affordances.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "29bef37d",
   "metadata": {},
   "source": [
    "# Affordance Heatmaps\n",
    "\n",
    "This notebook visualizes the pick and place affordance predictions of a pre-trained `multi-language-conditioned` agent from the quickstart guide.\n",
    "\n",
    "### Setup\n",
    "\n",
    "- Set the root folder environment variable with `export CLIPORT_ROOT=<cliport_root>`\n",
    "- Complete the [quickstart guide](https://github.com/cliport/cliport#quickstart) in README.md\n",
    "- Generate `val` and `test` splits for the task you want to evaluate on by running `python cliport/demos.py n=10 mode=test task=stack-block-pyramid-seq-seen-colors`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ca5b47c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: CUDA_VISIBLE_DEVICES=5\n",
      "env: CLIPORT_ROOT='../'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "pybullet build time: Nov 28 2023 23:52:03\n"
     ]
    }
   ],
   "source": [
    "# set GPU\n",
    "%env CUDA_VISIBLE_DEVICES=5\n",
    "%env CLIPORT_ROOT='../'\n",
    "\n",
    "import os\n",
    "import sys\n",
    "import json\n",
    "\n",
    "import numpy as np\n",
    "from cliport import tasks\n",
    "from cliport import agents\n",
    "from cliport.utils import utils\n",
    "\n",
    "import torch\n",
    "import cv2\n",
    "from cliport.dataset import RavensDataset\n",
    "from cliport.environments.environment import Environment\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pybullet as p"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8bd6b5a6",
   "metadata": {},
   "source": [
    "### Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4d00e1a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_demos = 1000 # number training demonstrations used to train agent\n",
    "n_eval = 5 # number of evaluation instances\n",
    "mode = 'test' # val or test\n",
    "\n",
    "agent_name = 'cliport'\n",
    "model_task = 'multi-language-conditioned' # multi-task agent conditioned with language goals\n",
    "\n",
    "model_folder = './' # path to pre-trained checkpoint\n",
    "ckpt_name = 'steps=400000-val_loss=0.00014655.ckpt' # name of checkpoint to load\n",
    "\n",
    "draw_grasp_lines = False\n",
    "affordance_heatmap_scale = 30\n",
    "\n",
    "### Uncomment the task you want to evaluate on ###\n",
    "# eval_task = 'align-rope'\n",
    "# eval_task = 'assembling-kits-seq-seen-colors'\n",
    "#eval_task = 'assembling-kits-seq-unseen-colors'\n",
    "#eval_task = 'packing-boxes'\n",
    "# eval_task = 'packing-boxes-pairs-seen-colors'\n",
    "# eval_task = 'packing-boxes-pairs-unseen-colors'\n",
    "# eval_task = 'packing-seen-google-objects-seq'\n",
    "# eval_task = 'packing-unseen-google-objects-seq'\n",
    "# eval_task = 'packing-seen-google-objects-group'\n",
    "# eval_task = 'packing-unseen-google-objects-group'\n",
    "# eval_task = 'put-block-in-bowl-seen-colors'\n",
    "# eval_task = 'put-block-in-bowl-unseen-colors'\n",
    "eval_task = 'stack-block-pyramid-seq-unseen-colors-'\n",
    "# eval_task = 'stack-block-pyramid-seq-unseen-colors'\n",
    "# eval_task = 'separating-piles-seen-colors'\n",
    "# eval_task = 'separating-piles-unseen-colors'\n",
    "# eval_task = 'towers-of-hanoi-seq-seen-colors'\n",
    "# eval_task = 'towers-of-hanoi-seq-unseen-colors'"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c812da35",
   "metadata": {},
   "source": [
    "### Load Configs and Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0042f541",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/home/zhang/workspace/yinxu/cliport/cfg/train.yaml'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 18\u001b[0m\n\u001b[1;32m     15\u001b[0m vcfg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain_config\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m../cliport/cfg/train.yaml\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     16\u001b[0m vcfg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel_path\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvcfg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel_task\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m-\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvcfg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124magent\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m-n\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtrain_demos\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m-train/checkpoints/\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 18\u001b[0m tcfg \u001b[38;5;241m=\u001b[39m \u001b[43mutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_hydra_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvcfg\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtrain_config\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;66;03m# Load dataset\u001b[39;00m\n\u001b[1;32m     21\u001b[0m ds \u001b[38;5;241m=\u001b[39m RavensDataset(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(vcfg[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata_dir\u001b[39m\u001b[38;5;124m'\u001b[39m], \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvcfg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meval_task\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m-\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvcfg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m), \n\u001b[1;32m     22\u001b[0m                    tcfg, \n\u001b[1;32m     23\u001b[0m                    n_demos\u001b[38;5;241m=\u001b[39mn_eval,\n\u001b[1;32m     24\u001b[0m                    augment\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "File \u001b[0;32m~/workspace/yinxu/LoHo-Ravens/cliport/utils/utils.py:1290\u001b[0m, in \u001b[0;36mload_hydra_config\u001b[0;34m(config_path)\u001b[0m\n\u001b[1;32m   1289\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_hydra_config\u001b[39m(config_path):\n\u001b[0;32m-> 1290\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mOmegaConf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig_path\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/safe_llm/lib/python3.9/site-packages/omegaconf/omegaconf.py:189\u001b[0m, in \u001b[0;36mOmegaConf.load\u001b[0;34m(file_)\u001b[0m\n\u001b[1;32m    186\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_yaml_loader\n\u001b[1;32m    188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(file_, (\u001b[38;5;28mstr\u001b[39m, pathlib\u001b[38;5;241m.\u001b[39mPath)):\n\u001b[0;32m--> 189\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mabspath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile_\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m    190\u001b[0m         obj \u001b[38;5;241m=\u001b[39m yaml\u001b[38;5;241m.\u001b[39mload(f, Loader\u001b[38;5;241m=\u001b[39mget_yaml_loader())\n\u001b[1;32m    191\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(file_, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mread\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/home/zhang/workspace/yinxu/cliport/cfg/train.yaml'"
     ]
    }
   ],
   "source": [
    "root_dir = '../'\n",
    "assets_root = os.path.join(root_dir, 'cliport/environments/assets/')\n",
    "config_file = 'eval.yaml' \n",
    "\n",
    "vcfg = utils.load_hydra_config(os.path.join('/home/zhang/workspace/yinxu/original/cliport-batchify/cliport/cfg/eval.yaml'))\n",
    "vcfg['data_dir'] = '/mnt/bear1/users/zhangkang/yinxu/Workfolder/data/primitive/'\n",
    "vcfg['mode'] = mode\n",
    "\n",
    "vcfg['model_task'] = model_task\n",
    "vcfg['eval_task'] = eval_task\n",
    "vcfg['agent'] = agent_name\n",
    "\n",
    "# Model and training config paths\n",
    "model_path = os.path.join(root_dir, model_folder)\n",
    "vcfg['train_config'] = \"../cliport/cfg/train.yaml\"\n",
    "vcfg['model_path'] = f\"{model_path}/{vcfg['model_task']}-{vcfg['agent']}-n{train_demos}-train/checkpoints/\"\n",
    "\n",
    "tcfg = utils.load_hydra_config(vcfg['train_config'])\n",
    "\n",
    "# Load dataset\n",
    "ds = RavensDataset(os.path.join(vcfg['data_dir'], f'{vcfg[\"eval_task\"]}-{vcfg[\"mode\"]}'), \n",
    "                   tcfg, \n",
    "                   n_demos=n_eval,\n",
    "                   augment=False)\n",
    "\n",
    "eval_run = 0\n",
    "name = '{}-{}-{}-{}'.format(vcfg['eval_task'], vcfg['agent'], n_eval, eval_run)\n",
    "print(f'\\nEval ID: {name}\\n')\n",
    "\n",
    "# Initialize agent\n",
    "utils.set_seed(eval_run, torch=True)\n",
    "agent = agents.names[vcfg['agent']](name, tcfg, None, ds)\n",
    "\n",
    "# Load checkpoint\n",
    "ckpt_path = '/mnt/bear1/users/zhangkang/yinxu/Workfolder/exp/checkpoints/steps=07501.ckpt'\n",
    "print(f'\\nLoading checkpoint: {ckpt_path}')\n",
    "agent.load(ckpt_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e7b6f8a",
   "metadata": {},
   "source": [
    "###Simulate environmental perturbations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcf437cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_confusing_object(env,task,obj_id, obj_type):\n",
    "  __, __, obj_size = env.info[obj_id]\n",
    "  if obj_type==\"block\":\n",
    "    obj_template = 'box/box-template.urdf'\n",
    "    params={'DIM': obj_size}\n",
    "  elif obj_type==\"stand\":\n",
    "    obj_template = 'stacking/stand.urdf'\n",
    "    params={'DIM': obj_size}\n",
    "  else:\n",
    "    obj_size = task.get_random_size(0.05, 0.3, 0.05, 0.3, 0.05, 0.05)\n",
    "    #zone_pose = task.get_random_pose(env, zone_size)\n",
    "    obj_template = 'container/container-template.urdf'\n",
    "    half = np.float32(obj_size) / 2\n",
    "    params = {'DIM': obj_size, 'HALF': half}\n",
    "  \n",
    "  urdf = task.fill_template(obj_template, params)\n",
    "  addition_pose = task.get_random_pose(env, obj_size)\n",
    "  addition_id=env.add_object(urdf, addition_pose)\n",
    "  icolor = p.getVisualShapeData(obj_id)[0][-1]\n",
    "  p.changeVisualShape(addition_id, -1, rgbaColor=icolor)\n",
    "  os.remove(urdf)\n",
    "  return addition_id"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2a6832c0",
   "metadata": {},
   "source": [
    "### Spawn Environment"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0c138c4a",
   "metadata": {},
   "source": [
    "### Evaluate Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b86bb611",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5text argument:../cliport/environments/assets/\n",
      "int args: [\n",
      "\n",
      "Evaluation Instance: 1/5\n",
      "Step: 0 (12 max)\n",
      "Step: 1 (12 max)\n",
      "p0_pix [107 150]\n",
      "p0_theta 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_200368/371113307.py:121: DeprecationWarning: an integer is required (got type numpy.float64).  Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n",
      "  'p0': torch.LongTensor(pick).unsqueeze(0).cuda(),\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lang Goal: put the red block on the lightest brown block of the stand.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max steps reached. Task failed.\n",
      "\n",
      "Evaluation Instance: 2/5\n",
      "Step: 0 (12 max)\n",
      "Step: 1 (12 max)\n",
      "p0_pix [202  84]\n",
      "p0_theta 0.0\n",
      "Lang Goal: put the gray block on the lightest brown block of the stand.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max steps reached. Task failed.\n",
      "\n",
      "Evaluation Instance: 3/5\n",
      "Step: 0 (12 max)\n",
      "Step: 1 (12 max)\n",
      "p0_pix [199 147]\n",
      "p0_theta 0.0\n",
      "Lang Goal: put the brown block on the lightest brown block of the stand.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max steps reached. Task failed.\n",
      "\n",
      "Evaluation Instance: 4/5\n",
      "Step: 0 (12 max)\n",
      "Step: 1 (12 max)\n",
      "p0_pix [198 108]\n",
      "p0_theta 0.0\n",
      "Lang Goal: put the red block on the lightest brown block of the stand.\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[5], line 390\u001b[0m\n\u001b[1;32m    157\u001b[0m     \u001b[38;5;66;03m### add confusing objects and then visualize the affordance map\u001b[39;00m\n\u001b[1;32m    158\u001b[0m     \u001b[38;5;66;03m# obj_id=env.obj_ids['fixed'][0]\u001b[39;00m\n\u001b[1;32m    159\u001b[0m     \u001b[38;5;66;03m# #obj_id=env.obj_ids['rigid'][0]\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    385\u001b[0m     \u001b[38;5;66;03m# axs[5][0].imshow(pick_logits_disp_masked, alpha=0.75)\u001b[39;00m\n\u001b[1;32m    386\u001b[0m     \u001b[38;5;66;03m# axs[5][1].imshow(place_logits_disp_masked, cmap='viridis', alpha=0.75)\u001b[39;00m\n\u001b[1;32m    389\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLang Goal: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mstr\u001b[39m(info[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlang_goal\u001b[39m\u001b[38;5;124m'\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 390\u001b[0m     \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    391\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m    392\u001b[0m \u001b[38;5;66;03m# Act with the predicted actions\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/lohoravens/lib/python3.8/site-packages/matplotlib/pyplot.py:446\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    402\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    403\u001b[0m \u001b[38;5;124;03mDisplay all open figures.\u001b[39;00m\n\u001b[1;32m    404\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    443\u001b[0m \u001b[38;5;124;03mexplicitly there.\u001b[39;00m\n\u001b[1;32m    444\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    445\u001b[0m _warn_if_gui_out_of_main_thread()\n\u001b[0;32m--> 446\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_get_backend_mod\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/lohoravens/lib/python3.8/site-packages/matplotlib_inline/backend_inline.py:90\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     88\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     89\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m figure_manager \u001b[38;5;129;01min\u001b[39;00m Gcf\u001b[38;5;241m.\u001b[39mget_all_fig_managers():\n\u001b[0;32m---> 90\u001b[0m         \u001b[43mdisplay\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     91\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfigure_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     92\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_fetch_figure_metadata\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfigure_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     93\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     95\u001b[0m     show\u001b[38;5;241m.\u001b[39m_to_draw \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m~/miniconda3/envs/lohoravens/lib/python3.8/site-packages/IPython/core/display_functions.py:298\u001b[0m, in \u001b[0;36mdisplay\u001b[0;34m(include, exclude, metadata, transient, display_id, raw, clear, *objs, **kwargs)\u001b[0m\n\u001b[1;32m    296\u001b[0m     publish_display_data(data\u001b[38;5;241m=\u001b[39mobj, metadata\u001b[38;5;241m=\u001b[39mmetadata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    297\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 298\u001b[0m     format_dict, md_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minclude\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexclude\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    299\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m format_dict:\n\u001b[1;32m    300\u001b[0m         \u001b[38;5;66;03m# nothing to display (e.g. _ipython_display_ took over)\u001b[39;00m\n\u001b[1;32m    301\u001b[0m         \u001b[38;5;28;01mcontinue\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/lohoravens/lib/python3.8/site-packages/IPython/core/formatters.py:179\u001b[0m, in \u001b[0;36mDisplayFormatter.format\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    177\u001b[0m md \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    178\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 179\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mformatter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    180\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m    181\u001b[0m     \u001b[38;5;66;03m# FIXME: log the exception\u001b[39;00m\n\u001b[1;32m    182\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/lohoravens/lib/python3.8/site-packages/decorator.py:232\u001b[0m, in \u001b[0;36mdecorate.<locals>.fun\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    230\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kwsyntax:\n\u001b[1;32m    231\u001b[0m     args, kw \u001b[38;5;241m=\u001b[39m fix(args, kw, sig)\n\u001b[0;32m--> 232\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcaller\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mextras\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/lohoravens/lib/python3.8/site-packages/IPython/core/formatters.py:223\u001b[0m, in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    221\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"show traceback on failed format call\"\"\"\u001b[39;00m\n\u001b[1;32m    222\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 223\u001b[0m     r \u001b[38;5;241m=\u001b[39m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    224\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m:\n\u001b[1;32m    225\u001b[0m     \u001b[38;5;66;03m# don't warn on NotImplementedErrors\u001b[39;00m\n\u001b[1;32m    226\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_return(\u001b[38;5;28;01mNone\u001b[39;00m, args[\u001b[38;5;241m0\u001b[39m])\n",
      "File \u001b[0;32m~/miniconda3/envs/lohoravens/lib/python3.8/site-packages/IPython/core/formatters.py:340\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    338\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m    339\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 340\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    341\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m    342\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n",
      "File \u001b[0;32m~/miniconda3/envs/lohoravens/lib/python3.8/site-packages/IPython/core/pylabtools.py:152\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m    150\u001b[0m     FigureCanvasBase(fig)\n\u001b[0;32m--> 152\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    153\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m    154\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
      "File \u001b[0;32m~/miniconda3/envs/lohoravens/lib/python3.8/site-packages/matplotlib/backend_bases.py:2342\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2336\u001b[0m     renderer \u001b[38;5;241m=\u001b[39m _get_renderer(\n\u001b[1;32m   2337\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure,\n\u001b[1;32m   2338\u001b[0m         functools\u001b[38;5;241m.\u001b[39mpartial(\n\u001b[1;32m   2339\u001b[0m             print_method, orientation\u001b[38;5;241m=\u001b[39morientation)\n\u001b[1;32m   2340\u001b[0m     )\n\u001b[1;32m   2341\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_draw_disabled\u001b[39m\u001b[38;5;124m\"\u001b[39m, nullcontext)():\n\u001b[0;32m-> 2342\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2344\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[1;32m   2345\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[0;32m~/miniconda3/envs/lohoravens/lib/python3.8/site-packages/matplotlib/artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 95\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     96\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m     97\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n",
      "File \u001b[0;32m~/miniconda3/envs/lohoravens/lib/python3.8/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/envs/lohoravens/lib/python3.8/site-packages/matplotlib/figure.py:3175\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3172\u001b[0m         \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m   3174\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3175\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3176\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3178\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[1;32m   3179\u001b[0m     sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n",
      "File \u001b[0;32m~/miniconda3/envs/lohoravens/lib/python3.8/site-packages/matplotlib/image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 131\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    132\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    134\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m~/miniconda3/envs/lohoravens/lib/python3.8/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/envs/lohoravens/lib/python3.8/site-packages/matplotlib/axes/_base.py:3064\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3061\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artists_rasterized:\n\u001b[1;32m   3062\u001b[0m     _draw_rasterized(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, artists_rasterized, renderer)\n\u001b[0;32m-> 3064\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3065\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3067\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m   3068\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/lohoravens/lib/python3.8/site-packages/matplotlib/image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 131\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    132\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    134\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m~/miniconda3/envs/lohoravens/lib/python3.8/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     70\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/envs/lohoravens/lib/python3.8/site-packages/matplotlib/image.py:641\u001b[0m, in \u001b[0;36m_ImageBase.draw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m    639\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mdraw_image(gc, l, b, im, trans)\n\u001b[1;32m    640\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 641\u001b[0m     im, l, b, trans \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmake_image\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    642\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_image_magnification\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    643\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m im \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    644\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mdraw_image(gc, l, b, im)\n",
      "File \u001b[0;32m~/miniconda3/envs/lohoravens/lib/python3.8/site-packages/matplotlib/image.py:949\u001b[0m, in \u001b[0;36mAxesImage.make_image\u001b[0;34m(self, renderer, magnification, unsampled)\u001b[0m\n\u001b[1;32m    946\u001b[0m transformed_bbox \u001b[38;5;241m=\u001b[39m TransformedBbox(bbox, trans)\n\u001b[1;32m    947\u001b[0m clip \u001b[38;5;241m=\u001b[39m ((\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_clip_box() \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes\u001b[38;5;241m.\u001b[39mbbox) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_clip_on()\n\u001b[1;32m    948\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mbbox)\n\u001b[0;32m--> 949\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_image\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_A\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbbox\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtransformed_bbox\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclip\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    950\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mmagnification\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munsampled\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munsampled\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/lohoravens/lib/python3.8/site-packages/matplotlib/image.py:553\u001b[0m, in \u001b[0;36m_ImageBase._make_image\u001b[0;34m(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border)\u001b[0m\n\u001b[1;32m    551\u001b[0m     A \u001b[38;5;241m=\u001b[39m _rgb_to_rgba(A)\n\u001b[1;32m    552\u001b[0m alpha \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_scalar_alpha()\n\u001b[0;32m--> 553\u001b[0m output_alpha \u001b[38;5;241m=\u001b[39m \u001b[43m_resample\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# resample alpha channel\u001b[39;49;00m\n\u001b[1;32m    554\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mA\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout_shape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malpha\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    555\u001b[0m output \u001b[38;5;241m=\u001b[39m _resample(  \u001b[38;5;66;03m# resample rgb channels\u001b[39;00m\n\u001b[1;32m    556\u001b[0m     \u001b[38;5;28mself\u001b[39m, _rgb_to_rgba(A[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, :\u001b[38;5;241m3\u001b[39m]), out_shape, t, alpha\u001b[38;5;241m=\u001b[39malpha)\n\u001b[1;32m    557\u001b[0m output[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, \u001b[38;5;241m3\u001b[39m] \u001b[38;5;241m=\u001b[39m output_alpha  \u001b[38;5;66;03m# recombine rgb and alpha\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/lohoravens/lib/python3.8/site-packages/matplotlib/image.py:207\u001b[0m, in \u001b[0;36m_resample\u001b[0;34m(image_obj, data, out_shape, transform, resample, alpha)\u001b[0m\n\u001b[1;32m    205\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m resample \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    206\u001b[0m     resample \u001b[38;5;241m=\u001b[39m image_obj\u001b[38;5;241m.\u001b[39mget_resample()\n\u001b[0;32m--> 207\u001b[0m \u001b[43m_image\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtransform\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    208\u001b[0m \u001b[43m                \u001b[49m\u001b[43m_interpd_\u001b[49m\u001b[43m[\u001b[49m\u001b[43minterpolation\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    209\u001b[0m \u001b[43m                \u001b[49m\u001b[43mresample\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    210\u001b[0m \u001b[43m                \u001b[49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    211\u001b[0m \u001b[43m                \u001b[49m\u001b[43mimage_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_filternorm\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    212\u001b[0m \u001b[43m                \u001b[49m\u001b[43mimage_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_filterrad\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    213\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Initialize environment and task.\n",
    "env = Environment(\n",
    "    assets_root,\n",
    "    disp=False,\n",
    "    shared_memory=False,\n",
    "    hz=480,\n",
    "    record_cfg=False\n",
    ")\n",
    "\n",
    "episode = 0\n",
    "num_eval_instances = min(n_eval, ds.n_episodes)\n",
    "print(num_eval_instances)\n",
    "for i in range(num_eval_instances):\n",
    "    print(f'\\nEvaluation Instance: {i + 1}/{num_eval_instances}')\n",
    "    \n",
    "    # Load episode\n",
    "    episode, seed = ds.load(i)\n",
    "    goal = episode[-1]\n",
    "    total_reward = 0\n",
    "    np.random.seed(seed)\n",
    "\n",
    "    # Set task\n",
    "    task_name = vcfg['eval_task']\n",
    "    task = tasks.names[task_name]()\n",
    "    #print(task.obj_info)\n",
    "    task.mode = mode\n",
    "    \n",
    "    # Set environment\n",
    "    env.seed(seed)\n",
    "    env.set_task(task)\n",
    "    obs = env.reset()\n",
    "    info = env.info\n",
    "    reward = 0\n",
    "    \n",
    "    step = 0\n",
    "    done = False\n",
    "    \n",
    "    # Rollout\n",
    "    while (step <= task.max_steps) and not done:\n",
    "        print(f\"Step: {step} ({task.max_steps} max)\")\n",
    "        \n",
    "        # Get batch\n",
    "        if step == task.max_steps-1:\n",
    "            batch = ds.process_goal((obs, None, reward, info), perturb_params=None)\n",
    "        else:\n",
    "            batch = ds.process_sample((obs, None, reward, info), augment=False)\n",
    "\n",
    "\n",
    "        act = agent.act(obs, info, goal=None)\n",
    "        if step==1:\n",
    "            fig, axs = plt.subplots(2, 2, figsize=(20, 16))\n",
    "        \n",
    "            # Get color and depth inputs\n",
    "            img = batch['img']\n",
    "            # img = torch.from_numpy(img)\n",
    "            color = np.uint8(img.detach().cpu().numpy())[:,:,:3]\n",
    "            color = color.transpose(1,0,2)\n",
    "            depth = np.array(img.detach().cpu().numpy())[:,:,3]\n",
    "            depth = depth.transpose(1,0)\n",
    "            \n",
    "            # Display input color\n",
    "            axs[0,0].imshow(color)\n",
    "            axs[0,0].axes.xaxis.set_visible(False)\n",
    "            axs[0,0].axes.yaxis.set_visible(False)\n",
    "            axs[0,0].set_title('Input RGB')\n",
    "            \n",
    "            # Display input depth\n",
    "            axs[0,1].imshow(depth)\n",
    "            axs[0,1].axes.xaxis.set_visible(False)\n",
    "            axs[0,1].axes.yaxis.set_visible(False)        \n",
    "            axs[0,1].set_title('Input Depth')\n",
    "            \n",
    "            # Display predicted pick affordance\n",
    "            axs[1,0].imshow(color)\n",
    "            axs[1,0].axes.xaxis.set_visible(False)\n",
    "            axs[1,0].axes.yaxis.set_visible(False)\n",
    "            axs[1,0].set_title('Pick Affordance')\n",
    "            \n",
    "            # Display predicted place affordance\n",
    "            axs[1,1].imshow(color)\n",
    "            axs[1,1].axes.xaxis.set_visible(False)\n",
    "            axs[1,1].axes.yaxis.set_visible(False)\n",
    "            axs[1,1].set_title('Place Affordance')\n",
    "            \n",
    "            # Get action predictions\n",
    "            #print(task.block_info[step][1])\n",
    "            #info[\"lang_goal\"]=info[\"lang_goal\"].format(pick_color=task.block_info[step][1])\n",
    "            l = str(info['lang_goal'])\n",
    "            act = agent.act(obs, info, goal=None)\n",
    "            pick, place = act['pick'], act['place']\n",
    "            \n",
    "            # Visualize pick affordance\n",
    "            pick_inp = {'inp_img': batch['img'].unsqueeze(0).cuda(), 'lang_goal': [l]}\n",
    "            pick_conf = agent.attn_forward(pick_inp)\n",
    "            assert pick_conf.dim() == 4\n",
    "            pick_conf = pick_conf.permute(0, 2, 3, 1)\n",
    "            pick_conf = pick_conf.detach().cpu().numpy()\n",
    "            argmax = np.argmax(pick_conf.reshape(pick_conf.shape[0], -1), axis=1)\n",
    "            coord0, coord1, coord2 = np.unravel_index(argmax, shape=pick_conf.shape[1:])\n",
    "            p0_pix = np.stack([coord0, coord1], axis=1)\n",
    "            p0_theta = coord2 * (2 * np.pi / pick_conf.shape[3])\n",
    "            assert p0_pix.shape[0] == p0_theta.shape[0] == 1\n",
    "            p0_pix = p0_pix[0]\n",
    "            p0_theta = p0_theta[0]\n",
    "\n",
    "            print(\"p0_pix\", p0_pix)\n",
    "            print(\"p0_theta\", p0_theta)\n",
    "\n",
    "            pick_conf = pick_conf[0]\n",
    "            logits = pick_conf\n",
    "        \n",
    "            line_len = 30\n",
    "            pick0 = (pick[0] + line_len/2.0 * np.sin(p0_theta), pick[1] + line_len/2.0 * np.cos(p0_theta))\n",
    "            pick1 = (pick[0] - line_len/2.0 * np.sin(p0_theta), pick[1] - line_len/2.0 * np.cos(p0_theta))\n",
    "\n",
    "            if draw_grasp_lines:\n",
    "                axs[1,0].plot((pick1[0], pick0[0]), (pick1[1], pick0[1]), color='r', linewidth=1)\n",
    "            \n",
    "            # Visualize place affordance\n",
    "            place_inp = {'inp_img': batch['img'].unsqueeze(0).cuda(), \n",
    "                        'p0': torch.LongTensor(pick).unsqueeze(0).cuda(),\n",
    "                        'lang_goal': [l]}\n",
    "            place_conf = agent.trans_forward(place_inp)\n",
    "\n",
    "            assert place_conf.dim() == 4\n",
    "            place_conf = place_conf.permute(0, 2, 3, 1)\n",
    "            place_conf = place_conf.detach().cpu().numpy()\n",
    "            argmax = np.argmax(place_conf.reshape(place_conf.shape[0], -1), axis=1)\n",
    "            coord0, coord1, coord2 = np.unravel_index(argmax, shape=place_conf.shape[1:])\n",
    "            p1_pix = np.stack([coord0, coord1], axis=1)\n",
    "            p1_theta = coord2 * (2 * np.pi / place_conf.shape[3]) * -1.0\n",
    "            assert p1_pix.shape[0] == p1_theta.shape[0] == 1\n",
    "            p1_pix = p1_pix[0]\n",
    "            p1_theta = p1_theta[0]\n",
    "\n",
    "            place_conf = place_conf[0]\n",
    "            #print(\"p1_pix\", p1_pix)\n",
    "            #print(\"p1_theta\", p1_theta)\n",
    "            \n",
    "            line_len = 30\n",
    "            place0 = (place[0] + line_len/2.0 * np.sin(p1_theta), place[1] + line_len/2.0 * np.cos(p1_theta))\n",
    "            place1 = (place[0] - line_len/2.0 * np.sin(p1_theta), place[1] - line_len/2.0 * np.cos(p1_theta))\n",
    "\n",
    "            if draw_grasp_lines:\n",
    "                axs[1,1].plot((place1[0], place0[0]), (place1[1], place0[1]), color='g', linewidth=1)\n",
    "\n",
    "            # Overlay affordances on RGB input\n",
    "            pick_logits_disp = np.uint8(logits * 255 * affordance_heatmap_scale).transpose(1,0,2)\n",
    "            place_logits_disp = np.uint8(np.sum(place_conf, axis=2)[:,:,None] * 255 * affordance_heatmap_scale).transpose(1,0,2)    \n",
    "\n",
    "            pick_logits_disp_masked = np.ma.masked_where(pick_logits_disp < 0, pick_logits_disp)\n",
    "            place_logits_disp_masked = np.ma.masked_where(place_logits_disp < 0, place_logits_disp)\n",
    "\n",
    "            axs[1][0].imshow(pick_logits_disp_masked, alpha=0.75)\n",
    "            axs[1][1].imshow(place_logits_disp_masked, cmap='viridis', alpha=0.75)\n",
    "            \n",
    "            ### add confusing objects and then visualize the affordance map\n",
    "            # obj_id=env.obj_ids['fixed'][0]\n",
    "            # #obj_id=env.obj_ids['rigid'][0]\n",
    "            # #add_confusing_object(env,task,obj_id,obj_type=\"block\")\n",
    "            # addition_id=add_confusing_object(env,task,obj_id,obj_type=\"container\")\n",
    "            \n",
    "            # obs, __, __, __= env.step()\n",
    "            # batch = ds.process_sample((obs, None, reward, info), augment=False)\n",
    "            # # Get color and depth inputs\n",
    "            # img = batch['img']\n",
    "            # # img = torch.from_numpy(img)\n",
    "            # color = np.uint8(img.detach().cpu().numpy())[:,:,:3]\n",
    "            # color = color.transpose(1,0,2)\n",
    "            # depth = np.array(img.detach().cpu().numpy())[:,:,3]\n",
    "            # depth = depth.transpose(1,0)\n",
    "            \n",
    "            # # Display input color\n",
    "            # axs[2,0].imshow(color)\n",
    "            # axs[2,0].axes.xaxis.set_visible(False)\n",
    "            # axs[2,0].axes.yaxis.set_visible(False)\n",
    "            # axs[2,0].set_title('Input RGB after perturbation')\n",
    "            \n",
    "            # # Display input depth\n",
    "            # axs[2,1].imshow(depth)\n",
    "            # axs[2,1].axes.xaxis.set_visible(False)\n",
    "            # axs[2,1].axes.yaxis.set_visible(False)        \n",
    "            # axs[2,1].set_title('Input Depth after perturbation')\n",
    "            \n",
    "            \n",
    "            # # Display predicted pick affordance\n",
    "            # axs[3,0].imshow(color)\n",
    "            # axs[3,0].axes.xaxis.set_visible(False)\n",
    "            # axs[3,0].axes.yaxis.set_visible(False)\n",
    "            # axs[3,0].set_title('Pick Affordance')\n",
    "            \n",
    "            # # Display predicted place affordance\n",
    "            # axs[3,1].imshow(color)\n",
    "            # axs[3,1].axes.xaxis.set_visible(False)\n",
    "            # axs[3,1].axes.yaxis.set_visible(False)\n",
    "            # axs[3,1].set_title('Place Affordance')\n",
    "            \n",
    "            \n",
    "            \n",
    "\n",
    "            \n",
    "            # act = agent.act(obs, info, goal=None)\n",
    "            # pick, place = act['pick'], act['place']\n",
    "            \n",
    "            # # Visualize pick affordance\n",
    "            # pick_inp = {'inp_img': batch['img'].unsqueeze(0).cuda(), 'lang_goal': [l]}\n",
    "            # pick_conf = agent.attn_forward(pick_inp)\n",
    "            # assert pick_conf.dim() == 4\n",
    "            # pick_conf = pick_conf.permute(0, 2, 3, 1)\n",
    "            # pick_conf = pick_conf.detach().cpu().numpy()\n",
    "            # argmax = np.argmax(pick_conf.reshape(pick_conf.shape[0], -1), axis=1)\n",
    "            # coord0, coord1, coord2 = np.unravel_index(argmax, shape=pick_conf.shape[1:])\n",
    "            # p0_pix = np.stack([coord0, coord1], axis=1)\n",
    "            # p0_theta = coord2 * (2 * np.pi / pick_conf.shape[3])\n",
    "            # assert p0_pix.shape[0] == p0_theta.shape[0] == 1\n",
    "            # p0_pix = p0_pix[0]\n",
    "            # p0_theta = p0_theta[0]\n",
    "\n",
    "            # print(\"p0_pix\", p0_pix)\n",
    "            # print(\"p0_theta\", p0_theta)\n",
    "\n",
    "            # pick_conf = pick_conf[0]\n",
    "            # logits = pick_conf\n",
    "        \n",
    "            # line_len = 30\n",
    "            # pick0 = (pick[0] + line_len/2.0 * np.sin(p0_theta), pick[1] + line_len/2.0 * np.cos(p0_theta))\n",
    "            # pick1 = (pick[0] - line_len/2.0 * np.sin(p0_theta), pick[1] - line_len/2.0 * np.cos(p0_theta))\n",
    "\n",
    "            # if draw_grasp_lines:\n",
    "            #     axs[3,0].plot((pick1[0], pick0[0]), (pick1[1], pick0[1]), color='r', linewidth=1)\n",
    "            \n",
    "            # # Visualize place affordance\n",
    "            # place_inp = {'inp_img': batch['img'].unsqueeze(0).cuda(), \n",
    "            #             'p0': torch.LongTensor(pick).unsqueeze(0).cuda(),\n",
    "            #             'lang_goal': [l]}\n",
    "            # place_conf = agent.trans_forward(place_inp)\n",
    "\n",
    "            # assert place_conf.dim() == 4\n",
    "            # place_conf = place_conf.permute(0, 2, 3, 1)\n",
    "            # place_conf = place_conf.detach().cpu().numpy()\n",
    "            # argmax = np.argmax(place_conf.reshape(place_conf.shape[0], -1), axis=1)\n",
    "            # coord0, coord1, coord2 = np.unravel_index(argmax, shape=place_conf.shape[1:])\n",
    "            # p1_pix = np.stack([coord0, coord1], axis=1)\n",
    "            # p1_theta = coord2 * (2 * np.pi / place_conf.shape[3]) * -1.0\n",
    "            # assert p1_pix.shape[0] == p1_theta.shape[0] == 1\n",
    "            # p1_pix = p1_pix[0]\n",
    "            # p1_theta = p1_theta[0]\n",
    "\n",
    "            # place_conf = place_conf[0]\n",
    "            # #print(\"p1_pix\", p1_pix)\n",
    "            # #print(\"p1_theta\", p1_theta)\n",
    "            \n",
    "            # line_len = 30\n",
    "            # place0 = (place[0] + line_len/2.0 * np.sin(p1_theta), place[1] + line_len/2.0 * np.cos(p1_theta))\n",
    "            # place1 = (place[0] - line_len/2.0 * np.sin(p1_theta), place[1] - line_len/2.0 * np.cos(p1_theta))\n",
    "\n",
    "            # if draw_grasp_lines:\n",
    "            #     axs[3,1].plot((place1[0], place0[0]), (place1[1], place0[1]), color='g', linewidth=1)\n",
    "\n",
    "            # # Overlay affordances on RGB input\n",
    "            # pick_logits_disp = np.uint8(logits * 255 * affordance_heatmap_scale).transpose(1,0,2)\n",
    "            # place_logits_disp = np.uint8(np.sum(place_conf, axis=2)[:,:,None] * 255 * affordance_heatmap_scale).transpose(1,0,2)    \n",
    "\n",
    "            # pick_logits_disp_masked = np.ma.masked_where(pick_logits_disp < 0, pick_logits_disp)\n",
    "            # place_logits_disp_masked = np.ma.masked_where(place_logits_disp < 0, place_logits_disp)\n",
    "\n",
    "            # axs[3][0].imshow(pick_logits_disp_masked, alpha=0.75)\n",
    "            # axs[3][1].imshow(place_logits_disp_masked, cmap='viridis', alpha=0.75)\n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            \n",
    "            # ### add another perturbation\n",
    "            # env.remove_object(addition_id)\n",
    "            # obj_id=task.block_info[step][0]\n",
    "            # #obj_id=env.obj_ids['rigid'][0]\n",
    "            # #add_confusing_object(env,task,obj_id,obj_type=\"block\")\n",
    "            # add_confusing_object(env,task,obj_id,obj_type=\"block\")\n",
    "            \n",
    "            # obs, __, __, __= env.step()\n",
    "            # batch = ds.process_sample((obs, None, reward, info), augment=False)\n",
    "            # # Get color and depth inputs\n",
    "            # img = batch['img']\n",
    "            # # img = torch.from_numpy(img)\n",
    "            # color = np.uint8(img.detach().cpu().numpy())[:,:,:3]\n",
    "            # color = color.transpose(1,0,2)\n",
    "            # depth = np.array(img.detach().cpu().numpy())[:,:,3]\n",
    "            # depth = depth.transpose(1,0)\n",
    "            \n",
    "            # # Display input color\n",
    "            # axs[4,0].imshow(color)\n",
    "            # axs[4,0].axes.xaxis.set_visible(False)\n",
    "            # axs[4,0].axes.yaxis.set_visible(False)\n",
    "            # axs[4,0].set_title('Input RGB after perturbation')\n",
    "            \n",
    "            # # Display input depth\n",
    "            # axs[4,1].imshow(depth)\n",
    "            # axs[4,1].axes.xaxis.set_visible(False)\n",
    "            # axs[4,1].axes.yaxis.set_visible(False)        \n",
    "            # axs[4,1].set_title('Input Depth after perturbation')\n",
    "            \n",
    "            \n",
    "            # # Display predicted pick affordance\n",
    "            # axs[5,0].imshow(color)\n",
    "            # axs[5,0].axes.xaxis.set_visible(False)\n",
    "            # axs[5,0].axes.yaxis.set_visible(False)\n",
    "            # axs[5,0].set_title('Pick Affordance')\n",
    "            \n",
    "            # # Display predicted place affordance\n",
    "            # axs[5,1].imshow(color)\n",
    "            # axs[5,1].axes.xaxis.set_visible(False)\n",
    "            # axs[5,1].axes.yaxis.set_visible(False)\n",
    "            # axs[5,1].set_title('Place Affordance')\n",
    "            \n",
    "            \n",
    "            \n",
    "\n",
    "            \n",
    "            # act = agent.act(obs, info, goal=None)\n",
    "            # pick, place = act['pick'], act['place']\n",
    "            \n",
    "            # # Visualize pick affordance\n",
    "            # pick_inp = {'inp_img': batch['img'].unsqueeze(0).cuda(), 'lang_goal': [l]}\n",
    "            # pick_conf = agent.attn_forward(pick_inp)\n",
    "            # assert pick_conf.dim() == 4\n",
    "            # pick_conf = pick_conf.permute(0, 2, 3, 1)\n",
    "            # pick_conf = pick_conf.detach().cpu().numpy()\n",
    "            # argmax = np.argmax(pick_conf.reshape(pick_conf.shape[0], -1), axis=1)\n",
    "            # coord0, coord1, coord2 = np.unravel_index(argmax, shape=pick_conf.shape[1:])\n",
    "            # p0_pix = np.stack([coord0, coord1], axis=1)\n",
    "            # p0_theta = coord2 * (2 * np.pi / pick_conf.shape[3])\n",
    "            # assert p0_pix.shape[0] == p0_theta.shape[0] == 1\n",
    "            # p0_pix = p0_pix[0]\n",
    "            # p0_theta = p0_theta[0]\n",
    "\n",
    "            # print(\"p0_pix\", p0_pix)\n",
    "            # print(\"p0_theta\", p0_theta)\n",
    "\n",
    "            # pick_conf = pick_conf[0]\n",
    "            # logits = pick_conf\n",
    "        \n",
    "            # line_len = 30\n",
    "            # pick0 = (pick[0] + line_len/2.0 * np.sin(p0_theta), pick[1] + line_len/2.0 * np.cos(p0_theta))\n",
    "            # pick1 = (pick[0] - line_len/2.0 * np.sin(p0_theta), pick[1] - line_len/2.0 * np.cos(p0_theta))\n",
    "\n",
    "            # if draw_grasp_lines:\n",
    "            #     axs[3,0].plot((pick1[0], pick0[0]), (pick1[1], pick0[1]), color='r', linewidth=1)\n",
    "            \n",
    "            # # Visualize place affordance\n",
    "            # place_inp = {'inp_img': batch['img'].unsqueeze(0).cuda(), \n",
    "            #             'p0': torch.LongTensor(pick).unsqueeze(0).cuda(),\n",
    "            #             'lang_goal': [l]}\n",
    "            # place_conf = agent.trans_forward(place_inp)\n",
    "\n",
    "            # assert place_conf.dim() == 4\n",
    "            # place_conf = place_conf.permute(0, 2, 3, 1)\n",
    "            # place_conf = place_conf.detach().cpu().numpy()\n",
    "            # argmax = np.argmax(place_conf.reshape(place_conf.shape[0], -1), axis=1)\n",
    "            # coord0, coord1, coord2 = np.unravel_index(argmax, shape=place_conf.shape[1:])\n",
    "            # p1_pix = np.stack([coord0, coord1], axis=1)\n",
    "            # p1_theta = coord2 * (2 * np.pi / place_conf.shape[3]) * -1.0\n",
    "            # assert p1_pix.shape[0] == p1_theta.shape[0] == 1\n",
    "            # p1_pix = p1_pix[0]\n",
    "            # p1_theta = p1_theta[0]\n",
    "\n",
    "            # place_conf = place_conf[0]\n",
    "            # #print(\"p1_pix\", p1_pix)\n",
    "            # #print(\"p1_theta\", p1_theta)\n",
    "            \n",
    "            # line_len = 30\n",
    "            # place0 = (place[0] + line_len/2.0 * np.sin(p1_theta), place[1] + line_len/2.0 * np.cos(p1_theta))\n",
    "            # place1 = (place[0] - line_len/2.0 * np.sin(p1_theta), place[1] - line_len/2.0 * np.cos(p1_theta))\n",
    "\n",
    "            # if draw_grasp_lines:\n",
    "            #     axs[5,1].plot((place1[0], place0[0]), (place1[1], place0[1]), color='g', linewidth=1)\n",
    "\n",
    "            # # Overlay affordances on RGB input\n",
    "            # pick_logits_disp = np.uint8(logits * 255 * affordance_heatmap_scale).transpose(1,0,2)\n",
    "            # place_logits_disp = np.uint8(np.sum(place_conf, axis=2)[:,:,None] * 255 * affordance_heatmap_scale).transpose(1,0,2)    \n",
    "\n",
    "            # pick_logits_disp_masked = np.ma.masked_where(pick_logits_disp < 0, pick_logits_disp)\n",
    "            # place_logits_disp_masked = np.ma.masked_where(place_logits_disp < 0, place_logits_disp)\n",
    "\n",
    "            # axs[5][0].imshow(pick_logits_disp_masked, alpha=0.75)\n",
    "            # axs[5][1].imshow(place_logits_disp_masked, cmap='viridis', alpha=0.75)\n",
    "            \n",
    "            \n",
    "            print(f\"Lang Goal: {str(info['lang_goal'])}\")\n",
    "            plt.show()\n",
    "            break\n",
    "        # Act with the predicted actions\n",
    "        obs, reward, done, info = env.step(act)\n",
    "        step += 1\n",
    "        \n",
    "    if done:\n",
    "        print(\"Done. Success.\")\n",
    "    else:\n",
    "        print(\"Max steps reached. Task failed.\")\n"
   ]
  }
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