demo_dl.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import pandas as pd\n",
    "from torch.utils.data.dataset import Dataset\n",
    "from logger import Logger\n",
    "from model_instance import Model_Instance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "train_transform = transforms.Compose(\n",
    "    [transforms.RandomRotation(20),\n",
    "     transforms.RandomHorizontalFlip(0.3),\n",
    "     transforms.ColorJitter(brightness = 0.1, # Randomly adjust color jitter of the images\n",
    "                            contrast = 0.1, \n",
    "                            saturation = 0.1), \n",
    "     transforms.ToTensor(),\n",
    "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "test_transform = transforms.Compose(\n",
    "    [transforms.ToTensor(),\n",
    "     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "\n",
    "def get_dataloader(batch_size):\n",
    "    \n",
    "\n",
    "    trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
    "                                            download=True, transform=train_transform)\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n",
    "                                            shuffle=True, num_workers=4)\n",
    "\n",
    "    testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
    "                                        download=True, transform=test_transform)\n",
    "    testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n",
    "                                            shuffle=False, num_workers=4)\n",
    "    return trainloader,testloader\n",
    "trainloader,testloader = get_dataloader(32)\n",
    "classes = ('plane', 'car', 'bird', 'cat',\n",
    "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def evaluation(model_instance,dataloader,logger=None):\n",
    "  # prepare to count predictions for each class\n",
    "    correct_pred = {classname: 0 for classname in classes}\n",
    "    total_pred = {classname: 0 for classname in classes}\n",
    "    classes_acc={classname: 0 for classname in classes}\n",
    "    correct=0\n",
    "    total=0\n",
    "\n",
    "    outcome,record_df = model_instance.run_dataloader(dataloader,update=False,display_result=False,logger=logger)\n",
    "    for prediction , label in zip(outcome['pred'],outcome['label']):\n",
    "        if label == prediction.argmax(axis=0):\n",
    "            correct+=1\n",
    "            correct_pred[classes[label]] += 1\n",
    "        total_pred[classes[label]] += 1\n",
    "        total+=1\n",
    "\n",
    "    print(f'Accuracy of the network on the {total} test images: {100 * correct // total} %')\n",
    "    # print accuracy for each class\n",
    "    for classname, correct_count in correct_pred.items():\n",
    "        accuracy = 100 * float(correct_count) / total_pred[classname]\n",
    "        classes_acc[classname]=[accuracy]\n",
    "        print(f'Accuracy for class: {classname:5s} is {accuracy:.1f} %')\n",
    "    record_df = pd.DataFrame.from_dict(classes_acc)\n",
    "    return record_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:08<00:00, 11.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.284, 'f1score': 0.022, 'loss': 2.055}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 12.57it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.397, 'f1score': 0.392, 'loss': 1.697}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 12.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.455, 'f1score': 0.451, 'loss': 1.555}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 12.53it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.491, 'f1score': 0.487, 'loss': 1.457}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 12.55it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.511, 'f1score': 0.508, 'loss': 1.373}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train   9%|█▊                  | 9/98 [00:00<00:07, 11.21it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================= Step: 500 =================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Run Dataloader 100%|████████████████████| 98/98 [00:04<00:00, 21.15it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------\n",
      "【Train】:\n",
      "acc\t:0.535\n",
      "f1score\t:0.531\n",
      "loss\t:1.288\n",
      "step\t:98\n",
      "---------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Valid 100%|████████████████████| 20/20 [00:01<00:00, 17.94it/s]\n",
      "Train  14%|██▊                 | 14/98 [00:07<00:55,  1.52it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 53 %\n",
      "Accuracy for class: plane is 59.5 %\n",
      "Accuracy for class: car   is 76.6 %\n",
      "Accuracy for class: bird  is 25.3 %\n",
      "Accuracy for class: cat   is 50.0 %\n",
      "Accuracy for class: deer  is 38.4 %\n",
      "Accuracy for class: dog   is 40.5 %\n",
      "Accuracy for class: frog  is 74.0 %\n",
      "Accuracy for class: horse is 59.2 %\n",
      "Accuracy for class: ship  is 63.1 %\n",
      "Accuracy for class: truck is 49.9 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:13<00:00,  7.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.537, 'f1score': 0.535, 'loss': 1.297}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 13.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.565, 'f1score': 0.562, 'loss': 1.221}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 12.93it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.562, 'f1score': 0.56, 'loss': 1.233}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 13.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.578, 'f1score': 0.576, 'loss': 1.183}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 12.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.616, 'f1score': 0.614, 'loss': 1.085}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train  18%|███▋                | 18/98 [00:01<00:04, 16.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================= Step: 1000 =================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Run Dataloader 100%|████████████████████| 98/98 [00:04<00:00, 20.87it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------\n",
      "【Train】:\n",
      "acc\t:0.311\n",
      "f1score\t:0.297\n",
      "loss\t:5.576\n",
      "step\t:98\n",
      "---------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Valid 100%|████████████████████| 20/20 [00:01<00:00, 17.59it/s]\n",
      "Train  22%|████▍               | 22/98 [00:07<00:49,  1.54it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 32 %\n",
      "Accuracy for class: plane is 26.7 %\n",
      "Accuracy for class: car   is 58.1 %\n",
      "Accuracy for class: bird  is 67.1 %\n",
      "Accuracy for class: cat   is 25.1 %\n",
      "Accuracy for class: deer  is 11.9 %\n",
      "Accuracy for class: dog   is 31.4 %\n",
      "Accuracy for class: frog  is 38.4 %\n",
      "Accuracy for class: horse is 9.0 %\n",
      "Accuracy for class: ship  is 48.0 %\n",
      "Accuracy for class: truck is 5.9 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:13<00:00,  7.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.546, 'f1score': 0.544, 'loss': 1.284}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 12.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.609, 'f1score': 0.607, 'loss': 1.105}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 13.33it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.639, 'f1score': 0.638, 'loss': 1.017}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 12.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.663, 'f1score': 0.662, 'loss': 0.953}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 12.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.684, 'f1score': 0.683, 'loss': 0.899}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train  30%|█████▉              | 29/98 [00:02<00:05, 12.93it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================= Step: 1500 =================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Run Dataloader 100%|████████████████████| 98/98 [00:04<00:00, 21.01it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------\n",
      "【Train】:\n",
      "acc\t:0.697\n",
      "f1score\t:0.699\n",
      "loss\t:0.849\n",
      "step\t:98\n",
      "---------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Valid 100%|████████████████████| 20/20 [00:00<00:00, 23.75it/s]\n",
      "Train  35%|██████▉             | 34/98 [00:08<00:36,  1.74it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 64 %\n",
      "Accuracy for class: plane is 60.5 %\n",
      "Accuracy for class: car   is 78.2 %\n",
      "Accuracy for class: bird  is 59.9 %\n",
      "Accuracy for class: cat   is 38.3 %\n",
      "Accuracy for class: deer  is 57.7 %\n",
      "Accuracy for class: dog   is 70.9 %\n",
      "Accuracy for class: frog  is 69.8 %\n",
      "Accuracy for class: horse is 64.0 %\n",
      "Accuracy for class: ship  is 80.2 %\n",
      "Accuracy for class: truck is 69.0 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:13<00:00,  7.45it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.698, 'f1score': 0.697, 'loss': 0.855}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 12.87it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.716, 'f1score': 0.716, 'loss': 0.806}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 12.83it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.732, 'f1score': 0.732, 'loss': 0.762}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 12.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.744, 'f1score': 0.744, 'loss': 0.725}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 12.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.761, 'f1score': 0.76, 'loss': 0.687}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train  39%|███████▊            | 38/98 [00:02<00:04, 13.82it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================= Step: 2000 =================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Run Dataloader 100%|████████████████████| 98/98 [00:04<00:00, 21.08it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------\n",
      "【Train】:\n",
      "acc\t:0.778\n",
      "f1score\t:0.78\n",
      "loss\t:0.627\n",
      "step\t:98\n",
      "---------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Valid 100%|████████████████████| 20/20 [00:01<00:00, 18.00it/s]\n",
      "Train  43%|████████▌           | 42/98 [00:09<00:37,  1.49it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 68 %\n",
      "Accuracy for class: plane is 71.8 %\n",
      "Accuracy for class: car   is 79.9 %\n",
      "Accuracy for class: bird  is 62.1 %\n",
      "Accuracy for class: cat   is 51.6 %\n",
      "Accuracy for class: deer  is 62.3 %\n",
      "Accuracy for class: dog   is 57.5 %\n",
      "Accuracy for class: frog  is 71.9 %\n",
      "Accuracy for class: horse is 71.0 %\n",
      "Accuracy for class: ship  is 82.4 %\n",
      "Accuracy for class: truck is 76.4 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:12<00:00,  7.54it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.775, 'f1score': 0.774, 'loss': 0.646}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 12.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.788, 'f1score': 0.787, 'loss': 0.606}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 12.91it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.801, 'f1score': 0.801, 'loss': 0.571}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 12.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.816, 'f1score': 0.815, 'loss': 0.53}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 12.84it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.826, 'f1score': 0.826, 'loss': 0.493}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train  49%|█████████▊          | 48/98 [00:03<00:02, 18.59it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================= Step: 2500 =================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Run Dataloader 100%|████████████████████| 98/98 [00:04<00:00, 21.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------\n",
      "【Train】:\n",
      "acc\t:0.849\n",
      "f1score\t:0.849\n",
      "loss\t:0.44\n",
      "step\t:98\n",
      "---------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Valid 100%|████████████████████| 20/20 [00:01<00:00, 17.76it/s]\n",
      "Train  53%|██████████▌         | 52/98 [00:09<00:27,  1.68it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 70 %\n",
      "Accuracy for class: plane is 75.3 %\n",
      "Accuracy for class: car   is 80.0 %\n",
      "Accuracy for class: bird  is 57.9 %\n",
      "Accuracy for class: cat   is 55.1 %\n",
      "Accuracy for class: deer  is 62.7 %\n",
      "Accuracy for class: dog   is 58.3 %\n",
      "Accuracy for class: frog  is 79.2 %\n",
      "Accuracy for class: horse is 76.4 %\n",
      "Accuracy for class: ship  is 83.6 %\n",
      "Accuracy for class: truck is 75.5 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:12<00:00,  7.55it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.838, 'f1score': 0.838, 'loss': 0.47}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 12.81it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.848, 'f1score': 0.848, 'loss': 0.443}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 12.88it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.858, 'f1score': 0.858, 'loss': 0.416}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 12.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.86, 'f1score': 0.86, 'loss': 0.409}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train 100%|████████████████████| 98/98 [00:07<00:00, 12.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'acc': 0.864, 'f1score': 0.864, 'loss': 0.395}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train  60%|████████████        | 59/98 [00:04<00:03, 12.88it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================= Step: 3000 =================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Run Dataloader 100%|████████████████████| 98/98 [00:04<00:00, 20.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------\n",
      "【Train】:\n",
      "acc\t:0.867\n",
      "f1score\t:0.867\n",
      "loss\t:0.388\n",
      "step\t:98\n",
      "---------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Valid 100%|████████████████████| 20/20 [00:01<00:00, 18.31it/s]\n",
      "Train  60%|████████████        | 59/98 [00:10<00:07,  5.41it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 70 %\n",
      "Accuracy for class: plane is 76.5 %\n",
      "Accuracy for class: car   is 80.9 %\n",
      "Accuracy for class: bird  is 59.5 %\n",
      "Accuracy for class: cat   is 52.1 %\n",
      "Accuracy for class: deer  is 62.7 %\n",
      "Accuracy for class: dog   is 61.4 %\n",
      "Accuracy for class: frog  is 76.6 %\n",
      "Accuracy for class: horse is 76.1 %\n",
      "Accuracy for class: ship  is 80.4 %\n",
      "Accuracy for class: truck is 75.5 %\n",
      "{'acc': 0.867, 'f1score': 0.867, 'loss': 0.395}\n"
     ]
    }
   ],
   "source": [
    "import torch.optim as optim\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "#from resnet import get_resnet34\n",
    "logger = Logger('test')\n",
    "config=logger.config\n",
    "config['train_step']=3000\n",
    "config['valid_step']=500\n",
    "config['lr']=1e-3\n",
    "config['batch_size']=512\n",
    "config['device']= torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "print(config['device'])\n",
    "# config['device']='cpu'\n",
    "trainloader,testloader = get_dataloader(config.batch_size)\n",
    "\n",
    "torch.cuda.empty_cache()\n",
    "model = torchvision.models.resnet101()\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.AdamW(model.parameters(),lr=config['lr'])\n",
    "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, config['train_step'])\n",
    "model_instance = Model_Instance(model=model,\n",
    "                                optimizer=optimizer,\n",
    "                                loss_function=criterion,\n",
    "                                evaluation_metrics=['acc','f1score'],\n",
    "                                scheduler=scheduler,#iter\n",
    "                                scheduler_epoch=False,# default False\n",
    "                                device=config.device,\n",
    "                                amp=False)\n",
    "\n",
    "a=model_instance.run_step_dataloader(trainloader,\n",
    "                                     run_step=config.train_step,\n",
    "                                     valid_step=config.valid_step,\n",
    "                                     logger=logger['Train'],\n",
    "                                     evaluation_function=lambda: evaluation(model_instance,\n",
    "                                                                            testloader,\n",
    "                                                                            logger['Valid']))#model_instance.run_dataloader(testloader,logger=logger['Valid'],update=False))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experiment_name</th>\n",
       "      <th>tag</th>\n",
       "      <th>acc</th>\n",
       "      <th>f1score</th>\n",
       "      <th>loss</th>\n",
       "      <th>step</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>Train</td>\n",
       "      <td>0.535</td>\n",
       "      <td>0.531</td>\n",
       "      <td>1.288</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>test</td>\n",
       "      <td>Valid</td>\n",
       "      <td>0.536</td>\n",
       "      <td>0.531</td>\n",
       "      <td>1.297</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1000.0</td>\n",
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       "      <td>Valid</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.307</td>\n",
       "      <td>5.148</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>Train</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.699</td>\n",
       "      <td>0.849</td>\n",
       "      <td>1500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>Valid</td>\n",
       "      <td>0.648</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.993</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>Train</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.627</td>\n",
       "      <td>2000.0</td>\n",
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       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>Valid</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.688</td>\n",
       "      <td>0.945</td>\n",
       "      <td>20.0</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>Train</td>\n",
       "      <td>0.849</td>\n",
       "      <td>0.849</td>\n",
       "      <td>0.440</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>Valid</td>\n",
       "      <td>0.704</td>\n",
       "      <td>0.704</td>\n",
       "      <td>0.984</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>test</td>\n",
       "      <td>Train</td>\n",
       "      <td>0.867</td>\n",
       "      <td>0.867</td>\n",
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       "      <td>3000.0</td>\n",
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      ],
      "text/plain": [
       "  experiment_name    tag    acc  f1score   loss    step\n",
       "0            test  Train  0.535    0.531  1.288   500.0\n",
       "0            test  Valid  0.536    0.531  1.297    20.0\n",
       "0            test  Train  0.311    0.297  5.576  1000.0\n",
       "0            test  Valid  0.322    0.307  5.148    20.0\n",
       "0            test  Train  0.697    0.699  0.849  1500.0\n",
       "0            test  Valid  0.648    0.650  0.993    20.0\n",
       "0            test  Train  0.778    0.780  0.627  2000.0\n",
       "0            test  Valid  0.687    0.688  0.945    20.0\n",
       "0            test  Train  0.849    0.849  0.440  2500.0\n",
       "0            test  Valid  0.704    0.704  0.984    20.0\n",
       "0            test  Train  0.867    0.867  0.388  3000.0\n",
       "0            test  Valid  0.702    0.701  1.021    20.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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      "text/plain": [
       "<Figure size 4660x750 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Export Success! \n",
      " File Path:logger_file/test/logger_history\n"
     ]
    }
   ],
   "source": [
    "display(logger.history['records'])\n",
    "logger.plot_experiments()\n",
    "logger.export_logger()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torch",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.15"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "43f092e7e4a21b43dca84c0822620b32f2411ff482ecb3addc9cc958ce82dbac"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}