demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../')\n",
    "from dataset import get_activitynet_dataset, get_kineticslike_dataset\n",
    "from dataset import get_son2parent, get_emb, get_dataloader\n",
    "from metric import Metric\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
    "\n",
    "dataset_name, emb_type = 'activitynet', 'cone'\n",
    "\n",
    "if dataset_name == 'activitynet':\n",
    "    tree_file = '../activity_net_depth_v5.csv'\n",
    "    if emb_type == 'cone' : emb_file = '../cone_activity_net300.pth' \n",
    "    if emb_type == 'glove' : emb_file = '../w2v_activity_300d.pth'\n",
    "    feat_path, anno_fn = '../data.pickle', '../activity_net.v1-3.json'\n",
    "    Xtr, Xval, ytr, yval, label_set = get_activitynet_dataset(feat_path, anno_fn)\n",
    "    \n",
    "elif dataset_name == 'kinetics':\n",
    "    tree_file = '../kinetics_depth_v2.csv'\n",
    "    if emb_type == 'cone' : emb_file = '../cones_kinetics300.pth'\n",
    "    if emb_type == 'glove' : emb_file = '../w2v_kinetics_300d.pth'\n",
    "    train_pth_path, valid_pth_path = '../kinetics_train.pth', '../kinetics_val.pth'\n",
    "    Xtr, Xval, ytr, yval, label_set = get_kineticslike_dataset(train_pth_path, valid_pth_path)\n",
    "    \n",
    "elif dataset_name == 'moments':\n",
    "    tree_file = '../moments_depth_v4.csv'\n",
    "    if emb_type == 'cone' : emb_file = '../cones_moments300.pth'\n",
    "    if emb_type == 'glove' : emb_file = '../w2v_moments_300d.pth'\n",
    "    train_pth_path, valid_pth_path = '../moments_train.pth', '../moments_val.pth'\n",
    "    Xtr, Xval, ytr, yval, label_set = get_kineticslike_dataset(train_pth_path, valid_pth_path)\n",
    "\n",
    "son2parent = get_son2parent(tree_file)\n",
    "emb = get_emb('hyp', emb_file, label_set)\n",
    "train_loader, val_loader = get_dataloader(Xtr, Xval, ytr, yval, emb, batch_size = 128)\n",
    "metric = Metric(label_set, son2parent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### T=0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss:1.291,acc:0.669,1hop_acc:0.829,2hop_acc:0.908,mAP:0.584,1hop_mAP:0.767,2hop_mAP:0.871\n",
      "mAP:0.620,1hop_mAP:0.958,2hop_mAP:0.983\n",
      "loss:1.185,acc:0.711,1hop_acc:0.838,2hop_acc:0.908,mAP:0.623,1hop_mAP:0.791,2hop_mAP:0.885\n",
      "mAP:0.712,1hop_mAP:0.955,2hop_mAP:0.979\n",
      "loss:1.226,acc:0.716,1hop_acc:0.837,2hop_acc:0.909,mAP:0.636,1hop_mAP:0.798,2hop_mAP:0.889\n",
      "mAP:0.720,1hop_mAP:0.960,2hop_mAP:0.982\n",
      "loss:1.216,acc:0.726,1hop_acc:0.849,2hop_acc:0.914,mAP:0.643,1hop_mAP:0.802,2hop_mAP:0.891\n",
      "mAP:0.730,1hop_mAP:0.960,2hop_mAP:0.981\n",
      "loss:1.272,acc:0.726,1hop_acc:0.849,2hop_acc:0.911,mAP:0.645,1hop_mAP:0.803,2hop_mAP:0.890\n",
      "mAP:0.730,1hop_mAP:0.962,2hop_mAP:0.981\n",
      "loss:1.268,acc:0.734,1hop_acc:0.849,2hop_acc:0.915,mAP:0.651,1hop_mAP:0.805,2hop_mAP:0.892\n",
      "mAP:0.740,1hop_mAP:0.960,2hop_mAP:0.981\n",
      "loss:1.262,acc:0.733,1hop_acc:0.847,2hop_acc:0.913,mAP:0.652,1hop_mAP:0.806,2hop_mAP:0.893\n",
      "mAP:0.740,1hop_mAP:0.959,2hop_mAP:0.981\n",
      "loss:1.299,acc:0.720,1hop_acc:0.840,2hop_acc:0.912,mAP:0.653,1hop_mAP:0.803,2hop_mAP:0.890\n",
      "mAP:0.741,1hop_mAP:0.959,2hop_mAP:0.981\n",
      "loss:1.333,acc:0.722,1hop_acc:0.843,2hop_acc:0.905,mAP:0.653,1hop_mAP:0.805,2hop_mAP:0.890\n",
      "mAP:0.740,1hop_mAP:0.956,2hop_mAP:0.978\n",
      "loss:1.379,acc:0.708,1hop_acc:0.831,2hop_acc:0.906,mAP:0.649,1hop_mAP:0.800,2hop_mAP:0.889\n",
      "mAP:0.737,1hop_mAP:0.957,2hop_mAP:0.979\n"
     ]
    }
   ],
   "source": [
    "from pmath import pair_wise_cos, pair_wise_eud, pair_wise_hyp\n",
    "from model import RegressNet\n",
    "import torch\n",
    "c, T, epochs, dist_func, eval_dist = .1,.1, 50, pair_wise_hyp, pair_wise_cos\n",
    "torch.manual_seed(42)\n",
    "emb = emb.cuda()\n",
    "\n",
    "train_loader, val_loader = get_dataloader(Xtr, Xval, ytr, yval, emb, batch_size = 128)\n",
    "model = RegressNet(T, c, dist_func, eval_dist, train_loader, val_loader, emb, metric)\n",
    "model = model.cuda()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
    "model._train(optimizer, epochs, T, eval_interval = 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### T = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss:3.328,acc:0.635,1hop_acc:0.821,2hop_acc:0.898,mAP:0.568,1hop_mAP:0.811,2hop_mAP:0.895\n",
      "mAP:0.595,1hop_mAP:0.977,2hop_mAP:0.988\n",
      "loss:3.025,acc:0.731,1hop_acc:0.858,2hop_acc:0.920,mAP:0.646,1hop_mAP:0.847,2hop_mAP:0.913\n",
      "mAP:0.752,1hop_mAP:0.965,2hop_mAP:0.981\n",
      "loss:2.968,acc:0.744,1hop_acc:0.858,2hop_acc:0.919,mAP:0.667,1hop_mAP:0.849,2hop_mAP:0.913\n",
      "mAP:0.778,1hop_mAP:0.960,2hop_mAP:0.977\n",
      "loss:2.945,acc:0.749,1hop_acc:0.859,2hop_acc:0.919,mAP:0.675,1hop_mAP:0.847,2hop_mAP:0.911\n",
      "mAP:0.788,1hop_mAP:0.955,2hop_mAP:0.974\n",
      "loss:2.934,acc:0.751,1hop_acc:0.858,2hop_acc:0.918,mAP:0.678,1hop_mAP:0.844,2hop_mAP:0.909\n",
      "mAP:0.790,1hop_mAP:0.951,2hop_mAP:0.972\n",
      "loss:2.925,acc:0.750,1hop_acc:0.857,2hop_acc:0.918,mAP:0.679,1hop_mAP:0.842,2hop_mAP:0.908\n",
      "mAP:0.791,1hop_mAP:0.948,2hop_mAP:0.971\n",
      "loss:2.917,acc:0.750,1hop_acc:0.856,2hop_acc:0.918,mAP:0.679,1hop_mAP:0.840,2hop_mAP:0.908\n",
      "mAP:0.792,1hop_mAP:0.945,2hop_mAP:0.969\n",
      "loss:2.910,acc:0.751,1hop_acc:0.857,2hop_acc:0.918,mAP:0.680,1hop_mAP:0.840,2hop_mAP:0.907\n",
      "mAP:0.791,1hop_mAP:0.944,2hop_mAP:0.968\n",
      "loss:2.905,acc:0.751,1hop_acc:0.857,2hop_acc:0.919,mAP:0.680,1hop_mAP:0.839,2hop_mAP:0.907\n",
      "mAP:0.790,1hop_mAP:0.942,2hop_mAP:0.967\n",
      "loss:2.901,acc:0.750,1hop_acc:0.856,2hop_acc:0.918,mAP:0.679,1hop_mAP:0.838,2hop_mAP:0.907\n",
      "mAP:0.791,1hop_mAP:0.942,2hop_mAP:0.967\n"
     ]
    }
   ],
   "source": [
    "from pmath import pair_wise_cos, pair_wise_eud, pair_wise_hyp\n",
    "from model import RegressNet\n",
    "import torch\n",
    "c, T, epochs, dist_func, eval_dist = .1,1, 50, pair_wise_hyp, pair_wise_cos\n",
    "torch.manual_seed(42)\n",
    "emb = emb.cuda()\n",
    "\n",
    "train_loader, val_loader = get_dataloader(Xtr, Xval, ytr, yval, emb, batch_size = 128)\n",
    "model = RegressNet(T, c, dist_func, eval_dist, train_loader, val_loader, emb, metric)\n",
    "model = model.cuda()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
    "model._train(optimizer, epochs, T, eval_interval = 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step2 200D hyperbolic embedding, for all three datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../')\n",
    "from dataset import get_activitynet_dataset, get_kineticslike_dataset\n",
    "from dataset import get_son2parent, get_emb, get_dataloader\n",
    "from metric import Metric\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
    "\n",
    "dataset_name, emb_type = 'activitynet', 'cone'\n",
    "\n",
    "if dataset_name == 'activitynet':\n",
    "    tree_file = '../activity_net_depth_v5.csv'\n",
    "    if emb_type == 'cone' : emb_file = '../cone_activity_net200.pth' \n",
    "    if emb_type == 'glove' : emb_file = '../w2v_activity_300d.pth'\n",
    "    feat_path, anno_fn = '../data.pickle', '../activity_net.v1-3.json'\n",
    "    Xtr, Xval, ytr, yval, label_set = get_activitynet_dataset(feat_path, anno_fn)\n",
    "    \n",
    "elif dataset_name == 'kinetics':\n",
    "    tree_file = '../kinetics_depth_v2.csv'\n",
    "    if emb_type == 'cone' : emb_file = '../cones_kinetics300.pth'\n",
    "    if emb_type == 'glove' : emb_file = '../w2v_kinetics_300d.pth'\n",
    "    train_pth_path, valid_pth_path = '../kinetics_train.pth', '../kinetics_val.pth'\n",
    "    Xtr, Xval, ytr, yval, label_set = get_kineticslike_dataset(train_pth_path, valid_pth_path)\n",
    "    \n",
    "elif dataset_name == 'moments':\n",
    "    tree_file = '../moments_depth_v4.csv'\n",
    "    if emb_type == 'cone' : emb_file = '../cones_moments300.pth'\n",
    "    if emb_type == 'glove' : emb_file = '../w2v_moments_300d.pth'\n",
    "    train_pth_path, valid_pth_path = '../moments_train.pth', '../moments_val.pth'\n",
    "    Xtr, Xval, ytr, yval, label_set = get_kineticslike_dataset(train_pth_path, valid_pth_path)\n",
    "\n",
    "son2parent = get_son2parent(tree_file)\n",
    "emb = get_emb('hyp', emb_file, label_set)\n",
    "train_loader, val_loader = get_dataloader(Xtr, Xval, ytr, yval, emb, batch_size = 128)\n",
    "metric = Metric(label_set, son2parent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss:3.318,acc:0.649,1hop_acc:0.822,2hop_acc:0.900,mAP:0.571,1hop_mAP:0.812,2hop_mAP:0.896\n",
      "mAP:0.608,1hop_mAP:0.974,2hop_mAP:0.986\n",
      "loss:3.034,acc:0.729,1hop_acc:0.858,2hop_acc:0.917,mAP:0.647,1hop_mAP:0.847,2hop_mAP:0.913\n",
      "mAP:0.756,1hop_mAP:0.966,2hop_mAP:0.981\n",
      "loss:2.980,acc:0.742,1hop_acc:0.860,2hop_acc:0.919,mAP:0.666,1hop_mAP:0.850,2hop_mAP:0.914\n",
      "mAP:0.778,1hop_mAP:0.961,2hop_mAP:0.978\n",
      "loss:2.955,acc:0.748,1hop_acc:0.858,2hop_acc:0.918,mAP:0.674,1hop_mAP:0.848,2hop_mAP:0.911\n",
      "mAP:0.787,1hop_mAP:0.955,2hop_mAP:0.974\n",
      "loss:2.942,acc:0.749,1hop_acc:0.859,2hop_acc:0.918,mAP:0.676,1hop_mAP:0.845,2hop_mAP:0.909\n",
      "mAP:0.789,1hop_mAP:0.952,2hop_mAP:0.972\n",
      "loss:2.933,acc:0.747,1hop_acc:0.858,2hop_acc:0.916,mAP:0.678,1hop_mAP:0.843,2hop_mAP:0.908\n",
      "mAP:0.789,1hop_mAP:0.950,2hop_mAP:0.971\n",
      "loss:2.925,acc:0.749,1hop_acc:0.858,2hop_acc:0.916,mAP:0.679,1hop_mAP:0.843,2hop_mAP:0.907\n",
      "mAP:0.789,1hop_mAP:0.948,2hop_mAP:0.970\n",
      "loss:2.921,acc:0.750,1hop_acc:0.858,2hop_acc:0.917,mAP:0.679,1hop_mAP:0.842,2hop_mAP:0.907\n",
      "mAP:0.789,1hop_mAP:0.945,2hop_mAP:0.969\n",
      "loss:2.915,acc:0.751,1hop_acc:0.859,2hop_acc:0.918,mAP:0.679,1hop_mAP:0.841,2hop_mAP:0.907\n",
      "mAP:0.790,1hop_mAP:0.945,2hop_mAP:0.969\n",
      "loss:2.909,acc:0.750,1hop_acc:0.858,2hop_acc:0.918,mAP:0.679,1hop_mAP:0.841,2hop_mAP:0.907\n",
      "mAP:0.791,1hop_mAP:0.945,2hop_mAP:0.968\n"
     ]
    }
   ],
   "source": [
    "from pmath import pair_wise_cos, pair_wise_eud, pair_wise_hyp\n",
    "from model import RegressNet\n",
    "import torch\n",
    "c, T, epochs, dist_func, eval_dist = .1,1, 50, pair_wise_hyp, pair_wise_cos\n",
    "torch.manual_seed(42)\n",
    "emb = emb.cuda()\n",
    "\n",
    "train_loader, val_loader = get_dataloader(Xtr, Xval, ytr, yval, emb, batch_size = 128)\n",
    "model = RegressNet(T, c, dist_func, eval_dist, train_loader, val_loader, emb, metric)\n",
    "model = model.cuda()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
    "model._train(optimizer, epochs, T, eval_interval = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../')\n",
    "from dataset import get_activitynet_dataset, get_kineticslike_dataset\n",
    "from dataset import get_son2parent, get_emb, get_dataloader\n",
    "from metric import Metric\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
    "\n",
    "dataset_name, emb_type = 'kinetics', 'cone'\n",
    "\n",
    "if dataset_name == 'activitynet':\n",
    "    tree_file = '../activity_net_depth_v5.csv'\n",
    "    if emb_type == 'cone' : emb_file = '../cone_activity_net200.pth' \n",
    "    if emb_type == 'glove' : emb_file = '../w2v_activity_300d.pth'\n",
    "    feat_path, anno_fn = '../data.pickle', '../activity_net.v1-3.json'\n",
    "    Xtr, Xval, ytr, yval, label_set = get_activitynet_dataset(feat_path, anno_fn)\n",
    "    \n",
    "elif dataset_name == 'kinetics':\n",
    "    tree_file = '../kinetics_depth_v2.csv'\n",
    "    if emb_type == 'cone' : emb_file = '../cone_kinetics_200.pth'\n",
    "    if emb_type == 'glove' : emb_file = '../w2v_kinetics_300d.pth'\n",
    "    train_pth_path, valid_pth_path = '../kinetics_train.pth', '../kinetics_val.pth'\n",
    "    Xtr, Xval, ytr, yval, label_set = get_kineticslike_dataset(train_pth_path, valid_pth_path)\n",
    "    \n",
    "elif dataset_name == 'moments':\n",
    "    tree_file = '../moments_depth_v4.csv'\n",
    "    if emb_type == 'cone' : emb_file = '../cones_moments300.pth'\n",
    "    if emb_type == 'glove' : emb_file = '../w2v_moments_300d.pth'\n",
    "    train_pth_path, valid_pth_path = '../moments_train.pth', '../moments_val.pth'\n",
    "    Xtr, Xval, ytr, yval, label_set = get_kineticslike_dataset(train_pth_path, valid_pth_path)\n",
    "\n",
    "son2parent = get_son2parent(tree_file)\n",
    "emb = get_emb('hyp', emb_file, label_set)\n",
    "train_loader, val_loader = get_dataloader(Xtr, Xval, ytr, yval, emb, batch_size = 128)\n",
    "metric = Metric(label_set, son2parent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss:2.935,acc:0.762,1hop_acc:0.872,2hop_acc:0.914,mAP:0.573,1hop_mAP:0.844,2hop_mAP:0.894\n",
      "mAP:0.698,1hop_mAP:0.956,2hop_mAP:0.969\n",
      "loss:2.845,acc:0.770,1hop_acc:0.870,2hop_acc:0.913,mAP:0.595,1hop_mAP:0.834,2hop_mAP:0.887\n",
      "mAP:0.719,1hop_mAP:0.937,2hop_mAP:0.957\n",
      "loss:2.846,acc:0.771,1hop_acc:0.870,2hop_acc:0.912,mAP:0.594,1hop_mAP:0.827,2hop_mAP:0.882\n",
      "mAP:0.717,1hop_mAP:0.929,2hop_mAP:0.951\n",
      "loss:2.850,acc:0.769,1hop_acc:0.867,2hop_acc:0.911,mAP:0.593,1hop_mAP:0.824,2hop_mAP:0.880\n",
      "mAP:0.715,1hop_mAP:0.928,2hop_mAP:0.950\n",
      "loss:2.854,acc:0.762,1hop_acc:0.864,2hop_acc:0.908,mAP:0.590,1hop_mAP:0.821,2hop_mAP:0.878\n",
      "mAP:0.713,1hop_mAP:0.927,2hop_mAP:0.949\n",
      "loss:2.853,acc:0.763,1hop_acc:0.863,2hop_acc:0.906,mAP:0.590,1hop_mAP:0.822,2hop_mAP:0.878\n",
      "mAP:0.712,1hop_mAP:0.926,2hop_mAP:0.949\n",
      "loss:2.856,acc:0.762,1hop_acc:0.863,2hop_acc:0.908,mAP:0.589,1hop_mAP:0.819,2hop_mAP:0.876\n",
      "mAP:0.711,1hop_mAP:0.924,2hop_mAP:0.947\n",
      "loss:2.854,acc:0.763,1hop_acc:0.860,2hop_acc:0.903,mAP:0.589,1hop_mAP:0.816,2hop_mAP:0.871\n",
      "mAP:0.711,1hop_mAP:0.922,2hop_mAP:0.944\n",
      "loss:2.857,acc:0.763,1hop_acc:0.859,2hop_acc:0.903,mAP:0.588,1hop_mAP:0.814,2hop_mAP:0.871\n",
      "mAP:0.711,1hop_mAP:0.921,2hop_mAP:0.944\n",
      "loss:2.853,acc:0.766,1hop_acc:0.863,2hop_acc:0.904,mAP:0.589,1hop_mAP:0.815,2hop_mAP:0.869\n",
      "mAP:0.711,1hop_mAP:0.919,2hop_mAP:0.942\n"
     ]
    }
   ],
   "source": [
    "from pmath import pair_wise_cos, pair_wise_eud, pair_wise_hyp\n",
    "from model import RegressNet\n",
    "import torch\n",
    "c, T, epochs, dist_func, eval_dist = .1,1, 50, pair_wise_hyp, pair_wise_cos\n",
    "torch.manual_seed(42)\n",
    "emb = emb.cuda()\n",
    "\n",
    "train_loader, val_loader = get_dataloader(Xtr, Xval, ytr, yval, emb, batch_size = 128)\n",
    "model = RegressNet(T, c, dist_func, eval_dist, train_loader, val_loader, emb, metric)\n",
    "model = model.cuda()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
    "model._train(optimizer, epochs, T, eval_interval = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../')\n",
    "from dataset import get_activitynet_dataset, get_kineticslike_dataset\n",
    "from dataset import get_son2parent, get_emb, get_dataloader\n",
    "from metric import Metric\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
    "\n",
    "dataset_name, emb_type = 'moments', 'cone'\n",
    "\n",
    "if dataset_name == 'activitynet':\n",
    "    tree_file = '../activity_net_depth_v5.csv'\n",
    "    if emb_type == 'cone' : emb_file = '../cone_activity_net200.pth' \n",
    "    if emb_type == 'glove' : emb_file = '../w2v_activity_300d.pth'\n",
    "    feat_path, anno_fn = '../data.pickle', '../activity_net.v1-3.json'\n",
    "    Xtr, Xval, ytr, yval, label_set = get_activitynet_dataset(feat_path, anno_fn)\n",
    "    \n",
    "elif dataset_name == 'kinetics':\n",
    "    tree_file = '../kinetics_depth_v2.csv'\n",
    "    if emb_type == 'cone' : emb_file = '../cone_kinetics_200.pth'\n",
    "    if emb_type == 'glove' : emb_file = '../w2v_kinetics_300d.pth'\n",
    "    train_pth_path, valid_pth_path = '../kinetics_train.pth', '../kinetics_val.pth'\n",
    "    Xtr, Xval, ytr, yval, label_set = get_kineticslike_dataset(train_pth_path, valid_pth_path)\n",
    "    \n",
    "elif dataset_name == 'moments':\n",
    "    tree_file = '../moments_depth_v5.csv'\n",
    "    if emb_type == 'cone' : emb_file = '../cone_moments_200.pth'\n",
    "    if emb_type == 'glove' : emb_file = '../w2v_moments_300d.pth'\n",
    "    train_pth_path, valid_pth_path = '../moments_train.pth', '../moments_val.pth'\n",
    "    Xtr, Xval, ytr, yval, label_set = get_kineticslike_dataset(train_pth_path, valid_pth_path)\n",
    "\n",
    "son2parent = get_son2parent(tree_file)\n",
    "emb = get_emb('hyp', emb_file, label_set)\n",
    "train_loader, val_loader = get_dataloader(Xtr, Xval, ytr, yval, emb, batch_size = 128)\n",
    "metric = Metric(label_set, son2parent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss:4.406,acc:0.133,1hop_acc:0.188,2hop_acc:0.375,mAP:0.149,1hop_mAP:0.182,2hop_mAP:0.365\n",
      "mAP:0.209,1hop_mAP:0.388,2hop_mAP:0.527\n",
      "loss:4.365,acc:0.153,1hop_acc:0.207,2hop_acc:0.380,mAP:0.157,1hop_mAP:0.190,2hop_mAP:0.373\n",
      "mAP:0.254,1hop_mAP:0.414,2hop_mAP:0.557\n",
      "loss:4.353,acc:0.160,1hop_acc:0.214,2hop_acc:0.391,mAP:0.160,1hop_mAP:0.196,2hop_mAP:0.378\n",
      "mAP:0.273,1hop_mAP:0.417,2hop_mAP:0.561\n",
      "loss:4.352,acc:0.164,1hop_acc:0.218,2hop_acc:0.393,mAP:0.162,1hop_mAP:0.199,2hop_mAP:0.380\n",
      "mAP:0.281,1hop_mAP:0.419,2hop_mAP:0.563\n",
      "loss:4.356,acc:0.164,1hop_acc:0.219,2hop_acc:0.398,mAP:0.163,1hop_mAP:0.200,2hop_mAP:0.381\n",
      "mAP:0.287,1hop_mAP:0.420,2hop_mAP:0.564\n",
      "loss:4.358,acc:0.166,1hop_acc:0.222,2hop_acc:0.403,mAP:0.163,1hop_mAP:0.201,2hop_mAP:0.381\n",
      "mAP:0.292,1hop_mAP:0.419,2hop_mAP:0.560\n",
      "loss:4.366,acc:0.166,1hop_acc:0.222,2hop_acc:0.402,mAP:0.163,1hop_mAP:0.201,2hop_mAP:0.381\n",
      "mAP:0.296,1hop_mAP:0.419,2hop_mAP:0.561\n",
      "loss:4.370,acc:0.167,1hop_acc:0.223,2hop_acc:0.406,mAP:0.163,1hop_mAP:0.201,2hop_mAP:0.382\n",
      "mAP:0.300,1hop_mAP:0.415,2hop_mAP:0.561\n",
      "loss:4.374,acc:0.166,1hop_acc:0.222,2hop_acc:0.409,mAP:0.163,1hop_mAP:0.201,2hop_mAP:0.383\n",
      "mAP:0.302,1hop_mAP:0.415,2hop_mAP:0.559\n",
      "loss:4.381,acc:0.164,1hop_acc:0.218,2hop_acc:0.407,mAP:0.163,1hop_mAP:0.200,2hop_mAP:0.382\n",
      "mAP:0.307,1hop_mAP:0.414,2hop_mAP:0.564\n"
     ]
    }
   ],
   "source": [
    "from pmath import pair_wise_cos, pair_wise_eud, pair_wise_hyp\n",
    "from model import RegressNet\n",
    "import torch\n",
    "c, T, epochs, dist_func, eval_dist = .1,1, 20, pair_wise_hyp, pair_wise_cos\n",
    "torch.manual_seed(42)\n",
    "emb = emb.cuda()\n",
    "\n",
    "train_loader, val_loader = get_dataloader(Xtr, Xval, ytr, yval, emb, batch_size = 128)\n",
    "model = RegressNet(T, c, dist_func, eval_dist, train_loader, val_loader, emb, metric)\n",
    "model = model.cuda()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
    "model._train(optimizer, epochs, T, eval_interval = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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