# Copyright (c) 2020 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 __future__ import absolute_import from __future__ import division from __future__ import print_function import os import sys import copy import time from tqdm import tqdm import numpy as np import typing from PIL import Image, ImageOps, ImageFile ImageFile.LOAD_TRUNCATED_IMAGES = True import paddle import paddle.nn as nn import paddle.distributed as dist from paddle.distributed import fleet from paddle import amp from paddle.static import InputSpec from ppdet.optimizer import ModelEMA from ppdet.core.workspace import create from ppdet.utils.checkpoint import load_weight, load_pretrain_weight from ppdet.utils.visualizer import visualize_results, save_result from ppdet.metrics import Metric, COCOMetric, VOCMetric, WiderFaceMetric, get_infer_results, KeyPointTopDownCOCOEval, KeyPointTopDownMPIIEval from ppdet.metrics import RBoxMetric, JDEDetMetric, SNIPERCOCOMetric from ppdet.data.source.sniper_coco import SniperCOCODataSet from ppdet.data.source.category import get_categories import ppdet.utils.stats as stats from ppdet.utils import profiler from .callbacks import Callback, ComposeCallback, LogPrinter, Checkpointer, WiferFaceEval, VisualDLWriter, SniperProposalsGenerator from .export_utils import _dump_infer_config, _prune_input_spec from ppdet.utils.logger import setup_logger logger = setup_logger('ppdet.engine') __all__ = ['Trainer'] MOT_ARCH = ['DeepSORT', 'JDE', 'FairMOT', 'ByteTrack'] class Trainer(object): def __init__(self, cfg, mode='train'): self.cfg = cfg assert mode.lower() in ['train', 'eval', 'test'], \ "mode should be 'train', 'eval' or 'test'" self.mode = mode.lower() self.optimizer = None self.is_loaded_weights = False # build data loader if cfg.architecture in MOT_ARCH and self.mode in ['eval', 'test']: self.dataset = cfg['{}MOTDataset'.format(self.mode.capitalize())] else: self.dataset = cfg['{}Dataset'.format(self.mode.capitalize())] if cfg.architecture == 'DeepSORT' and self.mode == 'train': logger.error('DeepSORT has no need of training on mot dataset.') sys.exit(1) if cfg.architecture == 'FairMOT' and self.mode == 'eval': images = self.parse_mot_images(cfg) self.dataset.set_images(images) if self.mode == 'train': self.loader = create('{}Reader'.format(self.mode.capitalize()))( self.dataset, cfg.worker_num) if cfg.architecture == 'JDE' and self.mode == 'train': cfg['JDEEmbeddingHead'][ 'num_identities'] = self.dataset.num_identities_dict[0] # JDE only support single class MOT now. if cfg.architecture == 'FairMOT' and self.mode == 'train': cfg['FairMOTEmbeddingHead'][ 'num_identities_dict'] = self.dataset.num_identities_dict # FairMOT support single class and multi-class MOT now. # build model if 'model' not in self.cfg: self.model = create(cfg.architecture) else: self.model = self.cfg.model self.is_loaded_weights = True if cfg.architecture == 'YOLOX': for k, m in self.model.named_sublayers(): if isinstance(m, nn.BatchNorm2D): m._epsilon = 1e-3 # for amp(fp16) m._momentum = 0.97 # 0.03 in pytorch #normalize params for deploy if 'slim' in cfg and cfg['slim_type'] == 'OFA': self.model.model.load_meanstd(cfg['TestReader'][ 'sample_transforms']) elif 'slim' in cfg and cfg['slim_type'] == 'Distill': self.model.student_model.load_meanstd(cfg['TestReader'][ 'sample_transforms']) elif 'slim' in cfg and cfg[ 'slim_type'] == 'DistillPrune' and self.mode == 'train': self.model.student_model.load_meanstd(cfg['TestReader'][ 'sample_transforms']) else: self.model.load_meanstd(cfg['TestReader']['sample_transforms']) self.use_ema = ('use_ema' in cfg and cfg['use_ema']) if self.use_ema: ema_decay = self.cfg.get('ema_decay', 0.9998) cycle_epoch = self.cfg.get('cycle_epoch', -1) ema_decay_type = self.cfg.get('ema_decay_type', 'threshold') self.ema = ModelEMA( self.model, decay=ema_decay, ema_decay_type=ema_decay_type, cycle_epoch=cycle_epoch) # EvalDataset build with BatchSampler to evaluate in single device # TODO: multi-device evaluate if self.mode == 'eval': if cfg.architecture == 'FairMOT': self.loader = create('EvalMOTReader')(self.dataset, 0) else: self._eval_batch_sampler = paddle.io.BatchSampler( self.dataset, batch_size=self.cfg.EvalReader['batch_size']) reader_name = '{}Reader'.format(self.mode.capitalize()) # If metric is VOC, need to be set collate_batch=False. if cfg.metric == 'VOC': cfg[reader_name]['collate_batch'] = False self.loader = create(reader_name)(self.dataset, cfg.worker_num, self._eval_batch_sampler) # TestDataset build after user set images, skip loader creation here # build optimizer in train mode if self.mode == 'train': steps_per_epoch = len(self.loader) self.lr = create('LearningRate')(steps_per_epoch) self.optimizer = create('OptimizerBuilder')(self.lr, self.model) # Unstructured pruner is only enabled in the train mode. if self.cfg.get('unstructured_prune'): self.pruner = create('UnstructuredPruner')(self.model, steps_per_epoch) self._nranks = dist.get_world_size() self._local_rank = dist.get_rank() self.status = {} self.start_epoch = 0 self.end_epoch = 0 if 'epoch' not in cfg else cfg.epoch # initial default callbacks self._init_callbacks() # initial default metrics self._init_metrics() self._reset_metrics() def _init_callbacks(self): if self.mode == 'train': self._callbacks = [LogPrinter(self), Checkpointer(self)] if self.cfg.get('use_vdl', False): self._callbacks.append(VisualDLWriter(self)) if self.cfg.get('save_proposals', False): self._callbacks.append(SniperProposalsGenerator(self)) self._compose_callback = ComposeCallback(self._callbacks) elif self.mode == 'eval': self._callbacks = [LogPrinter(self)] if self.cfg.metric == 'WiderFace': self._callbacks.append(WiferFaceEval(self)) self._compose_callback = ComposeCallback(self._callbacks) elif self.mode == 'test' and self.cfg.get('use_vdl', False): self._callbacks = [VisualDLWriter(self)] self._compose_callback = ComposeCallback(self._callbacks) else: self._callbacks = [] self._compose_callback = None def _init_metrics(self, validate=False): if self.mode == 'test' or (self.mode == 'train' and not validate): self._metrics = [] return classwise = self.cfg['classwise'] if 'classwise' in self.cfg else False if self.cfg.metric == 'COCO' or self.cfg.metric == "SNIPERCOCO": # TODO: bias should be unified bias = self.cfg['bias'] if 'bias' in self.cfg else 0 output_eval = self.cfg['output_eval'] \ if 'output_eval' in self.cfg else None save_prediction_only = self.cfg.get('save_prediction_only', False) # pass clsid2catid info to metric instance to avoid multiple loading # annotation file clsid2catid = {v: k for k, v in self.dataset.catid2clsid.items()} \ if self.mode == 'eval' else None # when do validation in train, annotation file should be get from # EvalReader instead of self.dataset(which is TrainReader) anno_file = self.dataset.get_anno() dataset = self.dataset if self.mode == 'train' and validate: eval_dataset = self.cfg['EvalDataset'] eval_dataset.check_or_download_dataset() anno_file = eval_dataset.get_anno() dataset = eval_dataset IouType = self.cfg['IouType'] if 'IouType' in self.cfg else 'bbox' if self.cfg.metric == "COCO": self._metrics = [ COCOMetric( anno_file=anno_file, clsid2catid=clsid2catid, classwise=classwise, output_eval=output_eval, bias=bias, IouType=IouType, save_prediction_only=save_prediction_only) ] elif self.cfg.metric == "SNIPERCOCO": # sniper self._metrics = [ SNIPERCOCOMetric( anno_file=anno_file, dataset=dataset, clsid2catid=clsid2catid, classwise=classwise, output_eval=output_eval, bias=bias, IouType=IouType, save_prediction_only=save_prediction_only) ] elif self.cfg.metric == 'RBOX': # TODO: bias should be unified bias = self.cfg['bias'] if 'bias' in self.cfg else 0 output_eval = self.cfg['output_eval'] \ if 'output_eval' in self.cfg else None save_prediction_only = self.cfg.get('save_prediction_only', False) # pass clsid2catid info to metric instance to avoid multiple loading # annotation file clsid2catid = {v: k for k, v in self.dataset.catid2clsid.items()} \ if self.mode == 'eval' else None # when do validation in train, annotation file should be get from # EvalReader instead of self.dataset(which is TrainReader) anno_file = self.dataset.get_anno() if self.mode == 'train' and validate: eval_dataset = self.cfg['EvalDataset'] eval_dataset.check_or_download_dataset() anno_file = eval_dataset.get_anno() self._metrics = [ RBoxMetric( anno_file=anno_file, clsid2catid=clsid2catid, classwise=classwise, output_eval=output_eval, bias=bias, save_prediction_only=save_prediction_only) ] elif self.cfg.metric == 'VOC': self._metrics = [ VOCMetric( label_list=self.dataset.get_label_list(), class_num=self.cfg.num_classes, map_type=self.cfg.map_type, classwise=classwise) ] elif self.cfg.metric == 'WiderFace': multi_scale = self.cfg.multi_scale_eval if 'multi_scale_eval' in self.cfg else True self._metrics = [ WiderFaceMetric( image_dir=os.path.join(self.dataset.dataset_dir, self.dataset.image_dir), anno_file=self.dataset.get_anno(), multi_scale=multi_scale) ] elif self.cfg.metric == 'KeyPointTopDownCOCOEval': eval_dataset = self.cfg['EvalDataset'] eval_dataset.check_or_download_dataset() anno_file = eval_dataset.get_anno() save_prediction_only = self.cfg.get('save_prediction_only', False) self._metrics = [ KeyPointTopDownCOCOEval( anno_file, len(eval_dataset), self.cfg.num_joints, self.cfg.save_dir, save_prediction_only=save_prediction_only) ] elif self.cfg.metric == 'KeyPointTopDownMPIIEval': eval_dataset = self.cfg['EvalDataset'] eval_dataset.check_or_download_dataset() anno_file = eval_dataset.get_anno() save_prediction_only = self.cfg.get('save_prediction_only', False) self._metrics = [ KeyPointTopDownMPIIEval( anno_file, len(eval_dataset), self.cfg.num_joints, self.cfg.save_dir, save_prediction_only=save_prediction_only) ] elif self.cfg.metric == 'MOTDet': self._metrics = [JDEDetMetric(), ] else: logger.warning("Metric not support for metric type {}".format( self.cfg.metric)) self._metrics = [] def _reset_metrics(self): for metric in self._metrics: metric.reset() def register_callbacks(self, callbacks): callbacks = [c for c in list(callbacks) if c is not None] for c in callbacks: assert isinstance(c, Callback), \ "metrics shoule be instances of subclass of Metric" self._callbacks.extend(callbacks) self._compose_callback = ComposeCallback(self._callbacks) def register_metrics(self, metrics): metrics = [m for m in list(metrics) if m is not None] for m in metrics: assert isinstance(m, Metric), \ "metrics shoule be instances of subclass of Metric" self._metrics.extend(metrics) def load_weights(self, weights): if self.is_loaded_weights: return self.start_epoch = 0 load_pretrain_weight(self.model, weights) logger.debug("Load weights {} to start training".format(weights)) def load_weights_sde(self, det_weights, reid_weights): if self.model.detector: load_weight(self.model.detector, det_weights) load_weight(self.model.reid, reid_weights) else: load_weight(self.model.reid, reid_weights) def resume_weights(self, weights): # support Distill resume weights if hasattr(self.model, 'student_model'): self.start_epoch = load_weight(self.model.student_model, weights, self.optimizer) else: self.start_epoch = load_weight(self.model, weights, self.optimizer, self.ema if self.use_ema else None) logger.debug("Resume weights of epoch {}".format(self.start_epoch)) def train(self, validate=False): assert self.mode == 'train', "Model not in 'train' mode" Init_mark = False sync_bn = (getattr(self.cfg, 'norm_type', None) == 'sync_bn' and self.cfg.use_gpu and self._nranks > 1) if sync_bn: self.model = paddle.nn.SyncBatchNorm.convert_sync_batchnorm( self.model) model = self.model if self.cfg.get('fleet', False): model = fleet.distributed_model(model) self.optimizer = fleet.distributed_optimizer(self.optimizer) elif self._nranks > 1: find_unused_parameters = self.cfg[ 'find_unused_parameters'] if 'find_unused_parameters' in self.cfg else False model = paddle.DataParallel( self.model, find_unused_parameters=find_unused_parameters) # enabel auto mixed precision mode if self.cfg.get('amp', False): scaler = amp.GradScaler( enable=self.cfg.use_gpu or self.cfg.use_npu, init_loss_scaling=1024) self.status.update({ 'epoch_id': self.start_epoch, 'step_id': 0, 'steps_per_epoch': len(self.loader) }) self.status['batch_time'] = stats.SmoothedValue( self.cfg.log_iter, fmt='{avg:.4f}') self.status['data_time'] = stats.SmoothedValue( self.cfg.log_iter, fmt='{avg:.4f}') self.status['training_staus'] = stats.TrainingStats(self.cfg.log_iter) if self.cfg.get('print_flops', False): flops_loader = create('{}Reader'.format(self.mode.capitalize()))( self.dataset, self.cfg.worker_num) self._flops(flops_loader) profiler_options = self.cfg.get('profiler_options', None) self._compose_callback.on_train_begin(self.status) for epoch_id in range(self.start_epoch, self.cfg.epoch): self.status['mode'] = 'train' self.status['epoch_id'] = epoch_id self._compose_callback.on_epoch_begin(self.status) self.loader.dataset.set_epoch(epoch_id) model.train() iter_tic = time.time() for step_id, data in enumerate(self.loader): self.status['data_time'].update(time.time() - iter_tic) self.status['step_id'] = step_id profiler.add_profiler_step(profiler_options) self._compose_callback.on_step_begin(self.status) data['epoch_id'] = epoch_id if self.cfg.get('amp', False): with amp.auto_cast(enable=self.cfg.use_gpu): # model forward outputs = model(data) loss = outputs['loss'] # model backward scaled_loss = scaler.scale(loss) scaled_loss.backward() # in dygraph mode, optimizer.minimize is equal to optimizer.step scaler.minimize(self.optimizer, scaled_loss) else: # model forward outputs = model(data) loss = outputs['loss'] # model backward loss.backward() self.optimizer.step() curr_lr = self.optimizer.get_lr() self.lr.step() if self.cfg.get('unstructured_prune'): self.pruner.step() self.optimizer.clear_grad() self.status['learning_rate'] = curr_lr if self._nranks < 2 or self._local_rank == 0: self.status['training_staus'].update(outputs) self.status['batch_time'].update(time.time() - iter_tic) self._compose_callback.on_step_end(self.status) if self.use_ema: self.ema.update() iter_tic = time.time() if self.cfg.get('unstructured_prune'): self.pruner.update_params() is_snapshot = (self._nranks < 2 or self._local_rank == 0) \ and ((epoch_id + 1) % self.cfg.snapshot_epoch == 0 or epoch_id == self.end_epoch - 1) if is_snapshot and self.use_ema: # apply ema weight on model weight = copy.deepcopy(self.model.state_dict()) self.model.set_dict(self.ema.apply()) self.status['weight'] = weight self._compose_callback.on_epoch_end(self.status) if validate and is_snapshot: if not hasattr(self, '_eval_loader'): # build evaluation dataset and loader self._eval_dataset = self.cfg.EvalDataset self._eval_batch_sampler = \ paddle.io.BatchSampler( self._eval_dataset, batch_size=self.cfg.EvalReader['batch_size']) # If metric is VOC, need to be set collate_batch=False. if self.cfg.metric == 'VOC': self.cfg['EvalReader']['collate_batch'] = False self._eval_loader = create('EvalReader')( self._eval_dataset, self.cfg.worker_num, batch_sampler=self._eval_batch_sampler) # if validation in training is enabled, metrics should be re-init # Init_mark makes sure this code will only execute once if validate and Init_mark == False: Init_mark = True self._init_metrics(validate=validate) self._reset_metrics() with paddle.no_grad(): self.status['save_best_model'] = True self._eval_with_loader(self._eval_loader) if is_snapshot and self.use_ema: # reset original weight self.model.set_dict(weight) self.status.pop('weight') self._compose_callback.on_train_end(self.status) def _eval_with_loader(self, loader): sample_num = 0 tic = time.time() self._compose_callback.on_epoch_begin(self.status) self.status['mode'] = 'eval' self.model.eval() if self.cfg.get('print_flops', False): flops_loader = create('{}Reader'.format(self.mode.capitalize()))( self.dataset, self.cfg.worker_num, self._eval_batch_sampler) self._flops(flops_loader) for step_id, data in enumerate(loader): self.status['step_id'] = step_id self._compose_callback.on_step_begin(self.status) # forward outs = self.model(data) # update metrics for metric in self._metrics: metric.update(data, outs) # multi-scale inputs: all inputs have same im_id if isinstance(data, typing.Sequence): sample_num += data[0]['im_id'].numpy().shape[0] else: sample_num += data['im_id'].numpy().shape[0] self._compose_callback.on_step_end(self.status) self.status['sample_num'] = sample_num self.status['cost_time'] = time.time() - tic # accumulate metric to log out for metric in self._metrics: metric.accumulate() metric.log() self._compose_callback.on_epoch_end(self.status) # reset metric states for metric may performed multiple times self._reset_metrics() def evaluate(self): with paddle.no_grad(): self._eval_with_loader(self.loader) def predict(self, images, draw_threshold=0.5, output_dir='output', save_results=False): self.dataset.set_images(images) loader = create('TestReader')(self.dataset, 0) def setup_metrics_for_loader(): # mem metrics = copy.deepcopy(self._metrics) mode = self.mode save_prediction_only = self.cfg[ 'save_prediction_only'] if 'save_prediction_only' in self.cfg else None output_eval = self.cfg[ 'output_eval'] if 'output_eval' in self.cfg else None # modify self.mode = '_test' self.cfg['save_prediction_only'] = True self.cfg['output_eval'] = output_dir self._init_metrics() # restore self.mode = mode self.cfg.pop('save_prediction_only') if save_prediction_only is not None: self.cfg['save_prediction_only'] = save_prediction_only self.cfg.pop('output_eval') if output_eval is not None: self.cfg['output_eval'] = output_eval _metrics = copy.deepcopy(self._metrics) self._metrics = metrics return _metrics if save_results: metrics = setup_metrics_for_loader() else: metrics = [] imid2path = self.dataset.get_imid2path() anno_file = self.dataset.get_anno() clsid2catid, catid2name = get_categories( self.cfg.metric, anno_file=anno_file) # Run Infer self.status['mode'] = 'test' self.model.eval() if self.cfg.get('print_flops', False): flops_loader = create('TestReader')(self.dataset, 0) self._flops(flops_loader) results = [] for step_id, data in enumerate(tqdm(loader)): self.status['step_id'] = step_id # forward outs = self.model(data) for _m in metrics: _m.update(data, outs) for key in ['im_shape', 'scale_factor', 'im_id']: if isinstance(data, typing.Sequence): outs[key] = data[0][key] else: outs[key] = data[key] for key, value in outs.items(): if hasattr(value, 'numpy'): outs[key] = value.numpy() results.append(outs) # sniper if type(self.dataset) == SniperCOCODataSet: results = self.dataset.anno_cropper.aggregate_chips_detections( results) for _m in metrics: _m.accumulate() _m.reset() for outs in results: batch_res = get_infer_results(outs, clsid2catid) bbox_num = outs['bbox_num'] start = 0 for i, im_id in enumerate(outs['im_id']): image_path = imid2path[int(im_id)] image = Image.open(image_path).convert('RGB') image = ImageOps.exif_transpose(image) self.status['original_image'] = np.array(image.copy()) end = start + bbox_num[i] bbox_res = batch_res['bbox'][start:end] \ if 'bbox' in batch_res else None mask_res = batch_res['mask'][start:end] \ if 'mask' in batch_res else None segm_res = batch_res['segm'][start:end] \ if 'segm' in batch_res else None keypoint_res = batch_res['keypoint'][start:end] \ if 'keypoint' in batch_res else None image = visualize_results( image, bbox_res, mask_res, segm_res, keypoint_res, int(im_id), catid2name, draw_threshold) self.status['result_image'] = np.array(image.copy()) if self._compose_callback: self._compose_callback.on_step_end(self.status) # save image with detection save_name = self._get_save_image_name(output_dir, image_path) logger.info("Detection bbox results save in {}".format( save_name)) image.save(save_name, quality=95) start = end def _get_save_image_name(self, output_dir, image_path): """ Get save image name from source image path. """ if not os.path.exists(output_dir): os.makedirs(output_dir) image_name = os.path.split(image_path)[-1] name, ext = os.path.splitext(image_name) return os.path.join(output_dir, "{}".format(name)) + ext def _get_infer_cfg_and_input_spec(self, save_dir, prune_input=True): image_shape = None im_shape = [None, 2] scale_factor = [None, 2] if self.cfg.architecture in MOT_ARCH: test_reader_name = 'TestMOTReader' else: test_reader_name = 'TestReader' if 'inputs_def' in self.cfg[test_reader_name]: inputs_def = self.cfg[test_reader_name]['inputs_def'] image_shape = inputs_def.get('image_shape', None) # set image_shape=[None, 3, -1, -1] as default if image_shape is None: image_shape = [None, 3, -1, -1] if len(image_shape) == 3: image_shape = [None] + image_shape else: im_shape = [image_shape[0], 2] scale_factor = [image_shape[0], 2] if hasattr(self.model, 'deploy'): self.model.deploy = True for layer in self.model.sublayers(): if hasattr(layer, 'convert_to_deploy'): layer.convert_to_deploy() export_post_process = self.cfg['export'].get( 'post_process', False) if hasattr(self.cfg, 'export') else True export_nms = self.cfg['export'].get('nms', False) if hasattr( self.cfg, 'export') else True export_benchmark = self.cfg['export'].get( 'benchmark', False) if hasattr(self.cfg, 'export') else False if hasattr(self.model, 'fuse_norm'): self.model.fuse_norm = self.cfg['TestReader'].get('fuse_normalize', False) if hasattr(self.model, 'export_post_process'): self.model.export_post_process = export_post_process if not export_benchmark else False if hasattr(self.model, 'export_nms'): self.model.export_nms = export_nms if not export_benchmark else False if export_post_process and not export_benchmark: image_shape = [None] + image_shape[1:] # Save infer cfg _dump_infer_config(self.cfg, os.path.join(save_dir, 'infer_cfg.yml'), image_shape, self.model) input_spec = [{ "image": InputSpec( shape=image_shape, name='image'), "im_shape": InputSpec( shape=im_shape, name='im_shape'), "scale_factor": InputSpec( shape=scale_factor, name='scale_factor') }] if self.cfg.architecture == 'DeepSORT': input_spec[0].update({ "crops": InputSpec( shape=[None, 3, 192, 64], name='crops') }) if prune_input: static_model = paddle.jit.to_static( self.model, input_spec=input_spec) # NOTE: dy2st do not pruned program, but jit.save will prune program # input spec, prune input spec here and save with pruned input spec pruned_input_spec = _prune_input_spec( input_spec, static_model.forward.main_program, static_model.forward.outputs) else: static_model = None pruned_input_spec = input_spec # TODO: Hard code, delete it when support prune input_spec. if self.cfg.architecture == 'PicoDet' and not export_post_process: pruned_input_spec = [{ "image": InputSpec( shape=image_shape, name='image') }] return static_model, pruned_input_spec def export(self, output_dir='output_inference'): self.model.eval() model_name = os.path.splitext(os.path.split(self.cfg.filename)[-1])[0] save_dir = os.path.join(output_dir, model_name) if not os.path.exists(save_dir): os.makedirs(save_dir) static_model, pruned_input_spec = self._get_infer_cfg_and_input_spec( save_dir) # dy2st and save model if 'slim' not in self.cfg or self.cfg['slim_type'] != 'QAT': paddle.jit.save( static_model, os.path.join(save_dir, 'model'), input_spec=pruned_input_spec) else: self.cfg.slim.save_quantized_model( self.model, os.path.join(save_dir, 'model'), input_spec=pruned_input_spec) logger.info("Export model and saved in {}".format(save_dir)) def post_quant(self, output_dir='output_inference'): model_name = os.path.splitext(os.path.split(self.cfg.filename)[-1])[0] save_dir = os.path.join(output_dir, model_name) if not os.path.exists(save_dir): os.makedirs(save_dir) for idx, data in enumerate(self.loader): self.model(data) if idx == int(self.cfg.get('quant_batch_num', 10)): break # TODO: support prune input_spec _, pruned_input_spec = self._get_infer_cfg_and_input_spec( save_dir, prune_input=False) self.cfg.slim.save_quantized_model( self.model, os.path.join(save_dir, 'model'), input_spec=pruned_input_spec) logger.info("Export Post-Quant model and saved in {}".format(save_dir)) def _flops(self, loader): self.model.eval() try: import paddleslim except Exception as e: logger.warning( 'Unable to calculate flops, please install paddleslim, for example: `pip install paddleslim`' ) return from paddleslim.analysis import dygraph_flops as flops input_data = None for data in loader: input_data = data break input_spec = [{ "image": input_data['image'][0].unsqueeze(0), "im_shape": input_data['im_shape'][0].unsqueeze(0), "scale_factor": input_data['scale_factor'][0].unsqueeze(0) }] flops = flops(self.model, input_spec) / (1000**3) logger.info(" Model FLOPs : {:.6f}G. (image shape is {})".format( flops, input_data['image'][0].unsqueeze(0).shape)) def parse_mot_images(self, cfg): import glob # for quant dataset_dir = cfg['EvalMOTDataset'].dataset_dir data_root = cfg['EvalMOTDataset'].data_root data_root = '{}/{}'.format(dataset_dir, data_root) seqs = os.listdir(data_root) seqs.sort() all_images = [] for seq in seqs: infer_dir = os.path.join(data_root, seq) assert infer_dir is None or os.path.isdir(infer_dir), \ "{} is not a directory".format(infer_dir) images = set() exts = ['jpg', 'jpeg', 'png', 'bmp'] exts += [ext.upper() for ext in exts] for ext in exts: images.update(glob.glob('{}/*.{}'.format(infer_dir, ext))) images = list(images) images.sort() assert len(images) > 0, "no image found in {}".format(infer_dir) all_images.extend(images) logger.info("Found {} inference images in total.".format( len(images))) return all_images