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[WIP]separation parameter #9176

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[WIP]separation parameter #9176

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8 changes: 6 additions & 2 deletions ppdet/engine/callbacks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@
from __future__ import print_function

import os
import gc
import sys
import datetime
import six
Expand Down Expand Up @@ -179,6 +180,7 @@ def __init__(self, model):
self.best_ap = -1000.
self.save_dir = self.model.cfg.save_dir
self.uniform_output_enabled = self.model.cfg.get("uniform_output_enabled", False)
self.export_during_train = self.model.cfg.get("export_during_train", False)
if hasattr(self.model.model, 'student_model'):
self.weight = self.model.model.student_model
else:
Expand Down Expand Up @@ -261,8 +263,9 @@ def on_epoch_end(self, status):
save_name,
epoch_id + 1,
ema_model=weight)
if self.uniform_output_enabled:
if self.export_during_train:
self.model.export(output_dir=os.path.join(self.save_dir, save_name, "inference"), for_fd=True)
gc.collect()
else:
# save model(student model) and ema_model(teacher model)
# in DenseTeacher SSOD, the teacher model will be higher,
Expand All @@ -281,8 +284,9 @@ def on_epoch_end(self, status):
else:
save_model(weight, self.model.optimizer, os.path.join(self.save_dir, save_name) if self.uniform_output_enabled else self.save_dir,
save_name, epoch_id + 1)
if self.uniform_output_enabled:
if self.export_during_train:
self.model.export(output_dir=os.path.join(self.save_dir, save_name, "inference"), for_fd=True)
gc.collect()


class WiferFaceEval(Callback):
Expand Down
3 changes: 2 additions & 1 deletion ppdet/engine/trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -77,7 +77,8 @@ def __init__(self, cfg, mode='train'):
self.custom_black_list = self.cfg.get('custom_black_list', None)
self.use_master_grad = self.cfg.get('master_grad', False)
self.uniform_output_enabled = self.cfg.get('uniform_output_enabled', False)
if ('slim' in cfg and cfg['slim_type'] == 'PTQ') or self.uniform_output_enabled:
self.export_during_train = self.cfg.get('export_during_train', False)
if ('slim' in cfg and cfg['slim_type'] == 'PTQ') or self.export_during_train:
self.cfg['TestDataset'] = create('TestDataset')()
log_ranks = cfg.get('log_ranks', '0')
if isinstance(log_ranks, str):
Expand Down