{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Using device `cuda:0`'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "from pathlib import Path\n",
    "\n",
    "DATA = Path('/mnt/wsl/PHYSICALDRIVE1/data/unsplash').glob('*.jpg')\n",
    "CACHE_PATH = Path('/mnt/wsl/PHYSICALDRIVE1/data/cache2')\n",
    "CACHE_PATH.mkdir(exist_ok=True, parents=True)\n",
    "BINS = 32\n",
    "NUM_EPOCHS = 10\n",
    "BATCH_SIZE = 32\n",
    "LEARNING_RATE = 0.005\n",
    "SCHEDULER_GAMMA = 0.2\n",
    "EDIT_COUNT = 25\n",
    "LOSS_DAMPING = 2\n",
    "MODELS_PATH = Path('models')\n",
    "MODELS_PATH.mkdir(exist_ok=True, parents=True)\n",
    "\n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "f'Using device `{device}`'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Loaded 561982 training images and 62443 test images'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torch.utils.data import DataLoader, random_split\n",
    "from editor.training import HistogramDataset\n",
    "\n",
    "dataset = HistogramDataset(DATA, edit_count=EDIT_COUNT, bin_count=BINS, delete_corrupt_images=False, cache_path=CACHE_PATH)\n",
    "total_size = len(dataset)\n",
    "train_size = int(0.9 * total_size)\n",
    "test_size = total_size - train_size\n",
    "train_dataset, test_dataset = random_split(dataset, [train_size, test_size], generator=torch.Generator().manual_seed(42))\n",
    "\n",
    "train_dataloader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=32)\n",
    "test_dataloader = DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=32)\n",
    "\n",
    "f'Loaded {len(train_dataset)} training images and {len(test_dataset)} test images'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class HistogramRestorationNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(HistogramRestorationNet, self).__init__()\n",
    "\n",
    "        self.conv1 = nn.Conv3d(in_channels=1, out_channels=16, kernel_size=3, padding=1)\n",
    "        self.bn1 = nn.BatchNorm3d(16)\n",
    "        self.conv2 = nn.Conv3d(16, 32, 3, padding=1)\n",
    "        self.bn2 = nn.BatchNorm3d(32)\n",
    "        self.conv3 = nn.Conv3d(32, 64, 3, padding=1)\n",
    "        self.bn3 = nn.BatchNorm3d(64)\n",
    "        \n",
    "        # Adjusted residual connections with proper downsampling and channel matching\n",
    "        self.res1 = nn.Sequential(\n",
    "            nn.Conv3d(16, 32, 1, stride=1, padding=0),  # Match channels\n",
    "            nn.BatchNorm3d(32),\n",
    "            nn.MaxPool3d(2)  # Downsample to match size\n",
    "        )\n",
    "        self.res2 = nn.Sequential(\n",
    "            nn.Conv3d(32, 64, 1, stride=1, padding=0),  # Match channels\n",
    "            nn.BatchNorm3d(64),\n",
    "            nn.MaxPool3d(2)  # Downsample to match size\n",
    "        )\n",
    "\n",
    "        self.fc1 = nn.Linear(64 * 4 * 4 * 4, 512)\n",
    "        self.fc_bn1 = nn.BatchNorm1d(512)\n",
    "        self.fc2 = nn.Linear(512, 32 * 32 * 32)\n",
    "        self.apply(HistogramRestorationNet._init_weights_he)\n",
    "\n",
    "    @staticmethod\n",
    "    def _init_weights_he(m):\n",
    "        if isinstance(m, nn.Linear) or isinstance(m, nn.Conv3d):\n",
    "            torch.nn.init.kaiming_uniform_(m.weight, nonlinearity='relu')\n",
    "            if m.bias is not None:\n",
    "                torch.nn.init.zeros_(m.bias)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # Input dimensions: (batch_size, channels(1), 32, 32, 32)\n",
    "\n",
    "        x = F.relu(self.bn1(self.conv1(x)))\n",
    "        x = F.max_pool3d(x, 2)\n",
    "\n",
    "        # Apply first adjusted residual connection\n",
    "        res = self.res1(x)\n",
    "        x = F.relu(self.bn2(self.conv2(x)))\n",
    "        x = F.max_pool3d(x, 2)\n",
    "        x += res  # Add adjusted residual\n",
    "\n",
    "        # Apply second adjusted residual connection\n",
    "        res = self.res2(x)\n",
    "        x = F.relu(self.bn3(self.conv3(x)))\n",
    "        x = F.max_pool3d(x, 2)\n",
    "        x += res  # Add adjusted residual\n",
    "\n",
    "        # Flatten for fully connected layers\n",
    "        x = x.view(x.size(0), -1)\n",
    "\n",
    "        x = F.relu(self.fc_bn1(self.fc1(x)))\n",
    "        x = self.fc2(x)\n",
    "\n",
    "        # Reshape back to the histogram shape\n",
    "        x = x.view(-1, 32, 32, 32)\n",
    "        x /= torch.sum(x, (1, 2, 3)).view(x.size()[0], 1, 1, 1)\n",
    "\n",
    "        return x\n",
    "    \n",
    "edited, og = next(iter(train_dataloader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def plot_histograms(original_histogram, edited_histogram, predicted_histogram):\n",
    "    fig = plt.figure(figsize=(15, 5))\n",
    "    tensors = [original_histogram.numpy().squeeze(), edited_histogram.numpy().squeeze(), predicted_histogram.numpy().squeeze()]\n",
    "\n",
    "    for i, tensor in enumerate(tensors, 1):\n",
    "        ax = fig.add_subplot(1, 3, i, projection='3d')\n",
    "\n",
    "        x, y, z = np.indices(tensor.shape)\n",
    "        x = x.flatten()\n",
    "        y = y.flatten()\n",
    "        z = z.flatten()\n",
    "        values = tensor.flatten()\n",
    "\n",
    "        sizes = values * 5000 \n",
    "\n",
    "        colors = np.vstack((x, y, z)).T / 31\n",
    "\n",
    "        sc = ax.scatter(x, y, z, c=colors, s=sizes, marker='o', alpha=0.5)\n",
    "\n",
    "        ax.set_xlim([0, 31])\n",
    "        ax.set_ylim([0, 31])\n",
    "        ax.set_zlim([0, 31])\n",
    "\n",
    "        ax.set_title(f'Tensor {i}')\n",
    "    return fig\n",
    "\n",
    "\n",
    "# plot_histograms(original_histogram, edited_histogram, edited_histogram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b1a1b0d47c494d52af3f9a6a8eda7127",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 0:   0%|          | 0/17562 [00:00<?, ?batch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/andras/miniconda3/envs/bipolaroid/lib/python3.12/site-packages/matplotlib/collections.py:996: RuntimeWarning: invalid value encountered in sqrt\n",
      "  scale = np.sqrt(self._sizes) * dpi / 72.0 * self._factor\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "006686e41c8f4144abe4fbf0be7221ae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 1:   0%|          | 0/17562 [00:00<?, ?batch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b95d628692ad41c28675519557231fb3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 2:   0%|          | 0/17562 [00:00<?, ?batch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bf13345b762f402b86a4b639e9edb8fe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 3:   0%|          | 0/17562 [00:00<?, ?batch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "294c96a0052e45e88ad33d8a01fc9bd7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 4:   0%|          | 0/17562 [00:00<?, ?batch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "822b5a9ec0514a888b33fba658e25e06",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 5:   0%|          | 0/17562 [00:00<?, ?batch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d417f4778dbb446986dac677d33daab1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 6:   0%|          | 0/17562 [00:00<?, ?batch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e7c367cde9da4bc9a86ce8093fd24d54",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 7:   0%|          | 0/17562 [00:00<?, ?batch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "97b25685e4c64731817b1f6bdd0334e7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 8:   0%|          | 0/17562 [00:00<?, ?batch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81528672e35a430e8c555b6d56846798",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 9:   0%|          | 0/17562 [00:00<?, ?batch/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from torch.utils.tensorboard import SummaryWriter\n",
    "from torch.optim import Adam\n",
    "from tqdm.notebook import tqdm\n",
    "from torch.nn.utils import clip_grad_norm_\n",
    "from editor.training import ProgressivePoolingLoss\n",
    "# from geomloss import SamplesLoss \n",
    "# import numpy as np\n",
    "\n",
    "writer = SummaryWriter()\n",
    "model = HistogramRestorationNet().train().to(device)\n",
    "writer.add_graph(model, next(iter(train_dataloader))[0].to(device))\n",
    "writer.add_scalar(\"Bins\", BINS)\n",
    "writer.add_scalar(\"Batch size\", BATCH_SIZE)\n",
    "writer.add_scalar(\"Learning rate\", LEARNING_RATE)\n",
    "writer.add_scalar(\"Scheduler gamma\", SCHEDULER_GAMMA)\n",
    "writer.add_scalar(\"Loss damping\", LOSS_DAMPING)\n",
    "\n",
    "optimizer = Adam(model.parameters(), lr=LEARNING_RATE)\n",
    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=SCHEDULER_GAMMA)\n",
    "loss_function = ProgressivePoolingLoss(target_sizes=[16, 32], damping=LOSS_DAMPING).to(device)\n",
    "# loss_function = torch.nn.KLDivLoss(reduction='batchmean')\n",
    "# loss_function = SamplesLoss(backend='online')\n",
    "\n",
    "last_model_path = None\n",
    "try:\n",
    "    for epoch in range(NUM_EPOCHS):           \n",
    "        epoch_loss = 0\n",
    "        writer.add_scalar(\"Actual learning rate\", scheduler.get_last_lr()[0], epoch)\n",
    "        for batch_id, (edited_histogram, original_histogram) in enumerate(\n",
    "            tqdm(train_dataloader, desc=f'Epoch {epoch}', unit='batch')\n",
    "        ):\n",
    "            edited_histogram = edited_histogram.to(device)\n",
    "            original_histogram = original_histogram.to(device)\n",
    "            \n",
    "            optimizer.zero_grad()\n",
    "            predicted_original = model(edited_histogram)\n",
    "            # predicted_original = torch.clamp(predicted_original, 0.0000000000000000000000001, 1)\n",
    "            # histogram_points = torch.from_numpy(np.array(np.meshgrid(np.arange(BINS), np.arange(BINS), np.arange(BINS))).T.reshape(-1,3)).to(device)\n",
    "            # original_weights = original_histogram[:, histogram_points[:, 0], histogram_points[:, 1], histogram_points[:, 2]].float()\n",
    "            # predicted_weights = predicted_original[:, histogram_points[:, 0], histogram_points[:, 1], histogram_points[:, 2]].float()\n",
    "            # histogram_points = histogram_points.unsqueeze(0).repeat(BATCH_SIZE, 1, 1)\n",
    "            # loss = loss_function(original_weights, histogram_points.float(), predicted_weights, histogram_points.float())\n",
    "            # loss = loss_function(torch.log(predicted_original.unsqueeze(1)), original_histogram)\n",
    "            loss = loss_function(predicted_original.unsqueeze(1), original_histogram)\n",
    "            epoch_loss += loss.item()\n",
    "            writer.add_scalar(\"Loss/train/batch\", loss, epoch * len(train_dataloader) + batch_id)\n",
    "            writer.flush()\n",
    "\n",
    "            # loss = loss.sum()\n",
    "            loss.backward()\n",
    "            # clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
    "            optimizer.step()\n",
    "        \n",
    "        writer.add_scalar(\"Loss/train/epoch\", epoch_loss, epoch)\n",
    "        with torch.no_grad():\n",
    "            model.eval()\n",
    "            loader = iter(test_dataloader)\n",
    "            edited_histogram, original_histogram = next(loader)\n",
    "            edited_histogram = edited_histogram.to(device)\n",
    "            original_histogram = original_histogram.to(device)\n",
    "            predicted_original = model(edited_histogram)\n",
    "            writer.add_figure(\"Histograms/train/original\", plot_histograms(\n",
    "                original_histogram.cpu()[0], edited_histogram.cpu()[0], predicted_original.cpu()[0]\n",
    "            ), epoch)\n",
    "            model.train()\n",
    "        last_model_path = MODELS_PATH / f'model-{epoch}.pth'\n",
    "        torch.save(model.state_dict(), last_model_path)\n",
    "        scheduler.step()\n",
    "except KeyboardInterrupt:\n",
    "    print('Interrupted, saving last model')\n",
    "    last_model_path = MODELS_PATH / f'model-final.pth'\n",
    "    torch.save(model.state_dict(), last_model_path)\n",
    "finally:\n",
    "    writer.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = HistogramRestorationNet().to(device)\n",
    "model.load_state_dict(torch.load(last_model_path))\n",
    "model.eval()\n",
    "loader = iter(test_dataloader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from editor.ploting import plot_histograms\n",
    "\n",
    "\n",
    "edited_histogram, original_histogram = next(loader)\n",
    "edited_histogram = edited_histogram.to(device)\n",
    "original_histogram = original_histogram.to(device)\n",
    "predicted_original = model(edited_histogram)\n",
    "plot_histograms([\n",
    "    original_histogram.cpu().numpy().squeeze(),\n",
    "    edited_histogram.cpu().numpy().squeeze(),\n",
    "    predicted_original.cpu().detach().numpy().squeeze()\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 1, 32, 32, 32])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch.nn.functional as F\n",
    "\n",
    "original_histogram.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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{\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenu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     "metadata": {},
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   ],
   "source": [
    "\n",
    "# 32 / 16 -> 2\n",
    "# 32 / 8 -> 4\n",
    "# 4 -> 8\n",
    "\n",
    "a =  F.avg_pool3d(original_histogram, 4)\n",
    "a /= torch.sum(a)\n",
    "plot_histograms([\n",
    "    original_histogram.cpu().numpy().squeeze(),\n",
    "   a.cpu().numpy().squeeze(),\n",
    "])"
   ]
  }
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