# 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 unittest import numpy as np import paddle.inference as paddle_infer from functools import partial from typing import Optional, List, Callable, Dict, Any, Set class TrtConvertBatchNormTest(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input1(attrs: List[Dict[str, Any]], batch): if self.dims == 4: if attrs[0]['data_layout'] == "NCHW": return np.ones([batch, 3, 24, 24]).astype(np.float32) elif attrs[0]['data_layout'] == "NHWC": return np.ones([batch, 24, 24, 3]).astype(np.float32) elif self.dims == 3: return np.ones([batch, 3, 24]).astype(np.float32) elif self.dims == 2: return np.ones([batch, 3]).astype(np.float32) def generate_bias(attrs: List[Dict[str, Any]], batch): return np.full((3), 0.9).astype("float32") def generate_mean(attrs: List[Dict[str, Any]], batch): return np.full((3), 0.9).astype("float32") def generate_scale(attrs: List[Dict[str, Any]], batch): return np.full((3), 1.1).astype("float32") def generate_variance(attrs: List[Dict[str, Any]], batch): return np.full((3), 1.2).astype("float32") def generate_MomentumTensor(attrs: List[Dict[str, Any]], batch): return np.full((3), 0.9).astype("float32") for dims in [2, 3, 4]: for num_input in [0, 1]: for batch in [1, 4]: for epsilon in [1e-6, 1e-5, 1e-4]: for data_layout in ["NCHW"]: for momentum in [0.9, 0.8]: self.num_input = num_input self.dims = dims dics = [{ "epsilon": epsilon, "data_layout": data_layout, "momentum": momentum, "is_test": True, "trainable_statistics": False }, {}] dics_intput = [{ "X": ["batch_norm_input"], "Bias": ["Bias"], "Mean": ["Mean"], "Scale": ["Scale"], "Variance": ["Variance"], "MomentumTensor": ["MomentumTensor"] }, { "X": ["batch_norm_input"], "Bias": ["Bias"], "Mean": ["Mean"], "Scale": ["Scale"], "Variance": ["Variance"] }] dics_intputs = [{ "Bias": TensorConfig(data_gen=partial( generate_bias, dics, batch)), "Mean": TensorConfig(data_gen=partial( generate_mean, dics, batch)), "Scale": TensorConfig(data_gen=partial( generate_scale, dics, batch)), "Variance": TensorConfig(data_gen=partial( generate_variance, dics, batch)), "MomentumTensor": TensorConfig(data_gen=partial( generate_MomentumTensor, dics, batch)), }, { "Bias": TensorConfig(data_gen=partial( generate_bias, dics, batch)), "Mean": TensorConfig(data_gen=partial( generate_mean, dics, batch)), "Scale": TensorConfig(data_gen=partial( generate_scale, dics, batch)), "Variance": TensorConfig(data_gen=partial( generate_variance, dics, batch)) }] ops_config = [{ "op_type": "batch_norm", "op_inputs": dics_intput[num_input], "op_outputs": { "Y": ["batch_norm_out"], "MeanOut": ["Mean"], "VarianceOut": ["Variance"], "SavedMean": ["SavedMean"], "SavedVariance": ["SavedVariance"] }, "op_attrs": dics[0] }] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights=dics_intputs[num_input], inputs={ "batch_norm_input": TensorConfig( data_gen=partial(generate_input1, dics, batch)) }, outputs=["batch_norm_out"]) 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: if attrs[0]['data_layout'] == "NCHW": self.dynamic_shape.min_input_shape = { "batch_norm_input": [1, 3, 12, 12] } self.dynamic_shape.max_input_shape = { "batch_norm_input": [4, 3, 24, 24] } self.dynamic_shape.opt_input_shape = { "batch_norm_input": [1, 3, 24, 24] } elif attrs[0]['data_layout'] == "NHWC": self.dynamic_shape.min_input_shape = { "batch_norm_input": [1, 12, 12, 3] } self.dynamic_shape.max_input_shape = { "batch_norm_input": [4, 24, 24, 3] } self.dynamic_shape.opt_input_shape = { "batch_norm_input": [1, 24, 24, 3] } elif self.dims == 3: self.dynamic_shape.min_input_shape = { "batch_norm_input": [1, 3, 12] } self.dynamic_shape.max_input_shape = { "batch_norm_input": [4, 3, 24] } self.dynamic_shape.opt_input_shape = { "batch_norm_input": [1, 3, 24] } elif self.dims == 2: self.dynamic_shape.min_input_shape = { "batch_norm_input": [1, 3] } self.dynamic_shape.max_input_shape = { "batch_norm_input": [4, 3] } self.dynamic_shape.opt_input_shape = { "batch_norm_input": [1, 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): 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 add_skip_trt_case(self): def teller1(program_config, predictor_config): if len(program_config.weights) == 5: return True return False self.add_skip_case(teller1, SkipReasons.TRT_NOT_SUPPORT, "INPUT MomentumTensor NOT SUPPORT") def test(self): self.add_skip_trt_case() self.run_test() if __name__ == "__main__": unittest.main()