# 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 SkipReasons, TrtLayerAutoScanTest import paddle.inference as paddle_infer class TrtConvertDepthwiseConv2dTransposeTest(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 ( inputs['input_data'].shape[1] != weights['conv2d_weight'].shape[1] * attrs[0]['groups'] ): return False if inputs['input_data'].shape[1] != weights['conv2d_weight'].shape[1]: return False if inputs['input_data'].shape[1] != attrs[0]['groups']: return False if attrs[0]['dilations'][0] != 1 or attrs[0]['dilations'][1] != 1: return False ver = paddle_infer.get_trt_compile_version() if ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 7000: return False return True def sample_program_configs(self): self.trt_param.workspace_size = 1073741824 def generate_input1(batch, attrs: List[Dict[str, Any]]): return np.ones([batch, attrs[0]['groups'], 64, 64]).astype( np.float32 ) def generate_weight1(attrs: List[Dict[str, Any]]): return np.random.random([attrs[0]['groups'], 1, 3, 3]).astype( np.float32 ) for batch in [1, 2, 4]: for strides in [[1, 1], [2, 2], [1, 2]]: for paddings in [[0, 3], [1, 2, 3, 4]]: for groups in [1, 2, 3]: for padding_algorithm in ['EXPLICIT', 'SAME', 'VALID']: for dilations in [[1, 1], [2, 2], [1, 2]]: for data_format in ['NCHW']: dics = [ { "data_fromat": data_format, "dilations": dilations, "padding_algorithm": padding_algorithm, "groups": groups, "paddings": paddings, "strides": strides, "data_format": data_format, "output_size": [], "output_padding": [], } ] ops_config = [ { "op_type": "conv2d_transpose", "op_inputs": { "Input": ["input_data"], "Filter": ["conv2d_weight"], }, "op_outputs": { "Output": ["output_data"] }, "op_attrs": dics[0], } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={ "conv2d_weight": TensorConfig( data_gen=partial( generate_weight1, dics ) ) }, inputs={ "input_data": TensorConfig( data_gen=partial( generate_input1, batch, dics ) ) }, 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": [1, attrs[0]['groups'], 32, 32], "output_data": [1, attrs[0]['groups'], 32, 32], } self.dynamic_shape.max_input_shape = { "input_data": [4, attrs[0]['groups'], 64, 64], "output_data": [4, attrs[0]['groups'], 64, 64], } self.dynamic_shape.opt_input_shape = { "input_data": [1, attrs[0]['groups'], 64, 64], "output_data": [1, attrs[0]['groups'], 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-3, 1e-3) # self.trt_param.precision = paddle_infer.PrecisionType.Int8 # yield self.create_inference_config(), generate_trt_nodes_num( # attrs, False), (1e-5, 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 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) # self.trt_param.precision = paddle_infer.PrecisionType.Int8 # yield self.create_inference_config(), generate_trt_nodes_num( # attrs, True), (1e-5, 1e-5) def add_skip_trt_case(self): def teller1(program_config, predictor_config): if self.trt_param.precision == paddle_infer.PrecisionType.Int8: return True return False self.add_skip_case( teller1, SkipReasons.TRT_NOT_IMPLEMENTED, "When precisionType is int8 without relu op, output is different between Trt and Paddle.", ) def test(self): self.add_skip_trt_case() self.run_test() def test_quant(self): self.add_skip_trt_case() self.run_test(quant=True) if __name__ == "__main__": unittest.main()