From f070aa5c135c275b1597ee725acc8d7a1bfcaf49 Mon Sep 17 00:00:00 2001 From: JingZhuangzhuang <75348594+JZZ-NOTE@users.noreply.github.com> Date: Tue, 14 Sep 2021 20:27:20 +0800 Subject: [PATCH] Add clip convert test (#35694) * add anchor_generator test * add anchor_generator test * add clip convert test * delete wrong file Co-authored-by: xiaoxiaohehe001 --- paddle/fluid/inference/tensorrt/op_teller.cc | 24 +++ .../ir/inference/test_trt_convert_clip.py | 154 ++++++++++++++++++ 2 files changed, 178 insertions(+) create mode 100644 python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_clip.py diff --git a/paddle/fluid/inference/tensorrt/op_teller.cc b/paddle/fluid/inference/tensorrt/op_teller.cc index 906521d77aa..a4df1622ed5 100644 --- a/paddle/fluid/inference/tensorrt/op_teller.cc +++ b/paddle/fluid/inference/tensorrt/op_teller.cc @@ -903,6 +903,30 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8, return false; } + if (op_type == "clip") { + // Paddle-TRT does not support the input tensors: Min and Max + auto clip_inputs = desc.Inputs(); + if (clip_inputs.find("Min") != clip_inputs.end()) { + if (desc.Input("Min").size() >= 1) { + return false; + } + } + if (clip_inputs.find("Max") != clip_inputs.end()) { + if (desc.Input("Max").size() >= 1) { + return false; + } + } + + auto* block = desc.Block(); + auto x_var_name = desc.Input("X")[0]; + auto* x_var_desc = block->FindVar(x_var_name); + const auto x_shape = x_var_desc->GetShape(); + if (x_shape.size() == 1) { + VLOG(3) << "clip op does not support input's dim is 1 in tensorrt."; + return false; + } + } + if (op_type == "reduce_sum" || op_type == "reduce_mean") { if (!(desc.HasAttr("keep_dim") && desc.HasAttr("dim") && desc.HasAttr("reduce_all"))) { diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_clip.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_clip.py new file mode 100644 index 00000000000..95b4fb83d5b --- /dev/null +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_clip.py @@ -0,0 +1,154 @@ +# 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 paddle.inference as paddle_infer +from functools import partial +from typing import Optional, List, Callable, Dict, Any, Set + + +class TrtConvertClipTest(TrtLayerAutoScanTest): + def is_program_valid(self, program_config: ProgramConfig) -> bool: + return True + + def sample_program_configs(self): + def generate_input1(dims, batch, attrs: List[Dict[str, Any]]): + if dims == 1: + return np.ones([64]).astype(np.float32) + elif dims == 2: + return np.ones([3, 64]).astype(np.float32) + elif dims == 3: + return np.ones([3, 64, 64]).astype(np.float32) + else: + return np.ones([batch, 3, 64, 64]).astype(np.float32) + + def generate_weight1(attrs: List[Dict[str, Any]]): + return np.array([np.random.uniform(1, 10)]).astype("float32") + + def generate_weight2(attrs: List[Dict[str, Any]]): + return np.array([np.random.uniform(10, 20)]).astype("float32") + + for dims in [1, 2, 3, 4]: + for batch in [1, 2, 4]: + for op_inputs in [{ + "X": ["input_data"] + }, { + "X": ["input_data"], + "Min": ["Min_"], + "Max": ["Max_"] + }]: + self.input_num = len(op_inputs) + self.dims = dims + dics = [{ + "min": np.random.uniform(1, 10), + "max": np.random.uniform(10, 20) + }, { + "op_inputs": op_inputs + }] + ops_config = [{ + "op_type": "clip", + "op_inputs": op_inputs, + "op_outputs": { + "Out": ["output_data"] + }, + "op_attrs": dics[0] + }] + ops = self.generate_op_config(ops_config) + + program_config = ProgramConfig( + ops=ops, + weights={ + "Min_": TensorConfig(data_gen=partial( + generate_weight1, dics)), + "Max_": TensorConfig(data_gen=partial( + generate_weight2, dics)) + }, + inputs={ + "input_data": TensorConfig(data_gen=partial( + generate_input1, dims, 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): + if self.dims == 1: + self.dynamic_shape.min_input_shape = {"input_data": [1]} + self.dynamic_shape.max_input_shape = {"input_data": [128]} + self.dynamic_shape.opt_input_shape = {"input_data": [64]} + elif self.dims == 2: + self.dynamic_shape.min_input_shape = {"input_data": [1, 32]} + self.dynamic_shape.max_input_shape = {"input_data": [4, 64]} + self.dynamic_shape.opt_input_shape = {"input_data": [3, 64]} + elif self.dims == 3: + self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 32]} + self.dynamic_shape.max_input_shape = { + "input_data": [10, 64, 64] + } + self.dynamic_shape.opt_input_shape = {"input_data": [3, 64, 64]} + else: + self.dynamic_shape.min_input_shape = { + "input_data": [1, 3, 32, 32] + } + 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): + if self.input_num == 3 or self.dims == 1: + return 0, 3 + else: + 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-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-5 + + def test(self): + self.run_test() + + +if __name__ == "__main__": + unittest.main() -- GitLab