jupyter_test.ipynb

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   "source": [
    "import os\n",
    "import numpy as np\n",
    "\n",
    "import torch\n",
    "\n",
    "from torch.autograd import Variable\n",
    "\n",
    "import import_ipynb\n",
    "from prednet import PredNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "SET_NAME = 'Set9'\n",
    "batch_size = 1\n",
    "A_channels = (3, 48, 96, 192)\n",
    "R_channels = (3, 48, 96, 192)\n",
    "DATA_DIR = 'D:/WS/PredNet/Result/{}/Original'.format(SET_NAME)"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "model = PredNet(R_channels, A_channels, output_mode='out_all')\n",
    "model.load_state_dict(torch.load('models/training_0020.pt', map_location=torch.device('cpu')))\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    print(\"Using GPU.\")\n",
    "    model.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save np.load\n",
    "np_load_old = np.load\n",
    "\n",
    "# modify the default parameters of np.load\n",
    "np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "## KITTI\n",
    "\n",
    "allinputs = []\n",
    "for i in [0,2,4,6,8]:\n",
    "    inputfn = os.path.join(DATA_DIR, '{:010d}.npy'.format(i))\n",
    "    inputarray = np.load(inputfn).transpose(2,0,1).tolist()\n",
    "    allinputs.append(inputarray)\n",
    "allinputs = np.array([allinputs])\n",
    "print(allinputs.shape)\n",
    "\n",
    "inputs = torch.from_numpy(allinputs)\n",
    "inputs = Variable(inputs)\n",
    "nt = inputs.shape[1]\n",
    "\n",
    "ALL = model(inputs)"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "# ## DOT\n",
    "# \n",
    "# inputfn = os.path.join(DATA_DIR, '{}.npy'.format(SET_NAME))\n",
    "# inputarray = np.load(inputfn)\n",
    "# \n",
    "# inputs = inputarray[0]\n",
    "# inputs = np.transpose(inputs,(0,3,1,2))\n",
    "# inputs = np.array([inputs])\n",
    "# \n",
    "# inputs = torch.from_numpy(inputs)\n",
    "# inputs = Variable(inputs)\n",
    "# nt = inputs.shape[1]\n",
    "# \n",
    "# ALL = model(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "namelist = [\"A\", \"Ahat\", \"E\"]\n",
    "for i in range(len(ALL)-1):\n",
    "    for t in range(5):\n",
    "        for l in range(4):\n",
    "            org = ALL[i]\n",
    "            orgLayer = org[t*4+l][0]\n",
    "            fn = \"{}{}_t{}.npy\".format(namelist[i],l,t)\n",
    "            orgdir = \"0711res/kitti/{}\".format(fn)\n",
    "            np.save(orgdir, orgLayer)\n",
    "R = ALL[len(ALL)-1]\n",
    "for t in range(5):\n",
    "    for l in range(4):\n",
    "        orgR = R[t*4+l][0]\n",
    "        fn = \"R{}_t{}.npy\".format((3-l),t)\n",
    "        Rdir = \"0711res/kitti/{}\".format(fn)\n",
    "        np.save(Rdir,orgR)"
   ]
  }
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