narrowlyapplicable · December 1, 2019 13:26
diff --git a/FactorVAE.ipynb b/FactorVAE.ipynb
 {"nbformat":4,"nbformat_minor":0,"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.5"},"colab":{"name":"FactorVAE.ipynb","provenance":[],"collapsed_sections":[],"machine_shape":"hm"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"CBdtkdJ37cT0","colab_type":"text"},"source":["# FactorVAEの構成確認\n","- PyTorchの練習も兼ねて、FactorVAEの実装例を追う。\n","  - 実装例は<https://github.com/1Konny/FactorVAE/blob/master/model.py>から"]},{"cell_type":"code","metadata":{"id":"8hKc1Rq3AIUx","colab_type":"code","outputId":"d6eb9f22-7897-4dbc-b90f-f7e83fde850f","executionInfo":{"status":"ok","timestamp":1575186292534,"user_tz":-540,"elapsed":3869,"user":{"displayName":"玉井雄介","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mByoBodcBhoKVJl1rvo_BtNmaEPQgvLc4PF74st=s64","userId":"06916507031009543111"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["!cat /proc/uptime | awk '{print $1 /60 /60 /24 \"days (\" $1 \"sec)\"}'"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0.00722558days (624.29sec)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"cOiQwn-v7jlq","colab_type":"text"},"source":["## Google Coalbの準備"]},{"cell_type":"code","metadata":{"id":"JM3yUpUy7iKU","colab_type":"code","outputId":"442b0137-db72-461a-a2fe-ae5fdc877915","executionInfo":{"status":"ok","timestamp":1575186292538,"user_tz":-540,"elapsed":3853,"user":{"displayName":"玉井雄介","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mByoBodcBhoKVJl1rvo_BtNmaEPQgvLc4PF74st=s64","userId":"06916507031009543111"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["# Google Driveにマウント\n","from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"P7lh6YHy7vWB","colab_type":"code","outputId":"3070af50-505f-47e9-f0f7-b64c21e17800","executionInfo":{"status":"ok","timestamp":1575186292542,"user_tz":-540,"elapsed":3841,"user":{"displayName":"玉井雄介","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mByoBodcBhoKVJl1rvo_BtNmaEPQgvLc4PF74st=s64","userId":"06916507031009543111"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["cd /content/drive/My\\ Drive/Colab\\ Notebooks/factor-vae/"],"execution_count":0,"outputs":[{"output_type":"stream","text":["/content/drive/My Drive/Colab Notebooks/factor-vae\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"tnAb8a647vmG","colab_type":"code","outputId":"e4f3a971-241e-48bd-e872-d81d317006bc","executionInfo":{"status":"ok","timestamp":1575186294384,"user_tz":-540,"elapsed":5664,"user":{"displayName":"玉井雄介","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mByoBodcBhoKVJl1rvo_BtNmaEPQgvLc4PF74st=s64","userId":"06916507031009543111"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["ls"],"execution_count":0,"outputs":[{"output_type":"stream","text":["\u001b[0m\u001b[01;34mcheckpoints\u001b[0m/  \u001b[01;34mdata\u001b[0m/  FactorVAE.ipynb  \u001b[01;34moutputs\u001b[0m/  url.txt  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/root/.cache/pip/wheels/b1/c3/d6/9a1cc8f3a99a0fc1124cae20153f36af59a6e683daca0a0814\n","Successfully built visdom torchfile\n","Installing collected packages: jsonpointer, jsonpatch, torchfile, websocket-client, visdom\n","Successfully installed jsonpatch-1.24 jsonpointer-2.0 torchfile-0.1.0 visdom-0.1.8.9 websocket-client-0.56.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"NTdnw4MC7cT8","colab_type":"code","colab":{}},"source":["import argparse\n","import os\n","import random\n","import subprocess\n","#import matplotlib.pyplot as plt\n","import numpy as np\n","import pandas as pd\n","#import seaborn as sns\n","import visdom\n","from tqdm import tqdm\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.init as init\n","import torch.nn.functional as F\n","import torch.optim as optim\n","from torch.utils.data import Dataset, DataLoader\n","from torchvision.datasets import ImageFolder\n","from torchvision import transforms\n","from torchvision.utils import make_grid, save_image"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"wwBhWGe27cUP","colab_type":"code","colab":{}},"source":["import warnings\n","warnings.simplefilter('ignore')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"DUuLgWM79Vj8","colab_type":"code","outputId":"258a51bf-1173-460b-a940-64e58e332678","executionInfo":{"status":"ok","timestamp":1575179912952,"user_tz":-540,"elapsed":30923,"user":{"displayName":"玉井雄介","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mByoBodcBhoKVJl1rvo_BtNmaEPQgvLc4PF74st=s64","userId":"06916507031009543111"}},"colab":{"base_uri":"https://localhost:8080/","height":146}},"source":["! npm install -g localtunnel\n","get_ipython().system_raw('python3 -m pip install visdom')\n","get_ipython().system_raw('python3 -m visdom.server -port 8097 >> visdomlog.txt 2>&1 &')\n","get_ipython().system_raw('lt --port 8097 >> url.txt 2>&1 &')\n","import time\n","time.sleep(5)\n","! cat url.txt\n","import visdom\n","time.sleep(5)\n","vis = visdom.Visdom(port='8097')\n","print(vis)\n","time.sleep(3)\n","vis.text('testing')\n","! cat visdomlog.txt"],"execution_count":0,"outputs":[{"output_type":"stream","text":["\u001b[K\u001b[?25h/tools/node/bin/lt -> /tools/node/lib/node_modules/localtunnel/bin/lt.js\n","+ [email protected]\n","added 35 packages from 21 contributors in 1.818s\n","your url is: https://good-snail-36.localtunnel.me\n","your url is: https://jolly-skunk-53.localtunnel.me\n","your url is: https://chatty-termite-62.localtunnel.me\n","your url is: https://bitter-cobra-100.localtunnel.me\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"crsove4m7cUd","colab_type":"text"},"source":["## 1. モデル定義\n","### 1.1. 初期化用関数"]},{"cell_type":"markdown","metadata":{"id":"iFxF1Gqh7cUg","colab_type":"text"},"source":["#### (1). kaiming_normal\n","- He(2015)の正規分布に従ってNN全体を初期化する。\n","  - Heの正規分布については下記参照  \n","  <<https://nykergoto.hatenablog.jp/entry/2017/10/15/%E3%83%8B%E3%83%A5%E3%83%BC%E3%83%A9%E3%83%AB%E3%83%8D%E3%83%83%E3%83%88%E3%81%AB%E3%81%8A%E3%81%91%E3%82%8B%E5%A4%89%E6%95%B0%E3%81%AE%E5%88%9D%E6%9C%9F%E5%8C%96%E3%81%AB%E3%81%A4%E3%81%84%E3%81%A6>>\n","- nn.Linear層とnn.Conv2d層に関してはHeの正規分布で初期化し、バイアス項は0を埋めている。\n","- nn.BatchNorm1d系に関しては、値を全て1で埋めている。"]},{"cell_type":"code","metadata":{"id":"MqAi_x0J7cUl","colab_type":"code","colab":{}},"source":["def kaiming_init(m):\n","    if isinstance(m, (nn.Linear, nn.Conv2d)):\n","        init.kaiming_normal_(m.weight)\n","        if m.bias is not None:\n","            m.bias.data.fill_(0)\n","    elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):\n","        m.weight.data.fill_(1)\n","        if m.bias is not None:\n","            m.bias.data.fill_(0)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"p8XphRbP7cUw","colab_type":"text"},"source":["#### (2). kaiming_normal\n","- 決め打ちの正規分布で正規化する。"]},{"cell_type":"code","metadata":{"id":"lO6ydDo77cUz","colab_type":"code","colab":{}},"source":["def normal_init(m):\n","    if isinstance(m, (nn.Linear, nn.Conv2d)):\n","        init.normal_(m.weight, 0, 0.02)\n","        if m.bias is not None:\n","            m.bias.data.fill_(0)\n","    elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):\n","        m.weight.data.fill_(1)\n","        if m.bias is not None:\n","            m.bias.data.fill_(0)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"zcfp6gQE7cU_","colab_type":"text"},"source":["### 1.2. Discriminator\n","- FactorVAE後段に置くDiscriminatorを定義"]},{"cell_type":"code","metadata":{"id":"VxV-ZweO7cVC","colab_type":"code","colab":{}},"source":["class Discriminator(nn.Module):\n","    def __init__(self, z_dim):\n","        super(Discriminator, self).__init__()\n","        self.z_dim = z_dim\n","        self.net = nn.Sequential(\n","            nn.Linear(z_dim, 1000),\n","            nn.LeakyReLU(0.2, True),\n","            nn.Linear(1000, 1000),\n","            nn.LeakyReLU(0.2, True),\n","            nn.Linear(1000, 1000),\n","            nn.LeakyReLU(0.2, True),\n","            nn.Linear(1000, 1000),\n","            nn.LeakyReLU(0.2, True),\n","            nn.Linear(1000, 1000),\n","            nn.LeakyReLU(0.2, True),\n","            nn.Linear(1000, 2),\n","        )\n","        self.weight_init()\n","\n","    def weight_init(self, mode='normal'):\n","        if mode == 'kaiming':\n","            initializer = kaiming_init\n","        elif mode == 'normal':\n","            initializer = normal_init\n","\n","        for block in self._modules:\n","            for m in self._modules[block]:\n","                initializer(m)\n","\n","    def forward(self, z):\n","        return self.net(z).squeeze()"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"gSqLghRw7cVL","colab_type":"text"},"source":["### 1.3. FactorVAE\n","#### (1). 2Dデータ用"]},{"cell_type":"code","metadata":{"id":"-GrPTGYf7cVN","colab_type":"code","colab":{}},"source":["class FactorVAE1(nn.Module):\n","    \"\"\"Encoder and Decoder architecture for 2D Shapes data.\"\"\"\n","    def __init__(self, z_dim=10):\n","        super(FactorVAE1, self).__init__()\n","        self.z_dim = z_dim\n","        self.encode = nn.Sequential(\n","            nn.Conv2d(1, 32, 4, 2, 1),\n","            nn.ReLU(True),\n","            nn.Conv2d(32, 32, 4, 2, 1),\n","            nn.ReLU(True),\n","            nn.Conv2d(32, 64, 4, 2, 1),\n","            nn.ReLU(True),\n","            nn.Conv2d(64, 64, 4, 2, 1),\n","            nn.ReLU(True),\n","            nn.Conv2d(64, 128, 4, 1),\n","            nn.ReLU(True),\n","            nn.Conv2d(128, 2*z_dim, 1)\n","        )\n","        self.decode = nn.Sequential(\n","            nn.Conv2d(z_dim, 128, 1),\n","            nn.ReLU(True),\n","            nn.ConvTranspose2d(128, 64, 4),\n","            nn.ReLU(True),\n","            nn.ConvTranspose2d(64, 64, 4, 2, 1),\n","            nn.ReLU(True),\n","            nn.ConvTranspose2d(64, 32, 4, 2, 1),\n","            nn.ReLU(True),\n","            nn.ConvTranspose2d(32, 32, 4, 2, 1),\n","            nn.ReLU(True),\n","            nn.ConvTranspose2d(32, 1, 4, 2, 1),\n","        )\n","        self.weight_init()\n","\n","    def weight_init(self, mode='normal'):\n","        if mode == 'kaiming':\n","            initializer = kaiming_init\n","        elif mode == 'normal':\n","            initializer = normal_init\n","\n","        for block in self._modules:\n","            for m in self._modules[block]:\n","                initializer(m)\n","\n","    def reparametrize(self, mu, logvar):\n","        std = logvar.mul(0.5).exp_()\n","        eps = std.data.new(std.size()).normal_()\n","        return eps.mul(std).add_(mu)\n","\n","    def forward(self, x, no_dec=False):\n","        stats = self.encode(x)\n","        mu = stats[:, :self.z_dim]\n","        logvar = stats[:, self.z_dim:]\n","        z = self.reparametrize(mu, logvar)\n","\n","        if no_dec:\n","            return z.squeeze()\n","        else:\n","            x_recon = self.decode(z).view(x.size())\n","            return x_recon, mu, logvar, z.squeeze()"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"0M7dnz0d7cVW","colab_type":"text"},"source":["## 2. ops\n","- loss function + alpha."]},{"cell_type":"markdown","metadata":{"id":"EFQyhDwQ7cVY","colab_type":"text"},"source":["#### 再構成誤差\n","- sigmoidとBCEを一つにまとめたBCE_with_logits関数を使っている。こちらの方が数値的に安定するとのこと。"]},{"cell_type":"code","metadata":{"id":"d0NjrbZE7cVZ","colab_type":"code","colab":{}},"source":["def recon_loss(x, x_recon):\n","    n = x.size(0)\n","    loss = F.binary_cross_entropy_with_logits(x_recon, x, size_average=False).div(n)\n","    return loss"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"-Q9vlW-d7cVh","colab_type":"text"},"source":["#### KLダイバージェンス\n","- 探せば実装もありそうだが、VAE専用の関数を定義している。"]},{"cell_type":"code","metadata":{"id":"oYyj9m9o7cVi","colab_type":"code","colab":{}},"source":["def kl_divergence(mu, logvar):\n","    kld = -0.5*(1+logvar-mu**2-logvar.exp()).sum(1).mean()\n","    return kld"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"5Esno0an7cVq","colab_type":"text"},"source":["#### zの入れ替え\n","- FactorVAEに特有の操作を実装している。\n","  - randpermはランダムな順列を返す関数。この結果をindexとすることで、zの各次元を入れ替えている。"]},{"cell_type":"code","metadata":{"id":"1ZiZkOpo7cVs","colab_type":"code","colab":{}},"source":["def permute_dims(z):\n","    assert z.dim() == 2\n","\n","    B, _ = z.size()\n","    perm_z = []\n","    for z_j in z.split(1, 1):\n","        perm = torch.randperm(B).to(z.device)\n","        perm_z_j = z_j[perm]\n","        perm_z.append(perm_z_j)\n","\n","    return torch.cat(perm_z, 1)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"KZw54Igs7cVy","colab_type":"text"},"source":["## 3. dataset\n","- PyTorch用のカスタムDataSet & DataLoaderを作成する。"]},{"cell_type":"markdown","metadata":{"id":"Qw40nfhA7cV0","colab_type":"text"},"source":["### 3.1. データセット読み込み\n","- PyTorchのImageFolderを継承して、データセット作成用クラスを作る。\n","  - ImegeFolder : ストレージ内の画像からデータセットを作成するtorchvision.datasetsのクラス"]},{"cell_type":"code","metadata":{"id":"i4iNBbRP7cV3","colab_type":"code","colab":{}},"source":["class CustomImageFolder(ImageFolder):\n","    def __init__(self, root, transform=None):\n","        super(CustomImageFolder, self).__init__(root, transform)\n","        self.indices = range(len(self))\n","\n","    def __getitem__(self, index1):\n","        index2 = random.choice(self.indices)\n","\n","        path1 = self.imgs[index1][0]\n","        path2 = self.imgs[index2][0]\n","        img1 = self.loader(path1)\n","        img2 = self.loader(path2)\n","        if self.transform is not None:\n","            img1 = self.transform(img1)\n","            img2 = self.transform(img2)\n","\n","        return img1, img2"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"MzYEXUTg7cV9","colab_type":"text"},"source":["### 3.2. データセット作成\n","- `torch.util.data.Dataset`を継承し、カスタムデータセットを作るクラスを定義。"]},{"cell_type":"code","metadata":{"id":"7Mpxoid37cV-","colab_type":"code","colab":{}},"source":["class CustomTensorDataset(Dataset):\n","    def __init__(self, data_tensor, transform=None):\n","        self.data_tensor = data_tensor\n","        self.transform = transform\n","        self.indices = range(len(self))\n","\n","    def __getitem__(self, index1):\n","        index2 = random.choice(self.indices)\n","\n","        img1 = self.data_tensor[index1]\n","        img2 = self.data_tensor[index2]\n","        if self.transform is not None:\n","            img1 = self.transform(img1)\n","            img2 = self.transform(img2)\n","\n","        return img1, img2\n","\n","    def __len__(self):\n","        return self.data_tensor.size(0)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"47vY56_77cWE","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"6kQ0x_w57cWK","colab_type":"text"},"source":["### 3.3. dataloader作成"]},{"cell_type":"code","metadata":{"id":"rCd5AoRO7cWN","colab_type":"code","colab":{}},"source":["def return_data(args):\n","    name = args.dataset\n","    dset_dir = args.dset_dir\n","    batch_size = args.batch_size\n","    num_workers = args.num_workers\n","    image_size = args.image_size\n","    assert image_size == 64, 'currently only image size of 64 is supported'\n","\n","    transform = transforms.Compose([\n","        transforms.Resize((image_size, image_size)),\n","        transforms.ToTensor(),])\n","\n","    if name.lower() == 'celeba':\n","        root = os.path.join(dset_dir, 'CelebA')\n","        train_kwargs = {'root':root, 'transform':transform}\n","        dset = CustomImageFolder\n","    elif name.lower() == '3dchairs':\n","        root = os.path.join(dset_dir, '3DChairs')\n","        train_kwargs = {'root':root, 'transform':transform}\n","        dset = CustomImageFolder\n","    elif name.lower() == 'dsprites':\n","        root = os.path.join(dset_dir, 'dsprites-dataset/dsprites_ndarray_co1sh3sc6or40x32y32_64x64.npz')\n","        data = np.load(root, encoding='latin1')\n","        data = torch.from_numpy(data['imgs']).unsqueeze(1).float()\n","        train_kwargs = {'data_tensor':data}\n","        dset = CustomTensorDataset\n","    else:\n","        raise NotImplementedError\n","\n","\n","    train_data = dset(**train_kwargs)\n","    train_loader = DataLoader(train_data,\n","                              batch_size=batch_size,\n","                              shuffle=True,\n","                              num_workers=num_workers,\n","                              pin_memory=True,\n","                              drop_last=True)\n","\n","    data_loader = train_loader\n","    return data_loader"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"LWX8DH1e7cWT","colab_type":"text"},"source":["## 4. utils"]},{"cell_type":"markdown","metadata":{"id":"92KcSg8Z7cWT","colab_type":"text"},"source":["#### 2の冪乗か否かを判定"]},{"cell_type":"code","metadata":{"id":"zWVuYVgb7cWV","colab_type":"code","colab":{}},"source":["def is_power_of_2(num):\n","    return ((num & (num - 1)) == 0) and num != 0"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"fEeOjAWd7cWb","colab_type":"text"},"source":["#### 文字引数をTrue/Falseに変換"]},{"cell_type":"code","metadata":{"id":"1KjYTn3B7cWd","colab_type":"code","colab":{}},"source":["def str2bool(v):\n","    # codes from : https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse\n","    if v.lower() in ('yes', 'true', 't', 'y', '1'):\n","        return True\n","    elif v.lower() in ('no', 'false', 'f', 'n', '0'):\n","        return False"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"KAs7YtWx7cWh","colab_type":"text"},"source":["#### GIF作成"]},{"cell_type":"code","metadata":{"id":"by0jUWAG7cWj","colab_type":"code","colab":{}},"source":["def grid2gif(image_str, output_gif, delay=100):\n","    \"\"\"Make GIF from images.\n","    code from:\n","        https://stackoverflow.com/questions/753190/programmatically-generate-video-or-animated-gif-in-python/34555939#34555939\n","    \"\"\"\n","    str1 = 'convert -delay '+str(delay)+' -loop 0 ' + image_str  + ' ' + output_gif\n","    subprocess.call(str1, shell=True)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"yE0mFOcA7cWo","colab_type":"text"},"source":["#### ディレクトリ作成"]},{"cell_type":"code","metadata":{"id":"8eaEmmDg7cWq","colab_type":"code","colab":{}},"source":["def mkdirs(path):\n","    if not os.path.exists(path):\n","        os.makedirs(path)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"6aUPcFLR7cWv","colab_type":"text"},"source":["#### データの統合"]},{"cell_type":"code","metadata":{"id":"MJSk736q7cWx","colab_type":"code","colab":{}},"source":["class DataGather(object):\n","    def __init__(self, *args):\n","        self.keys = args\n","        self.data = self.get_empty_data_dict()\n","\n","    def get_empty_data_dict(self):\n","        return {arg:[] for arg in self.keys}\n","\n","    def insert(self, **kwargs):\n","        for key in kwargs:\n","            self.data[key].append(kwargs[key])\n","\n","    def flush(self):\n","        self.data = self.get_empty_data_dict()"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"fbFw4wHj7cW0","colab_type":"text"},"source":["## 5. solver\n","- メインの学習&結果の保存・可視化を行う。"]},{"cell_type":"code","metadata":{"id":"Cd2soIuG7cW3","colab_type":"code","colab":{}},"source":["class Solver:\n","    def __init__(self, args):\n","        # Misc\n","        use_cuda = args.cuda and torch.cuda.is_available()\n","        self.device = 'cuda' if use_cuda else 'cpu'\n","        self.name = args.name\n","        self.max_iter = int(args.max_iter)\n","        self.print_iter = args.print_iter\n","        self.global_iter = 0\n","        self.pbar = tqdm(total=self.max_iter)\n","\n","        # Data\n","        self.dset_dir = args.dset_dir\n","        self.dataset = args.dataset\n","        self.batch_size = args.batch_size\n","        self.data_loader = return_data(args)\n","\n","        # Networks & Optimizers\n","        self.z_dim = args.z_dim\n","        self.gamma = args.gamma\n","\n","        self.lr_VAE = args.lr_VAE\n","        self.beta1_VAE = args.beta1_VAE\n","        self.beta2_VAE = args.beta2_VAE\n","\n","        self.lr_D = args.lr_D\n","        self.beta1_D = args.beta1_D\n","        self.beta2_D = args.beta2_D\n","\n","        if args.dataset == 'dsprites':\n","            self.VAE = FactorVAE1(self.z_dim).to(self.device)\n","            self.nc = 1\n","        else:\n","            self.VAE = FactorVAE2(self.z_dim).to(self.device)\n","            self.nc = 3\n","        self.optim_VAE = optim.Adam(self.VAE.parameters(), lr=self.lr_VAE,\n","                                    betas=(self.beta1_VAE, self.beta2_VAE))\n","\n","        self.D = Discriminator(self.z_dim).to(self.device)\n","        self.optim_D = optim.Adam(self.D.parameters(), lr=self.lr_D,\n","                                  betas=(self.beta1_D, self.beta2_D))\n","\n","        self.nets = [self.VAE, self.D]\n","\n","        # Visdom\n","        self.viz_on = args.viz_on\n","        self.win_id = dict(D_z='win_D_z', recon='win_recon', kld='win_kld', acc='win_acc')\n","        self.line_gather = DataGather('iter', 'soft_D_z', 'soft_D_z_pperm', 'recon', 'kld', 'acc')\n","        self.image_gather = DataGather('true', 'recon')\n","        if self.viz_on:\n","            self.viz_port = args.viz_port\n","            self.viz = visdom.Visdom(port=self.viz_port)\n","            self.viz_ll_iter = args.viz_ll_iter\n","            self.viz_la_iter = args.viz_la_iter\n","            self.viz_ra_iter = args.viz_ra_iter\n","            self.viz_ta_iter = args.viz_ta_iter\n","            if not self.viz.win_exists(env=self.name+'/lines', win=self.win_id['D_z']):\n","                self.viz_init()\n","\n","        # Checkpoint\n","        self.ckpt_dir = os.path.join(args.ckpt_dir, args.name)\n","        self.ckpt_save_iter = args.ckpt_save_iter\n","        mkdirs(self.ckpt_dir)\n","        if args.ckpt_load:\n","            self.load_checkpoint(args.ckpt_load)\n","\n","        # Output(latent traverse GIF)\n","        self.output_dir = os.path.join(args.output_dir, args.name)\n","        self.output_save = args.output_save\n","        mkdirs(self.output_dir)\n","\n","    def train(self):\n","        self.net_mode(train=True)\n","\n","        ones = torch.ones(self.batch_size, dtype=torch.long, device=self.device)\n","        zeros = torch.zeros(self.batch_size, dtype=torch.long, device=self.device)\n","\n","        out = False\n","        while not out:\n","            for x_true1, x_true2 in self.data_loader:\n","                self.global_iter += 1\n","                self.pbar.update(1)\n","\n","                x_true1 = x_true1.to(self.device)\n","                x_recon, mu, logvar, z = self.VAE(x_true1)\n","                vae_recon_loss = recon_loss(x_true1, x_recon)\n","                vae_kld = kl_divergence(mu, logvar)\n","\n","                D_z = self.D(z)\n","                vae_tc_loss = (D_z[:, :1] - D_z[:, 1:]).mean()\n","\n","                vae_loss = vae_recon_loss + vae_kld + self.gamma*vae_tc_loss\n","\n","                self.optim_VAE.zero_grad()\n","                vae_loss.backward(retain_graph=True)\n","                self.optim_VAE.step()\n","\n","                x_true2 = x_true2.to(self.device)\n","                z_prime = self.VAE(x_true2, no_dec=True)\n","                z_pperm = permute_dims(z_prime).detach()\n","                D_z_pperm = self.D(z_pperm)\n","                D_tc_loss = 0.5*(F.cross_entropy(D_z, zeros) + F.cross_entropy(D_z_pperm, ones))\n","\n","                self.optim_D.zero_grad()\n","                D_tc_loss.backward()\n","                self.optim_D.step()\n","\n","                if self.global_iter%self.print_iter == 0:\n","                    self.pbar.write('[{}] vae_recon_loss:{:.3f} vae_kld:{:.3f} vae_tc_loss:{:.3f} D_tc_loss:{:.3f}'.format(\n","                        self.global_iter, vae_recon_loss.item(), vae_kld.item(), vae_tc_loss.item(), D_tc_loss.item()))\n","\n","                if self.global_iter%self.ckpt_save_iter == 0:\n","                    self.save_checkpoint(self.global_iter)\n","\n","                if self.viz_on and (self.global_iter%self.viz_ll_iter == 0):\n","                    soft_D_z = F.softmax(D_z, 1)[:, :1].detach()\n","                    soft_D_z_pperm = F.softmax(D_z_pperm, 1)[:, :1].detach()\n","                    D_acc = ((soft_D_z >= 0.5).sum() + (soft_D_z_pperm < 0.5).sum()).float()\n","                    D_acc /= 2*self.batch_size\n","                    self.line_gather.insert(iter=self.global_iter,\n","                                            soft_D_z=soft_D_z.mean().item(),\n","                                            soft_D_z_pperm=soft_D_z_pperm.mean().item(),\n","                                            recon=vae_recon_loss.item(),\n","                                            kld=vae_kld.item(),\n","                                            acc=D_acc.item())\n","\n","                if self.viz_on and (self.global_iter%self.viz_la_iter == 0):\n","                    self.visualize_line()\n","                    self.line_gather.flush()\n","\n","                if self.viz_on and (self.global_iter%self.viz_ra_iter == 0):\n","                    self.image_gather.insert(true=x_true1.data.cpu(),\n","                                             recon=F.sigmoid(x_recon).data.cpu())\n","                    self.visualize_recon()\n","                    self.image_gather.flush()\n","\n","                if self.viz_on and (self.global_iter%self.viz_ta_iter == 0):\n","                    if self.dataset.lower() == '3dchairs':\n","                        self.visualize_traverse(limit=2, inter=0.5)\n","                    else:\n","                        self.visualize_traverse(limit=3, inter=2/3)\n","\n","                if self.global_iter >= self.max_iter:\n","                    out = True\n","                    break\n","\n","        self.pbar.write(\"[Training Finished]\")\n","        self.pbar.close()\n","\n","    def visualize_recon(self):\n","        data = self.image_gather.data\n","        true_image = data['true'][0]\n","        recon_image = data['recon'][0]\n","\n","        true_image = make_grid(true_image)\n","        recon_image = make_grid(recon_image)\n","        sample = torch.stack([true_image, recon_image], dim=0)\n","        self.viz.images(sample, env=self.name+'/recon_image',\n","                        opts=dict(title=str(self.global_iter)))\n","\n","    def visualize_line(self):\n","        data = self.line_gather.data\n","        iters = torch.Tensor(data['iter'])\n","        recon = torch.Tensor(data['recon'])\n","        kld = torch.Tensor(data['kld'])\n","        D_acc = torch.Tensor(data['acc'])\n","        soft_D_z = torch.Tensor(data['soft_D_z'])\n","        soft_D_z_pperm = torch.Tensor(data['soft_D_z_pperm'])\n","        soft_D_zs = torch.stack([soft_D_z, soft_D_z_pperm], -1)\n","\n","        self.viz.line(X=iters,\n","                      Y=soft_D_zs,\n","                      env=self.name+'/lines',\n","                      win=self.win_id['D_z'],\n","                      update='append',\n","                      opts=dict(\n","                        xlabel='iteration',\n","                        ylabel='D(.)',\n","                        legend=['D(z)', 'D(z_perm)']))\n","        self.viz.line(X=iters,\n","                      Y=recon,\n","                      env=self.name+'/lines',\n","                      win=self.win_id['recon'],\n","                      update='append',\n","                      opts=dict(\n","                        xlabel='iteration',\n","                        ylabel='reconstruction loss',))\n","        self.viz.line(X=iters,\n","                      Y=D_acc,\n","                      env=self.name+'/lines',\n","                      win=self.win_id['acc'],\n","                      update='append',\n","                      opts=dict(\n","                        xlabel='iteration',\n","                        ylabel='discriminator accuracy',))\n","        self.viz.line(X=iters,\n","                      Y=kld,\n","                      env=self.name+'/lines',\n","                      win=self.win_id['kld'],\n","                      update='append',\n","                      opts=dict(\n","                        xlabel='iteration',\n","                        ylabel='kl divergence',))\n","\n","    def visualize_traverse(self, limit=3, inter=2/3, loc=-1):\n","        self.net_mode(train=False)\n","\n","        decoder = self.VAE.decode\n","        encoder = self.VAE.encode\n","        interpolation = torch.arange(-limit, limit+0.1, inter)\n","\n","        random_img = self.data_loader.dataset.__getitem__(0)[1]\n","        random_img = random_img.to(self.device).unsqueeze(0)\n","        random_img_z = encoder(random_img)[:, :self.z_dim]\n","\n","        if self.dataset.lower() == 'dsprites':\n","            fixed_idx1 = 87040 # square\n","            fixed_idx2 = 332800 # ellipse\n","            fixed_idx3 = 578560 # heart\n","\n","            fixed_img1 = self.data_loader.dataset.__getitem__(fixed_idx1)[0]\n","            fixed_img1 = fixed_img1.to(self.device).unsqueeze(0)\n","            fixed_img_z1 = encoder(fixed_img1)[:, :self.z_dim]\n","\n","            fixed_img2 = self.data_loader.dataset.__getitem__(fixed_idx2)[0]\n","            fixed_img2 = fixed_img2.to(self.device).unsqueeze(0)\n","            fixed_img_z2 = encoder(fixed_img2)[:, :self.z_dim]\n","\n","            fixed_img3 = self.data_loader.dataset.__getitem__(fixed_idx3)[0]\n","            fixed_img3 = fixed_img3.to(self.device).unsqueeze(0)\n","            fixed_img_z3 = encoder(fixed_img3)[:, :self.z_dim]\n","\n","            Z = {'fixed_square':fixed_img_z1, 'fixed_ellipse':fixed_img_z2,\n","                 'fixed_heart':fixed_img_z3, 'random_img':random_img_z}\n","\n","        elif self.dataset.lower() == 'celeba':\n","            fixed_idx1 = 191281 # 'CelebA/img_align_celeba/191282.jpg'\n","            fixed_idx2 = 143307 # 'CelebA/img_align_celeba/143308.jpg'\n","            fixed_idx3 = 101535 # 'CelebA/img_align_celeba/101536.jpg'\n","            fixed_idx4 = 70059  # 'CelebA/img_align_celeba/070060.jpg'\n","\n","            fixed_img1 = self.data_loader.dataset.__getitem__(fixed_idx1)[0]\n","            fixed_img1 = fixed_img1.to(self.device).unsqueeze(0)\n","            fixed_img_z1 = encoder(fixed_img1)[:, :self.z_dim]\n","\n","            fixed_img2 = self.data_loader.dataset.__getitem__(fixed_idx2)[0]\n","            fixed_img2 = fixed_img2.to(self.device).unsqueeze(0)\n","            fixed_img_z2 = encoder(fixed_img2)[:, :self.z_dim]\n","\n","            fixed_img3 = self.data_loader.dataset.__getitem__(fixed_idx3)[0]\n","            fixed_img3 = fixed_img3.to(self.device).unsqueeze(0)\n","            fixed_img_z3 = encoder(fixed_img3)[:, :self.z_dim]\n","\n","            fixed_img4 = self.data_loader.dataset.__getitem__(fixed_idx4)[0]\n","            fixed_img4 = fixed_img4.to(self.device).unsqueeze(0)\n","            fixed_img_z4 = encoder(fixed_img4)[:, :self.z_dim]\n","\n","            Z = {'fixed_1':fixed_img_z1, 'fixed_2':fixed_img_z2,\n","                 'fixed_3':fixed_img_z3, 'fixed_4':fixed_img_z4,\n","                 'random':random_img_z}\n","\n","        elif self.dataset.lower() == '3dchairs':\n","            fixed_idx1 = 40919 # 3DChairs/images/4682_image_052_p030_t232_r096.png\n","            fixed_idx2 = 5172  # 3DChairs/images/14657_image_020_p020_t232_r096.png\n","            fixed_idx3 = 22330 # 3DChairs/images/30099_image_052_p030_t232_r096.png\n","\n","            fixed_img1 = self.data_loader.dataset.__getitem__(fixed_idx1)[0]\n","            fixed_img1 = fixed_img1.to(self.device).unsqueeze(0)\n","            fixed_img_z1 = encoder(fixed_img1)[:, :self.z_dim]\n","\n","            fixed_img2 = self.data_loader.dataset.__getitem__(fixed_idx2)[0]\n","            fixed_img2 = fixed_img2.to(self.device).unsqueeze(0)\n","            fixed_img_z2 = encoder(fixed_img2)[:, :self.z_dim]\n","\n","            fixed_img3 = self.data_loader.dataset.__getitem__(fixed_idx3)[0]\n","            fixed_img3 = fixed_img3.to(self.device).unsqueeze(0)\n","            fixed_img_z3 = encoder(fixed_img3)[:, :self.z_dim]\n","\n","            Z = {'fixed_1':fixed_img_z1, 'fixed_2':fixed_img_z2,\n","                 'fixed_3':fixed_img_z3, 'random':random_img_z}\n","        else:\n","            fixed_idx = 0\n","            fixed_img = self.data_loader.dataset.__getitem__(fixed_idx)[0]\n","            fixed_img = fixed_img.to(self.device).unsqueeze(0)\n","            fixed_img_z = encoder(fixed_img)[:, :self.z_dim]\n","\n","            random_z = torch.rand(1, self.z_dim, 1, 1, device=self.device)\n","\n","            Z = {'fixed_img':fixed_img_z, 'random_img':random_img_z, 'random_z':random_z}\n","\n","        gifs = []\n","        for key in Z:\n","            z_ori = Z[key]\n","            samples = []\n","            for row in range(self.z_dim):\n","                if loc != -1 and row != loc:\n","                    continue\n","                z = z_ori.clone()\n","                for val in interpolation:\n","                    z[:, row] = val\n","                    sample = F.sigmoid(decoder(z)).data\n","                    samples.append(sample)\n","                    gifs.append(sample)\n","            samples = torch.cat(samples, dim=0).cpu()\n","            title = '{}_latent_traversal(iter:{})'.format(key, self.global_iter)\n","            self.viz.images(samples, env=self.name+'/traverse',\n","                            opts=dict(title=title), nrow=len(interpolation))\n","\n","        if self.output_save:\n","            output_dir = os.path.join(self.output_dir, str(self.global_iter))\n","            mkdirs(output_dir)\n","            gifs = torch.cat(gifs)\n","            gifs = gifs.view(len(Z), self.z_dim, len(interpolation), self.nc, 64, 64).transpose(1, 2)\n","            for i, key in enumerate(Z.keys()):\n","                for j, val in enumerate(interpolation):\n","                    save_image(tensor=gifs[i][j].cpu(),\n","                               filename=os.path.join(output_dir, '{}_{}.jpg'.format(key, j)),\n","                               nrow=self.z_dim, pad_value=1)\n","\n","                grid2gif(str(os.path.join(output_dir, key+'*.jpg')),\n","                         str(os.path.join(output_dir, key+'.gif')), delay=10)\n","\n","        self.net_mode(train=True)\n","\n","    def viz_init(self):\n","        zero_init = torch.zeros([1])\n","        self.viz.line(X=zero_init,\n","                      Y=torch.stack([zero_init, zero_init], -1),\n","                      env=self.name+'/lines',\n","                      win=self.win_id['D_z'],\n","                      opts=dict(\n","                        xlabel='iteration',\n","                        ylabel='D(.)',\n","                        legend=['D(z)', 'D(z_perm)']))\n","        self.viz.line(X=zero_init,\n","                      Y=zero_init,\n","                      env=self.name+'/lines',\n","                      win=self.win_id['recon'],\n","                      opts=dict(\n","                        xlabel='iteration',\n","                        ylabel='reconstruction loss',))\n","        self.viz.line(X=zero_init,\n","                      Y=zero_init,\n","                      env=self.name+'/lines',\n","                      win=self.win_id['acc'],\n","                      opts=dict(\n","                        xlabel='iteration',\n","                        ylabel='discriminator accuracy',))\n","        self.viz.line(X=zero_init,\n","                      Y=zero_init,\n","                      env=self.name+'/lines',\n","                      win=self.win_id['kld'],\n","                      opts=dict(\n","                        xlabel='iteration',\n","                        ylabel='kl divergence',))\n","\n","    def net_mode(self, train):\n","        if not isinstance(train, bool):\n","            raise ValueError('Only bool type is supported. True|False')\n","\n","        for net in self.nets:\n","            if train:\n","                net.train()\n","            else:\n","                net.eval()\n","\n","    def save_checkpoint(self, ckptname='last', verbose=True):\n","        model_states = {'D':self.D.state_dict(),\n","                        'VAE':self.VAE.state_dict()}\n","        optim_states = {'optim_D':self.optim_D.state_dict(),\n","                        'optim_VAE':self.optim_VAE.state_dict()}\n","        states = {'iter':self.global_iter,\n","                  'model_states':model_states,\n","                  'optim_states':optim_states}\n","\n","        filepath = os.path.join(self.ckpt_dir, str(ckptname))\n","        with open(filepath, 'wb+') as f:\n","            torch.save(states, f)\n","        if verbose:\n","            self.pbar.write(\"=> saved checkpoint '{}' (iter {})\".format(filepath, self.global_iter))\n","\n","    def load_checkpoint(self, ckptname='last', verbose=True):\n","        if ckptname == 'last':\n","            ckpts = os.listdir(self.ckpt_dir)\n","            if not ckpts:\n","                if verbose:\n","                    self.pbar.write(\"=> no checkpoint found\")\n","                return\n","\n","            ckpts = [int(ckpt) for ckpt in ckpts]\n","            ckpts.sort(reverse=True)\n","            ckptname = str(ckpts[0])\n","\n","        filepath = os.path.join(self.ckpt_dir, ckptname)\n","        if os.path.isfile(filepath):\n","            with open(filepath, 'rb') as f:\n","                checkpoint = torch.load(f)\n","\n","            self.global_iter = checkpoint['iter']\n","            self.VAE.load_state_dict(checkpoint['model_states']['VAE'])\n","            self.D.load_state_dict(checkpoint['model_states']['D'])\n","            self.optim_VAE.load_state_dict(checkpoint['optim_states']['optim_VAE'])\n","            self.optim_D.load_state_dict(checkpoint['optim_states']['optim_D'])\n","            self.pbar.update(self.global_iter)\n","            if verbose:\n","                self.pbar.write(\"=> loaded checkpoint '{} (iter {})'\".format(filepath, self.global_iter))\n","        else:\n","            if verbose:\n","                self.pbar.write(\"=> no checkpoint found at '{}'\".format(filepath))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"DfiHPuyz7cW7","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"GrdVkg6-7cW_","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"UYDNf68R7cXG","colab_type":"text"},"source":["## 6. main"]},{"cell_type":"markdown","metadata":{"id":"FKNzGUsw7cXJ","colab_type":"text"},"source":["#### cudnn"]},{"cell_type":"code","metadata":{"id":"OFiL53Jv7cXL","colab_type":"code","colab":{}},"source":["torch.backends.cudnn.enabled"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"cZ4bGKJc7cXT","colab_type":"code","colab":{}},"source":["torch.backends.cudnn.benchmark"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"HWcjxvYf7cXX","colab_type":"text"},"source":["#### seed"]},{"cell_type":"code","metadata":{"id":"VuvoZtpq7cXZ","colab_type":"code","colab":{}},"source":["init_seed = 1\n","torch.manual_seed(init_seed)\n","torch.cuda.manual_seed(init_seed)\n","np.random.seed(init_seed)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"aduUvTGd7cXc","colab_type":"text"},"source":["#### parser"]},{"cell_type":"code","metadata":{"id":"JgT77NLw7cXd","colab_type":"code","colab":{}},"source":["parser = argparse.ArgumentParser(description='Factor-VAE')\n","\n","parser.add_argument('--name', default='main', type=str, help='name of the experiment')\n","parser.add_argument('--cuda', default=True, type=str2bool, help='enable cuda')\n","parser.add_argument('--max_iter', default=1e6, type=float, help='maximum training iteration')\n","parser.add_argument('--batch_size', default=64, type=int, help='batch size')\n","\n","parser.add_argument('--z_dim', default=10, type=int, help='dimension of the representation z')\n","parser.add_argument('--gamma', default=6.4, type=float, help='gamma hyperparameter')\n","parser.add_argument('--lr_VAE', default=1e-4, type=float, help='learning rate of the VAE')\n","parser.add_argument('--beta1_VAE', default=0.9, type=float, help='beta1 parameter of the Adam optimizer for the VAE')\n","parser.add_argument('--beta2_VAE', default=0.999, type=float, help='beta2 parameter of the Adam optimizer for the VAE')\n","parser.add_argument('--lr_D', default=1e-4, type=float, help='learning rate of the discriminator')\n","parser.add_argument('--beta1_D', default=0.5, type=float, help='beta1 parameter of the Adam optimizer for the discriminator')\n","parser.add_argument('--beta2_D', default=0.9, type=float, help='beta2 parameter of the Adam optimizer for the discriminator')\n","\n","parser.add_argument('--dset_dir', default='data', type=str, help='dataset directory')\n","parser.add_argument('--dataset', default='CelebA', type=str, help='dataset name')\n","parser.add_argument('--image_size', default=64, type=int, help='image size. now only (64,64) is supported')\n","parser.add_argument('--num_workers', default=2, type=int, help='dataloader num_workers')\n","\n","parser.add_argument('--viz_on', default=True, type=str2bool, help='enable visdom visualization')\n","parser.add_argument('--viz_port', default=8097, type=int, help='visdom port number')\n","parser.add_argument('--viz_ll_iter', default=1000, type=int, help='visdom line data logging iter')\n","parser.add_argument('--viz_la_iter', default=5000, type=int, help='visdom line data applying iter')\n","parser.add_argument('--viz_ra_iter', default=10000, type=int, help='visdom recon image applying iter')\n","parser.add_argument('--viz_ta_iter', default=10000, type=int, help='visdom traverse applying iter')\n","\n","parser.add_argument('--print_iter', default=500, type=int, help='print losses iter')\n","\n","parser.add_argument('--ckpt_dir', default='checkpoints', type=str, help='checkpoint directory')\n","parser.add_argument('--ckpt_load', default=None, type=str, help='checkpoint name to load')\n","parser.add_argument('--ckpt_save_iter', default=10000, type=int, help='checkpoint save iter')\n","\n","parser.add_argument('--output_dir', default='outputs', type=str, help='output directory')\n","parser.add_argument('--output_save', default=True, type=str2bool, help='whether to save traverse results')\n","\n","args = parser.parse_args(args=['--dataset', 'dsprites'])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"vqdMfyNB7cXi","colab_type":"code","outputId":"dc9aad78-c097-4557-fa7a-912c30bfa5fb","colab":{"base_uri":"https://localhost:8080/"}},"source":["net = Solver(args)\n","net.train()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["  0%|          | 0/1000000 [00:00<?, ?it/s]Setting up a new session...\n","  0%|          | 513/1000000 [00:27<4:37:55, 59.94it/s]"],"name":"stderr"},{"output_type":"stream","text":["[500] vae_recon_loss:578.680 vae_kld:11.289 vae_tc_loss:0.005 D_tc_loss:0.694\n"],"name":"stdout"},{"output_type":"stream","text":["  0%|          | 1008/1000000 [00:35<5:02:49, 54.98it/s]"],"name":"stderr"},{"output_type":"stream","text":["[1000] vae_recon_loss:529.956 vae_kld:7.405 vae_tc_loss:0.014 D_tc_loss:0.694\n"],"name":"stdout"},{"output_type":"stream","text":["  0%|          | 1512/1000000 [00:44<4:49:13, 57.54it/s]"],"name":"stderr"},{"output_type":"stream","text":["[1500] vae_recon_loss:208.961 vae_kld:28.136 vae_tc_loss:2.036 D_tc_loss:0.244\n"],"name":"stdout"},{"output_type":"stream","text":["  0%|          | 2010/1000000 [00:52<4:45:12, 58.32it/s]"],"name":"stderr"},{"output_type":"stream","text":["[2000] vae_recon_loss:149.252 vae_kld:20.223 vae_tc_loss:2.909 D_tc_loss:0.304\n"],"name":"stdout"},{"output_type":"stream","text":["  0%|          | 2512/1000000 [01:01<4:48:09, 57.69it/s]"],"name":"stderr"},{"output_type":"stream","text":["[2500] vae_recon_loss:148.198 vae_kld:14.289 vae_tc_loss:1.428 D_tc_loss:0.425\n"],"name":"stdout"},{"output_type":"stream","text":["  0%|          | 3009/1000000 [01:09<4:48:17, 57.64it/s]"],"name":"stderr"},{"output_type":"stream","text":["[3000] vae_recon_loss:130.892 vae_kld:18.177 vae_tc_loss:1.889 D_tc_loss:0.272\n"],"name":"stdout"},{"output_type":"stream","text":["  0%|          | 3507/1000000 [01:18<4:42:42, 58.75it/s]"],"name":"stderr"},{"output_type":"stream","text":["[3500] vae_recon_loss:134.920 vae_kld:12.536 vae_tc_loss:1.335 D_tc_loss:0.375\n"],"name":"stdout"},{"output_type":"stream","text":["  0%|          | 4012/1000000 [01:26<4:45:05, 58.23it/s]"],"name":"stderr"},{"output_type":"stream","text":["[4000] vae_recon_loss:126.589 vae_kld:17.862 vae_tc_loss:1.443 D_tc_loss:0.314\n"],"name":"stdout"},{"output_type":"stream","text":["  0%|          | 4512/1000000 [01:35<4:50:19, 57.15it/s]"],"name":"stderr"},{"output_type":"stream","text":["[4500] vae_recon_loss:129.483 vae_kld:26.025 vae_tc_loss:1.577 D_tc_loss:0.554\n"],"name":"stdout"},{"output_type":"stream","text":["  1%|          | 5006/1000000 [01:43<4:59:07, 55.44it/s]"],"name":"stderr"},{"output_type":"stream","text":["[5000] vae_recon_loss:111.592 vae_kld:22.255 vae_tc_loss:0.453 D_tc_loss:0.655\n"],"name":"stdout"},{"output_type":"stream","text":["  1%|          | 5508/1000000 [01:52<4:44:59, 58.16it/s]"],"name":"stderr"},{"output_type":"stream","text":["[5500] vae_recon_loss:125.552 vae_kld:14.735 vae_tc_loss:0.058 D_tc_loss:0.597\n"],"name":"stdout"},{"output_type":"stream","text":["  1%|          | 6010/1000000 [02:00<4:35:38, 60.10it/s]"],"name":"stderr"},{"output_type":"stream","text":["[6000] vae_recon_loss:112.986 vae_kld:13.730 vae_tc_loss:0.171 D_tc_loss:0.575\n"],"name":"stdout"},{"output_type":"stream","text":["  1%|          | 6511/1000000 [02:09<4:51:08, 56.87it/s]"],"name":"stderr"},{"output_type":"stream","text":["[6500] vae_recon_loss:117.020 vae_kld:14.736 vae_tc_loss:0.539 D_tc_loss:0.618\n"],"name":"stdout"},{"output_type":"stream","text":["  1%|          | 7012/1000000 [02:17<4:33:46, 60.45it/s]"],"name":"stderr"},{"output_type":"stream","text":["[7000] vae_recon_loss:101.064 vae_kld:12.034 vae_tc_loss:0.280 D_tc_loss:0.613\n"],"name":"stdout"},{"output_type":"stream","text":["  1%|          | 7507/1000000 [02:26<5:02:01, 54.77it/s]"],"name":"stderr"},{"output_type":"stream","text":["[7500] vae_recon_loss:116.972 vae_kld:11.923 vae_tc_loss:0.184 D_tc_loss:0.640\n"],"name":"stdout"},{"output_type":"stream","text":["  1%|          | 8007/1000000 [02:34<4:47:35, 57.49it/s]"],"name":"stderr"},{"output_type":"stream","text":["[8000] vae_recon_loss:104.887 vae_kld:11.755 vae_tc_loss:0.161 D_tc_loss:0.648\n"],"name":"stdout"},{"output_type":"stream","text":["  1%|          | 8513/1000000 [02:43<4:36:12, 59.83it/s]"],"name":"stderr"},{"output_type":"stream","text":["[8500] vae_recon_loss:107.934 vae_kld:11.438 vae_tc_loss:0.199 D_tc_loss:0.619\n"],"name":"stdout"},{"output_type":"stream","text":["  1%|          | 9010/1000000 [02:51<4:36:25, 59.75it/s]"],"name":"stderr"},{"output_type":"stream","text":["[9000] vae_recon_loss:98.573 vae_kld:11.222 vae_tc_loss:0.284 D_tc_loss:0.562\n"],"name":"stdout"},{"output_type":"stream","text":["  1%|          | 9513/1000000 [03:00<4:40:42, 58.81it/s]"],"name":"stderr"},{"output_type":"stream","text":["[9500] vae_recon_loss:103.489 vae_kld:11.272 vae_tc_loss:0.189 D_tc_loss:0.659\n"],"name":"stdout"},{"output_type":"stream","text":["  1%|          | 10000/1000000 [03:08<4:41:01, 58.71it/s]"],"name":"stderr"},{"output_type":"stream","text":["[10000] vae_recon_loss:109.952 vae_kld:11.177 vae_tc_loss:0.184 D_tc_loss:0.661\n"],"name":"stdout"},{"output_type":"stream","text":["  1%|          | 10000/1000000 [03:09<4:41:01, 58.71it/s]"],"name":"stderr"},{"output_type":"stream","text":["=> saved checkpoint 'checkpoints/main/10000' (iter 10000)\n"],"name":"stdout"},{"output_type":"stream","text":["  1%|          | 10507/1000000 [03:27<4:37:47, 59.37it/s]"],"name":"stderr"},{"output_type":"stream","text":["[10500] vae_recon_loss:103.490 vae_kld:10.848 vae_tc_loss:0.139 D_tc_loss:0.658\n"],"name":"stdout"},{"output_type":"stream","text":["  1%|          | 11012/1000000 [03:36<4:40:35, 58.75it/s]"],"name":"stderr"},{"output_type":"stream","text":["[11000] vae_recon_loss:107.130 vae_kld:10.780 vae_tc_loss:0.028 D_tc_loss:0.667\n"],"name":"stdout"},{"output_type":"stream","text":["  1%|          | 11511/1000000 [03:44<4:43:25, 58.13it/s]"],"name":"stderr"},{"output_type":"stream","text":["[11500] vae_recon_loss:100.854 vae_kld:10.630 vae_tc_loss:0.065 D_tc_loss:0.642\n"],"name":"stdout"},{"output_type":"stream","text":["  1%|          | 12010/1000000 [03:54<4:51:07, 56.56it/s]"],"name":"stderr"},{"output_type":"stream","text":["[12000] vae_recon_loss:102.562 vae_kld:10.783 vae_tc_loss:0.147 D_tc_loss:0.684\n"],"name":"stdout"},{"output_type":"stream","text":["  1%|▏         | 12508/1000000 [04:02<4:52:39, 56.24it/s]"],"name":"stderr"},{"output_type":"stream","text":["[12500] vae_recon_loss:114.736 vae_kld:10.997 vae_tc_loss:0.280 D_tc_loss:0.655\n"],"name":"stdout"},{"output_type":"stream","text":["  1%|▏         | 13010/1000000 [04:11<4:40:35, 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59.63it/s]"],"name":"stderr"},{"output_type":"stream","text":["=> saved checkpoint 'checkpoints/main/30000' (iter 30000)\n"],"name":"stdout"},{"output_type":"stream","text":["  3%|▎         | 30508/1000000 [09:27<4:45:29, 56.60it/s]"],"name":"stderr"},{"output_type":"stream","text":["[30500] vae_recon_loss:100.288 vae_kld:10.666 vae_tc_loss:0.126 D_tc_loss:0.633\n"],"name":"stdout"},{"output_type":"stream","text":["  3%|▎         | 31007/1000000 [09:36<4:37:01, 58.30it/s]"],"name":"stderr"},{"output_type":"stream","text":["[31000] vae_recon_loss:95.063 vae_kld:10.743 vae_tc_loss:0.166 D_tc_loss:0.628\n"],"name":"stdout"},{"output_type":"stream","text":["  3%|▎         | 31508/1000000 [09:44<4:44:46, 56.68it/s]"],"name":"stderr"},{"output_type":"stream","text":["[31500] vae_recon_loss:99.083 vae_kld:10.280 vae_tc_loss:0.302 D_tc_loss:0.626\n"],"name":"stdout"},{"output_type":"stream","text":["  3%|▎         | 32009/1000000 [09:52<4:27:33, 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60.28it/s]"],"name":"stderr"},{"output_type":"stream","text":["[220500] vae_recon_loss:78.213 vae_kld:13.589 vae_tc_loss:0.113 D_tc_loss:0.653\n"],"name":"stdout"},{"output_type":"stream","text":[" 22%|██▏       | 221011/1000000 [1:06:59<3:44:23, 57.86it/s]"],"name":"stderr"},{"output_type":"stream","text":["[221000] vae_recon_loss:72.966 vae_kld:13.406 vae_tc_loss:0.090 D_tc_loss:0.690\n"],"name":"stdout"},{"output_type":"stream","text":[" 22%|██▏       | 221508/1000000 [1:07:08<3:46:14, 57.35it/s]"],"name":"stderr"},{"output_type":"stream","text":["[221500] vae_recon_loss:78.591 vae_kld:13.240 vae_tc_loss:0.091 D_tc_loss:0.638\n"],"name":"stdout"},{"output_type":"stream","text":[" 22%|██▏       | 222011/1000000 [1:07:16<3:34:24, 60.48it/s]"],"name":"stderr"},{"output_type":"stream","text":["[222000] vae_recon_loss:78.130 vae_kld:13.773 vae_tc_loss:0.008 D_tc_loss:0.691\n"],"name":"stdout"},{"output_type":"stream","text":[" 22%|██▏       | 222510/1000000 [1:07:24<3:36:37, 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56.57it/s]"],"name":"stderr"},{"output_type":"stream","text":["=> saved checkpoint 'checkpoints/main/230000' (iter 230000)\n"],"name":"stdout"},{"output_type":"stream","text":[" 23%|██▎       | 230510/1000000 [1:09:51<3:37:47, 58.89it/s]"],"name":"stderr"},{"output_type":"stream","text":["[230500] vae_recon_loss:86.171 vae_kld:13.913 vae_tc_loss:0.131 D_tc_loss:0.657\n"],"name":"stdout"},{"output_type":"stream","text":[" 23%|██▎       | 231010/1000000 [1:10:00<3:47:51, 56.25it/s]"],"name":"stderr"},{"output_type":"stream","text":["[231000] vae_recon_loss:76.381 vae_kld:13.887 vae_tc_loss:0.128 D_tc_loss:0.664\n"],"name":"stdout"},{"output_type":"stream","text":[" 23%|██▎       | 231509/1000000 [1:10:08<3:46:06, 56.65it/s]"],"name":"stderr"},{"output_type":"stream","text":["[231500] vae_recon_loss:71.143 vae_kld:13.871 vae_tc_loss:-0.045 D_tc_loss:0.703\n"],"name":"stdout"},{"output_type":"stream","text":[" 23%|██▎       | 232013/1000000 [1:10:17<3:32:18, 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430000)\n"],"name":"stdout"},{"output_type":"stream","text":[" 43%|████▎     | 430509/1000000 [2:09:09<2:50:49, 55.56it/s]"],"name":"stderr"},{"output_type":"stream","text":["[430500] vae_recon_loss:31.937 vae_kld:17.709 vae_tc_loss:0.516 D_tc_loss:0.592\n"],"name":"stdout"},{"output_type":"stream","text":[" 43%|████▎     | 431009/1000000 [2:09:18<2:51:21, 55.34it/s]"],"name":"stderr"},{"output_type":"stream","text":["[431000] vae_recon_loss:35.178 vae_kld:18.410 vae_tc_loss:0.527 D_tc_loss:0.600\n"],"name":"stdout"},{"output_type":"stream","text":[" 43%|████▎     | 431512/1000000 [2:09:27<2:44:47, 57.50it/s]"],"name":"stderr"},{"output_type":"stream","text":["[431500] vae_recon_loss:29.162 vae_kld:17.829 vae_tc_loss:0.816 D_tc_loss:0.544\n"],"name":"stdout"},{"output_type":"stream","text":[" 43%|████▎     | 432010/1000000 [2:09:36<2:48:25, 56.20it/s]"],"name":"stderr"},{"output_type":"stream","text":["[432000] vae_recon_loss:30.950 vae_kld:17.605 vae_tc_loss:0.666 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D_tc_loss:0.620\n","=> saved checkpoint 'checkpoints/main/440000' (iter 440000)\n"],"name":"stdout"},{"output_type":"stream","text":[" 44%|████▍     | 440510/1000000 [2:12:08<2:52:45, 53.98it/s]"],"name":"stderr"},{"output_type":"stream","text":["[440500] vae_recon_loss:35.194 vae_kld:17.805 vae_tc_loss:0.288 D_tc_loss:0.579\n"],"name":"stdout"},{"output_type":"stream","text":[" 44%|████▍     | 441012/1000000 [2:12:17<2:45:52, 56.16it/s]"],"name":"stderr"},{"output_type":"stream","text":["[441000] vae_recon_loss:30.951 vae_kld:18.202 vae_tc_loss:0.663 D_tc_loss:0.509\n"],"name":"stdout"},{"output_type":"stream","text":[" 44%|████▍     | 441507/1000000 [2:12:26<2:46:43, 55.83it/s]"],"name":"stderr"},{"output_type":"stream","text":["[441500] vae_recon_loss:35.320 vae_kld:18.344 vae_tc_loss:0.450 D_tc_loss:0.558\n"],"name":"stdout"},{"output_type":"stream","text":[" 44%|████▍     | 442007/1000000 [2:12:35<2:43:04, 57.03it/s]"],"name":"stderr"},{"output_type":"stream","text":["[442000] 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