["mode"] == "all": return np.random.random([1]).astype(np.float32) elif attrs[0]["mode"] == "channel": return np.random.random([3]).astype(np.float32) elif attrs[0]["mode"] == "element": if self.dims == 1: return np.random.random([16]).astype(np.float32) elif self.dims == 2: return np.random.random([1, 3]).astype(np.float32) elif self.dims == 3: if attrs[0]["data_format"] == "NCHW": return np.random.random([1, 3, 16]).astype(np.float32) elif attrs[0]["data_format"] == "NHWC": return np.random.random([1, 16, 3]).astype(np.float32) else: raise AssertionError() else: if attrs[0]["data_format"] == "NCHW": return np.random.random([1, 3, 16, 32]).astype( np.float32 ) elif attrs[0]["data_format"] == "NHWC": return np.random.random([1, 16, 32, 3]).astype( np.float32 ) else: raise AssertionError() for batch in [1, 4]: for dims in [0, 1, 2, 3, 4]: for mode in ["all", "element", "channel"]: for data_format in ["NCHW", "NHWC"]: if (mode == "element" or mode == "all") and dims == 0: continue if mode == "channel" and dims != 4: continue self.dims = dims dics = [{"mode": mode, "data_format": data_format}] ops_config = [ { "op_type": "prelu", "op_inputs": { "X": ["input_data"], "Alpha": ["alpha_weight"], }, "op_outputs": {"Out": ["output_data"]}, "op_attrs": dics[0], } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={ "alpha_weight": TensorConfig( data_gen=partial(generate_alpha, dics) ) }, inputs={ "input_data": TensorConfig( data_gen=partial( generate_input, dics, batch ) ), }, outputs=["output_data"], ) yield program_config def sample_predictor_configs( self, program_config ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.dims == 0: self.dynamic_shape.min_input_shape = {"input_data": []} self.dynamic_shape.max_input_shape = {"input_data": []} self.dynamic_shape.opt_input_shape = {"input_data": []} elif self.dims == 1: self.dynamic_shape.min_input_shape = {"input_data": [16]} self.dynamic_shape.max_input_shape = {"input_data": [16]} self.dynamic_shape.opt_input_shape = {"input_data": [16]} elif self.dims == 2: self.dynamic_shape.min_input_shape = {"input_data": [1, 3]} self.dynamic_shape.max_input_shape = {"input_data": [1, 3]} self.dynamic_shape.opt_input_shape = {"input_data": [1, 3]} elif self.dims == 3: if attrs[0]["data_format"] == "NCHW": self.dynamic_shape.min_input_shape = { "input_data": [1, 3, 16] } self.dynamic_shape.max_input_shape = { "input_data": [4, 3, 16] } self.dynamic_shape.opt_input_shape = { "input_data": [1, 3, 16] } elif attrs[0]["data_format"] == "NHWC": self.dynamic_shape.min_input_shape = { "input_data": [1, 16, 3] } self.dynamic_shape.max_input_shape = { "input_data": [4, 16, 3] } self.dynamic_shape.opt_input_shape = { "input_data": [1, 16, 3] } else: raise AssertionError() else: if attrs[0]["data_format"] == "NCHW": self.dynamic_shape.min_input_shape = { "input_data": [1, 3, 16, 32] } self.dynamic_shape.max_input_shape = { "input_data": [4, 3, 16, 32] } self.dynamic_shape.opt_input_shape = { "input_data": [1, 3, 16, 32] } elif attrs[0]["data_format"] == "NHWC": self.dynamic_shape.min_input_shape = { "input_data": [1, 16, 32, 3] } self.dynamic_shape.max_input_shape = { "input_data": [4, 16, 32, 3] } self.dynamic_shape.opt_input_shape = { "input_data": [1, 16, 32, 3] } else: raise AssertionError() def clear_dynamic_shape(): self.dynamic_shape.max_input_shape = {} self.dynamic_shape.min_input_shape = {} self.dynamic_shape.opt_input_shape = {} attrs = [ program_config.ops[i].attrs for i in range(len(program_config.ops)) ] def generate_trt_nodes_num(attrs, dynamic_shape): if not dynamic_shape and (self.dims == 1 or self.dims == 0): return 0, 3 return 1, 2 # 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-3, 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, 1e-3) def test(self): self.run_test() if __name__ == "__main__": unittest.main()