examples/swav_iwang_224-new.ipynb

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   "source": [
    "from fastai.vision.all import *\n",
    "from self_supervised.augmentations import *\n",
    "from self_supervised.layers import *\n",
    "from self_supervised.swav import *\n",
    "from fastai.callback.wandb import WandbCallback\n",
    "import wandb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pretraining"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sqrmom=0.99\n",
    "mom=0.95\n",
    "beta=0.\n",
    "eps=1e-4\n",
    "opt_func = partial(ranger, mom=mom, sqr_mom=sqrmom, eps=eps, beta=beta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dls(size, bs, workers=None):\n",
    "    path = URLs.IMAGEWANG_160 if size <= 160 else URLs.IMAGEWANG\n",
    "    source = untar_data(path)\n",
    "    \n",
    "    files = get_image_files(source)\n",
    "    tfms = [[PILImage.create, ToTensor, RandomResizedCrop(size, min_scale=1.)], \n",
    "            [parent_label, Categorize()]]\n",
    "    \n",
    "    dsets = Datasets(files, tfms=tfms, splits=RandomSplitter(valid_pct=0.1)(files))\n",
    "    \n",
    "    batch_tfms = [IntToFloatTensor]\n",
    "    dls = dsets.dataloaders(bs=bs, num_workers=workers, after_batch=batch_tfms)\n",
    "    return dls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "bs=96\n",
    "resize, size = 256, 224"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "arch = \"xresnet34\"\n",
    "encoder = create_encoder(arch, pretrained=False, n_in=3)"
   ]
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   "execution_count": 10,
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       "Find user logs for this run at: <code>/home/code-base/extra_space/self_supervised/examples/wandb/run-20210208_214939-5pb4724k/logs/debug.log</code>"
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       "Find internal logs for this run at: <code>/home/code-base/extra_space/self_supervised/examples/wandb/run-20210208_214939-5pb4724k/logs/debug-internal.log</code>"
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       "\n",
       "                    <br/>Synced <strong style=\"color:#cdcd00\">royal-glade-8</strong>: <a href=\"https://wandb.ai/keremturgutlu/self-supervised-imagewang/runs/5pb4724k\" target=\"_blank\">https://wandb.ai/keremturgutlu/self-supervised-imagewang/runs/5pb4724k</a><br/>\n",
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       "...Successfully finished last run (ID:5pb4724k). Initializing new run:<br/><br/>"
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       "\n",
       "                Tracking run with wandb version 0.10.17<br/>\n",
       "                Syncing run <strong style=\"color:#cdcd00\">celestial-music-9</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
       "                Project page: <a href=\"https://wandb.ai/keremturgutlu/self-supervised-imagewang\" target=\"_blank\">https://wandb.ai/keremturgutlu/self-supervised-imagewang</a><br/>\n",
       "                Run page: <a href=\"https://wandb.ai/keremturgutlu/self-supervised-imagewang/runs/3ggbze22\" target=\"_blank\">https://wandb.ai/keremturgutlu/self-supervised-imagewang/runs/3ggbze22</a><br/>\n",
       "                Run data is saved locally in <code>/home/code-base/extra_space/self_supervised/examples/wandb/run-20210208_214948-3ggbze22</code><br/><br/>\n",
       "            "
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    "xtra_config = {\"Arch\":arch, \"Resize\":resize, \"Size\":size, \"Algorithm\":\"SWAV\"}\n",
    "wandb.init(project=\"self-supervised-imagewang\", config=xtra_config);"
   ]
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   "execution_count": 11,
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     "text": [
      "Pipeline: RandomResizedCrop -> RandomHorizontalFlip -> ColorJitter -> RandomGrayscale -> Rotate -- {'size': None, 'mode': 'bilinear', 'pad_mode': 'reflection', 'mode_mask': 'nearest', 'align_corners': True, 'p': 1.0} -> Normalize -- {'mean': tensor([[[[0.4850]],\n",
      "\n",
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      "Pipeline: RandomResizedCrop -> RandomHorizontalFlip -> ColorJitter -> RandomGrayscale -> Rotate -- {'size': None, 'mode': 'bilinear', 'pad_mode': 'reflection', 'mode_mask': 'nearest', 'align_corners': True, 'p': 1.0} -> Normalize -- {'mean': tensor([[[[0.4850]],\n",
      "\n",
      "         [[0.4560]],\n",
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      "         [[0.4060]]]], device='cuda:0'), 'std': tensor([[[[0.2290]],\n",
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      "         [[0.2250]]]], device='cuda:0'), 'axes': (0, 2, 3)}\n",
      "Pipeline: RandomResizedCrop -> RandomHorizontalFlip -> ColorJitter -> RandomGrayscale -> Rotate -- {'size': None, 'mode': 'bilinear', 'pad_mode': 'reflection', 'mode_mask': 'nearest', 'align_corners': True, 'p': 1.0} -> Normalize -- {'mean': tensor([[[[0.4850]],\n",
      "\n",
      "         [[0.4560]],\n",
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      "         [[0.4060]]]], device='cuda:0'), 'std': tensor([[[[0.2290]],\n",
      "\n",
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      "\n",
      "         [[0.2250]]]], device='cuda:0'), 'axes': (0, 2, 3)}\n",
      "Pipeline: RandomResizedCrop -> RandomHorizontalFlip -> ColorJitter -> RandomGrayscale -> Rotate -- {'size': None, 'mode': 'bilinear', 'pad_mode': 'reflection', 'mode_mask': 'nearest', 'align_corners': True, 'p': 1.0} -> Normalize -- {'mean': tensor([[[[0.4850]],\n",
      "\n",
      "         [[0.4560]],\n",
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      "         [[0.2250]]]], device='cuda:0'), 'axes': (0, 2, 3)}\n",
      "Pipeline: RandomResizedCrop -> RandomHorizontalFlip -> ColorJitter -> RandomGrayscale -> Rotate -- {'size': None, 'mode': 'bilinear', 'pad_mode': 'reflection', 'mode_mask': 'nearest', 'align_corners': True, 'p': 1.0} -> Normalize -- {'mean': tensor([[[[0.4850]],\n",
      "\n",
      "         [[0.4560]],\n",
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      "         [[0.4060]]]], device='cuda:0'), 'std': tensor([[[[0.2290]],\n",
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      "Pipeline: RandomResizedCrop -> RandomHorizontalFlip -> ColorJitter -> RandomGrayscale -> Rotate -- {'size': None, 'mode': 'bilinear', 'pad_mode': 'reflection', 'mode_mask': 'nearest', 'align_corners': True, 'p': 1.0} -> Normalize -- {'mean': tensor([[[[0.4850]],\n",
      "\n",
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      "\n",
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      "\n",
      "         [[0.2250]]]], device='cuda:0'), 'axes': (0, 2, 3)}\n",
      "Pipeline: RandomResizedCrop -> RandomHorizontalFlip -> ColorJitter -> RandomGrayscale -> Rotate -- {'size': None, 'mode': 'bilinear', 'pad_mode': 'reflection', 'mode_mask': 'nearest', 'align_corners': True, 'p': 1.0} -> Normalize -- {'mean': tensor([[[[0.4850]],\n",
      "\n",
      "         [[0.4560]],\n",
      "\n",
      "         [[0.4060]]]], device='cuda:0'), 'std': tensor([[[[0.2290]],\n",
      "\n",
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      "         [[0.2250]]]], device='cuda:0'), 'axes': (0, 2, 3)}\n",
      "Pipeline: RandomResizedCrop -> RandomHorizontalFlip -> ColorJitter -> RandomGrayscale -> Rotate -- {'size': None, 'mode': 'bilinear', 'pad_mode': 'reflection', 'mode_mask': 'nearest', 'align_corners': True, 'p': 1.0} -> Normalize -- {'mean': tensor([[[[0.4850]],\n",
      "\n",
      "         [[0.4560]],\n",
      "\n",
      "         [[0.4060]]]], device='cuda:0'), 'std': tensor([[[[0.2290]],\n",
      "\n",
      "         [[0.2240]],\n",
      "\n",
      "         [[0.2250]]]], device='cuda:0'), 'axes': (0, 2, 3)}\n"
     ]
    }
   ],
   "source": [
    "dls = get_dls(resize, bs)\n",
    "model = create_swav_model(encoder, n_in=3)\n",
    "learn = Learner(dls, model, SWAVLoss(),\n",
    "                cbs=[SWAV(aug_func=get_batch_augs,\n",
    "                          crop_sizes=[size,int(3/4*size)], #4/3 - large to small crop size ratio is an important hyperparam for optimization! If we kept small crop 96 and just increased 128 to 192 training has hard time\n",
    "                          num_crops=[2,6],\n",
    "                          min_scales=[0.25,0.2],\n",
    "                          max_scales=[1.0,0.35],                \n",
    "                          rotate=True,\n",
    "                          rotate_deg=10,\n",
    "                          jitter=True,\n",
    "                          bw=True,\n",
    "                          blur=False\n",
    "                          ),\n",
    "                     TerminateOnNaNCallback(),\n",
    "                     WandbCallback(log_preds=False,log_model=False)\n",
    "                     ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1728x216 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = dls.one_batch()\n",
    "learn._split(b)\n",
    "learn('before_batch')\n",
    "learn.swav.show_one()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.to_fp16();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=1e-2\n",
    "wd=1e-2\n",
    "epochs=100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>7.346763</td>\n",
       "      <td>7.319592</td>\n",
       "      <td>03:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>7.085961</td>\n",
       "      <td>7.102826</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6.826987</td>\n",
       "      <td>7.432413</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>6.657548</td>\n",
       "      <td>6.684697</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>6.484780</td>\n",
       "      <td>6.750178</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6.332900</td>\n",
       "      <td>6.453974</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6.237092</td>\n",
       "      <td>6.448156</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>6.127450</td>\n",
       "      <td>6.266057</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>6.031301</td>\n",
       "      <td>6.170720</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>5.977430</td>\n",
       "      <td>6.152627</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>5.886191</td>\n",
       "      <td>6.071438</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>5.815827</td>\n",
       "      <td>6.066660</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>5.810672</td>\n",
       "      <td>6.028860</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>5.783502</td>\n",
       "      <td>6.036624</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>5.734086</td>\n",
       "      <td>5.959934</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>5.691333</td>\n",
       "      <td>5.828962</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>5.697824</td>\n",
       "      <td>5.850079</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>5.669659</td>\n",
       "      <td>5.833512</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>5.616106</td>\n",
       "      <td>5.816678</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>5.604099</td>\n",
       "      <td>5.856925</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>5.563162</td>\n",
       "      <td>5.826210</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>5.546981</td>\n",
       "      <td>5.759083</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>5.512746</td>\n",
       "      <td>5.673390</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>5.522406</td>\n",
       "      <td>5.753300</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>5.486694</td>\n",
       "      <td>5.710245</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>5.467843</td>\n",
       "      <td>5.675628</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>5.448352</td>\n",
       "      <td>5.629094</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>5.427695</td>\n",
       "      <td>5.586243</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>5.445467</td>\n",
       "      <td>5.623706</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>5.432904</td>\n",
       "      <td>5.613710</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>5.406093</td>\n",
       "      <td>5.630359</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>5.410904</td>\n",
       "      <td>5.596660</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>5.379332</td>\n",
       "      <td>5.612282</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>5.376659</td>\n",
       "      <td>5.609948</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>5.366118</td>\n",
       "      <td>5.609379</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>5.347384</td>\n",
       "      <td>5.622696</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>5.349342</td>\n",
       "      <td>5.591712</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>5.330217</td>\n",
       "      <td>5.606980</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>5.308704</td>\n",
       "      <td>5.561160</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>5.334291</td>\n",
       "      <td>5.584899</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>5.330826</td>\n",
       "      <td>5.524928</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>5.312093</td>\n",
       "      <td>5.562461</td>\n",
       "      <td>03:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>5.312520</td>\n",
       "      <td>5.596872</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>5.327497</td>\n",
       "      <td>5.531265</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>5.331987</td>\n",
       "      <td>5.563127</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>5.312850</td>\n",
       "      <td>5.524671</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>5.305676</td>\n",
       "      <td>5.589220</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>5.308321</td>\n",
       "      <td>5.626301</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>5.307263</td>\n",
       "      <td>5.521525</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>5.267408</td>\n",
       "      <td>5.496075</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>5.223699</td>\n",
       "      <td>5.497714</td>\n",
       "      <td>02:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>5.264105</td>\n",
       "      <td>5.505012</td>\n",
       "      <td>02:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>5.245604</td>\n",
       "      <td>5.574551</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>5.257089</td>\n",
       "      <td>5.568157</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>5.238290</td>\n",
       "      <td>5.503451</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>5.235277</td>\n",
       "      <td>5.507579</td>\n",
       "      <td>02:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>5.255403</td>\n",
       "      <td>5.559513</td>\n",
       "      <td>02:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>5.240900</td>\n",
       "      <td>5.534863</td>\n",
       "      <td>02:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>5.213208</td>\n",
       "      <td>5.486213</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>5.197606</td>\n",
       "      <td>5.478808</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>5.197224</td>\n",
       "      <td>5.482157</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>5.191122</td>\n",
       "      <td>5.463331</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>5.163327</td>\n",
       "      <td>5.491376</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>5.198448</td>\n",
       "      <td>5.445905</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>5.150698</td>\n",
       "      <td>5.484091</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>5.162223</td>\n",
       "      <td>5.432713</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>5.136093</td>\n",
       "      <td>5.400705</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>5.119492</td>\n",
       "      <td>5.411208</td>\n",
       "      <td>02:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>5.116789</td>\n",
       "      <td>5.434815</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>5.107651</td>\n",
       "      <td>5.390078</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>5.094916</td>\n",
       "      <td>5.443214</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>5.083064</td>\n",
       "      <td>5.380498</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>5.066286</td>\n",
       "      <td>5.383089</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>5.064909</td>\n",
       "      <td>5.406197</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>5.026994</td>\n",
       "      <td>5.418057</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>5.024372</td>\n",
       "      <td>5.371900</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>5.010478</td>\n",
       "      <td>5.358855</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>5.009789</td>\n",
       "      <td>5.352871</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>4.985011</td>\n",
       "      <td>5.398735</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>4.981392</td>\n",
       "      <td>5.380107</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>4.969063</td>\n",
       "      <td>5.336713</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>4.947324</td>\n",
       "      <td>5.347535</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>4.930379</td>\n",
       "      <td>5.312095</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>4.918865</td>\n",
       "      <td>5.366760</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>4.889992</td>\n",
       "      <td>5.331993</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>4.915460</td>\n",
       "      <td>5.322830</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>4.892452</td>\n",
       "      <td>5.315125</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>4.894144</td>\n",
       "      <td>5.333621</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>4.883802</td>\n",
       "      <td>5.326740</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>4.851794</td>\n",
       "      <td>5.294724</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>4.873789</td>\n",
       "      <td>5.315515</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>4.855323</td>\n",
       "      <td>5.303772</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>4.842680</td>\n",
       "      <td>5.299001</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>4.830575</td>\n",
       "      <td>5.307305</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>4.829955</td>\n",
       "      <td>5.310102</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>4.839103</td>\n",
       "      <td>5.306712</td>\n",
       "      <td>03:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>4.814095</td>\n",
       "      <td>5.287855</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>4.813604</td>\n",
       "      <td>5.325490</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>4.837542</td>\n",
       "      <td>5.336023</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>4.803956</td>\n",
       "      <td>5.338683</td>\n",
       "      <td>03:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "learn.unfreeze()\n",
    "learn.fit_flat_cos(epochs, lr, wd=wd, pct_start=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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    {
     "data": {
      "text/html": [
       "Find user logs for this run at: <code>/home/code-base/extra_space/self_supervised/examples/wandb/run-20210208_214948-3ggbze22/logs/debug.log</code>"
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      "text/plain": [
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     "data": {
      "text/html": [
       "Find internal logs for this run at: <code>/home/code-base/extra_space/self_supervised/examples/wandb/run-20210208_214948-3ggbze22/logs/debug-internal.log</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h3>Run summary:</h3><br/><style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
       "    </style><table class=\"wandb\">\n",
       "<tr><td>epoch</td><td>100</td></tr><tr><td>train_loss</td><td>4.80396</td></tr><tr><td>raw_loss</td><td>4.80031</td></tr><tr><td>wd_0</td><td>0.01</td></tr><tr><td>sqr_mom_0</td><td>0.99</td></tr><tr><td>lr_0</td><td>0.0</td></tr><tr><td>mom_0</td><td>0.9</td></tr><tr><td>eps_0</td><td>1e-05</td></tr><tr><td>_step</td><td>24699</td></tr><tr><td>_runtime</td><td>18095</td></tr><tr><td>_timestamp</td><td>1612839086</td></tr><tr><td>valid_loss</td><td>5.33868</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h3>Run history:</h3><br/><style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
       "    </style><table class=\"wandb\">\n",
       "<tr><td>epoch</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>train_loss</td><td>█▇▆▄▄▄▄▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁</td></tr><tr><td>raw_loss</td><td>█▇▆▅▄▅▄▄▃▃▃▂▃▃▃▂▂▃▃▃▂▃▃▂▂▃▂▂▁▁▂▂▁▂▁▂▁▃▁▁</td></tr><tr><td>wd_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>sqr_mom_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>lr_0</td><td>███████████████████████▇▇▇▆▆▅▅▄▄▃▃▂▂▂▁▁▁</td></tr><tr><td>mom_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>eps_0</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>_step</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>_runtime</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>_timestamp</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>valid_loss</td><td>██▅▄▄▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr></table><br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Synced 5 W&B file(s), 1 media file(s), 0 artifact file(s) and 0 other file(s)"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                    <br/>Synced <strong style=\"color:#cdcd00\">celestial-music-9</strong>: <a href=\"https://wandb.ai/keremturgutlu/self-supervised-imagewang/runs/3ggbze22\" target=\"_blank\">https://wandb.ai/keremturgutlu/self-supervised-imagewang/runs/3ggbze22</a><br/>\n",
       "                "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wandb.finish()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_name = f'swav_iwang_sz{size}_epc{epochs}'\n",
    "learn.save(save_name)\n",
    "torch.save(learn.model.encoder.state_dict(), learn.path/learn.model_dir/f'{save_name}_encoder.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_loss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downstream Task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96, 224)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bs, size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dls(size, bs, workers=None):\n",
    "    path = URLs.IMAGEWANG_160 if size <= 160 else URLs.IMAGEWANG\n",
    "    source = untar_data(path)\n",
    "    files = get_image_files(source, folders=['train', 'val'])\n",
    "    splits = GrandparentSplitter(valid_name='val')(files)\n",
    "    \n",
    "    item_aug = [RandomResizedCrop(size, min_scale=0.35), FlipItem(0.5)]\n",
    "    tfms = [[PILImage.create, ToTensor, *item_aug], \n",
    "            [parent_label, Categorize()]]\n",
    "    \n",
    "    dsets = Datasets(files, tfms=tfms, splits=splits)\n",
    "    \n",
    "    batch_tfms = [IntToFloatTensor, Normalize.from_stats(*imagenet_stats)]\n",
    "    dls = dsets.dataloaders(bs=bs, num_workers=workers, after_batch=batch_tfms)\n",
    "    return dls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_func(m): return L(m[0], m[1]).map(params)\n",
    "\n",
    "def create_learner(size=size, arch='xresnet34', encoder_path=\"models/swav_iwang_sz128_epc100_encoder.pth\"):\n",
    "    \n",
    "    dls = get_dls(size, bs=bs//2)\n",
    "    pretrained_encoder = torch.load(encoder_path)\n",
    "    encoder = create_encoder(arch, pretrained=False, n_in=3)\n",
    "    encoder.load_state_dict(pretrained_encoder)\n",
    "    nf = encoder(torch.randn(2,3,224,224)).size(-1)\n",
    "    classifier = create_cls_module(nf, dls.c)\n",
    "    model = nn.Sequential(encoder, classifier)\n",
    "    learn = Learner(dls, model, opt_func=opt_func, splitter=split_func,\n",
    "                metrics=[accuracy,top_k_accuracy], loss_func=LabelSmoothingCrossEntropy())\n",
    "    return learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def finetune(size, epochs, arch, encoder_path, lr=1e-2, wd=1e-2):\n",
    "    learn = create_learner(size, arch, encoder_path)\n",
    "    learn.unfreeze()\n",
    "    learn.fit_flat_cos(epochs, lr, wd=wd)\n",
    "    final_acc = learn.recorder.values[-1][-2]\n",
    "    return final_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5 epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.003726</td>\n",
       "      <td>1.702733</td>\n",
       "      <td>0.637312</td>\n",
       "      <td>0.902011</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.907711</td>\n",
       "      <td>1.588652</td>\n",
       "      <td>0.684907</td>\n",
       "      <td>0.920336</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.880427</td>\n",
       "      <td>1.381752</td>\n",
       "      <td>0.744973</td>\n",
       "      <td>0.945788</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.821257</td>\n",
       "      <td>1.412441</td>\n",
       "      <td>0.735047</td>\n",
       "      <td>0.944770</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.753851</td>\n",
       "      <td>1.245431</td>\n",
       "      <td>0.782896</td>\n",
       "      <td>0.959023</td>\n",
       "      <td>00:56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.977824</td>\n",
       "      <td>1.644421</td>\n",
       "      <td>0.656656</td>\n",
       "      <td>0.904556</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.941114</td>\n",
       "      <td>1.702319</td>\n",
       "      <td>0.652838</td>\n",
       "      <td>0.910664</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.877325</td>\n",
       "      <td>1.466865</td>\n",
       "      <td>0.731993</td>\n",
       "      <td>0.939170</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.826462</td>\n",
       "      <td>1.414049</td>\n",
       "      <td>0.732756</td>\n",
       "      <td>0.944006</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.754504</td>\n",
       "      <td>1.248356</td>\n",
       "      <td>0.789005</td>\n",
       "      <td>0.963604</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.991975</td>\n",
       "      <td>1.696295</td>\n",
       "      <td>0.649020</td>\n",
       "      <td>0.912446</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.927800</td>\n",
       "      <td>1.564077</td>\n",
       "      <td>0.675999</td>\n",
       "      <td>0.914482</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.883308</td>\n",
       "      <td>1.452437</td>\n",
       "      <td>0.706032</td>\n",
       "      <td>0.953678</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.825139</td>\n",
       "      <td>1.405012</td>\n",
       "      <td>0.732247</td>\n",
       "      <td>0.944770</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.755022</td>\n",
       "      <td>1.269651</td>\n",
       "      <td>0.771189</td>\n",
       "      <td>0.959786</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.981242</td>\n",
       "      <td>1.705830</td>\n",
       "      <td>0.651820</td>\n",
       "      <td>0.918554</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.930540</td>\n",
       "      <td>1.530243</td>\n",
       "      <td>0.704250</td>\n",
       "      <td>0.932807</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.874156</td>\n",
       "      <td>1.428301</td>\n",
       "      <td>0.734538</td>\n",
       "      <td>0.944006</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.828597</td>\n",
       "      <td>1.432052</td>\n",
       "      <td>0.732756</td>\n",
       "      <td>0.944261</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.749793</td>\n",
       "      <td>1.270651</td>\n",
       "      <td>0.777043</td>\n",
       "      <td>0.957241</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.990188</td>\n",
       "      <td>1.664461</td>\n",
       "      <td>0.659964</td>\n",
       "      <td>0.913209</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.916905</td>\n",
       "      <td>1.746949</td>\n",
       "      <td>0.653347</td>\n",
       "      <td>0.919827</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.876274</td>\n",
       "      <td>1.540925</td>\n",
       "      <td>0.699160</td>\n",
       "      <td>0.928481</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.822780</td>\n",
       "      <td>1.427482</td>\n",
       "      <td>0.736065</td>\n",
       "      <td>0.945279</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.746668</td>\n",
       "      <td>1.283033</td>\n",
       "      <td>0.777297</td>\n",
       "      <td>0.957241</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = []\n",
    "runs = 5\n",
    "for i in range(runs): acc += [finetune(size, epochs=5, arch='xresnet34', encoder_path=f'models/swav_iwang_sz{size}_epc100_encoder.pth')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7794858813285828"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 20 epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.984659</td>\n",
       "      <td>1.648162</td>\n",
       "      <td>0.665818</td>\n",
       "      <td>0.928481</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.920816</td>\n",
       "      <td>1.480424</td>\n",
       "      <td>0.721303</td>\n",
       "      <td>0.933571</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.872141</td>\n",
       "      <td>1.387076</td>\n",
       "      <td>0.739119</td>\n",
       "      <td>0.947060</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.814974</td>\n",
       "      <td>1.395511</td>\n",
       "      <td>0.745482</td>\n",
       "      <td>0.946042</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.815932</td>\n",
       "      <td>1.444554</td>\n",
       "      <td>0.733520</td>\n",
       "      <td>0.936880</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.784586</td>\n",
       "      <td>1.364161</td>\n",
       "      <td>0.752100</td>\n",
       "      <td>0.949606</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.763978</td>\n",
       "      <td>1.464598</td>\n",
       "      <td>0.723085</td>\n",
       "      <td>0.945279</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.756061</td>\n",
       "      <td>1.513858</td>\n",
       "      <td>0.691016</td>\n",
       "      <td>0.928990</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.743778</td>\n",
       "      <td>1.414075</td>\n",
       "      <td>0.726139</td>\n",
       "      <td>0.938152</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.738189</td>\n",
       "      <td>1.410384</td>\n",
       "      <td>0.745482</td>\n",
       "      <td>0.953678</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.729744</td>\n",
       "      <td>1.355721</td>\n",
       "      <td>0.746755</td>\n",
       "      <td>0.949096</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.714316</td>\n",
       "      <td>1.460312</td>\n",
       "      <td>0.718503</td>\n",
       "      <td>0.937389</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.731326</td>\n",
       "      <td>1.391350</td>\n",
       "      <td>0.742683</td>\n",
       "      <td>0.941206</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.719519</td>\n",
       "      <td>1.462322</td>\n",
       "      <td>0.711886</td>\n",
       "      <td>0.934334</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.715189</td>\n",
       "      <td>1.472617</td>\n",
       "      <td>0.708068</td>\n",
       "      <td>0.927717</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.703142</td>\n",
       "      <td>1.476813</td>\n",
       "      <td>0.721303</td>\n",
       "      <td>0.927971</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.684320</td>\n",
       "      <td>1.306895</td>\n",
       "      <td>0.762026</td>\n",
       "      <td>0.951896</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.660385</td>\n",
       "      <td>1.299886</td>\n",
       "      <td>0.763299</td>\n",
       "      <td>0.941970</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.644466</td>\n",
       "      <td>1.221008</td>\n",
       "      <td>0.784678</td>\n",
       "      <td>0.952914</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.645878</td>\n",
       "      <td>1.221697</td>\n",
       "      <td>0.788750</td>\n",
       "      <td>0.954187</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.990826</td>\n",
       "      <td>1.926803</td>\n",
       "      <td>0.562739</td>\n",
       "      <td>0.896666</td>\n",
       "      <td>00:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.925727</td>\n",
       "      <td>1.520635</td>\n",
       "      <td>0.707559</td>\n",
       "      <td>0.939679</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.877482</td>\n",
       "      <td>1.419136</td>\n",
       "      <td>0.731993</td>\n",
       "      <td>0.946297</td>\n",
       "      <td>00:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.834636</td>\n",
       "      <td>1.372909</td>\n",
       "      <td>0.741156</td>\n",
       "      <td>0.940443</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.802381</td>\n",
       "      <td>1.417018</td>\n",
       "      <td>0.724103</td>\n",
       "      <td>0.950878</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.782618</td>\n",
       "      <td>1.384342</td>\n",
       "      <td>0.745228</td>\n",
       "      <td>0.955968</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.762937</td>\n",
       "      <td>1.416170</td>\n",
       "      <td>0.739883</td>\n",
       "      <td>0.945024</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.760333</td>\n",
       "      <td>1.416006</td>\n",
       "      <td>0.730720</td>\n",
       "      <td>0.948587</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.738668</td>\n",
       "      <td>1.327097</td>\n",
       "      <td>0.749555</td>\n",
       "      <td>0.944515</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.730946</td>\n",
       "      <td>1.483327</td>\n",
       "      <td>0.714686</td>\n",
       "      <td>0.935607</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.735652</td>\n",
       "      <td>1.423437</td>\n",
       "      <td>0.735047</td>\n",
       "      <td>0.947824</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.726877</td>\n",
       "      <td>1.306741</td>\n",
       "      <td>0.762535</td>\n",
       "      <td>0.952405</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.723073</td>\n",
       "      <td>1.465621</td>\n",
       "      <td>0.722321</td>\n",
       "      <td>0.929499</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.713759</td>\n",
       "      <td>1.311549</td>\n",
       "      <td>0.756936</td>\n",
       "      <td>0.941206</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.701419</td>\n",
       "      <td>1.423864</td>\n",
       "      <td>0.732247</td>\n",
       "      <td>0.941970</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.705997</td>\n",
       "      <td>1.427778</td>\n",
       "      <td>0.736065</td>\n",
       "      <td>0.938916</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.682911</td>\n",
       "      <td>1.380643</td>\n",
       "      <td>0.745228</td>\n",
       "      <td>0.936371</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.659642</td>\n",
       "      <td>1.313483</td>\n",
       "      <td>0.768643</td>\n",
       "      <td>0.948842</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.644884</td>\n",
       "      <td>1.227343</td>\n",
       "      <td>0.790786</td>\n",
       "      <td>0.956732</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.643427</td>\n",
       "      <td>1.215912</td>\n",
       "      <td>0.792568</td>\n",
       "      <td>0.955714</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.989855</td>\n",
       "      <td>1.620532</td>\n",
       "      <td>0.683635</td>\n",
       "      <td>0.914991</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.923530</td>\n",
       "      <td>1.533112</td>\n",
       "      <td>0.714177</td>\n",
       "      <td>0.934843</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.862308</td>\n",
       "      <td>1.643399</td>\n",
       "      <td>0.664546</td>\n",
       "      <td>0.925172</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.832433</td>\n",
       "      <td>1.360041</td>\n",
       "      <td>0.740901</td>\n",
       "      <td>0.949351</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.800033</td>\n",
       "      <td>1.378103</td>\n",
       "      <td>0.750827</td>\n",
       "      <td>0.939425</td>\n",
       "      <td>00:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.768974</td>\n",
       "      <td>1.396923</td>\n",
       "      <td>0.759481</td>\n",
       "      <td>0.939679</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.766265</td>\n",
       "      <td>1.413500</td>\n",
       "      <td>0.724866</td>\n",
       "      <td>0.934080</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.751104</td>\n",
       "      <td>1.383515</td>\n",
       "      <td>0.752354</td>\n",
       "      <td>0.949096</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.736254</td>\n",
       "      <td>1.407612</td>\n",
       "      <td>0.753881</td>\n",
       "      <td>0.942479</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.730978</td>\n",
       "      <td>1.415057</td>\n",
       "      <td>0.734793</td>\n",
       "      <td>0.935098</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.731433</td>\n",
       "      <td>1.421862</td>\n",
       "      <td>0.722321</td>\n",
       "      <td>0.932044</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.725414</td>\n",
       "      <td>1.462966</td>\n",
       "      <td>0.725375</td>\n",
       "      <td>0.940443</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.721521</td>\n",
       "      <td>1.351979</td>\n",
       "      <td>0.750827</td>\n",
       "      <td>0.952914</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.719478</td>\n",
       "      <td>1.405232</td>\n",
       "      <td>0.737338</td>\n",
       "      <td>0.947060</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.704920</td>\n",
       "      <td>1.355567</td>\n",
       "      <td>0.758463</td>\n",
       "      <td>0.942225</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.707933</td>\n",
       "      <td>1.422607</td>\n",
       "      <td>0.727412</td>\n",
       "      <td>0.941970</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.686819</td>\n",
       "      <td>1.363768</td>\n",
       "      <td>0.750573</td>\n",
       "      <td>0.945279</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.657534</td>\n",
       "      <td>1.294799</td>\n",
       "      <td>0.769407</td>\n",
       "      <td>0.951896</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.645342</td>\n",
       "      <td>1.253585</td>\n",
       "      <td>0.776533</td>\n",
       "      <td>0.951896</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.640570</td>\n",
       "      <td>1.240647</td>\n",
       "      <td>0.785442</td>\n",
       "      <td>0.951133</td>\n",
       "      <td>00:56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.788919985294342"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc = []\n",
    "runs = 3\n",
    "for i in range(runs): acc += [finetune(size, epochs=20, arch='xresnet34', encoder_path=f'models/swav_iwang_sz{size}_epc100_encoder.pth')]\n",
    "np.mean(acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 80 epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.006964</td>\n",
       "      <td>1.835560</td>\n",
       "      <td>0.641385</td>\n",
       "      <td>0.883685</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.928978</td>\n",
       "      <td>1.511517</td>\n",
       "      <td>0.707050</td>\n",
       "      <td>0.934589</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.869080</td>\n",
       "      <td>1.435580</td>\n",
       "      <td>0.721558</td>\n",
       "      <td>0.944006</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.832356</td>\n",
       "      <td>1.475267</td>\n",
       "      <td>0.703741</td>\n",
       "      <td>0.941715</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.785432</td>\n",
       "      <td>1.527628</td>\n",
       "      <td>0.716467</td>\n",
       "      <td>0.941206</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.783196</td>\n",
       "      <td>1.392344</td>\n",
       "      <td>0.746500</td>\n",
       "      <td>0.946806</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.764544</td>\n",
       "      <td>1.376068</td>\n",
       "      <td>0.730720</td>\n",
       "      <td>0.937643</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.750309</td>\n",
       "      <td>1.455704</td>\n",
       "      <td>0.722576</td>\n",
       "      <td>0.935098</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.745672</td>\n",
       "      <td>1.381837</td>\n",
       "      <td>0.750827</td>\n",
       "      <td>0.945788</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.739380</td>\n",
       "      <td>1.433963</td>\n",
       "      <td>0.730466</td>\n",
       "      <td>0.939425</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.723529</td>\n",
       "      <td>1.396730</td>\n",
       "      <td>0.739119</td>\n",
       "      <td>0.938407</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.728593</td>\n",
       "      <td>1.301539</td>\n",
       "      <td>0.762789</td>\n",
       "      <td>0.951133</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.723337</td>\n",
       "      <td>1.539744</td>\n",
       "      <td>0.707559</td>\n",
       "      <td>0.934843</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.737634</td>\n",
       "      <td>1.401320</td>\n",
       "      <td>0.721049</td>\n",
       "      <td>0.933316</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.715232</td>\n",
       "      <td>1.447023</td>\n",
       "      <td>0.719012</td>\n",
       "      <td>0.936880</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.704936</td>\n",
       "      <td>1.383101</td>\n",
       "      <td>0.730211</td>\n",
       "      <td>0.945279</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.704030</td>\n",
       "      <td>1.524006</td>\n",
       "      <td>0.711631</td>\n",
       "      <td>0.925172</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.708099</td>\n",
       "      <td>1.363805</td>\n",
       "      <td>0.749809</td>\n",
       "      <td>0.943752</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.706292</td>\n",
       "      <td>1.416293</td>\n",
       "      <td>0.739883</td>\n",
       "      <td>0.938916</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.697576</td>\n",
       "      <td>1.447763</td>\n",
       "      <td>0.729957</td>\n",
       "      <td>0.925426</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.698248</td>\n",
       "      <td>1.498276</td>\n",
       "      <td>0.707559</td>\n",
       "      <td>0.917027</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.692724</td>\n",
       "      <td>1.401238</td>\n",
       "      <td>0.730975</td>\n",
       "      <td>0.941461</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.681901</td>\n",
       "      <td>1.463799</td>\n",
       "      <td>0.724357</td>\n",
       "      <td>0.933571</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.698454</td>\n",
       "      <td>1.396493</td>\n",
       "      <td>0.744210</td>\n",
       "      <td>0.939170</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.690055</td>\n",
       "      <td>1.475645</td>\n",
       "      <td>0.720031</td>\n",
       "      <td>0.939425</td>\n",
       "      <td>00:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.684283</td>\n",
       "      <td>1.412133</td>\n",
       "      <td>0.733011</td>\n",
       "      <td>0.938916</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.696717</td>\n",
       "      <td>1.386610</td>\n",
       "      <td>0.740901</td>\n",
       "      <td>0.943243</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.697309</td>\n",
       "      <td>1.354248</td>\n",
       "      <td>0.746755</td>\n",
       "      <td>0.939934</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.694943</td>\n",
       "      <td>1.543622</td>\n",
       "      <td>0.693561</td>\n",
       "      <td>0.923390</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.692300</td>\n",
       "      <td>1.441512</td>\n",
       "      <td>0.721303</td>\n",
       "      <td>0.920845</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.690814</td>\n",
       "      <td>1.486224</td>\n",
       "      <td>0.705778</td>\n",
       "      <td>0.922372</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.694924</td>\n",
       "      <td>1.549793</td>\n",
       "      <td>0.699160</td>\n",
       "      <td>0.917536</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.690185</td>\n",
       "      <td>1.575127</td>\n",
       "      <td>0.696360</td>\n",
       "      <td>0.919318</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.677899</td>\n",
       "      <td>1.420722</td>\n",
       "      <td>0.732502</td>\n",
       "      <td>0.928735</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.701723</td>\n",
       "      <td>1.540780</td>\n",
       "      <td>0.694833</td>\n",
       "      <td>0.908628</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.690799</td>\n",
       "      <td>1.491939</td>\n",
       "      <td>0.710868</td>\n",
       "      <td>0.910410</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.699683</td>\n",
       "      <td>1.594946</td>\n",
       "      <td>0.685671</td>\n",
       "      <td>0.920590</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.696029</td>\n",
       "      <td>1.486275</td>\n",
       "      <td>0.703487</td>\n",
       "      <td>0.923899</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.692194</td>\n",
       "      <td>1.571023</td>\n",
       "      <td>0.678035</td>\n",
       "      <td>0.901247</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.687103</td>\n",
       "      <td>1.474924</td>\n",
       "      <td>0.719522</td>\n",
       "      <td>0.931026</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.691896</td>\n",
       "      <td>1.538751</td>\n",
       "      <td>0.694070</td>\n",
       "      <td>0.920336</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0.679582</td>\n",
       "      <td>1.565147</td>\n",
       "      <td>0.695597</td>\n",
       "      <td>0.926190</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>0.685645</td>\n",
       "      <td>1.542227</td>\n",
       "      <td>0.684398</td>\n",
       "      <td>0.913209</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>0.681636</td>\n",
       "      <td>1.485691</td>\n",
       "      <td>0.711631</td>\n",
       "      <td>0.934080</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>0.693798</td>\n",
       "      <td>1.578841</td>\n",
       "      <td>0.683889</td>\n",
       "      <td>0.914482</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>0.686720</td>\n",
       "      <td>1.529884</td>\n",
       "      <td>0.697633</td>\n",
       "      <td>0.928735</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>0.688417</td>\n",
       "      <td>1.559355</td>\n",
       "      <td>0.693306</td>\n",
       "      <td>0.912700</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>0.692981</td>\n",
       "      <td>1.547655</td>\n",
       "      <td>0.687452</td>\n",
       "      <td>0.913464</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>0.681289</td>\n",
       "      <td>1.535568</td>\n",
       "      <td>0.698142</td>\n",
       "      <td>0.913973</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>0.691402</td>\n",
       "      <td>1.530416</td>\n",
       "      <td>0.697887</td>\n",
       "      <td>0.918300</td>\n",
       "      <td>00:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>0.689610</td>\n",
       "      <td>1.547159</td>\n",
       "      <td>0.693561</td>\n",
       "      <td>0.914991</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.704537</td>\n",
       "      <td>1.607475</td>\n",
       "      <td>0.684907</td>\n",
       "      <td>0.910664</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.683805</td>\n",
       "      <td>1.538770</td>\n",
       "      <td>0.701960</td>\n",
       "      <td>0.917791</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.680030</td>\n",
       "      <td>1.502178</td>\n",
       "      <td>0.695342</td>\n",
       "      <td>0.912955</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.693130</td>\n",
       "      <td>1.507548</td>\n",
       "      <td>0.707305</td>\n",
       "      <td>0.920845</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.688406</td>\n",
       "      <td>1.385802</td>\n",
       "      <td>0.740646</td>\n",
       "      <td>0.934334</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.686518</td>\n",
       "      <td>1.547207</td>\n",
       "      <td>0.703996</td>\n",
       "      <td>0.917791</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.685814</td>\n",
       "      <td>1.614876</td>\n",
       "      <td>0.680071</td>\n",
       "      <td>0.905065</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.691067</td>\n",
       "      <td>1.496006</td>\n",
       "      <td>0.709341</td>\n",
       "      <td>0.918300</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.679938</td>\n",
       "      <td>1.504920</td>\n",
       "      <td>0.700178</td>\n",
       "      <td>0.920336</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.689400</td>\n",
       "      <td>1.735247</td>\n",
       "      <td>0.632985</td>\n",
       "      <td>0.888776</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.680476</td>\n",
       "      <td>1.609240</td>\n",
       "      <td>0.675490</td>\n",
       "      <td>0.906083</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.680244</td>\n",
       "      <td>1.665053</td>\n",
       "      <td>0.661491</td>\n",
       "      <td>0.908119</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.677594</td>\n",
       "      <td>1.694178</td>\n",
       "      <td>0.656401</td>\n",
       "      <td>0.905828</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.670590</td>\n",
       "      <td>1.537750</td>\n",
       "      <td>0.697633</td>\n",
       "      <td>0.907865</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.659842</td>\n",
       "      <td>1.488384</td>\n",
       "      <td>0.707305</td>\n",
       "      <td>0.913209</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.647976</td>\n",
       "      <td>1.531747</td>\n",
       "      <td>0.695342</td>\n",
       "      <td>0.913464</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.647509</td>\n",
       "      <td>1.429043</td>\n",
       "      <td>0.726393</td>\n",
       "      <td>0.921863</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.640351</td>\n",
       "      <td>1.515520</td>\n",
       "      <td>0.702978</td>\n",
       "      <td>0.913719</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.633499</td>\n",
       "      <td>1.364841</td>\n",
       "      <td>0.744719</td>\n",
       "      <td>0.931535</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.633666</td>\n",
       "      <td>1.342478</td>\n",
       "      <td>0.752609</td>\n",
       "      <td>0.939425</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.630881</td>\n",
       "      <td>1.368544</td>\n",
       "      <td>0.742174</td>\n",
       "      <td>0.927717</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.624069</td>\n",
       "      <td>1.441913</td>\n",
       "      <td>0.729957</td>\n",
       "      <td>0.921354</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.617660</td>\n",
       "      <td>1.322569</td>\n",
       "      <td>0.760244</td>\n",
       "      <td>0.938407</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.619092</td>\n",
       "      <td>1.362574</td>\n",
       "      <td>0.752354</td>\n",
       "      <td>0.930008</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.617422</td>\n",
       "      <td>1.364635</td>\n",
       "      <td>0.752609</td>\n",
       "      <td>0.926190</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.616865</td>\n",
       "      <td>1.368864</td>\n",
       "      <td>0.752609</td>\n",
       "      <td>0.927971</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.618172</td>\n",
       "      <td>1.356623</td>\n",
       "      <td>0.755154</td>\n",
       "      <td>0.930008</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.615551</td>\n",
       "      <td>1.329600</td>\n",
       "      <td>0.761517</td>\n",
       "      <td>0.933825</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.620276</td>\n",
       "      <td>1.344752</td>\n",
       "      <td>0.757954</td>\n",
       "      <td>0.933571</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.7579537034034729"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc = []\n",
    "runs = 1\n",
    "for i in range(runs): acc += [finetune(size, epochs=80, arch='xresnet34',encoder_path=f'models/swav_iwang_sz{size}_epc100_encoder.pth')]\n",
    "np.mean(acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 200 epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='39' class='' max='200' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      19.50% [39/200 36:01<2:28:44]\n",
       "    </div>\n",
       "    \n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.963223</td>\n",
       "      <td>1.624201</td>\n",
       "      <td>0.673454</td>\n",
       "      <td>0.932807</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.949285</td>\n",
       "      <td>1.703292</td>\n",
       "      <td>0.633495</td>\n",
       "      <td>0.873505</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.871079</td>\n",
       "      <td>1.506654</td>\n",
       "      <td>0.709341</td>\n",
       "      <td>0.941715</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.834461</td>\n",
       "      <td>1.515007</td>\n",
       "      <td>0.704759</td>\n",
       "      <td>0.929244</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.802906</td>\n",
       "      <td>1.325958</td>\n",
       "      <td>0.759990</td>\n",
       "      <td>0.954696</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.773226</td>\n",
       "      <td>1.405822</td>\n",
       "      <td>0.739119</td>\n",
       "      <td>0.945279</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.752312</td>\n",
       "      <td>1.440087</td>\n",
       "      <td>0.724866</td>\n",
       "      <td>0.926953</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.755499</td>\n",
       "      <td>1.418935</td>\n",
       "      <td>0.742937</td>\n",
       "      <td>0.948333</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.746847</td>\n",
       "      <td>1.398286</td>\n",
       "      <td>0.734284</td>\n",
       "      <td>0.935862</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.737169</td>\n",
       "      <td>1.443338</td>\n",
       "      <td>0.721049</td>\n",
       "      <td>0.942988</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.727426</td>\n",
       "      <td>1.358974</td>\n",
       "      <td>0.756936</td>\n",
       "      <td>0.951133</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.730754</td>\n",
       "      <td>1.477595</td>\n",
       "      <td>0.719776</td>\n",
       "      <td>0.941970</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.717104</td>\n",
       "      <td>1.456343</td>\n",
       "      <td>0.710104</td>\n",
       "      <td>0.941970</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.718931</td>\n",
       "      <td>1.435890</td>\n",
       "      <td>0.727921</td>\n",
       "      <td>0.928481</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.721263</td>\n",
       "      <td>1.502325</td>\n",
       "      <td>0.706796</td>\n",
       "      <td>0.927462</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.704004</td>\n",
       "      <td>1.431562</td>\n",
       "      <td>0.733774</td>\n",
       "      <td>0.941206</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.722174</td>\n",
       "      <td>1.384548</td>\n",
       "      <td>0.741665</td>\n",
       "      <td>0.941206</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.698924</td>\n",
       "      <td>1.398302</td>\n",
       "      <td>0.744210</td>\n",
       "      <td>0.944261</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.709611</td>\n",
       "      <td>1.481906</td>\n",
       "      <td>0.707305</td>\n",
       "      <td>0.919827</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.701380</td>\n",
       "      <td>1.484859</td>\n",
       "      <td>0.731484</td>\n",
       "      <td>0.938916</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.697446</td>\n",
       "      <td>1.432695</td>\n",
       "      <td>0.724103</td>\n",
       "      <td>0.924408</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.713865</td>\n",
       "      <td>1.439622</td>\n",
       "      <td>0.732247</td>\n",
       "      <td>0.924663</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.700996</td>\n",
       "      <td>1.481006</td>\n",
       "      <td>0.726648</td>\n",
       "      <td>0.927717</td>\n",
       "      <td>00:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.696026</td>\n",
       "      <td>1.504403</td>\n",
       "      <td>0.726393</td>\n",
       "      <td>0.923899</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.688778</td>\n",
       "      <td>1.610080</td>\n",
       "      <td>0.673963</td>\n",
       "      <td>0.913719</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.691736</td>\n",
       "      <td>1.464858</td>\n",
       "      <td>0.722321</td>\n",
       "      <td>0.925681</td>\n",
       "      <td>00:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.690028</td>\n",
       "      <td>1.443982</td>\n",
       "      <td>0.730975</td>\n",
       "      <td>0.940443</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.688034</td>\n",
       "      <td>1.492388</td>\n",
       "      <td>0.714940</td>\n",
       "      <td>0.927462</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.687660</td>\n",
       "      <td>1.455014</td>\n",
       "      <td>0.715958</td>\n",
       "      <td>0.927971</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.698066</td>\n",
       "      <td>1.494515</td>\n",
       "      <td>0.703232</td>\n",
       "      <td>0.926444</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.680180</td>\n",
       "      <td>1.405783</td>\n",
       "      <td>0.748791</td>\n",
       "      <td>0.928990</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.691784</td>\n",
       "      <td>1.441025</td>\n",
       "      <td>0.720540</td>\n",
       "      <td>0.935607</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.683654</td>\n",
       "      <td>1.510540</td>\n",
       "      <td>0.709341</td>\n",
       "      <td>0.926953</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.691187</td>\n",
       "      <td>1.459951</td>\n",
       "      <td>0.718503</td>\n",
       "      <td>0.929753</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.693315</td>\n",
       "      <td>1.495772</td>\n",
       "      <td>0.707050</td>\n",
       "      <td>0.928226</td>\n",
       "      <td>00:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.693724</td>\n",
       "      <td>1.455134</td>\n",
       "      <td>0.702723</td>\n",
       "      <td>0.931789</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.689293</td>\n",
       "      <td>1.519282</td>\n",
       "      <td>0.700433</td>\n",
       "      <td>0.914737</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.693902</td>\n",
       "      <td>1.626301</td>\n",
       "      <td>0.666836</td>\n",
       "      <td>0.909137</td>\n",
       "      <td>00:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.694570</td>\n",
       "      <td>1.503048</td>\n",
       "      <td>0.704505</td>\n",
       "      <td>0.937643</td>\n",
       "      <td>00:55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>\n",
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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       "      <progress value='208' class='' max='305' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      68.20% [208/305 00:30<00:14 0.6926]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = []\n",
    "runs = 1\n",
    "for i in range(runs): acc += [finetune(size, epochs=200, arch='xresnet34', encoder_path=f'models/swav_iwang_sz{size}_epc100_encoder.pth')]\n",
    "np.mean(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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