scCC - ANN - No Bank.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from torch import nn\n",
    "import torch\n",
    "import argparse\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scanpy as sc\n",
    "import os\n",
    "import anndata\n",
    "import math\n",
    "import hnswlib\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from modules import network,mlp\n",
    "from utils import yaml_config_hook,save_model\n",
    "\n",
    "parser = argparse.ArgumentParser()\n",
    "config = yaml_config_hook(\"config/config.yaml\")\n",
    "for k, v in config.items():\n",
    "    parser.add_argument(f\"--{k}\", default=v, type=type(v))\n",
    "args = parser.parse_args([])\n",
    "if not os.path.exists(args.model_path):\n",
    "    os.makedirs(args.model_path)\n",
    "\n",
    "# torch.manual_seed(args.seed)\n",
    "# torch.cuda.manual_seed_all(args.seed)\n",
    "# torch.cuda.manual_seed(args.seed)\n",
    "# np.random.seed(args.seed)\n",
    "class_num = args.classnum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8569, 2000) (8569, 1)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2</th>\n",
       "      <th>10</th>\n",
       "      <th>13</th>\n",
       "      <th>41</th>\n",
       "      <th>45</th>\n",
       "      <th>62</th>\n",
       "      <th>68</th>\n",
       "      <th>106</th>\n",
       "      <th>133</th>\n",
       "      <th>147</th>\n",
       "      <th>...</th>\n",
       "      <th>19763</th>\n",
       "      <th>19786</th>\n",
       "      <th>19808</th>\n",
       "      <th>19854</th>\n",
       "      <th>19883</th>\n",
       "      <th>20021</th>\n",
       "      <th>20073</th>\n",
       "      <th>20109</th>\n",
       "      <th>20121</th>\n",
       "      <th>20124</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.302199</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.36896</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.637877</td>\n",
       "      <td>0.368960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.351171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.888292</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.711146</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.417500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.93785</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.937850</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.509045</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.509045</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.509045</td>\n",
       "      <td>0.509045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 2000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     2        10   13   41   45        62       68  106  133  147  ...  19763  \\\n",
       "0  0.0  1.302199  0.0  0.0  0.0  0.000000  0.36896  0.0  0.0  0.0  ...    0.0   \n",
       "1  0.0  1.351171  0.0  0.0  0.0  0.000000  0.00000  0.0  0.0  0.0  ...    0.0   \n",
       "2  0.0  0.000000  0.0  0.0  0.0  0.000000  0.00000  0.0  0.0  0.0  ...    0.0   \n",
       "3  0.0  0.000000  0.0  0.0  0.0  0.711146  0.00000  0.0  0.0  0.0  ...    0.0   \n",
       "4  0.0  0.000000  0.0  0.0  0.0  0.509045  0.00000  0.0  0.0  0.0  ...    0.0   \n",
       "\n",
       "      19786  19808  19854    19883  20021  20073  20109     20121     20124  \n",
       "0  0.000000    0.0    0.0  0.00000    0.0    0.0    0.0  0.637877  0.368960  \n",
       "1  0.000000    0.0    0.0  0.00000    0.0    0.0    0.0  0.888292  0.000000  \n",
       "2  0.000000    0.0    0.0  0.00000    0.0    0.0    0.0  0.000000  0.000000  \n",
       "3  0.417500    0.0    0.0  0.93785    0.0    0.0    0.0  0.937850  0.000000  \n",
       "4  0.509045    0.0    0.0  0.00000    0.0    0.0    0.0  0.509045  0.509045  \n",
       "\n",
       "[5 rows x 2000 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.sparse\n",
    "sparse_X = scipy.sparse.load_npz('data/filtered_Counts.npz')\n",
    "annoData = pd.read_table('data/annoData.txt')\n",
    "y = annoData[\"cellIden\"].to_numpy()\n",
    "high_var_gene = args.num_genes\n",
    "# normlization and feature selection\n",
    "adataSC = anndata.AnnData(X=sparse_X, obs=np.arange(sparse_X.shape[0]), var=np.arange(sparse_X.shape[1]))\n",
    "sc.pp.filter_genes(adataSC, min_cells=10)\n",
    "adataSC.raw = adataSC\n",
    "sc.pp.highly_variable_genes(adataSC, n_top_genes=high_var_gene, flavor='seurat_v3')\n",
    "sc.pp.normalize_total(adataSC, target_sum=1e4)\n",
    "sc.pp.log1p(adataSC)\n",
    "\n",
    "adataNorm = adataSC[:, adataSC.var.highly_variable]\n",
    "dataframe = adataNorm.to_df()\n",
    "x_ndarray = dataframe.values.squeeze()\n",
    "y_ndarray = np.expand_dims(y, axis=1)\n",
    "print(x_ndarray.shape,y_ndarray.shape)\n",
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000\n",
      "512\n",
      "512\n",
      "2000\n",
      "512\n",
      "512\n"
     ]
    }
   ],
   "source": [
    "from torch.utils.data import DataLoader,random_split,TensorDataset\n",
    "scDataset = TensorDataset(torch.tensor(x_ndarray, dtype=torch.float32),\n",
    "                              torch.tensor(y_ndarray, dtype=torch.float32))\n",
    "\n",
    "scDataLoader = DataLoader(scDataset, shuffle=True, batch_size=args.batch_size,drop_last=True)\n",
    "\n",
    "for features, labels in scDataLoader:\n",
    "    print(len(features[-1]))\n",
    "    print(len(features))\n",
    "    print(len(labels))\n",
    "    break\n",
    "\n",
    "scGenDataLoader = DataLoader(scDataset, shuffle=False, batch_size=args.batch_size,drop_last=True)\n",
    "\n",
    "for features, labels in scDataLoader:\n",
    "    print(len(features[-1]))\n",
    "    print(len(features))\n",
    "    print(len(labels))\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8569 2000\n"
     ]
    }
   ],
   "source": [
    "num_elements=len(scDataset)\n",
    "dim=high_var_gene\n",
    "print(num_elements,dim)\n",
    "p=hnswlib.Index(space='cosine',dim=dim)\n",
    "p.init_index(max_elements=num_elements, ef_construction=100, M=16)\n",
    "p.set_ef(100)\n",
    "p.set_num_threads(4)\n",
    "p.add_items(x_ndarray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# initialize model\n",
    "mlp = mlp.MLP(num_genes=args.num_genes)\n",
    "model = network.Network(mlp, args.feature_dim, args.classnum)\n",
    "model = model.to('cuda')\n",
    "# optimizer / loss\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class InstanceLoss(nn.Module):\n",
    "\n",
    "    def __init__(self,batch_size,temperature,device):\n",
    "        super(InstanceLoss,self).__init__()\n",
    "        self.batch_size=batch_size\n",
    "        self.temperature=temperature\n",
    "        self.device=device\n",
    "\n",
    "        self.mask=self.mask_correlated_samples(batch_size)\n",
    "        self.criterion = nn.CrossEntropyLoss(reduction=\"sum\")\n",
    "\n",
    "\n",
    "    def mask_correlated_samples(self,batch_size):\n",
    "        N=2*batch_size\n",
    "        mask=torch.ones(N,N)\n",
    "        mask=mask.fill_diagonal_(0)\n",
    "        for i in range(batch_size):\n",
    "            mask[i,batch_size+1]=0\n",
    "            mask[batch_size+i,i]=0\n",
    "        mask=mask.bool()\n",
    "        return mask\n",
    "\n",
    "    def forward(self,z_i,z_j):\n",
    "        N=2*self.batch_size\n",
    "        z=torch.cat((z_i,z_j),dim=0)\n",
    "\n",
    "        sim=torch.matmul(z,z.T)/self.temperature\n",
    "        sim_i_j=torch.diag(sim,self.batch_size)\n",
    "        sim_j_i=torch.diag(sim,-self.batch_size)\n",
    "\n",
    "        positive_samples= torch.cat((sim_i_j,sim_j_i),dim=0).reshape(N,1)\n",
    "        negative_samples=sim[self.mask].reshape(N,-1)\n",
    "\n",
    "        labels = torch.zeros(N).to(positive_samples.device).long()\n",
    "        logits = torch.cat((positive_samples, negative_samples), dim=1)\n",
    "        loss = self.criterion(logits, labels)\n",
    "        loss /= N\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "class ClusterLoss(nn.Module):\n",
    "    def __init__(self, class_num, temperature, device):\n",
    "        super(ClusterLoss, self).__init__()\n",
    "        self.class_num = class_num\n",
    "        self.temperature = temperature\n",
    "        self.device = device\n",
    "\n",
    "        self.mask = self.mask_correlated_clusters(class_num)\n",
    "        self.criterion = nn.CrossEntropyLoss(reduction=\"sum\")\n",
    "        self.similarity_f = nn.CosineSimilarity(dim=2)\n",
    "\n",
    "    def mask_correlated_clusters(self, class_num):\n",
    "        N = 2 * class_num\n",
    "        mask = torch.ones((N, N))\n",
    "        mask = mask.fill_diagonal_(0)\n",
    "        for i in range(class_num):\n",
    "            mask[i, class_num + i] = 0\n",
    "            mask[class_num + i, i] = 0\n",
    "        mask = mask.bool()\n",
    "        return mask\n",
    "\n",
    "    def forward(self, c_i, c_j):\n",
    "        p_i = c_i.sum(0).view(-1)\n",
    "        p_i /= p_i.sum()\n",
    "        ne_i = math.log(p_i.size(0)) + (p_i * torch.log(p_i)).sum()\n",
    "        p_j = c_j.sum(0).view(-1)\n",
    "        p_j /= p_j.sum()\n",
    "        ne_j = math.log(p_j.size(0)) + (p_j * torch.log(p_j)).sum()\n",
    "        ne_loss = ne_i + ne_j\n",
    "\n",
    "        c_i = c_i.t()\n",
    "        c_j = c_j.t()\n",
    "        N = 2 * self.class_num\n",
    "        c = torch.cat((c_i, c_j), dim=0)\n",
    "\n",
    "        sim = self.similarity_f(c.unsqueeze(1), c.unsqueeze(0)) / self.temperature\n",
    "        sim_i_j = torch.diag(sim, self.class_num)\n",
    "        sim_j_i = torch.diag(sim, -self.class_num)\n",
    "\n",
    "        positive_clusters = torch.cat((sim_i_j, sim_j_i), dim=0).reshape(N, 1)\n",
    "        negative_clusters = sim[self.mask].reshape(N, -1)\n",
    "\n",
    "        labels = torch.zeros(N).to(positive_clusters.device).long()\n",
    "        logits = torch.cat((positive_clusters, negative_clusters), dim=1)\n",
    "        loss = self.criterion(logits, labels)\n",
    "        loss /= N\n",
    "\n",
    "        return loss + ne_loss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MemoryBank():\n",
    "\n",
    "    # 初始化，传入参数\n",
    "    def __init__(self,batch_size,full_data,topK=10):\n",
    "        self.topK=topK\n",
    "        self.batch_size=batch_size\n",
    "        self.bank=None\n",
    "        self.full_data=full_data\n",
    "\n",
    "    # 根据在updateBank中更新的hnsw对象以及输入的数据data（这里可以是embedding）提取TopK个近邻的数据\n",
    "    # 返回的结果是一个形状为[TopK,batch_size,num_genes]的数组，从第一个维度来看，\n",
    "    # 每个[batch_size,num_genes]的子数组都是根据输入的数据data寻找的一个近邻，一共TopK个\n",
    "    def generateContrast(self,data):\n",
    "        if self.bank is not None:\n",
    "            contrasts=np.empty((self.topK,args.batch_size,args.num_genes))\n",
    "            labels,distances=self.bank.knn_query(data,k=self.topK)\n",
    "            \n",
    "            # print(labels)\n",
    "\n",
    "            for step,label in enumerate(labels):\n",
    "                contrasts[:,step]=self.full_data[label.tolist()]\n",
    "            return contrasts\n",
    "        else:\n",
    "            print('Memory Bank has not been initialized......')\n",
    "            raise NotImplementedError()\n",
    "\n",
    "    # 根据输入的embedding更新hnsw对象\n",
    "    def updateBank(self,embedding):\n",
    "        num_elements=len(embedding)\n",
    "        dim=embedding.shape[1]\n",
    "        self.bank=hnswlib.Index(space='cosine',dim=dim)\n",
    "        self.bank.init_index(max_elements=num_elements, ef_construction=100, M=16)\n",
    "        self.bank.set_ef(100)\n",
    "        self.bank.set_num_threads(4)\n",
    "        self.bank.add_items(embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "# def train(instance_loss,cluster_loss):\n",
    "#     loss_epoch = 0\n",
    "#     for step, (data,label) in enumerate(scDataset):\n",
    "#         optimizer.zero_grad()\n",
    "        \n",
    "#         x_i=data.clone().numpy()\n",
    "        \n",
    "#         labels, distances = p.knn_query(x_i, k=31)\n",
    "#         labels=labels.squeeze()\n",
    "#         contrastive=x_ndarray[labels.tolist()]\n",
    "#         input=np.row_stack((contrastive,x_i))\n",
    "#         input_1=input.copy()\n",
    "#         np.random.shuffle(input_1)\n",
    "#         x_a=torch.from_numpy(input).to('cuda')\n",
    "#         x_b=torch.from_numpy(input_1).to('cuda')\n",
    "        \n",
    "#         z_i,z_j,c_i,c_j=model(x_a,x_b)\n",
    "#         loss_instance=instance_loss(z_i,z_j)\n",
    "#         loss_cluster=cluster_loss(c_i,c_j)\n",
    "#         loss = loss_instance + loss_cluster\n",
    "#         loss.backward()\n",
    "#         optimizer.step()\n",
    "#         if step % 1000 == 0:\n",
    "#             print(f\"Step [{step}/{len(scDataLoader)}]\\t loss_instance: {loss_instance.item()}\\t loss_cluster: {loss_cluster.item()}\")\n",
    "#         loss_epoch += loss.item()\n",
    "#     return loss_epoch\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(instance_loss,cluster_loss,memory_bank,model,epoch):\n",
    "    loss_epoch=0\n",
    "    for step,(data,label) in enumerate(scDataLoader):\n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        # if epoch<20:\n",
    "        embedding_data=data.numpy().copy()\n",
    "        data=data.to('cuda')\n",
    "        # else:\n",
    "        #     data=data.to('cuda')\n",
    "        #     embedding_data=model.forward_embedding(data).cpu().detach().numpy()    \n",
    "        contrast_samples=memory_bank.generateContrast(embedding_data)\n",
    "        iter_times=contrast_samples.shape[0]\n",
    "        for sample in contrast_samples:\n",
    "            # print(f'sample shape:\\n{sample.shape}')\n",
    "            # print(f'data shape:\\n{data.shape}')\n",
    "            x_i=data.clone()\n",
    "            x_j=torch.tensor(sample,dtype=torch.float32).to('cuda')\n",
    "\n",
    "            z_i,z_j,c_i,c_j=model(x_i,x_j)\n",
    "            loss_instance=instance_loss(z_i,z_j)\n",
    "            loss_cluster=cluster_loss(c_i,c_j)\n",
    "            loss = loss_instance + loss_cluster\n",
    "            # print(f'------ loss:\\n{loss}')\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            loss_epoch+=loss.item()\n",
    "        if step % 2 == 0:\n",
    "                print(f\"Step [{step}/{len(scDataLoader)}]\\t loss_instance: {loss_instance.item()}\\t loss_cluster: {loss_cluster.item()}\")\n",
    "            \n",
    "    return loss_epoch/iter_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def inference(loader, model, device):\n",
    "    model.eval()\n",
    "    feature_vector = []\n",
    "    labels_vector = []\n",
    "    for step, (x, y) in enumerate(loader):\n",
    "        x = x.to(device)\n",
    "        with torch.no_grad():\n",
    "            c = model.forward_cluster(x)\n",
    "        c = c.detach()\n",
    "        feature_vector.extend(c.cpu().detach().numpy())\n",
    "        labels_vector.extend(y.numpy())\n",
    "        if step % 1000 == 0:\n",
    "            print(f\"Step [{step}/{len(loader)}]\\t Computing features...\")\n",
    "    feature_vector = np.array(feature_vector)\n",
    "    labels_vector = np.array(labels_vector)\n",
    "    print(\"Features shape {}\".format(feature_vector.shape))\n",
    "    # print(feature_vector.shape, labels_vector.shape)\n",
    "    return feature_vector, labels_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from evaluation import evaluation\n",
    "def test():\n",
    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "    X, Y = inference(scDataLoader, model, device)\n",
    "    # print(X.shape,Y.shape)\n",
    "    nmi, ari, f, acc = evaluation.evaluate(Y.reshape(-1), X)\n",
    "    # print('NMI = {:.4f} ARI = {:.4f} F = {:.4f} ACC = {:.4f}'.format(nmi, ari, f, acc))\n",
    "    return nmi, ari, f, acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step [0/16]\t loss_instance: 6.913857460021973\t loss_cluster: 3.2991929054260254\n",
      "Step [2/16]\t loss_instance: 6.712862491607666\t loss_cluster: 3.298283576965332\n",
      "Step [4/16]\t loss_instance: 6.383283615112305\t loss_cluster: 3.2948877811431885\n",
      "Step [6/16]\t loss_instance: 6.3004961013793945\t loss_cluster: 3.2854509353637695\n",
      "Step [8/16]\t loss_instance: 6.1375861167907715\t loss_cluster: 3.265096664428711\n",
      "Step [10/16]\t loss_instance: 5.950763702392578\t loss_cluster: 3.230727434158325\n",
      "Step [12/16]\t loss_instance: 5.897871971130371\t loss_cluster: 3.1586647033691406\n",
      "Step [14/16]\t loss_instance: 5.796322822570801\t loss_cluster: 3.0672667026519775\n",
      "\n",
      "Epoch [1/300]\t Loss: 9.495668607950211 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.753137588500977\t loss_cluster: 2.9943418502807617\n",
      "Step [2/16]\t loss_instance: 5.756695747375488\t loss_cluster: 2.8654565811157227\n",
      "Step [4/16]\t loss_instance: 5.723991870880127\t loss_cluster: 2.768895149230957\n",
      "Step [6/16]\t loss_instance: 5.684011459350586\t loss_cluster: 2.649139404296875\n",
      "Step [8/16]\t loss_instance: 5.6662726402282715\t loss_cluster: 2.571167230606079\n",
      "Step [10/16]\t loss_instance: 5.673282623291016\t loss_cluster: 2.499586820602417\n",
      "Step [12/16]\t loss_instance: 5.611105442047119\t loss_cluster: 2.437824010848999\n",
      "Step [14/16]\t loss_instance: 5.653970241546631\t loss_cluster: 2.391754627227783\n",
      "\n",
      "Epoch [2/300]\t Loss: 8.328922188282013 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.597115516662598\t loss_cluster: 2.321256160736084\n",
      "Step [2/16]\t loss_instance: 5.610922336578369\t loss_cluster: 2.245945930480957\n",
      "Step [4/16]\t loss_instance: 5.581927299499512\t loss_cluster: 2.2025556564331055\n",
      "Step [6/16]\t loss_instance: 5.557734966278076\t loss_cluster: 2.1326420307159424\n",
      "Step [8/16]\t loss_instance: 5.561939716339111\t loss_cluster: 2.1007955074310303\n",
      "Step [10/16]\t loss_instance: 5.590679168701172\t loss_cluster: 2.1103861331939697\n",
      "Step [12/16]\t loss_instance: 5.591104507446289\t loss_cluster: 2.155946969985962\n",
      "Step [14/16]\t loss_instance: 5.580905437469482\t loss_cluster: 2.065976142883301\n",
      "\n",
      "Epoch [3/300]\t Loss: 7.7321917682886125 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.568933010101318\t loss_cluster: 2.075925827026367\n",
      "Step [2/16]\t loss_instance: 5.5390520095825195\t loss_cluster: 1.9850374460220337\n",
      "Step [4/16]\t loss_instance: 5.517529487609863\t loss_cluster: 1.9706709384918213\n",
      "Step [6/16]\t loss_instance: 5.541860103607178\t loss_cluster: 1.958804726600647\n",
      "Step [8/16]\t loss_instance: 5.513359546661377\t loss_cluster: 1.9594013690948486\n",
      "Step [10/16]\t loss_instance: 5.523978233337402\t loss_cluster: 1.959222435951233\n",
      "Step [12/16]\t loss_instance: 5.510314464569092\t loss_cluster: 1.8925329446792603\n",
      "Step [14/16]\t loss_instance: 5.507368087768555\t loss_cluster: 1.9198057651519775\n",
      "\n",
      "Epoch [4/300]\t Loss: 7.4642174452543255 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.522740364074707\t loss_cluster: 1.916527271270752\n",
      "Step [2/16]\t loss_instance: 5.515199184417725\t loss_cluster: 1.9377856254577637\n",
      "Step [4/16]\t loss_instance: 5.530066967010498\t loss_cluster: 1.9284030199050903\n",
      "Step [6/16]\t loss_instance: 5.483078956604004\t loss_cluster: 1.8869061470031738\n",
      "Step [8/16]\t loss_instance: 5.514851093292236\t loss_cluster: 1.8854432106018066\n",
      "Step [10/16]\t loss_instance: 5.505980968475342\t loss_cluster: 1.9075627326965332\n",
      "Step [12/16]\t loss_instance: 5.482364177703857\t loss_cluster: 1.866175651550293\n",
      "Step [14/16]\t loss_instance: 5.479496002197266\t loss_cluster: 1.8720786571502686\n",
      "\n",
      "Epoch [5/300]\t Loss: 7.348539799451828 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.4749884605407715\t loss_cluster: 1.8383138179779053\n",
      "Step [2/16]\t loss_instance: 5.484776973724365\t loss_cluster: 1.8451924324035645\n",
      "Step [4/16]\t loss_instance: 5.493249416351318\t loss_cluster: 1.8533785343170166\n",
      "Step [6/16]\t loss_instance: 5.482458114624023\t loss_cluster: 1.868274450302124\n",
      "Step [8/16]\t loss_instance: 5.519843101501465\t loss_cluster: 1.9165093898773193\n",
      "Step [10/16]\t loss_instance: 5.463873386383057\t loss_cluster: 1.825200080871582\n",
      "Step [12/16]\t loss_instance: 5.5169243812561035\t loss_cluster: 1.865167260169983\n",
      "Step [14/16]\t loss_instance: 5.456074237823486\t loss_cluster: 1.8143740892410278\n",
      "\n",
      "Epoch [6/300]\t Loss: 7.296215954422951 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.478443145751953\t loss_cluster: 1.8692946434020996\n",
      "Step [2/16]\t loss_instance: 5.490517616271973\t loss_cluster: 1.8726919889450073\n",
      "Step [4/16]\t loss_instance: 5.480159759521484\t loss_cluster: 1.8435479402542114\n",
      "Step [6/16]\t loss_instance: 5.480886459350586\t loss_cluster: 1.8368268013000488\n",
      "Step [8/16]\t loss_instance: 5.47507905960083\t loss_cluster: 1.8498178720474243\n",
      "Step [10/16]\t loss_instance: 5.478150367736816\t loss_cluster: 1.8873655796051025\n",
      "Step [12/16]\t loss_instance: 5.476963043212891\t loss_cluster: 1.8409663438796997\n",
      "Step [14/16]\t loss_instance: 5.502669334411621\t loss_cluster: 1.8503293991088867\n",
      "\n",
      "Epoch [7/300]\t Loss: 7.282403662800789 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.468949794769287\t loss_cluster: 1.789175033569336\n",
      "Step [2/16]\t loss_instance: 5.470996856689453\t loss_cluster: 1.8270219564437866\n",
      "Step [4/16]\t loss_instance: 5.455573081970215\t loss_cluster: 1.800964117050171\n",
      "Step [6/16]\t loss_instance: 5.457625865936279\t loss_cluster: 1.8231501579284668\n",
      "Step [8/16]\t loss_instance: 5.471193790435791\t loss_cluster: 1.8063101768493652\n",
      "Step [10/16]\t loss_instance: 5.479262351989746\t loss_cluster: 1.808341145515442\n",
      "Step [12/16]\t loss_instance: 5.479740142822266\t loss_cluster: 1.798192024230957\n",
      "Step [14/16]\t loss_instance: 5.47307825088501\t loss_cluster: 1.8361009359359741\n",
      "\n",
      "Epoch [8/300]\t Loss: 7.2388851791620255 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.464277267456055\t loss_cluster: 1.7980780601501465\n",
      "Step [2/16]\t loss_instance: 5.451183795928955\t loss_cluster: 1.7859950065612793\n",
      "Step [4/16]\t loss_instance: 5.456385135650635\t loss_cluster: 1.810935378074646\n",
      "Step [6/16]\t loss_instance: 5.42469596862793\t loss_cluster: 1.7688047885894775\n",
      "Step [8/16]\t loss_instance: 5.4689836502075195\t loss_cluster: 1.808424472808838\n",
      "Step [10/16]\t loss_instance: 5.448159694671631\t loss_cluster: 1.7849034070968628\n",
      "Step [12/16]\t loss_instance: 5.426823616027832\t loss_cluster: 1.7915310859680176\n",
      "Step [14/16]\t loss_instance: 5.465444564819336\t loss_cluster: 1.7814960479736328\n",
      "\n",
      "Epoch [9/300]\t Loss: 7.214872747659683 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.446814060211182\t loss_cluster: 1.789561152458191\n",
      "Step [2/16]\t loss_instance: 5.442336082458496\t loss_cluster: 1.8098232746124268\n",
      "Step [4/16]\t loss_instance: 5.448482513427734\t loss_cluster: 1.7664929628372192\n",
      "Step [6/16]\t loss_instance: 5.4660491943359375\t loss_cluster: 1.7974705696105957\n",
      "Step [8/16]\t loss_instance: 5.449591159820557\t loss_cluster: 1.8051849603652954\n",
      "Step [10/16]\t loss_instance: 5.441982746124268\t loss_cluster: 1.8132526874542236\n",
      "Step [12/16]\t loss_instance: 5.452888011932373\t loss_cluster: 1.7731255292892456\n",
      "Step [14/16]\t loss_instance: 5.451430320739746\t loss_cluster: 1.8088716268539429\n",
      "\n",
      "Epoch [10/300]\t Loss: 7.19921697974205 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.441089630126953\t loss_cluster: 1.7864346504211426\n",
      "Step [2/16]\t loss_instance: 5.44898796081543\t loss_cluster: 1.7872744798660278\n",
      "Step [4/16]\t loss_instance: 5.437901973724365\t loss_cluster: 1.7793487310409546\n",
      "Step [6/16]\t loss_instance: 5.457622051239014\t loss_cluster: 1.7886911630630493\n",
      "Step [8/16]\t loss_instance: 5.41055965423584\t loss_cluster: 1.73866605758667\n",
      "Step [10/16]\t loss_instance: 5.4154510498046875\t loss_cluster: 1.7476146221160889\n",
      "Step [12/16]\t loss_instance: 5.44829797744751\t loss_cluster: 1.7852673530578613\n",
      "Step [14/16]\t loss_instance: 5.424195289611816\t loss_cluster: 1.7854750156402588\n",
      "\n",
      "Epoch [11/300]\t Loss: 7.174546635150909 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.455684661865234\t loss_cluster: 1.7985550165176392\n",
      "Step [2/16]\t loss_instance: 5.4548726081848145\t loss_cluster: 1.7894617319107056\n",
      "Step [4/16]\t loss_instance: 5.445486545562744\t loss_cluster: 1.7768336534500122\n",
      "Step [6/16]\t loss_instance: 5.413389205932617\t loss_cluster: 1.7522019147872925\n",
      "Step [8/16]\t loss_instance: 5.420774936676025\t loss_cluster: 1.7711164951324463\n",
      "Step [10/16]\t loss_instance: 5.447828769683838\t loss_cluster: 1.7874680757522583\n",
      "Step [12/16]\t loss_instance: 5.420336723327637\t loss_cluster: 1.762007236480713\n",
      "Step [14/16]\t loss_instance: 5.397138595581055\t loss_cluster: 1.7370421886444092\n",
      "\n",
      "Epoch [12/300]\t Loss: 7.15703949034214 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.426036357879639\t loss_cluster: 1.7560254335403442\n",
      "Step [2/16]\t loss_instance: 5.441290855407715\t loss_cluster: 1.8006665706634521\n",
      "Step [4/16]\t loss_instance: 5.450018882751465\t loss_cluster: 1.7733898162841797\n",
      "Step [6/16]\t loss_instance: 5.424660682678223\t loss_cluster: 1.737143874168396\n",
      "Step [8/16]\t loss_instance: 5.417857646942139\t loss_cluster: 1.7857924699783325\n",
      "Step [10/16]\t loss_instance: 5.4085845947265625\t loss_cluster: 1.7919220924377441\n",
      "Step [12/16]\t loss_instance: 5.425687789916992\t loss_cluster: 1.7927240133285522\n",
      "Step [14/16]\t loss_instance: 5.4491777420043945\t loss_cluster: 1.8015646934509277\n",
      "\n",
      "Epoch [13/300]\t Loss: 7.151377454400063 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.44112491607666\t loss_cluster: 1.7748746871948242\n",
      "Step [2/16]\t loss_instance: 5.416289329528809\t loss_cluster: 1.7909013032913208\n",
      "Step [4/16]\t loss_instance: 5.429006576538086\t loss_cluster: 1.7810076475143433\n",
      "Step [6/16]\t loss_instance: 5.466796398162842\t loss_cluster: 1.7952547073364258\n",
      "Step [8/16]\t loss_instance: 5.4064154624938965\t loss_cluster: 1.728911280632019\n",
      "Step [10/16]\t loss_instance: 5.424759864807129\t loss_cluster: 1.7970808744430542\n",
      "Step [12/16]\t loss_instance: 5.394744396209717\t loss_cluster: 1.7297589778900146\n",
      "Step [14/16]\t loss_instance: 5.4278106689453125\t loss_cluster: 1.776492953300476\n",
      "\n",
      "Epoch [14/300]\t Loss: 7.142032980918884 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.415767192840576\t loss_cluster: 1.777522087097168\n",
      "Step [2/16]\t loss_instance: 5.407140254974365\t loss_cluster: 1.7717658281326294\n",
      "Step [4/16]\t loss_instance: 5.404459476470947\t loss_cluster: 1.747465968132019\n",
      "Step [6/16]\t loss_instance: 5.407020568847656\t loss_cluster: 1.748482584953308\n",
      "Step [8/16]\t loss_instance: 5.393637180328369\t loss_cluster: 1.743066668510437\n",
      "Step [10/16]\t loss_instance: 5.405760765075684\t loss_cluster: 1.740921974182129\n",
      "Step [12/16]\t loss_instance: 5.409542083740234\t loss_cluster: 1.7570158243179321\n",
      "Step [14/16]\t loss_instance: 5.4239044189453125\t loss_cluster: 1.7726668119430542\n",
      "\n",
      "Epoch [15/300]\t Loss: 7.121612626314163 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.401046276092529\t loss_cluster: 1.7468231916427612\n",
      "Step [2/16]\t loss_instance: 5.4082350730896\t loss_cluster: 1.748176097869873\n",
      "Step [4/16]\t loss_instance: 5.418869972229004\t loss_cluster: 1.753604531288147\n",
      "Step [6/16]\t loss_instance: 5.421517372131348\t loss_cluster: 1.7769564390182495\n",
      "Step [8/16]\t loss_instance: 5.408228874206543\t loss_cluster: 1.7793755531311035\n",
      "Step [10/16]\t loss_instance: 5.407018661499023\t loss_cluster: 1.7390270233154297\n",
      "Step [12/16]\t loss_instance: 5.406591892242432\t loss_cluster: 1.7430123090744019\n",
      "Step [14/16]\t loss_instance: 5.411741733551025\t loss_cluster: 1.7550644874572754\n",
      "\n",
      "Epoch [16/300]\t Loss: 7.10700889825821 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.405425548553467\t loss_cluster: 1.7343144416809082\n",
      "Step [2/16]\t loss_instance: 5.4228835105896\t loss_cluster: 1.784638524055481\n",
      "Step [4/16]\t loss_instance: 5.400583267211914\t loss_cluster: 1.75360107421875\n",
      "Step [6/16]\t loss_instance: 5.390758514404297\t loss_cluster: 1.7561060190200806\n",
      "Step [8/16]\t loss_instance: 5.423159599304199\t loss_cluster: 1.7562118768692017\n",
      "Step [10/16]\t loss_instance: 5.397352695465088\t loss_cluster: 1.7308984994888306\n",
      "Step [12/16]\t loss_instance: 5.382796764373779\t loss_cluster: 1.7423896789550781\n",
      "Step [14/16]\t loss_instance: 5.430908203125\t loss_cluster: 1.7887927293777466\n",
      "\n",
      "Epoch [17/300]\t Loss: 7.100915464758873 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.38547420501709\t loss_cluster: 1.7155084609985352\n",
      "Step [2/16]\t loss_instance: 5.382345676422119\t loss_cluster: 1.721909761428833\n",
      "Step [4/16]\t loss_instance: 5.4194464683532715\t loss_cluster: 1.7624238729476929\n",
      "Step [6/16]\t loss_instance: 5.4159040451049805\t loss_cluster: 1.7884626388549805\n",
      "Step [8/16]\t loss_instance: 5.388368606567383\t loss_cluster: 1.7509007453918457\n",
      "Step [10/16]\t loss_instance: 5.4027180671691895\t loss_cluster: 1.7533506155014038\n",
      "Step [12/16]\t loss_instance: 5.4486918449401855\t loss_cluster: 1.7943928241729736\n",
      "Step [14/16]\t loss_instance: 5.391160488128662\t loss_cluster: 1.7311859130859375\n",
      "\n",
      "Epoch [18/300]\t Loss: 7.08565225303173 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.415882587432861\t loss_cluster: 1.7955774068832397\n",
      "Step [2/16]\t loss_instance: 5.374388217926025\t loss_cluster: 1.7367535829544067\n",
      "Step [4/16]\t loss_instance: 5.357913017272949\t loss_cluster: 1.684895634651184\n",
      "Step [6/16]\t loss_instance: 5.414494037628174\t loss_cluster: 1.7446074485778809\n",
      "Step [8/16]\t loss_instance: 5.401799201965332\t loss_cluster: 1.7377513647079468\n",
      "Step [10/16]\t loss_instance: 5.369967460632324\t loss_cluster: 1.7060033082962036\n",
      "Step [12/16]\t loss_instance: 5.409226894378662\t loss_cluster: 1.757483959197998\n",
      "Step [14/16]\t loss_instance: 5.405636787414551\t loss_cluster: 1.7841250896453857\n",
      "\n",
      "Epoch [19/300]\t Loss: 7.078556102514267 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.397818088531494\t loss_cluster: 1.7401000261306763\n",
      "Step [2/16]\t loss_instance: 5.391729354858398\t loss_cluster: 1.7340822219848633\n",
      "Step [4/16]\t loss_instance: 5.394189834594727\t loss_cluster: 1.7289035320281982\n",
      "Step [6/16]\t loss_instance: 5.38332462310791\t loss_cluster: 1.742369294166565\n",
      "Step [8/16]\t loss_instance: 5.392189979553223\t loss_cluster: 1.7757503986358643\n",
      "Step [10/16]\t loss_instance: 5.3996500968933105\t loss_cluster: 1.7521448135375977\n",
      "Step [12/16]\t loss_instance: 5.392541408538818\t loss_cluster: 1.744735836982727\n",
      "Step [14/16]\t loss_instance: 5.384908676147461\t loss_cluster: 1.721522569656372\n",
      "\n",
      "Epoch [20/300]\t Loss: 7.0799222648143765 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3915629386901855\t loss_cluster: 1.7356797456741333\n",
      "Step [2/16]\t loss_instance: 5.384221076965332\t loss_cluster: 1.7218161821365356\n",
      "Step [4/16]\t loss_instance: 5.393490791320801\t loss_cluster: 1.7457726001739502\n",
      "Step [6/16]\t loss_instance: 5.389463424682617\t loss_cluster: 1.7390851974487305\n",
      "Step [8/16]\t loss_instance: 5.3953471183776855\t loss_cluster: 1.7280257940292358\n",
      "Step [10/16]\t loss_instance: 5.391528129577637\t loss_cluster: 1.7772552967071533\n",
      "Step [12/16]\t loss_instance: 5.37247896194458\t loss_cluster: 1.7480510473251343\n",
      "Step [14/16]\t loss_instance: 5.386538028717041\t loss_cluster: 1.7366435527801514\n",
      "\n",
      "Epoch [21/300]\t Loss: 7.066431340575218 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.383673191070557\t loss_cluster: 1.724520206451416\n",
      "Step [2/16]\t loss_instance: 5.394871711730957\t loss_cluster: 1.7459923028945923\n",
      "Step [4/16]\t loss_instance: 5.4224853515625\t loss_cluster: 1.7674243450164795\n",
      "Step [6/16]\t loss_instance: 5.368570327758789\t loss_cluster: 1.738113522529602\n",
      "Step [8/16]\t loss_instance: 5.378284931182861\t loss_cluster: 1.7271620035171509\n",
      "Step [10/16]\t loss_instance: 5.407606601715088\t loss_cluster: 1.7405571937561035\n",
      "Step [12/16]\t loss_instance: 5.406097888946533\t loss_cluster: 1.7316735982894897\n",
      "Step [14/16]\t loss_instance: 5.381948947906494\t loss_cluster: 1.712232232093811\n",
      "\n",
      "Epoch [22/300]\t Loss: 7.058607673645019 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3681440353393555\t loss_cluster: 1.7124508619308472\n",
      "Step [2/16]\t loss_instance: 5.388570785522461\t loss_cluster: 1.7299529314041138\n",
      "Step [4/16]\t loss_instance: 5.396749019622803\t loss_cluster: 1.7488068342208862\n",
      "Step [6/16]\t loss_instance: 5.403031826019287\t loss_cluster: 1.7470693588256836\n",
      "Step [8/16]\t loss_instance: 5.3708672523498535\t loss_cluster: 1.7011592388153076\n",
      "Step [10/16]\t loss_instance: 5.389651298522949\t loss_cluster: 1.728454351425171\n",
      "Step [12/16]\t loss_instance: 5.397627830505371\t loss_cluster: 1.7363892793655396\n",
      "Step [14/16]\t loss_instance: 5.373769283294678\t loss_cluster: 1.7356523275375366\n",
      "\n",
      "Epoch [23/300]\t Loss: 7.05570932328701 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.372824192047119\t loss_cluster: 1.7392699718475342\n",
      "Step [2/16]\t loss_instance: 5.364149570465088\t loss_cluster: 1.7361611127853394\n",
      "Step [4/16]\t loss_instance: 5.3902268409729\t loss_cluster: 1.7650350332260132\n",
      "Step [6/16]\t loss_instance: 5.400113582611084\t loss_cluster: 1.7373138666152954\n",
      "Step [8/16]\t loss_instance: 5.374461650848389\t loss_cluster: 1.7184913158416748\n",
      "Step [10/16]\t loss_instance: 5.393463611602783\t loss_cluster: 1.7317067384719849\n",
      "Step [12/16]\t loss_instance: 5.3740973472595215\t loss_cluster: 1.739134430885315\n",
      "Step [14/16]\t loss_instance: 5.372922897338867\t loss_cluster: 1.7196048498153687\n",
      "\n",
      "Epoch [24/300]\t Loss: 7.061055973172188 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.4066901206970215\t loss_cluster: 1.7665534019470215\n",
      "Step [2/16]\t loss_instance: 5.411509037017822\t loss_cluster: 1.7473149299621582\n",
      "Step [4/16]\t loss_instance: 5.365232467651367\t loss_cluster: 1.7292309999465942\n",
      "Step [6/16]\t loss_instance: 5.386579990386963\t loss_cluster: 1.7415096759796143\n",
      "Step [8/16]\t loss_instance: 5.36849308013916\t loss_cluster: 1.7082231044769287\n",
      "Step [10/16]\t loss_instance: 5.383379936218262\t loss_cluster: 1.7237530946731567\n",
      "Step [12/16]\t loss_instance: 5.389829158782959\t loss_cluster: 1.7423608303070068\n",
      "Step [14/16]\t loss_instance: 5.382927894592285\t loss_cluster: 1.7484058141708374\n",
      "\n",
      "Epoch [25/300]\t Loss: 7.045472925901413 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.353095054626465\t loss_cluster: 1.7114317417144775\n",
      "Step [2/16]\t loss_instance: 5.402163505554199\t loss_cluster: 1.738794207572937\n",
      "Step [4/16]\t loss_instance: 5.409673690795898\t loss_cluster: 1.7594009637832642\n",
      "Step [6/16]\t loss_instance: 5.407606601715088\t loss_cluster: 1.7436881065368652\n",
      "Step [8/16]\t loss_instance: 5.383804798126221\t loss_cluster: 1.728622317314148\n",
      "Step [10/16]\t loss_instance: 5.364866733551025\t loss_cluster: 1.7086248397827148\n",
      "Step [12/16]\t loss_instance: 5.3795037269592285\t loss_cluster: 1.7590991258621216\n",
      "Step [14/16]\t loss_instance: 5.391635894775391\t loss_cluster: 1.7355642318725586\n",
      "\n",
      "Epoch [26/300]\t Loss: 7.0569405376911165 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.375552654266357\t loss_cluster: 1.7252131700515747\n",
      "Step [2/16]\t loss_instance: 5.395263671875\t loss_cluster: 1.7363351583480835\n",
      "Step [4/16]\t loss_instance: 5.4095258712768555\t loss_cluster: 1.7691808938980103\n",
      "Step [6/16]\t loss_instance: 5.36764669418335\t loss_cluster: 1.7052842378616333\n",
      "Step [8/16]\t loss_instance: 5.4028801918029785\t loss_cluster: 1.724192500114441\n",
      "Step [10/16]\t loss_instance: 5.375378608703613\t loss_cluster: 1.7172075510025024\n",
      "Step [12/16]\t loss_instance: 5.370726108551025\t loss_cluster: 1.703943133354187\n",
      "Step [14/16]\t loss_instance: 5.387027740478516\t loss_cluster: 1.7565360069274902\n",
      "\n",
      "Epoch [27/300]\t Loss: 7.024720230698586 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.343642711639404\t loss_cluster: 1.6685909032821655\n",
      "Step [2/16]\t loss_instance: 5.388798236846924\t loss_cluster: 1.7608789205551147\n",
      "Step [4/16]\t loss_instance: 5.363296985626221\t loss_cluster: 1.6875642538070679\n",
      "Step [6/16]\t loss_instance: 5.394539833068848\t loss_cluster: 1.7078567743301392\n",
      "Step [8/16]\t loss_instance: 5.389533996582031\t loss_cluster: 1.7423161268234253\n",
      "Step [10/16]\t loss_instance: 5.383151531219482\t loss_cluster: 1.7072532176971436\n",
      "Step [12/16]\t loss_instance: 5.398410320281982\t loss_cluster: 1.7209910154342651\n",
      "Step [14/16]\t loss_instance: 5.380133152008057\t loss_cluster: 1.736911654472351\n",
      "\n",
      "Epoch [28/300]\t Loss: 7.026482889056206 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3763227462768555\t loss_cluster: 1.7234344482421875\n",
      "Step [2/16]\t loss_instance: 5.411369323730469\t loss_cluster: 1.7496142387390137\n",
      "Step [4/16]\t loss_instance: 5.364472389221191\t loss_cluster: 1.7021781206130981\n",
      "Step [6/16]\t loss_instance: 5.385202407836914\t loss_cluster: 1.7432351112365723\n",
      "Step [8/16]\t loss_instance: 5.3782057762146\t loss_cluster: 1.7348653078079224\n",
      "Step [10/16]\t loss_instance: 5.374026775360107\t loss_cluster: 1.713535189628601\n",
      "Step [12/16]\t loss_instance: 5.3773674964904785\t loss_cluster: 1.702752709388733\n",
      "Step [14/16]\t loss_instance: 5.355412483215332\t loss_cluster: 1.6882776021957397\n",
      "\n",
      "Epoch [29/300]\t Loss: 7.026650628447532 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.345792770385742\t loss_cluster: 1.694089651107788\n",
      "Step [2/16]\t loss_instance: 5.372624397277832\t loss_cluster: 1.7185112237930298\n",
      "Step [4/16]\t loss_instance: 5.401309967041016\t loss_cluster: 1.715455174446106\n",
      "Step [6/16]\t loss_instance: 5.3887104988098145\t loss_cluster: 1.7391659021377563\n",
      "Step [8/16]\t loss_instance: 5.3844709396362305\t loss_cluster: 1.7536183595657349\n",
      "Step [10/16]\t loss_instance: 5.365609645843506\t loss_cluster: 1.75108003616333\n",
      "Step [12/16]\t loss_instance: 5.395884037017822\t loss_cluster: 1.7482078075408936\n",
      "Step [14/16]\t loss_instance: 5.376211166381836\t loss_cluster: 1.724812626838684\n",
      "\n",
      "Epoch [30/300]\t Loss: 7.0215322226285934 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.37141752243042\t loss_cluster: 1.701085090637207\n",
      "Step [2/16]\t loss_instance: 5.3611650466918945\t loss_cluster: 1.7035945653915405\n",
      "Step [4/16]\t loss_instance: 5.369216442108154\t loss_cluster: 1.7143322229385376\n",
      "Step [6/16]\t loss_instance: 5.378476619720459\t loss_cluster: 1.715480923652649\n",
      "Step [8/16]\t loss_instance: 5.359005451202393\t loss_cluster: 1.7062413692474365\n",
      "Step [10/16]\t loss_instance: 5.3576340675354\t loss_cluster: 1.6815166473388672\n",
      "Step [12/16]\t loss_instance: 5.409067153930664\t loss_cluster: 1.7630064487457275\n",
      "Step [14/16]\t loss_instance: 5.378317832946777\t loss_cluster: 1.7093677520751953\n",
      "\n",
      "Epoch [31/300]\t Loss: 7.011630210280418 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.378653049468994\t loss_cluster: 1.7537055015563965\n",
      "Step [2/16]\t loss_instance: 5.348686695098877\t loss_cluster: 1.7152656316757202\n",
      "Step [4/16]\t loss_instance: 5.361860275268555\t loss_cluster: 1.7050780057907104\n",
      "Step [6/16]\t loss_instance: 5.375289440155029\t loss_cluster: 1.7249000072479248\n",
      "Step [8/16]\t loss_instance: 5.3670148849487305\t loss_cluster: 1.7426480054855347\n",
      "Step [10/16]\t loss_instance: 5.379065036773682\t loss_cluster: 1.7281031608581543\n",
      "Step [12/16]\t loss_instance: 5.3422040939331055\t loss_cluster: 1.6849092245101929\n",
      "Step [14/16]\t loss_instance: 5.370859146118164\t loss_cluster: 1.7058262825012207\n",
      "\n",
      "Epoch [32/300]\t Loss: 7.010704144835472 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.363039970397949\t loss_cluster: 1.7050803899765015\n",
      "Step [2/16]\t loss_instance: 5.3643012046813965\t loss_cluster: 1.7113054990768433\n",
      "Step [4/16]\t loss_instance: 5.380313873291016\t loss_cluster: 1.7187848091125488\n",
      "Step [6/16]\t loss_instance: 5.357102870941162\t loss_cluster: 1.7082880735397339\n",
      "Step [8/16]\t loss_instance: 5.379260540008545\t loss_cluster: 1.730512022972107\n",
      "Step [10/16]\t loss_instance: 5.372478485107422\t loss_cluster: 1.720067024230957\n",
      "Step [12/16]\t loss_instance: 5.386356830596924\t loss_cluster: 1.7400600910186768\n",
      "Step [14/16]\t loss_instance: 5.350157737731934\t loss_cluster: 1.694355845451355\n",
      "\n",
      "Epoch [33/300]\t Loss: 7.010278508067131 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.375329494476318\t loss_cluster: 1.7096121311187744\n",
      "Step [2/16]\t loss_instance: 5.3848772048950195\t loss_cluster: 1.7224867343902588\n",
      "Step [4/16]\t loss_instance: 5.396183490753174\t loss_cluster: 1.7530673742294312\n",
      "Step [6/16]\t loss_instance: 5.3695292472839355\t loss_cluster: 1.718276023864746\n",
      "Step [8/16]\t loss_instance: 5.38470458984375\t loss_cluster: 1.7004051208496094\n",
      "Step [10/16]\t loss_instance: 5.358010768890381\t loss_cluster: 1.6894959211349487\n",
      "Step [12/16]\t loss_instance: 5.374180316925049\t loss_cluster: 1.7186882495880127\n",
      "Step [14/16]\t loss_instance: 5.371525287628174\t loss_cluster: 1.7062602043151855\n",
      "\n",
      "Epoch [34/300]\t Loss: 7.003046616911888 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.382527828216553\t loss_cluster: 1.7180615663528442\n",
      "Step [2/16]\t loss_instance: 5.386909008026123\t loss_cluster: 1.7346954345703125\n",
      "Step [4/16]\t loss_instance: 5.367302894592285\t loss_cluster: 1.7472375631332397\n",
      "Step [6/16]\t loss_instance: 5.3456549644470215\t loss_cluster: 1.6638702154159546\n",
      "Step [8/16]\t loss_instance: 5.356817245483398\t loss_cluster: 1.714910626411438\n",
      "Step [10/16]\t loss_instance: 5.373692512512207\t loss_cluster: 1.7075749635696411\n",
      "Step [12/16]\t loss_instance: 5.335597991943359\t loss_cluster: 1.6693106889724731\n",
      "Step [14/16]\t loss_instance: 5.386404037475586\t loss_cluster: 1.748267412185669\n",
      "\n",
      "Epoch [35/300]\t Loss: 7.008011111617089 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.354836940765381\t loss_cluster: 1.7118383646011353\n",
      "Step [2/16]\t loss_instance: 5.36523962020874\t loss_cluster: 1.696237564086914\n",
      "Step [4/16]\t loss_instance: 5.382408618927002\t loss_cluster: 1.714154839515686\n",
      "Step [6/16]\t loss_instance: 5.352643966674805\t loss_cluster: 1.6932787895202637\n",
      "Step [8/16]\t loss_instance: 5.386389255523682\t loss_cluster: 1.7197591066360474\n",
      "Step [10/16]\t loss_instance: 5.3394775390625\t loss_cluster: 1.712315320968628\n",
      "Step [12/16]\t loss_instance: 5.370972156524658\t loss_cluster: 1.7045587301254272\n",
      "Step [14/16]\t loss_instance: 5.381028175354004\t loss_cluster: 1.7061593532562256\n",
      "\n",
      "Epoch [36/300]\t Loss: 6.9959546536207196 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.365662097930908\t loss_cluster: 1.7131134271621704\n",
      "Step [2/16]\t loss_instance: 5.364719867706299\t loss_cluster: 1.7023192644119263\n",
      "Step [4/16]\t loss_instance: 5.393059730529785\t loss_cluster: 1.7158600091934204\n",
      "Step [6/16]\t loss_instance: 5.372891902923584\t loss_cluster: 1.7074605226516724\n",
      "Step [8/16]\t loss_instance: 5.375432014465332\t loss_cluster: 1.725781798362732\n",
      "Step [10/16]\t loss_instance: 5.368890762329102\t loss_cluster: 1.721879243850708\n",
      "Step [12/16]\t loss_instance: 5.3768744468688965\t loss_cluster: 1.719541072845459\n",
      "Step [14/16]\t loss_instance: 5.334574222564697\t loss_cluster: 1.6910003423690796\n",
      "\n",
      "Epoch [37/300]\t Loss: 6.995342370867729 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.373050689697266\t loss_cluster: 1.700210452079773\n",
      "Step [2/16]\t loss_instance: 5.350554943084717\t loss_cluster: 1.7094447612762451\n",
      "Step [4/16]\t loss_instance: 5.3625168800354\t loss_cluster: 1.6951308250427246\n",
      "Step [6/16]\t loss_instance: 5.345234394073486\t loss_cluster: 1.6841012239456177\n",
      "Step [8/16]\t loss_instance: 5.3762335777282715\t loss_cluster: 1.7310495376586914\n",
      "Step [10/16]\t loss_instance: 5.349833011627197\t loss_cluster: 1.6970336437225342\n",
      "Step [12/16]\t loss_instance: 5.348530292510986\t loss_cluster: 1.6790584325790405\n",
      "Step [14/16]\t loss_instance: 5.369417667388916\t loss_cluster: 1.7253435850143433\n",
      "\n",
      "Epoch [38/300]\t Loss: 6.984240180253982 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.382781505584717\t loss_cluster: 1.7248669862747192\n",
      "Step [2/16]\t loss_instance: 5.359701156616211\t loss_cluster: 1.6941231489181519\n",
      "Step [4/16]\t loss_instance: 5.3421950340271\t loss_cluster: 1.6715691089630127\n",
      "Step [6/16]\t loss_instance: 5.396628379821777\t loss_cluster: 1.744902491569519\n",
      "Step [8/16]\t loss_instance: 5.3517656326293945\t loss_cluster: 1.71510910987854\n",
      "Step [10/16]\t loss_instance: 5.354312419891357\t loss_cluster: 1.6982678174972534\n",
      "Step [12/16]\t loss_instance: 5.367690563201904\t loss_cluster: 1.7257589101791382\n",
      "Step [14/16]\t loss_instance: 5.348606586456299\t loss_cluster: 1.6877278089523315\n",
      "\n",
      "Epoch [39/300]\t Loss: 6.992610916495323 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.360514163970947\t loss_cluster: 1.6991347074508667\n",
      "Step [2/16]\t loss_instance: 5.372259140014648\t loss_cluster: 1.7238706350326538\n",
      "Step [4/16]\t loss_instance: 5.374634742736816\t loss_cluster: 1.7145498991012573\n",
      "Step [6/16]\t loss_instance: 5.331303596496582\t loss_cluster: 1.6758909225463867\n",
      "Step [8/16]\t loss_instance: 5.374042510986328\t loss_cluster: 1.7298541069030762\n",
      "Step [10/16]\t loss_instance: 5.3679375648498535\t loss_cluster: 1.696773886680603\n",
      "Step [12/16]\t loss_instance: 5.373523235321045\t loss_cluster: 1.7309828996658325\n",
      "Step [14/16]\t loss_instance: 5.332851409912109\t loss_cluster: 1.6895623207092285\n",
      "\n",
      "Epoch [40/300]\t Loss: 6.983617636561394 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.355611324310303\t loss_cluster: 1.7074040174484253\n",
      "Step [2/16]\t loss_instance: 5.34556245803833\t loss_cluster: 1.7006640434265137\n",
      "Step [4/16]\t loss_instance: 5.368514537811279\t loss_cluster: 1.7179628610610962\n",
      "Step [6/16]\t loss_instance: 5.367661476135254\t loss_cluster: 1.7101540565490723\n",
      "Step [8/16]\t loss_instance: 5.335794925689697\t loss_cluster: 1.675744652748108\n",
      "Step [10/16]\t loss_instance: 5.3730926513671875\t loss_cluster: 1.728860855102539\n",
      "Step [12/16]\t loss_instance: 5.36647367477417\t loss_cluster: 1.723559856414795\n",
      "Step [14/16]\t loss_instance: 5.3722944259643555\t loss_cluster: 1.725597858428955\n",
      "\n",
      "Epoch [41/300]\t Loss: 6.983166280388832 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.361525535583496\t loss_cluster: 1.7031110525131226\n",
      "Step [2/16]\t loss_instance: 5.342450141906738\t loss_cluster: 1.681470513343811\n",
      "Step [4/16]\t loss_instance: 5.354760646820068\t loss_cluster: 1.6828328371047974\n",
      "Step [6/16]\t loss_instance: 5.369841575622559\t loss_cluster: 1.7229628562927246\n",
      "Step [8/16]\t loss_instance: 5.382798671722412\t loss_cluster: 1.722050428390503\n",
      "Step [10/16]\t loss_instance: 5.358086585998535\t loss_cluster: 1.7177010774612427\n",
      "Step [12/16]\t loss_instance: 5.3563432693481445\t loss_cluster: 1.6971606016159058\n",
      "Step [14/16]\t loss_instance: 5.353381633758545\t loss_cluster: 1.6969445943832397\n",
      "\n",
      "Epoch [42/300]\t Loss: 6.978626710176468 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3709797859191895\t loss_cluster: 1.7302031517028809\n",
      "Step [2/16]\t loss_instance: 5.3447442054748535\t loss_cluster: 1.709944486618042\n",
      "Step [4/16]\t loss_instance: 5.341556072235107\t loss_cluster: 1.671012282371521\n",
      "Step [6/16]\t loss_instance: 5.3756866455078125\t loss_cluster: 1.7179174423217773\n",
      "Step [8/16]\t loss_instance: 5.356163024902344\t loss_cluster: 1.6985950469970703\n",
      "Step [10/16]\t loss_instance: 5.350220203399658\t loss_cluster: 1.6859694719314575\n",
      "Step [12/16]\t loss_instance: 5.352065086364746\t loss_cluster: 1.7018972635269165\n",
      "Step [14/16]\t loss_instance: 5.350409030914307\t loss_cluster: 1.689314365386963\n",
      "\n",
      "Epoch [43/300]\t Loss: 6.981354314088821 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.390474796295166\t loss_cluster: 1.728721022605896\n",
      "Step [2/16]\t loss_instance: 5.341413974761963\t loss_cluster: 1.7043405771255493\n",
      "Step [4/16]\t loss_instance: 5.349969387054443\t loss_cluster: 1.7041987180709839\n",
      "Step [6/16]\t loss_instance: 5.331704616546631\t loss_cluster: 1.6762734651565552\n",
      "Step [8/16]\t loss_instance: 5.350049018859863\t loss_cluster: 1.7022039890289307\n",
      "Step [10/16]\t loss_instance: 5.374850749969482\t loss_cluster: 1.7164698839187622\n",
      "Step [12/16]\t loss_instance: 5.362029075622559\t loss_cluster: 1.6977813243865967\n",
      "Step [14/16]\t loss_instance: 5.34372091293335\t loss_cluster: 1.6966296434402466\n",
      "\n",
      "Epoch [44/300]\t Loss: 6.974841776490211 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.340594291687012\t loss_cluster: 1.694005012512207\n",
      "Step [2/16]\t loss_instance: 5.3365349769592285\t loss_cluster: 1.6828374862670898\n",
      "Step [4/16]\t loss_instance: 5.361494541168213\t loss_cluster: 1.698479175567627\n",
      "Step [6/16]\t loss_instance: 5.352590560913086\t loss_cluster: 1.7026458978652954\n",
      "Step [8/16]\t loss_instance: 5.3601298332214355\t loss_cluster: 1.727550983428955\n",
      "Step [10/16]\t loss_instance: 5.3570780754089355\t loss_cluster: 1.6915363073349\n",
      "Step [12/16]\t loss_instance: 5.366892337799072\t loss_cluster: 1.7063987255096436\n",
      "Step [14/16]\t loss_instance: 5.355156421661377\t loss_cluster: 1.6888326406478882\n",
      "\n",
      "Epoch [45/300]\t Loss: 6.977383115887642 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.379402160644531\t loss_cluster: 1.7396233081817627\n",
      "Step [2/16]\t loss_instance: 5.340420246124268\t loss_cluster: 1.6780750751495361\n",
      "Step [4/16]\t loss_instance: 5.334540843963623\t loss_cluster: 1.6750553846359253\n",
      "Step [6/16]\t loss_instance: 5.380003452301025\t loss_cluster: 1.7419321537017822\n",
      "Step [8/16]\t loss_instance: 5.357137680053711\t loss_cluster: 1.7203017473220825\n",
      "Step [10/16]\t loss_instance: 5.3535637855529785\t loss_cluster: 1.7058011293411255\n",
      "Step [12/16]\t loss_instance: 5.383641242980957\t loss_cluster: 1.7218317985534668\n",
      "Step [14/16]\t loss_instance: 5.379504680633545\t loss_cluster: 1.7258448600769043\n",
      "\n",
      "Epoch [46/300]\t Loss: 6.974450203776359 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.354910850524902\t loss_cluster: 1.7072813510894775\n",
      "Step [2/16]\t loss_instance: 5.379660129547119\t loss_cluster: 1.7199159860610962\n",
      "Step [4/16]\t loss_instance: 5.34594202041626\t loss_cluster: 1.6881816387176514\n",
      "Step [6/16]\t loss_instance: 5.3488078117370605\t loss_cluster: 1.6987801790237427\n",
      "Step [8/16]\t loss_instance: 5.354214191436768\t loss_cluster: 1.731655478477478\n",
      "Step [10/16]\t loss_instance: 5.330940246582031\t loss_cluster: 1.6968679428100586\n",
      "Step [12/16]\t loss_instance: 5.359138488769531\t loss_cluster: 1.6964317560195923\n",
      "Step [14/16]\t loss_instance: 5.363067150115967\t loss_cluster: 1.6867644786834717\n",
      "\n",
      "Epoch [47/300]\t Loss: 6.969542998075485 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.346909523010254\t loss_cluster: 1.6903177499771118\n",
      "Step [2/16]\t loss_instance: 5.370521545410156\t loss_cluster: 1.7080880403518677\n",
      "Step [4/16]\t loss_instance: 5.333599090576172\t loss_cluster: 1.6803114414215088\n",
      "Step [6/16]\t loss_instance: 5.364835262298584\t loss_cluster: 1.7148358821868896\n",
      "Step [8/16]\t loss_instance: 5.338607311248779\t loss_cluster: 1.6795340776443481\n",
      "Step [10/16]\t loss_instance: 5.363168239593506\t loss_cluster: 1.699110746383667\n",
      "Step [12/16]\t loss_instance: 5.368149280548096\t loss_cluster: 1.7327520847320557\n",
      "Step [14/16]\t loss_instance: 5.339908599853516\t loss_cluster: 1.6786607503890991\n",
      "\n",
      "Epoch [48/300]\t Loss: 6.9719437003135685 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.339724540710449\t loss_cluster: 1.6738994121551514\n",
      "Step [2/16]\t loss_instance: 5.355855941772461\t loss_cluster: 1.69878089427948\n",
      "Step [4/16]\t loss_instance: 5.353999614715576\t loss_cluster: 1.7056559324264526\n",
      "Step [6/16]\t loss_instance: 5.3603105545043945\t loss_cluster: 1.693907618522644\n",
      "Step [8/16]\t loss_instance: 5.370303630828857\t loss_cluster: 1.7252180576324463\n",
      "Step [10/16]\t loss_instance: 5.341930866241455\t loss_cluster: 1.679138422012329\n",
      "Step [12/16]\t loss_instance: 5.362419128417969\t loss_cluster: 1.7092552185058594\n",
      "Step [14/16]\t loss_instance: 5.368512153625488\t loss_cluster: 1.7198245525360107\n",
      "\n",
      "Epoch [49/300]\t Loss: 6.978185594081879 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.353445529937744\t loss_cluster: 1.7334742546081543\n",
      "Step [2/16]\t loss_instance: 5.36208963394165\t loss_cluster: 1.7105003595352173\n",
      "Step [4/16]\t loss_instance: 5.356367111206055\t loss_cluster: 1.6954923868179321\n",
      "Step [6/16]\t loss_instance: 5.325006008148193\t loss_cluster: 1.6919910907745361\n",
      "Step [8/16]\t loss_instance: 5.335453510284424\t loss_cluster: 1.6823673248291016\n",
      "Step [10/16]\t loss_instance: 5.364686965942383\t loss_cluster: 1.7190707921981812\n",
      "Step [12/16]\t loss_instance: 5.325339317321777\t loss_cluster: 1.6842135190963745\n",
      "Step [14/16]\t loss_instance: 5.329086780548096\t loss_cluster: 1.6761871576309204\n",
      "\n",
      "Epoch [50/300]\t Loss: 6.970906120538712 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.34188175201416\t loss_cluster: 1.69048273563385\n",
      "Step [2/16]\t loss_instance: 5.338404655456543\t loss_cluster: 1.6992191076278687\n",
      "Step [4/16]\t loss_instance: 5.349012851715088\t loss_cluster: 1.7009856700897217\n",
      "Step [6/16]\t loss_instance: 5.3392462730407715\t loss_cluster: 1.680623173713684\n",
      "Step [8/16]\t loss_instance: 5.342742919921875\t loss_cluster: 1.6774744987487793\n",
      "Step [10/16]\t loss_instance: 5.341029644012451\t loss_cluster: 1.696027398109436\n",
      "Step [12/16]\t loss_instance: 5.335219383239746\t loss_cluster: 1.685011863708496\n",
      "Step [14/16]\t loss_instance: 5.317089080810547\t loss_cluster: 1.6578134298324585\n",
      "\n",
      "Epoch [51/300]\t Loss: 6.963189813494682 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.353156566619873\t loss_cluster: 1.6996961832046509\n",
      "Step [2/16]\t loss_instance: 5.338140964508057\t loss_cluster: 1.7016619443893433\n",
      "Step [4/16]\t loss_instance: 5.340495586395264\t loss_cluster: 1.6878151893615723\n",
      "Step [6/16]\t loss_instance: 5.372757434844971\t loss_cluster: 1.7154114246368408\n",
      "Step [8/16]\t loss_instance: 5.321053504943848\t loss_cluster: 1.6601890325546265\n",
      "Step [10/16]\t loss_instance: 5.3525238037109375\t loss_cluster: 1.6937601566314697\n",
      "Step [12/16]\t loss_instance: 5.354773044586182\t loss_cluster: 1.693861722946167\n",
      "Step [14/16]\t loss_instance: 5.374441623687744\t loss_cluster: 1.7170294523239136\n",
      "\n",
      "Epoch [52/300]\t Loss: 6.971747037768364 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.331336498260498\t loss_cluster: 1.695525050163269\n",
      "Step [2/16]\t loss_instance: 5.38068151473999\t loss_cluster: 1.7339107990264893\n",
      "Step [4/16]\t loss_instance: 5.3510942459106445\t loss_cluster: 1.6943912506103516\n",
      "Step [6/16]\t loss_instance: 5.314839839935303\t loss_cluster: 1.6642531156539917\n",
      "Step [8/16]\t loss_instance: 5.353553771972656\t loss_cluster: 1.6990904808044434\n",
      "Step [10/16]\t loss_instance: 5.344197750091553\t loss_cluster: 1.6886155605316162\n",
      "Step [12/16]\t loss_instance: 5.346116065979004\t loss_cluster: 1.6929153203964233\n",
      "Step [14/16]\t loss_instance: 5.374544143676758\t loss_cluster: 1.7293670177459717\n",
      "\n",
      "Epoch [53/300]\t Loss: 6.964696860313415 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.345211029052734\t loss_cluster: 1.6978647708892822\n",
      "Step [2/16]\t loss_instance: 5.340505123138428\t loss_cluster: 1.6974939107894897\n",
      "Step [4/16]\t loss_instance: 5.334834575653076\t loss_cluster: 1.6856563091278076\n",
      "Step [6/16]\t loss_instance: 5.3516845703125\t loss_cluster: 1.7113158702850342\n",
      "Step [8/16]\t loss_instance: 5.345802307128906\t loss_cluster: 1.6866825819015503\n",
      "Step [10/16]\t loss_instance: 5.332458972930908\t loss_cluster: 1.6870367527008057\n",
      "Step [12/16]\t loss_instance: 5.367325305938721\t loss_cluster: 1.709924578666687\n",
      "Step [14/16]\t loss_instance: 5.35536003112793\t loss_cluster: 1.7004395723342896\n",
      "\n",
      "Epoch [54/300]\t Loss: 6.972756388783455 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.356751441955566\t loss_cluster: 1.7003693580627441\n",
      "Step [2/16]\t loss_instance: 5.373615264892578\t loss_cluster: 1.712048888206482\n",
      "Step [4/16]\t loss_instance: 5.352421283721924\t loss_cluster: 1.7110530138015747\n",
      "Step [6/16]\t loss_instance: 5.344283580780029\t loss_cluster: 1.6989058256149292\n",
      "Step [8/16]\t loss_instance: 5.329864978790283\t loss_cluster: 1.677777647972107\n",
      "Step [10/16]\t loss_instance: 5.326417922973633\t loss_cluster: 1.6776882410049438\n",
      "Step [12/16]\t loss_instance: 5.350569725036621\t loss_cluster: 1.7130247354507446\n",
      "Step [14/16]\t loss_instance: 5.368350982666016\t loss_cluster: 1.7180697917938232\n",
      "\n",
      "Epoch [55/300]\t Loss: 6.96443652510643 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.365029335021973\t loss_cluster: 1.711401343345642\n",
      "Step [2/16]\t loss_instance: 5.341710567474365\t loss_cluster: 1.685468316078186\n",
      "Step [4/16]\t loss_instance: 5.360528945922852\t loss_cluster: 1.7054682970046997\n",
      "Step [6/16]\t loss_instance: 5.351736068725586\t loss_cluster: 1.6916441917419434\n",
      "Step [8/16]\t loss_instance: 5.350057601928711\t loss_cluster: 1.682554006576538\n",
      "Step [10/16]\t loss_instance: 5.363966941833496\t loss_cluster: 1.7046138048171997\n",
      "Step [12/16]\t loss_instance: 5.346792697906494\t loss_cluster: 1.6757793426513672\n",
      "Step [14/16]\t loss_instance: 5.340686321258545\t loss_cluster: 1.6836532354354858\n",
      "\n",
      "Epoch [56/300]\t Loss: 6.959215450286865 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.334854602813721\t loss_cluster: 1.6739087104797363\n",
      "Step [2/16]\t loss_instance: 5.339092254638672\t loss_cluster: 1.6740247011184692\n",
      "Step [4/16]\t loss_instance: 5.334880352020264\t loss_cluster: 1.6669856309890747\n",
      "Step [6/16]\t loss_instance: 5.345268249511719\t loss_cluster: 1.695872187614441\n",
      "Step [8/16]\t loss_instance: 5.360040187835693\t loss_cluster: 1.7196258306503296\n",
      "Step [10/16]\t loss_instance: 5.369302749633789\t loss_cluster: 1.7298065423965454\n",
      "Step [12/16]\t loss_instance: 5.3332295417785645\t loss_cluster: 1.6860045194625854\n",
      "Step [14/16]\t loss_instance: 5.376038551330566\t loss_cluster: 1.7438486814498901\n",
      "\n",
      "Epoch [57/300]\t Loss: 6.965666025876999 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.365076065063477\t loss_cluster: 1.7110215425491333\n",
      "Step [2/16]\t loss_instance: 5.3660125732421875\t loss_cluster: 1.6917301416397095\n",
      "Step [4/16]\t loss_instance: 5.380802631378174\t loss_cluster: 1.733655571937561\n",
      "Step [6/16]\t loss_instance: 5.343804359436035\t loss_cluster: 1.6895033121109009\n",
      "Step [8/16]\t loss_instance: 5.327712059020996\t loss_cluster: 1.6784677505493164\n",
      "Step [10/16]\t loss_instance: 5.331036567687988\t loss_cluster: 1.683579921722412\n",
      "Step [12/16]\t loss_instance: 5.3242645263671875\t loss_cluster: 1.6712274551391602\n",
      "Step [14/16]\t loss_instance: 5.319277286529541\t loss_cluster: 1.6668775081634521\n",
      "\n",
      "Epoch [58/300]\t Loss: 6.961450761556625 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.349010944366455\t loss_cluster: 1.691894769668579\n",
      "Step [2/16]\t loss_instance: 5.341949462890625\t loss_cluster: 1.6785231828689575\n",
      "Step [4/16]\t loss_instance: 5.3998212814331055\t loss_cluster: 1.7606571912765503\n",
      "Step [6/16]\t loss_instance: 5.342212677001953\t loss_cluster: 1.6822311878204346\n",
      "Step [8/16]\t loss_instance: 5.362830638885498\t loss_cluster: 1.7065938711166382\n",
      "Step [10/16]\t loss_instance: 5.349758148193359\t loss_cluster: 1.6786963939666748\n",
      "Step [12/16]\t loss_instance: 5.354319095611572\t loss_cluster: 1.6988900899887085\n",
      "Step [14/16]\t loss_instance: 5.35471248626709\t loss_cluster: 1.694894552230835\n",
      "\n",
      "Epoch [59/300]\t Loss: 6.957308575510979 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.318739891052246\t loss_cluster: 1.6747976541519165\n",
      "Step [2/16]\t loss_instance: 5.35496711730957\t loss_cluster: 1.6921573877334595\n",
      "Step [4/16]\t loss_instance: 5.356204509735107\t loss_cluster: 1.708568811416626\n",
      "Step [6/16]\t loss_instance: 5.33946418762207\t loss_cluster: 1.6954331398010254\n",
      "Step [8/16]\t loss_instance: 5.369190692901611\t loss_cluster: 1.7142658233642578\n",
      "Step [10/16]\t loss_instance: 5.329949378967285\t loss_cluster: 1.6860089302062988\n",
      "Step [12/16]\t loss_instance: 5.332118034362793\t loss_cluster: 1.668685793876648\n",
      "Step [14/16]\t loss_instance: 5.343361854553223\t loss_cluster: 1.6993070840835571\n",
      "\n",
      "Epoch [60/300]\t Loss: 6.961477026343346 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.353756904602051\t loss_cluster: 1.7058751583099365\n",
      "Step [2/16]\t loss_instance: 5.353882789611816\t loss_cluster: 1.7255817651748657\n",
      "Step [4/16]\t loss_instance: 5.336740016937256\t loss_cluster: 1.6834834814071655\n",
      "Step [6/16]\t loss_instance: 5.368802547454834\t loss_cluster: 1.7201554775238037\n",
      "Step [8/16]\t loss_instance: 5.3680901527404785\t loss_cluster: 1.7084537744522095\n",
      "Step [10/16]\t loss_instance: 5.333683013916016\t loss_cluster: 1.6730644702911377\n",
      "Step [12/16]\t loss_instance: 5.342077255249023\t loss_cluster: 1.7105474472045898\n",
      "Step [14/16]\t loss_instance: 5.330773830413818\t loss_cluster: 1.665527105331421\n",
      "\n",
      "Epoch [61/300]\t Loss: 6.963220182061195 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.347103118896484\t loss_cluster: 1.6901135444641113\n",
      "Step [2/16]\t loss_instance: 5.350285530090332\t loss_cluster: 1.6802374124526978\n",
      "Step [4/16]\t loss_instance: 5.353157043457031\t loss_cluster: 1.693359375\n",
      "Step [6/16]\t loss_instance: 5.333730220794678\t loss_cluster: 1.674133062362671\n",
      "Step [8/16]\t loss_instance: 5.358041763305664\t loss_cluster: 1.734782099723816\n",
      "Step [10/16]\t loss_instance: 5.342952251434326\t loss_cluster: 1.668624758720398\n",
      "Step [12/16]\t loss_instance: 5.339356422424316\t loss_cluster: 1.675034999847412\n",
      "Step [14/16]\t loss_instance: 5.369226455688477\t loss_cluster: 1.6998518705368042\n",
      "\n",
      "Epoch [62/300]\t Loss: 6.953755414485931 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.345705032348633\t loss_cluster: 1.691594123840332\n",
      "Step [2/16]\t loss_instance: 5.3741230964660645\t loss_cluster: 1.711073398590088\n",
      "Step [4/16]\t loss_instance: 5.369955539703369\t loss_cluster: 1.6905485391616821\n",
      "Step [6/16]\t loss_instance: 5.361724853515625\t loss_cluster: 1.7003166675567627\n",
      "Step [8/16]\t loss_instance: 5.335103511810303\t loss_cluster: 1.6862019300460815\n",
      "Step [10/16]\t loss_instance: 5.3628764152526855\t loss_cluster: 1.7033131122589111\n",
      "Step [12/16]\t loss_instance: 5.36854362487793\t loss_cluster: 1.706727385520935\n",
      "Step [14/16]\t loss_instance: 5.355656623840332\t loss_cluster: 1.7118546962738037\n",
      "\n",
      "Epoch [63/300]\t Loss: 6.954327195882797 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.345992565155029\t loss_cluster: 1.6849347352981567\n",
      "Step [2/16]\t loss_instance: 5.354321479797363\t loss_cluster: 1.730851650238037\n",
      "Step [4/16]\t loss_instance: 5.3472371101379395\t loss_cluster: 1.6933743953704834\n",
      "Step [6/16]\t loss_instance: 5.361542224884033\t loss_cluster: 1.711876392364502\n",
      "Step [8/16]\t loss_instance: 5.3422770500183105\t loss_cluster: 1.6860836744308472\n",
      "Step [10/16]\t loss_instance: 5.347562789916992\t loss_cluster: 1.716626524925232\n",
      "Step [12/16]\t loss_instance: 5.38060188293457\t loss_cluster: 1.7079815864562988\n",
      "Step [14/16]\t loss_instance: 5.373712062835693\t loss_cluster: 1.713013768196106\n",
      "\n",
      "Epoch [64/300]\t Loss: 6.958410641551017 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.320372104644775\t loss_cluster: 1.6589330434799194\n",
      "Step [2/16]\t loss_instance: 5.335387706756592\t loss_cluster: 1.6841553449630737\n",
      "Step [4/16]\t loss_instance: 5.387563228607178\t loss_cluster: 1.719435453414917\n",
      "Step [6/16]\t loss_instance: 5.3370771408081055\t loss_cluster: 1.692452311515808\n",
      "Step [8/16]\t loss_instance: 5.33584451675415\t loss_cluster: 1.6828111410140991\n",
      "Step [10/16]\t loss_instance: 5.3607563972473145\t loss_cluster: 1.7107195854187012\n",
      "Step [12/16]\t loss_instance: 5.351199626922607\t loss_cluster: 1.6980029344558716\n",
      "Step [14/16]\t loss_instance: 5.378143310546875\t loss_cluster: 1.730958104133606\n",
      "\n",
      "Epoch [65/300]\t Loss: 6.951398313045502 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3494977951049805\t loss_cluster: 1.692007064819336\n",
      "Step [2/16]\t loss_instance: 5.377908706665039\t loss_cluster: 1.6999109983444214\n",
      "Step [4/16]\t loss_instance: 5.334227561950684\t loss_cluster: 1.7188700437545776\n",
      "Step [6/16]\t loss_instance: 5.333123207092285\t loss_cluster: 1.668194055557251\n",
      "Step [8/16]\t loss_instance: 5.339791774749756\t loss_cluster: 1.7131998538970947\n",
      "Step [10/16]\t loss_instance: 5.347487449645996\t loss_cluster: 1.682142734527588\n",
      "Step [12/16]\t loss_instance: 5.331580638885498\t loss_cluster: 1.6944584846496582\n",
      "Step [14/16]\t loss_instance: 5.364123344421387\t loss_cluster: 1.7055102586746216\n",
      "\n",
      "Epoch [66/300]\t Loss: 6.950014641880989 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.32969856262207\t loss_cluster: 1.6671451330184937\n",
      "Step [2/16]\t loss_instance: 5.348101615905762\t loss_cluster: 1.6829427480697632\n",
      "Step [4/16]\t loss_instance: 5.334519386291504\t loss_cluster: 1.6879523992538452\n",
      "Step [6/16]\t loss_instance: 5.375237464904785\t loss_cluster: 1.7232732772827148\n",
      "Step [8/16]\t loss_instance: 5.379336357116699\t loss_cluster: 1.7346543073654175\n",
      "Step [10/16]\t loss_instance: 5.371302604675293\t loss_cluster: 1.7212270498275757\n",
      "Step [12/16]\t loss_instance: 5.3117265701293945\t loss_cluster: 1.6586371660232544\n",
      "Step [14/16]\t loss_instance: 5.357763767242432\t loss_cluster: 1.7125563621520996\n",
      "\n",
      "Epoch [67/300]\t Loss: 6.955819624662399 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.349971294403076\t loss_cluster: 1.6953411102294922\n",
      "Step [2/16]\t loss_instance: 5.340997219085693\t loss_cluster: 1.692673921585083\n",
      "Step [4/16]\t loss_instance: 5.343521595001221\t loss_cluster: 1.7098784446716309\n",
      "Step [6/16]\t loss_instance: 5.33926248550415\t loss_cluster: 1.6832600831985474\n",
      "Step [8/16]\t loss_instance: 5.344658374786377\t loss_cluster: 1.6780085563659668\n",
      "Step [10/16]\t loss_instance: 5.338362693786621\t loss_cluster: 1.6939663887023926\n",
      "Step [12/16]\t loss_instance: 5.327666282653809\t loss_cluster: 1.6961588859558105\n",
      "Step [14/16]\t loss_instance: 5.34821081161499\t loss_cluster: 1.69483482837677\n",
      "\n",
      "Epoch [68/300]\t Loss: 6.953750282526016 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.32953405380249\t loss_cluster: 1.6808289289474487\n",
      "Step [2/16]\t loss_instance: 5.337292194366455\t loss_cluster: 1.693520188331604\n",
      "Step [4/16]\t loss_instance: 5.343002796173096\t loss_cluster: 1.684105634689331\n",
      "Step [6/16]\t loss_instance: 5.356039047241211\t loss_cluster: 1.7026489973068237\n",
      "Step [8/16]\t loss_instance: 5.355662822723389\t loss_cluster: 1.712139368057251\n",
      "Step [10/16]\t loss_instance: 5.3524489402771\t loss_cluster: 1.7048078775405884\n",
      "Step [12/16]\t loss_instance: 5.373762130737305\t loss_cluster: 1.7241164445877075\n",
      "Step [14/16]\t loss_instance: 5.32379150390625\t loss_cluster: 1.6555203199386597\n",
      "\n",
      "Epoch [69/300]\t Loss: 6.952985528111458 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.349907875061035\t loss_cluster: 1.6925817728042603\n",
      "Step [2/16]\t loss_instance: 5.358424186706543\t loss_cluster: 1.7065058946609497\n",
      "Step [4/16]\t loss_instance: 5.353634357452393\t loss_cluster: 1.7076729536056519\n",
      "Step [6/16]\t loss_instance: 5.360874652862549\t loss_cluster: 1.7038910388946533\n",
      "Step [8/16]\t loss_instance: 5.323850631713867\t loss_cluster: 1.66314697265625\n",
      "Step [10/16]\t loss_instance: 5.360661506652832\t loss_cluster: 1.7098636627197266\n",
      "Step [12/16]\t loss_instance: 5.346327781677246\t loss_cluster: 1.692777156829834\n",
      "Step [14/16]\t loss_instance: 5.32895565032959\t loss_cluster: 1.67876398563385\n",
      "\n",
      "Epoch [70/300]\t Loss: 6.952282345294952 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3597025871276855\t loss_cluster: 1.6843185424804688\n",
      "Step [2/16]\t loss_instance: 5.345893859863281\t loss_cluster: 1.7174160480499268\n",
      "Step [4/16]\t loss_instance: 5.331146717071533\t loss_cluster: 1.6781662702560425\n",
      "Step [6/16]\t loss_instance: 5.352183818817139\t loss_cluster: 1.6947455406188965\n",
      "Step [8/16]\t loss_instance: 5.366519451141357\t loss_cluster: 1.7264348268508911\n",
      "Step [10/16]\t loss_instance: 5.317544460296631\t loss_cluster: 1.6838375329971313\n",
      "Step [12/16]\t loss_instance: 5.354298114776611\t loss_cluster: 1.6944266557693481\n",
      "Step [14/16]\t loss_instance: 5.367895603179932\t loss_cluster: 1.7081087827682495\n",
      "\n",
      "Epoch [71/300]\t Loss: 6.9512637794017795 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.333118438720703\t loss_cluster: 1.6902878284454346\n",
      "Step [2/16]\t loss_instance: 5.34574031829834\t loss_cluster: 1.6889917850494385\n",
      "Step [4/16]\t loss_instance: 5.3398003578186035\t loss_cluster: 1.6863927841186523\n",
      "Step [6/16]\t loss_instance: 5.340664386749268\t loss_cluster: 1.6833361387252808\n",
      "Step [8/16]\t loss_instance: 5.363389492034912\t loss_cluster: 1.7075754404067993\n",
      "Step [10/16]\t loss_instance: 5.374005317687988\t loss_cluster: 1.704002022743225\n",
      "Step [12/16]\t loss_instance: 5.340756893157959\t loss_cluster: 1.67866051197052\n",
      "Step [14/16]\t loss_instance: 5.33927059173584\t loss_cluster: 1.6801024675369263\n",
      "\n",
      "Epoch [72/300]\t Loss: 6.9467531710863115 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.321509838104248\t loss_cluster: 1.664336919784546\n",
      "Step [2/16]\t loss_instance: 5.337489128112793\t loss_cluster: 1.7025164365768433\n",
      "Step [4/16]\t loss_instance: 5.342046737670898\t loss_cluster: 1.6770455837249756\n",
      "Step [6/16]\t loss_instance: 5.320672512054443\t loss_cluster: 1.6843417882919312\n",
      "Step [8/16]\t loss_instance: 5.365874290466309\t loss_cluster: 1.7105611562728882\n",
      "Step [10/16]\t loss_instance: 5.357726573944092\t loss_cluster: 1.7107774019241333\n",
      "Step [12/16]\t loss_instance: 5.357689380645752\t loss_cluster: 1.689752459526062\n",
      "Step [14/16]\t loss_instance: 5.332800388336182\t loss_cluster: 1.670894742012024\n",
      "\n",
      "Epoch [73/300]\t Loss: 6.951781898736954 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.34460973739624\t loss_cluster: 1.6909979581832886\n",
      "Step [2/16]\t loss_instance: 5.366823196411133\t loss_cluster: 1.6970881223678589\n",
      "Step [4/16]\t loss_instance: 5.324432373046875\t loss_cluster: 1.6679819822311401\n",
      "Step [6/16]\t loss_instance: 5.382723808288574\t loss_cluster: 1.7472479343414307\n",
      "Step [8/16]\t loss_instance: 5.328676700592041\t loss_cluster: 1.6684805154800415\n",
      "Step [10/16]\t loss_instance: 5.3071818351745605\t loss_cluster: 1.6501418352127075\n",
      "Step [12/16]\t loss_instance: 5.333410263061523\t loss_cluster: 1.661294937133789\n",
      "Step [14/16]\t loss_instance: 5.35677433013916\t loss_cluster: 1.7039752006530762\n",
      "\n",
      "Epoch [74/300]\t Loss: 6.946702003479004 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.346192836761475\t loss_cluster: 1.6862688064575195\n",
      "Step [2/16]\t loss_instance: 5.324133396148682\t loss_cluster: 1.7046964168548584\n",
      "Step [4/16]\t loss_instance: 5.361024379730225\t loss_cluster: 1.6994081735610962\n",
      "Step [6/16]\t loss_instance: 5.344175338745117\t loss_cluster: 1.700584053993225\n",
      "Step [8/16]\t loss_instance: 5.328946113586426\t loss_cluster: 1.671491026878357\n",
      "Step [10/16]\t loss_instance: 5.347751140594482\t loss_cluster: 1.7058566808700562\n",
      "Step [12/16]\t loss_instance: 5.3954057693481445\t loss_cluster: 1.720058798789978\n",
      "Step [14/16]\t loss_instance: 5.373891353607178\t loss_cluster: 1.7194591760635376\n",
      "\n",
      "Epoch [75/300]\t Loss: 6.945051118731499 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.356741905212402\t loss_cluster: 1.6936331987380981\n",
      "Step [2/16]\t loss_instance: 5.34786319732666\t loss_cluster: 1.6967079639434814\n",
      "Step [4/16]\t loss_instance: 5.390407562255859\t loss_cluster: 1.737769365310669\n",
      "Step [6/16]\t loss_instance: 5.334624290466309\t loss_cluster: 1.6864029169082642\n",
      "Step [8/16]\t loss_instance: 5.3479132652282715\t loss_cluster: 1.7059013843536377\n",
      "Step [10/16]\t loss_instance: 5.307888984680176\t loss_cluster: 1.6570097208023071\n",
      "Step [12/16]\t loss_instance: 5.323215484619141\t loss_cluster: 1.6812479496002197\n",
      "Step [14/16]\t loss_instance: 5.381326675415039\t loss_cluster: 1.733137845993042\n",
      "\n",
      "Epoch [76/300]\t Loss: 6.947742635011673 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.370720386505127\t loss_cluster: 1.712579369544983\n",
      "Step [2/16]\t loss_instance: 5.349005699157715\t loss_cluster: 1.6987419128417969\n",
      "Step [4/16]\t loss_instance: 5.3298749923706055\t loss_cluster: 1.6827023029327393\n",
      "Step [6/16]\t loss_instance: 5.33970308303833\t loss_cluster: 1.6883459091186523\n",
      "Step [8/16]\t loss_instance: 5.31772518157959\t loss_cluster: 1.6909756660461426\n",
      "Step [10/16]\t loss_instance: 5.31817626953125\t loss_cluster: 1.671349048614502\n",
      "Step [12/16]\t loss_instance: 5.352756023406982\t loss_cluster: 1.6780592203140259\n",
      "Step [14/16]\t loss_instance: 5.34029483795166\t loss_cluster: 1.698033094406128\n",
      "\n",
      "Epoch [77/300]\t Loss: 6.946014633774757 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.350533485412598\t loss_cluster: 1.7071014642715454\n",
      "Step [2/16]\t loss_instance: 5.361601829528809\t loss_cluster: 1.7084558010101318\n",
      "Step [4/16]\t loss_instance: 5.36542272567749\t loss_cluster: 1.7237290143966675\n",
      "Step [6/16]\t loss_instance: 5.314754486083984\t loss_cluster: 1.66705322265625\n",
      "Step [8/16]\t loss_instance: 5.327518939971924\t loss_cluster: 1.6796636581420898\n",
      "Step [10/16]\t loss_instance: 5.373728275299072\t loss_cluster: 1.697085976600647\n",
      "Step [12/16]\t loss_instance: 5.345639705657959\t loss_cluster: 1.6833919286727905\n",
      "Step [14/16]\t loss_instance: 5.353926658630371\t loss_cluster: 1.688938021659851\n",
      "\n",
      "Epoch [78/300]\t Loss: 6.948090371489525 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.363711357116699\t loss_cluster: 1.70255446434021\n",
      "Step [2/16]\t loss_instance: 5.339012622833252\t loss_cluster: 1.688847541809082\n",
      "Step [4/16]\t loss_instance: 5.344394683837891\t loss_cluster: 1.6961156129837036\n",
      "Step [6/16]\t loss_instance: 5.345795154571533\t loss_cluster: 1.7174617052078247\n",
      "Step [8/16]\t loss_instance: 5.35957145690918\t loss_cluster: 1.717240571975708\n",
      "Step [10/16]\t loss_instance: 5.314513206481934\t loss_cluster: 1.6615475416183472\n",
      "Step [12/16]\t loss_instance: 5.319489479064941\t loss_cluster: 1.657033920288086\n",
      "Step [14/16]\t loss_instance: 5.370107650756836\t loss_cluster: 1.704920768737793\n",
      "\n",
      "Epoch [79/300]\t Loss: 6.946373653411865 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3730387687683105\t loss_cluster: 1.7104859352111816\n",
      "Step [2/16]\t loss_instance: 5.340729713439941\t loss_cluster: 1.6861716508865356\n",
      "Step [4/16]\t loss_instance: 5.335728168487549\t loss_cluster: 1.6730836629867554\n",
      "Step [6/16]\t loss_instance: 5.371264934539795\t loss_cluster: 1.702373743057251\n",
      "Step [8/16]\t loss_instance: 5.35364294052124\t loss_cluster: 1.7186833620071411\n",
      "Step [10/16]\t loss_instance: 5.342996597290039\t loss_cluster: 1.7074891328811646\n",
      "Step [12/16]\t loss_instance: 5.317947864532471\t loss_cluster: 1.6783729791641235\n",
      "Step [14/16]\t loss_instance: 5.357580661773682\t loss_cluster: 1.6878774166107178\n",
      "\n",
      "Epoch [80/300]\t Loss: 6.942400106787682 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3350677490234375\t loss_cluster: 1.6560850143432617\n",
      "Step [2/16]\t loss_instance: 5.333549499511719\t loss_cluster: 1.6927735805511475\n",
      "Step [4/16]\t loss_instance: 5.369189739227295\t loss_cluster: 1.7139595746994019\n",
      "Step [6/16]\t loss_instance: 5.336678981781006\t loss_cluster: 1.6984472274780273\n",
      "Step [8/16]\t loss_instance: 5.353959083557129\t loss_cluster: 1.7129446268081665\n",
      "Step [10/16]\t loss_instance: 5.335507392883301\t loss_cluster: 1.6822973489761353\n",
      "Step [12/16]\t loss_instance: 5.344518184661865\t loss_cluster: 1.688850998878479\n",
      "Step [14/16]\t loss_instance: 5.378645420074463\t loss_cluster: 1.7113081216812134\n",
      "\n",
      "Epoch [81/300]\t Loss: 6.9419327795505525 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.358026504516602\t loss_cluster: 1.7073980569839478\n",
      "Step [2/16]\t loss_instance: 5.32749605178833\t loss_cluster: 1.6852725744247437\n",
      "Step [4/16]\t loss_instance: 5.338940143585205\t loss_cluster: 1.6968110799789429\n",
      "Step [6/16]\t loss_instance: 5.340780735015869\t loss_cluster: 1.6868749856948853\n",
      "Step [8/16]\t loss_instance: 5.32788610458374\t loss_cluster: 1.6741266250610352\n",
      "Step [10/16]\t loss_instance: 5.337820529937744\t loss_cluster: 1.6795188188552856\n",
      "Step [12/16]\t loss_instance: 5.321849822998047\t loss_cluster: 1.6766810417175293\n",
      "Step [14/16]\t loss_instance: 5.350094318389893\t loss_cluster: 1.6758854389190674\n",
      "\n",
      "Epoch [82/300]\t Loss: 6.94048852622509 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.358537197113037\t loss_cluster: 1.712980031967163\n",
      "Step [2/16]\t loss_instance: 5.3220086097717285\t loss_cluster: 1.6870399713516235\n",
      "Step [4/16]\t loss_instance: 5.320615768432617\t loss_cluster: 1.693755030632019\n",
      "Step [6/16]\t loss_instance: 5.343865394592285\t loss_cluster: 1.7054957151412964\n",
      "Step [8/16]\t loss_instance: 5.330577850341797\t loss_cluster: 1.6769636869430542\n",
      "Step [10/16]\t loss_instance: 5.3120551109313965\t loss_cluster: 1.6893060207366943\n",
      "Step [12/16]\t loss_instance: 5.344081878662109\t loss_cluster: 1.6941533088684082\n",
      "Step [14/16]\t loss_instance: 5.377475261688232\t loss_cluster: 1.707403302192688\n",
      "\n",
      "Epoch [83/300]\t Loss: 6.950186923146248 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.354091644287109\t loss_cluster: 1.7089124917984009\n",
      "Step [2/16]\t loss_instance: 5.351796627044678\t loss_cluster: 1.694905161857605\n",
      "Step [4/16]\t loss_instance: 5.3501410484313965\t loss_cluster: 1.7037354707717896\n",
      "Step [6/16]\t loss_instance: 5.329950332641602\t loss_cluster: 1.6847026348114014\n",
      "Step [8/16]\t loss_instance: 5.340436935424805\t loss_cluster: 1.6888213157653809\n",
      "Step [10/16]\t loss_instance: 5.360265731811523\t loss_cluster: 1.7269260883331299\n",
      "Step [12/16]\t loss_instance: 5.319275856018066\t loss_cluster: 1.6635161638259888\n",
      "Step [14/16]\t loss_instance: 5.3596320152282715\t loss_cluster: 1.6735605001449585\n",
      "\n",
      "Epoch [84/300]\t Loss: 6.944491574168206 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.356451988220215\t loss_cluster: 1.7060718536376953\n",
      "Step [2/16]\t loss_instance: 5.353542327880859\t loss_cluster: 1.7340604066848755\n",
      "Step [4/16]\t loss_instance: 5.333932399749756\t loss_cluster: 1.6732995510101318\n",
      "Step [6/16]\t loss_instance: 5.3361968994140625\t loss_cluster: 1.6822521686553955\n",
      "Step [8/16]\t loss_instance: 5.322664260864258\t loss_cluster: 1.6554332971572876\n",
      "Step [10/16]\t loss_instance: 5.343073844909668\t loss_cluster: 1.6835261583328247\n",
      "Step [12/16]\t loss_instance: 5.355587005615234\t loss_cluster: 1.702628254890442\n",
      "Step [14/16]\t loss_instance: 5.345820426940918\t loss_cluster: 1.701383352279663\n",
      "\n",
      "Epoch [85/300]\t Loss: 6.94399442076683 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.368381500244141\t loss_cluster: 1.7264310121536255\n",
      "Step [2/16]\t loss_instance: 5.372651100158691\t loss_cluster: 1.7100157737731934\n",
      "Step [4/16]\t loss_instance: 5.325955390930176\t loss_cluster: 1.671759009361267\n",
      "Step [6/16]\t loss_instance: 5.332417964935303\t loss_cluster: 1.663102388381958\n",
      "Step [8/16]\t loss_instance: 5.344310283660889\t loss_cluster: 1.6805092096328735\n",
      "Step [10/16]\t loss_instance: 5.361403465270996\t loss_cluster: 1.7026442289352417\n",
      "Step [12/16]\t loss_instance: 5.356685161590576\t loss_cluster: 1.7113810777664185\n",
      "Step [14/16]\t loss_instance: 5.349754810333252\t loss_cluster: 1.6946691274642944\n",
      "\n",
      "Epoch [86/300]\t Loss: 6.940601164102555 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.346329689025879\t loss_cluster: 1.687774419784546\n",
      "Step [2/16]\t loss_instance: 5.302798271179199\t loss_cluster: 1.6434146165847778\n",
      "Step [4/16]\t loss_instance: 5.342720031738281\t loss_cluster: 1.6833935976028442\n",
      "Step [6/16]\t loss_instance: 5.360348224639893\t loss_cluster: 1.718065619468689\n",
      "Step [8/16]\t loss_instance: 5.319451808929443\t loss_cluster: 1.6612173318862915\n",
      "Step [10/16]\t loss_instance: 5.351447582244873\t loss_cluster: 1.6944046020507812\n",
      "Step [12/16]\t loss_instance: 5.343675136566162\t loss_cluster: 1.6906417608261108\n",
      "Step [14/16]\t loss_instance: 5.313006401062012\t loss_cluster: 1.6731795072555542\n",
      "\n",
      "Epoch [87/300]\t Loss: 6.932579442858696 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.357503414154053\t loss_cluster: 1.71703040599823\n",
      "Step [2/16]\t loss_instance: 5.353412628173828\t loss_cluster: 1.703191876411438\n",
      "Step [4/16]\t loss_instance: 5.3467841148376465\t loss_cluster: 1.692403793334961\n",
      "Step [6/16]\t loss_instance: 5.309598445892334\t loss_cluster: 1.651055932044983\n",
      "Step [8/16]\t loss_instance: 5.337209701538086\t loss_cluster: 1.6568607091903687\n",
      "Step [10/16]\t loss_instance: 5.342099189758301\t loss_cluster: 1.6864897012710571\n",
      "Step [12/16]\t loss_instance: 5.336164951324463\t loss_cluster: 1.6883997917175293\n",
      "Step [14/16]\t loss_instance: 5.341815948486328\t loss_cluster: 1.686171293258667\n",
      "\n",
      "Epoch [88/300]\t Loss: 6.933761888742447 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3218488693237305\t loss_cluster: 1.6724358797073364\n",
      "Step [2/16]\t loss_instance: 5.3522443771362305\t loss_cluster: 1.7089687585830688\n",
      "Step [4/16]\t loss_instance: 5.37251091003418\t loss_cluster: 1.7559932470321655\n",
      "Step [6/16]\t loss_instance: 5.357080936431885\t loss_cluster: 1.686429738998413\n",
      "Step [8/16]\t loss_instance: 5.329221248626709\t loss_cluster: 1.6731503009796143\n",
      "Step [10/16]\t loss_instance: 5.346059799194336\t loss_cluster: 1.678419828414917\n",
      "Step [12/16]\t loss_instance: 5.358445644378662\t loss_cluster: 1.6933590173721313\n",
      "Step [14/16]\t loss_instance: 5.333385467529297\t loss_cluster: 1.6863057613372803\n",
      "\n",
      "Epoch [89/300]\t Loss: 6.948363158106804 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.338681697845459\t loss_cluster: 1.6896294355392456\n",
      "Step [2/16]\t loss_instance: 5.341197490692139\t loss_cluster: 1.6739364862442017\n",
      "Step [4/16]\t loss_instance: 5.332500457763672\t loss_cluster: 1.6730390787124634\n",
      "Step [6/16]\t loss_instance: 5.3431782722473145\t loss_cluster: 1.6824226379394531\n",
      "Step [8/16]\t loss_instance: 5.348728179931641\t loss_cluster: 1.6922751665115356\n",
      "Step [10/16]\t loss_instance: 5.342453956604004\t loss_cluster: 1.694541573524475\n",
      "Step [12/16]\t loss_instance: 5.340681076049805\t loss_cluster: 1.6833120584487915\n",
      "Step [14/16]\t loss_instance: 5.350880146026611\t loss_cluster: 1.6922869682312012\n",
      "\n",
      "Epoch [90/300]\t Loss: 6.935889378190041 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.362677574157715\t loss_cluster: 1.6909335851669312\n",
      "Step [2/16]\t loss_instance: 5.314444065093994\t loss_cluster: 1.676453709602356\n",
      "Step [4/16]\t loss_instance: 5.336915969848633\t loss_cluster: 1.689013123512268\n",
      "Step [6/16]\t loss_instance: 5.354327201843262\t loss_cluster: 1.7113653421401978\n",
      "Step [8/16]\t loss_instance: 5.378139972686768\t loss_cluster: 1.711177110671997\n",
      "Step [10/16]\t loss_instance: 5.348113536834717\t loss_cluster: 1.685370683670044\n",
      "Step [12/16]\t loss_instance: 5.369574069976807\t loss_cluster: 1.719979166984558\n",
      "Step [14/16]\t loss_instance: 5.322389602661133\t loss_cluster: 1.6709705591201782\n",
      "\n",
      "Epoch [91/300]\t Loss: 6.939641511440277 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.349308013916016\t loss_cluster: 1.6811586618423462\n",
      "Step [2/16]\t loss_instance: 5.3250732421875\t loss_cluster: 1.6610924005508423\n",
      "Step [4/16]\t loss_instance: 5.380435943603516\t loss_cluster: 1.7096420526504517\n",
      "Step [6/16]\t loss_instance: 5.3539509773254395\t loss_cluster: 1.707204818725586\n",
      "Step [8/16]\t loss_instance: 5.337360858917236\t loss_cluster: 1.692470908164978\n",
      "Step [10/16]\t loss_instance: 5.362272262573242\t loss_cluster: 1.6945675611495972\n",
      "Step [12/16]\t loss_instance: 5.304299354553223\t loss_cluster: 1.6400402784347534\n",
      "Step [14/16]\t loss_instance: 5.3359527587890625\t loss_cluster: 1.6861799955368042\n",
      "\n",
      "Epoch [92/300]\t Loss: 6.932153582572937 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.361006736755371\t loss_cluster: 1.694284200668335\n",
      "Step [2/16]\t loss_instance: 5.335193634033203\t loss_cluster: 1.6683833599090576\n",
      "Step [4/16]\t loss_instance: 5.3441548347473145\t loss_cluster: 1.7007991075515747\n",
      "Step [6/16]\t loss_instance: 5.371042251586914\t loss_cluster: 1.7321854829788208\n",
      "Step [8/16]\t loss_instance: 5.341949939727783\t loss_cluster: 1.679770827293396\n",
      "Step [10/16]\t loss_instance: 5.337279319763184\t loss_cluster: 1.6813374757766724\n",
      "Step [12/16]\t loss_instance: 5.326016426086426\t loss_cluster: 1.6759703159332275\n",
      "Step [14/16]\t loss_instance: 5.36509895324707\t loss_cluster: 1.7111719846725464\n",
      "\n",
      "Epoch [93/300]\t Loss: 6.937629619240761 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.365396976470947\t loss_cluster: 1.7057455778121948\n",
      "Step [2/16]\t loss_instance: 5.3279032707214355\t loss_cluster: 1.6799507141113281\n",
      "Step [4/16]\t loss_instance: 5.348726272583008\t loss_cluster: 1.6811513900756836\n",
      "Step [6/16]\t loss_instance: 5.3574299812316895\t loss_cluster: 1.6984881162643433\n",
      "Step [8/16]\t loss_instance: 5.32174825668335\t loss_cluster: 1.6725128889083862\n",
      "Step [10/16]\t loss_instance: 5.345643043518066\t loss_cluster: 1.682637095451355\n",
      "Step [12/16]\t loss_instance: 5.343133926391602\t loss_cluster: 1.6800010204315186\n",
      "Step [14/16]\t loss_instance: 5.3670477867126465\t loss_cluster: 1.7227404117584229\n",
      "\n",
      "Epoch [94/300]\t Loss: 6.936679872870445 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.337339401245117\t loss_cluster: 1.6788307428359985\n",
      "Step [2/16]\t loss_instance: 5.363497257232666\t loss_cluster: 1.7173947095870972\n",
      "Step [4/16]\t loss_instance: 5.348243236541748\t loss_cluster: 1.6764380931854248\n",
      "Step [6/16]\t loss_instance: 5.3264241218566895\t loss_cluster: 1.67271089553833\n",
      "Step [8/16]\t loss_instance: 5.355997562408447\t loss_cluster: 1.6909774541854858\n",
      "Step [10/16]\t loss_instance: 5.346068382263184\t loss_cluster: 1.7020474672317505\n",
      "Step [12/16]\t loss_instance: 5.325074195861816\t loss_cluster: 1.682034969329834\n",
      "Step [14/16]\t loss_instance: 5.338205814361572\t loss_cluster: 1.6882044076919556\n",
      "\n",
      "Epoch [95/300]\t Loss: 6.934780257940292 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.359999656677246\t loss_cluster: 1.6930936574935913\n",
      "Step [2/16]\t loss_instance: 5.340622901916504\t loss_cluster: 1.6792606115341187\n",
      "Step [4/16]\t loss_instance: 5.357197284698486\t loss_cluster: 1.6963566541671753\n",
      "Step [6/16]\t loss_instance: 5.341030597686768\t loss_cluster: 1.6718906164169312\n",
      "Step [8/16]\t loss_instance: 5.3429789543151855\t loss_cluster: 1.6732122898101807\n",
      "Step [10/16]\t loss_instance: 5.311341762542725\t loss_cluster: 1.6666524410247803\n",
      "Step [12/16]\t loss_instance: 5.382923603057861\t loss_cluster: 1.7002696990966797\n",
      "Step [14/16]\t loss_instance: 5.329162120819092\t loss_cluster: 1.6551392078399658\n",
      "\n",
      "Epoch [96/300]\t Loss: 6.933992829918862 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.334173202514648\t loss_cluster: 1.678651213645935\n",
      "Step [2/16]\t loss_instance: 5.330939769744873\t loss_cluster: 1.6819382905960083\n",
      "Step [4/16]\t loss_instance: 5.3669586181640625\t loss_cluster: 1.7154053449630737\n",
      "Step [6/16]\t loss_instance: 5.340487957000732\t loss_cluster: 1.7042531967163086\n",
      "Step [8/16]\t loss_instance: 5.336179733276367\t loss_cluster: 1.6896075010299683\n",
      "Step [10/16]\t loss_instance: 5.333995819091797\t loss_cluster: 1.6670618057250977\n",
      "Step [12/16]\t loss_instance: 5.358857154846191\t loss_cluster: 1.6953608989715576\n",
      "Step [14/16]\t loss_instance: 5.3385090827941895\t loss_cluster: 1.6821526288986206\n",
      "\n",
      "Epoch [97/300]\t Loss: 6.937731400132179 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.329925537109375\t loss_cluster: 1.663236141204834\n",
      "Step [2/16]\t loss_instance: 5.31380033493042\t loss_cluster: 1.6880254745483398\n",
      "Step [4/16]\t loss_instance: 5.387607574462891\t loss_cluster: 1.7412614822387695\n",
      "Step [6/16]\t loss_instance: 5.331290245056152\t loss_cluster: 1.6704822778701782\n",
      "Step [8/16]\t loss_instance: 5.320763111114502\t loss_cluster: 1.6632089614868164\n",
      "Step [10/16]\t loss_instance: 5.344788551330566\t loss_cluster: 1.684492588043213\n",
      "Step [12/16]\t loss_instance: 5.364402770996094\t loss_cluster: 1.7325595617294312\n",
      "Step [14/16]\t loss_instance: 5.371551513671875\t loss_cluster: 1.70236337184906\n",
      "\n",
      "Epoch [98/300]\t Loss: 6.937357729673385 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.362922191619873\t loss_cluster: 1.6995954513549805\n",
      "Step [2/16]\t loss_instance: 5.344227313995361\t loss_cluster: 1.6929410696029663\n",
      "Step [4/16]\t loss_instance: 5.359237194061279\t loss_cluster: 1.6987262964248657\n",
      "Step [6/16]\t loss_instance: 5.321742057800293\t loss_cluster: 1.6777489185333252\n",
      "Step [8/16]\t loss_instance: 5.322591781616211\t loss_cluster: 1.6730178594589233\n",
      "Step [10/16]\t loss_instance: 5.3277716636657715\t loss_cluster: 1.6787396669387817\n",
      "Step [12/16]\t loss_instance: 5.349772930145264\t loss_cluster: 1.6986955404281616\n",
      "Step [14/16]\t loss_instance: 5.373168468475342\t loss_cluster: 1.7225584983825684\n",
      "\n",
      "Epoch [99/300]\t Loss: 6.9394742280244825 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.31878662109375\t loss_cluster: 1.6813069581985474\n",
      "Step [2/16]\t loss_instance: 5.337146282196045\t loss_cluster: 1.6795151233673096\n",
      "Step [4/16]\t loss_instance: 5.343164920806885\t loss_cluster: 1.6835882663726807\n",
      "Step [6/16]\t loss_instance: 5.341932773590088\t loss_cluster: 1.685707926750183\n",
      "Step [8/16]\t loss_instance: 5.341076374053955\t loss_cluster: 1.6682058572769165\n",
      "Step [10/16]\t loss_instance: 5.3659234046936035\t loss_cluster: 1.7007975578308105\n",
      "Step [12/16]\t loss_instance: 5.32051420211792\t loss_cluster: 1.6757441759109497\n",
      "Step [14/16]\t loss_instance: 5.334944248199463\t loss_cluster: 1.7009665966033936\n",
      "\n",
      "Epoch [100/300]\t Loss: 6.932613942027092 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.327536582946777\t loss_cluster: 1.6843591928482056\n",
      "Step [2/16]\t loss_instance: 5.334084987640381\t loss_cluster: 1.6918525695800781\n",
      "Step [4/16]\t loss_instance: 5.34721040725708\t loss_cluster: 1.7061431407928467\n",
      "Step [6/16]\t loss_instance: 5.344176769256592\t loss_cluster: 1.6774693727493286\n",
      "Step [8/16]\t loss_instance: 5.342015266418457\t loss_cluster: 1.6964445114135742\n",
      "Step [10/16]\t loss_instance: 5.355666637420654\t loss_cluster: 1.7006134986877441\n",
      "Step [12/16]\t loss_instance: 5.368452548980713\t loss_cluster: 1.691999077796936\n",
      "Step [14/16]\t loss_instance: 5.327603816986084\t loss_cluster: 1.6840959787368774\n",
      "\n",
      "Epoch [101/300]\t Loss: 6.937314638495446 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.331014156341553\t loss_cluster: 1.6734322309494019\n",
      "Step [2/16]\t loss_instance: 5.319542407989502\t loss_cluster: 1.6743686199188232\n",
      "Step [4/16]\t loss_instance: 5.385130405426025\t loss_cluster: 1.7190618515014648\n",
      "Step [6/16]\t loss_instance: 5.348178386688232\t loss_cluster: 1.6785547733306885\n",
      "Step [8/16]\t loss_instance: 5.333673477172852\t loss_cluster: 1.6879489421844482\n",
      "Step [10/16]\t loss_instance: 5.37943172454834\t loss_cluster: 1.7167502641677856\n",
      "Step [12/16]\t loss_instance: 5.328359127044678\t loss_cluster: 1.6724483966827393\n",
      "Step [14/16]\t loss_instance: 5.326534271240234\t loss_cluster: 1.7032827138900757\n",
      "\n",
      "Epoch [102/300]\t Loss: 6.93256756067276 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.369293689727783\t loss_cluster: 1.721009612083435\n",
      "Step [2/16]\t loss_instance: 5.33998966217041\t loss_cluster: 1.6949647665023804\n",
      "Step [4/16]\t loss_instance: 5.3469014167785645\t loss_cluster: 1.6847697496414185\n",
      "Step [6/16]\t loss_instance: 5.360133171081543\t loss_cluster: 1.7053558826446533\n",
      "Step [8/16]\t loss_instance: 5.364132881164551\t loss_cluster: 1.736568570137024\n",
      "Step [10/16]\t loss_instance: 5.336947917938232\t loss_cluster: 1.6882153749465942\n",
      "Step [12/16]\t loss_instance: 5.390649318695068\t loss_cluster: 1.724429965019226\n",
      "Step [14/16]\t loss_instance: 5.3354411125183105\t loss_cluster: 1.6921024322509766\n",
      "\n",
      "Epoch [103/300]\t Loss: 6.931090781092644 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.348274230957031\t loss_cluster: 1.695827841758728\n",
      "Step [2/16]\t loss_instance: 5.3378190994262695\t loss_cluster: 1.6857339143753052\n",
      "Step [4/16]\t loss_instance: 5.339780807495117\t loss_cluster: 1.6944576501846313\n",
      "Step [6/16]\t loss_instance: 5.367077827453613\t loss_cluster: 1.7136832475662231\n",
      "Step [8/16]\t loss_instance: 5.36815071105957\t loss_cluster: 1.6886812448501587\n",
      "Step [10/16]\t loss_instance: 5.353022575378418\t loss_cluster: 1.6909849643707275\n",
      "Step [12/16]\t loss_instance: 5.34060001373291\t loss_cluster: 1.6877825260162354\n",
      "Step [14/16]\t loss_instance: 5.311084747314453\t loss_cluster: 1.666212558746338\n",
      "\n",
      "Epoch [104/300]\t Loss: 6.9371548056602474 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.370217800140381\t loss_cluster: 1.7315517663955688\n",
      "Step [2/16]\t loss_instance: 5.331398010253906\t loss_cluster: 1.674738883972168\n",
      "Step [4/16]\t loss_instance: 5.328287124633789\t loss_cluster: 1.6722208261489868\n",
      "Step [6/16]\t loss_instance: 5.361545562744141\t loss_cluster: 1.7142632007598877\n",
      "Step [8/16]\t loss_instance: 5.362977504730225\t loss_cluster: 1.7197719812393188\n",
      "Step [10/16]\t loss_instance: 5.331409931182861\t loss_cluster: 1.672025442123413\n",
      "Step [12/16]\t loss_instance: 5.344930171966553\t loss_cluster: 1.678735375404358\n",
      "Step [14/16]\t loss_instance: 5.3577775955200195\t loss_cluster: 1.721373438835144\n",
      "\n",
      "Epoch [105/300]\t Loss: 6.934502649307251 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.324797630310059\t loss_cluster: 1.6939233541488647\n",
      "Step [2/16]\t loss_instance: 5.351123809814453\t loss_cluster: 1.6900115013122559\n",
      "Step [4/16]\t loss_instance: 5.341588973999023\t loss_cluster: 1.7066715955734253\n",
      "Step [6/16]\t loss_instance: 5.3306169509887695\t loss_cluster: 1.65178382396698\n",
      "Step [8/16]\t loss_instance: 5.366302013397217\t loss_cluster: 1.7270625829696655\n",
      "Step [10/16]\t loss_instance: 5.353658199310303\t loss_cluster: 1.7202274799346924\n",
      "Step [12/16]\t loss_instance: 5.3265275955200195\t loss_cluster: 1.6739139556884766\n",
      "Step [14/16]\t loss_instance: 5.325625896453857\t loss_cluster: 1.6831672191619873\n",
      "\n",
      "Epoch [106/300]\t Loss: 6.932814425230026 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.360882759094238\t loss_cluster: 1.7064319849014282\n",
      "Step [2/16]\t loss_instance: 5.3460693359375\t loss_cluster: 1.682802438735962\n",
      "Step [4/16]\t loss_instance: 5.32270622253418\t loss_cluster: 1.693552851676941\n",
      "Step [6/16]\t loss_instance: 5.346488952636719\t loss_cluster: 1.6773297786712646\n",
      "Step [8/16]\t loss_instance: 5.3379034996032715\t loss_cluster: 1.6780502796173096\n",
      "Step [10/16]\t loss_instance: 5.335263252258301\t loss_cluster: 1.6755082607269287\n",
      "Step [12/16]\t loss_instance: 5.356000900268555\t loss_cluster: 1.6822479963302612\n",
      "Step [14/16]\t loss_instance: 5.334045886993408\t loss_cluster: 1.682662844657898\n",
      "\n",
      "Epoch [107/300]\t Loss: 6.926555779576302 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.332881450653076\t loss_cluster: 1.6769710779190063\n",
      "Step [2/16]\t loss_instance: 5.350611686706543\t loss_cluster: 1.6821010112762451\n",
      "Step [4/16]\t loss_instance: 5.368799209594727\t loss_cluster: 1.712265133857727\n",
      "Step [6/16]\t loss_instance: 5.381110668182373\t loss_cluster: 1.7193113565444946\n",
      "Step [8/16]\t loss_instance: 5.325736045837402\t loss_cluster: 1.678806185722351\n",
      "Step [10/16]\t loss_instance: 5.330420017242432\t loss_cluster: 1.694456934928894\n",
      "Step [12/16]\t loss_instance: 5.366185188293457\t loss_cluster: 1.7096660137176514\n",
      "Step [14/16]\t loss_instance: 5.338905334472656\t loss_cluster: 1.6997712850570679\n",
      "\n",
      "Epoch [108/300]\t Loss: 6.936816841363907 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.323073863983154\t loss_cluster: 1.6713910102844238\n",
      "Step [2/16]\t loss_instance: 5.385279178619385\t loss_cluster: 1.7050621509552002\n",
      "Step [4/16]\t loss_instance: 5.325906276702881\t loss_cluster: 1.6754372119903564\n",
      "Step [6/16]\t loss_instance: 5.347793102264404\t loss_cluster: 1.699783205986023\n",
      "Step [8/16]\t loss_instance: 5.332387924194336\t loss_cluster: 1.6698333024978638\n",
      "Step [10/16]\t loss_instance: 5.366917133331299\t loss_cluster: 1.6938022375106812\n",
      "Step [12/16]\t loss_instance: 5.3262038230896\t loss_cluster: 1.6753861904144287\n",
      "Step [14/16]\t loss_instance: 5.383087635040283\t loss_cluster: 1.7190067768096924\n",
      "\n",
      "Epoch [109/300]\t Loss: 6.93256955742836 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.327059745788574\t loss_cluster: 1.676985263824463\n",
      "Step [2/16]\t loss_instance: 5.35183048248291\t loss_cluster: 1.7012765407562256\n",
      "Step [4/16]\t loss_instance: 5.336907386779785\t loss_cluster: 1.6960196495056152\n",
      "Step [6/16]\t loss_instance: 5.329212665557861\t loss_cluster: 1.673839807510376\n",
      "Step [8/16]\t loss_instance: 5.345383644104004\t loss_cluster: 1.7034322023391724\n",
      "Step [10/16]\t loss_instance: 5.345301628112793\t loss_cluster: 1.6828914880752563\n",
      "Step [12/16]\t loss_instance: 5.353156089782715\t loss_cluster: 1.7054662704467773\n",
      "Step [14/16]\t loss_instance: 5.356091022491455\t loss_cluster: 1.7143117189407349\n",
      "\n",
      "Epoch [110/300]\t Loss: 6.930730605125428 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.314076900482178\t loss_cluster: 1.6705316305160522\n",
      "Step [2/16]\t loss_instance: 5.322606563568115\t loss_cluster: 1.6890290975570679\n",
      "Step [4/16]\t loss_instance: 5.333811283111572\t loss_cluster: 1.6766587495803833\n",
      "Step [6/16]\t loss_instance: 5.373119354248047\t loss_cluster: 1.7309532165527344\n",
      "Step [8/16]\t loss_instance: 5.351982593536377\t loss_cluster: 1.69780695438385\n",
      "Step [10/16]\t loss_instance: 5.353692531585693\t loss_cluster: 1.6834803819656372\n",
      "Step [12/16]\t loss_instance: 5.344328880310059\t loss_cluster: 1.6691827774047852\n",
      "Step [14/16]\t loss_instance: 5.371527671813965\t loss_cluster: 1.7152271270751953\n",
      "\n",
      "Epoch [111/300]\t Loss: 6.938105714321137 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.335939884185791\t loss_cluster: 1.6744359731674194\n",
      "Step [2/16]\t loss_instance: 5.361005783081055\t loss_cluster: 1.7057952880859375\n",
      "Step [4/16]\t loss_instance: 5.357767581939697\t loss_cluster: 1.7225024700164795\n",
      "Step [6/16]\t loss_instance: 5.357728481292725\t loss_cluster: 1.699546217918396\n",
      "Step [8/16]\t loss_instance: 5.331381320953369\t loss_cluster: 1.6860480308532715\n",
      "Step [10/16]\t loss_instance: 5.33187198638916\t loss_cluster: 1.70512855052948\n",
      "Step [12/16]\t loss_instance: 5.318317413330078\t loss_cluster: 1.6545934677124023\n",
      "Step [14/16]\t loss_instance: 5.326094150543213\t loss_cluster: 1.6782937049865723\n",
      "\n",
      "Epoch [112/300]\t Loss: 6.931792351603508 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3230695724487305\t loss_cluster: 1.6579821109771729\n",
      "Step [2/16]\t loss_instance: 5.343415260314941\t loss_cluster: 1.68453049659729\n",
      "Step [4/16]\t loss_instance: 5.356090545654297\t loss_cluster: 1.7055506706237793\n",
      "Step [6/16]\t loss_instance: 5.340847492218018\t loss_cluster: 1.7020031213760376\n",
      "Step [8/16]\t loss_instance: 5.339313507080078\t loss_cluster: 1.6719228029251099\n",
      "Step [10/16]\t loss_instance: 5.317227840423584\t loss_cluster: 1.6718640327453613\n",
      "Step [12/16]\t loss_instance: 5.349066734313965\t loss_cluster: 1.698294997215271\n",
      "Step [14/16]\t loss_instance: 5.354795455932617\t loss_cluster: 1.6938974857330322\n",
      "\n",
      "Epoch [113/300]\t Loss: 6.927634593844414 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.365358829498291\t loss_cluster: 1.703705906867981\n",
      "Step [2/16]\t loss_instance: 5.348669052124023\t loss_cluster: 1.6707125902175903\n",
      "Step [4/16]\t loss_instance: 5.336216926574707\t loss_cluster: 1.6774444580078125\n",
      "Step [6/16]\t loss_instance: 5.3280463218688965\t loss_cluster: 1.6818146705627441\n",
      "Step [8/16]\t loss_instance: 5.330238342285156\t loss_cluster: 1.6733015775680542\n",
      "Step [10/16]\t loss_instance: 5.340723037719727\t loss_cluster: 1.6862412691116333\n",
      "Step [12/16]\t loss_instance: 5.343852519989014\t loss_cluster: 1.693846344947815\n",
      "Step [14/16]\t loss_instance: 5.326235771179199\t loss_cluster: 1.6586191654205322\n",
      "\n",
      "Epoch [114/300]\t Loss: 6.933851489424706 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.325101375579834\t loss_cluster: 1.6673389673233032\n",
      "Step [2/16]\t loss_instance: 5.328980445861816\t loss_cluster: 1.660380244255066\n",
      "Step [4/16]\t loss_instance: 5.315948486328125\t loss_cluster: 1.6748064756393433\n",
      "Step [6/16]\t loss_instance: 5.345531463623047\t loss_cluster: 1.695860743522644\n",
      "Step [8/16]\t loss_instance: 5.363247871398926\t loss_cluster: 1.6944042444229126\n",
      "Step [10/16]\t loss_instance: 5.350600242614746\t loss_cluster: 1.6849600076675415\n",
      "Step [12/16]\t loss_instance: 5.321207046508789\t loss_cluster: 1.6754140853881836\n",
      "Step [14/16]\t loss_instance: 5.3258280754089355\t loss_cluster: 1.6649856567382812\n",
      "\n",
      "Epoch [115/300]\t Loss: 6.927909451723099 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.358080863952637\t loss_cluster: 1.7432820796966553\n",
      "Step [2/16]\t loss_instance: 5.3400702476501465\t loss_cluster: 1.67707097530365\n",
      "Step [4/16]\t loss_instance: 5.329074859619141\t loss_cluster: 1.6869094371795654\n",
      "Step [6/16]\t loss_instance: 5.362342357635498\t loss_cluster: 1.709038257598877\n",
      "Step [8/16]\t loss_instance: 5.349497318267822\t loss_cluster: 1.7226378917694092\n",
      "Step [10/16]\t loss_instance: 5.356190204620361\t loss_cluster: 1.7079017162322998\n",
      "Step [12/16]\t loss_instance: 5.358892917633057\t loss_cluster: 1.7116193771362305\n",
      "Step [14/16]\t loss_instance: 5.3536057472229\t loss_cluster: 1.7030116319656372\n",
      "\n",
      "Epoch [116/300]\t Loss: 6.931874769926071 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3441243171691895\t loss_cluster: 1.6856554746627808\n",
      "Step [2/16]\t loss_instance: 5.351781845092773\t loss_cluster: 1.6863203048706055\n",
      "Step [4/16]\t loss_instance: 5.350799560546875\t loss_cluster: 1.6840656995773315\n",
      "Step [6/16]\t loss_instance: 5.346117973327637\t loss_cluster: 1.69613516330719\n",
      "Step [8/16]\t loss_instance: 5.367595195770264\t loss_cluster: 1.699967384338379\n",
      "Step [10/16]\t loss_instance: 5.34616231918335\t loss_cluster: 1.693717360496521\n",
      "Step [12/16]\t loss_instance: 5.349916458129883\t loss_cluster: 1.7113574743270874\n",
      "Step [14/16]\t loss_instance: 5.337954044342041\t loss_cluster: 1.6898961067199707\n",
      "\n",
      "Epoch [117/300]\t Loss: 6.935320165753365 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.340475082397461\t loss_cluster: 1.7025660276412964\n",
      "Step [2/16]\t loss_instance: 5.317657947540283\t loss_cluster: 1.6955143213272095\n",
      "Step [4/16]\t loss_instance: 5.342618465423584\t loss_cluster: 1.7191026210784912\n",
      "Step [6/16]\t loss_instance: 5.334660530090332\t loss_cluster: 1.6928132772445679\n",
      "Step [8/16]\t loss_instance: 5.3729987144470215\t loss_cluster: 1.719056248664856\n",
      "Step [10/16]\t loss_instance: 5.350175857543945\t loss_cluster: 1.6884669065475464\n",
      "Step [12/16]\t loss_instance: 5.308712005615234\t loss_cluster: 1.6745284795761108\n",
      "Step [14/16]\t loss_instance: 5.348776340484619\t loss_cluster: 1.692747950553894\n",
      "\n",
      "Epoch [118/300]\t Loss: 6.94293304681778 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3464131355285645\t loss_cluster: 1.7021926641464233\n",
      "Step [2/16]\t loss_instance: 5.344724178314209\t loss_cluster: 1.6933972835540771\n",
      "Step [4/16]\t loss_instance: 5.3404107093811035\t loss_cluster: 1.688748836517334\n",
      "Step [6/16]\t loss_instance: 5.325913429260254\t loss_cluster: 1.6636090278625488\n",
      "Step [8/16]\t loss_instance: 5.342255115509033\t loss_cluster: 1.692598581314087\n",
      "Step [10/16]\t loss_instance: 5.372756004333496\t loss_cluster: 1.7227444648742676\n",
      "Step [12/16]\t loss_instance: 5.363847732543945\t loss_cluster: 1.6995584964752197\n",
      "Step [14/16]\t loss_instance: 5.314918518066406\t loss_cluster: 1.6611689329147339\n",
      "\n",
      "Epoch [119/300]\t Loss: 6.927705001831055 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.337085723876953\t loss_cluster: 1.6782729625701904\n",
      "Step [2/16]\t loss_instance: 5.347405433654785\t loss_cluster: 1.6949552297592163\n",
      "Step [4/16]\t loss_instance: 5.3335418701171875\t loss_cluster: 1.6769592761993408\n",
      "Step [6/16]\t loss_instance: 5.374495983123779\t loss_cluster: 1.7107237577438354\n",
      "Step [8/16]\t loss_instance: 5.335052013397217\t loss_cluster: 1.6707119941711426\n",
      "Step [10/16]\t loss_instance: 5.332338809967041\t loss_cluster: 1.6891167163848877\n",
      "Step [12/16]\t loss_instance: 5.318819046020508\t loss_cluster: 1.668196201324463\n",
      "Step [14/16]\t loss_instance: 5.326056003570557\t loss_cluster: 1.6659663915634155\n",
      "\n",
      "Epoch [120/300]\t Loss: 6.927842158079147 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.351567268371582\t loss_cluster: 1.6824560165405273\n",
      "Step [2/16]\t loss_instance: 5.366396427154541\t loss_cluster: 1.7231669425964355\n",
      "Step [4/16]\t loss_instance: 5.338523864746094\t loss_cluster: 1.675400972366333\n",
      "Step [6/16]\t loss_instance: 5.319667816162109\t loss_cluster: 1.6633940935134888\n",
      "Step [8/16]\t loss_instance: 5.346895694732666\t loss_cluster: 1.673065185546875\n",
      "Step [10/16]\t loss_instance: 5.3462324142456055\t loss_cluster: 1.677791714668274\n",
      "Step [12/16]\t loss_instance: 5.360815048217773\t loss_cluster: 1.6991606950759888\n",
      "Step [14/16]\t loss_instance: 5.3339080810546875\t loss_cluster: 1.6937222480773926\n",
      "\n",
      "Epoch [121/300]\t Loss: 6.925088024139404 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.35178279876709\t loss_cluster: 1.694345235824585\n",
      "Step [2/16]\t loss_instance: 5.349344730377197\t loss_cluster: 1.6789562702178955\n",
      "Step [4/16]\t loss_instance: 5.3382697105407715\t loss_cluster: 1.7114473581314087\n",
      "Step [6/16]\t loss_instance: 5.336449146270752\t loss_cluster: 1.6722079515457153\n",
      "Step [8/16]\t loss_instance: 5.338686943054199\t loss_cluster: 1.6845767498016357\n",
      "Step [10/16]\t loss_instance: 5.354176044464111\t loss_cluster: 1.6799935102462769\n",
      "Step [12/16]\t loss_instance: 5.360927581787109\t loss_cluster: 1.729822039604187\n",
      "Step [14/16]\t loss_instance: 5.360633850097656\t loss_cluster: 1.694380283355713\n",
      "\n",
      "Epoch [122/300]\t Loss: 6.9300584435462955 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.358341693878174\t loss_cluster: 1.690822720527649\n",
      "Step [2/16]\t loss_instance: 5.325572967529297\t loss_cluster: 1.6704570055007935\n",
      "Step [4/16]\t loss_instance: 5.321893215179443\t loss_cluster: 1.6735531091690063\n",
      "Step [6/16]\t loss_instance: 5.36868143081665\t loss_cluster: 1.7321476936340332\n",
      "Step [8/16]\t loss_instance: 5.364332675933838\t loss_cluster: 1.6909626722335815\n",
      "Step [10/16]\t loss_instance: 5.334487438201904\t loss_cluster: 1.6847046613693237\n",
      "Step [12/16]\t loss_instance: 5.359596252441406\t loss_cluster: 1.7036192417144775\n",
      "Step [14/16]\t loss_instance: 5.34770393371582\t loss_cluster: 1.674662470817566\n",
      "\n",
      "Epoch [123/300]\t Loss: 6.928983968496323 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.322015762329102\t loss_cluster: 1.6717239618301392\n",
      "Step [2/16]\t loss_instance: 5.344355583190918\t loss_cluster: 1.7163523435592651\n",
      "Step [4/16]\t loss_instance: 5.3563618659973145\t loss_cluster: 1.696506142616272\n",
      "Step [6/16]\t loss_instance: 5.360023498535156\t loss_cluster: 1.7013286352157593\n",
      "Step [8/16]\t loss_instance: 5.345271587371826\t loss_cluster: 1.6856335401535034\n",
      "Step [10/16]\t loss_instance: 5.3467936515808105\t loss_cluster: 1.6965967416763306\n",
      "Step [12/16]\t loss_instance: 5.355678558349609\t loss_cluster: 1.6958277225494385\n",
      "Step [14/16]\t loss_instance: 5.3530192375183105\t loss_cluster: 1.6831750869750977\n",
      "\n",
      "Epoch [124/300]\t Loss: 6.929095879197121 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.339419841766357\t loss_cluster: 1.7016721963882446\n",
      "Step [2/16]\t loss_instance: 5.340663433074951\t loss_cluster: 1.6921470165252686\n",
      "Step [4/16]\t loss_instance: 5.324110507965088\t loss_cluster: 1.6551604270935059\n",
      "Step [6/16]\t loss_instance: 5.3312506675720215\t loss_cluster: 1.6700055599212646\n",
      "Step [8/16]\t loss_instance: 5.319336891174316\t loss_cluster: 1.6712043285369873\n",
      "Step [10/16]\t loss_instance: 5.36422872543335\t loss_cluster: 1.6911596059799194\n",
      "Step [12/16]\t loss_instance: 5.343117713928223\t loss_cluster: 1.6728211641311646\n",
      "Step [14/16]\t loss_instance: 5.32810115814209\t loss_cluster: 1.6789230108261108\n",
      "\n",
      "Epoch [125/300]\t Loss: 6.925602898001671 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.340485572814941\t loss_cluster: 1.705307126045227\n",
      "Step [2/16]\t loss_instance: 5.341552734375\t loss_cluster: 1.6848273277282715\n",
      "Step [4/16]\t loss_instance: 5.330883979797363\t loss_cluster: 1.6780239343643188\n",
      "Step [6/16]\t loss_instance: 5.32515287399292\t loss_cluster: 1.678056240081787\n",
      "Step [8/16]\t loss_instance: 5.345218181610107\t loss_cluster: 1.6637656688690186\n",
      "Step [10/16]\t loss_instance: 5.327524185180664\t loss_cluster: 1.6657744646072388\n",
      "Step [12/16]\t loss_instance: 5.373941898345947\t loss_cluster: 1.7073649168014526\n",
      "Step [14/16]\t loss_instance: 5.333621978759766\t loss_cluster: 1.6941039562225342\n",
      "\n",
      "Epoch [126/300]\t Loss: 6.92551555633545 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.360774517059326\t loss_cluster: 1.7131556272506714\n",
      "Step [2/16]\t loss_instance: 5.3623270988464355\t loss_cluster: 1.7113558053970337\n",
      "Step [4/16]\t loss_instance: 5.366580009460449\t loss_cluster: 1.7071778774261475\n",
      "Step [6/16]\t loss_instance: 5.376049041748047\t loss_cluster: 1.7142255306243896\n",
      "Step [8/16]\t loss_instance: 5.341739177703857\t loss_cluster: 1.6802012920379639\n",
      "Step [10/16]\t loss_instance: 5.336238861083984\t loss_cluster: 1.6727157831192017\n",
      "Step [12/16]\t loss_instance: 5.344990253448486\t loss_cluster: 1.685755968093872\n",
      "Step [14/16]\t loss_instance: 5.321686744689941\t loss_cluster: 1.690755009651184\n",
      "\n",
      "Epoch [127/300]\t Loss: 6.931027558445931 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.327119827270508\t loss_cluster: 1.6601430177688599\n",
      "Step [2/16]\t loss_instance: 5.363175868988037\t loss_cluster: 1.6882691383361816\n",
      "Step [4/16]\t loss_instance: 5.312620162963867\t loss_cluster: 1.6710307598114014\n",
      "Step [6/16]\t loss_instance: 5.34831428527832\t loss_cluster: 1.7129353284835815\n",
      "Step [8/16]\t loss_instance: 5.328369140625\t loss_cluster: 1.7034034729003906\n",
      "Step [10/16]\t loss_instance: 5.3943376541137695\t loss_cluster: 1.7309191226959229\n",
      "Step [12/16]\t loss_instance: 5.32520866394043\t loss_cluster: 1.6574301719665527\n",
      "Step [14/16]\t loss_instance: 5.340364933013916\t loss_cluster: 1.6934128999710083\n",
      "\n",
      "Epoch [128/300]\t Loss: 6.924407723546028 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.346704006195068\t loss_cluster: 1.7019329071044922\n",
      "Step [2/16]\t loss_instance: 5.368285179138184\t loss_cluster: 1.7070133686065674\n",
      "Step [4/16]\t loss_instance: 5.34512996673584\t loss_cluster: 1.7019399404525757\n",
      "Step [6/16]\t loss_instance: 5.360973358154297\t loss_cluster: 1.697077989578247\n",
      "Step [8/16]\t loss_instance: 5.353999137878418\t loss_cluster: 1.6954286098480225\n",
      "Step [10/16]\t loss_instance: 5.3255815505981445\t loss_cluster: 1.671618103981018\n",
      "Step [12/16]\t loss_instance: 5.36046028137207\t loss_cluster: 1.683275580406189\n",
      "Step [14/16]\t loss_instance: 5.31947660446167\t loss_cluster: 1.6836732625961304\n",
      "\n",
      "Epoch [129/300]\t Loss: 6.930504226684571 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.339404582977295\t loss_cluster: 1.6968281269073486\n",
      "Step [2/16]\t loss_instance: 5.335834503173828\t loss_cluster: 1.6708581447601318\n",
      "Step [4/16]\t loss_instance: 5.354101657867432\t loss_cluster: 1.6881383657455444\n",
      "Step [6/16]\t loss_instance: 5.333644390106201\t loss_cluster: 1.6773875951766968\n",
      "Step [8/16]\t loss_instance: 5.351811408996582\t loss_cluster: 1.704085111618042\n",
      "Step [10/16]\t loss_instance: 5.344501495361328\t loss_cluster: 1.6785671710968018\n",
      "Step [12/16]\t loss_instance: 5.3512749671936035\t loss_cluster: 1.6773865222930908\n",
      "Step [14/16]\t loss_instance: 5.33652400970459\t loss_cluster: 1.6805249452590942\n",
      "\n",
      "Epoch [130/300]\t Loss: 6.921935907006263 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.334151268005371\t loss_cluster: 1.6811004877090454\n",
      "Step [2/16]\t loss_instance: 5.350364685058594\t loss_cluster: 1.6935828924179077\n",
      "Step [4/16]\t loss_instance: 5.365987777709961\t loss_cluster: 1.708804726600647\n",
      "Step [6/16]\t loss_instance: 5.365050315856934\t loss_cluster: 1.6879955530166626\n",
      "Step [8/16]\t loss_instance: 5.343642234802246\t loss_cluster: 1.6776312589645386\n",
      "Step [10/16]\t loss_instance: 5.35813045501709\t loss_cluster: 1.7173078060150146\n",
      "Step [12/16]\t loss_instance: 5.353837013244629\t loss_cluster: 1.6949195861816406\n",
      "Step [14/16]\t loss_instance: 5.368544578552246\t loss_cluster: 1.6907743215560913\n",
      "\n",
      "Epoch [131/300]\t Loss: 6.925946986675262 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3235249519348145\t loss_cluster: 1.661776065826416\n",
      "Step [2/16]\t loss_instance: 5.35127592086792\t loss_cluster: 1.680982232093811\n",
      "Step [4/16]\t loss_instance: 5.329622745513916\t loss_cluster: 1.693970799446106\n",
      "Step [6/16]\t loss_instance: 5.3261027336120605\t loss_cluster: 1.6960550546646118\n",
      "Step [8/16]\t loss_instance: 5.3341145515441895\t loss_cluster: 1.6926751136779785\n",
      "Step [10/16]\t loss_instance: 5.3756513595581055\t loss_cluster: 1.7242733240127563\n",
      "Step [12/16]\t loss_instance: 5.35867977142334\t loss_cluster: 1.6940006017684937\n",
      "Step [14/16]\t loss_instance: 5.362804889678955\t loss_cluster: 1.7158268690109253\n",
      "\n",
      "Epoch [132/300]\t Loss: 6.929390826821328 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.37229585647583\t loss_cluster: 1.7327462434768677\n",
      "Step [2/16]\t loss_instance: 5.33547306060791\t loss_cluster: 1.6959569454193115\n",
      "Step [4/16]\t loss_instance: 5.348834991455078\t loss_cluster: 1.6974647045135498\n",
      "Step [6/16]\t loss_instance: 5.323152542114258\t loss_cluster: 1.6596647500991821\n",
      "Step [8/16]\t loss_instance: 5.3585991859436035\t loss_cluster: 1.704838752746582\n",
      "Step [10/16]\t loss_instance: 5.3409104347229\t loss_cluster: 1.6833434104919434\n",
      "Step [12/16]\t loss_instance: 5.3676042556762695\t loss_cluster: 1.7045440673828125\n",
      "Step [14/16]\t loss_instance: 5.3550124168396\t loss_cluster: 1.6867083311080933\n",
      "\n",
      "Epoch [133/300]\t Loss: 6.924046477675438 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.341067790985107\t loss_cluster: 1.6751327514648438\n",
      "Step [2/16]\t loss_instance: 5.361073970794678\t loss_cluster: 1.6997207403182983\n",
      "Step [4/16]\t loss_instance: 5.308348178863525\t loss_cluster: 1.6503524780273438\n",
      "Step [6/16]\t loss_instance: 5.360857009887695\t loss_cluster: 1.7038758993148804\n",
      "Step [8/16]\t loss_instance: 5.328717231750488\t loss_cluster: 1.688218355178833\n",
      "Step [10/16]\t loss_instance: 5.352104663848877\t loss_cluster: 1.7008216381072998\n",
      "Step [12/16]\t loss_instance: 5.333132266998291\t loss_cluster: 1.6957429647445679\n",
      "Step [14/16]\t loss_instance: 5.32688570022583\t loss_cluster: 1.6822853088378906\n",
      "\n",
      "Epoch [134/300]\t Loss: 6.927084264159203 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.372945308685303\t loss_cluster: 1.7059739828109741\n",
      "Step [2/16]\t loss_instance: 5.3283467292785645\t loss_cluster: 1.666334867477417\n",
      "Step [4/16]\t loss_instance: 5.351729393005371\t loss_cluster: 1.6896804571151733\n",
      "Step [6/16]\t loss_instance: 5.349332809448242\t loss_cluster: 1.7019646167755127\n",
      "Step [8/16]\t loss_instance: 5.341979026794434\t loss_cluster: 1.6862412691116333\n",
      "Step [10/16]\t loss_instance: 5.343522071838379\t loss_cluster: 1.6831098794937134\n",
      "Step [12/16]\t loss_instance: 5.355128765106201\t loss_cluster: 1.7009093761444092\n",
      "Step [14/16]\t loss_instance: 5.305270671844482\t loss_cluster: 1.6460893154144287\n",
      "\n",
      "Epoch [135/300]\t Loss: 6.927501103281974 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.378253936767578\t loss_cluster: 1.7328643798828125\n",
      "Step [2/16]\t loss_instance: 5.318066120147705\t loss_cluster: 1.66023588180542\n",
      "Step [4/16]\t loss_instance: 5.33263635635376\t loss_cluster: 1.6943823099136353\n",
      "Step [6/16]\t loss_instance: 5.333105087280273\t loss_cluster: 1.6721032857894897\n",
      "Step [8/16]\t loss_instance: 5.344505310058594\t loss_cluster: 1.6904104948043823\n",
      "Step [10/16]\t loss_instance: 5.334745407104492\t loss_cluster: 1.667762279510498\n",
      "Step [12/16]\t loss_instance: 5.337456703186035\t loss_cluster: 1.6813178062438965\n",
      "Step [14/16]\t loss_instance: 5.36466121673584\t loss_cluster: 1.7188888788223267\n",
      "\n",
      "Epoch [136/300]\t Loss: 6.934300595521927 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.378373622894287\t loss_cluster: 1.7216933965682983\n",
      "Step [2/16]\t loss_instance: 5.356515407562256\t loss_cluster: 1.7010018825531006\n",
      "Step [4/16]\t loss_instance: 5.329922676086426\t loss_cluster: 1.6746673583984375\n",
      "Step [6/16]\t loss_instance: 5.338413238525391\t loss_cluster: 1.7046313285827637\n",
      "Step [8/16]\t loss_instance: 5.349828720092773\t loss_cluster: 1.6862987279891968\n",
      "Step [10/16]\t loss_instance: 5.3389573097229\t loss_cluster: 1.704293131828308\n",
      "Step [12/16]\t loss_instance: 5.2967610359191895\t loss_cluster: 1.648587703704834\n",
      "Step [14/16]\t loss_instance: 5.332344055175781\t loss_cluster: 1.6643084287643433\n",
      "\n",
      "Epoch [137/300]\t Loss: 6.9273436814546585 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.323991298675537\t loss_cluster: 1.6750434637069702\n",
      "Step [2/16]\t loss_instance: 5.357959747314453\t loss_cluster: 1.6935218572616577\n",
      "Step [4/16]\t loss_instance: 5.299104690551758\t loss_cluster: 1.6700670719146729\n",
      "Step [6/16]\t loss_instance: 5.346237659454346\t loss_cluster: 1.6820563077926636\n",
      "Step [8/16]\t loss_instance: 5.34340238571167\t loss_cluster: 1.7031826972961426\n",
      "Step [10/16]\t loss_instance: 5.322676181793213\t loss_cluster: 1.6571941375732422\n",
      "Step [12/16]\t loss_instance: 5.352514743804932\t loss_cluster: 1.7051879167556763\n",
      "Step [14/16]\t loss_instance: 5.361460208892822\t loss_cluster: 1.6871191263198853\n",
      "\n",
      "Epoch [138/300]\t Loss: 6.9268717736005785 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.307339191436768\t loss_cluster: 1.6535069942474365\n",
      "Step [2/16]\t loss_instance: 5.321859836578369\t loss_cluster: 1.6546566486358643\n",
      "Step [4/16]\t loss_instance: 5.336339473724365\t loss_cluster: 1.6842807531356812\n",
      "Step [6/16]\t loss_instance: 5.335790634155273\t loss_cluster: 1.6767643690109253\n",
      "Step [8/16]\t loss_instance: 5.352466583251953\t loss_cluster: 1.6987920999526978\n",
      "Step [10/16]\t loss_instance: 5.3765387535095215\t loss_cluster: 1.7165330648422241\n",
      "Step [12/16]\t loss_instance: 5.334874629974365\t loss_cluster: 1.694644570350647\n",
      "Step [14/16]\t loss_instance: 5.358706951141357\t loss_cluster: 1.7142647504806519\n",
      "\n",
      "Epoch [139/300]\t Loss: 6.923611578345299 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.334997653961182\t loss_cluster: 1.6652812957763672\n",
      "Step [2/16]\t loss_instance: 5.328156471252441\t loss_cluster: 1.6767950057983398\n",
      "Step [4/16]\t loss_instance: 5.355984210968018\t loss_cluster: 1.7021722793579102\n",
      "Step [6/16]\t loss_instance: 5.338029384613037\t loss_cluster: 1.7148994207382202\n",
      "Step [8/16]\t loss_instance: 5.335601329803467\t loss_cluster: 1.6765090227127075\n",
      "Step [10/16]\t loss_instance: 5.328472137451172\t loss_cluster: 1.6610029935836792\n",
      "Step [12/16]\t loss_instance: 5.360682010650635\t loss_cluster: 1.6988898515701294\n",
      "Step [14/16]\t loss_instance: 5.339694976806641\t loss_cluster: 1.6952364444732666\n",
      "\n",
      "Epoch [140/300]\t Loss: 6.925977569818497 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.330255508422852\t loss_cluster: 1.659609317779541\n",
      "Step [2/16]\t loss_instance: 5.323962211608887\t loss_cluster: 1.683310627937317\n",
      "Step [4/16]\t loss_instance: 5.359071731567383\t loss_cluster: 1.6990485191345215\n",
      "Step [6/16]\t loss_instance: 5.317838191986084\t loss_cluster: 1.6615493297576904\n",
      "Step [8/16]\t loss_instance: 5.321035385131836\t loss_cluster: 1.682739019393921\n",
      "Step [10/16]\t loss_instance: 5.3862738609313965\t loss_cluster: 1.7409024238586426\n",
      "Step [12/16]\t loss_instance: 5.355064392089844\t loss_cluster: 1.6838573217391968\n",
      "Step [14/16]\t loss_instance: 5.354997634887695\t loss_cluster: 1.6885650157928467\n",
      "\n",
      "Epoch [141/300]\t Loss: 6.923552542924881 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.332926273345947\t loss_cluster: 1.6783270835876465\n",
      "Step [2/16]\t loss_instance: 5.3281097412109375\t loss_cluster: 1.655895471572876\n",
      "Step [4/16]\t loss_instance: 5.357327938079834\t loss_cluster: 1.702290415763855\n",
      "Step [6/16]\t loss_instance: 5.330563068389893\t loss_cluster: 1.676507592201233\n",
      "Step [8/16]\t loss_instance: 5.358371734619141\t loss_cluster: 1.6979917287826538\n",
      "Step [10/16]\t loss_instance: 5.358964920043945\t loss_cluster: 1.688036561012268\n",
      "Step [12/16]\t loss_instance: 5.342832088470459\t loss_cluster: 1.6876904964447021\n",
      "Step [14/16]\t loss_instance: 5.346756935119629\t loss_cluster: 1.712561845779419\n",
      "\n",
      "Epoch [142/300]\t Loss: 6.919003966450691 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3462815284729\t loss_cluster: 1.6968344449996948\n",
      "Step [2/16]\t loss_instance: 5.3362250328063965\t loss_cluster: 1.692123532295227\n",
      "Step [4/16]\t loss_instance: 5.381805896759033\t loss_cluster: 1.728258728981018\n",
      "Step [6/16]\t loss_instance: 5.320371627807617\t loss_cluster: 1.679273247718811\n",
      "Step [8/16]\t loss_instance: 5.326919078826904\t loss_cluster: 1.6821951866149902\n",
      "Step [10/16]\t loss_instance: 5.345557689666748\t loss_cluster: 1.6914862394332886\n",
      "Step [12/16]\t loss_instance: 5.3642754554748535\t loss_cluster: 1.7232728004455566\n",
      "Step [14/16]\t loss_instance: 5.36265754699707\t loss_cluster: 1.687865972518921\n",
      "\n",
      "Epoch [143/300]\t Loss: 6.9298561096191404 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.362154483795166\t loss_cluster: 1.687330961227417\n",
      "Step [2/16]\t loss_instance: 5.325380325317383\t loss_cluster: 1.678139090538025\n",
      "Step [4/16]\t loss_instance: 5.357694625854492\t loss_cluster: 1.6910094022750854\n",
      "Step [6/16]\t loss_instance: 5.35770320892334\t loss_cluster: 1.6904704570770264\n",
      "Step [8/16]\t loss_instance: 5.330739974975586\t loss_cluster: 1.6785283088684082\n",
      "Step [10/16]\t loss_instance: 5.3266706466674805\t loss_cluster: 1.652431607246399\n",
      "Step [12/16]\t loss_instance: 5.351047992706299\t loss_cluster: 1.6849993467330933\n",
      "Step [14/16]\t loss_instance: 5.359118461608887\t loss_cluster: 1.7019164562225342\n",
      "\n",
      "Epoch [144/300]\t Loss: 6.922053998708725 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.341493129730225\t loss_cluster: 1.6999309062957764\n",
      "Step [2/16]\t loss_instance: 5.345499038696289\t loss_cluster: 1.6779204607009888\n",
      "Step [4/16]\t loss_instance: 5.334479331970215\t loss_cluster: 1.672425627708435\n",
      "Step [6/16]\t loss_instance: 5.346468448638916\t loss_cluster: 1.6900676488876343\n",
      "Step [8/16]\t loss_instance: 5.35511589050293\t loss_cluster: 1.7012341022491455\n",
      "Step [10/16]\t loss_instance: 5.344480991363525\t loss_cluster: 1.6695865392684937\n",
      "Step [12/16]\t loss_instance: 5.341132640838623\t loss_cluster: 1.6924827098846436\n",
      "Step [14/16]\t loss_instance: 5.360421180725098\t loss_cluster: 1.7078851461410522\n",
      "\n",
      "Epoch [145/300]\t Loss: 6.9233928442001345 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.343979358673096\t loss_cluster: 1.7034331560134888\n",
      "Step [2/16]\t loss_instance: 5.349199295043945\t loss_cluster: 1.6924158334732056\n",
      "Step [4/16]\t loss_instance: 5.348522663116455\t loss_cluster: 1.6720571517944336\n",
      "Step [6/16]\t loss_instance: 5.367419719696045\t loss_cluster: 1.7155426740646362\n",
      "Step [8/16]\t loss_instance: 5.364522933959961\t loss_cluster: 1.7016013860702515\n",
      "Step [10/16]\t loss_instance: 5.342315196990967\t loss_cluster: 1.6946882009506226\n",
      "Step [12/16]\t loss_instance: 5.361217975616455\t loss_cluster: 1.7190990447998047\n",
      "Step [14/16]\t loss_instance: 5.330138683319092\t loss_cluster: 1.668979287147522\n",
      "\n",
      "Epoch [146/300]\t Loss: 6.9245175391435625 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.339084148406982\t loss_cluster: 1.6918346881866455\n",
      "Step [2/16]\t loss_instance: 5.375493049621582\t loss_cluster: 1.7140425443649292\n",
      "Step [4/16]\t loss_instance: 5.357442378997803\t loss_cluster: 1.6815556287765503\n",
      "Step [6/16]\t loss_instance: 5.343317985534668\t loss_cluster: 1.7050151824951172\n",
      "Step [8/16]\t loss_instance: 5.354656219482422\t loss_cluster: 1.7024937868118286\n",
      "Step [10/16]\t loss_instance: 5.369520664215088\t loss_cluster: 1.7113808393478394\n",
      "Step [12/16]\t loss_instance: 5.345494747161865\t loss_cluster: 1.6777963638305664\n",
      "Step [14/16]\t loss_instance: 5.334496974945068\t loss_cluster: 1.690979242324829\n",
      "\n",
      "Epoch [147/300]\t Loss: 6.9260263532400135 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.377801418304443\t loss_cluster: 1.6981608867645264\n",
      "Step [2/16]\t loss_instance: 5.3573784828186035\t loss_cluster: 1.684269905090332\n",
      "Step [4/16]\t loss_instance: 5.336973190307617\t loss_cluster: 1.6850863695144653\n",
      "Step [6/16]\t loss_instance: 5.353567123413086\t loss_cluster: 1.6939340829849243\n",
      "Step [8/16]\t loss_instance: 5.324233531951904\t loss_cluster: 1.645284652709961\n",
      "Step [10/16]\t loss_instance: 5.3514862060546875\t loss_cluster: 1.6728731393814087\n",
      "Step [12/16]\t loss_instance: 5.349674701690674\t loss_cluster: 1.7182612419128418\n",
      "Step [14/16]\t loss_instance: 5.3052544593811035\t loss_cluster: 1.6499614715576172\n",
      "\n",
      "Epoch [148/300]\t Loss: 6.925185886025429 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.338974475860596\t loss_cluster: 1.6770179271697998\n",
      "Step [2/16]\t loss_instance: 5.3557209968566895\t loss_cluster: 1.7016631364822388\n",
      "Step [4/16]\t loss_instance: 5.336531639099121\t loss_cluster: 1.6744598150253296\n",
      "Step [6/16]\t loss_instance: 5.343955039978027\t loss_cluster: 1.6781463623046875\n",
      "Step [8/16]\t loss_instance: 5.351815223693848\t loss_cluster: 1.690278172492981\n",
      "Step [10/16]\t loss_instance: 5.347622394561768\t loss_cluster: 1.7023358345031738\n",
      "Step [12/16]\t loss_instance: 5.36105489730835\t loss_cluster: 1.7115784883499146\n",
      "Step [14/16]\t loss_instance: 5.329959392547607\t loss_cluster: 1.699845790863037\n",
      "\n",
      "Epoch [149/300]\t Loss: 6.924244204163552 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.323766708374023\t loss_cluster: 1.6878058910369873\n",
      "Step [2/16]\t loss_instance: 5.357200622558594\t loss_cluster: 1.711631178855896\n",
      "Step [4/16]\t loss_instance: 5.340925216674805\t loss_cluster: 1.6802293062210083\n",
      "Step [6/16]\t loss_instance: 5.337370872497559\t loss_cluster: 1.6819499731063843\n",
      "Step [8/16]\t loss_instance: 5.326300144195557\t loss_cluster: 1.6698994636535645\n",
      "Step [10/16]\t loss_instance: 5.349153995513916\t loss_cluster: 1.6821731328964233\n",
      "Step [12/16]\t loss_instance: 5.340126991271973\t loss_cluster: 1.7098281383514404\n",
      "Step [14/16]\t loss_instance: 5.360475540161133\t loss_cluster: 1.6903388500213623\n",
      "\n",
      "Epoch [150/300]\t Loss: 6.924006664752961 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.335557460784912\t loss_cluster: 1.674926519393921\n",
      "Step [2/16]\t loss_instance: 5.3517327308654785\t loss_cluster: 1.6783632040023804\n",
      "Step [4/16]\t loss_instance: 5.33344030380249\t loss_cluster: 1.6798162460327148\n",
      "Step [6/16]\t loss_instance: 5.368057727813721\t loss_cluster: 1.7292288541793823\n",
      "Step [8/16]\t loss_instance: 5.35857629776001\t loss_cluster: 1.6864959001541138\n",
      "Step [10/16]\t loss_instance: 5.359575271606445\t loss_cluster: 1.6866559982299805\n",
      "Step [12/16]\t loss_instance: 5.345186233520508\t loss_cluster: 1.6938115358352661\n",
      "Step [14/16]\t loss_instance: 5.320476531982422\t loss_cluster: 1.6855342388153076\n",
      "\n",
      "Epoch [151/300]\t Loss: 6.925771880149841 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.32365608215332\t loss_cluster: 1.691451072692871\n",
      "Step [2/16]\t loss_instance: 5.3529486656188965\t loss_cluster: 1.7061437368392944\n",
      "Step [4/16]\t loss_instance: 5.355250835418701\t loss_cluster: 1.684186339378357\n",
      "Step [6/16]\t loss_instance: 5.357379913330078\t loss_cluster: 1.7214515209197998\n",
      "Step [8/16]\t loss_instance: 5.36005163192749\t loss_cluster: 1.7097210884094238\n",
      "Step [10/16]\t loss_instance: 5.319718837738037\t loss_cluster: 1.6837950944900513\n",
      "Step [12/16]\t loss_instance: 5.3737382888793945\t loss_cluster: 1.7105776071548462\n",
      "Step [14/16]\t loss_instance: 5.313311576843262\t loss_cluster: 1.673018455505371\n",
      "\n",
      "Epoch [152/300]\t Loss: 6.926704278588295 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3459978103637695\t loss_cluster: 1.690619945526123\n",
      "Step [2/16]\t loss_instance: 5.334378242492676\t loss_cluster: 1.6902259588241577\n",
      "Step [4/16]\t loss_instance: 5.376418113708496\t loss_cluster: 1.7014738321304321\n",
      "Step [6/16]\t loss_instance: 5.346183776855469\t loss_cluster: 1.6851770877838135\n",
      "Step [8/16]\t loss_instance: 5.3315229415893555\t loss_cluster: 1.669474482536316\n",
      "Step [10/16]\t loss_instance: 5.326257705688477\t loss_cluster: 1.6652475595474243\n",
      "Step [12/16]\t loss_instance: 5.351210594177246\t loss_cluster: 1.707792043685913\n",
      "Step [14/16]\t loss_instance: 5.358749866485596\t loss_cluster: 1.7196682691574097\n",
      "\n",
      "Epoch [153/300]\t Loss: 6.919224968552589 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.360163688659668\t loss_cluster: 1.6851190328598022\n",
      "Step [2/16]\t loss_instance: 5.374349117279053\t loss_cluster: 1.7337762117385864\n",
      "Step [4/16]\t loss_instance: 5.341887950897217\t loss_cluster: 1.6859697103500366\n",
      "Step [6/16]\t loss_instance: 5.335958003997803\t loss_cluster: 1.6922889947891235\n",
      "Step [8/16]\t loss_instance: 5.3430633544921875\t loss_cluster: 1.6990679502487183\n",
      "Step [10/16]\t loss_instance: 5.33850622177124\t loss_cluster: 1.6795276403427124\n",
      "Step [12/16]\t loss_instance: 5.356988430023193\t loss_cluster: 1.7056413888931274\n",
      "Step [14/16]\t loss_instance: 5.325843811035156\t loss_cluster: 1.6625406742095947\n",
      "\n",
      "Epoch [154/300]\t Loss: 6.921045020222664 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.356682777404785\t loss_cluster: 1.7008224725723267\n",
      "Step [2/16]\t loss_instance: 5.339764595031738\t loss_cluster: 1.690558671951294\n",
      "Step [4/16]\t loss_instance: 5.329533100128174\t loss_cluster: 1.6787984371185303\n",
      "Step [6/16]\t loss_instance: 5.318541049957275\t loss_cluster: 1.6725609302520752\n",
      "Step [8/16]\t loss_instance: 5.326264381408691\t loss_cluster: 1.6750121116638184\n",
      "Step [10/16]\t loss_instance: 5.389468669891357\t loss_cluster: 1.6973447799682617\n",
      "Step [12/16]\t loss_instance: 5.354053020477295\t loss_cluster: 1.6878222227096558\n",
      "Step [14/16]\t loss_instance: 5.34406042098999\t loss_cluster: 1.6872812509536743\n",
      "\n",
      "Epoch [155/300]\t Loss: 6.916882240772248 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.354115009307861\t loss_cluster: 1.7026140689849854\n",
      "Step [2/16]\t loss_instance: 5.351168632507324\t loss_cluster: 1.6918314695358276\n",
      "Step [4/16]\t loss_instance: 5.335343837738037\t loss_cluster: 1.6723676919937134\n",
      "Step [6/16]\t loss_instance: 5.332779407501221\t loss_cluster: 1.6731599569320679\n",
      "Step [8/16]\t loss_instance: 5.336823463439941\t loss_cluster: 1.683677315711975\n",
      "Step [10/16]\t loss_instance: 5.3734517097473145\t loss_cluster: 1.7103471755981445\n",
      "Step [12/16]\t loss_instance: 5.316153526306152\t loss_cluster: 1.644782543182373\n",
      "Step [14/16]\t loss_instance: 5.374550819396973\t loss_cluster: 1.7296593189239502\n",
      "\n",
      "Epoch [156/300]\t Loss: 6.922212880849838 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.354780673980713\t loss_cluster: 1.6886074542999268\n",
      "Step [2/16]\t loss_instance: 5.340912818908691\t loss_cluster: 1.6846067905426025\n",
      "Step [4/16]\t loss_instance: 5.344517707824707\t loss_cluster: 1.6782491207122803\n",
      "Step [6/16]\t loss_instance: 5.337796688079834\t loss_cluster: 1.6663750410079956\n",
      "Step [8/16]\t loss_instance: 5.327462196350098\t loss_cluster: 1.6871455907821655\n",
      "Step [10/16]\t loss_instance: 5.35023832321167\t loss_cluster: 1.6898266077041626\n",
      "Step [12/16]\t loss_instance: 5.344393253326416\t loss_cluster: 1.6819536685943604\n",
      "Step [14/16]\t loss_instance: 5.338408470153809\t loss_cluster: 1.677181363105774\n",
      "\n",
      "Epoch [157/300]\t Loss: 6.914373570680619 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.344870090484619\t loss_cluster: 1.6849050521850586\n",
      "Step [2/16]\t loss_instance: 5.3437018394470215\t loss_cluster: 1.671082854270935\n",
      "Step [4/16]\t loss_instance: 5.340391635894775\t loss_cluster: 1.683349847793579\n",
      "Step [6/16]\t loss_instance: 5.314600944519043\t loss_cluster: 1.654196858406067\n",
      "Step [8/16]\t loss_instance: 5.37566614151001\t loss_cluster: 1.7082077264785767\n",
      "Step [10/16]\t loss_instance: 5.342780113220215\t loss_cluster: 1.6927238702774048\n",
      "Step [12/16]\t loss_instance: 5.368826866149902\t loss_cluster: 1.7030452489852905\n",
      "Step [14/16]\t loss_instance: 5.375411510467529\t loss_cluster: 1.7141892910003662\n",
      "\n",
      "Epoch [158/300]\t Loss: 6.9194712191820145 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.361780643463135\t loss_cluster: 1.7024260759353638\n",
      "Step [2/16]\t loss_instance: 5.347766876220703\t loss_cluster: 1.7039837837219238\n",
      "Step [4/16]\t loss_instance: 5.353201389312744\t loss_cluster: 1.7124391794204712\n",
      "Step [6/16]\t loss_instance: 5.351762294769287\t loss_cluster: 1.6930515766143799\n",
      "Step [8/16]\t loss_instance: 5.344267845153809\t loss_cluster: 1.68711256980896\n",
      "Step [10/16]\t loss_instance: 5.358475208282471\t loss_cluster: 1.7010620832443237\n",
      "Step [12/16]\t loss_instance: 5.3665056228637695\t loss_cluster: 1.7048920392990112\n",
      "Step [14/16]\t loss_instance: 5.339191436767578\t loss_cluster: 1.6820133924484253\n",
      "\n",
      "Epoch [159/300]\t Loss: 6.9265347748994825 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.340725898742676\t loss_cluster: 1.7003027200698853\n",
      "Step [2/16]\t loss_instance: 5.362590312957764\t loss_cluster: 1.7138307094573975\n",
      "Step [4/16]\t loss_instance: 5.330334186553955\t loss_cluster: 1.685901403427124\n",
      "Step [6/16]\t loss_instance: 5.348944187164307\t loss_cluster: 1.7163983583450317\n",
      "Step [8/16]\t loss_instance: 5.340953826904297\t loss_cluster: 1.6652270555496216\n",
      "Step [10/16]\t loss_instance: 5.361554145812988\t loss_cluster: 1.7021124362945557\n",
      "Step [12/16]\t loss_instance: 5.339141368865967\t loss_cluster: 1.672423005104065\n",
      "Step [14/16]\t loss_instance: 5.335251331329346\t loss_cluster: 1.6701301336288452\n",
      "\n",
      "Epoch [160/300]\t Loss: 6.923432004451752 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3770575523376465\t loss_cluster: 1.6901241540908813\n",
      "Step [2/16]\t loss_instance: 5.353692531585693\t loss_cluster: 1.6941279172897339\n",
      "Step [4/16]\t loss_instance: 5.345188140869141\t loss_cluster: 1.7077655792236328\n",
      "Step [6/16]\t loss_instance: 5.342545986175537\t loss_cluster: 1.6840860843658447\n",
      "Step [8/16]\t loss_instance: 5.327245235443115\t loss_cluster: 1.6728609800338745\n",
      "Step [10/16]\t loss_instance: 5.337557315826416\t loss_cluster: 1.6922093629837036\n",
      "Step [12/16]\t loss_instance: 5.319441795349121\t loss_cluster: 1.6642919778823853\n",
      "Step [14/16]\t loss_instance: 5.339931964874268\t loss_cluster: 1.7152478694915771\n",
      "\n",
      "Epoch [161/300]\t Loss: 6.924586543440819 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.363531589508057\t loss_cluster: 1.7103686332702637\n",
      "Step [2/16]\t loss_instance: 5.3542070388793945\t loss_cluster: 1.7007068395614624\n",
      "Step [4/16]\t loss_instance: 5.33549690246582\t loss_cluster: 1.6771697998046875\n",
      "Step [6/16]\t loss_instance: 5.373517990112305\t loss_cluster: 1.7231451272964478\n",
      "Step [8/16]\t loss_instance: 5.342824935913086\t loss_cluster: 1.6874337196350098\n",
      "Step [10/16]\t loss_instance: 5.335379123687744\t loss_cluster: 1.6811431646347046\n",
      "Step [12/16]\t loss_instance: 5.357199668884277\t loss_cluster: 1.70363450050354\n",
      "Step [14/16]\t loss_instance: 5.326227188110352\t loss_cluster: 1.6599665880203247\n",
      "\n",
      "Epoch [162/300]\t Loss: 6.927084565162659 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.344629764556885\t loss_cluster: 1.681102991104126\n",
      "Step [2/16]\t loss_instance: 5.337028980255127\t loss_cluster: 1.6939162015914917\n",
      "Step [4/16]\t loss_instance: 5.335261344909668\t loss_cluster: 1.6801021099090576\n",
      "Step [6/16]\t loss_instance: 5.335658073425293\t loss_cluster: 1.6820539236068726\n",
      "Step [8/16]\t loss_instance: 5.331179618835449\t loss_cluster: 1.6778334379196167\n",
      "Step [10/16]\t loss_instance: 5.331957817077637\t loss_cluster: 1.6721738576889038\n",
      "Step [12/16]\t loss_instance: 5.370163917541504\t loss_cluster: 1.6999391317367554\n",
      "Step [14/16]\t loss_instance: 5.370099067687988\t loss_cluster: 1.7176915407180786\n",
      "\n",
      "Epoch [163/300]\t Loss: 6.920884454250336 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.37546443939209\t loss_cluster: 1.7359421253204346\n",
      "Step [2/16]\t loss_instance: 5.336520195007324\t loss_cluster: 1.6875070333480835\n",
      "Step [4/16]\t loss_instance: 5.337735652923584\t loss_cluster: 1.682015061378479\n",
      "Step [6/16]\t loss_instance: 5.341228485107422\t loss_cluster: 1.6800732612609863\n",
      "Step [8/16]\t loss_instance: 5.324705123901367\t loss_cluster: 1.6720820665359497\n",
      "Step [10/16]\t loss_instance: 5.334161281585693\t loss_cluster: 1.6788361072540283\n",
      "Step [12/16]\t loss_instance: 5.339467525482178\t loss_cluster: 1.6826993227005005\n",
      "Step [14/16]\t loss_instance: 5.375394821166992\t loss_cluster: 1.705501914024353\n",
      "\n",
      "Epoch [164/300]\t Loss: 6.919702881574631 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.313936710357666\t loss_cluster: 1.6468528509140015\n",
      "Step [2/16]\t loss_instance: 5.332769393920898\t loss_cluster: 1.6959894895553589\n",
      "Step [4/16]\t loss_instance: 5.368011951446533\t loss_cluster: 1.7277541160583496\n",
      "Step [6/16]\t loss_instance: 5.351974964141846\t loss_cluster: 1.6937215328216553\n",
      "Step [8/16]\t loss_instance: 5.321800231933594\t loss_cluster: 1.6678307056427002\n",
      "Step [10/16]\t loss_instance: 5.332026481628418\t loss_cluster: 1.6906402111053467\n",
      "Step [12/16]\t loss_instance: 5.375661849975586\t loss_cluster: 1.7326008081436157\n",
      "Step [14/16]\t loss_instance: 5.361227989196777\t loss_cluster: 1.697597861289978\n",
      "\n",
      "Epoch [165/300]\t Loss: 6.9250085055828094 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.326100826263428\t loss_cluster: 1.660325050354004\n",
      "Step [2/16]\t loss_instance: 5.363494873046875\t loss_cluster: 1.7096811532974243\n",
      "Step [4/16]\t loss_instance: 5.361641883850098\t loss_cluster: 1.6839275360107422\n",
      "Step [6/16]\t loss_instance: 5.355942726135254\t loss_cluster: 1.6893950700759888\n",
      "Step [8/16]\t loss_instance: 5.364996433258057\t loss_cluster: 1.7074986696243286\n",
      "Step [10/16]\t loss_instance: 5.3304266929626465\t loss_cluster: 1.6668018102645874\n",
      "Step [12/16]\t loss_instance: 5.3546462059021\t loss_cluster: 1.7277655601501465\n",
      "Step [14/16]\t loss_instance: 5.372800350189209\t loss_cluster: 1.6958132982254028\n",
      "\n",
      "Epoch [166/300]\t Loss: 6.919459214806556 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.317836284637451\t loss_cluster: 1.6578953266143799\n",
      "Step [2/16]\t loss_instance: 5.344578266143799\t loss_cluster: 1.6913388967514038\n",
      "Step [4/16]\t loss_instance: 5.3678741455078125\t loss_cluster: 1.698766827583313\n",
      "Step [6/16]\t loss_instance: 5.355055332183838\t loss_cluster: 1.6837462186813354\n",
      "Step [8/16]\t loss_instance: 5.350119113922119\t loss_cluster: 1.7048149108886719\n",
      "Step [10/16]\t loss_instance: 5.316767692565918\t loss_cluster: 1.651490569114685\n",
      "Step [12/16]\t loss_instance: 5.368518829345703\t loss_cluster: 1.729251742362976\n",
      "Step [14/16]\t loss_instance: 5.345938205718994\t loss_cluster: 1.694232702255249\n",
      "\n",
      "Epoch [167/300]\t Loss: 6.923801818490029 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.339934349060059\t loss_cluster: 1.6704360246658325\n",
      "Step [2/16]\t loss_instance: 5.341938018798828\t loss_cluster: 1.6814426183700562\n",
      "Step [4/16]\t loss_instance: 5.344727516174316\t loss_cluster: 1.6776624917984009\n",
      "Step [6/16]\t loss_instance: 5.338952541351318\t loss_cluster: 1.6666358709335327\n",
      "Step [8/16]\t loss_instance: 5.33743953704834\t loss_cluster: 1.673892855644226\n",
      "Step [10/16]\t loss_instance: 5.343589782714844\t loss_cluster: 1.6757975816726685\n",
      "Step [12/16]\t loss_instance: 5.322874546051025\t loss_cluster: 1.6648375988006592\n",
      "Step [14/16]\t loss_instance: 5.358379364013672\t loss_cluster: 1.7060792446136475\n",
      "\n",
      "Epoch [168/300]\t Loss: 6.915544772148133 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.345213413238525\t loss_cluster: 1.6819283962249756\n",
      "Step [2/16]\t loss_instance: 5.339386940002441\t loss_cluster: 1.6677764654159546\n",
      "Step [4/16]\t loss_instance: 5.336460113525391\t loss_cluster: 1.6935652494430542\n",
      "Step [6/16]\t loss_instance: 5.319673538208008\t loss_cluster: 1.6827222108840942\n",
      "Step [8/16]\t loss_instance: 5.311069965362549\t loss_cluster: 1.6529691219329834\n",
      "Step [10/16]\t loss_instance: 5.350474834442139\t loss_cluster: 1.708715558052063\n",
      "Step [12/16]\t loss_instance: 5.344518661499023\t loss_cluster: 1.6884998083114624\n",
      "Step [14/16]\t loss_instance: 5.355165958404541\t loss_cluster: 1.6878329515457153\n",
      "\n",
      "Epoch [169/300]\t Loss: 6.92018024623394 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.336583614349365\t loss_cluster: 1.6851195096969604\n",
      "Step [2/16]\t loss_instance: 5.331876277923584\t loss_cluster: 1.669027328491211\n",
      "Step [4/16]\t loss_instance: 5.316043376922607\t loss_cluster: 1.652873158454895\n",
      "Step [6/16]\t loss_instance: 5.373534679412842\t loss_cluster: 1.7186399698257446\n",
      "Step [8/16]\t loss_instance: 5.355404853820801\t loss_cluster: 1.681575894355774\n",
      "Step [10/16]\t loss_instance: 5.362934112548828\t loss_cluster: 1.7374931573867798\n",
      "Step [12/16]\t loss_instance: 5.352806568145752\t loss_cluster: 1.6931726932525635\n",
      "Step [14/16]\t loss_instance: 5.325981140136719\t loss_cluster: 1.6836456060409546\n",
      "\n",
      "Epoch [170/300]\t Loss: 6.923536765575409 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.333492755889893\t loss_cluster: 1.6634527444839478\n",
      "Step [2/16]\t loss_instance: 5.344162940979004\t loss_cluster: 1.6977853775024414\n",
      "Step [4/16]\t loss_instance: 5.311240196228027\t loss_cluster: 1.6665374040603638\n",
      "Step [6/16]\t loss_instance: 5.323944568634033\t loss_cluster: 1.6727945804595947\n",
      "Step [8/16]\t loss_instance: 5.374390125274658\t loss_cluster: 1.700153112411499\n",
      "Step [10/16]\t loss_instance: 5.319691181182861\t loss_cluster: 1.6680115461349487\n",
      "Step [12/16]\t loss_instance: 5.323646068572998\t loss_cluster: 1.6781269311904907\n",
      "Step [14/16]\t loss_instance: 5.36496639251709\t loss_cluster: 1.7142640352249146\n",
      "\n",
      "Epoch [171/300]\t Loss: 6.919940659403801 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.367758750915527\t loss_cluster: 1.7062535285949707\n",
      "Step [2/16]\t loss_instance: 5.352808475494385\t loss_cluster: 1.7022905349731445\n",
      "Step [4/16]\t loss_instance: 5.365270614624023\t loss_cluster: 1.7097370624542236\n",
      "Step [6/16]\t loss_instance: 5.337026596069336\t loss_cluster: 1.6733661890029907\n",
      "Step [8/16]\t loss_instance: 5.337853908538818\t loss_cluster: 1.6766525506973267\n",
      "Step [10/16]\t loss_instance: 5.394843101501465\t loss_cluster: 1.746673345565796\n",
      "Step [12/16]\t loss_instance: 5.332618236541748\t loss_cluster: 1.6783018112182617\n",
      "Step [14/16]\t loss_instance: 5.336236476898193\t loss_cluster: 1.6572606563568115\n",
      "\n",
      "Epoch [172/300]\t Loss: 6.920145905017852 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.320470333099365\t loss_cluster: 1.6604598760604858\n",
      "Step [2/16]\t loss_instance: 5.336130142211914\t loss_cluster: 1.6680220365524292\n",
      "Step [4/16]\t loss_instance: 5.354018688201904\t loss_cluster: 1.7042902708053589\n",
      "Step [6/16]\t loss_instance: 5.339133262634277\t loss_cluster: 1.6966367959976196\n",
      "Step [8/16]\t loss_instance: 5.312422752380371\t loss_cluster: 1.659591555595398\n",
      "Step [10/16]\t loss_instance: 5.337845325469971\t loss_cluster: 1.67336905002594\n",
      "Step [12/16]\t loss_instance: 5.3608198165893555\t loss_cluster: 1.711512565612793\n",
      "Step [14/16]\t loss_instance: 5.3414106369018555\t loss_cluster: 1.6871185302734375\n",
      "\n",
      "Epoch [173/300]\t Loss: 6.916166508197785 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.311837673187256\t loss_cluster: 1.6566798686981201\n",
      "Step [2/16]\t loss_instance: 5.369424819946289\t loss_cluster: 1.687050461769104\n",
      "Step [4/16]\t loss_instance: 5.3371782302856445\t loss_cluster: 1.6797329187393188\n",
      "Step [6/16]\t loss_instance: 5.349717617034912\t loss_cluster: 1.70560884475708\n",
      "Step [8/16]\t loss_instance: 5.3241071701049805\t loss_cluster: 1.6454873085021973\n",
      "Step [10/16]\t loss_instance: 5.334992408752441\t loss_cluster: 1.682212471961975\n",
      "Step [12/16]\t loss_instance: 5.35994291305542\t loss_cluster: 1.7016104459762573\n",
      "Step [14/16]\t loss_instance: 5.387876510620117\t loss_cluster: 1.720654010772705\n",
      "\n",
      "Epoch [174/300]\t Loss: 6.916924595832825 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3682475090026855\t loss_cluster: 1.7299734354019165\n",
      "Step [2/16]\t loss_instance: 5.337712287902832\t loss_cluster: 1.6810088157653809\n",
      "Step [4/16]\t loss_instance: 5.352499961853027\t loss_cluster: 1.7122046947479248\n",
      "Step [6/16]\t loss_instance: 5.367701053619385\t loss_cluster: 1.713561773300171\n",
      "Step [8/16]\t loss_instance: 5.342070579528809\t loss_cluster: 1.693077564239502\n",
      "Step [10/16]\t loss_instance: 5.336248874664307\t loss_cluster: 1.6776361465454102\n",
      "Step [12/16]\t loss_instance: 5.36416482925415\t loss_cluster: 1.694008231163025\n",
      "Step [14/16]\t loss_instance: 5.3228654861450195\t loss_cluster: 1.690231442451477\n",
      "\n",
      "Epoch [175/300]\t Loss: 6.920628616213799 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.331946849822998\t loss_cluster: 1.6671247482299805\n",
      "Step [2/16]\t loss_instance: 5.364950656890869\t loss_cluster: 1.7003366947174072\n",
      "Step [4/16]\t loss_instance: 5.339214324951172\t loss_cluster: 1.6830075979232788\n",
      "Step [6/16]\t loss_instance: 5.334899425506592\t loss_cluster: 1.6991502046585083\n",
      "Step [8/16]\t loss_instance: 5.363757133483887\t loss_cluster: 1.7022571563720703\n",
      "Step [10/16]\t loss_instance: 5.335873126983643\t loss_cluster: 1.6596976518630981\n",
      "Step [12/16]\t loss_instance: 5.329955577850342\t loss_cluster: 1.6704217195510864\n",
      "Step [14/16]\t loss_instance: 5.354478359222412\t loss_cluster: 1.6810942888259888\n",
      "\n",
      "Epoch [176/300]\t Loss: 6.9205257833004 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.335970878601074\t loss_cluster: 1.6820151805877686\n",
      "Step [2/16]\t loss_instance: 5.360258102416992\t loss_cluster: 1.7044761180877686\n",
      "Step [4/16]\t loss_instance: 5.370213985443115\t loss_cluster: 1.6969308853149414\n",
      "Step [6/16]\t loss_instance: 5.3414835929870605\t loss_cluster: 1.6918036937713623\n",
      "Step [8/16]\t loss_instance: 5.3582444190979\t loss_cluster: 1.7001630067825317\n",
      "Step [10/16]\t loss_instance: 5.309410095214844\t loss_cluster: 1.6460630893707275\n",
      "Step [12/16]\t loss_instance: 5.375873565673828\t loss_cluster: 1.7146798372268677\n",
      "Step [14/16]\t loss_instance: 5.347692966461182\t loss_cluster: 1.691205382347107\n",
      "\n",
      "Epoch [177/300]\t Loss: 6.920262721180916 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.362895965576172\t loss_cluster: 1.708657145500183\n",
      "Step [2/16]\t loss_instance: 5.349545955657959\t loss_cluster: 1.6989301443099976\n",
      "Step [4/16]\t loss_instance: 5.341892242431641\t loss_cluster: 1.6929771900177002\n",
      "Step [6/16]\t loss_instance: 5.35373592376709\t loss_cluster: 1.6878135204315186\n",
      "Step [8/16]\t loss_instance: 5.343318462371826\t loss_cluster: 1.6961562633514404\n",
      "Step [10/16]\t loss_instance: 5.339542388916016\t loss_cluster: 1.6951955556869507\n",
      "Step [12/16]\t loss_instance: 5.345841884613037\t loss_cluster: 1.674494743347168\n",
      "Step [14/16]\t loss_instance: 5.315943241119385\t loss_cluster: 1.670698881149292\n",
      "\n",
      "Epoch [178/300]\t Loss: 6.917916551232338 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.337677478790283\t loss_cluster: 1.6852964162826538\n",
      "Step [2/16]\t loss_instance: 5.3508710861206055\t loss_cluster: 1.6916859149932861\n",
      "Step [4/16]\t loss_instance: 5.368953704833984\t loss_cluster: 1.7165131568908691\n",
      "Step [6/16]\t loss_instance: 5.348180294036865\t loss_cluster: 1.6959856748580933\n",
      "Step [8/16]\t loss_instance: 5.330280780792236\t loss_cluster: 1.680290699005127\n",
      "Step [10/16]\t loss_instance: 5.32593297958374\t loss_cluster: 1.6737860441207886\n",
      "Step [12/16]\t loss_instance: 5.351456642150879\t loss_cluster: 1.682908535003662\n",
      "Step [14/16]\t loss_instance: 5.354716777801514\t loss_cluster: 1.6983572244644165\n",
      "\n",
      "Epoch [179/300]\t Loss: 6.9202668339014055 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.343750476837158\t loss_cluster: 1.7049627304077148\n",
      "Step [2/16]\t loss_instance: 5.351086616516113\t loss_cluster: 1.6920733451843262\n",
      "Step [4/16]\t loss_instance: 5.353959560394287\t loss_cluster: 1.702446699142456\n",
      "Step [6/16]\t loss_instance: 5.335315227508545\t loss_cluster: 1.6973286867141724\n",
      "Step [8/16]\t loss_instance: 5.366675853729248\t loss_cluster: 1.697706937789917\n",
      "Step [10/16]\t loss_instance: 5.358608722686768\t loss_cluster: 1.6906691789627075\n",
      "Step [12/16]\t loss_instance: 5.351149559020996\t loss_cluster: 1.685247540473938\n",
      "Step [14/16]\t loss_instance: 5.312639236450195\t loss_cluster: 1.6501481533050537\n",
      "\n",
      "Epoch [180/300]\t Loss: 6.915763336420059 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.355419158935547\t loss_cluster: 1.6993989944458008\n",
      "Step [2/16]\t loss_instance: 5.355891227722168\t loss_cluster: 1.699240803718567\n",
      "Step [4/16]\t loss_instance: 5.36639404296875\t loss_cluster: 1.6857842206954956\n",
      "Step [6/16]\t loss_instance: 5.356772422790527\t loss_cluster: 1.6909422874450684\n",
      "Step [8/16]\t loss_instance: 5.340853691101074\t loss_cluster: 1.6702783107757568\n",
      "Step [10/16]\t loss_instance: 5.334144592285156\t loss_cluster: 1.6655477285385132\n",
      "Step [12/16]\t loss_instance: 5.309380531311035\t loss_cluster: 1.6617242097854614\n",
      "Step [14/16]\t loss_instance: 5.335057258605957\t loss_cluster: 1.6875319480895996\n",
      "\n",
      "Epoch [181/300]\t Loss: 6.917705774307251 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.328599452972412\t loss_cluster: 1.6768710613250732\n",
      "Step [2/16]\t loss_instance: 5.362425327301025\t loss_cluster: 1.709498405456543\n",
      "Step [4/16]\t loss_instance: 5.346353530883789\t loss_cluster: 1.6965370178222656\n",
      "Step [6/16]\t loss_instance: 5.391871452331543\t loss_cluster: 1.7201197147369385\n",
      "Step [8/16]\t loss_instance: 5.33140754699707\t loss_cluster: 1.6769869327545166\n",
      "Step [10/16]\t loss_instance: 5.375402927398682\t loss_cluster: 1.736465573310852\n",
      "Step [12/16]\t loss_instance: 5.340312480926514\t loss_cluster: 1.683899998664856\n",
      "Step [14/16]\t loss_instance: 5.346367359161377\t loss_cluster: 1.6774441003799438\n",
      "\n",
      "Epoch [182/300]\t Loss: 6.9221054196357725 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.305670738220215\t loss_cluster: 1.6459912061691284\n",
      "Step [2/16]\t loss_instance: 5.378122806549072\t loss_cluster: 1.7203333377838135\n",
      "Step [4/16]\t loss_instance: 5.32099723815918\t loss_cluster: 1.6721841096878052\n",
      "Step [6/16]\t loss_instance: 5.347593307495117\t loss_cluster: 1.695781946182251\n",
      "Step [8/16]\t loss_instance: 5.367098331451416\t loss_cluster: 1.710652232170105\n",
      "Step [10/16]\t loss_instance: 5.346625804901123\t loss_cluster: 1.6973912715911865\n",
      "Step [12/16]\t loss_instance: 5.350963115692139\t loss_cluster: 1.6985019445419312\n",
      "Step [14/16]\t loss_instance: 5.349985122680664\t loss_cluster: 1.697413444519043\n",
      "\n",
      "Epoch [183/300]\t Loss: 6.917957517504692 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.331954002380371\t loss_cluster: 1.6882638931274414\n",
      "Step [2/16]\t loss_instance: 5.349246025085449\t loss_cluster: 1.6962159872055054\n",
      "Step [4/16]\t loss_instance: 5.377199649810791\t loss_cluster: 1.7057124376296997\n",
      "Step [6/16]\t loss_instance: 5.3469719886779785\t loss_cluster: 1.7006345987319946\n",
      "Step [8/16]\t loss_instance: 5.324975490570068\t loss_cluster: 1.678802251815796\n",
      "Step [10/16]\t loss_instance: 5.370882987976074\t loss_cluster: 1.7161825895309448\n",
      "Step [12/16]\t loss_instance: 5.350790023803711\t loss_cluster: 1.704395055770874\n",
      "Step [14/16]\t loss_instance: 5.306022644042969\t loss_cluster: 1.6546497344970703\n",
      "\n",
      "Epoch [184/300]\t Loss: 6.920271539688111 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.344438076019287\t loss_cluster: 1.684475064277649\n",
      "Step [2/16]\t loss_instance: 5.344383716583252\t loss_cluster: 1.685405969619751\n",
      "Step [4/16]\t loss_instance: 5.372056007385254\t loss_cluster: 1.6827150583267212\n",
      "Step [6/16]\t loss_instance: 5.348775386810303\t loss_cluster: 1.6846345663070679\n",
      "Step [8/16]\t loss_instance: 5.36809778213501\t loss_cluster: 1.7022236585617065\n",
      "Step [10/16]\t loss_instance: 5.346963405609131\t loss_cluster: 1.7007540464401245\n",
      "Step [12/16]\t loss_instance: 5.346791744232178\t loss_cluster: 1.6828583478927612\n",
      "Step [14/16]\t loss_instance: 5.387085437774658\t loss_cluster: 1.7277352809906006\n",
      "\n",
      "Epoch [185/300]\t Loss: 6.917470726370811 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.362112045288086\t loss_cluster: 1.6855207681655884\n",
      "Step [2/16]\t loss_instance: 5.339240550994873\t loss_cluster: 1.6878687143325806\n",
      "Step [4/16]\t loss_instance: 5.318243980407715\t loss_cluster: 1.6648802757263184\n",
      "Step [6/16]\t loss_instance: 5.350437164306641\t loss_cluster: 1.6857558488845825\n",
      "Step [8/16]\t loss_instance: 5.308990478515625\t loss_cluster: 1.6579022407531738\n",
      "Step [10/16]\t loss_instance: 5.350000858306885\t loss_cluster: 1.7176355123519897\n",
      "Step [12/16]\t loss_instance: 5.340767860412598\t loss_cluster: 1.699960470199585\n",
      "Step [14/16]\t loss_instance: 5.348455905914307\t loss_cluster: 1.7008039951324463\n",
      "\n",
      "Epoch [186/300]\t Loss: 6.917350050806999 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.348743438720703\t loss_cluster: 1.7066142559051514\n",
      "Step [2/16]\t loss_instance: 5.350003242492676\t loss_cluster: 1.6846230030059814\n",
      "Step [4/16]\t loss_instance: 5.301756858825684\t loss_cluster: 1.6694551706314087\n",
      "Step [6/16]\t loss_instance: 5.3118367195129395\t loss_cluster: 1.692519187927246\n",
      "Step [8/16]\t loss_instance: 5.3270440101623535\t loss_cluster: 1.6691020727157593\n",
      "Step [10/16]\t loss_instance: 5.345414161682129\t loss_cluster: 1.7016972303390503\n",
      "Step [12/16]\t loss_instance: 5.365906238555908\t loss_cluster: 1.7079060077667236\n",
      "Step [14/16]\t loss_instance: 5.367181301116943\t loss_cluster: 1.717625379562378\n",
      "\n",
      "Epoch [187/300]\t Loss: 6.925541949272156 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.372607231140137\t loss_cluster: 1.7223560810089111\n",
      "Step [2/16]\t loss_instance: 5.317687511444092\t loss_cluster: 1.6624237298965454\n",
      "Step [4/16]\t loss_instance: 5.337620258331299\t loss_cluster: 1.6907135248184204\n",
      "Step [6/16]\t loss_instance: 5.319963455200195\t loss_cluster: 1.679402470588684\n",
      "Step [8/16]\t loss_instance: 5.342467784881592\t loss_cluster: 1.6765450239181519\n",
      "Step [10/16]\t loss_instance: 5.354503154754639\t loss_cluster: 1.704693078994751\n",
      "Step [12/16]\t loss_instance: 5.337782382965088\t loss_cluster: 1.6912323236465454\n",
      "Step [14/16]\t loss_instance: 5.331716537475586\t loss_cluster: 1.6777697801589966\n",
      "\n",
      "Epoch [188/300]\t Loss: 6.92519266307354 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.398076057434082\t loss_cluster: 1.7309139966964722\n",
      "Step [2/16]\t loss_instance: 5.380033493041992\t loss_cluster: 1.7148650884628296\n",
      "Step [4/16]\t loss_instance: 5.330631256103516\t loss_cluster: 1.6933997869491577\n",
      "Step [6/16]\t loss_instance: 5.360829830169678\t loss_cluster: 1.7020119428634644\n",
      "Step [8/16]\t loss_instance: 5.312368869781494\t loss_cluster: 1.659461259841919\n",
      "Step [10/16]\t loss_instance: 5.333959102630615\t loss_cluster: 1.6619991064071655\n",
      "Step [12/16]\t loss_instance: 5.3493123054504395\t loss_cluster: 1.673535943031311\n",
      "Step [14/16]\t loss_instance: 5.322605133056641\t loss_cluster: 1.680495023727417\n",
      "\n",
      "Epoch [189/300]\t Loss: 6.9170742392539974 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.345007419586182\t loss_cluster: 1.688556432723999\n",
      "Step [2/16]\t loss_instance: 5.329014778137207\t loss_cluster: 1.6884135007858276\n",
      "Step [4/16]\t loss_instance: 5.309179782867432\t loss_cluster: 1.661407709121704\n",
      "Step [6/16]\t loss_instance: 5.3729681968688965\t loss_cluster: 1.6936253309249878\n",
      "Step [8/16]\t loss_instance: 5.353763580322266\t loss_cluster: 1.706880807876587\n",
      "Step [10/16]\t loss_instance: 5.337936878204346\t loss_cluster: 1.6763126850128174\n",
      "Step [12/16]\t loss_instance: 5.358121871948242\t loss_cluster: 1.6919273138046265\n",
      "Step [14/16]\t loss_instance: 5.345048904418945\t loss_cluster: 1.7178285121917725\n",
      "\n",
      "Epoch [190/300]\t Loss: 6.919325339794159 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.32283878326416\t loss_cluster: 1.6545391082763672\n",
      "Step [2/16]\t loss_instance: 5.36700963973999\t loss_cluster: 1.7051719427108765\n",
      "Step [4/16]\t loss_instance: 5.322554588317871\t loss_cluster: 1.655950903892517\n",
      "Step [6/16]\t loss_instance: 5.34846830368042\t loss_cluster: 1.6876634359359741\n",
      "Step [8/16]\t loss_instance: 5.362929821014404\t loss_cluster: 1.6853797435760498\n",
      "Step [10/16]\t loss_instance: 5.331107139587402\t loss_cluster: 1.668855905532837\n",
      "Step [12/16]\t loss_instance: 5.350418567657471\t loss_cluster: 1.682594895362854\n",
      "Step [14/16]\t loss_instance: 5.3384690284729\t loss_cluster: 1.702082633972168\n",
      "\n",
      "Epoch [191/300]\t Loss: 6.911715057492256 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.34313440322876\t loss_cluster: 1.6666229963302612\n",
      "Step [2/16]\t loss_instance: 5.330646991729736\t loss_cluster: 1.6722949743270874\n",
      "Step [4/16]\t loss_instance: 5.375990867614746\t loss_cluster: 1.7089790105819702\n",
      "Step [6/16]\t loss_instance: 5.339878082275391\t loss_cluster: 1.694084644317627\n",
      "Step [8/16]\t loss_instance: 5.32681131362915\t loss_cluster: 1.692352056503296\n",
      "Step [10/16]\t loss_instance: 5.3427019119262695\t loss_cluster: 1.693398118019104\n",
      "Step [12/16]\t loss_instance: 5.343896865844727\t loss_cluster: 1.6831759214401245\n",
      "Step [14/16]\t loss_instance: 5.360281944274902\t loss_cluster: 1.7253659963607788\n",
      "\n",
      "Epoch [192/300]\t Loss: 6.9165839612483975 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.348325729370117\t loss_cluster: 1.6891684532165527\n",
      "Step [2/16]\t loss_instance: 5.337112903594971\t loss_cluster: 1.671191692352295\n",
      "Step [4/16]\t loss_instance: 5.349460124969482\t loss_cluster: 1.695852518081665\n",
      "Step [6/16]\t loss_instance: 5.4019012451171875\t loss_cluster: 1.7379142045974731\n",
      "Step [8/16]\t loss_instance: 5.345548152923584\t loss_cluster: 1.6830629110336304\n",
      "Step [10/16]\t loss_instance: 5.338681221008301\t loss_cluster: 1.6789008378982544\n",
      "Step [12/16]\t loss_instance: 5.323714733123779\t loss_cluster: 1.6669775247573853\n",
      "Step [14/16]\t loss_instance: 5.314059257507324\t loss_cluster: 1.6643123626708984\n",
      "\n",
      "Epoch [193/300]\t Loss: 6.9110574334859844 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.353757858276367\t loss_cluster: 1.692034125328064\n",
      "Step [2/16]\t loss_instance: 5.376743316650391\t loss_cluster: 1.6931637525558472\n",
      "Step [4/16]\t loss_instance: 5.349617004394531\t loss_cluster: 1.6897401809692383\n",
      "Step [6/16]\t loss_instance: 5.330740451812744\t loss_cluster: 1.6800566911697388\n",
      "Step [8/16]\t loss_instance: 5.356934547424316\t loss_cluster: 1.6811950206756592\n",
      "Step [10/16]\t loss_instance: 5.3141350746154785\t loss_cluster: 1.6858370304107666\n",
      "Step [12/16]\t loss_instance: 5.3406829833984375\t loss_cluster: 1.6726409196853638\n",
      "Step [14/16]\t loss_instance: 5.3339362144470215\t loss_cluster: 1.6808897256851196\n",
      "\n",
      "Epoch [194/300]\t Loss: 6.91258405148983 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.336570739746094\t loss_cluster: 1.6912556886672974\n",
      "Step [2/16]\t loss_instance: 5.3273701667785645\t loss_cluster: 1.6682484149932861\n",
      "Step [4/16]\t loss_instance: 5.367759704589844\t loss_cluster: 1.7178044319152832\n",
      "Step [6/16]\t loss_instance: 5.364498615264893\t loss_cluster: 1.7014539241790771\n",
      "Step [8/16]\t loss_instance: 5.368705749511719\t loss_cluster: 1.7054249048233032\n",
      "Step [10/16]\t loss_instance: 5.358783721923828\t loss_cluster: 1.7017320394515991\n",
      "Step [12/16]\t loss_instance: 5.338310241699219\t loss_cluster: 1.6945356130599976\n",
      "Step [14/16]\t loss_instance: 5.331336498260498\t loss_cluster: 1.6817580461502075\n",
      "\n",
      "Epoch [195/300]\t Loss: 6.920561808347702 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.350936412811279\t loss_cluster: 1.6936615705490112\n",
      "Step [2/16]\t loss_instance: 5.369856357574463\t loss_cluster: 1.7251882553100586\n",
      "Step [4/16]\t loss_instance: 5.344661235809326\t loss_cluster: 1.681212306022644\n",
      "Step [6/16]\t loss_instance: 5.354334354400635\t loss_cluster: 1.6864521503448486\n",
      "Step [8/16]\t loss_instance: 5.3427653312683105\t loss_cluster: 1.6850899457931519\n",
      "Step [10/16]\t loss_instance: 5.343534469604492\t loss_cluster: 1.682311773300171\n",
      "Step [12/16]\t loss_instance: 5.325803279876709\t loss_cluster: 1.6660557985305786\n",
      "Step [14/16]\t loss_instance: 5.331350326538086\t loss_cluster: 1.667083501815796\n",
      "\n",
      "Epoch [196/300]\t Loss: 6.916560944914818 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.358453273773193\t loss_cluster: 1.714961051940918\n",
      "Step [2/16]\t loss_instance: 5.33308744430542\t loss_cluster: 1.6967601776123047\n",
      "Step [4/16]\t loss_instance: 5.346100330352783\t loss_cluster: 1.6810375452041626\n",
      "Step [6/16]\t loss_instance: 5.33366060256958\t loss_cluster: 1.6762728691101074\n",
      "Step [8/16]\t loss_instance: 5.3298187255859375\t loss_cluster: 1.6766222715377808\n",
      "Step [10/16]\t loss_instance: 5.323591232299805\t loss_cluster: 1.684175968170166\n",
      "Step [12/16]\t loss_instance: 5.364436149597168\t loss_cluster: 1.6818580627441406\n",
      "Step [14/16]\t loss_instance: 5.332053184509277\t loss_cluster: 1.6805557012557983\n",
      "\n",
      "Epoch [197/300]\t Loss: 6.914879468083382 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.353421211242676\t loss_cluster: 1.7080070972442627\n",
      "Step [2/16]\t loss_instance: 5.357947826385498\t loss_cluster: 1.701748013496399\n",
      "Step [4/16]\t loss_instance: 5.3098297119140625\t loss_cluster: 1.653853178024292\n",
      "Step [6/16]\t loss_instance: 5.324700355529785\t loss_cluster: 1.6723861694335938\n",
      "Step [8/16]\t loss_instance: 5.347095489501953\t loss_cluster: 1.671406626701355\n",
      "Step [10/16]\t loss_instance: 5.336930274963379\t loss_cluster: 1.660416603088379\n",
      "Step [12/16]\t loss_instance: 5.349944591522217\t loss_cluster: 1.6957587003707886\n",
      "Step [14/16]\t loss_instance: 5.339666843414307\t loss_cluster: 1.7009695768356323\n",
      "\n",
      "Epoch [198/300]\t Loss: 6.914159208536148 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.355771064758301\t loss_cluster: 1.695318341255188\n",
      "Step [2/16]\t loss_instance: 5.340140342712402\t loss_cluster: 1.681919813156128\n",
      "Step [4/16]\t loss_instance: 5.352204322814941\t loss_cluster: 1.7072670459747314\n",
      "Step [6/16]\t loss_instance: 5.3221211433410645\t loss_cluster: 1.6491284370422363\n",
      "Step [8/16]\t loss_instance: 5.350611686706543\t loss_cluster: 1.7011529207229614\n",
      "Step [10/16]\t loss_instance: 5.345908164978027\t loss_cluster: 1.704555630683899\n",
      "Step [12/16]\t loss_instance: 5.349796772003174\t loss_cluster: 1.6865829229354858\n",
      "Step [14/16]\t loss_instance: 5.356315612792969\t loss_cluster: 1.6883138418197632\n",
      "\n",
      "Epoch [199/300]\t Loss: 6.91644484102726 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.318393707275391\t loss_cluster: 1.664204478263855\n",
      "Step [2/16]\t loss_instance: 5.351200580596924\t loss_cluster: 1.7090140581130981\n",
      "Step [4/16]\t loss_instance: 5.350078582763672\t loss_cluster: 1.7201920747756958\n",
      "Step [6/16]\t loss_instance: 5.339468955993652\t loss_cluster: 1.677289366722107\n",
      "Step [8/16]\t loss_instance: 5.360960960388184\t loss_cluster: 1.7020214796066284\n",
      "Step [10/16]\t loss_instance: 5.3524346351623535\t loss_cluster: 1.6788551807403564\n",
      "Step [12/16]\t loss_instance: 5.332996368408203\t loss_cluster: 1.6798871755599976\n",
      "Step [14/16]\t loss_instance: 5.359274864196777\t loss_cluster: 1.691420555114746\n",
      "\n",
      "Epoch [200/300]\t Loss: 6.919105821847916 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.357664585113525\t loss_cluster: 1.7112857103347778\n",
      "Step [2/16]\t loss_instance: 5.342993259429932\t loss_cluster: 1.6912089586257935\n",
      "Step [4/16]\t loss_instance: 5.316864967346191\t loss_cluster: 1.6681174039840698\n",
      "Step [6/16]\t loss_instance: 5.376464366912842\t loss_cluster: 1.695784568786621\n",
      "Step [8/16]\t loss_instance: 5.3312764167785645\t loss_cluster: 1.712044596672058\n",
      "Step [10/16]\t loss_instance: 5.3723225593566895\t loss_cluster: 1.6971923112869263\n",
      "Step [12/16]\t loss_instance: 5.324727535247803\t loss_cluster: 1.6750059127807617\n",
      "Step [14/16]\t loss_instance: 5.355605125427246\t loss_cluster: 1.7034896612167358\n",
      "\n",
      "Epoch [201/300]\t Loss: 6.921567994356155 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.358480930328369\t loss_cluster: 1.6926188468933105\n",
      "Step [2/16]\t loss_instance: 5.329898357391357\t loss_cluster: 1.6746090650558472\n",
      "Step [4/16]\t loss_instance: 5.372788906097412\t loss_cluster: 1.6951771974563599\n",
      "Step [6/16]\t loss_instance: 5.366030216217041\t loss_cluster: 1.7076351642608643\n",
      "Step [8/16]\t loss_instance: 5.349059104919434\t loss_cluster: 1.6805099248886108\n",
      "Step [10/16]\t loss_instance: 5.323225498199463\t loss_cluster: 1.668966293334961\n",
      "Step [12/16]\t loss_instance: 5.352933406829834\t loss_cluster: 1.7063604593276978\n",
      "Step [14/16]\t loss_instance: 5.321285724639893\t loss_cluster: 1.677437424659729\n",
      "\n",
      "Epoch [202/300]\t Loss: 6.91905098259449 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.337838649749756\t loss_cluster: 1.6803011894226074\n",
      "Step [2/16]\t loss_instance: 5.354276180267334\t loss_cluster: 1.7166568040847778\n",
      "Step [4/16]\t loss_instance: 5.325798034667969\t loss_cluster: 1.667273759841919\n",
      "Step [6/16]\t loss_instance: 5.337662696838379\t loss_cluster: 1.6963647603988647\n",
      "Step [8/16]\t loss_instance: 5.3468217849731445\t loss_cluster: 1.6944371461868286\n",
      "Step [10/16]\t loss_instance: 5.354925632476807\t loss_cluster: 1.6954634189605713\n",
      "Step [12/16]\t loss_instance: 5.352794647216797\t loss_cluster: 1.6838462352752686\n",
      "Step [14/16]\t loss_instance: 5.356529712677002\t loss_cluster: 1.6885713338851929\n",
      "\n",
      "Epoch [203/300]\t Loss: 6.915365162491798 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.381698131561279\t loss_cluster: 1.7142828702926636\n",
      "Step [2/16]\t loss_instance: 5.369502067565918\t loss_cluster: 1.7262099981307983\n",
      "Step [4/16]\t loss_instance: 5.334212303161621\t loss_cluster: 1.6844720840454102\n",
      "Step [6/16]\t loss_instance: 5.304300308227539\t loss_cluster: 1.6493428945541382\n",
      "Step [8/16]\t loss_instance: 5.343182563781738\t loss_cluster: 1.6954513788223267\n",
      "Step [10/16]\t loss_instance: 5.345337390899658\t loss_cluster: 1.6822797060012817\n",
      "Step [12/16]\t loss_instance: 5.316015720367432\t loss_cluster: 1.666401982307434\n",
      "Step [14/16]\t loss_instance: 5.337054252624512\t loss_cluster: 1.6769075393676758\n",
      "\n",
      "Epoch [204/300]\t Loss: 6.91146596968174 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.332391738891602\t loss_cluster: 1.6540849208831787\n",
      "Step [2/16]\t loss_instance: 5.340412616729736\t loss_cluster: 1.681119441986084\n",
      "Step [4/16]\t loss_instance: 5.398227691650391\t loss_cluster: 1.7288700342178345\n",
      "Step [6/16]\t loss_instance: 5.314393520355225\t loss_cluster: 1.6623361110687256\n",
      "Step [8/16]\t loss_instance: 5.364373683929443\t loss_cluster: 1.7169443368911743\n",
      "Step [10/16]\t loss_instance: 5.320789337158203\t loss_cluster: 1.6604009866714478\n",
      "Step [12/16]\t loss_instance: 5.375480651855469\t loss_cluster: 1.7342517375946045\n",
      "Step [14/16]\t loss_instance: 5.351583957672119\t loss_cluster: 1.6972739696502686\n",
      "\n",
      "Epoch [205/300]\t Loss: 6.919072470068931 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.362874507904053\t loss_cluster: 1.700832486152649\n",
      "Step [2/16]\t loss_instance: 5.331688404083252\t loss_cluster: 1.6805521249771118\n",
      "Step [4/16]\t loss_instance: 5.331650257110596\t loss_cluster: 1.6840885877609253\n",
      "Step [6/16]\t loss_instance: 5.3375444412231445\t loss_cluster: 1.6525624990463257\n",
      "Step [8/16]\t loss_instance: 5.348079204559326\t loss_cluster: 1.672921061515808\n",
      "Step [10/16]\t loss_instance: 5.379961013793945\t loss_cluster: 1.7217713594436646\n",
      "Step [12/16]\t loss_instance: 5.340219497680664\t loss_cluster: 1.6730890274047852\n",
      "Step [14/16]\t loss_instance: 5.366995811462402\t loss_cluster: 1.7027554512023926\n",
      "\n",
      "Epoch [206/300]\t Loss: 6.915582278370858 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.356415271759033\t loss_cluster: 1.704884648323059\n",
      "Step [2/16]\t loss_instance: 5.312295913696289\t loss_cluster: 1.6535232067108154\n",
      "Step [4/16]\t loss_instance: 5.334887504577637\t loss_cluster: 1.674640417098999\n",
      "Step [6/16]\t loss_instance: 5.3676934242248535\t loss_cluster: 1.698900580406189\n",
      "Step [8/16]\t loss_instance: 5.322061538696289\t loss_cluster: 1.6755657196044922\n",
      "Step [10/16]\t loss_instance: 5.345583915710449\t loss_cluster: 1.6908341646194458\n",
      "Step [12/16]\t loss_instance: 5.34286642074585\t loss_cluster: 1.6762021780014038\n",
      "Step [14/16]\t loss_instance: 5.380248546600342\t loss_cluster: 1.7203598022460938\n",
      "\n",
      "Epoch [207/300]\t Loss: 6.913167375326156 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3598833084106445\t loss_cluster: 1.6894718408584595\n",
      "Step [2/16]\t loss_instance: 5.335439682006836\t loss_cluster: 1.6737643480300903\n",
      "Step [4/16]\t loss_instance: 5.340002536773682\t loss_cluster: 1.6802908182144165\n",
      "Step [6/16]\t loss_instance: 5.364270210266113\t loss_cluster: 1.708189845085144\n",
      "Step [8/16]\t loss_instance: 5.340604305267334\t loss_cluster: 1.6885708570480347\n",
      "Step [10/16]\t loss_instance: 5.324492454528809\t loss_cluster: 1.6770042181015015\n",
      "Step [12/16]\t loss_instance: 5.349278926849365\t loss_cluster: 1.683822751045227\n",
      "Step [14/16]\t loss_instance: 5.326930999755859\t loss_cluster: 1.690247893333435\n",
      "\n",
      "Epoch [208/300]\t Loss: 6.9209628969430925 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3271355628967285\t loss_cluster: 1.6691033840179443\n",
      "Step [2/16]\t loss_instance: 5.331616401672363\t loss_cluster: 1.6665289402008057\n",
      "Step [4/16]\t loss_instance: 5.325772762298584\t loss_cluster: 1.668745756149292\n",
      "Step [6/16]\t loss_instance: 5.350488185882568\t loss_cluster: 1.690748929977417\n",
      "Step [8/16]\t loss_instance: 5.359916687011719\t loss_cluster: 1.6883162260055542\n",
      "Step [10/16]\t loss_instance: 5.345330238342285\t loss_cluster: 1.6875792741775513\n",
      "Step [12/16]\t loss_instance: 5.3162946701049805\t loss_cluster: 1.672258973121643\n",
      "Step [14/16]\t loss_instance: 5.334918022155762\t loss_cluster: 1.6712833642959595\n",
      "\n",
      "Epoch [209/300]\t Loss: 6.914224100112915 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.356045246124268\t loss_cluster: 1.710605502128601\n",
      "Step [2/16]\t loss_instance: 5.344809532165527\t loss_cluster: 1.6831214427947998\n",
      "Step [4/16]\t loss_instance: 5.336686134338379\t loss_cluster: 1.6820961236953735\n",
      "Step [6/16]\t loss_instance: 5.346955299377441\t loss_cluster: 1.6738078594207764\n",
      "Step [8/16]\t loss_instance: 5.364298343658447\t loss_cluster: 1.7062534093856812\n",
      "Step [10/16]\t loss_instance: 5.34838342666626\t loss_cluster: 1.6927223205566406\n",
      "Step [12/16]\t loss_instance: 5.341799259185791\t loss_cluster: 1.7076319456100464\n",
      "Step [14/16]\t loss_instance: 5.324702262878418\t loss_cluster: 1.6853513717651367\n",
      "\n",
      "Epoch [210/300]\t Loss: 6.91771674156189 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.356282711029053\t loss_cluster: 1.701991081237793\n",
      "Step [2/16]\t loss_instance: 5.348121643066406\t loss_cluster: 1.6836625337600708\n",
      "Step [4/16]\t loss_instance: 5.338131904602051\t loss_cluster: 1.690350890159607\n",
      "Step [6/16]\t loss_instance: 5.348697662353516\t loss_cluster: 1.6989426612854004\n",
      "Step [8/16]\t loss_instance: 5.330944061279297\t loss_cluster: 1.6843445301055908\n",
      "Step [10/16]\t loss_instance: 5.343931198120117\t loss_cluster: 1.6726720333099365\n",
      "Step [12/16]\t loss_instance: 5.334684371948242\t loss_cluster: 1.6657888889312744\n",
      "Step [14/16]\t loss_instance: 5.3512678146362305\t loss_cluster: 1.6942980289459229\n",
      "\n",
      "Epoch [211/300]\t Loss: 6.913519191741943 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.345027446746826\t loss_cluster: 1.687273383140564\n",
      "Step [2/16]\t loss_instance: 5.3437819480896\t loss_cluster: 1.705454707145691\n",
      "Step [4/16]\t loss_instance: 5.3612823486328125\t loss_cluster: 1.7044991254806519\n",
      "Step [6/16]\t loss_instance: 5.348291873931885\t loss_cluster: 1.6708967685699463\n",
      "Step [8/16]\t loss_instance: 5.336441993713379\t loss_cluster: 1.6697040796279907\n",
      "Step [10/16]\t loss_instance: 5.330265045166016\t loss_cluster: 1.681215524673462\n",
      "Step [12/16]\t loss_instance: 5.351475715637207\t loss_cluster: 1.695486307144165\n",
      "Step [14/16]\t loss_instance: 5.359098434448242\t loss_cluster: 1.700069546699524\n",
      "\n",
      "Epoch [212/300]\t Loss: 6.913824757933616 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.37306022644043\t loss_cluster: 1.691071629524231\n",
      "Step [2/16]\t loss_instance: 5.345050811767578\t loss_cluster: 1.7081100940704346\n",
      "Step [4/16]\t loss_instance: 5.356459140777588\t loss_cluster: 1.6967475414276123\n",
      "Step [6/16]\t loss_instance: 5.329751968383789\t loss_cluster: 1.6679916381835938\n",
      "Step [8/16]\t loss_instance: 5.351484775543213\t loss_cluster: 1.7019946575164795\n",
      "Step [10/16]\t loss_instance: 5.347740173339844\t loss_cluster: 1.690718412399292\n",
      "Step [12/16]\t loss_instance: 5.369391918182373\t loss_cluster: 1.713625431060791\n",
      "Step [14/16]\t loss_instance: 5.351190567016602\t loss_cluster: 1.706785798072815\n",
      "\n",
      "Epoch [213/300]\t Loss: 6.911127498745918 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.349066734313965\t loss_cluster: 1.6863585710525513\n",
      "Step [2/16]\t loss_instance: 5.338042259216309\t loss_cluster: 1.6653233766555786\n",
      "Step [4/16]\t loss_instance: 5.312565803527832\t loss_cluster: 1.669388771057129\n",
      "Step [6/16]\t loss_instance: 5.349175453186035\t loss_cluster: 1.6826303005218506\n",
      "Step [8/16]\t loss_instance: 5.361362934112549\t loss_cluster: 1.6908148527145386\n",
      "Step [10/16]\t loss_instance: 5.341525077819824\t loss_cluster: 1.6821956634521484\n",
      "Step [12/16]\t loss_instance: 5.356525897979736\t loss_cluster: 1.707883596420288\n",
      "Step [14/16]\t loss_instance: 5.334156036376953\t loss_cluster: 1.6722350120544434\n",
      "\n",
      "Epoch [214/300]\t Loss: 6.913829055428505 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.36012601852417\t loss_cluster: 1.6857190132141113\n",
      "Step [2/16]\t loss_instance: 5.339271068572998\t loss_cluster: 1.6738561391830444\n",
      "Step [4/16]\t loss_instance: 5.346414089202881\t loss_cluster: 1.7068451642990112\n",
      "Step [6/16]\t loss_instance: 5.336619853973389\t loss_cluster: 1.6810592412948608\n",
      "Step [8/16]\t loss_instance: 5.366311073303223\t loss_cluster: 1.6981544494628906\n",
      "Step [10/16]\t loss_instance: 5.365744590759277\t loss_cluster: 1.6839784383773804\n",
      "Step [12/16]\t loss_instance: 5.355688571929932\t loss_cluster: 1.6890239715576172\n",
      "Step [14/16]\t loss_instance: 5.331611633300781\t loss_cluster: 1.6592144966125488\n",
      "\n",
      "Epoch [215/300]\t Loss: 6.914735108613968 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.326501846313477\t loss_cluster: 1.6885679960250854\n",
      "Step [2/16]\t loss_instance: 5.334234237670898\t loss_cluster: 1.6696635484695435\n",
      "Step [4/16]\t loss_instance: 5.357719898223877\t loss_cluster: 1.6937576532363892\n",
      "Step [6/16]\t loss_instance: 5.365052223205566\t loss_cluster: 1.6983457803726196\n",
      "Step [8/16]\t loss_instance: 5.346689701080322\t loss_cluster: 1.6993036270141602\n",
      "Step [10/16]\t loss_instance: 5.328248023986816\t loss_cluster: 1.6797813177108765\n",
      "Step [12/16]\t loss_instance: 5.3548407554626465\t loss_cluster: 1.6837537288665771\n",
      "Step [14/16]\t loss_instance: 5.359708309173584\t loss_cluster: 1.7013968229293823\n",
      "\n",
      "Epoch [216/300]\t Loss: 6.924852335453034 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.354910373687744\t loss_cluster: 1.6990455389022827\n",
      "Step [2/16]\t loss_instance: 5.33174467086792\t loss_cluster: 1.6726335287094116\n",
      "Step [4/16]\t loss_instance: 5.339777946472168\t loss_cluster: 1.6854591369628906\n",
      "Step [6/16]\t loss_instance: 5.349316120147705\t loss_cluster: 1.6884738206863403\n",
      "Step [8/16]\t loss_instance: 5.349457263946533\t loss_cluster: 1.6863983869552612\n",
      "Step [10/16]\t loss_instance: 5.332381725311279\t loss_cluster: 1.683922529220581\n",
      "Step [12/16]\t loss_instance: 5.359182834625244\t loss_cluster: 1.7215535640716553\n",
      "Step [14/16]\t loss_instance: 5.305688381195068\t loss_cluster: 1.6954147815704346\n",
      "\n",
      "Epoch [217/300]\t Loss: 6.914548766613007 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.325285911560059\t loss_cluster: 1.6752235889434814\n",
      "Step [2/16]\t loss_instance: 5.345966815948486\t loss_cluster: 1.671154499053955\n",
      "Step [4/16]\t loss_instance: 5.342419147491455\t loss_cluster: 1.6792757511138916\n",
      "Step [6/16]\t loss_instance: 5.34771728515625\t loss_cluster: 1.70063316822052\n",
      "Step [8/16]\t loss_instance: 5.3829216957092285\t loss_cluster: 1.731312870979309\n",
      "Step [10/16]\t loss_instance: 5.3305253982543945\t loss_cluster: 1.6819161176681519\n",
      "Step [12/16]\t loss_instance: 5.353297233581543\t loss_cluster: 1.6842502355575562\n",
      "Step [14/16]\t loss_instance: 5.33333683013916\t loss_cluster: 1.6847093105316162\n",
      "\n",
      "Epoch [218/300]\t Loss: 6.915807864069938 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.31632137298584\t loss_cluster: 1.6733951568603516\n",
      "Step [2/16]\t loss_instance: 5.319239616394043\t loss_cluster: 1.6812889575958252\n",
      "Step [4/16]\t loss_instance: 5.336516380310059\t loss_cluster: 1.6853206157684326\n",
      "Step [6/16]\t loss_instance: 5.3383870124816895\t loss_cluster: 1.6837239265441895\n",
      "Step [8/16]\t loss_instance: 5.354618072509766\t loss_cluster: 1.6863946914672852\n",
      "Step [10/16]\t loss_instance: 5.3669586181640625\t loss_cluster: 1.7218828201293945\n",
      "Step [12/16]\t loss_instance: 5.37329626083374\t loss_cluster: 1.7099753618240356\n",
      "Step [14/16]\t loss_instance: 5.362306118011475\t loss_cluster: 1.7079181671142578\n",
      "\n",
      "Epoch [219/300]\t Loss: 6.922193160653114 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.311359405517578\t loss_cluster: 1.6618456840515137\n",
      "Step [2/16]\t loss_instance: 5.3211212158203125\t loss_cluster: 1.6831272840499878\n",
      "Step [4/16]\t loss_instance: 5.339887619018555\t loss_cluster: 1.6787608861923218\n",
      "Step [6/16]\t loss_instance: 5.375779628753662\t loss_cluster: 1.7025511264801025\n",
      "Step [8/16]\t loss_instance: 5.337881565093994\t loss_cluster: 1.6746045351028442\n",
      "Step [10/16]\t loss_instance: 5.338920593261719\t loss_cluster: 1.6852020025253296\n",
      "Step [12/16]\t loss_instance: 5.3192949295043945\t loss_cluster: 1.6714179515838623\n",
      "Step [14/16]\t loss_instance: 5.3624587059021\t loss_cluster: 1.7084513902664185\n",
      "\n",
      "Epoch [220/300]\t Loss: 6.916393315792083 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.369856357574463\t loss_cluster: 1.7174824476242065\n",
      "Step [2/16]\t loss_instance: 5.3357391357421875\t loss_cluster: 1.6804448366165161\n",
      "Step [4/16]\t loss_instance: 5.332127094268799\t loss_cluster: 1.657826542854309\n",
      "Step [6/16]\t loss_instance: 5.322904586791992\t loss_cluster: 1.6800048351287842\n",
      "Step [8/16]\t loss_instance: 5.359130382537842\t loss_cluster: 1.6926171779632568\n",
      "Step [10/16]\t loss_instance: 5.325222015380859\t loss_cluster: 1.684670329093933\n",
      "Step [12/16]\t loss_instance: 5.363160610198975\t loss_cluster: 1.7119873762130737\n",
      "Step [14/16]\t loss_instance: 5.335088729858398\t loss_cluster: 1.6932843923568726\n",
      "\n",
      "Epoch [221/300]\t Loss: 6.923791816830635 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.328695297241211\t loss_cluster: 1.6773629188537598\n",
      "Step [2/16]\t loss_instance: 5.341866493225098\t loss_cluster: 1.7125200033187866\n",
      "Step [4/16]\t loss_instance: 5.315786361694336\t loss_cluster: 1.657106876373291\n",
      "Step [6/16]\t loss_instance: 5.365631103515625\t loss_cluster: 1.6999355554580688\n",
      "Step [8/16]\t loss_instance: 5.324281215667725\t loss_cluster: 1.650386929512024\n",
      "Step [10/16]\t loss_instance: 5.3402862548828125\t loss_cluster: 1.671112060546875\n",
      "Step [12/16]\t loss_instance: 5.3470025062561035\t loss_cluster: 1.687552809715271\n",
      "Step [14/16]\t loss_instance: 5.336121082305908\t loss_cluster: 1.6835798025131226\n",
      "\n",
      "Epoch [222/300]\t Loss: 6.9128339976072315 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.331326484680176\t loss_cluster: 1.6685041189193726\n",
      "Step [2/16]\t loss_instance: 5.338635444641113\t loss_cluster: 1.6938669681549072\n",
      "Step [4/16]\t loss_instance: 5.336281776428223\t loss_cluster: 1.6890124082565308\n",
      "Step [6/16]\t loss_instance: 5.329564571380615\t loss_cluster: 1.6696346998214722\n",
      "Step [8/16]\t loss_instance: 5.342743873596191\t loss_cluster: 1.684976577758789\n",
      "Step [10/16]\t loss_instance: 5.332327365875244\t loss_cluster: 1.6639783382415771\n",
      "Step [12/16]\t loss_instance: 5.3526201248168945\t loss_cluster: 1.6984211206436157\n",
      "Step [14/16]\t loss_instance: 5.3516106605529785\t loss_cluster: 1.708862066268921\n",
      "\n",
      "Epoch [223/300]\t Loss: 6.913800355792046 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.33610200881958\t loss_cluster: 1.67990243434906\n",
      "Step [2/16]\t loss_instance: 5.353207111358643\t loss_cluster: 1.6913408041000366\n",
      "Step [4/16]\t loss_instance: 5.3084917068481445\t loss_cluster: 1.6671772003173828\n",
      "Step [6/16]\t loss_instance: 5.339134693145752\t loss_cluster: 1.7036869525909424\n",
      "Step [8/16]\t loss_instance: 5.332676887512207\t loss_cluster: 1.689001202583313\n",
      "Step [10/16]\t loss_instance: 5.366036891937256\t loss_cluster: 1.6990625858306885\n",
      "Step [12/16]\t loss_instance: 5.34839391708374\t loss_cluster: 1.7171257734298706\n",
      "Step [14/16]\t loss_instance: 5.3446502685546875\t loss_cluster: 1.6810190677642822\n",
      "\n",
      "Epoch [224/300]\t Loss: 6.916387709975242 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.355401992797852\t loss_cluster: 1.6917200088500977\n",
      "Step [2/16]\t loss_instance: 5.346747398376465\t loss_cluster: 1.6708213090896606\n",
      "Step [4/16]\t loss_instance: 5.3389811515808105\t loss_cluster: 1.6790560483932495\n",
      "Step [6/16]\t loss_instance: 5.320784091949463\t loss_cluster: 1.6623908281326294\n",
      "Step [8/16]\t loss_instance: 5.3393425941467285\t loss_cluster: 1.703333854675293\n",
      "Step [10/16]\t loss_instance: 5.378434181213379\t loss_cluster: 1.713836431503296\n",
      "Step [12/16]\t loss_instance: 5.338024139404297\t loss_cluster: 1.6743344068527222\n",
      "Step [14/16]\t loss_instance: 5.346142768859863\t loss_cluster: 1.6917694807052612\n",
      "\n",
      "Epoch [225/300]\t Loss: 6.916104286909103 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.343874931335449\t loss_cluster: 1.6882025003433228\n",
      "Step [2/16]\t loss_instance: 5.323993682861328\t loss_cluster: 1.6743227243423462\n",
      "Step [4/16]\t loss_instance: 5.326292037963867\t loss_cluster: 1.6519758701324463\n",
      "Step [6/16]\t loss_instance: 5.342941761016846\t loss_cluster: 1.7059708833694458\n",
      "Step [8/16]\t loss_instance: 5.3399128913879395\t loss_cluster: 1.682841420173645\n",
      "Step [10/16]\t loss_instance: 5.333049297332764\t loss_cluster: 1.6687862873077393\n",
      "Step [12/16]\t loss_instance: 5.361198425292969\t loss_cluster: 1.6914070844650269\n",
      "Step [14/16]\t loss_instance: 5.372101306915283\t loss_cluster: 1.6899561882019043\n",
      "\n",
      "Epoch [226/300]\t Loss: 6.912188017368317 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3238630294799805\t loss_cluster: 1.6604524850845337\n",
      "Step [2/16]\t loss_instance: 5.361383438110352\t loss_cluster: 1.683459758758545\n",
      "Step [4/16]\t loss_instance: 5.33378267288208\t loss_cluster: 1.6760939359664917\n",
      "Step [6/16]\t loss_instance: 5.359805107116699\t loss_cluster: 1.7033801078796387\n",
      "Step [8/16]\t loss_instance: 5.343931198120117\t loss_cluster: 1.6769224405288696\n",
      "Step [10/16]\t loss_instance: 5.3699774742126465\t loss_cluster: 1.7261998653411865\n",
      "Step [12/16]\t loss_instance: 5.336962699890137\t loss_cluster: 1.690719723701477\n",
      "Step [14/16]\t loss_instance: 5.35652494430542\t loss_cluster: 1.6748497486114502\n",
      "\n",
      "Epoch [227/300]\t Loss: 6.915004006028175 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.342289924621582\t loss_cluster: 1.6770565509796143\n",
      "Step [2/16]\t loss_instance: 5.363656520843506\t loss_cluster: 1.7063323259353638\n",
      "Step [4/16]\t loss_instance: 5.330835819244385\t loss_cluster: 1.674371361732483\n",
      "Step [6/16]\t loss_instance: 5.3631510734558105\t loss_cluster: 1.7062511444091797\n",
      "Step [8/16]\t loss_instance: 5.337008476257324\t loss_cluster: 1.685459017753601\n",
      "Step [10/16]\t loss_instance: 5.329458713531494\t loss_cluster: 1.6699916124343872\n",
      "Step [12/16]\t loss_instance: 5.356389045715332\t loss_cluster: 1.6900200843811035\n",
      "Step [14/16]\t loss_instance: 5.351014614105225\t loss_cluster: 1.6959112882614136\n",
      "\n",
      "Epoch [228/300]\t Loss: 6.920787099003792 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3755717277526855\t loss_cluster: 1.7070298194885254\n",
      "Step [2/16]\t loss_instance: 5.316810607910156\t loss_cluster: 1.6578465700149536\n",
      "Step [4/16]\t loss_instance: 5.374647617340088\t loss_cluster: 1.7252442836761475\n",
      "Step [6/16]\t loss_instance: 5.330543518066406\t loss_cluster: 1.684861183166504\n",
      "Step [8/16]\t loss_instance: 5.367255210876465\t loss_cluster: 1.7220687866210938\n",
      "Step [10/16]\t loss_instance: 5.353009223937988\t loss_cluster: 1.686481237411499\n",
      "Step [12/16]\t loss_instance: 5.331057548522949\t loss_cluster: 1.6853080987930298\n",
      "Step [14/16]\t loss_instance: 5.35230827331543\t loss_cluster: 1.6932854652404785\n",
      "\n",
      "Epoch [229/300]\t Loss: 6.916286918520927 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.343197822570801\t loss_cluster: 1.6735671758651733\n",
      "Step [2/16]\t loss_instance: 5.328449249267578\t loss_cluster: 1.6766492128372192\n",
      "Step [4/16]\t loss_instance: 5.354824542999268\t loss_cluster: 1.6837248802185059\n",
      "Step [6/16]\t loss_instance: 5.339484691619873\t loss_cluster: 1.692671298980713\n",
      "Step [8/16]\t loss_instance: 5.343977451324463\t loss_cluster: 1.6773749589920044\n",
      "Step [10/16]\t loss_instance: 5.31740665435791\t loss_cluster: 1.649226427078247\n",
      "Step [12/16]\t loss_instance: 5.3303117752075195\t loss_cluster: 1.6638668775558472\n",
      "Step [14/16]\t loss_instance: 5.348024368286133\t loss_cluster: 1.715039610862732\n",
      "\n",
      "Epoch [230/300]\t Loss: 6.916110536456108 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3333587646484375\t loss_cluster: 1.6650084257125854\n",
      "Step [2/16]\t loss_instance: 5.347092628479004\t loss_cluster: 1.702741265296936\n",
      "Step [4/16]\t loss_instance: 5.3395843505859375\t loss_cluster: 1.6799513101577759\n",
      "Step [6/16]\t loss_instance: 5.340866565704346\t loss_cluster: 1.6776102781295776\n",
      "Step [8/16]\t loss_instance: 5.350707054138184\t loss_cluster: 1.709326982498169\n",
      "Step [10/16]\t loss_instance: 5.343680381774902\t loss_cluster: 1.6961941719055176\n",
      "Step [12/16]\t loss_instance: 5.353296279907227\t loss_cluster: 1.7037163972854614\n",
      "Step [14/16]\t loss_instance: 5.3505706787109375\t loss_cluster: 1.670812964439392\n",
      "\n",
      "Epoch [231/300]\t Loss: 6.915030771493912 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.352110862731934\t loss_cluster: 1.6714987754821777\n",
      "Step [2/16]\t loss_instance: 5.343168258666992\t loss_cluster: 1.6848642826080322\n",
      "Step [4/16]\t loss_instance: 5.347137451171875\t loss_cluster: 1.6790353059768677\n",
      "Step [6/16]\t loss_instance: 5.339012622833252\t loss_cluster: 1.6992813348770142\n",
      "Step [8/16]\t loss_instance: 5.379178047180176\t loss_cluster: 1.735398769378662\n",
      "Step [10/16]\t loss_instance: 5.348970413208008\t loss_cluster: 1.6915977001190186\n",
      "Step [12/16]\t loss_instance: 5.357211112976074\t loss_cluster: 1.691338062286377\n",
      "Step [14/16]\t loss_instance: 5.340158462524414\t loss_cluster: 1.6833237409591675\n",
      "\n",
      "Epoch [232/300]\t Loss: 6.910159656405449 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.342534065246582\t loss_cluster: 1.691936731338501\n",
      "Step [2/16]\t loss_instance: 5.352729320526123\t loss_cluster: 1.6921857595443726\n",
      "Step [4/16]\t loss_instance: 5.3281755447387695\t loss_cluster: 1.6703250408172607\n",
      "Step [6/16]\t loss_instance: 5.348588466644287\t loss_cluster: 1.6776760816574097\n",
      "Step [8/16]\t loss_instance: 5.3352131843566895\t loss_cluster: 1.700150489807129\n",
      "Step [10/16]\t loss_instance: 5.3397393226623535\t loss_cluster: 1.6988495588302612\n",
      "Step [12/16]\t loss_instance: 5.335367202758789\t loss_cluster: 1.682124376296997\n",
      "Step [14/16]\t loss_instance: 5.323836326599121\t loss_cluster: 1.6619011163711548\n",
      "\n",
      "Epoch [233/300]\t Loss: 6.917404440045357 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.339248180389404\t loss_cluster: 1.703131914138794\n",
      "Step [2/16]\t loss_instance: 5.329070091247559\t loss_cluster: 1.6820472478866577\n",
      "Step [4/16]\t loss_instance: 5.338741779327393\t loss_cluster: 1.6927741765975952\n",
      "Step [6/16]\t loss_instance: 5.366253852844238\t loss_cluster: 1.6972095966339111\n",
      "Step [8/16]\t loss_instance: 5.317950248718262\t loss_cluster: 1.653167963027954\n",
      "Step [10/16]\t loss_instance: 5.313955783843994\t loss_cluster: 1.652921199798584\n",
      "Step [12/16]\t loss_instance: 5.337686061859131\t loss_cluster: 1.6645127534866333\n",
      "Step [14/16]\t loss_instance: 5.3490753173828125\t loss_cluster: 1.7125747203826904\n",
      "\n",
      "Epoch [234/300]\t Loss: 6.91368882060051 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.354890823364258\t loss_cluster: 1.6777379512786865\n",
      "Step [2/16]\t loss_instance: 5.356420993804932\t loss_cluster: 1.7033873796463013\n",
      "Step [4/16]\t loss_instance: 5.3325347900390625\t loss_cluster: 1.6654640436172485\n",
      "Step [6/16]\t loss_instance: 5.31028413772583\t loss_cluster: 1.6952677965164185\n",
      "Step [8/16]\t loss_instance: 5.356391906738281\t loss_cluster: 1.6886067390441895\n",
      "Step [10/16]\t loss_instance: 5.343753337860107\t loss_cluster: 1.6795692443847656\n",
      "Step [12/16]\t loss_instance: 5.366795063018799\t loss_cluster: 1.7050648927688599\n",
      "Step [14/16]\t loss_instance: 5.342527389526367\t loss_cluster: 1.6878260374069214\n",
      "\n",
      "Epoch [235/300]\t Loss: 6.91213741004467 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.339000225067139\t loss_cluster: 1.6598031520843506\n",
      "Step [2/16]\t loss_instance: 5.324977397918701\t loss_cluster: 1.6521507501602173\n",
      "Step [4/16]\t loss_instance: 5.3306427001953125\t loss_cluster: 1.6724225282669067\n",
      "Step [6/16]\t loss_instance: 5.3553972244262695\t loss_cluster: 1.6865040063858032\n",
      "Step [8/16]\t loss_instance: 5.361141204833984\t loss_cluster: 1.7327550649642944\n",
      "Step [10/16]\t loss_instance: 5.362292289733887\t loss_cluster: 1.7282923460006714\n",
      "Step [12/16]\t loss_instance: 5.359067440032959\t loss_cluster: 1.6908622980117798\n",
      "Step [14/16]\t loss_instance: 5.375792980194092\t loss_cluster: 1.7243778705596924\n",
      "\n",
      "Epoch [236/300]\t Loss: 6.908706885576248 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.333994388580322\t loss_cluster: 1.6911993026733398\n",
      "Step [2/16]\t loss_instance: 5.313080310821533\t loss_cluster: 1.6517136096954346\n",
      "Step [4/16]\t loss_instance: 5.340224742889404\t loss_cluster: 1.683728814125061\n",
      "Step [6/16]\t loss_instance: 5.356320381164551\t loss_cluster: 1.7075608968734741\n",
      "Step [8/16]\t loss_instance: 5.355564117431641\t loss_cluster: 1.7095398902893066\n",
      "Step [10/16]\t loss_instance: 5.360479831695557\t loss_cluster: 1.6880611181259155\n",
      "Step [12/16]\t loss_instance: 5.339931488037109\t loss_cluster: 1.6770039796829224\n",
      "Step [14/16]\t loss_instance: 5.327986717224121\t loss_cluster: 1.6592631340026855\n",
      "\n",
      "Epoch [237/300]\t Loss: 6.914290472865105 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.317455291748047\t loss_cluster: 1.6719762086868286\n",
      "Step [2/16]\t loss_instance: 5.331447601318359\t loss_cluster: 1.6780149936676025\n",
      "Step [4/16]\t loss_instance: 5.328610897064209\t loss_cluster: 1.674428105354309\n",
      "Step [6/16]\t loss_instance: 5.351275444030762\t loss_cluster: 1.6884652376174927\n",
      "Step [8/16]\t loss_instance: 5.3597612380981445\t loss_cluster: 1.706614375114441\n",
      "Step [10/16]\t loss_instance: 5.346322059631348\t loss_cluster: 1.6932852268218994\n",
      "Step [12/16]\t loss_instance: 5.3585309982299805\t loss_cluster: 1.7078191041946411\n",
      "Step [14/16]\t loss_instance: 5.353396415710449\t loss_cluster: 1.6881189346313477\n",
      "\n",
      "Epoch [238/300]\t Loss: 6.91020700931549 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.33644437789917\t loss_cluster: 1.6945698261260986\n",
      "Step [2/16]\t loss_instance: 5.348416805267334\t loss_cluster: 1.6866387128829956\n",
      "Step [4/16]\t loss_instance: 5.364902973175049\t loss_cluster: 1.6963752508163452\n",
      "Step [6/16]\t loss_instance: 5.3258161544799805\t loss_cluster: 1.6766763925552368\n",
      "Step [8/16]\t loss_instance: 5.347476482391357\t loss_cluster: 1.692711353302002\n",
      "Step [10/16]\t loss_instance: 5.363077640533447\t loss_cluster: 1.702907681465149\n",
      "Step [12/16]\t loss_instance: 5.351000785827637\t loss_cluster: 1.704539179801941\n",
      "Step [14/16]\t loss_instance: 5.349303245544434\t loss_cluster: 1.66352117061615\n",
      "\n",
      "Epoch [239/300]\t Loss: 6.9105005919933316 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.368078708648682\t loss_cluster: 1.6935337781906128\n",
      "Step [2/16]\t loss_instance: 5.348049163818359\t loss_cluster: 1.6777242422103882\n",
      "Step [4/16]\t loss_instance: 5.346184253692627\t loss_cluster: 1.6864253282546997\n",
      "Step [6/16]\t loss_instance: 5.302960395812988\t loss_cluster: 1.6567226648330688\n",
      "Step [8/16]\t loss_instance: 5.350247859954834\t loss_cluster: 1.7029229402542114\n",
      "Step [10/16]\t loss_instance: 5.348231315612793\t loss_cluster: 1.690395474433899\n",
      "Step [12/16]\t loss_instance: 5.369523525238037\t loss_cluster: 1.6892942190170288\n",
      "Step [14/16]\t loss_instance: 5.340486526489258\t loss_cluster: 1.688685655593872\n",
      "\n",
      "Epoch [240/300]\t Loss: 6.912035566568375 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.328132629394531\t loss_cluster: 1.6690502166748047\n",
      "Step [2/16]\t loss_instance: 5.378034591674805\t loss_cluster: 1.7273019552230835\n",
      "Step [4/16]\t loss_instance: 5.343823432922363\t loss_cluster: 1.6897019147872925\n",
      "Step [6/16]\t loss_instance: 5.335036754608154\t loss_cluster: 1.6708741188049316\n",
      "Step [8/16]\t loss_instance: 5.359652996063232\t loss_cluster: 1.6884548664093018\n",
      "Step [10/16]\t loss_instance: 5.308736324310303\t loss_cluster: 1.6476181745529175\n",
      "Step [12/16]\t loss_instance: 5.364741325378418\t loss_cluster: 1.7054407596588135\n",
      "Step [14/16]\t loss_instance: 5.329124450683594\t loss_cluster: 1.6776942014694214\n",
      "\n",
      "Epoch [241/300]\t Loss: 6.9173777371644976 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.352604389190674\t loss_cluster: 1.6990694999694824\n",
      "Step [2/16]\t loss_instance: 5.331241130828857\t loss_cluster: 1.6655128002166748\n",
      "Step [4/16]\t loss_instance: 5.33775520324707\t loss_cluster: 1.6823281049728394\n",
      "Step [6/16]\t loss_instance: 5.3636322021484375\t loss_cluster: 1.702170491218567\n",
      "Step [8/16]\t loss_instance: 5.352424144744873\t loss_cluster: 1.6875802278518677\n",
      "Step [10/16]\t loss_instance: 5.3330078125\t loss_cluster: 1.6615198850631714\n",
      "Step [12/16]\t loss_instance: 5.35643196105957\t loss_cluster: 1.6942235231399536\n",
      "Step [14/16]\t loss_instance: 5.360483646392822\t loss_cluster: 1.6947095394134521\n",
      "\n",
      "Epoch [242/300]\t Loss: 6.913246333599091 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.319840908050537\t loss_cluster: 1.676544427871704\n",
      "Step [2/16]\t loss_instance: 5.353348255157471\t loss_cluster: 1.6740916967391968\n",
      "Step [4/16]\t loss_instance: 5.325603008270264\t loss_cluster: 1.6843445301055908\n",
      "Step [6/16]\t loss_instance: 5.33126974105835\t loss_cluster: 1.6869077682495117\n",
      "Step [8/16]\t loss_instance: 5.3674821853637695\t loss_cluster: 1.6897677183151245\n",
      "Step [10/16]\t loss_instance: 5.320728778839111\t loss_cluster: 1.662868857383728\n",
      "Step [12/16]\t loss_instance: 5.33439302444458\t loss_cluster: 1.6649478673934937\n",
      "Step [14/16]\t loss_instance: 5.344448089599609\t loss_cluster: 1.6952966451644897\n",
      "\n",
      "Epoch [243/300]\t Loss: 6.916593146324158 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.335310935974121\t loss_cluster: 1.6750117540359497\n",
      "Step [2/16]\t loss_instance: 5.356003284454346\t loss_cluster: 1.6900873184204102\n",
      "Step [4/16]\t loss_instance: 5.332696437835693\t loss_cluster: 1.6890029907226562\n",
      "Step [6/16]\t loss_instance: 5.337106704711914\t loss_cluster: 1.7029889822006226\n",
      "Step [8/16]\t loss_instance: 5.329023838043213\t loss_cluster: 1.6684271097183228\n",
      "Step [10/16]\t loss_instance: 5.349243640899658\t loss_cluster: 1.678964376449585\n",
      "Step [12/16]\t loss_instance: 5.366706848144531\t loss_cluster: 1.7019286155700684\n",
      "Step [14/16]\t loss_instance: 5.38110876083374\t loss_cluster: 1.7101585865020752\n",
      "\n",
      "Epoch [244/300]\t Loss: 6.910968020558357 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.348344802856445\t loss_cluster: 1.6781946420669556\n",
      "Step [2/16]\t loss_instance: 5.339758396148682\t loss_cluster: 1.7014336585998535\n",
      "Step [4/16]\t loss_instance: 5.365845203399658\t loss_cluster: 1.7213573455810547\n",
      "Step [6/16]\t loss_instance: 5.343289852142334\t loss_cluster: 1.686631202697754\n",
      "Step [8/16]\t loss_instance: 5.3693156242370605\t loss_cluster: 1.7083485126495361\n",
      "Step [10/16]\t loss_instance: 5.315609455108643\t loss_cluster: 1.6652815341949463\n",
      "Step [12/16]\t loss_instance: 5.344762325286865\t loss_cluster: 1.7014814615249634\n",
      "Step [14/16]\t loss_instance: 5.349863529205322\t loss_cluster: 1.6883832216262817\n",
      "\n",
      "Epoch [245/300]\t Loss: 6.915062507987022 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.359886169433594\t loss_cluster: 1.696192741394043\n",
      "Step [2/16]\t loss_instance: 5.331558704376221\t loss_cluster: 1.6850160360336304\n",
      "Step [4/16]\t loss_instance: 5.372979640960693\t loss_cluster: 1.7214853763580322\n",
      "Step [6/16]\t loss_instance: 5.3461079597473145\t loss_cluster: 1.6776623725891113\n",
      "Step [8/16]\t loss_instance: 5.340422630310059\t loss_cluster: 1.672554612159729\n",
      "Step [10/16]\t loss_instance: 5.3407487869262695\t loss_cluster: 1.6889904737472534\n",
      "Step [12/16]\t loss_instance: 5.354395389556885\t loss_cluster: 1.7081599235534668\n",
      "Step [14/16]\t loss_instance: 5.347528457641602\t loss_cluster: 1.6936945915222168\n",
      "\n",
      "Epoch [246/300]\t Loss: 6.913552942872047 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.375734806060791\t loss_cluster: 1.7037321329116821\n",
      "Step [2/16]\t loss_instance: 5.352991580963135\t loss_cluster: 1.6968778371810913\n",
      "Step [4/16]\t loss_instance: 5.361382961273193\t loss_cluster: 1.6990368366241455\n",
      "Step [6/16]\t loss_instance: 5.33969259262085\t loss_cluster: 1.6801090240478516\n",
      "Step [8/16]\t loss_instance: 5.336832046508789\t loss_cluster: 1.664551019668579\n",
      "Step [10/16]\t loss_instance: 5.359834671020508\t loss_cluster: 1.7285174131393433\n",
      "Step [12/16]\t loss_instance: 5.359498500823975\t loss_cluster: 1.7013274431228638\n",
      "Step [14/16]\t loss_instance: 5.342434883117676\t loss_cluster: 1.6844050884246826\n",
      "\n",
      "Epoch [247/300]\t Loss: 6.910883527994156 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.316192626953125\t loss_cluster: 1.6695293188095093\n",
      "Step [2/16]\t loss_instance: 5.334393501281738\t loss_cluster: 1.681931734085083\n",
      "Step [4/16]\t loss_instance: 5.3277997970581055\t loss_cluster: 1.663403868675232\n",
      "Step [6/16]\t loss_instance: 5.354971885681152\t loss_cluster: 1.6872897148132324\n",
      "Step [8/16]\t loss_instance: 5.3412394523620605\t loss_cluster: 1.6884597539901733\n",
      "Step [10/16]\t loss_instance: 5.34040641784668\t loss_cluster: 1.6742993593215942\n",
      "Step [12/16]\t loss_instance: 5.378087520599365\t loss_cluster: 1.7186939716339111\n",
      "Step [14/16]\t loss_instance: 5.34805965423584\t loss_cluster: 1.6824663877487183\n",
      "\n",
      "Epoch [248/300]\t Loss: 6.911394041776657 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.334192276000977\t loss_cluster: 1.6622203588485718\n",
      "Step [2/16]\t loss_instance: 5.338637351989746\t loss_cluster: 1.687685251235962\n",
      "Step [4/16]\t loss_instance: 5.345759391784668\t loss_cluster: 1.6915314197540283\n",
      "Step [6/16]\t loss_instance: 5.346248149871826\t loss_cluster: 1.6676477193832397\n",
      "Step [8/16]\t loss_instance: 5.32741641998291\t loss_cluster: 1.680107831954956\n",
      "Step [10/16]\t loss_instance: 5.365877628326416\t loss_cluster: 1.6945369243621826\n",
      "Step [12/16]\t loss_instance: 5.3441643714904785\t loss_cluster: 1.6845505237579346\n",
      "Step [14/16]\t loss_instance: 5.332553386688232\t loss_cluster: 1.677316665649414\n",
      "\n",
      "Epoch [249/300]\t Loss: 6.912853336334228 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.324435710906982\t loss_cluster: 1.6780227422714233\n",
      "Step [2/16]\t loss_instance: 5.362607002258301\t loss_cluster: 1.6917427778244019\n",
      "Step [4/16]\t loss_instance: 5.343930244445801\t loss_cluster: 1.6959308385849\n",
      "Step [6/16]\t loss_instance: 5.359672546386719\t loss_cluster: 1.6890231370925903\n",
      "Step [8/16]\t loss_instance: 5.345515251159668\t loss_cluster: 1.7001615762710571\n",
      "Step [10/16]\t loss_instance: 5.298970699310303\t loss_cluster: 1.653893232345581\n",
      "Step [12/16]\t loss_instance: 5.331450462341309\t loss_cluster: 1.663238286972046\n",
      "Step [14/16]\t loss_instance: 5.384342670440674\t loss_cluster: 1.7236617803573608\n",
      "\n",
      "Epoch [250/300]\t Loss: 6.913648694753647 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.32852029800415\t loss_cluster: 1.6926716566085815\n",
      "Step [2/16]\t loss_instance: 5.338544845581055\t loss_cluster: 1.6777106523513794\n",
      "Step [4/16]\t loss_instance: 5.371940612792969\t loss_cluster: 1.7168344259262085\n",
      "Step [6/16]\t loss_instance: 5.342921257019043\t loss_cluster: 1.6694244146347046\n",
      "Step [8/16]\t loss_instance: 5.3309760093688965\t loss_cluster: 1.6757804155349731\n",
      "Step [10/16]\t loss_instance: 5.338054656982422\t loss_cluster: 1.704196810722351\n",
      "Step [12/16]\t loss_instance: 5.338039875030518\t loss_cluster: 1.672333002090454\n",
      "Step [14/16]\t loss_instance: 5.32468843460083\t loss_cluster: 1.6632996797561646\n",
      "\n",
      "Epoch [251/300]\t Loss: 6.914151361584663 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.355321884155273\t loss_cluster: 1.714847207069397\n",
      "Step [2/16]\t loss_instance: 5.337958335876465\t loss_cluster: 1.6935251951217651\n",
      "Step [4/16]\t loss_instance: 5.348733425140381\t loss_cluster: 1.6765214204788208\n",
      "Step [6/16]\t loss_instance: 5.333437919616699\t loss_cluster: 1.6693930625915527\n",
      "Step [8/16]\t loss_instance: 5.335942268371582\t loss_cluster: 1.6626416444778442\n",
      "Step [10/16]\t loss_instance: 5.336100101470947\t loss_cluster: 1.6925404071807861\n",
      "Step [12/16]\t loss_instance: 5.342841625213623\t loss_cluster: 1.6721293926239014\n",
      "Step [14/16]\t loss_instance: 5.324488639831543\t loss_cluster: 1.6635806560516357\n",
      "\n",
      "Epoch [252/300]\t Loss: 6.912992709875107 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.373940467834473\t loss_cluster: 1.7043113708496094\n",
      "Step [2/16]\t loss_instance: 5.350183963775635\t loss_cluster: 1.7189511060714722\n",
      "Step [4/16]\t loss_instance: 5.344022274017334\t loss_cluster: 1.6795586347579956\n",
      "Step [6/16]\t loss_instance: 5.338805198669434\t loss_cluster: 1.6861181259155273\n",
      "Step [8/16]\t loss_instance: 5.375166893005371\t loss_cluster: 1.71552312374115\n",
      "Step [10/16]\t loss_instance: 5.327960014343262\t loss_cluster: 1.6626486778259277\n",
      "Step [12/16]\t loss_instance: 5.3190531730651855\t loss_cluster: 1.669785737991333\n",
      "Step [14/16]\t loss_instance: 5.3328447341918945\t loss_cluster: 1.6648952960968018\n",
      "\n",
      "Epoch [253/300]\t Loss: 6.910242235660553 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.335598468780518\t loss_cluster: 1.6929192543029785\n",
      "Step [2/16]\t loss_instance: 5.365694999694824\t loss_cluster: 1.7043193578720093\n",
      "Step [4/16]\t loss_instance: 5.341289043426514\t loss_cluster: 1.7045423984527588\n",
      "Step [6/16]\t loss_instance: 5.316186904907227\t loss_cluster: 1.6699401140213013\n",
      "Step [8/16]\t loss_instance: 5.2997260093688965\t loss_cluster: 1.653733491897583\n",
      "Step [10/16]\t loss_instance: 5.3363518714904785\t loss_cluster: 1.673399806022644\n",
      "Step [12/16]\t loss_instance: 5.312297344207764\t loss_cluster: 1.6608220338821411\n",
      "Step [14/16]\t loss_instance: 5.362685680389404\t loss_cluster: 1.699031114578247\n",
      "\n",
      "Epoch [254/300]\t Loss: 6.91320509314537 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.349430084228516\t loss_cluster: 1.692317247390747\n",
      "Step [2/16]\t loss_instance: 5.326507568359375\t loss_cluster: 1.6635043621063232\n",
      "Step [4/16]\t loss_instance: 5.378774166107178\t loss_cluster: 1.7176156044006348\n",
      "Step [6/16]\t loss_instance: 5.38203763961792\t loss_cluster: 1.7168749570846558\n",
      "Step [8/16]\t loss_instance: 5.3193230628967285\t loss_cluster: 1.6793121099472046\n",
      "Step [10/16]\t loss_instance: 5.326685428619385\t loss_cluster: 1.6594176292419434\n",
      "Step [12/16]\t loss_instance: 5.376617908477783\t loss_cluster: 1.7083930969238281\n",
      "Step [14/16]\t loss_instance: 5.350115776062012\t loss_cluster: 1.69844651222229\n",
      "\n",
      "Epoch [255/300]\t Loss: 6.915523773431778 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.322933673858643\t loss_cluster: 1.675294280052185\n",
      "Step [2/16]\t loss_instance: 5.325588226318359\t loss_cluster: 1.6684118509292603\n",
      "Step [4/16]\t loss_instance: 5.392240524291992\t loss_cluster: 1.7034271955490112\n",
      "Step [6/16]\t loss_instance: 5.341488838195801\t loss_cluster: 1.6737704277038574\n",
      "Step [8/16]\t loss_instance: 5.356245517730713\t loss_cluster: 1.6915314197540283\n",
      "Step [10/16]\t loss_instance: 5.315749168395996\t loss_cluster: 1.6705094575881958\n",
      "Step [12/16]\t loss_instance: 5.325305461883545\t loss_cluster: 1.6662474870681763\n",
      "Step [14/16]\t loss_instance: 5.328151226043701\t loss_cluster: 1.6530258655548096\n",
      "\n",
      "Epoch [256/300]\t Loss: 6.910813269019127 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.348789215087891\t loss_cluster: 1.6916207075119019\n",
      "Step [2/16]\t loss_instance: 5.38105583190918\t loss_cluster: 1.709721326828003\n",
      "Step [4/16]\t loss_instance: 5.3218512535095215\t loss_cluster: 1.6807373762130737\n",
      "Step [6/16]\t loss_instance: 5.332045555114746\t loss_cluster: 1.6653547286987305\n",
      "Step [8/16]\t loss_instance: 5.334658622741699\t loss_cluster: 1.6889712810516357\n",
      "Step [10/16]\t loss_instance: 5.36102819442749\t loss_cluster: 1.6859564781188965\n",
      "Step [12/16]\t loss_instance: 5.342803478240967\t loss_cluster: 1.709378719329834\n",
      "Step [14/16]\t loss_instance: 5.349861145019531\t loss_cluster: 1.7006539106369019\n",
      "\n",
      "Epoch [257/300]\t Loss: 6.908691218495369 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.338805675506592\t loss_cluster: 1.6719512939453125\n",
      "Step [2/16]\t loss_instance: 5.345106601715088\t loss_cluster: 1.678717017173767\n",
      "Step [4/16]\t loss_instance: 5.344756603240967\t loss_cluster: 1.7041772603988647\n",
      "Step [6/16]\t loss_instance: 5.346545219421387\t loss_cluster: 1.6553363800048828\n",
      "Step [8/16]\t loss_instance: 5.320298671722412\t loss_cluster: 1.6562583446502686\n",
      "Step [10/16]\t loss_instance: 5.3522748947143555\t loss_cluster: 1.7068036794662476\n",
      "Step [12/16]\t loss_instance: 5.354318618774414\t loss_cluster: 1.693623423576355\n",
      "Step [14/16]\t loss_instance: 5.3383965492248535\t loss_cluster: 1.6736118793487549\n",
      "\n",
      "Epoch [258/300]\t Loss: 6.9111333161592485 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.340216636657715\t loss_cluster: 1.7096713781356812\n",
      "Step [2/16]\t loss_instance: 5.333011150360107\t loss_cluster: 1.6691415309906006\n",
      "Step [4/16]\t loss_instance: 5.346646308898926\t loss_cluster: 1.6890755891799927\n",
      "Step [6/16]\t loss_instance: 5.3207879066467285\t loss_cluster: 1.6802420616149902\n",
      "Step [8/16]\t loss_instance: 5.384707450866699\t loss_cluster: 1.715016484260559\n",
      "Step [10/16]\t loss_instance: 5.341101169586182\t loss_cluster: 1.6765254735946655\n",
      "Step [12/16]\t loss_instance: 5.354828834533691\t loss_cluster: 1.6861826181411743\n",
      "Step [14/16]\t loss_instance: 5.34663724899292\t loss_cluster: 1.6784090995788574\n",
      "\n",
      "Epoch [259/300]\t Loss: 6.90898630619049 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.309384346008301\t loss_cluster: 1.6569870710372925\n",
      "Step [2/16]\t loss_instance: 5.358680248260498\t loss_cluster: 1.690651297569275\n",
      "Step [4/16]\t loss_instance: 5.368935585021973\t loss_cluster: 1.7217605113983154\n",
      "Step [6/16]\t loss_instance: 5.360138416290283\t loss_cluster: 1.691951036453247\n",
      "Step [8/16]\t loss_instance: 5.332067966461182\t loss_cluster: 1.674787163734436\n",
      "Step [10/16]\t loss_instance: 5.330966472625732\t loss_cluster: 1.6746097803115845\n",
      "Step [12/16]\t loss_instance: 5.349589824676514\t loss_cluster: 1.7037981748580933\n",
      "Step [14/16]\t loss_instance: 5.3231964111328125\t loss_cluster: 1.6757087707519531\n",
      "\n",
      "Epoch [260/300]\t Loss: 6.90636071562767 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.315607070922852\t loss_cluster: 1.6712766885757446\n",
      "Step [2/16]\t loss_instance: 5.316630840301514\t loss_cluster: 1.6728184223175049\n",
      "Step [4/16]\t loss_instance: 5.34710693359375\t loss_cluster: 1.6886041164398193\n",
      "Step [6/16]\t loss_instance: 5.355318546295166\t loss_cluster: 1.6800233125686646\n",
      "Step [8/16]\t loss_instance: 5.382089138031006\t loss_cluster: 1.7352778911590576\n",
      "Step [10/16]\t loss_instance: 5.359683990478516\t loss_cluster: 1.6965693235397339\n",
      "Step [12/16]\t loss_instance: 5.339309215545654\t loss_cluster: 1.6745933294296265\n",
      "Step [14/16]\t loss_instance: 5.336760997772217\t loss_cluster: 1.6914949417114258\n",
      "\n",
      "Epoch [261/300]\t Loss: 6.908215120434761 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.35152530670166\t loss_cluster: 1.6995863914489746\n",
      "Step [2/16]\t loss_instance: 5.341385841369629\t loss_cluster: 1.6807860136032104\n",
      "Step [4/16]\t loss_instance: 5.339567184448242\t loss_cluster: 1.681386947631836\n",
      "Step [6/16]\t loss_instance: 5.353520393371582\t loss_cluster: 1.705759048461914\n",
      "Step [8/16]\t loss_instance: 5.33862829208374\t loss_cluster: 1.6807748079299927\n",
      "Step [10/16]\t loss_instance: 5.360149383544922\t loss_cluster: 1.714536428451538\n",
      "Step [12/16]\t loss_instance: 5.324401378631592\t loss_cluster: 1.6615444421768188\n",
      "Step [14/16]\t loss_instance: 5.319767475128174\t loss_cluster: 1.6696559190750122\n",
      "\n",
      "Epoch [262/300]\t Loss: 6.9102971911430355 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.337010383605957\t loss_cluster: 1.689712643623352\n",
      "Step [2/16]\t loss_instance: 5.370877742767334\t loss_cluster: 1.709911584854126\n",
      "Step [4/16]\t loss_instance: 5.367964744567871\t loss_cluster: 1.7281135320663452\n",
      "Step [6/16]\t loss_instance: 5.35714054107666\t loss_cluster: 1.6940631866455078\n",
      "Step [8/16]\t loss_instance: 5.339234828948975\t loss_cluster: 1.689441204071045\n",
      "Step [10/16]\t loss_instance: 5.323266983032227\t loss_cluster: 1.6757022142410278\n",
      "Step [12/16]\t loss_instance: 5.353791236877441\t loss_cluster: 1.6973673105239868\n",
      "Step [14/16]\t loss_instance: 5.35554313659668\t loss_cluster: 1.6671282052993774\n",
      "\n",
      "Epoch [263/300]\t Loss: 6.9093495398759845 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3680033683776855\t loss_cluster: 1.7228249311447144\n",
      "Step [2/16]\t loss_instance: 5.335319519042969\t loss_cluster: 1.6578115224838257\n",
      "Step [4/16]\t loss_instance: 5.340875625610352\t loss_cluster: 1.678549885749817\n",
      "Step [6/16]\t loss_instance: 5.378945827484131\t loss_cluster: 1.7183210849761963\n",
      "Step [8/16]\t loss_instance: 5.332823276519775\t loss_cluster: 1.6845577955245972\n",
      "Step [10/16]\t loss_instance: 5.313224792480469\t loss_cluster: 1.6717087030410767\n",
      "Step [12/16]\t loss_instance: 5.322873115539551\t loss_cluster: 1.6489033699035645\n",
      "Step [14/16]\t loss_instance: 5.375423908233643\t loss_cluster: 1.7148847579956055\n",
      "\n",
      "Epoch [264/300]\t Loss: 6.908127278089523 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.360511779785156\t loss_cluster: 1.6850943565368652\n",
      "Step [2/16]\t loss_instance: 5.350901126861572\t loss_cluster: 1.7230652570724487\n",
      "Step [4/16]\t loss_instance: 5.353872299194336\t loss_cluster: 1.6944670677185059\n",
      "Step [6/16]\t loss_instance: 5.360565662384033\t loss_cluster: 1.6788862943649292\n",
      "Step [8/16]\t loss_instance: 5.344423770904541\t loss_cluster: 1.6885260343551636\n",
      "Step [10/16]\t loss_instance: 5.325572967529297\t loss_cluster: 1.685408353805542\n",
      "Step [12/16]\t loss_instance: 5.339709281921387\t loss_cluster: 1.7056139707565308\n",
      "Step [14/16]\t loss_instance: 5.368154525756836\t loss_cluster: 1.7010122537612915\n",
      "\n",
      "Epoch [265/300]\t Loss: 6.916890576481819 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.341586589813232\t loss_cluster: 1.6778233051300049\n",
      "Step [2/16]\t loss_instance: 5.344751834869385\t loss_cluster: 1.7054989337921143\n",
      "Step [4/16]\t loss_instance: 5.366060256958008\t loss_cluster: 1.7077281475067139\n",
      "Step [6/16]\t loss_instance: 5.362152576446533\t loss_cluster: 1.708957314491272\n",
      "Step [8/16]\t loss_instance: 5.3689069747924805\t loss_cluster: 1.7189395427703857\n",
      "Step [10/16]\t loss_instance: 5.326473236083984\t loss_cluster: 1.6821476221084595\n",
      "Step [12/16]\t loss_instance: 5.346803665161133\t loss_cluster: 1.6908036470413208\n",
      "Step [14/16]\t loss_instance: 5.359435558319092\t loss_cluster: 1.6836497783660889\n",
      "\n",
      "Epoch [266/300]\t Loss: 6.917960375547409 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.353116035461426\t loss_cluster: 1.6808857917785645\n",
      "Step [2/16]\t loss_instance: 5.333197593688965\t loss_cluster: 1.6864734888076782\n",
      "Step [4/16]\t loss_instance: 5.329418182373047\t loss_cluster: 1.6579315662384033\n",
      "Step [6/16]\t loss_instance: 5.320541858673096\t loss_cluster: 1.6735718250274658\n",
      "Step [8/16]\t loss_instance: 5.329353332519531\t loss_cluster: 1.6641916036605835\n",
      "Step [10/16]\t loss_instance: 5.347776889801025\t loss_cluster: 1.6890618801116943\n",
      "Step [12/16]\t loss_instance: 5.331047058105469\t loss_cluster: 1.6636542081832886\n",
      "Step [14/16]\t loss_instance: 5.3283233642578125\t loss_cluster: 1.658660650253296\n",
      "\n",
      "Epoch [267/300]\t Loss: 6.907405868172646 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.315005779266357\t loss_cluster: 1.67429780960083\n",
      "Step [2/16]\t loss_instance: 5.352815628051758\t loss_cluster: 1.6866196393966675\n",
      "Step [4/16]\t loss_instance: 5.317144870758057\t loss_cluster: 1.6752736568450928\n",
      "Step [6/16]\t loss_instance: 5.323685169219971\t loss_cluster: 1.656224012374878\n",
      "Step [8/16]\t loss_instance: 5.340696334838867\t loss_cluster: 1.6765986680984497\n",
      "Step [10/16]\t loss_instance: 5.334073066711426\t loss_cluster: 1.6771419048309326\n",
      "Step [12/16]\t loss_instance: 5.352472305297852\t loss_cluster: 1.695908546447754\n",
      "Step [14/16]\t loss_instance: 5.36180305480957\t loss_cluster: 1.7053049802780151\n",
      "\n",
      "Epoch [268/300]\t Loss: 6.914018198847771 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.353928565979004\t loss_cluster: 1.7280571460723877\n",
      "Step [2/16]\t loss_instance: 5.340713977813721\t loss_cluster: 1.6777427196502686\n",
      "Step [4/16]\t loss_instance: 5.341975212097168\t loss_cluster: 1.6710665225982666\n",
      "Step [6/16]\t loss_instance: 5.353152751922607\t loss_cluster: 1.691747784614563\n",
      "Step [8/16]\t loss_instance: 5.364154815673828\t loss_cluster: 1.7157801389694214\n",
      "Step [10/16]\t loss_instance: 5.322269439697266\t loss_cluster: 1.660804033279419\n",
      "Step [12/16]\t loss_instance: 5.379146575927734\t loss_cluster: 1.7186341285705566\n",
      "Step [14/16]\t loss_instance: 5.380253791809082\t loss_cluster: 1.7171059846878052\n",
      "\n",
      "Epoch [269/300]\t Loss: 6.907028758525849 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3628644943237305\t loss_cluster: 1.6967151165008545\n",
      "Step [2/16]\t loss_instance: 5.375824928283691\t loss_cluster: 1.720439076423645\n",
      "Step [4/16]\t loss_instance: 5.313176155090332\t loss_cluster: 1.653673768043518\n",
      "Step [6/16]\t loss_instance: 5.3358635902404785\t loss_cluster: 1.6561959981918335\n",
      "Step [8/16]\t loss_instance: 5.364707946777344\t loss_cluster: 1.7161940336227417\n",
      "Step [10/16]\t loss_instance: 5.328797340393066\t loss_cluster: 1.6744896173477173\n",
      "Step [12/16]\t loss_instance: 5.3304762840271\t loss_cluster: 1.6835159063339233\n",
      "Step [14/16]\t loss_instance: 5.357122421264648\t loss_cluster: 1.6798303127288818\n",
      "\n",
      "Epoch [270/300]\t Loss: 6.906324195861816 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3290863037109375\t loss_cluster: 1.6653379201889038\n",
      "Step [2/16]\t loss_instance: 5.313300132751465\t loss_cluster: 1.6411632299423218\n",
      "Step [4/16]\t loss_instance: 5.335930347442627\t loss_cluster: 1.69832444190979\n",
      "Step [6/16]\t loss_instance: 5.357547283172607\t loss_cluster: 1.6847501993179321\n",
      "Step [8/16]\t loss_instance: 5.355255603790283\t loss_cluster: 1.6925145387649536\n",
      "Step [10/16]\t loss_instance: 5.332709312438965\t loss_cluster: 1.6671912670135498\n",
      "Step [12/16]\t loss_instance: 5.355963706970215\t loss_cluster: 1.6844319105148315\n",
      "Step [14/16]\t loss_instance: 5.366786479949951\t loss_cluster: 1.712463617324829\n",
      "\n",
      "Epoch [271/300]\t Loss: 6.907150974869728 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.364120960235596\t loss_cluster: 1.7033121585845947\n",
      "Step [2/16]\t loss_instance: 5.373136520385742\t loss_cluster: 1.7219206094741821\n",
      "Step [4/16]\t loss_instance: 5.330001354217529\t loss_cluster: 1.6748831272125244\n",
      "Step [6/16]\t loss_instance: 5.330279350280762\t loss_cluster: 1.682530403137207\n",
      "Step [8/16]\t loss_instance: 5.357934951782227\t loss_cluster: 1.6962894201278687\n",
      "Step [10/16]\t loss_instance: 5.329290390014648\t loss_cluster: 1.6756126880645752\n",
      "Step [12/16]\t loss_instance: 5.350409984588623\t loss_cluster: 1.6994978189468384\n",
      "Step [14/16]\t loss_instance: 5.317437648773193\t loss_cluster: 1.6519429683685303\n",
      "\n",
      "Epoch [272/300]\t Loss: 6.9120156317949295 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3223676681518555\t loss_cluster: 1.669994831085205\n",
      "Step [2/16]\t loss_instance: 5.349143981933594\t loss_cluster: 1.6979024410247803\n",
      "Step [4/16]\t loss_instance: 5.367542266845703\t loss_cluster: 1.7275725603103638\n",
      "Step [6/16]\t loss_instance: 5.347930431365967\t loss_cluster: 1.699318528175354\n",
      "Step [8/16]\t loss_instance: 5.3720245361328125\t loss_cluster: 1.7206966876983643\n",
      "Step [10/16]\t loss_instance: 5.3550615310668945\t loss_cluster: 1.6823465824127197\n",
      "Step [12/16]\t loss_instance: 5.34472131729126\t loss_cluster: 1.6810001134872437\n",
      "Step [14/16]\t loss_instance: 5.356148719787598\t loss_cluster: 1.7018041610717773\n",
      "\n",
      "Epoch [273/300]\t Loss: 6.912808483839035 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3543853759765625\t loss_cluster: 1.6871737241744995\n",
      "Step [2/16]\t loss_instance: 5.329296588897705\t loss_cluster: 1.6649460792541504\n",
      "Step [4/16]\t loss_instance: 5.338836193084717\t loss_cluster: 1.6826205253601074\n",
      "Step [6/16]\t loss_instance: 5.357675075531006\t loss_cluster: 1.6942492723464966\n",
      "Step [8/16]\t loss_instance: 5.341731548309326\t loss_cluster: 1.6687909364700317\n",
      "Step [10/16]\t loss_instance: 5.349803447723389\t loss_cluster: 1.701284408569336\n",
      "Step [12/16]\t loss_instance: 5.366511344909668\t loss_cluster: 1.7126432657241821\n",
      "Step [14/16]\t loss_instance: 5.3471527099609375\t loss_cluster: 1.697558045387268\n",
      "\n",
      "Epoch [274/300]\t Loss: 6.912204971909523 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.348536014556885\t loss_cluster: 1.6980210542678833\n",
      "Step [2/16]\t loss_instance: 5.336847305297852\t loss_cluster: 1.6646523475646973\n",
      "Step [4/16]\t loss_instance: 5.364520072937012\t loss_cluster: 1.7137755155563354\n",
      "Step [6/16]\t loss_instance: 5.340203762054443\t loss_cluster: 1.672597050666809\n",
      "Step [8/16]\t loss_instance: 5.308167934417725\t loss_cluster: 1.6581088304519653\n",
      "Step [10/16]\t loss_instance: 5.339635372161865\t loss_cluster: 1.6632695198059082\n",
      "Step [12/16]\t loss_instance: 5.350109100341797\t loss_cluster: 1.6850436925888062\n",
      "Step [14/16]\t loss_instance: 5.337549209594727\t loss_cluster: 1.669763445854187\n",
      "\n",
      "Epoch [275/300]\t Loss: 6.904272601008415 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.316774368286133\t loss_cluster: 1.669626235961914\n",
      "Step [2/16]\t loss_instance: 5.335211277008057\t loss_cluster: 1.6936616897583008\n",
      "Step [4/16]\t loss_instance: 5.359484672546387\t loss_cluster: 1.706011176109314\n",
      "Step [6/16]\t loss_instance: 5.367325305938721\t loss_cluster: 1.6977074146270752\n",
      "Step [8/16]\t loss_instance: 5.341071128845215\t loss_cluster: 1.702307939529419\n",
      "Step [10/16]\t loss_instance: 5.34352445602417\t loss_cluster: 1.6734005212783813\n",
      "Step [12/16]\t loss_instance: 5.335590362548828\t loss_cluster: 1.6963411569595337\n",
      "Step [14/16]\t loss_instance: 5.382985591888428\t loss_cluster: 1.7195422649383545\n",
      "\n",
      "Epoch [276/300]\t Loss: 6.912867829203606 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.320017337799072\t loss_cluster: 1.6739453077316284\n",
      "Step [2/16]\t loss_instance: 5.341734409332275\t loss_cluster: 1.6841222047805786\n",
      "Step [4/16]\t loss_instance: 5.339035987854004\t loss_cluster: 1.6815060377120972\n",
      "Step [6/16]\t loss_instance: 5.349335670471191\t loss_cluster: 1.6772822141647339\n",
      "Step [8/16]\t loss_instance: 5.317000865936279\t loss_cluster: 1.6694388389587402\n",
      "Step [10/16]\t loss_instance: 5.366998195648193\t loss_cluster: 1.7010219097137451\n",
      "Step [12/16]\t loss_instance: 5.3511786460876465\t loss_cluster: 1.696200966835022\n",
      "Step [14/16]\t loss_instance: 5.360386848449707\t loss_cluster: 1.7112892866134644\n",
      "\n",
      "Epoch [277/300]\t Loss: 6.911555683612823 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.350342750549316\t loss_cluster: 1.6877361536026\n",
      "Step [2/16]\t loss_instance: 5.335066318511963\t loss_cluster: 1.6791177988052368\n",
      "Step [4/16]\t loss_instance: 5.3662428855896\t loss_cluster: 1.6919848918914795\n",
      "Step [6/16]\t loss_instance: 5.382325172424316\t loss_cluster: 1.70806086063385\n",
      "Step [8/16]\t loss_instance: 5.320159912109375\t loss_cluster: 1.6592228412628174\n",
      "Step [10/16]\t loss_instance: 5.330780029296875\t loss_cluster: 1.6900677680969238\n",
      "Step [12/16]\t loss_instance: 5.341975212097168\t loss_cluster: 1.6911373138427734\n",
      "Step [14/16]\t loss_instance: 5.31496000289917\t loss_cluster: 1.6615771055221558\n",
      "\n",
      "Epoch [278/300]\t Loss: 6.912784579396248 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.333619117736816\t loss_cluster: 1.684900164604187\n",
      "Step [2/16]\t loss_instance: 5.37730598449707\t loss_cluster: 1.729137659072876\n",
      "Step [4/16]\t loss_instance: 5.334798336029053\t loss_cluster: 1.6784048080444336\n",
      "Step [6/16]\t loss_instance: 5.360361576080322\t loss_cluster: 1.697649359703064\n",
      "Step [8/16]\t loss_instance: 5.367554187774658\t loss_cluster: 1.710175633430481\n",
      "Step [10/16]\t loss_instance: 5.344301700592041\t loss_cluster: 1.66845703125\n",
      "Step [12/16]\t loss_instance: 5.336276054382324\t loss_cluster: 1.7005746364593506\n",
      "Step [14/16]\t loss_instance: 5.334812641143799\t loss_cluster: 1.6722220182418823\n",
      "\n",
      "Epoch [279/300]\t Loss: 6.913022774457931 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.337636947631836\t loss_cluster: 1.6863656044006348\n",
      "Step [2/16]\t loss_instance: 5.381330490112305\t loss_cluster: 1.708905816078186\n",
      "Step [4/16]\t loss_instance: 5.3728532791137695\t loss_cluster: 1.692871332168579\n",
      "Step [6/16]\t loss_instance: 5.314306259155273\t loss_cluster: 1.6615016460418701\n",
      "Step [8/16]\t loss_instance: 5.400365352630615\t loss_cluster: 1.7414610385894775\n",
      "Step [10/16]\t loss_instance: 5.354187488555908\t loss_cluster: 1.6850391626358032\n",
      "Step [12/16]\t loss_instance: 5.338164329528809\t loss_cluster: 1.6805346012115479\n",
      "Step [14/16]\t loss_instance: 5.342574596405029\t loss_cluster: 1.68156898021698\n",
      "\n",
      "Epoch [280/300]\t Loss: 6.912697902321815 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.310202121734619\t loss_cluster: 1.6434985399246216\n",
      "Step [2/16]\t loss_instance: 5.338970184326172\t loss_cluster: 1.6848536729812622\n",
      "Step [4/16]\t loss_instance: 5.344466686248779\t loss_cluster: 1.6683008670806885\n",
      "Step [6/16]\t loss_instance: 5.365787029266357\t loss_cluster: 1.7036947011947632\n",
      "Step [8/16]\t loss_instance: 5.338278293609619\t loss_cluster: 1.7144944667816162\n",
      "Step [10/16]\t loss_instance: 5.337100505828857\t loss_cluster: 1.7073594331741333\n",
      "Step [12/16]\t loss_instance: 5.3314313888549805\t loss_cluster: 1.6843928098678589\n",
      "Step [14/16]\t loss_instance: 5.379220485687256\t loss_cluster: 1.7056063413619995\n",
      "\n",
      "Epoch [281/300]\t Loss: 6.911750653386116 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.320354461669922\t loss_cluster: 1.671494483947754\n",
      "Step [2/16]\t loss_instance: 5.35897970199585\t loss_cluster: 1.69906485080719\n",
      "Step [4/16]\t loss_instance: 5.334300994873047\t loss_cluster: 1.6819612979888916\n",
      "Step [6/16]\t loss_instance: 5.323615550994873\t loss_cluster: 1.6639710664749146\n",
      "Step [8/16]\t loss_instance: 5.339766979217529\t loss_cluster: 1.6762712001800537\n",
      "Step [10/16]\t loss_instance: 5.312824726104736\t loss_cluster: 1.674830436706543\n",
      "Step [12/16]\t loss_instance: 5.35719108581543\t loss_cluster: 1.7027286291122437\n",
      "Step [14/16]\t loss_instance: 5.360668182373047\t loss_cluster: 1.6929765939712524\n",
      "\n",
      "Epoch [282/300]\t Loss: 6.909279608726502 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.329250335693359\t loss_cluster: 1.6695163249969482\n",
      "Step [2/16]\t loss_instance: 5.316269874572754\t loss_cluster: 1.6673518419265747\n",
      "Step [4/16]\t loss_instance: 5.353586673736572\t loss_cluster: 1.7009093761444092\n",
      "Step [6/16]\t loss_instance: 5.337061882019043\t loss_cluster: 1.6637622117996216\n",
      "Step [8/16]\t loss_instance: 5.354691505432129\t loss_cluster: 1.7051823139190674\n",
      "Step [10/16]\t loss_instance: 5.344552040100098\t loss_cluster: 1.6881072521209717\n",
      "Step [12/16]\t loss_instance: 5.375838279724121\t loss_cluster: 1.7257364988327026\n",
      "Step [14/16]\t loss_instance: 5.355843544006348\t loss_cluster: 1.6728017330169678\n",
      "\n",
      "Epoch [283/300]\t Loss: 6.913613939285279 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.351341724395752\t loss_cluster: 1.6976689100265503\n",
      "Step [2/16]\t loss_instance: 5.36505126953125\t loss_cluster: 1.696961760520935\n",
      "Step [4/16]\t loss_instance: 5.32753849029541\t loss_cluster: 1.6723982095718384\n",
      "Step [6/16]\t loss_instance: 5.3515448570251465\t loss_cluster: 1.6733757257461548\n",
      "Step [8/16]\t loss_instance: 5.342131614685059\t loss_cluster: 1.686584711074829\n",
      "Step [10/16]\t loss_instance: 5.323648929595947\t loss_cluster: 1.6892756223678589\n",
      "Step [12/16]\t loss_instance: 5.346700191497803\t loss_cluster: 1.6903841495513916\n",
      "Step [14/16]\t loss_instance: 5.391568660736084\t loss_cluster: 1.7329024076461792\n",
      "\n",
      "Epoch [284/300]\t Loss: 6.9125840932130815 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.339791297912598\t loss_cluster: 1.677156686782837\n",
      "Step [2/16]\t loss_instance: 5.365025997161865\t loss_cluster: 1.6921536922454834\n",
      "Step [4/16]\t loss_instance: 5.3250226974487305\t loss_cluster: 1.6820158958435059\n",
      "Step [6/16]\t loss_instance: 5.329035758972168\t loss_cluster: 1.6922193765640259\n",
      "Step [8/16]\t loss_instance: 5.3215718269348145\t loss_cluster: 1.6786580085754395\n",
      "Step [10/16]\t loss_instance: 5.337580680847168\t loss_cluster: 1.6765791177749634\n",
      "Step [12/16]\t loss_instance: 5.366573333740234\t loss_cluster: 1.7084394693374634\n",
      "Step [14/16]\t loss_instance: 5.352370262145996\t loss_cluster: 1.7060577869415283\n",
      "\n",
      "Epoch [285/300]\t Loss: 6.908073833584785 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.327515125274658\t loss_cluster: 1.6701593399047852\n",
      "Step [2/16]\t loss_instance: 5.361475467681885\t loss_cluster: 1.7044814825057983\n",
      "Step [4/16]\t loss_instance: 5.343308925628662\t loss_cluster: 1.698072075843811\n",
      "Step [6/16]\t loss_instance: 5.346283435821533\t loss_cluster: 1.6938904523849487\n",
      "Step [8/16]\t loss_instance: 5.334099769592285\t loss_cluster: 1.6668695211410522\n",
      "Step [10/16]\t loss_instance: 5.343673229217529\t loss_cluster: 1.6833287477493286\n",
      "Step [12/16]\t loss_instance: 5.3327131271362305\t loss_cluster: 1.6764516830444336\n",
      "Step [14/16]\t loss_instance: 5.383229732513428\t loss_cluster: 1.754421353340149\n",
      "\n",
      "Epoch [286/300]\t Loss: 6.913662207126618 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.34078311920166\t loss_cluster: 1.69149911403656\n",
      "Step [2/16]\t loss_instance: 5.384484767913818\t loss_cluster: 1.734894037246704\n",
      "Step [4/16]\t loss_instance: 5.341136932373047\t loss_cluster: 1.6871715784072876\n",
      "Step [6/16]\t loss_instance: 5.342854976654053\t loss_cluster: 1.7210139036178589\n",
      "Step [8/16]\t loss_instance: 5.341401100158691\t loss_cluster: 1.7011024951934814\n",
      "Step [10/16]\t loss_instance: 5.3278937339782715\t loss_cluster: 1.6739753484725952\n",
      "Step [12/16]\t loss_instance: 5.352344036102295\t loss_cluster: 1.6918751001358032\n",
      "Step [14/16]\t loss_instance: 5.345142364501953\t loss_cluster: 1.7153241634368896\n",
      "\n",
      "Epoch [287/300]\t Loss: 6.913473919034004 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3424458503723145\t loss_cluster: 1.6760435104370117\n",
      "Step [2/16]\t loss_instance: 5.347524166107178\t loss_cluster: 1.696374535560608\n",
      "Step [4/16]\t loss_instance: 5.320206165313721\t loss_cluster: 1.6769088506698608\n",
      "Step [6/16]\t loss_instance: 5.343127250671387\t loss_cluster: 1.682228684425354\n",
      "Step [8/16]\t loss_instance: 5.3666205406188965\t loss_cluster: 1.6961257457733154\n",
      "Step [10/16]\t loss_instance: 5.387431621551514\t loss_cluster: 1.7278151512145996\n",
      "Step [12/16]\t loss_instance: 5.3428778648376465\t loss_cluster: 1.6686689853668213\n",
      "Step [14/16]\t loss_instance: 5.317982196807861\t loss_cluster: 1.6561533212661743\n",
      "\n",
      "Epoch [288/300]\t Loss: 6.906665751338005 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.354555606842041\t loss_cluster: 1.6894118785858154\n",
      "Step [2/16]\t loss_instance: 5.324070453643799\t loss_cluster: 1.678486943244934\n",
      "Step [4/16]\t loss_instance: 5.3788042068481445\t loss_cluster: 1.736618995666504\n",
      "Step [6/16]\t loss_instance: 5.327455997467041\t loss_cluster: 1.6597856283187866\n",
      "Step [8/16]\t loss_instance: 5.339034557342529\t loss_cluster: 1.665648102760315\n",
      "Step [10/16]\t loss_instance: 5.321938991546631\t loss_cluster: 1.6667510271072388\n",
      "Step [12/16]\t loss_instance: 5.348102569580078\t loss_cluster: 1.6943833827972412\n",
      "Step [14/16]\t loss_instance: 5.3297905921936035\t loss_cluster: 1.6920859813690186\n",
      "\n",
      "Epoch [289/300]\t Loss: 6.913759112358093 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.308657646179199\t loss_cluster: 1.6784905195236206\n",
      "Step [2/16]\t loss_instance: 5.3365583419799805\t loss_cluster: 1.6755393743515015\n",
      "Step [4/16]\t loss_instance: 5.343277931213379\t loss_cluster: 1.6789612770080566\n",
      "Step [6/16]\t loss_instance: 5.365407466888428\t loss_cluster: 1.70927894115448\n",
      "Step [8/16]\t loss_instance: 5.325217247009277\t loss_cluster: 1.6617639064788818\n",
      "Step [10/16]\t loss_instance: 5.351463794708252\t loss_cluster: 1.6857677698135376\n",
      "Step [12/16]\t loss_instance: 5.3658928871154785\t loss_cluster: 1.695532202720642\n",
      "Step [14/16]\t loss_instance: 5.346400260925293\t loss_cluster: 1.6984513998031616\n",
      "\n",
      "Epoch [290/300]\t Loss: 6.906536909937858 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.348416805267334\t loss_cluster: 1.6900314092636108\n",
      "Step [2/16]\t loss_instance: 5.357970237731934\t loss_cluster: 1.6876158714294434\n",
      "Step [4/16]\t loss_instance: 5.346532821655273\t loss_cluster: 1.6905791759490967\n",
      "Step [6/16]\t loss_instance: 5.340556621551514\t loss_cluster: 1.6890888214111328\n",
      "Step [8/16]\t loss_instance: 5.3320722579956055\t loss_cluster: 1.6553477048873901\n",
      "Step [10/16]\t loss_instance: 5.337525367736816\t loss_cluster: 1.6883463859558105\n",
      "Step [12/16]\t loss_instance: 5.357433319091797\t loss_cluster: 1.6915796995162964\n",
      "Step [14/16]\t loss_instance: 5.375686168670654\t loss_cluster: 1.7168385982513428\n",
      "\n",
      "Epoch [291/300]\t Loss: 6.905004087090492 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.329577922821045\t loss_cluster: 1.7009586095809937\n",
      "Step [2/16]\t loss_instance: 5.373104095458984\t loss_cluster: 1.6999949216842651\n",
      "Step [4/16]\t loss_instance: 5.346487045288086\t loss_cluster: 1.666128158569336\n",
      "Step [6/16]\t loss_instance: 5.334223747253418\t loss_cluster: 1.6676316261291504\n",
      "Step [8/16]\t loss_instance: 5.333588123321533\t loss_cluster: 1.6938947439193726\n",
      "Step [10/16]\t loss_instance: 5.349572658538818\t loss_cluster: 1.705815076828003\n",
      "Step [12/16]\t loss_instance: 5.3422627449035645\t loss_cluster: 1.663655161857605\n",
      "Step [14/16]\t loss_instance: 5.348467826843262\t loss_cluster: 1.6925307512283325\n",
      "\n",
      "Epoch [292/300]\t Loss: 6.907949247956276 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.333439826965332\t loss_cluster: 1.6736923456192017\n",
      "Step [2/16]\t loss_instance: 5.306946754455566\t loss_cluster: 1.6496800184249878\n",
      "Step [4/16]\t loss_instance: 5.3296051025390625\t loss_cluster: 1.6673539876937866\n",
      "Step [6/16]\t loss_instance: 5.3395466804504395\t loss_cluster: 1.6763544082641602\n",
      "Step [8/16]\t loss_instance: 5.356391906738281\t loss_cluster: 1.7089141607284546\n",
      "Step [10/16]\t loss_instance: 5.372409343719482\t loss_cluster: 1.7152312994003296\n",
      "Step [12/16]\t loss_instance: 5.33373498916626\t loss_cluster: 1.666692852973938\n",
      "Step [14/16]\t loss_instance: 5.3827595710754395\t loss_cluster: 1.726280927658081\n",
      "\n",
      "Epoch [293/300]\t Loss: 6.905887600779534 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.377564430236816\t loss_cluster: 1.7123428583145142\n",
      "Step [2/16]\t loss_instance: 5.361833095550537\t loss_cluster: 1.687703251838684\n",
      "Step [4/16]\t loss_instance: 5.348516941070557\t loss_cluster: 1.674580693244934\n",
      "Step [6/16]\t loss_instance: 5.349231719970703\t loss_cluster: 1.673062801361084\n",
      "Step [8/16]\t loss_instance: 5.3471293449401855\t loss_cluster: 1.6644219160079956\n",
      "Step [10/16]\t loss_instance: 5.321592330932617\t loss_cluster: 1.6694530248641968\n",
      "Step [12/16]\t loss_instance: 5.324091911315918\t loss_cluster: 1.6819406747817993\n",
      "Step [14/16]\t loss_instance: 5.38515043258667\t loss_cluster: 1.7119144201278687\n",
      "\n",
      "Epoch [294/300]\t Loss: 6.908394536375999 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.332208156585693\t loss_cluster: 1.6753993034362793\n",
      "Step [2/16]\t loss_instance: 5.320164203643799\t loss_cluster: 1.6598126888275146\n",
      "Step [4/16]\t loss_instance: 5.356276035308838\t loss_cluster: 1.696976661682129\n",
      "Step [6/16]\t loss_instance: 5.354100227355957\t loss_cluster: 1.6833451986312866\n",
      "Step [8/16]\t loss_instance: 5.3583598136901855\t loss_cluster: 1.6914364099502563\n",
      "Step [10/16]\t loss_instance: 5.343059539794922\t loss_cluster: 1.6704825162887573\n",
      "Step [12/16]\t loss_instance: 5.359671115875244\t loss_cluster: 1.7209062576293945\n",
      "Step [14/16]\t loss_instance: 5.368987083435059\t loss_cluster: 1.7036972045898438\n",
      "\n",
      "Epoch [295/300]\t Loss: 6.912094628810882 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.355004787445068\t loss_cluster: 1.685958743095398\n",
      "Step [2/16]\t loss_instance: 5.335260391235352\t loss_cluster: 1.6539961099624634\n",
      "Step [4/16]\t loss_instance: 5.339244842529297\t loss_cluster: 1.684777855873108\n",
      "Step [6/16]\t loss_instance: 5.340260982513428\t loss_cluster: 1.6886591911315918\n",
      "Step [8/16]\t loss_instance: 5.325057029724121\t loss_cluster: 1.685404896736145\n",
      "Step [10/16]\t loss_instance: 5.3277506828308105\t loss_cluster: 1.6690120697021484\n",
      "Step [12/16]\t loss_instance: 5.347966194152832\t loss_cluster: 1.7175095081329346\n",
      "Step [14/16]\t loss_instance: 5.327803611755371\t loss_cluster: 1.662394642829895\n",
      "\n",
      "Epoch [296/300]\t Loss: 6.906865882873535 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.33952522277832\t loss_cluster: 1.695721983909607\n",
      "Step [2/16]\t loss_instance: 5.325922966003418\t loss_cluster: 1.6630821228027344\n",
      "Step [4/16]\t loss_instance: 5.341137886047363\t loss_cluster: 1.6993309259414673\n",
      "Step [6/16]\t loss_instance: 5.351417064666748\t loss_cluster: 1.7005627155303955\n",
      "Step [8/16]\t loss_instance: 5.3499908447265625\t loss_cluster: 1.7107398509979248\n",
      "Step [10/16]\t loss_instance: 5.357841491699219\t loss_cluster: 1.6808348894119263\n",
      "Step [12/16]\t loss_instance: 5.338875770568848\t loss_cluster: 1.6773512363433838\n",
      "Step [14/16]\t loss_instance: 5.355754375457764\t loss_cluster: 1.704655647277832\n",
      "\n",
      "Epoch [297/300]\t Loss: 6.913636988401413 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.335354804992676\t loss_cluster: 1.6767882108688354\n",
      "Step [2/16]\t loss_instance: 5.367340564727783\t loss_cluster: 1.6964633464813232\n",
      "Step [4/16]\t loss_instance: 5.3388800621032715\t loss_cluster: 1.6768368482589722\n",
      "Step [6/16]\t loss_instance: 5.361533164978027\t loss_cluster: 1.6995035409927368\n",
      "Step [8/16]\t loss_instance: 5.332427978515625\t loss_cluster: 1.6889610290527344\n",
      "Step [10/16]\t loss_instance: 5.318068981170654\t loss_cluster: 1.6758400201797485\n",
      "Step [12/16]\t loss_instance: 5.33540153503418\t loss_cluster: 1.6656908988952637\n",
      "Step [14/16]\t loss_instance: 5.348880290985107\t loss_cluster: 1.6765284538269043\n",
      "\n",
      "Epoch [298/300]\t Loss: 6.912636199593544 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.316049098968506\t loss_cluster: 1.6708049774169922\n",
      "Step [2/16]\t loss_instance: 5.317589282989502\t loss_cluster: 1.6878069639205933\n",
      "Step [4/16]\t loss_instance: 5.353269577026367\t loss_cluster: 1.673589825630188\n",
      "Step [6/16]\t loss_instance: 5.329331874847412\t loss_cluster: 1.6736936569213867\n",
      "Step [8/16]\t loss_instance: 5.353923320770264\t loss_cluster: 1.6947768926620483\n",
      "Step [10/16]\t loss_instance: 5.36970853805542\t loss_cluster: 1.701283574104309\n",
      "Step [12/16]\t loss_instance: 5.360678672790527\t loss_cluster: 1.6820493936538696\n",
      "Step [14/16]\t loss_instance: 5.325390338897705\t loss_cluster: 1.6582006216049194\n",
      "\n",
      "Epoch [299/300]\t Loss: 6.906550380587578 \n",
      "\n",
      "Step [0/16]\t loss_instance: 5.3240966796875\t loss_cluster: 1.6661916971206665\n",
      "Step [2/16]\t loss_instance: 5.3373637199401855\t loss_cluster: 1.67539381980896\n",
      "Step [4/16]\t loss_instance: 5.391697883605957\t loss_cluster: 1.717804193496704\n",
      "Step [6/16]\t loss_instance: 5.325510025024414\t loss_cluster: 1.6500803232192993\n",
      "Step [8/16]\t loss_instance: 5.330032825469971\t loss_cluster: 1.690161108970642\n",
      "Step [10/16]\t loss_instance: 5.340555191040039\t loss_cluster: 1.6748853921890259\n",
      "Step [12/16]\t loss_instance: 5.341527462005615\t loss_cluster: 1.683942198753357\n",
      "Step [14/16]\t loss_instance: 5.339776039123535\t loss_cluster: 1.6778361797332764\n",
      "\n",
      "Epoch [300/300]\t Loss: 6.910854756832123 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "loss_device=torch.device('cuda')\n",
    "instance_loss=InstanceLoss(batch_size=args.batch_size,temperature=0.5,device=loss_device)\n",
    "cluster_loss=ClusterLoss(class_num=class_num,temperature=0.5,device=loss_device)\n",
    "accs=[]\n",
    "losses=[]\n",
    "aris=[]\n",
    "nmis=[]\n",
    "fs=[]\n",
    "\n",
    "memory_bank=MemoryBank(batch_size=args.batch_size,full_data=x_ndarray,topK=10)\n",
    "memory_bank.updateBank(x_ndarray)\n",
    "\n",
    "for epoch in range(args.start_epoch, args.epochs+1):\n",
    "    lr = optimizer.param_groups[0][\"lr\"]\n",
    "    # if epoch %20 ==0:\n",
    "    #     embeddings=np.empty((0,mlp.rep_dim))\n",
    "    #     model.eval()\n",
    "    #     with torch.no_grad():\n",
    "    #         for step, (x, y) in enumerate(scGenDataLoader):\n",
    "    #             x=x.to('cuda')\n",
    "    #             embedding=model.forward_embedding(x).cpu().detach().numpy()                 \n",
    "    #             embeddings=np.row_stack((embeddings,embedding))\n",
    "    #             memory_bank.updateBank(embeddings)\n",
    "    loss_epoch = train(instance_loss,cluster_loss,memory_bank,epoch=epoch,model=model)\n",
    "    losses.append(loss_epoch/len(scDataLoader))\n",
    "    if epoch % 10 == 0:\n",
    "        save_model(args, model, optimizer, epoch)\n",
    "    print(f\"\\nEpoch [{epoch}/{args.epochs}]\\t Loss: {loss_epoch / len(scDataLoader)} \\n\")\n",
    "    # nmi, ari, f, acc = test()\n",
    "    # accs.append(acc)\n",
    "    # aris.append(ari)\n",
    "    # nmis.append(nmi)\n",
    "    # fs.append(f)\n",
    "    # print('Test NMI = {:.4f} ARI = {:.4f} F = {:.4f} ACC = {:.4f}'.format(nmi, ari, f, acc))\n",
    "    # print('========'*8+'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2160x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from evaluation import evaluation_tools\n",
    "mlp_1 = mlp.MLP(num_genes=args.num_genes)\n",
    "model = network.Network(mlp_1, args.feature_dim,args.classnum)\n",
    "model = model.to('cuda')\n",
    "\n",
    "checkpoint = torch.load('save\\sc-static-bank\\checkpoint_180.tar')\n",
    "model.load_state_dict(checkpoint['net'])\n",
    "\n",
    "x=torch.tensor(x_ndarray,dtype=torch.float32).to('cuda')\n",
    "embeddings=model.forward_embedding(x)\n",
    "embeddings=embeddings.detach().cpu().numpy()\n",
    "results=evaluation_tools.cluster_embedding(embedding=embeddings,cluster_methods=['KMeans','Leiden'],cluster_number=args.classnum,cosine=False,Y=y_ndarray.squeeze(),save_pred=True)\n",
    "evaluation_tools.embedding_cluster_visualization(embeddings=embeddings,result=results,true_label=y_ndarray.squeeze(),args=args,epoch=180)\n",
    "# print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'kmeans_ari': 0.5485744616897491, 'kmeans_nmi': 0.7302188283689908, 'kmeans_sil': 0.35874087, 'kmeans_cal': 2820.0743903193, 'leiden_ari': 0.3036244818629452, 'leiden_nmi': 0.693425300566485, 'leiden_sil': 0.37151045, 'leiden_cal': 2006.463899696071}\n"
     ]
    }
   ],
   "source": [
    "checkpoint = torch.load('save\\sc-static-bank\\checkpoint_300.tar')\n",
    "model.load_state_dict(checkpoint['net'])\n",
    "\n",
    "x=torch.tensor(x_ndarray,dtype=torch.float32).to('cuda')\n",
    "embeddings=model.forward_embedding(x)\n",
    "embeddings=embeddings.detach().cpu().numpy()\n",
    "results=evaluation_tools.cluster_embedding(embedding=embeddings,cluster_methods=['KMeans','Leiden'],cluster_number=args.classnum,cosine=False,Y=y_ndarray.squeeze(),save_pred=False)\n",
    "# evaluation_tools.embedding_cluster_visualization(embeddings=embeddings,result=results,true_label=y_ndarray.squeeze(),args=args,epoch=286)\n",
    "print(results)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}