diff --git a/paddle/fluid/inference/tensorrt/op_teller.cc b/paddle/fluid/inference/tensorrt/op_teller.cc index 7c484b4722f36fa243adf61865afc5a5fd934dfb..cf7f312625e1354ac9d8da05ed2509b43806d99e 100644 --- a/paddle/fluid/inference/tensorrt/op_teller.cc +++ b/paddle/fluid/inference/tensorrt/op_teller.cc @@ -666,6 +666,33 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8, } } + if (op_type == "instance_norm") { + if (with_dynamic_shape) { + VLOG(3) << "trt instance_norm op does not support dynamic shape "; + return false; + } + if (desc.Input("X").size() != 1) { + VLOG(3) << "input of instance_norm op converter should be 1, got " + << desc.Input("X").size(); + return false; + } + if (desc.Input("Bias").size() != 1) { + VLOG(3) << "Bias of instance_norm op converter should be 1, got " + << desc.Input("Bias").size(); + return false; + } + if (desc.Input("Scale").size() != 1) { + VLOG(3) << "Scale of instance_norm op converter should be 1, got " + << desc.Input("Scale").size(); + return false; + } + if (desc.Output("Y").size() != 1) { + VLOG(3) << "output of layer_norm op converter should be 1, got " + << desc.Output("Y").size(); + return false; + } + } + if (op_type == "leaky_relu") { if (desc.Input("X").size() != 1) { VLOG(3) << "Invalid number of TRT leaky_relu op converter " diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_instance_norm.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_instance_norm.py new file mode 100644 index 0000000000000000000000000000000000000000..3f7c2a0fae6f06e929d17d06dc8cb841dd792c17 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_instance_norm.py @@ -0,0 +1,127 @@ +# 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 +import unittest + + +class TrtConvertInstanceNormTest(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 attrs[0]['epsilon'] < 0 or attrs[0]['epsilon'] > 0.001: + return False + + return True + + def sample_program_configs(self): + def generate_input1(attrs: List[Dict[str, Any]], shape_input): + return np.ones(shape_input).astype(np.float32) + + def generate_input2(attrs: List[Dict[str, Any]], shape_input): + return np.ones(len(shape_input) - 1).astype(np.float32) + + for epsilon in [0.0005, -1, 1]: + dics = [{"epsilon": epsilon}] + + ops_config = [{ + "op_type": "instance_norm", + "op_inputs": { + "X": ["input_data"], + "Scale": ["scale_data"], + "Bias": ["bias_data"] + }, + "op_outputs": { + "Y": ["y_data"], + "SavedMean": ["saved_mean_data"], + "SavedVariance": ["saved_variance_data"] + }, + "op_attrs": dics[0] + }] + ops = self.generate_op_config(ops_config) + shape_input = [1, 3, 64, 64] + program_config = ProgramConfig( + ops=ops, + weights={ + "bias_data": TensorConfig(data_gen=partial( + generate_input2, dics, shape_input)), + "scale_data": TensorConfig(data_gen=partial( + generate_input2, dics, shape_input)) + }, + inputs={ + "input_data": TensorConfig(data_gen=partial( + generate_input1, dics, shape_input)) + }, + outputs=["y_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, 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): + inputs = program_config.inputs + if dynamic_shape: + return 0, 3 + 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-2 + + # 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-2 + + def test(self): + self.run_test() + + +if __name__ == "__main__": + unittest.main()