forked from PulseFocusPlatform/PulseFocusPlatform
259 lines
9.6 KiB
Python
259 lines
9.6 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import sys
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import datetime
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import six
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import paddle.distributed as dist
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from ppdet.utils.checkpoint import save_model
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from ppdet.utils.logger import setup_logger
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logger = setup_logger('ppdet.engine')
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__all__ = ['Callback', 'ComposeCallback', 'LogPrinter', 'Checkpointer']
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class Callback(object):
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def __init__(self, model):
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self.model = model
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def on_step_begin(self, status):
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pass
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def on_step_end(self, status):
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pass
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def on_epoch_begin(self, status):
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pass
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def on_epoch_end(self, status):
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pass
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class ComposeCallback(object):
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def __init__(self, callbacks):
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callbacks = [c for c in list(callbacks) if c is not None]
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for c in callbacks:
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assert isinstance(
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c, Callback), "callback should be subclass of Callback"
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self._callbacks = callbacks
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def on_step_begin(self, status):
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for c in self._callbacks:
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c.on_step_begin(status)
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def on_step_end(self, status):
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for c in self._callbacks:
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c.on_step_end(status)
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def on_epoch_begin(self, status):
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for c in self._callbacks:
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c.on_epoch_begin(status)
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def on_epoch_end(self, status):
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for c in self._callbacks:
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c.on_epoch_end(status)
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class LogPrinter(Callback):
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def __init__(self, model):
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super(LogPrinter, self).__init__(model)
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def on_step_end(self, status):
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if dist.get_world_size() < 2 or dist.get_rank() == 0:
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mode = status['mode']
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if mode == 'train':
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epoch_id = status['epoch_id']
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step_id = status['step_id']
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steps_per_epoch = status['steps_per_epoch']
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training_staus = status['training_staus']
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batch_time = status['batch_time']
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data_time = status['data_time']
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epoches = self.model.cfg.epoch
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batch_size = self.model.cfg['{}Reader'.format(mode.capitalize(
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))]['batch_size']
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logs = training_staus.log()
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space_fmt = ':' + str(len(str(steps_per_epoch))) + 'd'
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if step_id % self.model.cfg.log_iter == 0:
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eta_steps = (epoches - epoch_id) * steps_per_epoch - step_id
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eta_sec = eta_steps * batch_time.global_avg
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eta_str = str(datetime.timedelta(seconds=int(eta_sec)))
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ips = float(batch_size) / batch_time.avg
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fmt = ' '.join([
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'Epoch: [{}]',
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'[{' + space_fmt + '}/{}]',
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'learning_rate: {lr:.6f}',
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'{meters}',
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'eta: {eta}',
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'batch_cost: {btime}',
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'data_cost: {dtime}',
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'ips: {ips:.4f} images/s',
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])
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fmt = fmt.format(
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epoch_id,
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step_id,
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steps_per_epoch,
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lr=status['learning_rate'],
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meters=logs,
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eta=eta_str,
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btime=str(batch_time),
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dtime=str(data_time),
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ips=ips)
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logger.info(fmt)
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if mode == 'eval':
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step_id = status['step_id']
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if step_id % 100 == 0:
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logger.info("Eval iter: {}".format(step_id))
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def on_epoch_end(self, status):
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if dist.get_world_size() < 2 or dist.get_rank() == 0:
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mode = status['mode']
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if mode == 'eval':
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sample_num = status['sample_num']
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cost_time = status['cost_time']
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logger.info('Total sample number: {}, averge FPS: {}'.format(
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sample_num, sample_num / cost_time))
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class Checkpointer(Callback):
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def __init__(self, model):
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super(Checkpointer, self).__init__(model)
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cfg = self.model.cfg
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self.best_ap = 0.
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self.save_dir = os.path.join(self.model.cfg.save_dir,
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self.model.cfg.filename)
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if hasattr(self.model.model, 'student_model'):
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self.weight = self.model.model.student_model
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else:
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self.weight = self.model.model
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def on_epoch_end(self, status):
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# Checkpointer only performed during training
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mode = status['mode']
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epoch_id = status['epoch_id']
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weight = None
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save_name = None
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if dist.get_world_size() < 2 or dist.get_rank() == 0:
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if mode == 'train':
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end_epoch = self.model.cfg.epoch
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if (
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epoch_id + 1
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) % self.model.cfg.snapshot_epoch == 0 or epoch_id == end_epoch - 1:
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save_name = str(
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epoch_id) if epoch_id != end_epoch - 1 else "model_final"
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weight = self.weight
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elif mode == 'eval':
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if 'save_best_model' in status and status['save_best_model']:
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for metric in self.model._metrics:
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map_res = metric.get_results()
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if 'bbox' in map_res:
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key = 'bbox'
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elif 'keypoint' in map_res:
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key = 'keypoint'
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else:
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key = 'mask'
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if key not in map_res:
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logger.warning("Evaluation results empty, this may be due to " \
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"training iterations being too few or not " \
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"loading the correct weights.")
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return
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if map_res[key][0] > self.best_ap:
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self.best_ap = map_res[key][0]
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save_name = 'best_model'
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weight = self.weight
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logger.info("Best test {} ap is {:0.3f}.".format(
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key, self.best_ap))
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if weight:
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save_model(weight, self.model.optimizer, self.save_dir,
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save_name, epoch_id + 1)
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class WiferFaceEval(Callback):
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def __init__(self, model):
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super(WiferFaceEval, self).__init__(model)
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def on_epoch_begin(self, status):
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assert self.model.mode == 'eval', \
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"WiferFaceEval can only be set during evaluation"
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for metric in self.model._metrics:
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metric.update(self.model.model)
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sys.exit()
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class VisualDLWriter(Callback):
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"""
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Use VisualDL to log data or image
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"""
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def __init__(self, model):
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super(VisualDLWriter, self).__init__(model)
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assert six.PY3, "VisualDL requires Python >= 3.5"
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try:
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from visualdl import LogWriter
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except Exception as e:
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logger.error('visualdl not found, plaese install visualdl. '
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'for example: `pip install visualdl`.')
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raise e
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self.vdl_writer = LogWriter(
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model.cfg.get('vdl_log_dir', 'vdl_log_dir/scalar'))
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self.vdl_loss_step = 0
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self.vdl_mAP_step = 0
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self.vdl_image_step = 0
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self.vdl_image_frame = 0
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def on_step_end(self, status):
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mode = status['mode']
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if dist.get_world_size() < 2 or dist.get_rank() == 0:
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if mode == 'train':
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training_staus = status['training_staus']
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for loss_name, loss_value in training_staus.get().items():
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self.vdl_writer.add_scalar(loss_name, loss_value,
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self.vdl_loss_step)
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self.vdl_loss_step += 1
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elif mode == 'test':
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ori_image = status['original_image']
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result_image = status['result_image']
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self.vdl_writer.add_image(
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"original/frame_{}".format(self.vdl_image_frame), ori_image,
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self.vdl_image_step)
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self.vdl_writer.add_image(
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"result/frame_{}".format(self.vdl_image_frame),
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result_image, self.vdl_image_step)
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self.vdl_image_step += 1
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# each frame can display ten pictures at most.
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if self.vdl_image_step % 10 == 0:
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self.vdl_image_step = 0
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self.vdl_image_frame += 1
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def on_epoch_end(self, status):
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mode = status['mode']
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if dist.get_world_size() < 2 or dist.get_rank() == 0:
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if mode == 'eval':
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for metric in self.model._metrics:
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for key, map_value in metric.get_results().items():
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self.vdl_writer.add_scalar("{}-mAP".format(key),
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map_value[0],
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self.vdl_mAP_step)
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self.vdl_mAP_step += 1
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