logo

towhee
/
resnet-image-embedding
repo-copy-icon

copied

Browse Source

add bird prediction example.

main
zhang chen 3 years ago
parent
8519bb727a
commit
1e18ecb529
5 changed files with 280 additions and 27 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 2
      default_config.yaml
  2. 2
      examples/1_quick_start.ipynb
  3. 4
      examples/2_train_on_mnist.ipynb
  4. 76
      examples/3_read_configs_from_yaml.ipynb
  5. 223
      examples/4_fine_tune_from_image_net.ipynb

2
default_config.yaml
View File

@ -26,7 +26,7 @@ metrics:
train:
batch_size: 8
dataloader_drop_last: true
dataloader_num_workers: -1
dataloader_num_workers: 0
dataloader_pin_memory: true
epoch_num: 2
eval_steps: null

2
examples/1_quick_start.ipynb
View File

@ -17,6 +17,8 @@
},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append('..')\n",
"from resnet_image_embedding import ResnetImageEmbedding\n",
"from towhee.trainer.training_config import TrainingConfig\n",
"from torchvision import transforms\n",

4
examples/2_train_on_mnist.ipynb
View File

@ -17,6 +17,8 @@
},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append('..')\n",
"import torch\n",
"import matplotlib.pyplot as plt\n",
"import random\n",
@ -136,7 +138,7 @@
"pil_img = img * std + mean\n",
"plt.imshow(pil_img)\n",
"plt.show()\n",
"test_img = eval_data.dataset[img_index][0].unsqueeze(0)\n",
"test_img = eval_data.dataset[img_index][0].unsqueeze(0).to(op.trainer.configs.device)\n",
"out = op.trainer.predict(test_img)\n",
"predict_num = torch.argmax(torch.softmax(out, dim=-1)).item()\n",
"print('this picture is number {}'.format(predict_num))"

76
examples/3_read_configs_from_yaml.ipynb
View File

@ -11,12 +11,14 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"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",
@ -27,7 +29,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 3,
"outputs": [],
"source": [
"from towhee.trainer.training_config import dump_default_yaml\n",
@ -54,13 +56,13 @@
},
{
"cell_type": "code",
"execution_count": 3,
"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": 3,
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@ -82,7 +84,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 5,
"outputs": [],
"source": [
"# prepare the fake dataset\n",
@ -99,37 +101,37 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 6,
"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"
"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"
]
},
{
"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"
"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"
]
}
],
@ -156,6 +158,30 @@
}
}
},
{
"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": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 7,
"outputs": [],
"source": [
"training_config.batch_size = 2\n",
"training_config.save_to_yaml('another_setting.yaml')\n"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": null,

223
examples/4_fine_tune_from_image_net.ipynb
View File

File diff suppressed because one or more lines are too long
Write Preview
Loading…
Cancel
Save