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- # 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.state_dict()
- 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.state_dict()
- logger.info("Best test {} ap is {:0.3f}.".format(
- key, self.best_ap))
- if weight:
- if self.model.use_ema:
- # save model and ema_model
- save_model(
- status['weight'],
- self.model.optimizer,
- self.save_dir,
- save_name,
- epoch_id + 1,
- ema_model=weight)
- else:
- save_model(weight, self.model.optimizer, self.save_dir,
- save_name, epoch_id + 1)
-
-
- 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 WandbCallback(Callback):
- def __init__(self, model):
- super(WandbCallback, self).__init__(model)
-
- try:
- import wandb
- self.wandb = wandb
- except Exception as e:
- logger.error('wandb not found, please install wandb. '
- 'Use: `pip install wandb`.')
- raise e
-
- self.wandb_params = model.cfg.get('wandb', None)
- self.save_dir = os.path.join(self.model.cfg.save_dir,
- self.model.cfg.filename)
- if self.wandb_params is None:
- self.wandb_params = {}
- for k, v in model.cfg.items():
- if k.startswith("wandb_"):
- self.wandb_params.update({
- k.lstrip("wandb_"): v
- })
-
- self._run = None
- if dist.get_world_size() < 2 or dist.get_rank() == 0:
- _ = self.run
- self.run.config.update(self.model.cfg)
- self.run.define_metric("epoch")
- self.run.define_metric("eval/*", step_metric="epoch")
-
- self.best_ap = 0
-
- @property
- def run(self):
- if self._run is None:
- if self.wandb.run is not None:
- logger.info("There is an ongoing wandb run which will be used"
- "for logging. Please use `wandb.finish()` to end that"
- "if the behaviour is not intended")
- self._run = self.wandb.run
- else:
- self._run = self.wandb.init(**self.wandb_params)
- return self._run
-
- def save_model(self,
- optimizer,
- save_dir,
- save_name,
- last_epoch,
- ema_model=None,
- ap=None,
- tags=None):
- if dist.get_world_size() < 2 or dist.get_rank() == 0:
- model_path = os.path.join(save_dir, save_name)
- metadata = {}
- metadata["last_epoch"] = last_epoch
- if ap:
- metadata["ap"] = ap
- if ema_model is None:
- ema_artifact = self.wandb.Artifact(name="ema_model-{}".format(self.run.id), type="model", metadata=metadata)
- model_artifact = self.wandb.Artifact(name="model-{}".format(self.run.id), type="model", metadata=metadata)
-
- ema_artifact.add_file(model_path + ".pdema", name="model_ema")
- model_artifact.add_file(model_path + ".pdparams", name="model")
-
- self.run.log_artifact(ema_artifact, aliases=tags)
- self.run.log_artfact(model_artifact, aliases=tags)
- else:
- model_artifact = self.wandb.Artifact(name="model-{}".format(self.run.id), type="model", metadata=metadata)
- model_artifact.add_file(model_path + ".pdparams", name="model")
- self.run.log_artifact(model_artifact, aliases=tags)
-
- def on_step_end(self, status):
-
- mode = status['mode']
- if dist.get_world_size() < 2 or dist.get_rank() == 0:
- if mode == 'train':
- training_status = status['training_staus'].get()
- for k, v in training_status.items():
- training_status[k] = float(v)
- metrics = {
- "train/" + k: v for k,v in training_status.items()
- }
- self.run.log(metrics)
-
- def on_epoch_end(self, status):
- mode = status['mode']
- epoch_id = status['epoch_id']
- 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"
- tags = ["latest", "epoch_{}".format(epoch_id)]
- self.save_model(
- self.model.optimizer,
- self.save_dir,
- save_name,
- epoch_id + 1,
- self.model.use_ema,
- tags=tags
- )
- if mode == 'eval':
- merged_dict = {}
- for metric in self.model._metrics:
- for key, map_value in metric.get_results().items():
- merged_dict["eval/{}-mAP".format(key)] = map_value[0]
- merged_dict["epoch"] = status["epoch_id"]
- self.run.log(merged_dict)
-
- 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'
- tags = ["best", "epoch_{}".format(epoch_id)]
-
- self.save_model(
- self.model.optimizer,
- self.save_dir,
- save_name,
- last_epoch=epoch_id + 1,
- ema_model=self.model.use_ema,
- ap=self.best_ap,
- tags=tags
- )
-
- def on_train_end(self, status):
- self.run.finish()
-
-
- 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)
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