# 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 datetime import six import copy import json import paddle import paddle.distributed as dist from ppdet.utils.checkpoint import save_model from ppdet.metrics import get_infer_results from ppdet.utils.logger import setup_logger logger = setup_logger('ppdet.engine') __all__ = [ 'Callback', 'ComposeCallback', 'LogPrinter', 'Checkpointer', 'VisualDLWriter', 'SniperProposalsGenerator' ] class Callback(object): def __init__(self, model): self.model = model def on_step_begin(self, status): pass def on_step_end(self, status): pass def on_epoch_begin(self, status): pass def on_epoch_end(self, status): pass def on_train_begin(self, status): pass def on_train_end(self, status): pass class ComposeCallback(object): def __init__(self, callbacks): callbacks = [c for c in list(callbacks) if c is not None] for c in callbacks: assert isinstance( c, Callback), "callback should be subclass of Callback" self._callbacks = callbacks def on_step_begin(self, status): for c in self._callbacks: c.on_step_begin(status) def on_step_end(self, status): for c in self._callbacks: c.on_step_end(status) def on_epoch_begin(self, status): for c in self._callbacks: c.on_epoch_begin(status) def on_epoch_end(self, status): for c in self._callbacks: c.on_epoch_end(status) def on_train_begin(self, status): for c in self._callbacks: c.on_train_begin(status) def on_train_end(self, status): for c in self._callbacks: c.on_train_end(status) class LogPrinter(Callback): def __init__(self, model): super(LogPrinter, self).__init__(model) def on_step_end(self, status): if dist.get_world_size() < 2 or dist.get_rank() == 0: mode = status['mode'] if mode == 'train': epoch_id = status['epoch_id'] step_id = status['step_id'] steps_per_epoch = status['steps_per_epoch'] training_staus = status['training_staus'] batch_time = status['batch_time'] data_time = status['data_time'] epoches = self.model.cfg.epoch batch_size = self.model.cfg['{}Reader'.format(mode.capitalize( ))]['batch_size'] logs = training_staus.log() space_fmt = ':' + str(len(str(steps_per_epoch))) + 'd' if step_id % self.model.cfg.log_iter == 0: eta_steps = (epoches - epoch_id) * steps_per_epoch - step_id eta_sec = eta_steps * batch_time.global_avg eta_str = str(datetime.timedelta(seconds=int(eta_sec))) ips = float(batch_size) / batch_time.avg fmt = ' '.join([ 'Epoch: [{}]', '[{' + space_fmt + '}/{}]', 'learning_rate: {lr:.6f}', '{meters}', 'eta: {eta}', 'batch_cost: {btime}', 'data_cost: {dtime}', 'ips: {ips:.4f} images/s', ]) fmt = fmt.format( epoch_id, step_id, steps_per_epoch, lr=status['learning_rate'], meters=logs, eta=eta_str, btime=str(batch_time), dtime=str(data_time), ips=ips) logger.info(fmt) if mode == 'eval': step_id = status['step_id'] if step_id % 100 == 0: logger.info("Eval iter: {}".format(step_id)) def on_epoch_end(self, status): if dist.get_world_size() < 2 or dist.get_rank() == 0: mode = status['mode'] if mode == 'eval': sample_num = status['sample_num'] cost_time = status['cost_time'] logger.info('Total sample number: {}, averge FPS: {}'.format( sample_num, sample_num / cost_time)) class Checkpointer(Callback): def __init__(self, model): super(Checkpointer, self).__init__(model) cfg = self.model.cfg self.best_ap = 0. self.save_dir = os.path.join(self.model.cfg.save_dir, self.model.cfg.filename) if hasattr(self.model.model, 'student_model'): self.weight = self.model.model.student_model else: self.weight = self.model.model def on_epoch_end(self, status): # Checkpointer only performed during training mode = status['mode'] epoch_id = status['epoch_id'] weight = None save_name = None if dist.get_world_size() < 2 or dist.get_rank() == 0: if mode == 'train': end_epoch = self.model.cfg.epoch if ( epoch_id + 1 ) % self.model.cfg.snapshot_epoch == 0 or epoch_id == end_epoch - 1: save_name = str( epoch_id) if epoch_id != end_epoch - 1 else "model_final" weight = self.weight elif mode == 'eval': if 'save_best_model' in status and status['save_best_model']: for metric in self.model._metrics: map_res = metric.get_results() if 'bbox' in map_res: key = 'bbox' elif 'keypoint' in map_res: key = 'keypoint' else: key = 'mask' if key not in map_res: logger.warning("Evaluation results empty, this may be due to " \ "training iterations being too few or not " \ "loading the correct weights.") return if map_res[key][0] > self.best_ap: self.best_ap = map_res[key][0] save_name = 'best_model' weight = self.weight logger.info("Best test {} ap is {:0.3f}.".format( key, self.best_ap)) if weight: if self.model.use_ema: save_model(status['weight'], self.save_dir, save_name, epoch_id + 1, self.model.optimizer) save_model(weight, self.save_dir, '{}_ema'.format(save_name), epoch_id + 1) else: save_model(weight, self.save_dir, save_name, epoch_id + 1, self.model.optimizer) class WiferFaceEval(Callback): def __init__(self, model): super(WiferFaceEval, self).__init__(model) def on_epoch_begin(self, status): assert self.model.mode == 'eval', \ "WiferFaceEval can only be set during evaluation" for metric in self.model._metrics: metric.update(self.model.model) sys.exit() class VisualDLWriter(Callback): """ Use VisualDL to log data or image """ def __init__(self, model): super(VisualDLWriter, self).__init__(model) assert six.PY3, "VisualDL requires Python >= 3.5" try: from visualdl import LogWriter except Exception as e: logger.error('visualdl not found, plaese install visualdl. ' 'for example: `pip install visualdl`.') raise e self.vdl_writer = LogWriter( model.cfg.get('vdl_log_dir', 'vdl_log_dir/scalar')) self.vdl_loss_step = 0 self.vdl_mAP_step = 0 self.vdl_image_step = 0 self.vdl_image_frame = 0 def on_step_end(self, status): mode = status['mode'] if dist.get_world_size() < 2 or dist.get_rank() == 0: if mode == 'train': training_staus = status['training_staus'] for loss_name, loss_value in training_staus.get().items(): self.vdl_writer.add_scalar(loss_name, loss_value, self.vdl_loss_step) self.vdl_loss_step += 1 elif mode == 'test': ori_image = status['original_image'] result_image = status['result_image'] self.vdl_writer.add_image( "original/frame_{}".format(self.vdl_image_frame), ori_image, self.vdl_image_step) self.vdl_writer.add_image( "result/frame_{}".format(self.vdl_image_frame), result_image, self.vdl_image_step) self.vdl_image_step += 1 # each frame can display ten pictures at most. if self.vdl_image_step % 10 == 0: self.vdl_image_step = 0 self.vdl_image_frame += 1 def on_epoch_end(self, status): mode = status['mode'] if dist.get_world_size() < 2 or dist.get_rank() == 0: if mode == 'eval': for metric in self.model._metrics: for key, map_value in metric.get_results().items(): self.vdl_writer.add_scalar("{}-mAP".format(key), map_value[0], self.vdl_mAP_step) self.vdl_mAP_step += 1 class SniperProposalsGenerator(Callback): def __init__(self, model): super(SniperProposalsGenerator, self).__init__(model) ori_dataset = self.model.dataset self.dataset = self._create_new_dataset(ori_dataset) self.loader = self.model.loader self.cfg = self.model.cfg self.infer_model = self.model.model def _create_new_dataset(self, ori_dataset): dataset = copy.deepcopy(ori_dataset) # init anno_cropper dataset.init_anno_cropper() # generate infer roidbs ori_roidbs = dataset.get_ori_roidbs() roidbs = dataset.anno_cropper.crop_infer_anno_records(ori_roidbs) # set new roidbs dataset.set_roidbs(roidbs) return dataset def _eval_with_loader(self, loader): results = [] with paddle.no_grad(): self.infer_model.eval() for step_id, data in enumerate(loader): outs = self.infer_model(data) for key in ['im_shape', 'scale_factor', 'im_id']: outs[key] = data[key] for key, value in outs.items(): if hasattr(value, 'numpy'): outs[key] = value.numpy() results.append(outs) return results def on_train_end(self, status): self.loader.dataset = self.dataset results = self._eval_with_loader(self.loader) results = self.dataset.anno_cropper.aggregate_chips_detections(results) # sniper proposals = [] clsid2catid = {v: k for k, v in self.dataset.catid2clsid.items()} for outs in results: batch_res = get_infer_results(outs, clsid2catid) start = 0 for i, im_id in enumerate(outs['im_id']): bbox_num = outs['bbox_num'] end = start + bbox_num[i] bbox_res = batch_res['bbox'][start:end] \ if 'bbox' in batch_res else None if bbox_res: proposals += bbox_res logger.info("save proposals in {}".format(self.cfg.proposals_path)) with open(self.cfg.proposals_path, 'w') as f: json.dump(proposals, f)