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| 158 | + <h1 id="config"><a class="header" href="#config">Config</a></h1> |
| 159 | +<p>To ease the usage of DD-Ranking, we allow users to specify the parameters of the evaluator in a config file. The config file is a YAML file that contains the parameters of the evaluator. We illustrate the config file with the following example.</p> |
| 160 | +<pre><code class="language-yaml">dataset: CIFAR100 # dataset name |
| 161 | +real_data_path: ./dataset/ # path to the real dataset |
| 162 | +ipc: 10 # image per class |
| 163 | +im_size: [32, 32] # image size |
| 164 | +model_name: ResNet-18-BN # model name |
| 165 | +stu_use_torchvision: true # whether to use torchvision to load student model |
| 166 | + |
| 167 | +tea_use_torchvision: true # whether to use torchvision to load teacher model |
| 168 | + |
| 169 | +teacher_dir: ./teacher_models # path to the pretrained teacher model |
| 170 | + |
| 171 | +data_aug_func: mixup # data augmentation function |
| 172 | +aug_params: |
| 173 | + lambda: 0.8 # data augmentation parameter; please follow this format for other parameters |
| 174 | + |
| 175 | +use_zca: false # whether to use ZCA whitening |
| 176 | + |
| 177 | +custom_train_trans: # custom torchvision-based transformations to process training data; please follow this format for your own transformations |
| 178 | + - name: RandomCrop |
| 179 | + args: |
| 180 | + size: 32 |
| 181 | + padding: 4 |
| 182 | + - name: RandomHorizontalFlip |
| 183 | + args: |
| 184 | + p: 0.5 |
| 185 | + - name: ToTensor |
| 186 | + - name: Normalize |
| 187 | + args: |
| 188 | + mean: [0.4914, 0.4822, 0.4465] |
| 189 | + std: [0.2023, 0.1994, 0.2010] |
| 190 | + |
| 191 | +custom_val_trans: null # custom torchvision-based transformations to process validation data; please follow the format above for your own transformations |
| 192 | + |
| 193 | +use_aug_for_hard: false # whether to use data augmentation for hard label evaluation |
| 194 | + |
| 195 | +soft_label_mode: M # soft label mode |
| 196 | +soft_label_criterion: kl # soft label criterion |
| 197 | +temperature: 30.0 # temperature for soft label |
| 198 | +optimizer: adamw # optimizer |
| 199 | +lr_scheduler: cosine # learning rate scheduler |
| 200 | +weight_decay: 0.01 # weight decay |
| 201 | +num_eval: 5 # number of evaluations |
| 202 | +num_epochs: 400 # number of training epochs |
| 203 | +default_lr: 0.001 # default learning rate |
| 204 | +num_workers: 4 # number of workers |
| 205 | +device: cuda # device |
| 206 | +syn_batch_size: 256 # batch size for synthetic data |
| 207 | +real_batch_size: 256 # batch size for real data |
| 208 | +save_path: ./results.csv # path to save the results |
| 209 | +</code></pre> |
| 210 | +<p>To use config file, you can follow the example below.</p> |
| 211 | +<pre><code class="language-python">from dd_ranking.metrics import SoftLabelEvaluator |
| 212 | + |
| 213 | +config = Config(config_path='./config.yaml') |
| 214 | +evaluator = SoftLabelEvaluator(config) |
| 215 | +</code></pre> |
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