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- # Copyright 2020 Huawei Technologies Co., Ltd
- #
- # 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.
- # ============================================================================
- """
- network config setting, will be used in train.py and eval.py
- """
- from easydict import EasyDict as edict
-
- # config for dpn,imagenet-1K
- config = edict()
-
- # model config
- config.image_size = (224, 224) # inpute image size
- config.num_classes = 1000 # dataset class number
- config.backbone = 'dpn92' # backbone network
- config.is_save_on_master = True
-
- # parallel config
- config.num_parallel_workers = 4 # number of workers to read the data
- config.rank = 0 # local rank of distributed
- config.group_size = 1 # group size of distributed
-
- # training config
- config.batch_size = 32 # batch_size
- config.global_step = 0 # start step of learning rate
- config.epoch_size = 180 # epoch_size
- config.loss_scale_num = 1024 # loss scale
- # optimizer config
- config.momentum = 0.9 # momentum (SGD)
- config.weight_decay = 1e-4 # weight_decay (SGD)
- # learning rate config
- config.lr_schedule = 'warmup' # learning rate schedule
- config.lr_init = 0.01 # init learning rate
- config.lr_max = 0.1 # max learning rate
- config.factor = 0.1 # factor of lr to drop
- config.epoch_number_to_drop = [5, 15] # learing rate will drop after these epochs
- config.warmup_epochs = 5 # warmup epochs in learning rate schedule
-
- # dataset config
- config.dataset = "imagenet-1K" # dataset
- config.label_smooth = False # label_smooth
- config.label_smooth_factor = 0.0 # label_smooth_factor
-
- # parameter save config
- config.keep_checkpoint_max = 3 # only keep the last keep_checkpoint_max checkpoint
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