resnet-image-embedding/examples/3_read_configs_from_yaml.ipynb

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   "source": [
    "# Read the configs from a yaml file."
   ],
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  {
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   "source": [
    "from resnet_image_embedding import ResnetImageEmbedding\n",
    "from torchvision import transforms\n",
    "from towhee import dataset\n",
    "\n",
    "# build an resnet op:\n",
    "op = ResnetImageEmbedding('resnet18', num_classes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "from towhee.trainer.training_config import dump_default_yaml\n",
    "\n",
    "# If you want to see the default setting yaml, run dump_default_yaml()\n",
    "dump_default_yaml('default_setting.yaml')"
   ],
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   "source": [
    "##### Then you can open `default_setting.yaml` to observe the yaml structure.\n",
    "##### Change `batch_size` to 5, `epoch_num` to 3, `tensorboard` to `null`, `output_dir` to `my_output`, `print_steps` to 1, and save it as `my_setting.yaml`"
   ],
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   "outputs": [
    {
     "data": {
      "text/plain": "TrainingConfig(output_dir='my_output', overwrite_output_dir=True, eval_strategy='epoch', eval_steps=None, batch_size=5, val_batch_size=-1, seed=42, epoch_num=3, dataloader_pin_memory=True, dataloader_drop_last=True, dataloader_num_workers=-1, lr=5e-05, metric='Accuracy', print_steps=1, load_best_model_at_end=False, early_stopping={'mode': 'max', 'monitor': 'eval_epoch_metric', 'patience': 4}, model_checkpoint={'every_n_epoch': 1}, tensorboard=None, loss='CrossEntropyLoss', optimizer='Adam', lr_scheduler_type='linear', warmup_ratio=0.0, warmup_steps=0, device_str=None, n_gpu=-1, sync_bn=False, freeze_bn=False)"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from towhee.trainer.training_config import TrainingConfig\n",
    "\n",
    "# now, read from your custom yaml.\n",
    "training_config = TrainingConfig()\n",
    "training_config.load_from_yaml('my_setting.yaml')\n",
    "training_config\n"
   ],
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  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "# prepare the fake dataset\n",
    "fake_transform = transforms.Compose([transforms.ToTensor()])\n",
    "train_data = dataset('fake', size=20, transform=fake_transform)\n",
    "eval_data = dataset('fake', size=10, transform=fake_transform)"
   ],
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   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "2022-03-02 16:00:26,226 - 8666785280 - trainer.py-trainer:390 - WARNING: TrainingConfig(output_dir='my_output', overwrite_output_dir=True, eval_strategy='epoch', eval_steps=None, batch_size=5, val_batch_size=-1, seed=42, epoch_num=3, dataloader_pin_memory=True, dataloader_drop_last=True, dataloader_num_workers=-1, lr=5e-05, metric='Accuracy', print_steps=1, load_best_model_at_end=False, early_stopping={'mode': 'max', 'monitor': 'eval_epoch_metric', 'patience': 4}, model_checkpoint={'every_n_epoch': 1}, tensorboard=None, loss='CrossEntropyLoss', optimizer='Adam', lr_scheduler_type='linear', warmup_ratio=0.0, warmup_steps=0, device_str=None, n_gpu=-1, sync_bn=False, freeze_bn=False)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch=1/3, global_step=1, epoch_loss=2.5702719688415527, epoch_metric=0.0\n",
      "epoch=1/3, global_step=2, epoch_loss=2.572024345397949, epoch_metric=0.0\n",
      "epoch=1/3, global_step=3, epoch_loss=2.558194160461426, epoch_metric=0.0\n",
      "epoch=1/3, global_step=4, epoch_loss=2.558873176574707, epoch_metric=0.15000000596046448\n",
      "epoch=1/3, eval_global_step=0, eval_epoch_loss=2.370976686477661, eval_epoch_metric=0.20000000298023224\n",
      "epoch=1/3, eval_global_step=1, eval_epoch_loss=2.2873291969299316, eval_epoch_metric=0.20000000298023224\n",
      "epoch=2/3, global_step=5, epoch_loss=1.3134113550186157, epoch_metric=0.20000000298023224\n",
      "epoch=2/3, global_step=6, epoch_loss=1.3073358535766602, epoch_metric=0.10000000149011612\n",
      "epoch=2/3, global_step=7, epoch_loss=1.41914701461792, epoch_metric=0.13333334028720856\n",
      "epoch=2/3, global_step=8, epoch_loss=1.3628838062286377, epoch_metric=0.15000000596046448\n",
      "epoch=2/3, eval_global_step=2, eval_epoch_loss=1.3158948421478271, eval_epoch_metric=0.20000000298023224\n",
      "epoch=2/3, eval_global_step=3, eval_epoch_loss=1.3246530294418335, eval_epoch_metric=0.20000000298023224\n",
      "epoch=3/3, global_step=9, epoch_loss=1.4589173793792725, epoch_metric=0.0\n",
      "epoch=3/3, global_step=10, epoch_loss=1.4343616962432861, epoch_metric=0.0\n",
      "epoch=3/3, global_step=11, epoch_loss=1.3701648712158203, epoch_metric=0.06666667014360428\n",
      "epoch=3/3, global_step=12, epoch_loss=1.1501117944717407, epoch_metric=0.10000000149011612\n",
      "epoch=3/3, eval_global_step=4, eval_epoch_loss=1.1129425764083862, eval_epoch_metric=0.0\n",
      "epoch=3/3, eval_global_step=5, eval_epoch_loss=1.1257113218307495, eval_epoch_metric=0.0\n"
     ]
    }
   ],
   "source": [
    "# start training,\n",
    "op.train(training_config, train_dataset=train_data, eval_dataset=eval_data)"
   ],
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   "source": [
    "### Because you have set the `print_steps` to 1, you will not see the progress bar, instead, you will see the every batch steps result printed on the screen. You can check whether other configs ares work correctly."
   ],
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