hhhh.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-06-16T13:51:35.162970800Z",
     "start_time": "2023-06-16T13:51:31.874120900Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "from Tencode import PFORT_encode\n",
    "from torchvision.transforms import transforms\n",
    "import h5py\n",
    "modss=PFORT_encode(ATTlays=6, bhlay=9, imglay=5, dim=512, heads=8, inner_dim=512, out_dim=48, pos_emb_drop=0.1,\n",
    "                         mlpdropout=0.05, attdropout=0.05)\n",
    "# aaaa = dastset('映射.json', 'fix1.json', './i')\n",
    "modss=modss.load_from_checkpoint('./post_LN/V6-epoch06-6-240000.ckpt',ATTlays=6, bhlay=9, imglay=5, dim=512, heads=8, inner_dim=512, out_dim=48, pos_emb_drop=0.1,\n",
    "                         mlpdropout=0.05, attdropout=0.05)\n",
    "modss.eval().cuda()\n",
    "\n",
    "\n",
    "with h5py.File('V2_dataset_stage21.hdf5', 'r') as f:\n",
    "    # for i in f:\n",
    "    #     print(i)\n",
    "    aaa=f['34_266'][()]\n",
    "# transform1 = transforms.Compose([\n",
    "#             transforms.ToTensor(),  # range [0, 255] -> [0.0,1.0]\n",
    "#         ]\n",
    "#         )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 512, 8, 8])\n"
     ]
    },
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x1e2141b8d30>"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "with h5py.File('V2_dataset_stage21.hdf5', 'r') as f:\n",
    "    # for i in f:\n",
    "    #     print(i)\n",
    "    aaa=f['33_6'][()]\n",
    "print(torch.tensor(aaa).unsqueeze(0).shape)\n",
    "opop=modss.decodess(torch.tensor(aaa).cuda().unsqueeze(0)).squeeze(0).cpu().detach().numpy()\n",
    "plt.imshow(opop[0])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-16T14:06:09.346874Z",
     "start_time": "2023-06-16T14:06:09.018597500Z"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}