# Copyright (c) 2022 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. import os import os.path as osp import time import copy import math import json from functools import partial, wraps from inspect import signature import yaml import paddle from paddle.io import DataLoader, DistributedBatchSampler from paddleslim import QAT from paddleslim.analysis import flops from paddleslim import L1NormFilterPruner, FPGMFilterPruner import paddlers import paddlers.utils.logging as logging from paddlers.utils import (seconds_to_hms, get_single_card_bs, dict2str, get_pretrain_weights, load_pretrain_weights, load_checkpoint, SmoothedValue, TrainingStats, _get_shared_memory_size_in_M, EarlyStop) from .slim.prune import _pruner_eval_fn, _pruner_template_input, sensitive_prune from .utils.infer_nets import InferNet, InferCDNet class ModelMeta(type): def __new__(cls, name, bases, attrs): def _deco(init_func): @wraps(init_func) def _wrapper(self, *args, **kwargs): if hasattr(self, '_raw_params'): ret = init_func(self, *args, **kwargs) else: sig = signature(init_func) bnd_args = sig.bind(self, *args, **kwargs) raw_params = bnd_args.arguments raw_params.pop('self') self._raw_params = raw_params ret = init_func(self, *args, **kwargs) return ret return _wrapper old_init_func = attrs['__init__'] attrs['__init__'] = _deco(old_init_func) return type.__new__(cls, name, bases, attrs) class BaseModel(metaclass=ModelMeta): def __init__(self, model_type): self.model_type = model_type self.in_channels = None self.num_classes = None self.labels = None self.version = paddlers.__version__ self.net = None self.optimizer = None self.test_inputs = None self.train_data_loader = None self.eval_data_loader = None self.eval_metrics = None self.best_accuracy = -1. self.best_model_epoch = -1 # 是否使用多卡间同步BatchNorm均值和方差 self.sync_bn = False self.status = 'Normal' # 已完成迭代轮数，为恢复训练时的起始轮数 self.completed_epochs = 0 self.pruner = None self.pruning_ratios = None self.quantizer = None self.quant_config = None self.fixed_input_shape = None def net_initialize(self, pretrain_weights=None, save_dir='.', resume_checkpoint=None, is_backbone_weights=False): if pretrain_weights is not None and \ not osp.exists(pretrain_weights): if not osp.isdir(save_dir): if osp.exists(save_dir): os.remove(save_dir) os.makedirs(save_dir) if self.model_type == 'classifier': pretrain_weights = get_pretrain_weights( pretrain_weights, self.model_name, save_dir) else: backbone_name = getattr(self, 'backbone_name', None) pretrain_weights = get_pretrain_weights( pretrain_weights, self.__class__.__name__, save_dir, backbone_name=backbone_name) if pretrain_weights is not None: if is_backbone_weights: load_pretrain_weights( self.net.backbone, pretrain_weights, model_name='backbone of ' + self.model_name) else: load_pretrain_weights( self.net, pretrain_weights, model_name=self.model_name) if resume_checkpoint is not None: if not osp.exists(resume_checkpoint): logging.error( "The checkpoint path {} to resume training from does not exist." .format(resume_checkpoint), exit=True) if not osp.exists(osp.join(resume_checkpoint, 'model.pdparams')): logging.error( "Model parameter state dictionary file 'model.pdparams' " "not found under given checkpoint path {}".format( resume_checkpoint), exit=True) if not osp.exists(osp.join(resume_checkpoint, 'model.pdopt')): logging.error( "Optimizer state dictionary file 'model.pdparams' " "not found under given checkpoint path {}".format( resume_checkpoint), exit=True) if not osp.exists(osp.join(resume_checkpoint, 'model.yml')): logging.error( "'model.yml' not found under given checkpoint path {}". format(resume_checkpoint), exit=True) with open(osp.join(resume_checkpoint, "model.yml")) as f: info = yaml.load(f.read(), Loader=yaml.Loader) self.completed_epochs = info['completed_epochs'] self.best_accuracy = info['_Attributes']['best_accuracy'] self.best_model_epoch = info['_Attributes']['best_model_epoch'] load_checkpoint( self.net, self.optimizer, model_name=self.model_name, checkpoint=resume_checkpoint) def get_model_info(self, get_raw_params=False, inplace=True): if inplace: init_params = self.init_params else: init_params = copy.deepcopy(self.init_params) info = dict() info['version'] = paddlers.__version__ info['Model'] = self.__class__.__name__ info['_Attributes'] = dict( [('model_type', self.model_type), ('in_channels', self.in_channels), ('num_classes', self.num_classes), ('labels', self.labels), ('fixed_input_shape', self.fixed_input_shape), ('best_accuracy', self.best_accuracy), ('best_model_epoch', self.best_model_epoch)]) if 'self' in init_params: del init_params['self'] if '__class__' in init_params: del init_params['__class__'] if 'model_name' in init_params: del init_params['model_name'] if 'params' in init_params: del init_params['params'] info['_init_params'] = init_params if get_raw_params: info['raw_params'] = self._raw_params try: primary_metric_key = list(self.eval_metrics.keys())[0] primary_metric_value = float(self.eval_metrics[primary_metric_key]) info['_Attributes']['eval_metrics'] = { primary_metric_key: primary_metric_value } except: pass if hasattr(self, 'test_transforms'): if self.test_transforms is not None: info['Transforms'] = list() for op in self.test_transforms.transforms: name = op.__class__.__name__ if name.startswith('Arrange'): continue attr = op.__dict__ info['Transforms'].append({name: attr}) info['completed_epochs'] = self.completed_epochs return info def get_pruning_info(self): info = dict() info['pruner'] = self.pruner.__class__.__name__ info['pruning_ratios'] = self.pruning_ratios pruner_inputs = self.pruner.inputs if self.model_type == 'detector': pruner_inputs = {k: v.tolist() for k, v in pruner_inputs[0].items()} info['pruner_inputs'] = pruner_inputs return info def get_quant_info(self): info = dict() info['quant_config'] = self.quant_config return info def save_model(self, save_dir): if not osp.isdir(save_dir): if osp.exists(save_dir): os.remove(save_dir) os.makedirs(save_dir) model_info = self.get_model_info(get_raw_params=True) model_info['status'] = self.status paddle.save(self.net.state_dict(), osp.join(save_dir, 'model.pdparams')) paddle.save(self.optimizer.state_dict(), osp.join(save_dir, 'model.pdopt')) with open( osp.join(save_dir, 'model.yml'), encoding='utf-8', mode='w') as f: yaml.dump(model_info, f) # 评估结果保存 if hasattr(self, 'eval_details'): with open(osp.join(save_dir, 'eval_details.json'), 'w') as f: json.dump(self.eval_details, f) if self.status == 'Pruned' and self.pruner is not None: pruning_info = self.get_pruning_info() with open( osp.join(save_dir, 'prune.yml'), encoding='utf-8', mode='w') as f: yaml.dump(pruning_info, f) if self.status == 'Quantized' and self.quantizer is not None: quant_info = self.get_quant_info() with open( osp.join(save_dir, 'quant.yml'), encoding='utf-8', mode='w') as f: yaml.dump(quant_info, f) # 模型保存成功的标志 open(osp.join(save_dir, '.success'), 'w').close() logging.info("Model saved in {}.".format(save_dir)) def build_data_loader(self, dataset, batch_size, mode='train'): if dataset.num_samples < batch_size: raise Exception( 'The volume of dataset({}) must be larger than batch size({}).' .format(dataset.num_samples, batch_size)) batch_size_each_card = get_single_card_bs(batch_size=batch_size) # TODO detection eval阶段需做判断 batch_sampler = DistributedBatchSampler( dataset, batch_size=batch_size_each_card, shuffle=dataset.shuffle, drop_last=mode == 'train') if dataset.num_workers > 0: shm_size = _get_shared_memory_size_in_M() if shm_size is None or shm_size < 1024.: use_shared_memory = False else: use_shared_memory = True else: use_shared_memory = False loader = DataLoader( dataset, batch_sampler=batch_sampler, collate_fn=dataset.batch_transforms, num_workers=dataset.num_workers, return_list=True, use_shared_memory=use_shared_memory) return loader def train_loop(self, num_epochs, train_dataset, train_batch_size, eval_dataset=None, save_interval_epochs=1, log_interval_steps=10, save_dir='output', ema=None, early_stop=False, early_stop_patience=5, use_vdl=True): self._check_transforms(train_dataset.transforms, 'train') if "RCNN" in self.__class__.__name__ and train_dataset.pos_num < len( train_dataset.file_list): nranks = 1 else: nranks = paddle.distributed.get_world_size() local_rank = paddle.distributed.get_rank() if nranks > 1: find_unused_parameters = getattr(self, 'find_unused_parameters', False) # Initialize parallel environment if not done. if not paddle.distributed.parallel.parallel_helper._is_parallel_ctx_initialized( ): paddle.distributed.init_parallel_env() ddp_net = paddle.DataParallel( self.net, find_unused_parameters=find_unused_parameters) else: ddp_net = paddle.DataParallel( self.net, find_unused_parameters=find_unused_parameters) if use_vdl: from visualdl import LogWriter vdl_logdir = osp.join(save_dir, 'vdl_log') log_writer = LogWriter(vdl_logdir) # task_id: refer to paddlers task_id = getattr(paddlers, "task_id", "") thresh = .0001 if early_stop: earlystop = EarlyStop(early_stop_patience, thresh) self.train_data_loader = self.build_data_loader( train_dataset, batch_size=train_batch_size, mode='train') if eval_dataset is not None: self.test_transforms = copy.deepcopy(eval_dataset.transforms) start_epoch = self.completed_epochs train_step_time = SmoothedValue(log_interval_steps) train_step_each_epoch = math.floor(train_dataset.num_samples / train_batch_size) train_total_step = train_step_each_epoch * (num_epochs - start_epoch) if eval_dataset is not None: eval_batch_size = train_batch_size eval_epoch_time = 0 current_step = 0 for i in range(start_epoch, num_epochs): self.net.train() if callable( getattr(self.train_data_loader.dataset, 'set_epoch', None)): self.train_data_loader.dataset.set_epoch(i) train_avg_metrics = TrainingStats() step_time_tic = time.time() for step, data in enumerate(self.train_data_loader()): if nranks > 1: outputs = self.run(ddp_net, data, mode='train') else: outputs = self.run(self.net, data, mode='train') loss = outputs['loss'] loss.backward() self.optimizer.step() self.optimizer.clear_grad() lr = self.optimizer.get_lr() if isinstance(self.optimizer._learning_rate, paddle.optimizer.lr.LRScheduler): # If ReduceOnPlateau is used as the scheduler, use the loss value as the metric. if isinstance(self.optimizer._learning_rate, paddle.optimizer.lr.ReduceOnPlateau): self.optimizer._learning_rate.step(loss.item()) else: self.optimizer._learning_rate.step() train_avg_metrics.update(outputs) outputs['lr'] = lr if ema is not None: ema.update(self.net) step_time_toc = time.time() train_step_time.update(step_time_toc - step_time_tic) step_time_tic = step_time_toc current_step += 1 # 每间隔log_interval_steps，输出loss信息 if current_step % log_interval_steps == 0 and local_rank == 0: if use_vdl: for k, v in outputs.items(): log_writer.add_scalar( '{}-Metrics/Training(Step): {}'.format( task_id, k), v, current_step) # 估算剩余时间 avg_step_time = train_step_time.avg() eta = avg_step_time * (train_total_step - current_step) if eval_dataset is not None: eval_num_epochs = math.ceil( (num_epochs - i - 1) / save_interval_epochs) if eval_epoch_time == 0: eta += avg_step_time * math.ceil( eval_dataset.num_samples / eval_batch_size) else: eta += eval_epoch_time * eval_num_epochs logging.info( "[TRAIN] Epoch={}/{}, Step={}/{}, {}, time_each_step={}s, eta={}" .format(i + 1, num_epochs, step + 1, train_step_each_epoch, dict2str(outputs), round(avg_step_time, 2), seconds_to_hms(eta))) logging.info('[TRAIN] Epoch {} finished, {} .' .format(i + 1, train_avg_metrics.log())) self.completed_epochs += 1 if ema is not None: weight = copy.deepcopy(self.net.state_dict()) self.net.set_state_dict(ema.apply()) eval_epoch_tic = time.time() # 每间隔save_interval_epochs, 在验证集上评估和对模型进行保存 if (i + 1) % save_interval_epochs == 0 or i == num_epochs - 1: if eval_dataset is not None and eval_dataset.num_samples > 0: eval_result = self.evaluate( eval_dataset, batch_size=eval_batch_size, return_details=True) # 保存最优模型 if local_rank == 0: self.eval_metrics, self.eval_details = eval_result if use_vdl: for k, v in self.eval_metrics.items(): try: log_writer.add_scalar( '{}-Metrics/Eval(Epoch): {}'.format( task_id, k), v, i + 1) except TypeError: pass logging.info('[EVAL] Finished, Epoch={}, {} .'.format( i + 1, dict2str(self.eval_metrics))) best_accuracy_key = list(self.eval_metrics.keys())[0] current_accuracy = self.eval_metrics[best_accuracy_key] if current_accuracy > self.best_accuracy: self.best_accuracy = current_accuracy self.best_model_epoch = i + 1 best_model_dir = osp.join(save_dir, "best_model") self.save_model(save_dir=best_model_dir) if self.best_model_epoch > 0: logging.info( 'Current evaluated best model on eval_dataset is epoch_{}, {}={}' .format(self.best_model_epoch, best_accuracy_key, self.best_accuracy)) eval_epoch_time = time.time() - eval_epoch_tic current_save_dir = osp.join(save_dir, "epoch_{}".format(i + 1)) if local_rank == 0: self.save_model(save_dir=current_save_dir) if eval_dataset is not None and early_stop: if earlystop(current_accuracy): break if ema is not None: self.net.set_state_dict(weight) def analyze_sensitivity(self, dataset, batch_size=8, criterion='l1_norm', save_dir='output'): """ Args: dataset(paddlers.dataset): Dataset used for evaluation during sensitivity analysis. batch_size(int, optional): Batch size used in evaluation. Defaults to 8. criterion({'l1_norm', 'fpgm'}, optional): Pruning criterion. Defaults to 'l1_norm'. save_dir(str, optional): The directory to save sensitivity file of the model. Defaults to 'output'. """ if self.__class__.__name__ in {'FasterRCNN', 'MaskRCNN', 'PicoDet'}: raise Exception("{} does not support pruning currently!".format( self.__class__.__name__)) assert criterion in {'l1_norm', 'fpgm'}, \ "Pruning criterion {} is not supported. Please choose from ['l1_norm', 'fpgm']" self._check_transforms(dataset.transforms, 'eval') if self.model_type == 'detector': self.net.eval() else: self.net.train() inputs = _pruner_template_input( sample=dataset[0], model_type=self.model_type) if criterion == 'l1_norm': self.pruner = L1NormFilterPruner(self.net, inputs=inputs) else: self.pruner = FPGMFilterPruner(self.net, inputs=inputs) if not osp.isdir(save_dir): os.makedirs(save_dir) sen_file = osp.join(save_dir, 'model.sensi.data') logging.info('Sensitivity analysis of model parameters starts...') self.pruner.sensitive( eval_func=partial(_pruner_eval_fn, self, dataset, batch_size), sen_file=sen_file) logging.info( 'Sensitivity analysis is complete. The result is saved at {}.'. format(sen_file)) def prune(self, pruned_flops, save_dir=None): """ Args: pruned_flops(float): Ratio of FLOPs to be pruned. save_dir(None or str, optional): If None, the pruned model will not be saved. Otherwise, the pruned model will be saved at save_dir. Defaults to None. """ if self.status == "Pruned": raise Exception( "A pruned model cannot be done model pruning again!") pre_pruning_flops = flops(self.net, self.pruner.inputs) logging.info("Pre-pruning FLOPs: {}. Pruning starts...".format( pre_pruning_flops)) _, self.pruning_ratios = sensitive_prune(self.pruner, pruned_flops) post_pruning_flops = flops(self.net, self.pruner.inputs) logging.info("Pruning is complete. Post-pruning FLOPs: {}".format( post_pruning_flops)) logging.warning("Pruning the model may hurt its performance, " "retraining is highly recommended") self.status = 'Pruned' if save_dir is not None: self.save_model(save_dir) logging.info("Pruned model is saved at {}".format(save_dir)) def _prepare_qat(self, quant_config): if self.status == 'Infer': logging.error( "Exported inference model does not support quantization aware training.", exit=True) if quant_config is None: # default quantization configuration quant_config = { # {None, 'PACT'}. Weight preprocess type. If None, no preprocessing is performed. 'weight_preprocess_type': None, # {None, 'PACT'}. Activation preprocess type. If None, no preprocessing is performed. 'activation_preprocess_type': None, # {'abs_max', 'channel_wise_abs_max', 'range_abs_max', 'moving_average_abs_max'}. # Weight quantization type. 'weight_quantize_type': 'channel_wise_abs_max', # {'abs_max', 'range_abs_max', 'moving_average_abs_max'}. Activation quantization type. 'activation_quantize_type': 'moving_average_abs_max', # The number of bits of weights after quantization. 'weight_bits': 8, # The number of bits of activation after quantization. 'activation_bits': 8, # Data type after quantization, such as 'uint8', 'int8', etc. 'dtype': 'int8', # Window size for 'range_abs_max' quantization. 'window_size': 10000, # Decay coefficient of moving average. 'moving_rate': .9, # Types of layers that will be quantized. 'quantizable_layer_type': ['Conv2D', 'Linear'] } if self.status != 'Quantized': self.quant_config = quant_config self.quantizer = QAT(config=self.quant_config) logging.info( "Preparing the model for quantization-aware training...") self.quantizer.quantize(self.net) logging.info("Model is ready for quantization-aware training.") self.status = 'Quantized' elif quant_config != self.quant_config: logging.error( "The model has been quantized with the following quant_config: {}." "Doing quantization-aware training with a quantized model " "using a different configuration is not supported." .format(self.quant_config), exit=True) def _get_pipeline_info(self, save_dir): pipeline_info = {} pipeline_info["pipeline_name"] = self.model_type nodes = [{ "src0": { "type": "Source", "next": "decode0" } }, { "decode0": { "type": "Decode", "next": "predict0" } }, { "predict0": { "type": "Predict", "init_params": { "use_gpu": False, "gpu_id": 0, "use_trt": False, "model_dir": save_dir, }, "next": "sink0" } }, { "sink0": { "type": "Sink" } }] pipeline_info["pipeline_nodes"] = nodes pipeline_info["version"] = "1.0.0" return pipeline_info def _build_inference_net(self): if self.model_type in ('classifier', 'detector'): infer_net = self.net elif self.model_type == 'changedetector': infer_net = InferCDNet(self.net) else: infer_net = InferNet(self.net, self.model_type) infer_net.eval() return infer_net def _export_inference_model(self, save_dir, image_shape=None): self.test_inputs = self._get_test_inputs(image_shape) infer_net = self._build_inference_net() if self.status == 'Quantized': self.quantizer.save_quantized_model(infer_net, osp.join(save_dir, 'model'), self.test_inputs) quant_info = self.get_quant_info() with open( osp.join(save_dir, 'quant.yml'), encoding='utf-8', mode='w') as f: yaml.dump(quant_info, f) else: static_net = paddle.jit.to_static( infer_net, input_spec=self.test_inputs) paddle.jit.save(static_net, osp.join(save_dir, 'model')) if self.status == 'Pruned': pruning_info = self.get_pruning_info() with open( osp.join(save_dir, 'prune.yml'), encoding='utf-8', mode='w') as f: yaml.dump(pruning_info, f) model_info = self.get_model_info() model_info['status'] = 'Infer' with open( osp.join(save_dir, 'model.yml'), encoding='utf-8', mode='w') as f: yaml.dump(model_info, f) pipeline_info = self._get_pipeline_info(save_dir) with open( osp.join(save_dir, 'pipeline.yml'), encoding='utf-8', mode='w') as f: yaml.dump(pipeline_info, f) # 模型保存成功的标志 open(osp.join(save_dir, '.success'), 'w').close() logging.info("The model for the inference deployment is saved in {}.". format(save_dir)) def _check_transforms(self, transforms, mode): # NOTE: Check transforms and transforms.arrange and give user-friendly error messages. if not isinstance(transforms, paddlers.transforms.Compose): raise TypeError("`transforms` must be paddlers.transforms.Compose.") arrange_obj = transforms.arrange if not isinstance(arrange_obj, paddlers.transforms.operators.Arrange): raise TypeError("`transforms.arrange` must be an Arrange object.") if arrange_obj.mode != mode: raise ValueError( f"Incorrect arrange mode! Expected {mode} but got {arrange_obj.mode}." )