{
"cells": [
{
"cell_type": "markdown",
"source": [
"# Read the configs from a yaml file."
],
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}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"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')"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "markdown",
"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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{
"cell_type": "code",
"execution_count": 3,
"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"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"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)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 5,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"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)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"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."
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
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