dlp_tutorial.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "369899b7",
      "metadata": {
        "id": "369899b7"
      },
      "source": [
        "# <img src=\"https://img.icons8.com/fluency/96/000000/particles.png\" style=\"height:50px;display:inline\"> Deep Latent Particles - Tutorial\n",
        "---\n",
        "\n",
        "Tal Daniel\n",
        "\n",
        "\n",
        "\n",
        "<center>\n",
        "    <a href=\"https://colab.research.google.com/github/taldatech/deep-latent-particles-pytorch/blob/main/dlp_tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
        "</center>\n",
        "\n",
        "* Paper: [**Unsupervised Image Representation Learning with Deep Latent Particles**, Tal Daniel and Aviv Tamar, ICML 2022](https://arxiv.org/abs/2205.15821)\n",
        "* GitHub: <a href=\"https://github.com/taldatech/deep-latent-particles-pytorch\">deep-latent-particles-pytorch</a>\n",
        "* Wepage: <a href=\"https://taldatech.github.io/deep-latent-particles-web/\">ICML 2022 - Deep Latent Particles</a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "65753934",
      "metadata": {
        "id": "65753934"
      },
      "source": [
        "<img src=\"https://raw.githubusercontent.com/taldatech/deep-latent-particles-web/main/assets/clevrer_manip_1.gif\" style=\"height:250px\">"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e73bfcc4",
      "metadata": {
        "id": "e73bfcc4"
      },
      "source": [
        "### <img src=\"https://img.icons8.com/color/96/000000/loading.png\" style=\"height:50px;display:inline\"> Running Instructions\n",
        "---\n",
        "* This Jupyter Notebook can be opened locally with Anaconda, or online via Google Colab.\n",
        "* To run online, go to https://colab.research.google.com/ and drag-and-drop the `dlp_tutorial.ipynb` file.\n",
        "    * On Colab, note the \"directory\" icon on the left, logs, figures and checkpoints are saved in this directory.\n",
        "* To run the training on the image dataset, it is better to have a GPU. In Google Cola select `Runtime->Change runtime type->GPU`."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7558396b",
      "metadata": {
        "id": "7558396b"
      },
      "source": [
        "### <img src=\"https://img.icons8.com/color/96/000000/software-installer.png\" style=\"height:50px;display:inline\"> Requirements\n",
        "---\n",
        "* If running locally, make sure to set up the environment, preferably with <a href=\"https://anaconda.org/\">Anaconda</a> by running `conda env create -f environment17/19.yml` or with `pip` by running `pip install - r requirements17/19.txt`.\n",
        "* If running online on <a href=\"https://colab.research.google.com\">Google Colab</a>, run the following cell to clone the repository."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "20fd2fb9",
      "metadata": {
        "id": "20fd2fb9",
        "outputId": "6efb04e6-d678-40ed-8464-429e7e7657f6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'deep-latent-particles-pytorch'...\n",
            "remote: Enumerating objects: 155, done.\u001b[K\n",
            "remote: Counting objects: 100% (155/155), done.\u001b[K\n",
            "remote: Compressing objects: 100% (106/106), done.\u001b[K\n",
            "remote: Total 155 (delta 67), reused 127 (delta 43), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (155/155), 18.21 MiB | 9.09 MiB/s, done.\n",
            "Resolving deltas: 100% (67/67), done.\n"
          ]
        }
      ],
      "source": [
        "# only run this cell if running on Google Colab\n",
        "!git clone https://github.com/taldatech/deep-latent-particles-pytorch.git\n",
        "%cd deep-latent-particles-pytorch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "78b8e5d3",
      "metadata": {
        "id": "78b8e5d3"
      },
      "outputs": [],
      "source": [
        "# only run this cell if running on Google Colab, Colab struggles with installing torch-geometric stuff\n",
        "import os\n",
        "import torch\n",
        "os.environ['TORCH'] = torch.__version__\n",
        "print(torch.__version__)\n",
        "\n",
        "!pip install -q torch-scatter -f https://data.pyg.org/whl/torch-${TORCH}.html\n",
        "!pip install -q torch-sparse -f https://data.pyg.org/whl/torch-${TORCH}.html\n",
        "!pip install -q torch-cluster -f https://data.pyg.org/whl/torch-${TORCH}.html\n",
        "!pip install -q git+https://github.com/pyg-team/pytorch_geometric.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# if opencv version is too high (>3.4.8.29) run this cell on Colab\n",
        "!pip install opencv-python==3.4.8.29"
      ],
      "metadata": {
        "id": "lcqUWxhLgA2T"
      },
      "id": "lcqUWxhLgA2T",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "2bef333e",
      "metadata": {
        "id": "2bef333e"
      },
      "source": [
        "### <img src=\"https://img.icons8.com/bubbles/50/000000/checklist.png\" style=\"height:50px;display:inline\"> Agenda\n",
        "---\n",
        "* [Introduction](#-Introduction)\n",
        "* [Random Shapes Dataset](#-Random-Shapes-Dataset)\n",
        "* [The Model](#-The-Model)\n",
        "* [Training and Optimization](#-Training-and-Optimization)\n",
        "* [Visualization and Manipulation](#Visualization-and-Manipulation)\n",
        "* [Where to Next?](#--Where-to-Next?)\n",
        "* [Credits](#-Credits)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4a50ceeb",
      "metadata": {
        "id": "4a50ceeb"
      },
      "outputs": [],
      "source": [
        "# imports\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm.auto import tqdm\n",
        "# torch\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import DataLoader\n",
        "import torchvision.utils as vutils\n",
        "import torch.optim as optim\n",
        "# model\n",
        "from models import KeyPointVAE\n",
        "from utils.util_func import *\n",
        "from utils.loss_functions import ChamferLossKL, calc_kl, calc_reconstruction_loss, VGGDistance\n",
        "# datasets\n",
        "from datasets.shapes_ds import generate_shape_dataset_torch"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7b30b711",
      "metadata": {
        "id": "7b30b711"
      },
      "source": [
        "### <img src=\"https://img.icons8.com/bubbles/50/000000/mention.png\" style=\"height:50px;display:inline\"> Introduction\n",
        "---\n",
        "* In this tutorial, we will give a brief overview of the deep latent particles (DLP) framework. \n",
        "* DLP is a new representation of visual data that disentangles object position from appearance. \n",
        "* DLP decomposes the visual input into low-dimensional latent ``particles'', where each particle is described by its spatial location ($z_p$) and features ($z_{\\alpha}$) of its surrounding region. $$ \\{(z_p^i, z_{\\alpha}^i) \\}_{i=1}^K $$\n",
        "* DLP is trained with a variational autoencoder (VAE) objective with a special prior for particle positions based on a spatial-softmax architecture, and a modification of the evidence lower bound (ELBO) loss inspired by the Chamfer distance between particles.\n",
        "* DLP comes in two flavors depending on the scene type: **(1) Masked model** and **(2) Object-based model**.\n",
        "* **Masked model**: designed for non-object scenes (e.g., faces from CelebA), PointNet++ and Gaussian maps model the local regions around the particles, and the rest (e.g., the background) is propagated from the encdoer ($\\Phi_{bypass}$). \n",
        "* **Object-based model**: designed for object-based scenes (e.g., CLEVRER), PointNet++ models the global regions (e.g., the background) and Gaussian maps (optionally) and a separate Glimpse decoder model the objects and their masks.\n",
        "* In this tutorial, *we will focus on the object-based model*."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "78de7d4b",
      "metadata": {
        "id": "78de7d4b"
      },
      "source": [
        "### <img src=\"https://img.icons8.com/external-icongeek26-linear-colour-icongeek26/64/000000/external-shapes-graphic-design-icongeek26-linear-colour-icongeek26.png\" style=\"height:50px;display:inline\"> Random Shapes Dataset\n",
        "---\n",
        "* First, we will take a look at data for the tutorial: images of random shapes of different sizes and colors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8451c3c6",
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "47d7734568ac4865914d344fa5109269"
          ]
        },
        "id": "8451c3c6",
        "outputId": "c97cde9e-5ef1-499d-99a2-c4a215ef959a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "47d7734568ac4865914d344fa5109269",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "sample_images: <torch.utils.data.dataset.TensorDataset object at 0x00000190288A3F88>\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sample_images = generate_shape_dataset_torch(img_size=64, num_images=5)  # [5, 3, 64, 64]\n",
        "print(f'sample_images: {sample_images}')\n",
        "# plot\n",
        "fig = plt.figure(figsize=(10, 10))\n",
        "for i in range(len(sample_images)):\n",
        "    ax = fig.add_subplot(1, len(sample_images), i + 1)\n",
        "    ax.imshow(sample_images[i][0].permute(1, 2, 0).data.cpu())\n",
        "    ax.set_axis_off()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bdb28d7a",
      "metadata": {
        "id": "bdb28d7a"
      },
      "source": [
        "### <img src=\"https://img.icons8.com/external-flaticons-flat-flat-icons/64/000000/external-neural-network-the-future-flaticons-flat-flat-icons.png\" style=\"height:50px;display:inline\"> The Model\n",
        "---\n",
        "* The model has 3 main components: encoder network, prior network, and decoder network(s)."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6b33e3f3",
      "metadata": {
        "id": "6b33e3f3"
      },
      "source": [
        "#### Encoder\n",
        "---\n",
        "<img src=\"https://raw.githubusercontent.com/taldatech/deep-latent-particles-web/main/assets/dlp_encoder.gif\" style=\"height:250px\">"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "01c36c26",
      "metadata": {
        "id": "01c36c26"
      },
      "source": [
        "The encoder is composed of two components:\n",
        "1. **Position encoder**: outputs keypoints -- the spatial location $z_p = (x,y)$ of interesting areas, where $(x,y)$ are the coordinates of pixels.\n",
        "2. **Appearance encoder**: extracts patches (or glimpses) of pre-determined size centered around $z_p$ and encodes them to latent variables $z_{\\alpha}$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7f2dc96d",
      "metadata": {
        "id": "7f2dc96d"
      },
      "outputs": [],
      "source": [
        "# PSEUDOCODE - DO NOT RUN\n",
        "# posterior\n",
        "enc_out = self.encode(x, return_heatmap=True, mask=mask)  # position encoder\n",
        "mu, logvar, kp_heatmap, mu_features, logvar_features, obj_on, _ = enc_out\n",
        "# obj_on is a transperency/presence variable, valeus between [0, 1]\n",
        "# kp_heatmap is a low dimension featuer map, output of the cnn - used to extract visual features later\n",
        "# mu/logvar_features = None [placeholders]\n",
        "# sample\n",
        "z = reparameterize(mu, logvar)\n",
        "# convert [x, y] to gaussian maps (gmaps) -- will be used in the decoder\n",
        "gmap_1_fg = self.to_gauss_map(z[:, :-1], self.features_dim, self.features_dim)\n",
        "# convert gmaps to binary mask -- to cover up parts of the visual feature maps\n",
        "fg_masks_sep = get_kp_mask_from_gmap(gmap_1_fg, threshold=self.mask_threshold,\n",
        "                                     binary=True, elementwise=True).detach()\n",
        "fg_masks = fg_masks_sep.sum(1, keepdim=True).clamp(0, 1)\n",
        "bg_masks = 1 - fg_masks\n",
        "masks_sep = torch.cat([fg_masks_sep, bg_masks], dim=1)\n",
        "# extract visual features around the particles\n",
        "feat_source = x if self.use_object_dec else kp_heatmap.detach()\n",
        "# feat_source is the source from which we extract regions (x is the original image)\n",
        "obj_enc_out = self.encode_object_features_sep(feat_source, z[:, :-1], kp_heatmap.detach(), masks_sep.detach())\n",
        "mu_features, logvar_features, cropped_objects = obj_enc_out[0], obj_enc_out[1], obj_enc_out[2]\n",
        "# note that the last particle is reserved for the bg and is located at (0, 0) [the center of the image]\n",
        "# this is why we use z[:, :-1] when we extract features for the particles"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "10a2a02c",
      "metadata": {
        "id": "10a2a02c"
      },
      "source": [
        "#### Prior\n",
        "---\n",
        "* The prior addresses the question: what are the interesting areas in the image?\n",
        "* It constrains the model such that the output of the position encoder is actually spatial positions and not just random values between $[-1, 1]$.\n",
        "* We extract points-of-intereset in the image by applying spatial-Softmax (SSM) over feature maps extracted from **patches** in the image. \n",
        "* We term the set of extracted prior keypoints as keypoint proposals.\n",
        "* Since we output one keypoint per patch, we may end up with a large set of keypoints, not all are informative.\n",
        "* To filter out some proposal keypoints, we either randomly sample from this set or use a heuristic where we take the top-K most distant keypoints from the center of the patch.\n",
        "* The reasoning behind the heuristic is that SSM is based on averaging, and contant areas in the image (e.g., a solid color patch) are not informative and SSM will result in a keypoint in the center of the patch."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e5e2d00a",
      "metadata": {
        "id": "e5e2d00a"
      },
      "outputs": [],
      "source": [
        "# PSEUDOCODE - DO NOT RUN\n",
        "# prior\n",
        "kp_p = self.prior(x_prior, global_kp=True)  \n",
        "# global_kp = True returns the coordinates in the whole image. \n",
        "# Otherwise, returns the local coordinates inside the patch.\n",
        "kp_p = kp_p.view(x_prior.shape[0], -1, 2)  # [batch_size, n_kp_total, 2]\n",
        "# filter proposals by distance to the patches' center\n",
        "dist_from_center = self.prior.get_distance_from_patch_centers(kp_p, global_kp=True)\n",
        "_, indices = torch.topk(dist_from_center, k=self.n_kp_prior, dim=-1, largest=True)\n",
        "batch_indices = torch.arange(kp_p.shape[0]).view(-1, 1).to(kp_p.device)\n",
        "kp_p = kp_p[batch_indices, indices]\n",
        "# random cnn features for prior also work\n",
        "kp_p = kp_p.detach()\n",
        "# alternatively, just sample random kp\n",
        "# kp_p = kp_p[:, torch.randperm(kp_p.shape[1])[:self.n_kp_prior]]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b834c4fa",
      "metadata": {
        "id": "b834c4fa"
      },
      "source": [
        "#### Decoder\n",
        "---\n",
        "<img src=\"https://raw.githubusercontent.com/taldatech/deep-latent-particles-web/main/assets/dlp_decoder.gif\" style=\"height:250px\">"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9a473d6a",
      "metadata": {
        "id": "9a473d6a"
      },
      "source": [
        "In the decoder for the **object-based** model PointNet++ models the global regions (e.g., the background) and Gaussian maps (optionally) and a separate Glimpse decoder model the objects and their masks."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4609d4ce",
      "metadata": {
        "id": "4609d4ce"
      },
      "outputs": [],
      "source": [
        "# PSEUDOCODE - DO NOT RUN\n",
        "# decoder\n",
        "gmap_1_fg = self.to_gauss_map(z[:, :-1], self.features_dim, self.features_dim)\n",
        "gmap_1_bg = 1 - gmap_1_fg.sum(1, keepdim=True).clamp(0, 1).detach()\n",
        "gmap_1 = torch.cat([gmap_1_fg, gmap_1_bg], dim=1)\n",
        "fg_masks_sep = get_kp_mask_from_gmap(gmap_1_fg, threshold=self.mask_threshold,\n",
        "                                     binary=True, elementwise=True).detach()\n",
        "fg_masks = fg_masks_sep.sum(1, keepdim=True).clamp(0, 1)\n",
        "bg_masks = 1 - fg_masks\n",
        "masks = torch.cat([fg_masks.expand_as(gmap_1_fg), bg_masks], dim=1)\n",
        "gmap_2 = self.pointnet(position=z.detach(), features=torch.cat([z.detach(), z_features], dim=-1))\n",
        "gmap = torch.cat([gmap_1[:, :-1], gmap_2], dim=1)\n",
        "rec = self.dec(gmap)  # bg elements\n",
        "# decode object and translate them to the positions of the keypoints\n",
        "object_dec_out = self.decode_objects(z, z_features, obj_on, bg=rec)\n",
        "dec_objects, dec_objects_trans, rec = object_dec_out"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a19ceb36",
      "metadata": {
        "id": "a19ceb36"
      },
      "source": [
        "#### Putting it All Together\n",
        "---\n",
        "<img src=\"https://raw.githubusercontent.com/taldatech/deep-latent-particles-web/main/assets/dlp_arch_all.PNG\" style=\"height:400px\">"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e900bd30",
      "metadata": {
        "id": "e900bd30"
      },
      "source": [
        "###  <img src=\"https://img.icons8.com/external-icongeek26-linear-colour-icongeek26/64/000000/external-shapes-graphic-design-icongeek26-linear-colour-icongeek26.png\" style=\"height:50px;display:inline\"> Training and Optimization\n",
        "---\n",
        "The model is optimized as a variational autoencoder (VAE) with objective of maximizing the evidence lower bound (ELBO): $$ \\log p_\\theta(x) \\geq \\mathbb{E}_{q(z|x)} \\left[\\log p_\\theta(x|z)\\right] - KL(q(z|x) \\Vert p(z)) \\doteq ELBO(x) $$\n",
        "The ELBO is decomposed to the <i>reconstruction error</i> and a <i>KL-divergence</i> regularization term."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "820643f0",
      "metadata": {
        "id": "820643f0"
      },
      "source": [
        "However, here we have two **unordered sets** (or point clouds) of position latent variables: the posterior keypoints and the keypoint proposals from the prior. Note that *the number of points in each set may also differ*.\n",
        "\n",
        "\n",
        "The Chamfer distance between two sets $S_1$ and $S_2$: \n",
        "$$ d_{CH}(S_1, S_2) = \\sum_{x \\in S_1}\\min_{y \\in S_2}||x-y||_2^2 + \\sum_{y \\in S_2}\\min_{x \\in S_1}||x-y||_2^2. $$\n",
        "<img src=\"https://raw.githubusercontent.com/taldatech/deep-latent-particles-web/main/assets/chamfer_distance.gif\" style=\"height:300px\">\n",
        "\n",
        "Animation by <a href=\"https://www.youtube.com/watch?v=P4IyrsWicfs\">Luke Hawkes</a>."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9773745a",
      "metadata": {
        "id": "9773745a"
      },
      "source": [
        "We propose the <b>Chamfer-KL</b>, a <i>novel modification</i> for the KL term:\n",
        "$$ d_{CH-KL}(S_1, S_2) = \\sum_{x \\in S_1}\\min_{y \\in S_2}KL(x \\Vert y) + \\sum_{y \\in S_2}\\min_{x \\in S_1}KL(x \\Vert y). $$\n",
        "Note that the Chamfer-KL is not a metric and maintains the properties of the standard KL term."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e4f18692",
      "metadata": {
        "id": "e4f18692"
      },
      "source": [
        "The complete training objective follows the $\\beta$-VAE formulation: $$ \\mathcal{L} =  \\mathcal{L}_{rec}(x, \\tilde{x}) +\\beta_{CKL}CH-KL(q_{\\phi}(z_p|x)\\Vert p_{\\psi}(z|x)) + \\beta_{KL} KL(q_{\\phi}(z_a|x) \\Vert p(z)).$$ We found it crucial to balance the two KL terms, where usually $\\beta_{CKL} > \\beta_{KL}$."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "781a1496",
      "metadata": {
        "id": "781a1496"
      },
      "source": [
        "#### Hyper-parameters\n",
        "---\n",
        "DLP has some trivial hyper-parameters just like any other model (e.g., learning rate, batch size and etc...), and the rest are described below:\n",
        "\n",
        "* `ds`: dataset name (str)\n",
        "* `enc_channels`: channels for the posterior CNN (takes in the whole image)\n",
        "* `prior_channels`: channels for prior CNN (takes in patches)\n",
        "* `n_kp`: number of kp to extract from each (!) patch\n",
        "* `n_kp_prior`: number of kp to filter from the set of prior kp (of size n_kp x num_patches)\n",
        "* `n_kp_enc`: number of posterior kp to be learned (this is the actual number of kp that will be learnt)\n",
        "* `use_logsoftmax`: for spatial-softmax, set True to use log-softmax for numerical stability\n",
        "* `pad_mode`: padding for the CNNs, 'zeros' or  'replicate' (default)\n",
        "* `sigma`: the prior std of the KP\n",
        "* `dropout`: dropout for the CNNs. We don't use it though...\n",
        "* `dec_bone`: decoder backbone -- \"gauss_pointnetpp_feat\": Masked Model, \"gauss_pointnetpp\": Object Model\n",
        "* `patch_size`: patch size for the prior KP proposals network (not to be confused with the glimpse size)\n",
        "* `kp_range`: the range of keypoints, can be $[-1, 1]$ (default) or $[0,1]$\n",
        "* `learned_feature_dim`: the latent visual features dimensions extracted from glimpses.\n",
        "* `kp_activation`: the type of activation to apply on the keypoints: \"tanh\" for kp_range $[-1, 1]$, \"sigmoid\" for $[0, 1]$\n",
        "* `mask_threshold`: activation threshold (>thresh -> 1, else 0) for the binary mask created from the Gaussian-maps.\n",
        "* `anchor_s`: defines the glimpse size as a ratio of image_size (e.g., 0.25 for image_size=128 -> glimpse_size=32)\n",
        "* `learn_order`: experimental feature to learn the order of keypoints - but it doesn't work yet.\n",
        "* `use_object_enc`: set True to use a separate encoder to encode visual features of glimpses.\n",
        "* `use_object_dec`: set True to use a separate decoder to decode glimpses (Object Model).\n",
        "* `iou_thresh`: intersection-over-union threshold for non-maximal suppression (nms) to filter bounding boxes\n",
        "* `use_tps`: set True to use a tps augmentation on the input image for datasets that support this option\n",
        "* `use_pairs`: for CelebA dataset, set True to use a tps-augmented image for the prior.\n",
        "* `topk`: the number top-k particles with the lowest variance (highest confidence) to filter for the plots.\n",
        "* `warmup_epoch`: (used for the Object Model) number of epochs where only the object decoder is trained.\n",
        "* `recon_loss_type`: tpe of pixel reconstruction loss (\"mse\", \"vgg\").\n",
        "* `beta_rec`: coefficient for the reconstruction loss (we use 1.0).\n",
        "* `beta_kl`: coefficient for the KL divergence term in the loss.\n",
        "* `kl_balance`: coefficient for the balance between the ChamferKL (for the KP) and the standard KL (for the visual features), kl_loss = beta_kl * (chamfer_kl + kl_balance * kl_features)\n",
        "* `exclusive_patches`: (mostly) enforce one particle pre object by masking up regions that were already encoded."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a64e3e00",
      "metadata": {
        "id": "a64e3e00"
      },
      "outputs": [],
      "source": [
        "# training\n",
        "# default hyper-parameters\n",
        "lr = 1e-3\n",
        "batch_size = 64\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "num_epochs = 100\n",
        "load_model = False\n",
        "eval_epoch_freq = 2\n",
        "n_kp = 1  # num kp per patch\n",
        "mask_threshold = 0.2  # mask threshold for the features from the encoder\n",
        "kp_range = (-1, 1)\n",
        "weight_decay = 0.0\n",
        "run_prefix = \"\"\n",
        "learn_order = False\n",
        "use_logsoftmax = False\n",
        "pad_mode = 'replicate'\n",
        "sigma = 0.1  # default sigma for the gaussian maps\n",
        "dropout = 0.0\n",
        "kp_activation = \"tanh\"\n",
        "iou_thresh = 0.2\n",
        "# ds specific hp\n",
        "ds = \"shapes\"\n",
        "beta_kl = 0.05\n",
        "beta_rec = 1.0\n",
        "n_kp_enc = 10  # total kp to output from the encoder / filter from prior\n",
        "n_kp_prior = 15\n",
        "patch_size = 8\n",
        "learned_feature_dim = 6  # additional features than x,y for each kp\n",
        "dec_bone = \"gauss_pointnetpp\"  # object model\n",
        "topk = min(10, n_kp_enc)  # display top-10 kp with smallest variance\n",
        "recon_loss_type = \"mse\"\n",
        "use_tps = False\n",
        "use_pairs = False\n",
        "use_object_enc = True  # separate object encoder\n",
        "use_object_dec = True  # separate object decoder\n",
        "warmup_epoch = 1\n",
        "anchor_s = 0.25\n",
        "kl_balance = 0.001\n",
        "exclusive_patches = False"
      ]
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "generating random shapes dataset\n"
          ]
        },
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              "  0%|          | 0/40000 [00:00<?, ?it/s]"
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          "text": [
            "decoder backbone: gauss_pointnetpp\n",
            "prior std: 0.1\n",
            "keypoints range: (-1, 1)\n",
            "total number of kp: 64 -> prior kp: 15\n",
            "number of kp from encoder: 10\n",
            "kp_activation: tanh\n",
            "learnable feature dim: 6\n",
            "mask threshold: 0.2\n",
            "object patch size: 16\n",
            "object encoder: True, object decoder: True\n",
            "learn particles order: False\n",
            "conv shape:  torch.Size([128, 16, 16])\n",
            "conv shape:  torch.Size([32, 4, 4])\n",
            "conv shape:  torch.Size([64, 2, 2])\n"
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          "text": [
            "epoch 0 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 20.318, rec: 16.962, kl: 67.107\n",
            "kl_balance: 0.0010, kl_kp: 66.557, kl_feat: 550.190\n",
            "mu max: 0.9220603704452515, mu min: -0.994138240814209\n",
            "obj_on max: 0.5406953692436218, obj_on min: 0.46821826696395874\n",
            "\n",
            "bb scores: max: 10.32, min: 8.34, mean: 9.25\n",
            "\n"
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          "text": [
            "epoch 1 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 390.838, rec: 379.946, kl: 217.854\n",
            "kl_balance: 0.0010, kl_kp: 216.093, kl_feat: 1760.987\n",
            "mu max: 0.8143038749694824, mu min: -0.87456876039505\n",
            "obj_on max: 0.5271044373512268, obj_on min: 0.4770619571208954\n",
            "mean psnr: 17.595\n",
            "\n"
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          "text": [
            "epoch 2 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 122.674, rec: 113.526, kl: 182.949\n",
            "kl_balance: 0.0010, kl_kp: 181.472, kl_feat: 1477.595\n",
            "mu max: 0.7596716284751892, mu min: -0.8739987015724182\n",
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            "mean psnr: 20.371\n",
            "\n",
            "bb scores: max: 24.40, min: 15.37, mean: 18.83\n",
            "\n"
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            "epoch 3 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 101.916, rec: 92.924, kl: 179.856\n",
            "kl_balance: 0.0010, kl_kp: 178.646, kl_feat: 1210.280\n",
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            "mean psnr: 21.227\n",
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            "epoch 4 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 91.068, rec: 82.212, kl: 177.132\n",
            "kl_balance: 0.0010, kl_kp: 176.001, kl_feat: 1130.567\n",
            "mu max: 0.9212254881858826, mu min: -0.9290873408317566\n",
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            "mean psnr: 21.760\n",
            "\n",
            "bb scores: max: 30.45, min: 17.83, mean: 22.86\n",
            "\n"
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            "epoch 5 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 66.834, rec: 58.049, kl: 175.697\n",
            "kl_balance: 0.0010, kl_kp: 174.719, kl_feat: 978.869\n",
            "mu max: 0.8949666023254395, mu min: -0.9380226135253906\n",
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            "mean psnr: 23.277\n",
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            "epoch 6 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 55.740, rec: 46.787, kl: 179.077\n",
            "kl_balance: 0.0010, kl_kp: 178.181, kl_feat: 895.483\n",
            "mu max: 0.8416098356246948, mu min: -0.917428195476532\n",
            "obj_on max: 0.9999988079071045, obj_on min: 0.01970219798386097\n",
            "mean psnr: 24.210\n",
            "\n",
            "bb scores: max: 27.53, min: 15.33, mean: 21.75\n",
            "\n"
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            "epoch 7 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 51.647, rec: 42.677, kl: 179.406\n",
            "kl_balance: 0.0010, kl_kp: 178.534, kl_feat: 871.211\n",
            "mu max: 0.836959719657898, mu min: -0.9047195911407471\n",
            "obj_on max: 0.9999998807907104, obj_on min: 0.008848834782838821\n",
            "mean psnr: 24.609\n",
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            "epoch 8 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 49.114, rec: 40.151, kl: 179.259\n",
            "kl_balance: 0.0010, kl_kp: 178.354, kl_feat: 904.590\n",
            "mu max: 0.9077540636062622, mu min: -0.9520664811134338\n",
            "obj_on max: 0.9999958276748657, obj_on min: 0.012792380526661873\n",
            "mean psnr: 24.874\n",
            "\n",
            "bb scores: max: 30.59, min: 13.13, mean: 22.35\n",
            "\n"
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            "epoch 9 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 46.979, rec: 37.950, kl: 180.599\n",
            "kl_balance: 0.0010, kl_kp: 179.692, kl_feat: 907.567\n",
            "mu max: 0.8774340152740479, mu min: -0.9096291065216064\n",
            "obj_on max: 1.0, obj_on min: 0.010667909868061543\n",
            "mean psnr: 25.120\n",
            "\n"
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          "text": [
            "epoch 10 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 44.993, rec: 35.986, kl: 180.145\n",
            "kl_balance: 0.0010, kl_kp: 179.215, kl_feat: 929.871\n",
            "mu max: 0.8482515811920166, mu min: -0.9133435487747192\n",
            "obj_on max: 0.9999982118606567, obj_on min: 0.025964386761188507\n",
            "mean psnr: 25.353\n",
            "\n",
            "bb scores: max: 29.61, min: 8.11, mean: 21.90\n",
            "\n"
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            "epoch 11 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 43.745, rec: 34.741, kl: 180.083\n",
            "kl_balance: 0.0010, kl_kp: 179.123, kl_feat: 960.411\n",
            "mu max: 0.8309618234634399, mu min: -0.9298042058944702\n",
            "obj_on max: 0.9999982118606567, obj_on min: 0.004609663970768452\n",
            "mean psnr: 25.505\n",
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            "epoch 12 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 42.112, rec: 33.203, kl: 178.191\n",
            "kl_balance: 0.0010, kl_kp: 177.210, kl_feat: 981.003\n",
            "mu max: 0.8208761215209961, mu min: -0.9347814321517944\n",
            "obj_on max: 1.0, obj_on min: 0.001264703692868352\n",
            "mean psnr: 25.704\n",
            "\n",
            "bb scores: max: 28.81, min: 7.25, mean: 21.39\n",
            "\n"
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          "text": [
            "epoch 13 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 40.643, rec: 31.795, kl: 176.964\n",
            "kl_balance: 0.0010, kl_kp: 175.978, kl_feat: 985.953\n",
            "mu max: 0.8243411183357239, mu min: -0.9438992142677307\n",
            "obj_on max: 1.0, obj_on min: 0.006016188766807318\n",
            "mean psnr: 25.895\n",
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            "epoch 14 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 39.815, rec: 31.033, kl: 175.624\n",
            "kl_balance: 0.0010, kl_kp: 174.596, kl_feat: 1028.706\n",
            "mu max: 0.8375452756881714, mu min: -0.9308322668075562\n",
            "obj_on max: 1.0, obj_on min: 0.005752075929194689\n",
            "mean psnr: 25.997\n",
            "\n",
            "bb scores: max: 29.20, min: 6.25, mean: 22.07\n",
            "\n"
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            "epoch 15 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 39.114, rec: 30.418, kl: 173.932\n",
            "kl_balance: 0.0010, kl_kp: 172.884, kl_feat: 1047.719\n",
            "mu max: 0.8799234628677368, mu min: -0.943925142288208\n",
            "obj_on max: 1.0, obj_on min: 0.0011613955721259117\n",
            "mean psnr: 26.086\n",
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            "epoch 16 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 38.627, rec: 30.031, kl: 171.919\n",
            "kl_balance: 0.0010, kl_kp: 170.852, kl_feat: 1067.368\n",
            "mu max: 0.8276530504226685, mu min: -0.9477713108062744\n",
            "obj_on max: 1.0, obj_on min: 0.00033329371945001185\n",
            "mean psnr: 26.140\n",
            "\n",
            "bb scores: max: 29.78, min: 7.62, mean: 21.72\n",
            "\n"
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            "epoch 17 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 37.608, rec: 29.043, kl: 171.305\n",
            "kl_balance: 0.0010, kl_kp: 170.215, kl_feat: 1090.673\n",
            "mu max: 0.8714057803153992, mu min: -0.976646363735199\n",
            "obj_on max: 1.0, obj_on min: 0.001266992767341435\n",
            "mean psnr: 26.288\n",
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            "epoch 18 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 36.692, rec: 28.232, kl: 169.200\n",
            "kl_balance: 0.0010, kl_kp: 168.114, kl_feat: 1086.276\n",
            "mu max: 0.8771125078201294, mu min: -0.9433962106704712\n",
            "obj_on max: 1.0, obj_on min: 0.001341684372164309\n",
            "mean psnr: 26.408\n",
            "\n",
            "bb scores: max: 30.78, min: 5.35, mean: 21.56\n",
            "\n"
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          "text": [
            "epoch 19 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 36.519, rec: 28.132, kl: 167.755\n",
            "kl_balance: 0.0010, kl_kp: 166.647, kl_feat: 1107.800\n",
            "mu max: 0.8538597226142883, mu min: -0.9460161328315735\n",
            "obj_on max: 1.0, obj_on min: 0.00036572542740032077\n",
            "mean psnr: 26.424\n",
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          "text": [
            "epoch 20 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 32.323, rec: 24.089, kl: 164.677\n",
            "kl_balance: 0.0010, kl_kp: 163.616, kl_feat: 1060.535\n",
            "mu max: 0.9301964640617371, mu min: -0.9796735048294067\n",
            "obj_on max: 1.0, obj_on min: 0.000521161942742765\n",
            "mean psnr: 27.099\n",
            "\n",
            "bb scores: max: 31.70, min: 5.26, mean: 21.79\n",
            "\n"
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            "epoch 21 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 31.843, rec: 23.707, kl: 162.716\n",
            "kl_balance: 0.0010, kl_kp: 161.693, kl_feat: 1023.086\n",
            "mu max: 0.8843681812286377, mu min: -0.9661332964897156\n",
            "obj_on max: 1.0, obj_on min: 0.0012414295924827456\n",
            "mean psnr: 27.168\n",
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            "epoch 22 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 31.479, rec: 23.388, kl: 161.816\n",
            "kl_balance: 0.0010, kl_kp: 160.825, kl_feat: 990.845\n",
            "mu max: 0.852033793926239, mu min: -0.9661705493927002\n",
            "obj_on max: 1.0, obj_on min: 0.0005525663727894425\n",
            "mean psnr: 27.226\n",
            "\n",
            "bb scores: max: 34.38, min: 7.65, mean: 21.59\n",
            "\n"
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          "text": [
            "epoch 23 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 31.145, rec: 23.155, kl: 159.782\n",
            "kl_balance: 0.0010, kl_kp: 158.788, kl_feat: 994.451\n",
            "mu max: 0.9152716994285583, mu min: -0.9351775050163269\n",
            "obj_on max: 1.0, obj_on min: 0.00090127281146124\n",
            "mean psnr: 27.270\n",
            "\n"
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            "epoch 24 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 30.854, rec: 22.922, kl: 158.636\n",
            "kl_balance: 0.0010, kl_kp: 157.666, kl_feat: 970.049\n",
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            "\n",
            "bb scores: max: 31.77, min: 5.58, mean: 21.38\n",
            "\n"
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            "loss: 30.373, rec: 22.438, kl: 158.697\n",
            "kl_balance: 0.0010, kl_kp: 157.732, kl_feat: 964.881\n",
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            "loss: 29.829, rec: 22.009, kl: 156.403\n",
            "kl_balance: 0.0010, kl_kp: 155.456, kl_feat: 946.402\n",
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            "\n",
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            "loss: 29.559, rec: 21.741, kl: 156.369\n",
            "kl_balance: 0.0010, kl_kp: 155.436, kl_feat: 932.905\n",
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            "loss: 29.499, rec: 21.736, kl: 155.270\n",
            "kl_balance: 0.0010, kl_kp: 154.353, kl_feat: 916.919\n",
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            "\n",
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            "epoch 29 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 29.064, rec: 21.298, kl: 155.315\n",
            "kl_balance: 0.0010, kl_kp: 154.400, kl_feat: 914.903\n",
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            "epoch 30 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 29.232, rec: 21.513, kl: 154.376\n",
            "kl_balance: 0.0010, kl_kp: 153.468, kl_feat: 908.154\n",
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            "mean psnr: 27.590\n",
            "\n",
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            "epoch 31 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 28.924, rec: 21.246, kl: 153.547\n",
            "kl_balance: 0.0010, kl_kp: 152.646, kl_feat: 900.687\n",
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            "epoch 32 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 28.797, rec: 21.169, kl: 152.565\n",
            "kl_balance: 0.0010, kl_kp: 151.673, kl_feat: 891.655\n",
            "mu max: 0.8587130308151245, mu min: -0.9334883689880371\n",
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            "\n",
            "bb scores: max: 33.80, min: 6.87, mean: 21.08\n",
            "\n"
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            "epoch 33 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 28.214, rec: 20.671, kl: 150.848\n",
            "kl_balance: 0.0010, kl_kp: 149.947, kl_feat: 901.428\n",
            "mu max: 0.837288498878479, mu min: -0.9524087905883789\n",
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            "epoch 34 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 28.160, rec: 20.580, kl: 151.605\n",
            "kl_balance: 0.0010, kl_kp: 150.722, kl_feat: 883.227\n",
            "mu max: 0.8719073534011841, mu min: -0.9670348763465881\n",
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            "mean psnr: 27.782\n",
            "\n",
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            "epoch 35 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 27.821, rec: 20.267, kl: 151.082\n",
            "kl_balance: 0.0010, kl_kp: 150.209, kl_feat: 872.284\n",
            "mu max: 0.9015540480613708, mu min: -0.980066180229187\n",
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            "mean psnr: 27.850\n",
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            "epoch 36 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 27.512, rec: 20.006, kl: 150.127\n",
            "kl_balance: 0.0010, kl_kp: 149.251, kl_feat: 876.406\n",
            "mu max: 0.7680054306983948, mu min: -0.9642571806907654\n",
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            "mean psnr: 27.904\n",
            "\n",
            "bb scores: max: 33.39, min: 6.70, mean: 20.84\n",
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            "epoch 37 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 27.567, rec: 20.113, kl: 149.075\n",
            "kl_balance: 0.0010, kl_kp: 148.200, kl_feat: 874.844\n",
            "mu max: 0.8447200059890747, mu min: -0.977231502532959\n",
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            "epoch 38 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 27.464, rec: 19.993, kl: 149.422\n",
            "kl_balance: 0.0010, kl_kp: 148.549, kl_feat: 873.232\n",
            "mu max: 0.8273630142211914, mu min: -0.9610006809234619\n",
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            "mean psnr: 27.907\n",
            "\n",
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            "epoch 39 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 26.983, rec: 19.553, kl: 148.600\n",
            "kl_balance: 0.0010, kl_kp: 147.724, kl_feat: 876.032\n",
            "mu max: 0.8355500102043152, mu min: -0.961559534072876\n",
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            "epoch 40 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 25.144, rec: 17.779, kl: 147.313\n",
            "kl_balance: 0.0010, kl_kp: 146.462, kl_feat: 850.840\n",
            "mu max: 0.8036053776741028, mu min: -0.9552882313728333\n",
            "obj_on max: 1.0, obj_on min: 0.00027074594981968403\n",
            "mean psnr: 28.416\n",
            "\n",
            "bb scores: max: 36.18, min: 6.75, mean: 21.19\n",
            "\n"
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            "epoch 41 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 25.053, rec: 17.714, kl: 146.767\n",
            "kl_balance: 0.0010, kl_kp: 145.939, kl_feat: 828.094\n",
            "mu max: 0.892311692237854, mu min: -0.9848070740699768\n",
            "obj_on max: 1.0, obj_on min: 1.7730179024511017e-05\n",
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            "epoch 42 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 24.780, rec: 17.502, kl: 145.552\n",
            "kl_balance: 0.0010, kl_kp: 144.736, kl_feat: 816.375\n",
            "mu max: 0.8598074316978455, mu min: -0.9633503556251526\n",
            "obj_on max: 1.0, obj_on min: 7.587993604829535e-05\n",
            "mean psnr: 28.486\n",
            "\n",
            "bb scores: max: 32.76, min: 6.36, mean: 20.63\n",
            "\n"
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            "epoch 43 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 24.685, rec: 17.401, kl: 145.682\n",
            "kl_balance: 0.0010, kl_kp: 144.874, kl_feat: 808.093\n",
            "mu max: 0.8871211409568787, mu min: -0.9598140716552734\n",
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            "epoch 44 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 24.469, rec: 17.227, kl: 144.821\n",
            "kl_balance: 0.0010, kl_kp: 144.027, kl_feat: 793.991\n",
            "mu max: 0.8571006059646606, mu min: -0.9570517539978027\n",
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            "mean psnr: 28.553\n",
            "\n",
            "bb scores: max: 32.65, min: 7.16, mean: 21.10\n",
            "\n"
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            "epoch 45 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 24.457, rec: 17.234, kl: 144.477\n",
            "kl_balance: 0.0010, kl_kp: 143.696, kl_feat: 780.857\n",
            "mu max: 0.8287784457206726, mu min: -0.9809794425964355\n",
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            "epoch 46 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 24.268, rec: 17.071, kl: 143.937\n",
            "kl_balance: 0.0010, kl_kp: 143.166, kl_feat: 770.954\n",
            "mu max: 0.859454870223999, mu min: -0.9537683129310608\n",
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            "\n",
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            "\n"
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            "\n",
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            "loss: 23.671, rec: 16.584, kl: 141.744\n",
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            "loss: 23.542, rec: 16.449, kl: 141.848\n",
            "kl_balance: 0.0010, kl_kp: 141.108, kl_feat: 740.335\n",
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            "\n",
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            "epoch 55 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 23.300, rec: 16.236, kl: 141.291\n",
            "kl_balance: 0.0010, kl_kp: 140.562, kl_feat: 728.619\n",
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            "epoch 56 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 23.305, rec: 16.239, kl: 141.321\n",
            "kl_balance: 0.0010, kl_kp: 140.595, kl_feat: 726.004\n",
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            "\n",
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            "epoch 57 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 23.225, rec: 16.155, kl: 141.404\n",
            "kl_balance: 0.0010, kl_kp: 140.673, kl_feat: 731.268\n",
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            "epoch 58 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 23.285, rec: 16.259, kl: 140.524\n",
            "kl_balance: 0.0010, kl_kp: 139.808, kl_feat: 716.448\n",
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            "\n",
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            "epoch 59 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 23.160, rec: 16.138, kl: 140.454\n",
            "kl_balance: 0.0010, kl_kp: 139.740, kl_feat: 713.214\n",
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            "epoch 60 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 23.124, rec: 16.105, kl: 140.382\n",
            "kl_balance: 0.0010, kl_kp: 139.670, kl_feat: 711.573\n",
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            "\n",
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            "epoch 61 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 22.935, rec: 15.944, kl: 139.813\n",
            "kl_balance: 0.0010, kl_kp: 139.101, kl_feat: 712.169\n",
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            "epoch 62 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 22.845, rec: 15.889, kl: 139.123\n",
            "kl_balance: 0.0010, kl_kp: 138.422, kl_feat: 700.953\n",
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            "epoch 63 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 22.737, rec: 15.766, kl: 139.422\n",
            "kl_balance: 0.0010, kl_kp: 138.726, kl_feat: 695.619\n",
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            "epoch 64 summary for dec backbone: gauss_pointnetpp\n",
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            "kl_balance: 0.0010, kl_kp: 138.013, kl_feat: 694.197\n",
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            "\n",
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            "epoch 65 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 22.568, rec: 15.619, kl: 138.986\n",
            "kl_balance: 0.0010, kl_kp: 138.286, kl_feat: 699.915\n",
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            "epoch 66 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 22.588, rec: 15.653, kl: 138.697\n",
            "kl_balance: 0.0010, kl_kp: 138.007, kl_feat: 689.635\n",
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            "\n",
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            "epoch 67 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 22.446, rec: 15.527, kl: 138.371\n",
            "kl_balance: 0.0010, kl_kp: 137.678, kl_feat: 692.849\n",
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            "epoch 68 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 22.400, rec: 15.495, kl: 138.113\n",
            "kl_balance: 0.0010, kl_kp: 137.424, kl_feat: 688.918\n",
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            "\n",
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            "loss: 21.883, rec: 15.093, kl: 135.796\n",
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            "loss: 21.774, rec: 14.954, kl: 136.401\n",
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            "loss: 20.926, rec: 14.159, kl: 135.353\n",
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            "kl_balance: 0.0010, kl_kp: 134.794, kl_feat: 641.031\n",
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            "kl_balance: 0.0010, kl_kp: 134.127, kl_feat: 642.856\n",
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            "loss: 20.521, rec: 13.775, kl: 134.923\n",
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            "epoch 85 summary for dec backbone: gauss_pointnetpp\n",
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            "epoch 86 summary for dec backbone: gauss_pointnetpp\n",
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            "kl_balance: 0.0010, kl_kp: 133.907, kl_feat: 637.561\n",
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            "epoch 87 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 20.313, rec: 13.584, kl: 134.569\n",
            "kl_balance: 0.0010, kl_kp: 133.937, kl_feat: 631.796\n",
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            "epoch 88 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 20.245, rec: 13.553, kl: 133.841\n",
            "kl_balance: 0.0010, kl_kp: 133.209, kl_feat: 632.007\n",
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            "\n",
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            "epoch 89 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 20.177, rec: 13.485, kl: 133.847\n",
            "kl_balance: 0.0010, kl_kp: 133.223, kl_feat: 623.997\n",
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            "epoch 90 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 20.198, rec: 13.515, kl: 133.648\n",
            "kl_balance: 0.0010, kl_kp: 133.027, kl_feat: 621.028\n",
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            "\n",
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          "text": [
            "epoch 91 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 20.178, rec: 13.480, kl: 133.957\n",
            "kl_balance: 0.0010, kl_kp: 133.340, kl_feat: 617.200\n",
            "mu max: 0.8412236571311951, mu min: -0.9818705320358276\n",
            "obj_on max: 1.0, obj_on min: 6.944325286895037e-05\n",
            "mean psnr: 29.617\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "83ccbd9254eb4d58a341c4498f36f19d",
              "version_major": 2,
              "version_minor": 0
            },
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              "  0%|          | 0/625 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 92 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 20.050, rec: 13.396, kl: 133.094\n",
            "kl_balance: 0.0010, kl_kp: 132.478, kl_feat: 615.220\n",
            "mu max: 0.8983055949211121, mu min: -0.9530709981918335\n",
            "obj_on max: 1.0, obj_on min: 1.3355971532291733e-05\n",
            "mean psnr: 29.647\n",
            "\n",
            "bb scores: max: 31.75, min: 6.70, mean: 20.66\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "136fab1692374ddcb4deb6873dc53f0a",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 93 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 20.039, rec: 13.373, kl: 133.313\n",
            "kl_balance: 0.0010, kl_kp: 132.704, kl_feat: 608.700\n",
            "mu max: 0.8605939149856567, mu min: -0.9754166007041931\n",
            "obj_on max: 1.0, obj_on min: 1.6803123799036257e-05\n",
            "mean psnr: 29.654\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "668e878e70434035a1c514b7c6700c4f",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 94 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 19.989, rec: 13.315, kl: 133.473\n",
            "kl_balance: 0.0010, kl_kp: 132.860, kl_feat: 613.688\n",
            "mu max: 0.8493151664733887, mu min: -0.977907657623291\n",
            "obj_on max: 1.0, obj_on min: 1.1165115211042576e-05\n",
            "mean psnr: 29.674\n",
            "\n",
            "bb scores: max: 30.42, min: 6.77, mean: 20.37\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "83cca9cdc67a45809d492b44a88987c4",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 95 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 19.962, rec: 13.297, kl: 133.291\n",
            "kl_balance: 0.0010, kl_kp: 132.683, kl_feat: 608.733\n",
            "mu max: 0.8414641618728638, mu min: -0.9939790368080139\n",
            "obj_on max: 1.0, obj_on min: 1.9187655198038556e-05\n",
            "mean psnr: 29.678\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "985cd105b3904cf2990e492a2e3ecffe",
              "version_major": 2,
              "version_minor": 0
            },
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              "  0%|          | 0/625 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 96 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 19.982, rec: 13.323, kl: 133.179\n",
            "kl_balance: 0.0010, kl_kp: 132.572, kl_feat: 607.072\n",
            "mu max: 0.8744240403175354, mu min: -0.9624093770980835\n",
            "obj_on max: 1.0, obj_on min: 3.4536262774054194e-06\n",
            "mean psnr: 29.671\n",
            "\n",
            "bb scores: max: 34.32, min: 6.25, mean: 20.76\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d2a78ead2d5341079c445cc700914afe",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 97 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 19.956, rec: 13.293, kl: 133.248\n",
            "kl_balance: 0.0010, kl_kp: 132.641, kl_feat: 606.888\n",
            "mu max: 0.8836831450462341, mu min: -0.9859519004821777\n",
            "obj_on max: 1.0, obj_on min: 7.796354111633264e-06\n",
            "mean psnr: 29.680\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d4323716789046778b0c81d88e33ee20",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/625 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 98 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 19.883, rec: 13.227, kl: 133.120\n",
            "kl_balance: 0.0010, kl_kp: 132.513, kl_feat: 607.411\n",
            "mu max: 0.8654155731201172, mu min: -0.995040774345398\n",
            "obj_on max: 1.0, obj_on min: 6.3751190282346215e-06\n",
            "mean psnr: 29.700\n",
            "\n",
            "bb scores: max: 32.44, min: 3.53, mean: 20.47\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e5254b920ebe438197be137a34722ca0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/625 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "epoch 99 summary for dec backbone: gauss_pointnetpp\n",
            "loss: 19.830, rec: 13.215, kl: 132.315\n",
            "kl_balance: 0.0010, kl_kp: 131.710, kl_feat: 605.558\n",
            "mu max: 0.8227019309997559, mu min: -0.9883113503456116\n",
            "obj_on max: 1.0, obj_on min: 3.5708762879949063e-06\n",
            "mean psnr: 29.704\n",
            "\n",
            "bb scores: max: 34.47, min: 5.62, mean: 20.67\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# load data\n",
        "if ds == \"shapes\":\n",
        "    image_size = 64\n",
        "    ch = 3\n",
        "    enc_channels = [32, 64, 128]\n",
        "    prior_channels = (16, 32, 64)\n",
        "    print('generating random shapes dataset')\n",
        "    dataset = generate_shape_dataset_torch(num_images=40_000)\n",
        "    milestones = (20, 40, 80)\n",
        "else:\n",
        "    raise NotImplementedError\n",
        "\n",
        "# save hyper-parameters\n",
        "hparams = {'ds': ds, 'batch_size': batch_size, 'lr': lr, 'kp_activation': kp_activation, 'pad_mode': pad_mode,\n",
        "           'num_epochs': num_epochs, 'n_kp': n_kp, 'recon_loss_type': recon_loss_type,\n",
        "           'use_logsoftmax': use_logsoftmax, 'sigma': sigma, 'beta_kl': beta_kl, 'beta_rec': beta_rec,\n",
        "           'dec_bone': dec_bone, 'patch_size': patch_size, 'topk': topk, 'n_kp_enc': n_kp_enc,\n",
        "           'eval_epoch_freq': eval_epoch_freq, 'learned_feature_dim': learned_feature_dim,\n",
        "           'n_kp_prior': n_kp_prior, 'weight_decay': weight_decay, 'kp_range': kp_range,\n",
        "           'run_prefix': run_prefix, 'mask_threshold': mask_threshold, 'use_tps': use_tps, 'use_pairs': use_pairs,\n",
        "           'use_object_enc': use_object_enc, 'use_object_dec': use_object_dec, 'warmup_epoch': warmup_epoch,\n",
        "           'iou_thresh': iou_thresh, 'anchor_s': anchor_s, 'learn_order': learn_order, 'kl_balance': kl_balance,\n",
        "           'milestones': milestones, 'image_size': image_size, 'enc_channels': enc_channels,\n",
        "           'prior_channels': prior_channels, 'exclusive_patches': exclusive_patches}\n",
        "\n",
        "# create dataloader\n",
        "dataloader = DataLoader(dataset, shuffle=True, batch_size=batch_size, num_workers=0, pin_memory=True,\n",
        "                        drop_last=True)\n",
        "# model\n",
        "model = KeyPointVAE(cdim=ch, enc_channels=enc_channels, prior_channels=prior_channels,\n",
        "                    image_size=image_size, n_kp=n_kp, learned_feature_dim=learned_feature_dim,\n",
        "                    use_logsoftmax=use_logsoftmax, pad_mode=pad_mode, sigma=sigma,\n",
        "                    dropout=dropout, dec_bone=dec_bone, patch_size=patch_size, n_kp_enc=n_kp_enc,\n",
        "                    n_kp_prior=n_kp_prior, kp_range=kp_range, kp_activation=kp_activation,\n",
        "                    mask_threshold=mask_threshold, use_object_enc=use_object_enc,\n",
        "                    exclusive_patches=exclusive_patches, use_object_dec=use_object_dec, anchor_s=anchor_s,\n",
        "                    learn_order=learn_order).to(device)\n",
        "\n",
        "logvar_p = torch.log(torch.tensor(sigma ** 2)).to(device)  # logvar of the constant std -> for the kl\n",
        "# prepare saving location\n",
        "run_name = f'{ds}_dlp_{dec_bone}' + run_prefix\n",
        "log_dir = prepare_logdir(runname=run_name, src_dir='./')\n",
        "fig_dir = os.path.join(log_dir, 'figures')\n",
        "save_dir = os.path.join(log_dir, 'saves')\n",
        "save_config(log_dir, hparams)\n",
        "\n",
        "kl_loss_func = ChamferLossKL(use_reverse_kl=False)\n",
        "if recon_loss_type == \"vgg\":\n",
        "    recon_loss_func = VGGDistance(device=device)\n",
        "else:\n",
        "    recon_loss_func = calc_reconstruction_loss\n",
        "betas = (0.9, 0.999)\n",
        "eps = 1e-4\n",
        "# we use separate optimizers for the encoder and decoder, but it is not really necessary...\n",
        "optimizer_e = optim.Adam(model.get_parameters(encoder=True, prior=True, decoder=False), lr=lr, betas=betas, eps=eps,\n",
        "                         weight_decay=weight_decay)\n",
        "optimizer_d = optim.Adam(model.get_parameters(encoder=False, prior=False, decoder=True), lr=lr, betas=betas,\n",
        "                         eps=eps, weight_decay=weight_decay)\n",
        "\n",
        "scheduler_e = optim.lr_scheduler.MultiStepLR(optimizer_e, milestones=milestones, gamma=0.5)\n",
        "scheduler_d = optim.lr_scheduler.MultiStepLR(optimizer_d, milestones=milestones, gamma=0.5)\n",
        "\n",
        "if load_model:\n",
        "    try:\n",
        "        model.load_state_dict(\n",
        "            torch.load(os.path.join(save_dir, f'{ds}_dlp_{dec_bone}.pth'), map_location=device))\n",
        "        print(\"loaded model from checkpoint\")\n",
        "    except:\n",
        "        print(\"model checkpoint not found\")\n",
        "\n",
        "# statistics\n",
        "losses = []\n",
        "losses_rec = []\n",
        "losses_kl = []\n",
        "losses_kl_kp = []\n",
        "losses_kl_feat = []\n",
        "\n",
        "# save PSNR values of the reconstruction\n",
        "psnrs = []\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    model.train()\n",
        "    batch_losses = []\n",
        "    batch_losses_rec = []\n",
        "    batch_losses_kl = []\n",
        "    batch_losses_kl_kp = []\n",
        "    batch_losses_kl_feat = []\n",
        "    batch_psnrs = []\n",
        "    pbar = tqdm(iterable=dataloader)\n",
        "    for batch in pbar:\n",
        "        if ds == 'shapes':\n",
        "            x = batch[0].to(device)\n",
        "            x_prior = x\n",
        "        else:\n",
        "            x = batch.to(device)\n",
        "            x_prior = x\n",
        "        batch_size = x.shape[0]\n",
        "        # forward pass\n",
        "        noisy_masks = (epoch < 5 * warmup_epoch)  # add small noise to the alpha masks\n",
        "        model_output = model(x, x_prior=x_prior, warmup=(epoch < warmup_epoch), noisy_masks=noisy_masks)\n",
        "        mu_p = model_output['kp_p']\n",
        "        gmap = model_output['gmap']\n",
        "        mu = model_output['mu']\n",
        "        logvar = model_output['logvar']\n",
        "        rec_x = model_output['rec']\n",
        "        mu_features = model_output['mu_features']\n",
        "        logvar_features = model_output['logvar_features']\n",
        "        # object stuff\n",
        "        dec_objects_original = model_output['dec_objects_original']\n",
        "        cropped_objects_original = model_output['cropped_objects_original']\n",
        "        obj_on = model_output['obj_on']  # [batch_size, n_kp]\n",
        "\n",
        "        # reconstruction error\n",
        "        if use_object_dec and dec_objects_original is not None and epoch < warmup_epoch:\n",
        "            # reconstruct patches in the warmup stage\n",
        "            if recon_loss_type == \"vgg\":\n",
        "                _, dec_objects_rgb = torch.split(dec_objects_original, [1, 3], dim=2)\n",
        "                dec_objects_rgb = dec_objects_rgb.reshape(-1, *dec_objects_rgb.shape[2:])\n",
        "                cropped_objects_original = cropped_objects_original.reshape(-1,\n",
        "                                                                            *cropped_objects_original.shape[2:])\n",
        "                # vgg has a minimal input size, so we interpolate if the patch is too small\n",
        "                if cropped_objects_original.shape[-1] < 32:\n",
        "                    cropped_objects_original = F.interpolate(cropped_objects_original, size=32, mode='bilinear',\n",
        "                                                             align_corners=False)\n",
        "                    dec_objects_rgb = F.interpolate(dec_objects_rgb, size=32, mode='bilinear',\n",
        "                                                    align_corners=False)\n",
        "                loss_rec_obj = recon_loss_func(cropped_objects_original, dec_objects_rgb, reduction=\"mean\")\n",
        "\n",
        "            else:\n",
        "                _, dec_objects_rgb = torch.split(dec_objects_original, [1, 3], dim=2)\n",
        "                dec_objects_rgb = dec_objects_rgb.reshape(-1, *dec_objects_rgb.shape[2:])\n",
        "                cropped_objects_original = cropped_objects_original.clone().reshape(-1,\n",
        "                                                                                    *cropped_objects_original.shape[\n",
        "                                                                                     2:])\n",
        "                loss_rec_obj = calc_reconstruction_loss(cropped_objects_original, dec_objects_rgb,\n",
        "                                                        loss_type='mse', reduction='mean')\n",
        "            loss_rec = loss_rec_obj\n",
        "        else:\n",
        "            # reconstruct full image\n",
        "            if recon_loss_type == \"vgg\":\n",
        "                loss_rec = recon_loss_func(x, rec_x, reduction=\"mean\")\n",
        "            else:\n",
        "                loss_rec = calc_reconstruction_loss(x, rec_x, loss_type='mse', reduction='mean')\n",
        "\n",
        "            with torch.no_grad():\n",
        "                psnr = -10 * torch.log10(F.mse_loss(rec_x, x))\n",
        "                batch_psnrs.append(psnr.data.cpu().item())\n",
        "\n",
        "        # kl-divergence\n",
        "        logvar_kp = logvar_p.expand_as(mu_p)\n",
        "\n",
        "        # the final kp is the bg kp which is located in the center (so no need for it)\n",
        "        # to reproduce the results on celeba, use `mu_post = mu`, `logvar_post = logvar`\n",
        "        mu_post = mu[:, :-1]\n",
        "        logvar_post = logvar[:, :-1]\n",
        "        # mu_post = mu\n",
        "        # logvar_post = logvar\n",
        "        mu_prior = mu_p\n",
        "        logvar_prior = logvar_kp\n",
        "\n",
        "        loss_kl_kp = kl_loss_func(mu_preds=mu_post, logvar_preds=logvar_post, mu_gts=mu_prior,\n",
        "                                  logvar_gts=logvar_prior).mean()\n",
        "\n",
        "        if learned_feature_dim > 0:\n",
        "            loss_kl_feat = calc_kl(logvar_features.view(-1, logvar_features.shape[-1]),\n",
        "                                   mu_features.view(-1, mu_features.shape[-1]), reduce='none')\n",
        "            loss_kl_feat = loss_kl_feat.view(batch_size, n_kp_enc + 1).sum(1).mean()\n",
        "        else:\n",
        "            loss_kl_feat = torch.tensor(0.0, device=device)\n",
        "        loss_kl = loss_kl_kp + kl_balance * loss_kl_feat\n",
        "\n",
        "        loss = beta_rec * loss_rec + beta_kl * loss_kl\n",
        "        # backprop\n",
        "        optimizer_e.zero_grad()\n",
        "        optimizer_d.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer_e.step()\n",
        "        optimizer_d.step()\n",
        "        # log\n",
        "        batch_losses.append(loss.data.cpu().item())\n",
        "        batch_losses_rec.append(loss_rec.data.cpu().item())\n",
        "        batch_losses_kl.append(loss_kl.data.cpu().item())\n",
        "        batch_losses_kl_kp.append(loss_kl_kp.data.cpu().item())\n",
        "        batch_losses_kl_feat.append(loss_kl_feat.data.cpu().item())\n",
        "        # progress bar\n",
        "        if use_object_dec and epoch < warmup_epoch:\n",
        "            pbar.set_description_str(f'epoch #{epoch} (warmup)')\n",
        "        elif use_object_dec and noisy_masks:\n",
        "            pbar.set_description_str(f'epoch #{epoch} (noisy masks)')\n",
        "        else:\n",
        "            pbar.set_description_str(f'epoch #{epoch}')\n",
        "        pbar.set_postfix(loss=loss.data.cpu().item(), rec=loss_rec.data.cpu().item(),\n",
        "                         kl=loss_kl.data.cpu().item())\n",
        "    pbar.close()\n",
        "    losses.append(np.mean(batch_losses))\n",
        "    losses_rec.append(np.mean(batch_losses_rec))\n",
        "    losses_kl.append(np.mean(batch_losses_kl))\n",
        "    losses_kl_kp.append(np.mean(batch_losses_kl_kp))\n",
        "    losses_kl_feat.append(np.mean(batch_losses_kl_feat))\n",
        "    if len(batch_psnrs) > 0:\n",
        "        psnrs.append(np.mean(batch_psnrs))\n",
        "    # keep track of bounding box scores to set a hard threshold (as bb scores are not normalized)\n",
        "    # epoch_bb_scores = torch.cat(batch_bb_scores, dim=0)\n",
        "    # bb_mean_score = epoch_bb_scores.mean().data.cpu().item()\n",
        "    # bb_mean_scores.append(bb_mean_score)\n",
        "    # schedulers\n",
        "    scheduler_e.step()\n",
        "    scheduler_d.step()\n",
        "    # epoch summary\n",
        "    log_str = f'epoch {epoch} summary for dec backbone: {dec_bone}\\n'\n",
        "    log_str += f'loss: {losses[-1]:.3f}, rec: {losses_rec[-1]:.3f}, kl: {losses_kl[-1]:.3f}\\n'\n",
        "    log_str += f'kl_balance: {kl_balance:.4f}, kl_kp: {losses_kl_kp[-1]:.3f}, kl_feat: {losses_kl_feat[-1]:.3f}\\n'\n",
        "    log_str += f'mu max: {mu.max()}, mu min: {mu.min()}\\n'\n",
        "    if obj_on is not None:\n",
        "        log_str += f'obj_on max: {obj_on.max()}, obj_on min: {obj_on.min()}\\n'\n",
        "    if len(psnrs) > 0:\n",
        "        log_str += f'mean psnr: {psnrs[-1]:.3f}\\n'\n",
        "    print(log_str)\n",
        "    log_line(log_dir, log_str)\n",
        "\n",
        "    if epoch % eval_epoch_freq == 0 or epoch == num_epochs - 1:\n",
        "        max_imgs = 8\n",
        "        img_with_kp = plot_keypoints_on_image_batch(mu[:, :-1].clamp(min=kp_range[0], max=kp_range[1]),\n",
        "                                                    x, radius=3, thickness=1, max_imgs=max_imgs, kp_range=kp_range)\n",
        "        img_with_kp_p = plot_keypoints_on_image_batch(mu_p, x_prior, radius=3, thickness=1, max_imgs=max_imgs,\n",
        "                                                      kp_range=kp_range)\n",
        "        # top-k\n",
        "        with torch.no_grad():\n",
        "            logvar_sum = logvar[:, :-1].sum(-1)\n",
        "            logvar_topk = torch.topk(logvar_sum, k=topk, dim=-1, largest=False)\n",
        "            indices = logvar_topk[1]  # [batch_size, topk]\n",
        "            batch_indices = torch.arange(mu.shape[0]).view(-1, 1).to(mu.device)\n",
        "            topk_kp = mu[batch_indices, indices]\n",
        "            # bounding boxes\n",
        "            masks = create_masks_fast(mu[:, :-1].detach(), anchor_s=model.anchor_s, feature_dim=x.shape[-1])\n",
        "            masks = torch.where(masks < mask_threshold, 0.0, 1.0)\n",
        "            bb_scores = -1 * logvar_sum\n",
        "            hard_threshold = bb_scores.mean()\n",
        "        if use_object_dec:\n",
        "            img_with_masks_nms, nms_ind = plot_bb_on_image_batch_from_masks_nms(masks, x, scores=bb_scores,\n",
        "                                                                                iou_thresh=iou_thresh,\n",
        "                                                                                thickness=1, max_imgs=max_imgs,\n",
        "                                                                                hard_thresh=hard_threshold)\n",
        "            # hard_thresh: a general threshold for bb scores (set None to not use it)\n",
        "            bb_str = f'bb scores: max: {bb_scores.max():.2f}, min: {bb_scores.min():.2f},' \\\n",
        "                     f' mean: {bb_scores.mean():.2f}\\n'\n",
        "            print(bb_str)\n",
        "        log_line(log_dir, bb_str)\n",
        "        img_with_kp_topk = plot_keypoints_on_image_batch(topk_kp.clamp(min=kp_range[0], max=kp_range[1]), x,\n",
        "                                                         radius=3, thickness=1, max_imgs=max_imgs,\n",
        "                                                         kp_range=kp_range)\n",
        "        if use_object_dec and dec_objects_original is not None:\n",
        "            dec_objects = model_output['dec_objects']\n",
        "            vutils.save_image(torch.cat([x[:max_imgs, -3:], img_with_kp[:max_imgs, -3:].to(device),\n",
        "                                         rec_x[:max_imgs, -3:], img_with_kp_p[:max_imgs, -3:].to(device),\n",
        "                                         img_with_kp_topk[:max_imgs, -3:].to(device),\n",
        "                                         dec_objects[:max_imgs, -3:],\n",
        "                                         img_with_masks_nms[:max_imgs, -3:].to(device)],\n",
        "                                        dim=0).data.cpu(), '{}/image_{}.jpg'.format(fig_dir, epoch),\n",
        "                              nrow=8, pad_value=1)\n",
        "            with torch.no_grad():\n",
        "                _, dec_objects_rgb = torch.split(dec_objects_original, [1, 3], dim=2)\n",
        "                dec_objects_rgb = dec_objects_rgb.reshape(-1, *dec_objects_rgb.shape[2:])\n",
        "                cropped_objects_original = cropped_objects_original.clone().reshape(-1, 3,\n",
        "                                                                                    cropped_objects_original.shape[\n",
        "                                                                                        -1],\n",
        "                                                                                    cropped_objects_original.shape[\n",
        "                                                                                        -1])\n",
        "                if cropped_objects_original.shape[-1] != dec_objects_rgb.shape[-1]:\n",
        "                    cropped_objects_original = F.interpolate(cropped_objects_original,\n",
        "                                                             size=dec_objects_rgb.shape[-1],\n",
        "                                                             align_corners=False, mode='bilinear')\n",
        "            vutils.save_image(\n",
        "                torch.cat([cropped_objects_original[:max_imgs * 2, -3:], dec_objects_rgb[:max_imgs * 2, -3:]],\n",
        "                          dim=0).data.cpu(), '{}/image_obj_{}.jpg'.format(fig_dir, epoch),\n",
        "                nrow=8, pad_value=1)\n",
        "        else:\n",
        "            vutils.save_image(torch.cat([x[:max_imgs, -3:], img_with_kp[:max_imgs, -3:].to(device),\n",
        "                                         rec_x[:max_imgs, -3:], img_with_kp_p[:max_imgs, -3:].to(device),\n",
        "                                         img_with_kp_topk[:max_imgs, -3:].to(device)],\n",
        "                                        dim=0).data.cpu(), '{}/image_{}.jpg'.format(fig_dir, epoch),\n",
        "                              nrow=8, pad_value=1)\n",
        "        torch.save(model.state_dict(),\n",
        "                   os.path.join(save_dir, f'{ds}_dlp_{dec_bone}{run_prefix}.pth'))\n",
        "    # plot graphs\n",
        "    if epoch > 0:\n",
        "        num_plots = 3\n",
        "        fig = plt.figure()\n",
        "        ax = fig.add_subplot(num_plots, 1, 1)\n",
        "        ax.plot(np.arange(len(losses[1:])), losses[1:], label=\"loss\")\n",
        "        ax.set_title(run_name)\n",
        "        ax.legend()\n",
        "\n",
        "        ax = fig.add_subplot(num_plots, 1, 2)\n",
        "        ax.plot(np.arange(len(losses_kl[1:])), losses_kl[1:], label=\"kl\", color='red')\n",
        "        if learned_feature_dim > 0:\n",
        "            ax.plot(np.arange(len(losses_kl_kp[1:])), losses_kl_kp[1:], label=\"kl_kp\", color='cyan')\n",
        "            ax.plot(np.arange(len(losses_kl_feat[1:])), losses_kl_feat[1:], label=\"kl_feat\", color='green')\n",
        "        ax.legend()\n",
        "\n",
        "        ax = fig.add_subplot(num_plots, 1, 3)\n",
        "        ax.plot(np.arange(len(losses_rec[1:])), losses_rec[1:], label=\"rec\", color='green')\n",
        "        ax.legend()\n",
        "        plt.tight_layout()\n",
        "        plt.savefig(f'{fig_dir}/{run_name}_graph.jpg')\n",
        "        plt.close('all')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "71d6bb46",
      "metadata": {
        "id": "71d6bb46"
      },
      "source": [
        "#### Visualization and Manipulation\n",
        "---\n",
        "* Let's visualize some of the model's output and manipulate learned particles."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f1989531",
      "metadata": {
        "id": "f1989531"
      },
      "outputs": [],
      "source": [
        "num_images = 4\n",
        "topk = 5  # top 5 particles with highest confidence\n",
        "hard_threshold = 24  # threshold score for bounding boxes\n",
        "iou_thresh = 0.2  # IoU for non-maximal supression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3b5220e1",
      "metadata": {
        "id": "3b5220e1"
      },
      "outputs": [],
      "source": [
        "gt_image = x[:num_images, -3:].permute(0, 2, 3, 1).data.cpu().numpy()\n",
        "img_with_posterior_kp = img_with_kp[:num_images, -3:].permute(0, 2, 3, 1).data.cpu().numpy()\n",
        "img_with_kp_proposals = img_with_kp_p[:num_images, -3:].permute(0, 2, 3, 1).data.cpu().numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3131a95e",
      "metadata": {
        "id": "3131a95e",
        "outputId": "4eacee2c-78a7-4a83-9281-862db1937282"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(10, 10))\n",
        "for i in range(num_images):\n",
        "    ax = fig.add_subplot(1, num_images, i + 1)\n",
        "    ax.imshow(gt_image[i])\n",
        "    ax.set_axis_off()\n",
        "    if i == 0:\n",
        "        ax.set_title('ground truth image')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "98fff901",
      "metadata": {
        "id": "98fff901",
        "outputId": "bf0b2782-f949-4c6c-eab6-0cf416ff6344"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(10, 10))\n",
        "for i in range(num_images):\n",
        "    ax = fig.add_subplot(1, num_images, i + 1)\n",
        "    ax.imshow(img_with_posterior_kp[i])\n",
        "    ax.set_axis_off()\n",
        "    if i == 0:\n",
        "        ax.set_title('learned particles')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bfd11704",
      "metadata": {
        "id": "bfd11704",
        "outputId": "188bc2f3-538f-4beb-bb40-c2ed4717811e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(10, 10))\n",
        "for i in range(num_images):\n",
        "    ax = fig.add_subplot(1, num_images, i + 1)\n",
        "    ax.imshow(img_with_kp_proposals[i])\n",
        "    ax.set_axis_off()\n",
        "    if i == 0:\n",
        "        ax.set_title('keypoint proposals (prior)')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9513f861",
      "metadata": {
        "id": "9513f861"
      },
      "outputs": [],
      "source": [
        "# top-k\n",
        "with torch.no_grad():\n",
        "    logvar_sum = logvar[:, :-1].sum(-1)\n",
        "    logvar_topk = torch.topk(logvar_sum, k=topk, dim=-1, largest=False)\n",
        "    indices = logvar_topk[1]  # [batch_size, topk]\n",
        "    batch_indices = torch.arange(mu.shape[0]).view(-1, 1).to(mu.device)\n",
        "    topk_kp = mu[batch_indices, indices]\n",
        "    img_with_kp_topk = plot_keypoints_on_image_batch(topk_kp.clamp(min=kp_range[0], max=kp_range[1]), x,\n",
        "                                                         radius=3, thickness=1, max_imgs=max_imgs,\n",
        "                                                         kp_range=kp_range)\n",
        "    img_with_topk_kp = img_with_kp_topk[:num_images, -3:].permute(0, 2, 3, 1).data.cpu().numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9b4afcc3",
      "metadata": {
        "id": "9b4afcc3",
        "outputId": "8d343357-34ca-4220-d49c-785916a46eb9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(10, 10))\n",
        "for i in range(num_images):\n",
        "    ax = fig.add_subplot(1, num_images, i + 1)\n",
        "    ax.imshow(img_with_topk_kp[i])\n",
        "    ax.set_axis_off()\n",
        "    if i == 0:\n",
        "        ax.set_title(f'top-{topk} particles')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8cbe5dc2",
      "metadata": {
        "id": "8cbe5dc2"
      },
      "outputs": [],
      "source": [
        "# bounding boxes\n",
        "with torch.no_grad():\n",
        "    masks = create_masks_fast(mu[:, :-1].detach(), anchor_s=model.anchor_s, feature_dim=x.shape[-1])\n",
        "    masks = torch.where(masks < mask_threshold, 0.0, 1.0)\n",
        "    img_with_masks_nms, nms_ind = plot_bb_on_image_batch_from_masks_nms(masks, x, scores=bb_scores,\n",
        "                                                                        iou_thresh=iou_thresh,\n",
        "                                                                        thickness=1, max_imgs=max_imgs,\n",
        "                                                                        hard_thresh=hard_threshold)\n",
        "    img_with_bb = img_with_masks_nms[:num_images, -3:].permute(0, 2, 3, 1).data.cpu().numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0b494cf6",
      "metadata": {
        "id": "0b494cf6",
        "outputId": "d547e828-20e3-45c2-f492-9e86292c58e9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# this can be further improved with smarter thresholding and learning the scale of the boxes\n",
        "fig = plt.figure(figsize=(10, 10))\n",
        "for i in range(num_images):\n",
        "    ax = fig.add_subplot(1, num_images, i + 1)\n",
        "    ax.imshow(img_with_bb[i])\n",
        "    ax.set_axis_off()\n",
        "    if i == 0:\n",
        "        ax.set_title(f'bounding boxes')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cd0839dd",
      "metadata": {
        "id": "cd0839dd"
      },
      "outputs": [],
      "source": [
        "# reconstruction and generated objects\n",
        "reconsturction = rec_x[:num_images, -3:].permute(0, 2, 3, 1).data.cpu().numpy().clip(0, 1)\n",
        "all_objects_with_masks = dec_objects[:num_images, -3:].permute(0, 2, 3, 1).data.cpu().numpy().clip(0, 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fb092909",
      "metadata": {
        "id": "fb092909",
        "outputId": "813d8f19-6fc6-40dc-947d-0ddfe898e390"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(10, 10))\n",
        "for i in range(num_images):\n",
        "    ax = fig.add_subplot(1, num_images, i + 1)\n",
        "    ax.imshow(reconsturction[i])\n",
        "    ax.set_axis_off()\n",
        "    if i == 0:\n",
        "        ax.set_title(f'reconstructions')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "34160d3a",
      "metadata": {
        "id": "34160d3a",
        "outputId": "3f1b2334-c571-4503-8602-11dfa58adb3a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(10, 10))\n",
        "for i in range(num_images):\n",
        "    ax = fig.add_subplot(1, num_images, i + 1)\n",
        "    ax.imshow(all_objects_with_masks[i])\n",
        "    ax.set_axis_off()\n",
        "    if i == 0:\n",
        "        ax.set_title(f'objects and masks')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "77eabf00",
      "metadata": {
        "id": "77eabf00"
      },
      "outputs": [],
      "source": [
        "# manipulation - let's move the particles a bit and see what happens\n",
        "mu_manip = mu + 0.3\n",
        "with torch.no_grad():\n",
        "    rec_new, _, _ = model.decode_all(mu_manip, mu_features, kp_heatmap=None, obj_on=obj_on, deterministic=True)\n",
        "    rec_new = rec_new.clamp(0, 1)\n",
        "    manip_reconstruction = rec_new[:num_images, -3:].permute(0, 2, 3, 1).data.cpu().numpy().clip(0, 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "008f61cf",
      "metadata": {
        "id": "008f61cf",
        "outputId": "874e8afd-6816-4534-a2ab-d49a20bd399d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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EIqSfexe3vIS5tQjt5qxzFgiMwDgz89n0ucV2wm9/7yGvPdvkXpqyIIpWo92HE73niCIiYFMik/G7P/2Ee67B//vOPR4sLkzJTX2I0NPjyOKck/HrPHOE0qeSZlSSFFWlFUd8595tLEpeigpzhOJk5zU7DkENOCuIBbFgXY5VR2YiUmNxSZfK9jZmYQGiozaGHiL0aP+/ocRZRpTlrOw0MG7X1F4UjCpRntOsVNhaqJFGlmwiS2uTE3qCwHMhGTJL1MKnzwjZvXvky0vYVYMkrdnk7VDCABs4jilHJxuXYjZc7aZ86cMnvLqxw/UsIxJFSwZ1oM6xR5IRRWyOcY4vPH/OvbzNL9+9yePFeuF9u68Ph24xBSarPRjKMclHWU4pzciBTmT5+MYVsAZnrc+eU0RBHP12ocbbhBEJWPGRetQvj+EUl2bEroXoZEVno0opTakkCVc3NonyvB8LUYAod5TSjPXlRZLIQqVMbu1BP+HTFvlR0WyOIQg8F5Uhle9qNbRUwr3zNpImpO03EJedfd4CgQuEAKU0p95NubrdYLXZwkiOCKgYvw6hihr8YG38CrOiOANxplQ7jlo3o9rNaJYi8ktvd3hWnIHQc8SVG9UynVJM9/VXiZxDI+OdS/rx0or/+g/5geUsQ3GcFyl6m0F3VMlFSON4IvkUoJwkLLZa/Oy//CrXX6xz5+FToqwXDdE7xBin2NzRLJfYqlX42pe/wK/86OfplEskE8rLabkAAs9JpkAnXCfvX2qUlcrjvI3OGBGIYzSK0Uq1yMet2eRlD5d96nrC2c9YxXbZy3hKFJodUaWaZNS7ST/Uv6FwHxYtlpL7JxUhIugLQDZXSqmj3klZ6CR0IkMuRUzYvnvxIcangTNkwga1IiRxRBJDq3LKCMl64M0J8zQkCVUq3YTlRpMf+cH7vProCbcfeIFHxS+ZqRhQxeRK11rakeX5lTW+886bOGNIo4j+BqgnYkxb3UPc8kWPME468se54QwFnh7HrHMenfhZFOkRDWtIx1CZbtAG0aNu+hw0salycQSeabaj0ceiCZtNHnVL6pcbllod/vAvfIN7z7f4HR89pJY7orJFjcFZUyxF+Dxpz35HQJyPrmKcRTE8iKs8rVf5yz/349y/vkSjUtr12jpwSye5x2M8iIYmfbb9c/pj0bB2NMolT7COcpaKoxMLPIeZP3isOqrdhD/0T/4Zrz98xBe/9x71ThtTuMursYXTmfSTkU6KaXV5evUaT65e5e/+np/ma1/8YbpxTG7N0X1qQhzWji6AhqfHCIU4C83lgWueIBOTlDvlwJsz4LIJNSMK4Sfp+Ie2haPSukyGIBO+x2NWPMppzkIr4Y1H69x9uUm9m2KNkEnstwAoNDWuZ+yAdxxgYNUiShWbZbz5bJ3rpZhr2y02Fqu0ynHfO+dAW9FhmRqzbYzU/C5D2znNvR0SvmAi7t4jHDOVYXy3zktZTi3p8saDh7x9/xOWGg0il9NdrHmB3vQ0PL02LUQux7aVmy9ecP3FBr/2+XepdBMyY/zmpjNcrr1AAk8gMMf0BJVzFwE1cBhxmvMT3/iQmy93sAibK4t849YKAJJ7wUbFz2j79juqIN5Ow0URai3GGAShvNVGnfI7P3zGZ9Yb/PyPvclmrXQOo+YGLgJGlS8+fsy9F8956/0PufngITbCu8s3FBVBpNBg9uyJLLjUkUQO20qIWjl3P/qEL62u8O23XuPJ1TWIo5mNgxdA4DlEwubor6fHKZfLhia5X619mthDw9IfMz8XipPc/DEz6cFJsfM2HJK54sE3OMOZwHq7jLMufhlm65Nmr8Ho/j+lJCNOczYXqzRFyZYqAJgk92eLr2814j1anKLGoGJwsRd4iCwiQqlaxqQOBMqZ6xuhjl+/o9/aKQ8I9NlXVieJp3SS4j5wzqh9/Ij89X5SWGm3uL69Q73VotzpkNdKOAGy3G9VJIVruvXjkN/k1OFEMaqIy6k1W6yubxEnCd5lUaHYMmMyS7WjcwEEnlkxYVuBwDlixLp3inQyTCcjft72HhjVuBB8emnowbGydw3pXWvgB1VEFbWCVixasrhq6MazII0tX/nRN4lyR+xcYYjcm5zsm4jtbzJF3faXAvA2Pf48ITfCVrU0JFLuWRHGtkuPKsvbLa5u7GAjS16r+J3bC82lt0DrCToMjFf+e1eOSE1EpdNh9ekLSq0Ou2u0s+ECjZQTtqAfiSHXmOZl93iJQRiUZskIFe1AWhlmq0v08baf2VdKqJFixl8IPMCuWWvvY8/9dO91RBVxilYs+dUKbiGGajTixC60l9NRlF/R/1SEZr18Jpc8m6oL7eNMmYYh+oFnxNGHH2CfQ06U5cRpBsag1hbCjhdoeu+Rg5cTfP9Qg98wN832eSsekp8D+Z5sm7xAAk/Ac6nXp+YK6WZE728Tf3+T6t96H0lBJfbLGRh83NTc/1V30BsUiuo09FQEgmI1I79Vp/szt8k+s0z+O6uh2gOBwOnYNwCJQpRkxJ0UJ5YsLuGKiZkrNDzOK3T8wcV76f1zCrnSddAEMmPA2pnaMV5ggWdgNja18h0ifY66dqvqlzxy5+079sz6QXLn1/tj796q0YA7335Pq5lZ818EjrEBG+ncHsOmL4JmgmwrJArkiDhkn8CD7sYjPZAj0YEU1a+DLzrIldGCqYaZ+1Q4TdOZK0L7GJ1TVPaJXcdHSVSGfjxdXhR1Di2eRTj1c6+edodd7yxEB0JG+XGvt0ybGyGxFmcM9OJLHcaUFy8usMAz5+SKSTKiZod4s4UrR7hS5GNzqBI1upgko3tlkbwaky1W0CP3RQnMG2oN+XIVWUjJZAmRQjoRLYQYRYn7qt6e2cegjWwvxoUMfOFEcXGNfKmGq54yYFkgEAj06E281AsrLs/JsgySHOk6KFuwfnLeX84CxBRL8L1JuzGIy7CZkkQltus1H/nZnE0cnsO4BAKPnsxi/pCkDvkwRhreBsM2OpSfbVN62aDyaNNrcmLrLdtVse0Ekzmar14hXa7SvHeVvOofogcazHSXPQN7GKNwBYiM3/NGevpeb5QqvXgsR7jhqOheu2WVgVN6M6sh15zKTDKwyyEeOccdt4dTDkYT6fOhfUyeoR4IZ3jd47T+h/zQF3S07x0o6tisV3m+vEhHDLXcIbk32JHCy8pHDe+7WfT/GlUwPvRCp16lubpEVopnrg29BALPnOEU082oPtpk9avvU3v/OYvfeoARMAIWxYg3TsUK61+4R+v2Klm9TOfaEnktnqmEHBgDEbRioGT3yiaihVem7gowgGqhJu55b6G7Wh5/xNnlPRAIXHy08KnqmVioK1YZHOIcH928SqtkefuXY+pJTlUdxhpc5KOImwEzi94WYEbBOsVFEYm1bN26xrPXX6G7WJ/5s+uSCDxD1gqO4lR1steTY3d27htU3OhQ+3Sd2scvqX/4kuhFB+dKfjPBQpfowMc4yAX7okvZ7bDw3lPizRaNN2+QV+O9Nj0Tzf9lY9TCOk2QDIfgAL/ZnhdrXN+B02/N5rU/okJvW20tlsCkbw2oA17sPvDX4fkPwtHZMZEAKvsYsxPv0ewFpse8F/IY+Su2RukLOS7HOIfkDlO8b1rLeqXCRzevk2Q5r71cp5LnqCu0zbYwUFZXaIW8iWHqhJfX1nh55QqP79xge22FtDT7DUTnWOC5YD1YwaSO8osGV3/lA6ofr7P4Gw9IKdMxS5jC6MtIz6XP2+tEn3aJH3WQPKN7c5Hu6gJ6pU5eLwfB5jzQCzwoKV530wu56+i17/6yVd/Avqfd6Wl6ingX2lv+kmK3bTlkNeUC9ZvAQQ7r9xdsyAxMkT3CjsPkOSbLsVmKyXNsliHOsRlFNBYW+FfvvMmD5SUWv/J11potyjkYEQymWMLqpakkmdBxwvv3XuVbP/4jfO/113h648as7xiYa4EHTteDjzL3PibNUTVB4+QmyyltNCk/2aH2g5dEL9tklHASYeg5Hvt1VJGeFkBQDKqKfd4hzqH2/jPsziLNd26ipTmvvnPBcZ5Wp8CBdHMkyfshKHZ9sXramd43stvc9yxjyZ5z+t4RQ3IeuEgc4W5zuNlXEHoCx9MPh+PtSU2eY9MMmyZESYJNU2zqBR9RxTjH+uoSmTX8xg+9zXKzydV2i0hzYuN/t1lWjEnCi5trPLpxk4/feYOPbt9ms74w/lLWlLy1zuETc7DgitKQAReX/YfNSZA+k+aUXzSoPthi4dvPcamQmAoqghHtv3Zd+wbiHahinraJtrssfPsR0dYKrTevo8FBh731OmKnGtZejj9pyPWOOcMp0s6QTrHNQCHA7k1jn4XOgLBzUOgpAn718iOzbtWXBOkVdCjtc88oVXiuNeejjFPa18b0tDs2TYi7XeJOh6jbJep2sVnWHydfXFlmfW2JRrVCvdHkjQ/uU+l2qaQJNsuIu4kPsmEs77/+Bt/8qR9n88oqW1dWZ263M8gcCzyHVdjgzOcIi/SJ7DA+wQHOgWll0HWklFBRnIjX5ogrDL50t71K8WhT+rYbIDhr/f47F5lhxT6SqYru7Vypg9Qhm22k2YU0hTzvtw2NYyhF6O1lKEV+P5hD63z8TqvFGrdPt5eMj78zmN5xOkjd9U/vq3b88zc8gM+EmZbzYV41cM6fzLMhFFkRNsdrdkyeYZOEuNOh1G5TajaJOx3iTgebpv24cGm5jBpLq1amWynRXaj7kDxFqBRRUPECz8bVNTaurtGtVk8u7Eypy82xwHMa5k+vK079TD9RMom8BkcMItp/FetZhe3GwENRC78dEdRY3GECjx54w0iSwhxJ4Ie6VO/R8B+R38GZeOqgnSFPG5hnO9BuIWmCusIouFqFeoV8rQ7WgLGTHRCFIqDk7mftC+q7wvrgLe0uVXnj5T1anlG0lfPV7GfPCRSA54fTqCtCQzm/nLIha6HhcQ6TZdg0Jep2iVstSs0m5XabUquFTRO/bx+CrVbJ45jWygpZucyjlVXyUky3XsfZiLxUKp5Pg0v2OjBZ2OfEMyPmR+A5al36RInNF2oN6WKZtLfbrHgTVkVRKTx1DjwLlf4GbYAaQ7pSJVuuDjSsC4YceDPCCbr34/Mm5sEWZnMbs7W9q+HJMsjzog8KGkcQW8zOC7RaIV+9Bks19O0re7UyJ72VXJGdFNPM+zPy3oJUX3OH7lPiDV5X+lq+XkvYHT7mr43PJRe0m4zOsIE1tJ1LS+EL0bfdyTLibpdSu0252aTa2KHUbFLqa3i8ptrmOVkck8cxJs9JqlUEJS1XQATnnN9va1rGNxNifgSesZnPAj0MNUJeiXFlW+hutHBFLx56A4Hk+iYDFMI4hWZABFcrkddKex+Mh2lFDv3uAtHzburdp1Nkq4N8tI59+QL78gUkGZLlaLFTrxbSpfZc4jZfoOUy7pagN5bh9dUhEUFPUI4OpF1EKB146Ow1O943Axq2wR4DYl3vvwterYFJEhrL+MzfKsEuI2gHemPXgWdD8TQZsN8x2e6yVtwulra6XWyaFmn59IzLSbpdVASbpjhrMS5HnfGaoMLz60jP0f5wd9xMZDplPz8CzwWfiUmeE2+3iJptjCag4NQWhslgikYs6EAAur72cbeBZr6BBgYo1oTkZQPz/nPMo3XsR0+gk+K6IM7sMcPoLw0VOz1oW6CbE6WfopsvcdpFbyzhPnfHxwk4TdYce2xAxFcm/WBfsisA7frmFfka/Kt70+kvfwbOjolqoQPzzbwKO5NB2PWuipNuYcPTotTxwk6cJLvPGRGc8ctbcdJFjcFmGXnuY/U450CdF3ZUfCyx3vL9nPWX+RF4DuPcDjI9iXZ35m7SDMky/xQUANs/zt+mF3aQAaFHd3/zk47iQXnBO+SJaKWYBxuY55uYjW1UjX95Y5qBAwdso3oSJ4pJt9G0C/fL3nrmnVsgp7DrObA51mC99Sx19pknj+15dS47RyAQOBX7Rwk5+ucDv+ueGDw297Y8Nsuwee6/c66/zG6dQ53/3rhdd/W+t1fPXGdgfd47w86X0DP/As8cFdapsIZssUR2pUJydwG7nWCfd73Xjhi/YfbA3kpaCD/ichDIVyukaxXSqwukq3WKvQkCA9hWk9KDT9Bmjjo7sJ48SG8jzsJ2pjcTUUFdCek4oocPcTbDPb4Di2V0tXqyDIm/jtfoOHoWO7JH6Bkm3kj//934PRTp0Dd0HjD3CpwFw1Y4QwUEzhW6Z85lVP1WEAoWwSIY7cWF6605eG9iI4Vjh7GoMf4lxhsrDxuM+sLP/Ag9U3pqhqF4P2oEV4nIFsqk1+pkyxW/qaShr8bxGh3xH3tLIaJgIVsuk61WyRfKuMrsN2GbKxTIHSQpptVCkhRVM2Qx+QiDOgEwkINptZFGC9lsQysdX5k2xDTn8EQOr8iD3tD7nri7Rj0D1wyav0Bg5vQ2ljqzZ+F+u8B9rz3Z6GmX2f1bhEjp/x00KBXpT8y9oLMr8Oze52Bih348ePyhTKfc5l/Dc0Fw5Yj2a1fpXl+mc3uN+g+esfpPv0+81SF+0UQlQtXSl6s1Q1Cy1TL5UomXv++zdG4v037rKnkl9rumBzzdDHneQl62IVXIB6YwPaGx9/nANH0gqrV4TQ9qkPU29lc+xH3mBvmtRTjRerSg1qDW4qQXSsB43ZKK3w1dd5e2dGA9a78s009RBSFH1GFc5tfPA2fPicfieTaGDVx4ZFeI0UKAccbiIu9anpXKmDxHER892fjj0mqVrFQiq9XJqlXySpW8XMbFMS6yfSEIMYU2etY3OpwpCTyhQ+9SlIUIrhyj1pKIEO10SW4tgRVsK8GZCCcxPYHH5IKIkq5VydYqdG8tkdxYJK+V0NjuSXr3yejXZU2eYHJvZLYbl7dYV0UPnMbC1WkXwnTJHbQS6GTeE2vXypeDbVH7Yo/s+bQrDCnGC06bHZ9u8e3xqtl9ah3BC6Zm8LcBm62BJJXBODt7U9v7RW/NXJHcb/oXOE+E+rpYHKE1HnrcKMcew0mWU/eoiwsv1UGhx1ryKMJFES6KcXkRq8wYMEIeeZf03stFkQ+C21vWKjxeh3tonYBRi3VMgobnjFFryJYqND97nfRKjeqHz1n85kOcDAo8YLMUxNH8/C2S6wu03ryKq5X8LumHYNIuUWebhc2PWH7xPaK8S5QnRC7Hak6UZ1iX45x3Iew/K3/vn53+jU8R6aaYJ5vIZhOcoD3rOdkrZAz2ooNCD4W2RVBi1FlIHGQ9LVEhFPUMf0ZAraBLFq0LVhJ/Jc1RjBeqCku/3W0jBvM4mHNT/OK9IQwZmqWYdoIkwWMvELg07HE3PZltjAo446P2Z3FMt1LGZDVM7kAMLoqIymVMlveXrrr1GlmpRHtpiaRcIalW+3F5nPG2PRMTdqbIORR4piT6nRWFO7GrxKRX6thml87dFk4ilN0lLZN7gad7c5F0rYarxGjU0+wMv3erXSrZBsuNR9x4/gOsJljtYsmxeIHHaI7LvXeS00OTOl84hSSFdDeo4HAOu9lC1bJHMJLDbYqHr4wNxwjEBleLvWWgRqgYHBbEFcbMvfVy71HWW+ba1c3tCjyi1se8qJZ84MT+0uZFqMhA4LxwzNP9gBp98Jxh1r3jXFd3356E3pJWodnxy1mxX7LKMoxz3vU8z3GFwJNWqmSlmLRUJivF5NYWGh4fz0z3p39apvRgOocCz8VAI+s1Pe/eovX2dQ603mL5QiPfqI6P/KvU8ufc7Pwan3n4VT7/9X+CK2W4Uo6rKlpSvDu84lL68X0uBLnDtNuFsXIRmVoGBWMZ+L/33S6iPT3P7oDUd2UfDOA1aPQ3qOk5omoUIa+V6bx1DVKDcyX6SZMjktNTLw/4Zu1LoxBq1HjFlVPczRLp7WXcamVu18sDo9Jbhh1VUJ8fr5fAGSPFfyccu/18W8BaNFYckAGJtRDF5JUKSbFxqBSCjzOGpFYjiyLaCwtkcUxSqfhlMBsVAtT5mHIdI/CctFdN89bPQ7GOQNFwNZIjl6n2cty9K0YySlmbemOLZMGRxopz4JyCYTcapjvLkpyyoaYIGkWFIfdum90f5vwoRcwuunvmaR8qVtB6hLtaJvvcMmSCatHlTLFEheP4i/U0TsUxDnTNp6uLYc6ylzGEhpGZ8ng2VBsQmG/2L5OP0p721e+BaMj9H4YfP+yQY9k7KdttaoozBrGGPIrIXIw4B/jvbZ4hufNOF2JIKmXyQhOU9+x3rOnvEKCD93MapjwLD6MljPok3HeOHl7Bgy42p+KYmdy+66gxpHEFSXNqm5tkFUtStrioiJVQaC5UtHCHvxgTRY0sbrGO7LS9EiQ33uNNBj2YjhN6dvUrfplpBO+nYdrqQUoGd6+Oe7VG9sWVQw46SeMrTjHHtI/AfHNhVKyB+WT44KDgnwfW4kRwxpBFEUmphK1WscWyVk/DoyLkcQlnDVmpjLMGF8XeofUsb+e48XYEgsADJyu8I6XZSTWDnm3J8dcQKFyVOyAZuYXcGPLCXsTH9/Fagp4n0/DAfNNiFNurE2qCShZdq6M7O4ihv2fWrlHfEWnuXXzGl0qGVCronUUfdLBn3TwyA4kKEJnJ97RLpxUYpW0cpyU76aUPTI9PntbQdEdNLwi4548BDcv+ej623o+o7AkozXtaGWeK8Uno2/WIc95WsLD3cVHsf4u8kOSFneOX9EfPzCE3c5jyoP/VwMVHyMeUBJ4pL2GcCWPMvCem0Rme+JG5GBiEjUuI822gS7dk6MaWxMZe2+GMfyl9gScvdvA699RK6JtXIGkikUOcweW7DymRIe1xcCWh7yXlDYatJrBi0R+7ha4tHCLcTrCzBybAGVTEtIa1UQSffW1wv818YI6RwTfzVVsq0jdAlihicKsIKAQeehstH2YFOa3MjXIVHfr2MIXEFDU8571LjjGAjmrwoYd+ODkDncm6lEpnB0uCq1iILCIRfs8us2v8SqHSxBwnTk2B4653gkFBgMjilmtkb9+CFx3kUcsLNYURs/TW8HqC7J7L+N9EM2/Ubaq4ch1dqUOtfPJb6bF/uT8wYcab5Z3+Umek8TlCi7xHWD9UeXoex9155ZiGdVgdyL73Ospz8ZBEBq9xYJI9ZsPf17Z00ERDeqE9CtW2UAg+xbGj2ur02nGeQ7HB6N58eyEKa/3fSW2XdET2zmhJa38OLmlHlCEfji2K0cpKgShLqLVeEmsHV7doKUJsDM7CQBRnFW/c68TiLsJTWAQiwd1YJv2Jz2C/85Do+SaaRWjum/juXuQHBwn/m8OQgFjyeIm8toy7vgSR3fcgCssKs2OYMHwBK2Nk4085+PGSDq1zybHVOMoy/wmve4Ike0tcEzVAVoU0gSRF8pw93jLGCzlarnih54DA0zMxOCbPvWNnt6R1HFOq6KkxJL8HtrU+aQs7/vzhvwxbUhGMRmAMrvD+UtPbMXzXxbqn3XEctsHmWTCFuo9jdHkJdy8jy0AebiGPthCXIbprhNyPRzjoYWEErSygSzXyd17F3VjyMW72C6knKq4Bm6LBy46VxHnpK9PmmMIbV5s2kWKd4oRu3AfPkZOq0IamxrCiHbm4R2w/R44BAw1/0Bpj5DWoMbSkI7Ynm3WxaYfqi0eUt18SdxvYrEvJ5ViFSEq4uMLL22/QrS6wvXzNR3Yegb3mCCPkueByGi3vH0SOrLd9GoH9Ksr9H0Z9MJ1qEDrswWsQjUAsrmS826AxODWoM37/N+0JO8X3F2mGXIrRUuz3eLmyjP1X7xM9X8ekCZKlxbLW8ApUE5PXl3A3V0l/5m2oDAb1O3D4CdE9f/YkeoGq4XIzbn8eoqUJXHLmqBGc4jll0w6l1ibLz95n6cmHLDSeU0oaLGQJsVOqskBeXuR7pRLbK9doLK2RH9jPfHczpL2Kq5ONm5dT4BmZ/eLxPun5NPRrbxz949EahtxW6Javk5fq2FixscNGzmt8nGK1WNRSxeFwml2MJa39lCJ0qYZ75xXypRrm+RZ2q4lppEg376+Bu5JFS5b09hKuWiZbW0OXqlCyXt0auDicjZXlWVwkEJgu49qCHWJKtPDsY1buf4v6+kMqO8+JsyZRniB55g2jBb+MpRkHAsMNEbT25qJ4EI/5PJ6xwHOmeufDrzdRjfRJlutGEXqGCTt7r+VMmSRexcU1jNX+ywmIFLGD+7tzC1a99cq5Z7/GLvIG23rvGu7WKvLRc+zjDeyzFqaZQu6jKue1CFePyT93G7dUIb+y7F3I51LYmZdl4BmVzTiX3W+qNY9MwkZikEHN8oG0560w5rF/7Wf/mHxYGZ7lvYxwrf2HHDVsHJbcWM5R+6SdwihZgOr6Y668/3WiZAebtYlMjhGHuNwfrQaJSqD53nRG1iqNL/ScAw3PvHXWCbJ/SeyoWx2hQtMUdhqw9Sxn86OEdl3p1DJULaoGs8+UJFe5kKVrk4yonbD40TMW7z9n6bceUPvoOXari3RyP5kQIVuukC+W2Ww36V5fYv0L98hqFbpLtfMxJgcCcKGHyMAUOUxQGHH+ve9NnyhpU+o0KDfXiTtbWBLE5BiTYUQxmmFUEW1hM8Py1iOwgrn1NjnxwbRP4Yx2IG+nO33aTKknH2fYeERlHstpJuKDlzsyj8Mv4nKlkyidhqP1MqPTVbrdHNTg7XuKs4o3esC18ZxxwBbL34tNMkrbbRYfvOTKtz9l9Rv3WXj/KbaZIUmOU793VbZaI1uuYK9XabW7tO5dpauQLFS88dyphZ4RNYrninGtggND0UPK8TRG8YERGM3n58IwopLkAHueReM91GzWpdzaoNRtEGUdxOSIUYw4jFG/hYU6hASjbWrtTZL2wh7HkkPzckrmWOC5gI1vHMZU4Ys6JFLMApQXYXEJ4iWIF9Q3pGHG1HqxStmkOXG7y/VvfMzdf/EdVr73kJXvPcJsd5BmQp4L6sDhY/PE3S7yQljaeEm2XOHadz5i+/UbvP8Hfzvd5RrtK0uzvqVAYARO6IccCMDk7FLxQQtXnn6fu9/+h5Q3nhNJ5s0pxGGl8AsWB0ZxmiHaYWnnAzRKibKELCr3ozuPHO9nDOZY4DnvTNHmYl/Svb29RRSJHbbkKJUgLyl5WXfdsAcmlYK3Ezuf4+RwzY7JcuJGl4VHG1z71scsfvCMxQ+fkWHIvdUSuQgUwQhNkmFwVDc7uFqEFUfU7lL9qXdRI3RWF32E0Vkyty7F4xgFnDHTev4f6647AzuroROZ3sAge/8eGb6/YE6qcDYcZgATGAVxOVHWpbrzjJUn34c0x1tL+imm95HVfv9UcQgp5e4mpe4SJk/9lhaFsKPWDiQ+mboIAs9FQRVxHaL0JTZrYjJFnBYRM70I32szPcc/OQ/GnaOgILmj9mSLm//qfW5+9T2u/9r7uI6jS4SK73ICxQwDvz2oeNf8lqkiiVL7wTNkp8vrV7/K+juv0v43f4K8WiKvlWZ9h6OxRzgaI65GIDCJWFGBS4uoY+XlB9z9wT9l+dF96i8bZEbIxaARqFXUgDNCZixGHFHWgdwR7zhKpkxt/QW2nBI5R1Kp0Fhd80Fyz/2S1rEToSk/hc9tZz4646IZNm9i8gTjQHLF5D1f9OJsoa/xOTOBZ5ht0mk0YAecUBRJHfF2m8X7L6k92iReb5BIRCbREO//woaJYiaBxbicUqNDvN5g8cMndOtV4kYHNTKGwDPDh8UBe6wBoWdiKutzxCQdlUYtu36THqVtz6CtjLNZ6VTbzCm9MybFvDg+7mcqzsun8TI7+kLiMuK0TbW5zvL6fSrNDcCgYsmjCC12N9JIIdoNjSI4rPNhU3IEk2VEJiHOMr+hqVI8pCbXJoKG5wJRynZYarzPQuMFtZaiEQgOtYIapRd3rx9+Ty/Gc9B2M6pPtln7zQfc/b9+ndLzl+zkKc4oufEdRpH+gJ8Xn92AkCA4HAbZanP1l74DWx1W332DxuvX2Vytz+zeRqYnzfbv6SLUbCAwJUL3KDjdWCGq1BvPeOXjX6S+8Zzydkq3vMTWm2ukcY0kqiFGvbmFcWAgLZVxUYQrG0SgmjXJ4hWSxWWUmChNyNRNpY5mI/CMG9joPDKN2cMhDaCnwVAxqCmRl6p0qwukZSErGZwVtLezxKDAc+56/fD8inPYbkq806H6bBtpdvuGySIGFUEMA0JP8XIUa8w+bUUxeU5pp0Nls0X1+Q7dq6MYLo84W1eH0ZzYJZSzVl8R0DPO64leia3iJCKXCA5EHh0lK+etXs+AE2mVh7jFDqY1D4gc1NicZDuKvhp04LuLzMhu12OedxYclYcT5U+9VrLfR8ZNREGVPCrTql+jWyrRqpZJoxppVCtiwHljZTGQFAJPVo3957SJMzXyKMbkBqPFttbFhqaTLPKg4bkI+JCVtGu3eHb7dxHnN7CmTlIukZRjRP3vxvlGZFToBxycgiX8WSNOiZOEUrtDZbOJOuejTdsYa2Oc+M7sCmO4HINDyIrgnuK8YXfkciLNqGVN0h3H1d98SL5Y4/mEFCaxS6gnm9ze/gGfefE18khwEeRW0EKtm2P5eOnH2CrdYrt8k8xUTn/hwAm5RJqyCzAOBCaHqH9W9Cdjh7QPBVoLN/jgsz+HqMPkzttMDvG02l3x7f3W+14xTil1Mqx2iTQlchnG5TjYa7x8SmYs8MyDuHwC5sWrYWBNXoE8qtOuvMLmSsqT2zlpHJNFEX4PKVM82L3AA2cYY3loNU+u7kUVk+X+5XKcqt8HyxjEGIwUE5hCuyPiBb5eCfj1ZEXE+OifCCZ1lLbbRK1k8EqHZOCYDKrDakY13eRa40OuN97nRuMH5JGQx/j9zoygTsjV0mWZSrlFZqq0YyU15REuEpgOYwg9s/KoG1ujM4Ytz2VlluGm9j9fZlBNxjmMusLGUciNObSdOYlw5YUxr7BXK27ynMg09rik92wsJ0nQ8FwUROiUbtKNr7K++BN88FpWNBYpLN0HVd8X6+Fp8pxSq0Op2yXSjNQIeRz5ZSzrAy0q2u+wPZ8tIwbVolic4n23lByDdHKqj7YobbT8b7Y38oxfdlZT6uk6r259k5+8/ze40nzI7e33yWPIy8YbmCvYriKZ8Gb9t9iu3OCfvf3v82TxTdarr5CZc+IpFggEJseMZNJSmhCnKc4anBg65YrX2uxnQgKJ4k0ynLVkpZi8FOOiKAg8p+KkZXfUzG2/19FUZIljEu3bgBhUSjiJ8crAAWPdmaqsT9trjy5byZWok2CSjL5QIgLG2y7tHljEK0IwCqqKqvSzN6j1MbkSt1JskvtJVk/WGacY1WE1pZZucnP7u1zf+QGrzYfUuy8x2gUHksmuwJODOKgnLxEcN3e+j2hGJ1qgHS2S2srBDEyiXs+7Td05y67nYk4+jmYe3KLGvLbOc/1MIhbWwNg6oPEz6ohchsOgYkhUOSQO8njXOSybIuRRRIqjU63TLVX62p5JcrkEnstC4Wp9WTBpRmWjQdzsFDMF+gJPz0J7sNtYVwQqxGu9vNDjBR+jAmqxiaO20aLc7J48X5pTzTa50XiPn7r/11hrPODO+nfAOFJbCFopOFWMQiaKWFhIH1NLn/PlT/8O69U7tOJF1muvslG9g5MpdNkZqc0Dl5XQ4OaKfbbqokqcp5SyxNeUiNfwGDNcyzMBVISkXCEpV2jXF/uuJJNmhNFznIY5p9LwmWg35mDGdkkND9UIubXk1hSGyYVap+954kNK74k4rWCcgpN+xGlVQVT8jvLGkJUr5PHJl5KM5lTSBvXuBmvNRyx2X+KXzYrJVM94XP23UniK+SbkqHfXyYlYbj8nMQtsVW7hplXFg+nOImLwSRhJMzVqgY1w3OXsXqfksEI7L0LP/nH9NPFsRkxqKhyjYSkopV3KacKt9aesNrZAITcWZyyNSv3wpa3DrjnyobsHHiXsGJdjnKPW2qGctOmN7c5GqHjHHFBYuz30/KDhCZx71BiSapm0VCITi0P2qKK9oOMQh5c0nPX9JFcv6ORFXB4niDqsKhpFtFdWSGq1E49LVjMWu89Zaz3klY33iFyH3PoOrc70tUo9d3SMtzNyxhtRL7cfU0ma3Nz6CHUxj5c+A8GWJ3BuCBLikcxK5juiWhY6DVYaW3z+/ne59/whmguJLZFEJZ6uXuNZHOPsKGLDlDQ0eUacJtx++jFXXz4CyUAcSbmOMxFRlvsyPZnAcx4k8IJpajdOJZGfk9nyXHO09sxFlu5ijW6tSjcq+81SHeAE8oHTtBBwnBa/Uwgd/tWPzWiEvF6mcfcKnasLhQ3UyfIdaULkUqw6jCq5FIJOEV/H2wZ53U4/aGBxu0YV4xxx1qWUd/0xZ6HFG6vJzvtD7Qw0rwfKac77/Jl4Z41S3vPeds6APZrV05w8+k9DDy/Gp8Vuh6utbaouw1g/EYyMcr21iY2E5sICLamQmmjA2+901x4pfy7Has7Nxx9x88l93v7+N7j1+D7O5Kj4GEBODDYvNDw/+vuGpnNBNDxz3HHOi+b2HJPHEa3VJVpLi3RKVaI0Jc7TA0bG2ltLcoo6KTQ+oM70164NINaQLVfZ+NxNWrdXBtIYr52JOuK8S+wSIlcoaouYSeLE2w4VGdNi6c0vcXkrZh9CwFFOO5TSjg/EdabM+YN77gmdPzADTvA4tKrELme12+B2Y4O6ZkgcgQELvLrzjOW8zdMr13DWkBm7u/R0cgfWMfKXEWdd3vjoN/ni1/85X/j1X+Luh98jtz7mT2+u2LcI+M//0tB0JizwjDqbmsQgME2vqGOuOa6twKnHveLksLHfUFxkSVZqtO+ssPHFV1h4tEH5o2cg4MT4CJ8YfGhlQbW3b++uRCRASVOIhNbSMtu3rrDx+Vs0X10uDhlDy6O9RSr1wo6mu1cz4u2GxBTNorDbQX2coJ7BNbvymorZtUs6Sy6DvDPOPZ5Kw7s/gTPsxLOKDxSYMCM8d07YrEppQr3bZmVniytb65S7bSTPkSxHVFl++Yyo0WDh+mt01dJarJAP+sVMqTmLy7Eu4/ajD7h3/9t89tu/zL0Pv47RTTavxcX4Ca54Nuoxw+QZaHgm5JJ8WLp7kj/p0sOYWTmRbeQknh6FGD2RcWsgkfNi7NzTcOzLr4st6XKV1p0VNr/wChbH6gdPwAku98EHfbcw7E5F9r5EHSVNyWyJ1vISO7fW2PjhmyTLA5GOx1wdEVUil/ioob3zCldL6c9KBtqsDORPdi+2K/CMcOGTaIGOrf+jJPYZGuufpksdm92jEj3pvQ45b+hlgmBy/pimimOE9nDSSxfjRSlLWWw3WW5ss7a9gXEp4nIkT5E8p7axQTkqs3B3m5YtIwsru9LFFJ8fRh1RnnDr8Yd8+df+Ma99+HVe/eTbtJcitq7GkEvh9FEIPtHRkQSmLPCcdcedsl5tLrgM9zgmRnDliPbtFZ7/zNuU0xT91sfYRLCJ30TUm+kUwkYv9o4qon47CTFCu16ncWeFH/zcF1n/7E26a1Vc+YRdxOXYLKHWaVLptjG5+iCItgiCqIUdkRkQJ0RRUTDaW/lCjGLJseSM0p+q+Q61bJtXmt/naucBzijO9LS9AmpIbI33Vn6Kll0kkSqXuj2daIg6TMibhDFDEHbOL+dvbO5ZEy502lzfXKfWbkPuiJIuNusSdduYNMN2E4zt8JlP3mNte51mfZFmpUa7NN2tb2rNba49+5RXP/wt3njvV6l11ulUI3KxSGa8naYOWC+IHlkF82/Dc9yYsp9DtAAT5SRpT3R54JIPivvr2AhasiRX6mz98B3WPniCLpZhO8W2M2+rbMTH5YG+d1SvHK2mqLF0FhbZubXKo595i8btFbKF8kD7GyjzEZYWxeWYPKOUdInTZGBV0gs0OAXj7Yh2XdHpj0Ba3JcYwfjwX8cVCgCVvMly8pw3tr/BGzvfJI+c3zW+CNWOs7SiVR7VP0MuEamtoMf1mXk3RTmhjdVwxrnR0erEMyxv81yoJ2XcOjgDi9cD6Y9S7idWmZzy/GFpTe96frhRakmX1eY2laQLTjFpSpR2KbVb2DRBshwrhleefUo16fC9N94lsxGdUvlIN/LTUu60WXvxmGtP73Pz4fdJapakYhEM4gy9eWD/UeA45wJPIDAieTmmfWWRF196jR/8kZ/ixm/c58bXP6HcahF1uyje0M6JQRFy66N7vrx5nc6VRZ789GfYuXeF9c/eJN0j7IzIoExkIpJSnYdrnwWrPNt6i0q2QS15Ri5KatVrmkzPXqeng1GizGEU2tEazfJVHq69zZPF18htfOilIzqUaPCZzV/lh599jbc2vsWdxkdksXgNTzEimFxITYWbW9s8qd/jH772x9mJV2naRc7b7PR8sX/2fxGFnXEJZTBrymmXpXaTa5vPufPyMdWkhRrFZClRp4tJUiTPIM8QhbWnj5B2h5WNF+QK29UFdIoxblUEZw0aCS4W1ApaTBR7TiforrECsxF4ptmQd213JHeHXE6LbQXMHiPQk17qSMZO+jJYgu5nSiqCfZoJjSxZZGm8coVnX36N2kaTtY+fUaJD7PJC+2nJvN0wRIIrWRo3l2ncXuPpl+/SvL1C6/oiao+yfttnRL7/+yJPmS2zWb1OPdtks36T5TYsNJ6jFkR6PbNwSS/OF1WsA3GGTnmZRukKG/XrbFev4IYZLhdlENOl5ja50fqYtza+wd2tD7neeExWKjYmLcrI5opiWe1u83DhIb9y6/eTS0Tb1vuGf8P7y3Ht9pyp82fS/aZ00RPZFI7KMTaUI3Oexrspt+Nji+IEZTWuoqcYN6Iso95psdhustTaQchQ0b6G2tvxONQ5xDkq3Q51sdRaDZqVuh+7DtMQT6BdqrC7ubItzIaGKNi1p+UZ0NwP4/xpeBSizTa2kbD4zSeYVurX8lR9BeUZUdKmfW+NzR+7i6uWyOul82OYGzg1revLpLUSzZurfPK7PseV9x+w+OgFXnkrpLGPypzUK6TVEhuv3SZZrrP1zk2yWskLyqdFhMyU2KjeJrVVfuHNP8Wd7ff40Qf/iMX2E9a2PvYCmIhfdlYl6igmFTYWbrNTWeNrd3+Op8uv88nS52nFy0O3lbCklGnx9vo3+NLTX+DNjW9yt/EeJWnTXIhQ9gr9edkPBlXd4E7rPf74b/2P3F/8LP/P2/8xzXiJRrR8+nsPnC1haAuMiQAWZSlpc3v7BUtpE2zPtlBRa3E2ppw3sWlKagW1lrQeo9WYO43nxCXDk/xVulIilemoefIopl2rk5ZLqC3MAaQI34HivW6Lg3tzriOsludf4Bn0Oim0OtF2h3ijzcIHz7E7SWG85DBZislS4k4Tm+e0X18jXa2TV+NisXLEkUEPvDni2OPtOUZNKnBC9nkmZZW4eJVo3lohW4xoX6kXvwpJbHGRobtUJa2U2blzjaxeoXvtOM3OmNkSQ9dW0bLh4fK7GIWN2vcwmWNFn+IKTZMpTHg0B82ElrnKdukmj5fe4fHyGzRKa8XGoQex5JRdi7XOE17f+C7XWw9YSDfJrZBGpjDQlr7Lu0ben6GUdannGW+vfwObZywmm2QSz7fAE/rQFBlXajpM4zfvlTSOXcwgp7VNOk1aE0R7douOcp5SSzostRtU0i4i2rf5VTGosd5zVEyhZTF+B/PIsthp0uk0qSUdUMhKlREdRMe7dxUhtxHOmH1yTLGW5Q/aTfoYRfP8CDz7S6tQTQ3ajErTYRtd7v7Cb7H44Dm3vv5dolYHp9Yf4NRHjMxT2p8ucfPb7/H0Jz/Lw9/7RbKFCvlCeRoZL/6Gada8kdXK5OWYx196i+c/dI9eHblC4+GMQY3gSnGxNjyNWDdCakqsl67TXa2wFV9lrfUJt7e/g0iKSALEqFpMYpE84uHKO2xXrvLgytu0Sgtk5vB2W+9u8Vrju7y++Zu8uf3rxNohj4qe4wovBqAfLdoJKpBZg8Gxuv2Cm/qAdx9/gwdLb7B+6zo6pdlaIBCYLQJYl7O2s8nbjz7mxvpTXn/0EVYUI0pvhSozZbQcka5VEHVkscUZEOcliptPn7G01SRKHc+Xr/Lt196hG5Vox5N9xto8o9JuUe4kxF0FnNeMq5dutAjh0dvfS4+J3jEfAs+AsNOPJjv4Xe6FmdJmi9JGm8UHL1j69Dn1lxtEnS6uH6bfG32KOkyWo52c5p01qo/X6dxcwVWiwltnCg+2UT1VLyWzKQQ1ghpLEtdIZmg7pRhSU6YZL+MWDN2oRG4VQ4KRBCVGiZAsAmd5svgmzdIqzdIKqe3tnTW8F1vNqKYNatk2tWyrry7SgQnQHmNqLayGjB8wojylnHVY6G5RTVuHW1vN2vRMD/1wDJdpIjKitvk0HCj6eXfhmxSTvMfZPSzEOUppl1qnyVpjnaXmFtVuExdF5KXYLxGJkJsIF1myOAaUPLbeWDj3m3dGnZRK0mFte4PURlSTDg6hHR1jPjK2fsDbOSoGxXrz3NyH71DwoTwKoUfxk7n5Fni02MVa3cBfLb73f812gm2kvP7//QbLHz/lyrfvE++06XRj0AgpgrSpofDCEUyWU9vc4vZXfpOV9z/l09/3ZR7/zs+TrtTI69ONHRAIDCM1MXlphXZc50Xt1WIturethNDrrakp48SSm+M1LX4oyDGSIzbre6HhitmY+tAU/UAVuaCixSYWQh4LLgZrMozJp1sAgQvIZRB2Lg7Vbou7jz/kWuMlt7Y/xVpl484VVCxqLJICGX5jZaeoKeLAW+PjggmAYvMUVKlkDVaaEXefPuTFwgrNa2WvObeT0RJ3y1U2rtxka+UWO4u3KSdblNo7ZGXxQQZNETW/Z6fYsw84hCkJPCNOBw/YyuwKOeIKwccpUTshbrSpP11n8dFz4p0WppOSaQTiVfO9wHKKkIuf4lqXUtppI1lG7dkGlfVtXCUir5aP8d46SSc+zHPnNGkGJscky/+kU2jBicWJIevver5/0bmIlzOGkb3009mblgxqefadUehCvfdDb+N2CW109qqsUTkDbU5gypy9OYQKOGNIohLb1QWILC6Ovc0OBkn9iormgOruuGCKMcl4r1KjGeIcJslolhdIbTTSBG3ce85tRKdSZ3PlOo9vv8WVF/dZ3ehgnCKZ4ozzkzfjtTxSbBB9GDPW8OwTcA55Lb14Tv3JBmsf3GfpgydkqSFXwZjCLV3wofql9x4chq6UiZOEerfJlfc+RkuGx7/jh0nrVTS2YMNoEZgFvZ21JpOSH6ro7RbW1+p440MvBPXXtvv9xCAG8siQWUMeeaPEwLS4qEs/F/W+Libtcp0PXn0Ho8q33I8CvTl6MR6NXJXFCKaFYbHxE7ncmLEma8fRLVdJ4zK/9hM/y4dv/Qj/2j/92/zkv/y7LDY2qHQbJFVDFlMEVoUodcUgOJwpCzyjzZYEb7vT/7vvZbLcv9IcSXO/zghI/858p5MiTomId1VTEb/m5xw2zYm6KSY/JjJRIHAoMvzjRJVH4waqAHH4GY9T72nhOxJ78ju4alZIR5r3XDoV4zKM9ravOGoWdthNT7lP7Xmunsf+O1gfp2gwR9pHKJL1Jor71HvibTNcKdrNx0mKcegtDCYUhJ95Rs2udjk5dWIH3kweMThraFcXWV+7xeM7b/HRmz/C7cfvs7LxlKRaLMsbP7mLurprBzyEubDhYYiWx+T5wGdFcsXlQl7YIIgoVgubgyJyLmgRYM1bbTvpjfD4EP3WIsZ6o+UQlydwATBOiVJHlDriVEkjxUVaqJ/B64B63gs+eBfiDbpFvZBks5xqukM5a2LIC51R6B/nkWing20lRGmCcX6na0TI4wgXR7SvLU/MviIQOCs6lTrdco2v/vQf5Ns/8lP85Ff+Dm9//1f7wk7PBMZkuY/AfAjHCDyTGvTGndnogU8OcCg+mvSAzQ/QiyftVfpS/Bu0bSg8dqy3ND9qN9WTEx4QhzNK2Ux7ZjiubdUYs+B+8z7NDP5k7SezMc1ynVa8QNssIGSIS73b5qBNTrEWr4Vhn1FvMJ1HQrdUYqN2lZ3ySrGWP6dM0zzuQNXNiy3P0e1QMq/1Lrc7xJ0upafbRNtt4iTxk8ZitpuXY/JyTJwk5JWYbr2Giyx5OT7Z5O/Q4jnK8yjYN+7lnD8zJjLujXotr6FsVxZQhE/u/ZCPz2P95UWdd3DK59qG5zBkT1vQQtDJFDLvV1to7F1fsPHeKYrBFAXQ090XIlJkyCve1c4bYM3gtgKBCdOOqzxeucOT5h2el++wlL1gMXnp17WtoloYKPdseCyAYjPfd7p1w9biEt+78SXWy7dwFCqgy8Q5vl3bTom321y9/5DVJ8+ofPic+PkOcbuDybJC4BFcNSavl9l+9xXaK4s8ffMuyWKN1s21c33/gctFp7JAp7LA137i30Z+WxGCA/r2RMcxXYHngLA/RBLsb3NazCy1tzjlhRgxBtvNiJttTJ7hxR/rrc2LFSvTM8bE9D21nEh/sBdAUsV0HJLp7gZjR01GJsZlnb2My1Gj7hTK8MDsZIRsjJ/4GOeczGMjM2V2ojUeLL7N12/9DK9tfId7m9+j0m1TcV2c9RpNV5huSFpYMxtoxYt8d+0n+HThs2yWrtOyS7su8mOz39vsnLA/y0M1PbPow8dodtIM281YeORjkq08fsbii3Wi9R1Mq4NJMnDO23jhtxEwzlH/9DnxRoOsk9G6ukJWKZFX4sJzdYz6O7RIzti2a2zGyc95GrvP3uOrz9kNd/026uzJRJfZanhkd9vEwWCATopQ+FEEqkStDtX1LWyaYlRJ8dPVTIy35ekbZJq+7Y5TQ473tzUKtuuIdvK+0KOjbjMRCJyYwYfldB6cXanSjV6hc7XCJyv3+Jn3/x717QZ3tj9lqblDWvFxdjT2S1q2EPY7CzE79av8vdf/E55V7vKk/Hqh3QkcZP48kWwnpbze4Op373Pn175LZXuHcqNJDjgVMmyxPOkN06Osi2l1qG5s4YywWPmYzddu0b6yRHd1kXZ1GlHoR2X+yvcgJ8njUeeE588sOEbgGVZZJ5gFyL6/e5Z1hws90PPYgvbaCuZOxvKVT4kbXWzTz16cs/581X7b6u1PhHNYl0PuyHJDe2mB7btXSJYqaCSHtMXTdLoZStiBEdhXP4dV9Umr8dCmM2ZC416/mPEkUmPHXuf+8ueo3dmhWfk6rZ0YV3JopEDuI5HnlsRWee/Wb+fpwus8K99lJ1rzju0jzfBn9GAaZgoyiayMXN6TeCiPcf6h5i7eRqH2YpPr3/yA5Y8fU9ppQJKRYnCFOYD2jNWLtBwGLawfFa8hqrzc4uqvv0fj3k2y5XrfsHmkSLkj39Oomr95HDdP63121DkT8to77JKzKE7ZP7Yed//satjPKL+z0fAU7uIeLYSeYkAZkHkkzxGU1rU1kqjMtZsfUNlpU+lsYLKcNhYngu3pbbWw9xHx4a+zFJySqqW5usjGm9fprlb9Xe/JQyBwvklNnVRq/GDtS2xUV+jWlc5WipgUMTmaJpArLi3RiK/y9+/+h7yo3WW9/ApObPBaPE+osvD4Ja9+5ZvEzTZxs0UqEV2s14z32Y33lGO84aNaBIekCZXnG9z4l9+itNNi5927pLUKSTynZp2BwAQ4Qes+Sko4ZNAcJnXKwJuehgZ23cgpNDzOQcWgy2Vefv51OmtL3P7lb1N9sc3Kxg5kjkSivk2OoFhcfyaz+dm7vPzSm7z48bdpvXKFrLetxGhbu56AIEUd5JSawokyYv2cx5gvIrTiVZ6L4RvXfpZPlr7oDfvFobn3YlBnSUyVZ7XXaUdLqJyXEA0D9XbAzfIEY9IRyY/543Q4ZKZe2m6y+Mljlj95RLnZRBNHii28U/2JMiDo7Fbt3kADTow3ZcwdlY0trv/WD9h69Sbrb92d4k3NAycZi8KzYnrs0/Ts56QmV4ecN2Fx/hj15SjqtoHf+sEHKxaimM13X6V1fY1r33tArdlm6WED6WY0TNV3ZwFLTkkTcmNJTET7zhUe/IEfo/nqVdq3Vidwj4HLxTiD0uwFh3a8QjteYb16b/KJz834PK515Ozr5dQUt1zaabL6/fssPnxKudmiS0QiUd/UfNB6QGWITXaxyaIzpljidFQ2d7jy3sfkpdgLPEXsnkDgdIyzDHyIIHqUEHPk8urw9jsF/eUpBhiF3qahgwEITZb5V01AY3Z+22twb4nFZYg6CcZWivgiBsWRmpyNa1d4+OZrrP/QPZr3rpEuVi7M2BcInDlzI+ychNN0/Pm68fJ2kxvf+ZDSZouECIfZK+yoDDwnZEBw0d3/+5NqwSlIK6XycJ341daZ3ksgcDSTf2BPacH2GNfEYWNIrycO7JTeF3qc35JeSiAS0X7zGmatQtLcgFYXopIXdoyPwZNZZeeNV3n0275I69oynWuLF8wra1Yq2MDRhHKfX46rm3GWyc6C/epw/zlud1l+8AzN8J5Y/eNl4CjZ4wyyR1mjsrtJrPqFLpvkxOtNbKs7Be2OHHw766IdmWlndBoFMhtj4MM5bb8a6AcTKKY5slDrd9XdDUVVD7xXlMZqlXYtol39YSR35FGMGkMexzhjyOOI1vIijRsrpLXKvmvMvAUEAoHAeCiYLCdKcyIVcsXbXzH42NzV6PSW+AefE3t9RQZsJZ36fQqzI2LyBwIXgDMSeI7R+Awe1jMm7hsV617tjyjdagmpRLQWyiiQRxFqLVmphLOWtFTCxTF5pVK4WU7jnuaZ07pTBs6eS9dIA2Mgqpjc7yto1W+hAz0tTm8itzf+hw6KQj1hp+/NuvteFEyuxYajx2Wkn2RgYlyQQh0p+7PVpM6Rhgc/G5Geu5WgRnDG+u0gAGJwziLWeg/L4vjcFgJPHOOsxcUxLopwcVwscw2uZQcCgcD5Q5xi1EfY6e0UUvwCDDi67Br0DJytA+92w3j0xCajQeQOXHxmJPAc7a7VE2R6KloVgxrFGTOwjQTFRqCCiyLUeEFHC4FH+zY9Q33hzzkjasz6v83TzOEkdXBBZkBDOW9t8rC6OG/3sZ95a1tDyvOA04r2/++tYEEh0BRu6v2NlntacpEDKRdD6pRqcMCMYN6K+Ng7Hvb7JG7iDLUco3hGnzbtU3Hc2D68Hww/5fibnCMNT2/K0dPuGC+8AKgWAozgnNL3ODDGbycRWdRYsmJpK7eRP9b6jRAvlsFyIBC4bKgRsnJMXorIjOCcX4rab5e4+8kNvO/FXaZYztodQ6Vn92O12JQwELi4zJHAs4v2Zyf0l6O00PRgtG/f4zU/0t9Dq6fR6Wl+/DGzu4/5Yd60PIFzx4FZ1Vl3rHGuNywex/lv/9obB5EBoUUYFHN6vxzQ8PgUjki9p+IJA2ZgHjhCs3PEIcemqlOLOBwIBAKBQCAwH5jjDwkEAoFAIBA43wSBJxAIBAKBwIUnCDyBQCAQCAQuPEHgCQQCgUAgcOEJAk8gEAgEAoELTxB4AoFAIBAIXHj+f/N2BlLBi5T1AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(10, 10))\n",
        "for i in range(num_images):\n",
        "    ax = fig.add_subplot(1, num_images, i + 1)\n",
        "    ax.imshow(manip_reconstruction[i])\n",
        "    ax.set_axis_off()\n",
        "    if i == 0:\n",
        "        ax.set_title(f'manipulation')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "29d394c1",
      "metadata": {
        "id": "29d394c1"
      },
      "source": [
        "### <img src=\"https://img.icons8.com/doodle/96/000000/where-to-quest.png\" style=\"height:50px;display:inline\">  Where to Next?\n",
        "---\n",
        "There is a lot of room for improvement:\n",
        "* Try to replace the MSE reconstruction loss with perceptual (VGG) loss.\n",
        "* Try to manipulate the features of each particle as well.\n",
        "* Architecture-wise: try replacing BatchNorm with GroupNorm, maybe other components will work better.\n",
        "* Hyper-parameters: try playing with the `beta_kl` and `kl_balance`.\n",
        "* What is the effect of the learned features dimensions?\n",
        "\n",
        "We think DLP is a super interesting framework, results can be pushed even further with more engineering!"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "955e3035",
      "metadata": {
        "id": "955e3035"
      },
      "source": [
        "## <img src=\"https://img.icons8.com/dusk/64/000000/prize.png\" style=\"height:50px;display:inline\"> Credits\n",
        "---\n",
        "* Icons from <a href=\"https://icons8.com/\">Icon8.com</a> - https://icons8.com"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.7.11"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}