{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Train resnet18 operator on mnist dataset."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from resnet_image_embedding import ResnetImageEmbedding\n",
    "from towhee.trainer.training_config import TrainingConfig\n",
    "from torchvision import transforms\n",
    "from towhee import dataset\n",
    "from torchvision.transforms import Lambda\n",
    "\n",
    "# build a resnet op with 10 classes output, because the mnist has 10 classes:\n",
    "op = ResnetImageEmbedding('resnet18', num_classes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "# build a training config:\n",
    "training_config = TrainingConfig()\n",
    "training_config.batch_size = 64\n",
    "training_config.epoch_num = 5\n",
    "training_config.tensorboard = None\n",
    "training_config.output_dir = 'mnist_output'\n",
    "training_config.dataloader_num_workers = 0"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "# prepare the mnist data\n",
    "mnist_transform = transforms.Compose([transforms.ToTensor(),\n",
    "                                          Lambda(lambda x: x.repeat(3, 1, 1)),\n",
    "                                          transforms.Normalize(mean=[0.1307,0.1307,0.1307], std=[0.3081,0.3081,0.3081])])\n",
    "train_data = dataset('mnist', transform=mnist_transform, download=True, root='data', train=True)\n",
    "eval_data = dataset('mnist', transform=mnist_transform, download=True, root='data', train=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-03-02 15:24:19,702 - 8669324800 - trainer.py-trainer:390 - WARNING: TrainingConfig(output_dir='mnist_output', overwrite_output_dir=True, eval_strategy='epoch', eval_steps=None, batch_size=64, val_batch_size=-1, seed=42, epoch_num=5, dataloader_pin_memory=True, dataloader_drop_last=True, dataloader_num_workers=0, lr=5e-05, metric='Accuracy', print_steps=None, load_best_model_at_end=False, early_stopping={'monitor': 'eval_epoch_metric', 'patience': 4, 'mode': 'max'}, 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",
      "[epoch 1/5] loss=0.388, metric=0.884:  41%|████      | 383/937 [07:38<11:00,  1.19s/step]"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "\u001B[0;32m/var/folders/wn/4wflyq8x0f9bhkwryvss30880000gn/T/ipykernel_5732/1544844912.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[0;32m----> 1\u001B[0;31m \u001B[0mop\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtrain\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mtraining_config\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mtrain_dataset\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mtrain_data\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0meval_dataset\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0meval_data\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[0;32m/opt/homebrew/anaconda3/envs/conda_towhee/lib/python3.9/site-packages/towhee-0.4.1.dev110-py3.9.egg/towhee/operator/base.py\u001B[0m in \u001B[0;36mtrain\u001B[0;34m(self, training_config, train_dataset, eval_dataset, resume_checkpoint_path, **kwargs)\u001B[0m\n\u001B[1;32m    136\u001B[0m         \"\"\"\n\u001B[1;32m    137\u001B[0m         \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msetup_trainer\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mtraining_config\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mtrain_dataset\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0meval_dataset\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 138\u001B[0;31m         \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtrainer\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtrain\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mresume_checkpoint_path\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    139\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    140\u001B[0m     \u001B[0;34m@\u001B[0m\u001B[0mproperty\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/opt/homebrew/anaconda3/envs/conda_towhee/lib/python3.9/site-packages/towhee-0.4.1.dev110-py3.9.egg/towhee/trainer/trainer.py\u001B[0m in \u001B[0;36mtrain\u001B[0;34m(self, resume_checkpoint_path)\u001B[0m\n\u001B[1;32m    281\u001B[0m         \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    282\u001B[0m             \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdistributed\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;32mFalse\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 283\u001B[0;31m             \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mrun_train\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mresume_checkpoint_path\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    284\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    285\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0m_spawn_train_process\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mresume_checkpoint_path\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mOptional\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mstr\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/opt/homebrew/anaconda3/envs/conda_towhee/lib/python3.9/site-packages/towhee-0.4.1.dev110-py3.9.egg/towhee/trainer/trainer.py\u001B[0m in \u001B[0;36mrun_train\u001B[0;34m(self, resume_checkpoint_path, rank, world_size)\u001B[0m\n\u001B[1;32m    407\u001B[0m                 \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcallbacks\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mon_train_batch_begin\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0minputs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mlogs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    408\u001B[0m                 \u001B[0minputs\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mprepare_inputs\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0minputs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 409\u001B[0;31m                 \u001B[0mstep_logs\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtrain_step\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmodel\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0minputs\u001B[0m\u001B[0;34m)\u001B[0m  \u001B[0;31m# , train_dataloader)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    410\u001B[0m                 \u001B[0mlogs\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m\"lr\"\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mlr_scheduler\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mget_lr\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    411\u001B[0m                 \u001B[0mlogs\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m\"global_step\"\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m+=\u001B[0m \u001B[0;36m1\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/opt/homebrew/anaconda3/envs/conda_towhee/lib/python3.9/site-packages/towhee-0.4.1.dev110-py3.9.egg/towhee/trainer/trainer.py\u001B[0m in \u001B[0;36mtrain_step\u001B[0;34m(self, model, inputs)\u001B[0m\n\u001B[1;32m    615\u001B[0m                 \u001B[0mStep\u001B[0m \u001B[0mlogs\u001B[0m \u001B[0mwhich\u001B[0m \u001B[0mcontains\u001B[0m \u001B[0mthe\u001B[0m \u001B[0mstep\u001B[0m \u001B[0mloss\u001B[0m \u001B[0;32mand\u001B[0m \u001B[0mmetric\u001B[0m \u001B[0minfos\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    616\u001B[0m         \"\"\"\n\u001B[0;32m--> 617\u001B[0;31m         \u001B[0mstep_loss\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcompute_loss\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmodel\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0minputs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    618\u001B[0m         \u001B[0mstep_loss\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mreduce_value\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mstep_loss\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0maverage\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mTrue\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    619\u001B[0m         \u001B[0mstep_loss\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mbackward\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/opt/homebrew/anaconda3/envs/conda_towhee/lib/python3.9/site-packages/towhee-0.4.1.dev110-py3.9.egg/towhee/trainer/trainer.py\u001B[0m in \u001B[0;36mcompute_loss\u001B[0;34m(self, model, inputs)\u001B[0m\n\u001B[1;32m    653\u001B[0m         \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mset_train_mode\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmodel\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    654\u001B[0m         \u001B[0mlabels\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0minputs\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 655\u001B[0;31m         \u001B[0moutputs\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mmodel\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0minputs\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    656\u001B[0m         \u001B[0mloss\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mloss\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0moutputs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mlabels\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    657\u001B[0m         \u001B[0;32mreturn\u001B[0m \u001B[0mloss\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/opt/homebrew/anaconda3/envs/conda_towhee/lib/python3.9/site-packages/torch/nn/modules/module.py\u001B[0m in \u001B[0;36m_call_impl\u001B[0;34m(self, *input, **kwargs)\u001B[0m\n\u001B[1;32m   1049\u001B[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001B[1;32m   1050\u001B[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001B[0;32m-> 1051\u001B[0;31m             \u001B[0;32mreturn\u001B[0m \u001B[0mforward_call\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0minput\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   1052\u001B[0m         \u001B[0;31m# Do not call functions when jit is used\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1053\u001B[0m         \u001B[0mfull_backward_hooks\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mnon_full_backward_hooks\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m[\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m[\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/opt/homebrew/anaconda3/envs/conda_towhee/lib/python3.9/site-packages/timm/models/resnet.py\u001B[0m in \u001B[0;36mforward\u001B[0;34m(self, x)\u001B[0m\n\u001B[1;32m    683\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    684\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0mforward\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mx\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 685\u001B[0;31m         \u001B[0mx\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mforward_features\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    686\u001B[0m         \u001B[0mx\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mglobal_pool\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    687\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdrop_rate\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/opt/homebrew/anaconda3/envs/conda_towhee/lib/python3.9/site-packages/timm/models/resnet.py\u001B[0m in \u001B[0;36mforward_features\u001B[0;34m(self, x)\u001B[0m\n\u001B[1;32m    676\u001B[0m         \u001B[0mx\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mmaxpool\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    677\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 678\u001B[0;31m         \u001B[0mx\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mlayer1\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    679\u001B[0m         \u001B[0mx\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mlayer2\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    680\u001B[0m         \u001B[0mx\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mlayer3\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/opt/homebrew/anaconda3/envs/conda_towhee/lib/python3.9/site-packages/torch/nn/modules/module.py\u001B[0m in \u001B[0;36m_call_impl\u001B[0;34m(self, *input, **kwargs)\u001B[0m\n\u001B[1;32m   1049\u001B[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001B[1;32m   1050\u001B[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001B[0;32m-> 1051\u001B[0;31m             \u001B[0;32mreturn\u001B[0m \u001B[0mforward_call\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0minput\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   1052\u001B[0m         \u001B[0;31m# Do not call functions when jit is used\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1053\u001B[0m         \u001B[0mfull_backward_hooks\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mnon_full_backward_hooks\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m[\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m[\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/opt/homebrew/anaconda3/envs/conda_towhee/lib/python3.9/site-packages/torch/nn/modules/container.py\u001B[0m in \u001B[0;36mforward\u001B[0;34m(self, input)\u001B[0m\n\u001B[1;32m    137\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0mforward\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0minput\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    138\u001B[0m         \u001B[0;32mfor\u001B[0m \u001B[0mmodule\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 139\u001B[0;31m             \u001B[0minput\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mmodule\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0minput\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    140\u001B[0m         \u001B[0;32mreturn\u001B[0m \u001B[0minput\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    141\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/opt/homebrew/anaconda3/envs/conda_towhee/lib/python3.9/site-packages/torch/nn/modules/module.py\u001B[0m in \u001B[0;36m_call_impl\u001B[0;34m(self, *input, **kwargs)\u001B[0m\n\u001B[1;32m   1049\u001B[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001B[1;32m   1050\u001B[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001B[0;32m-> 1051\u001B[0;31m             \u001B[0;32mreturn\u001B[0m \u001B[0mforward_call\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0minput\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   1052\u001B[0m         \u001B[0;31m# Do not call functions when jit is used\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1053\u001B[0m         \u001B[0mfull_backward_hooks\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mnon_full_backward_hooks\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m[\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m[\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/opt/homebrew/anaconda3/envs/conda_towhee/lib/python3.9/site-packages/timm/models/resnet.py\u001B[0m in \u001B[0;36mforward\u001B[0;34m(self, x)\u001B[0m\n\u001B[1;32m    339\u001B[0m             \u001B[0mx\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0maa\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    340\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 341\u001B[0;31m         \u001B[0mx\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mconv2\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    342\u001B[0m         \u001B[0mx\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mbn2\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    343\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdrop_block\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/opt/homebrew/anaconda3/envs/conda_towhee/lib/python3.9/site-packages/torch/nn/modules/module.py\u001B[0m in \u001B[0;36m_call_impl\u001B[0;34m(self, *input, **kwargs)\u001B[0m\n\u001B[1;32m   1049\u001B[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001B[1;32m   1050\u001B[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001B[0;32m-> 1051\u001B[0;31m             \u001B[0;32mreturn\u001B[0m \u001B[0mforward_call\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0minput\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   1052\u001B[0m         \u001B[0;31m# Do not call functions when jit is used\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1053\u001B[0m         \u001B[0mfull_backward_hooks\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mnon_full_backward_hooks\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m[\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m[\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/opt/homebrew/anaconda3/envs/conda_towhee/lib/python3.9/site-packages/torch/nn/modules/conv.py\u001B[0m in \u001B[0;36mforward\u001B[0;34m(self, input)\u001B[0m\n\u001B[1;32m    441\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    442\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0mforward\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0minput\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mTensor\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m->\u001B[0m \u001B[0mTensor\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 443\u001B[0;31m         \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_conv_forward\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0minput\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mweight\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mbias\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    444\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    445\u001B[0m \u001B[0;32mclass\u001B[0m \u001B[0mConv3d\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0m_ConvNd\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m/opt/homebrew/anaconda3/envs/conda_towhee/lib/python3.9/site-packages/torch/nn/modules/conv.py\u001B[0m in \u001B[0;36m_conv_forward\u001B[0;34m(self, input, weight, bias)\u001B[0m\n\u001B[1;32m    437\u001B[0m                             \u001B[0mweight\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mbias\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mstride\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    438\u001B[0m                             _pair(0), self.dilation, self.groups)\n\u001B[0;32m--> 439\u001B[0;31m         return F.conv2d(input, weight, bias, self.stride,\n\u001B[0m\u001B[1;32m    440\u001B[0m                         self.padding, self.dilation, self.groups)\n\u001B[1;32m    441\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "source": [
    "# start to train mnist, it will take about 30-100 minutes on a cpu machine.\n",
    "# if you train on a gpu machine, it will be much faster.\n",
    "op.train(training_config, train_dataset=train_data, eval_dataset=eval_data)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### if you observe loss is decreasing and metric is increasing, it means you are training the model correctly."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}