# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve. # # 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 os from abc import abstractmethod import lazy_paddle as paddle import numpy as np from ....utils.flags import FLAGS_json_format_model from ....utils import logging from ...utils.pp_option import PaddlePredictorOption from ..base import BaseComponent class BasePaddlePredictor(BaseComponent): """Predictor based on Paddle Inference""" OUTPUT_KEYS = "pred" DEAULT_OUTPUTS = {"pred": "pred"} ENABLE_BATCH = True def __init__(self, model_dir, model_prefix, option): super().__init__() self.model_dir = model_dir self.model_prefix = model_prefix self._update_option(option) def _update_option(self, option): if option: if self.option and option == self.option: return self._option = option self._option.attach(self) self.reset() @property def option(self): return self._option if hasattr(self, "_option") else None @option.setter def option(self, option): self._update_option(option) def reset(self): if not self.option: self.option = PaddlePredictorOption() ( self.predictor, self.inference_config, self.input_names, self.input_handlers, self.output_handlers, ) = self._create() logging.debug(f"Env: {self.option}") def _create(self): """_create""" from lazy_paddle.inference import Config, create_predictor model_postfix = ".json" if FLAGS_json_format_model else ".pdmodel" model_file = (self.model_dir / f"{self.model_prefix}{model_postfix}").as_posix() params_file = (self.model_dir / f"{self.model_prefix}.pdiparams").as_posix() config = Config(model_file, params_file) if self.option.device in ("gpu", "dcu"): config.enable_use_gpu(200, self.option.device_id) if paddle.is_compiled_with_rocm(): os.environ["FLAGS_conv_workspace_size_limit"] = "2000" elif hasattr(config, "enable_new_ir"): config.enable_new_ir(self.option.enable_new_ir) elif self.option.device == "npu": config.enable_custom_device("npu") os.environ["FLAGS_npu_jit_compile"] = "0" os.environ["FLAGS_use_stride_kernel"] = "0" os.environ["FLAGS_allocator_strategy"] = "auto_growth" os.environ["CUSTOM_DEVICE_BLACK_LIST"] = ( "pad3d,pad3d_grad,set_value,set_value_with_tensor" ) os.environ["FLAGS_npu_scale_aclnn"] = "True" os.environ["FLAGS_npu_split_aclnn"] = "True" elif self.option.device == "xpu": os.environ["BKCL_FORCE_SYNC"] = "1" os.environ["BKCL_TIMEOUT"] = "1800" os.environ["FLAGS_use_stride_kernel"] = "0" elif self.option.device == "mlu": config.enable_custom_device("mlu") os.environ["FLAGS_use_stride_kernel"] = "0" else: assert self.option.device == "cpu" config.disable_gpu() if hasattr(config, "enable_new_ir"): config.enable_new_ir(self.option.enable_new_ir) if hasattr(config, "enable_new_executor"): config.enable_new_executor(True) if "mkldnn" in self.option.run_mode: try: config.enable_mkldnn() config.set_cpu_math_library_num_threads(self.option.cpu_threads) if "bf16" in self.option.run_mode: config.enable_mkldnn_bfloat16() except Exception as e: logging.warning( "MKL-DNN is not available. We will disable MKL-DNN." ) precision_map = { "trt_int8": Config.Precision.Int8, "trt_fp32": Config.Precision.Float32, "trt_fp16": Config.Precision.Half, } if self.option.run_mode in precision_map.keys(): config.enable_tensorrt_engine( workspace_size=(1 << 25) * self.option.batch_size, max_batch_size=self.option.batch_size, min_subgraph_size=self.option.min_subgraph_size, precision_mode=precision_map[self.option.run_mode], use_static=self.option.trt_use_static, use_calib_mode=self.option.trt_calib_mode, ) if self.option.shape_info_filename is not None: if not os.path.exists(self.option.shape_info_filename): config.collect_shape_range_info(self.option.shape_info_filename) logging.info( f"Dynamic shape info is collected into: {self.option.shape_info_filename}" ) else: logging.info( f"A dynamic shape info file ( {self.option.shape_info_filename} ) already exists. \ No need to generate again." ) config.enable_tuned_tensorrt_dynamic_shape( self.option.shape_info_filename, True ) # Disable paddle inference logging config.disable_glog_info() for del_p in self.option.delete_pass: config.delete_pass(del_p) # Enable shared memory config.enable_memory_optim() config.switch_ir_optim(True) # Disable feed, fetch OP, needed by zero_copy_run config.switch_use_feed_fetch_ops(False) predictor = create_predictor(config) # Get input and output handlers input_names = predictor.get_input_names() input_names.sort() input_handlers = [] output_handlers = [] for input_name in input_names: input_handler = predictor.get_input_handle(input_name) input_handlers.append(input_handler) output_names = predictor.get_output_names() for output_name in output_names: output_handler = predictor.get_output_handle(output_name) output_handlers.append(output_handler) return predictor, config, input_names, input_handlers, output_handlers def get_input_names(self): """get input names""" return self.input_names def apply(self, **kwargs): x = self.to_batch(**kwargs) for idx in range(len(x)): self.input_handlers[idx].reshape(x[idx].shape) self.input_handlers[idx].copy_from_cpu(x[idx]) self.predictor.run() output = [] for out_tensor in self.output_handlers: batch = out_tensor.copy_to_cpu() output.append(batch) return self.format_output(output) def format_output(self, pred): return [{"pred": res} for res in zip(*pred)] @abstractmethod def to_batch(self): raise NotImplementedError class ImagePredictor(BasePaddlePredictor): INPUT_KEYS = "img" DEAULT_INPUTS = {"img": "img"} def to_batch(self, img): return [np.stack(img, axis=0).astype(dtype=np.float32, copy=False)] class ImageDetPredictor(BasePaddlePredictor): INPUT_KEYS = [["img", "scale_factors"], ["img", "scale_factors", "img_size"], ["img", "img_size"]] OUTPUT_KEYS = [["boxes"], ["boxes", "masks"]] DEAULT_INPUTS = {"img": "img", "scale_factors": "scale_factors"} DEAULT_OUTPUTS = None def to_batch(self, img, scale_factors=[[1., 1.]], img_size=None): scale_factors = [scale_factor[::-1] for scale_factor in scale_factors] if img_size is None: return [ np.stack(img, axis=0).astype(dtype=np.float32, copy=False), np.stack(scale_factors, axis=0).astype(dtype=np.float32, copy=False), ] else: img_size = [img_size[::-1] for img_size in img_size] return [ np.stack(img_size, axis=0).astype(dtype=np.float32, copy=False), np.stack(img, axis=0).astype(dtype=np.float32, copy=False), np.stack(scale_factors, axis=0).astype(dtype=np.float32, copy=False), ] def format_output(self, pred): box_idx_start = 0 pred_box = [] if len(pred) == 4: # Adapt to SOLOv2 pred_class_id = [] pred_mask = [] pred_class_id.append([pred[1], pred[2]]) pred_mask.append(pred[3]) return [ { "class_id": np.array(pred_class_id[i]), "masks": np.array(pred_mask[i]), } for i in range(len(pred_class_id)) ] if len(pred) == 3: # Adapt to Instance Segmentation pred_mask = [] for idx in range(len(pred[1])): np_boxes_num = pred[1][idx] box_idx_end = box_idx_start + np_boxes_num np_boxes = pred[0][box_idx_start:box_idx_end] pred_box.append(np_boxes) if len(pred) == 3: np_masks = pred[2][box_idx_start:box_idx_end] pred_mask.append(np_masks) box_idx_start = box_idx_end if len(pred) == 3: return [ {"boxes": np.array(pred_box[i]), "masks": np.array(pred_mask[i])} for i in range(len(pred_box)) ] else: return [{"boxes": np.array(res)} for res in pred_box] class TSPPPredictor(BasePaddlePredictor): INPUT_KEYS = "ts" DEAULT_INPUTS = {"ts": "ts"} def to_batch(self, ts): n = len(ts[0]) x = [np.stack([lst[i] for lst in ts], axis=0) for i in range(n)] return x