# 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 TrtConvertStackTest(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: inputs = program_config.inputs weights = program_config.weights outputs = program_config.outputs attrs = [ program_config.ops[i].attrs for i in range(len(program_config.ops)) ] # The input dimension should be less than the set axis. if len(inputs['stack_input1'].shape) < attrs[0]['axis']: return False return True def sample_program_configs(self): def generate_input1(attrs: List[Dict[str, Any]], batch): if self.dims == 4: return np.random.random([batch, 3, 24, 24]).astype(np.float32) elif self.dims == 3: return np.random.random([batch, 3, 24]).astype(np.float32) elif self.dims == 2: return np.random.random([batch, 24]).astype(np.float32) elif self.dims == 1: return np.random.random([24]).astype(np.float32) def generate_input2(attrs: List[Dict[str, Any]], batch): if self.dims == 4: return np.random.random([batch, 3, 24, 24]).astype(np.float32) elif self.dims == 3: return np.random.random([batch, 3, 24]).astype(np.float32) elif self.dims == 2: return np.random.random([batch, 24]).astype(np.float32) elif self.dims == 1: return np.random.random([24]).astype(np.float32) def generate_input3(attrs: List[Dict[str, Any]], batch): if self.dims == 4: return np.random.random([batch, 3, 24, 24]).astype(np.float32) elif self.dims == 3: return np.random.random([batch, 3, 24]).astype(np.float32) elif self.dims == 2: return np.random.random([batch, 24]).astype(np.float32) elif self.dims == 1: return np.random.random([24]).astype(np.float32) for dims in [1, 2, 3, 4]: for batch in [1, 4]: for axis in [-2, -1, 0, 1, 2, 3]: self.dims = dims dics = [{"axis": axis}, {}] ops_config = [ { "op_type": "stack", "op_inputs": { "X": [ "stack_input1", "stack_input2", "stack_input3", ] }, "op_outputs": {"Y": ["stack_output"]}, "op_attrs": dics[0], } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "stack_input1": TensorConfig( data_gen=partial(generate_input1, dics, batch) ), "stack_input2": TensorConfig( data_gen=partial(generate_input2, dics, batch) ), "stack_input3": TensorConfig( data_gen=partial(generate_input3, dics, batch) ), }, outputs=["stack_output"], ) yield program_config def sample_predictor_configs( self, program_config ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.dims == 4: self.dynamic_shape.min_input_shape = { "stack_input1": [1, 3, 24, 24], "stack_input2": [1, 3, 24, 24], "stack_input3": [1, 3, 24, 24], } self.dynamic_shape.max_input_shape = { "stack_input1": [4, 3, 48, 48], "stack_input2": [4, 3, 48, 48], "stack_input3": [4, 3, 48, 48], } self.dynamic_shape.opt_input_shape = { "stack_input1": [1, 3, 24, 24], "stack_input2": [1, 3, 24, 24], "stack_input3": [1, 3, 24, 24], } elif self.dims == 3: self.dynamic_shape.min_input_shape = { "stack_input1": [1, 3, 24], "stack_input2": [1, 3, 24], "stack_input3": [1, 3, 24], } self.dynamic_shape.max_input_shape = { "stack_input1": [4, 3, 48], "stack_input2": [4, 3, 48], "stack_input3": [4, 3, 48], } self.dynamic_shape.opt_input_shape = { "stack_input1": [1, 3, 24], "stack_input2": [1, 3, 24], "stack_input3": [1, 3, 24], } elif self.dims == 2: self.dynamic_shape.min_input_shape = { "stack_input1": [1, 24], "stack_input2": [1, 24], "stack_input3": [1, 24], } self.dynamic_shape.max_input_shape = { "stack_input1": [4, 48], "stack_input2": [4, 48], "stack_input3": [4, 48], } self.dynamic_shape.opt_input_shape = { "stack_input1": [1, 24], "stack_input2": [1, 24], "stack_input3": [1, 24], } elif self.dims == 1: self.dynamic_shape.min_input_shape = { "stack_input1": [24], "stack_input2": [24], "stack_input3": [24], } self.dynamic_shape.max_input_shape = { "stack_input1": [48], "stack_input2": [48], "stack_input3": [48], } self.dynamic_shape.opt_input_shape = { "stack_input1": [24], "stack_input2": [24], "stack_input3": [24], } 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): if dynamic_shape: return 1, 4 else: return 0, 5 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 # 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 def add_skip_trt_case(self): pass def test(self): self.add_skip_trt_case() self.run_test() if __name__ == "__main__": unittest.main()