72 lines
2.9 KiB
Python
72 lines
2.9 KiB
Python
# Copyright (c) 2021 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, division, print_function
|
|
|
|
from ppcls.data import build_dataloader
|
|
from ppcls.engine.train.utils import type_name
|
|
from ppcls.utils import logger
|
|
from .regular_train_epoch import regular_train_epoch
|
|
|
|
|
|
def train_epoch_progressive(engine, epoch_id, print_batch_step):
|
|
# 1. Build training hyper-parameters for different training stage
|
|
num_stage = 4
|
|
ratio_list = [(i + 1) / num_stage for i in range(num_stage)]
|
|
stones = [
|
|
int(engine.config["Global"]["epochs"] * ratio_list[i])
|
|
for i in range(num_stage)
|
|
]
|
|
stage_id = 0
|
|
for i in range(num_stage):
|
|
if epoch_id > stones[i]:
|
|
stage_id = i + 1
|
|
|
|
# 2. Adjust training hyper-parameters for different training stage
|
|
if not hasattr(engine, 'last_stage') or engine.last_stage < stage_id:
|
|
cur_dropout_rate = 0.0
|
|
|
|
def _change_dp_func(m):
|
|
global cur_dropout_rate
|
|
if type_name(m) == "Head" and hasattr(m, "_dropout"):
|
|
m._dropout.p = m.dropout_rate[stage_id]
|
|
cur_dropout_rate = m.dropout_rate[stage_id]
|
|
|
|
engine.model.apply(_change_dp_func)
|
|
|
|
cur_image_size = engine.config["DataLoader"]["Train"]["dataset"][
|
|
"transform_ops"][1]["RandCropImage"]["progress_size"][stage_id]
|
|
cur_magnitude = engine.config["DataLoader"]["Train"]["dataset"][
|
|
"transform_ops"][3]["RandAugmentV2"]["progress_magnitude"][
|
|
stage_id]
|
|
engine.config["DataLoader"]["Train"]["dataset"]["transform_ops"][1][
|
|
"RandCropImage"]["size"] = cur_image_size
|
|
engine.config["DataLoader"]["Train"]["dataset"]["transform_ops"][3][
|
|
"RandAugmentV2"]["magnitude"] = cur_magnitude
|
|
engine.train_dataloader = build_dataloader(
|
|
engine.config["DataLoader"],
|
|
"Train",
|
|
engine.device,
|
|
engine.use_dali,
|
|
seed=epoch_id)
|
|
engine.train_dataloader_iter = iter(engine.train_dataloader)
|
|
engine.last_stage = stage_id
|
|
logger.info(f"Training stage: [{stage_id+1}/{num_stage}]("
|
|
f"random_aug_magnitude={cur_magnitude}, "
|
|
f"train_image_size={cur_image_size}, "
|
|
f"dropout_rate={cur_dropout_rate}"
|
|
f")")
|
|
|
|
# 3. Train one epoch as usual at current stage
|
|
regular_train_epoch(engine, epoch_id, print_batch_step)
|