# 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. from trt_layer_auto_scan_test import TrtLayerAutoScanTest, SkipReasons from program_config import TensorConfig, ProgramConfig import numpy as np import unittest import paddle.inference as paddle_infer from functools import partial from typing import Any, Dict, List class TrtConvertConv2dTransposeTest(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[0]: 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, num_channels, attrs: List[Dict[str, Any]]): return np.ones([batch, num_channels, 64, 64]).astype(np.float32) def generate_weight1(num_channels, attrs: List[Dict[str, Any]]): if attrs[0]['groups'] == 1: return np.random.random([num_channels, num_channels, 3, 3]).astype(np.float32) else: return np.random.random( [num_channels, int(num_channels / 2), 3, 3]).astype(np.float32) for num_channels in [2, 4, 6]: for batch in [1, 4]: for strides in [[2, 2], [1, 2]]: for paddings in [[0, 3], [1, 2, 3, 4]]: for groups in [2]: for padding_algorithm in [ 'EXPLICIT', 'SAME', 'VALID' ]: for dilations in [[2, 2], [1, 2]]: for data_format in ['NCHW']: self.num_channels = num_channels 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, num_channels, dics)) }, inputs={ "input_data": TensorConfig(data_gen=partial( generate_input1, batch, num_channels, 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): if self.num_channels == 2: self.dynamic_shape.min_input_shape = { "input_data": [1, 2, 32, 32], "output_data": [1, 24, 32, 32] } self.dynamic_shape.max_input_shape = { "input_data": [4, 2, 64, 64], "output_data": [4, 24, 64, 64] } self.dynamic_shape.opt_input_shape = { "input_data": [1, 2, 64, 64], "output_data": [1, 24, 64, 64] } elif self.num_channels == 4: self.dynamic_shape.min_input_shape = { "input_data": [1, 4, 32, 32], "output_data": [1, 24, 32, 32] } self.dynamic_shape.max_input_shape = { "input_data": [4, 4, 64, 64], "output_data": [4, 24, 64, 64] } self.dynamic_shape.opt_input_shape = { "input_data": [1, 4, 64, 64], "output_data": [1, 24, 64, 64] } else: self.dynamic_shape.min_input_shape = { "input_data": [1, 6, 32, 32], "output_data": [1, 24, 32, 32] } self.dynamic_shape.max_input_shape = { "input_data": [4, 6, 64, 64], "output_data": [4, 24, 64, 64] } self.dynamic_shape.opt_input_shape = { "input_data": [1, 6, 64, 64], "output_data": [1, 24, 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 yield self.create_inference_config(), generate_trt_nodes_num( attrs, False), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half 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 yield self.create_inference_config(), generate_trt_nodes_num( attrs, True), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half 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) # Special case class TrtConvertConv2dTransposeTest2(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: 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, num_channels, attrs: List[Dict[str, Any]]): return np.ones([batch, num_channels, 20, 30]).astype(np.float32) def generate_weight1(num_channels, attrs: List[Dict[str, Any]]): return np.random.random([num_channels, 64, 3, 3]).astype(np.float32) num_channels = 128 batch = 1 self.num_channels = num_channels dics = [{ "data_fromat": 'NCHW', "dilations": [1, 1], "padding_algorithm": 'EXPLICIT', "groups": 1, "paddings": [1, 1], "strides": [2, 2], "output_padding": [1, 1], "output_size": [], }] 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, num_channels, dics)) }, inputs={ "input_data": TensorConfig(data_gen=partial(generate_input1, batch, num_channels, 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, 128, 20, 30], } self.dynamic_shape.max_input_shape = { "input_data": [1, 128, 20, 30], } self.dynamic_shape.opt_input_shape = { "input_data": [1, 128, 20, 30], } 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-4 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( attrs, False), (1e0, 1e-3) # 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-4 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( attrs, True), (1e0, 1e-3) def add_skip_trt_case(self): pass 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()