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

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   "cell_type": "markdown",
   "source": [
    "# Read the configs from a yaml file."
   ],
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  {
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   "source": [
    "import sys\n",
    "sys.path.append('..')\n",
    "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": 3,
   "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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   "execution_count": 4,
   "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": 4,
     "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",
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   "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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    }
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  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "2022-03-03 16:59:41,635 - 4310336896 - 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"
     ]
    },
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     "name": "stdout",
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     "text": [
      "epoch=1/3, global_step=1, epoch_loss=2.469155788421631, epoch_metric=0.20000000298023224\n",
      "epoch=1/3, global_step=2, epoch_loss=2.486016273498535, epoch_metric=0.20000000298023224\n",
      "epoch=1/3, global_step=3, epoch_loss=2.519146203994751, epoch_metric=0.20000000298023224\n",
      "epoch=1/3, global_step=4, epoch_loss=2.451723098754883, epoch_metric=0.20000000298023224\n",
      "epoch=1/3, eval_global_step=0, eval_epoch_loss=2.263216495513916, eval_epoch_metric=0.20000000298023224\n",
      "epoch=1/3, eval_global_step=1, eval_epoch_loss=2.1709983348846436, eval_epoch_metric=0.20000000298023224\n",
      "epoch=2/3, global_step=5, epoch_loss=1.2240798473358154, epoch_metric=0.20000000298023224\n",
      "epoch=2/3, global_step=6, epoch_loss=1.1725499629974365, epoch_metric=0.20000000298023224\n",
      "epoch=2/3, global_step=7, epoch_loss=1.2648464441299438, epoch_metric=0.20000000298023224\n",
      "epoch=2/3, global_step=8, epoch_loss=1.30061936378479, epoch_metric=0.15000000596046448\n",
      "epoch=2/3, eval_global_step=2, eval_epoch_loss=1.2398303747177124, eval_epoch_metric=0.0\n",
      "epoch=2/3, eval_global_step=3, eval_epoch_loss=1.2246357202529907, eval_epoch_metric=0.10000000149011612\n",
      "epoch=3/3, global_step=9, epoch_loss=1.501572847366333, epoch_metric=0.20000000298023224\n",
      "epoch=3/3, global_step=10, epoch_loss=1.365707516670227, epoch_metric=0.20000000298023224\n",
      "epoch=3/3, global_step=11, epoch_loss=1.2403526306152344, epoch_metric=0.13333334028720856\n",
      "epoch=3/3, global_step=12, epoch_loss=1.0921388864517212, epoch_metric=0.10000000149011612\n",
      "epoch=3/3, eval_global_step=4, eval_epoch_loss=1.0393352508544922, eval_epoch_metric=0.0\n",
      "epoch=3/3, eval_global_step=5, eval_epoch_loss=1.0277410745620728, eval_epoch_metric=0.10000000149011612\n"
     ]
    }
   ],
   "source": [
    "# start training,\n",
    "op.train(training_config, train_dataset=train_data, eval_dataset=eval_data)"
   ],
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   "cell_type": "markdown",
   "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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    "pycharm": {
     "name": "#%% md\n"
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  },
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   "cell_type": "markdown",
   "source": [
    "### By the way, you can change the config in your python code and save the config into a yaml file. So it's easy to convert between the python config instance and yaml file."
   ],
   "metadata": {
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   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "training_config.batch_size = 2\n",
    "training_config.save_to_yaml('another_setting.yaml')\n"
   ],
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     "name": "#%%\n"
    }
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