diff --git a/paddle/fluid/inference/tensorrt/op_teller.cc b/paddle/fluid/inference/tensorrt/op_teller.cc index 2d05d6aff9c7403262d2652f198f331a72437127..451fa0790e0aacbffa825ce171ef0c184a805c8a 100644 --- a/paddle/fluid/inference/tensorrt/op_teller.cc +++ b/paddle/fluid/inference/tensorrt/op_teller.cc @@ -402,6 +402,14 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8, auto data_layout = framework::StringToDataLayout( BOOST_GET_CONST(std::string, desc.GetAttr("data_layout"))); if (data_layout != framework::DataLayout::kNCHW) 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() == 2) { + return false; + } } if (op_type == "multiclass_nms") { diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_affine_channel.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_affine_channel.py new file mode 100644 index 0000000000000000000000000000000000000000..1e6c94f145497c4666b7f387f3242e9f9d5a37dc --- /dev/null +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_affine_channel.py @@ -0,0 +1,148 @@ +# 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 TrtConvertAffineChannelTest(TrtLayerAutoScanTest): + def is_program_valid(self, program_config: ProgramConfig) -> bool: + return True + + def sample_program_configs(self): + def generate_input1(batch, dims, attrs: List[Dict[str, Any]]): + if dims == 2: + return np.ones([batch, 64]).astype(np.float32) + else: + if attrs[0]['data_layout'] == "NCHW": + return np.ones([batch, 3, 64, 64]).astype(np.float32) + else: + return np.ones([batch, 64, 64, 3]).astype(np.float32) + + def generate_weight1(dims, attrs: List[Dict[str, Any]]): + if dims == 2: + return np.random.random([64]).astype(np.float32) + else: + return np.random.random([3]).astype(np.float32) + + for dims in [2, 4]: + for batch in [1, 2, 4]: + for data_layout in ["NCHW", "NHWC"]: + + self.dims = dims + dics = [{"data_layout": data_layout}] + + ops_config = [{ + "op_type": "affine_channel", + "op_inputs": { + "X": ["input_data"], + "Scale": ["scale"], + "Bias": ["bias"] + }, + "op_outputs": { + "Out": ["output_data"] + }, + "op_attrs": dics[0] + }] + ops = self.generate_op_config(ops_config) + + program_config = ProgramConfig( + ops=ops, + weights={ + "scale": TensorConfig(data_gen=partial( + generate_weight1, dims, dics)), + "bias": TensorConfig(data_gen=partial( + generate_weight1, dims, dics)) + }, + inputs={ + "input_data": TensorConfig(data_gen=partial( + generate_input1, batch, dims, 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 == 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": [2, 64]} + else: + if attrs[0]['data_layout'] == "NCHW": + 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] + } + else: + self.dynamic_shape.min_input_shape = { + "input_data": [1, 32, 32, 3] + } + self.dynamic_shape.max_input_shape = { + "input_data": [4, 64, 64, 3] + } + self.dynamic_shape.opt_input_shape = { + "input_data": [1, 64, 64, 3] + } + + 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.dims == 4 and attrs[0]['data_layout'] == "NCHW": + return 1, 2 + else: + return 0, 3 + + 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()