Residual FSQ MNIST Example

Residual_FSQ_Example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d4526f33-20b1-405f-8e24-f4efd311b09b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "import torch\n",
    "import torchinfo\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from vector_quantize_pytorch import FSQ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "077918e7-1c2e-4a87-80ca-2de2f3df7304",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Block(nn.Module):\n",
    "    def __init__(self, *, dim_in: int, inner_dim: int, dim_out: int):\n",
    "        super().__init__()\n",
    "        self.proj_in = nn.Linear(dim_in, inner_dim, bias=False)\n",
    "        self.layers = nn.ModuleList([nn.Linear(inner_dim, inner_dim, bias=False) for _ in range(1)])    \n",
    "        self.proj_out = nn.Linear(inner_dim, dim_out, bias=False)\n",
    "        \n",
    "    def forward(self, x: torch.Tensor):\n",
    "        x = self.proj_in(x)\n",
    "        for layer in self.layers:\n",
    "            #x = x + F.silu(layer(x))\n",
    "            x = F.silu(layer(x))\n",
    "        x = self.proj_out(x)\n",
    "        return x\n",
    "\n",
    "class LFSQ(nn.Module):\n",
    "    def __init__(self, *, dim: int, levels: list[int], inner_dim: int):\n",
    "        super().__init__()\n",
    "        self.fsq = FSQ(levels)\n",
    "        self.proj_in = Block(dim_in=dim, inner_dim=inner_dim, dim_out=self.fsq.dim)\n",
    "        self.proj_out = Block(dim_in=self.fsq.dim, inner_dim=1024, dim_out=dim)\n",
    "\n",
    "    def forward(self, x: torch.Tensor):\n",
    "        z = self.proj_in(x)\n",
    "        zhat, indices = self.fsq(z)\n",
    "        return self.proj_out(zhat), indices\n",
    "\n",
    "    def decode(self, indices: torch.Tensor):\n",
    "        zhat = self.fsq.indices_to_codes(indices)\n",
    "        return self.proj_out(zhat)\n",
    "\n",
    "class ResidualFSVQ(nn.Module):\n",
    "    def __init__(self, *, dim: int, levels: list[int], n_quantizers: int, inner_dim: int):\n",
    "        super().__init__()\n",
    "        self.layers = nn.ModuleList([LFSQ(dim=dim, levels=levels, inner_dim=inner_dim) for _ in range(n_quantizers)])\n",
    "    \n",
    "    def forward(self, x: torch.Tensor):\n",
    "        indices_stack = []\n",
    "        residual = x\n",
    "        quantized_out = 0\n",
    "        for i, layer in enumerate(self.layers):\n",
    "            q, indices = layer(residual)\n",
    "            indices_stack.append(indices)\n",
    "            residual = residual - q.detach()\n",
    "            quantized_out = quantized_out + q\n",
    "        return quantized_out, torch.stack(indices_stack, dim=1)\n",
    "\n",
    "    def decode(self, indices_stack: torch.Tensor):\n",
    "        quantized_out = 0\n",
    "        for layer, indices in zip(self.layers, indices_stack.transpose(0, 1)):\n",
    "            quantized_out += layer.decode(indices)\n",
    "        return quantized_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5fdd446b-c467-4268-84ed-9eba1eb16c74",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "transform = transforms.Compose(\n",
    "    [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]\n",
    ")\n",
    "train_dataset = DataLoader(\n",
    "    datasets.MNIST(\n",
    "        root=\"~/data/mnist\",\n",
    "        train=True,\n",
    "        download=True,\n",
    "        transform=transform,\n",
    "    ),\n",
    "    batch_size=256,\n",
    "    shuffle=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f5906915-d1bc-47df-a371-e9c7d96885d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "\t 0.9449350833892822\n",
      "\t 0.38362428545951843\n",
      "\t 0.3026482164859772\n",
      "\t 0.23744362592697144\n",
      "\t 0.2145228236913681\n",
      "\t 0.21569983661174774\n",
      "\t 0.20874753594398499\n",
      "\t 0.20240822434425354\n",
      "1\n",
      "\t 0.20177340507507324\n",
      "\t 0.19352513551712036\n",
      "\t 0.18616779148578644\n",
      "\t 0.1833460032939911\n",
      "\t 0.17546120285987854\n",
      "\t 0.17591439187526703\n",
      "\t 0.17490769922733307\n",
      "2\n",
      "\t 0.1696549654006958\n",
      "\t 0.17058797180652618\n",
      "\t 0.17241020500659943\n",
      "\t 0.1674705594778061\n",
      "\t 0.17151311039924622\n",
      "\t 0.16658423840999603\n",
      "\t 0.16760918498039246\n",
      "\t 0.16674788296222687\n",
      "3\n",
      "\t 0.16501620411872864\n",
      "\t 0.16709177196025848\n",
      "\t 0.1662142127752304\n",
      "\t 0.1652580201625824\n",
      "\t 0.16576175391674042\n",
      "\t 0.16851170361042023\n",
      "\t 0.1674565225839615\n",
      "4\n",
      "\t 0.1611906737089157\n",
      "\t 0.15916787087917328\n",
      "\t 0.16538867354393005\n",
      "\t 0.164993554353714\n",
      "\t 0.16003213822841644\n",
      "\t 0.15587331354618073\n",
      "\t 0.15846000611782074\n"
     ]
    }
   ],
   "source": [
    "device = \"cuda\"\n",
    "n_epochs = 5\n",
    "\n",
    "rvq = ResidualFSVQ(dim=28*28, levels=[8, 6, 5], n_quantizers=6, inner_dim=256).to(device)\n",
    "opt = torch.optim.AdamW(rvq.parameters(), lr=3e-4)\n",
    "\n",
    "losses = []\n",
    "global_step = 0\n",
    "\n",
    "for epoch in range(n_epochs):\n",
    "    print(epoch)\n",
    "    for x, _ in train_dataset:\n",
    "        x = x.to(device)\n",
    "        xhat, indices_stack = rvq(x.view(x.shape[0], -1))\n",
    "        xhat = xhat.view(x.shape)\n",
    "        loss = torch.nn.functional.l1_loss(x, xhat)\n",
    "        loss.backward()\n",
    "        opt.step()\n",
    "        opt.zero_grad()\n",
    "        loss = loss.item()\n",
    "        losses.append(loss)\n",
    "        if global_step % 32 == 0:\n",
    "            print(\"\\t\", loss)\n",
    "        \n",
    "        global_step += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e816cc91-2ad1-42b2-9a44-e3df297c0531",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dpqZNm4Zvv/3W1c1wiq1K57eKyuqwJURERPVbvQ5Tw4YNQ0BAgKub4RRbA3m3itgzRUREVFccDlMLFixA9+7dERgYiMDAQMTExGD9+vW12qjt27dj7NixiIiIgEKhwJo1ayyeN3/+fLRq1Qo+Pj7o378/9u7dW6vtkDO9rZ6pQvZMERER1RWHw1Tz5s0xb948pKSkYP/+/YiNjcW9996LI0eOWDx/586dKC837yk5evQosrLMK3gDQGFhIaKjozF//nyr7VixYgVmzpyJN954AwcOHEB0dDRGjx6N7Oxs4zk9evRA165dzf5kZGQ4+K3lx9Z+xiVabilDRERUV9SOvmHs2LGS5++++y4WLFiA3bt3o0uXLpLX9Ho9EhMT0b59eyxfvhwqlWFfuRMnTiA2NhYzZ87Eyy+/bPYZ8fHxiI+Pt9mOjz76CM888wwmT54MAFi4cCF+//13fP3115g1axYAIC0tzdGv5zZsDfNpdVzNR0REVFdqNGdKp9Nh+fLlKCwsRExMjPnFlUqsW7cOqampmDRpEvR6Pc6cOYPY2FiMGzfOYpCyR1lZGVJSUhAXFyf5rLi4OCQnJzv9fWyZP38+oqKi0Ldv39tyfUc90reF1dfKddZX+hEREVHtcrhnCgAOHz6MmJgYlJSUwN/fH6tXr0ZUVJTFcyMiIrB582YMHjwYEyZMQHJyMuLi4rBgwQKnG339+nXodDqEh4dLjoeHh+P48eN2XycuLg4HDx5EYWEhmjdvjpUrV1oMhQCQmJiIxMRE5OXlISgoyOm215bnYtuje/NghPprMP6LXZLXytkzRUREVGecClMdO3ZEWloacnNz8fPPPyMhIQHbtm2zGqgiIyOxbNkyDB06FG3atMHixYuhUChq1PDasGnTJlc3wWkatRIjo8Jx/nqh2WtaPXumiIiI6opTw3wajQbt2rVD7969MXfuXERHR+OTTz6xen5WVhamTJmCsWPHoqioCDNmzHC6wQAQGhoKlUplNoE9KysLTZo0qdG13Y1KaR5K2TNFRERUd2qlzpRer0dpaanF165fv44RI0agc+fOWLVqFZKSkrBixQq8+OKLTn+eRqNB7969kZSUJGlDUlKS1WE6T9UixA+z4jtJjnHOFBERUd1xeJhv9uzZiI+PR2RkJPLz8/HDDz9g69at+OOPP8zO1ev1iI+PR8uWLbFixQqo1WpERUVh48aNiI2NRbNmzSz2UhUUFOD06dPG5+fOnUNaWhpCQkIQGRkJAJg5cyYSEhLQp08f9OvXDx9//DEKCwuNq/vqk2eHtsW89VVzxbQMU0RERHXG4TCVnZ2NSZMm4erVqwgKCkL37t3xxx9/YOTIkWbnKpVKvPfeexg8eDA0Go3xeHR0NDZt2oTGjRtb/Iz9+/dj+PDhxuczZ84EACQkJGDp0qUAgIcffhjXrl3DnDlzkJmZiR49emDDhg1mk9LrIw7zERER1R2Hw9TixYsdOt9SyAKAnj17Wn3PsGHDbO49V2nq1KmYOnWqQ+2pDzjMR0REVHfq9d58nkqrZ88UERFRXWGY8kDsmSIiIqo7DFMeiNvJEBER1R2GKQ/EnikiIqK6wzDlgbiaj4iIqO4wTHkgbidDRERUdximPIR4q0MO8xEREdUdhikP8fOzdxgfc5iPiIio7jBMeYjeLRvigwe6AwBSLtyyq+gpERER1RzDlAcZ3ikMAHCzsAyXbxW7uDVERET1A8OUBwn190aovzcAoKBU6+LWEBER1Q8MUx4mwMew3eKYT/9CbnG5i1tDRETk+RimPIy/tyFM6QVg/pbTLm4NERGR52OY8jCVYQoAfk65jNh/b8UfRzJd2CIiIiLPxjDlYSqH+QDDRPSz1wrxf8tSXNgiIiIiz8Yw5WH8RWGKiIiIbj+GKQ/jp1G5uglERET1CsOUh2ngzZ4pIiKiusQw5WH8NQxTREREdYlhysP4sWeKiIioTjFMeZix0U1d3QQiIqJ6hWHKw4QF+OB/f4txdTOIiIjqDYYpD1S5Px8RERHdfgxTHsjHi+URiIiI6grDlAfyUTNMERER1RWGKQ/k7WX+1/rmr0dc0BIiIiLPxzDlgXy8VOjWLEhybOmu865pDBERkYdjmPJQj8e0dHUTiIiI6gWGKQ/lpVK4uglERET1AsOUhyrT6s2Onc4ucEFLiIiIPBvDlIcqKtOZHRv1n20uaAkREZFnY5jyUJbClF4Aisq0LmgNERGR52KY8lDjejazePx6flkdt4SIiMizMUx5qGbBvtg0c6jZ8euFpS5oDRERkedimPJgjRpozI5dz2eYIiIiqk0MUx6sYQMN/m9oG8mxS7eKXdQaIiIiz8Qw5eFm3dlJ8vx0dr6LWkJEROSZGKY8nEIhLd75495L+O9fZ6HVmdehIiIiIscxTNVD7/x+DD/svejqZhAREXkEhql66tDlXFc3gYiIyCMwTNUDS57oi1fv6oTIED9XN4WIiMjjMEzVA8M7hWHKkLZo4K02Hisu1+HijSIXtoqIiMgzMEzVI/7eKuPj3w9dxZAPtiD9Cof7iIiIaoJhqh7x06jNjv16MMMFLSEiIvIcDFP1SANRz1SlMi1LJBAREdUEw1Q94qM2D1NaPcMUERFRTTBM1SMqpcLsWLlWcEFLiIiIPAfDVD0S4ONldqxcp4dWp4dez1BFRETkDIapeqSRv8bsWHG5DiP/sx0PLNzlghYRERG5P/PlXeSxwgK8zY6lZ+Ti0s1inAOg1emhVjFfExEROYK/OeuRMd2bIjxQGqiyckuNj8t1HOojIiJyFMNUPeKnUWP37BGYHd/JeKxMV7Waj2USiIiIHMcwVc8oFAooFear+gBpsCIiIiL7MEzVQ0oLJRIAhikiIiJnMEzVQxFBPhaPl3OYj4iIyGEMU/XQ6C5N8Ldhbc2OH7qSiwVbz6BUq3NBq4iIiNwTSyPUQ0qlAq/c2QkZOcX4Ja1qo+Pnf0wFACgUwLNDzcMWERERmWPPVD3WqlEDi8cPX8mt45YQERG5L4apeqxNY8thysvKBHUiIiIyxzBVj8W0aYQAH/ORXi9WQSciIrIbf2vWY2GBPkiaORQ+XtJ/Bl5q/rMgIiKyF39r1nNhgT7o3DRQckwQuK0MERGRvRimCKkXcyTPC0pZGoGIiMheDFNk5reDGVi2+wJKtTqUsyo6ERGRTQxThCfuaGV27PU16ejz9iZj7SkiIiKyjEU7CXPujoJWr8fmY9nIyC0xHs8v1WJ9eiaKyrTw0/CfChERkSXsmSIolQq8M64bVvxfjMXXj13NQ2ZuCW4WltVxy4iIiOSP3Q1k1DjA2+Lxy7eKcf+CZADA+Xlj6rJJREREsseeKTLy8VJZPD5teZrxsU7PsglERERiDFMkoahmJ5kyLVf3ERERiTFMkcTa5wbZfL1UyxpUREREYgxTJNElIggtG/lZfb2UPVNEREQSDFNkRqW0PtZXWm5fmDp3vRDHrubVVpOIiIhki6v5yIzaVpiyc5hv+IdbAQAHXh+JkAaa2mgWERGRLLFnisyEBfhYfa3Ejp4prWgLmiu3imulTURERHLFMEVm3rq3CzqGB1h8rVSrg14v4K3fjmLl/kuYsSINP6dclpxTJgpTOoGlFIiIyLNxmI/MtGnsjz9mDMHAeZtxJcfQs9QxPAAnsvJRqtVj68lsfL3znPH81alX8EDv5sbn4nlVrEtFRESejj1TZFV0iyAAhtpT3l6Gfyr5JeX4ZNMps3NTLtyCviI4iVf8leu4+o+IiDwbwxRZ9fa9XfHkwNbYMG0IfNSG6uhfbD2Dg5dzzc69f8EubDyWBUA6Sb24nHWpiIjIs3GYj6xq5O+NOWOjAFT1TB2yEKQqnb1WCEBaJb2UYYqIiDwce6bILrZW+FUqLtMCkA7zsWeKiIg8HcMU2aVXy+Bqz9l68hoAk2G+Ms6ZIiIiz8YwRXbp3bJhteccupyL09kFktV87JkiIiJPxzBFdmkfZrnulKlTFeUTKlUO/REREXkqhimyi0qpwKq/31Hteb4alWSYL7+EYYqIiDwbwxTZrVdk9UN9mbklkp6pG4VlmL/lNPacvXE7m0ZEROQyLI1ATgv0USPPpOdp1qrDkufirWbOvncXynR6+Hip6qR9REREdYE9U+Q0ezY9Fmvz6jr0fnsj8krKb1OLiIiI6h7DFDlk5sgOxsdv3dvF4fcXlumw7cS12mwSERGRS3GYjxzy/Ij2eC62HbLzSxEe6GM2rGcPL5XiNrSMiIjINdgzRQ5TKBQIDzRURH93fFeH369S8p8dERF5DvZMUY1M6BeJgW1DkXrpFmasOGjXe1TMUkRE5EH4a41qRKFQoFVoA4zv2RxjujW16z35JVrEf/IXXl3t+BAhERGR3DBMUa158x77JqSvO3wVx67m4Yc9F29zi4iIiG4/himqNY0DvO06r6jM9n59JeU6nL1WUBtNIiIiuu0YpqjOlYg2PxYEwez1J5bsRey/tyHlwk2b1ykq02Lu+hM4cJ2rA4mIyHUYpui2U5hknX3nbxkfL95xDgWlVVXUBUHA7rOGEPVLWobVax66nIOoOX/g610X8M0pVlQnIiLXYZii2+7ZoW2tvvbO78fw+ebTxudXc0uMj5s39LX6vn9tOF47jSMiIqohhilyud8OZuDnlMsoKdehUNRLVWplu5pLN4uw87TljZPLtI5tcUNERFRTDFN02wkCsHRyX6uvX8kpxosrD+LjTadQKgpDBWWGYLXt5DVcvFFkPP788lSza+j1AhZsPYNub/6B1Iu3zF4nIiK6XRimqFY1qaiMbmpYxzA8F9vO5nu/33MBd3+2w/i8oESLfedvIuHrvRjywRaU6wxBK/Vijtl7S7V6/GvDcZRq9ZjtxBY3REREzmKYolr1/TP98UDv5tjy4jDjMQGGFXv+3rYL7ueXaCXPC0q1OHgpx/j85Z8PAQA0avN/tiXaqhWCZTrrQ32XbhZZXEFIRETkrHodpsaPH4+GDRvigQcecHVTPEbbxv748MFotA5tYDwWEWSYSB7g4+XQtQpKtFArq5YCrk69giMZuRbnRZWI5ldpdZbD0rLdFzD4/S34529HHWoHERGRLfU6TE2bNg3ffvutq5vhsb5+og8mxbTEo/0iAQABPo5tBVlQqoWXSS/UmE93WDz3gz9PGh9rrfRM/Wu9YQXg0l3n8UvaFYfaQkREZE29DlPDhg1DQECAq5vhsWI7heOte7sah+UcDVOpl3Lw2up0u8797VCm8XG53rxnKu1SjqSe1bTlaXj+x1SUam1XYyciIqqOw2Fq7ty56Nu3LwICAhAWFoZx48bhxIkTtdqo7du3Y+zYsYiIiIBCocCaNWssnjd//ny0atUKPj4+6N+/P/bu3Vur7aDaJZ7rlPr6SEyPa2/zfGfLHPh4Sf9Zl5TrMG7+TrPzfj2Yga93nLd4DZ2FQEZERGSJw2Fq27ZtSExMxO7du7Fx40aUl5dj1KhRKCwstHj+zp07UV5ebnb86NGjyMrKsviewsJCREdHY/78+VbbsWLFCsycORNvvPEGDhw4gOjoaIwePRrZ2dnGc3r06IGuXbua/cnIsF5Zm+pGkK8X2ofVrFdw/z/iLB73Ukn/WRfb2Asw7ZJ5GYUvtp5G9D//xLGreTVqHxER1Q+OjbsA2LBhg+T50qVLERYWhpSUFAwZMkTyml6vR2JiItq3b4/ly5dDpTJs+3HixAnExsZi5syZePnll80+Iz4+HvHx8Tbb8dFHH+GZZ57B5MmTAQALFy7E77//jq+//hqzZs0CAKSlpTn69eg26tEiGH4aFVo09INSqcDoLuG4q1sTrDucWf2bLQj194ZaqYDWpBdJqxOQU1SGwjIdmgX7Yu0h6+FZabrXDYD3Nxh6Wt9eexQ/PDPAqbYREVH9UeM5U7m5uQCAkJAQ84srlVi3bh1SU1MxadIk6PV6nDlzBrGxsRg3bpzFIGWPsrIypKSkIC6uqmdCqVQiLi4OycnJzn2RasyfPx9RUVHo29d68UmyzU+jRso/RmLt84MAAGqVEl9M7I237+3i9DV9vMz35SvX6dHjrY0YOG8zbhaW4fVfjlh9v1JpfZNkVcVru8/ewIUbhbhRUIq564/hzLUCp9tLRESep0ZhSq/XY/r06Rg4cCC6du1q8ZyIiAhs3rwZO3bswIQJExAbG4u4uDgsWLDA6c+9fv06dDodwsPDJcfDw8ORmWl/L0dcXBwefPBBrFu3Ds2bN7cZxBITE3H06FHs27fP6XYT4KtRmQ3DPVKx2s8ZfhrzMCWeaH4623bwUSoUuHSzCJ8mnUJOUZnkNbVSgWNX8/DIl7sx9IOteHvtUSzadhbxH//ldHuJiMjzODzMJ5aYmIj09HTs2GF5uXqlyMhILFu2DEOHDkWbNm2wePFiKCwMr9S1TZs2uboJBMMcp/88HI0ZKw46/N5WjfyQnV8qOSYu/lndJHaVAhj7+Q7kFJXjVlEZ3hhb1UumUipx+HKu8fmRDMMcKltFQYmIqP5xumdq6tSpWLt2LbZs2YLmzZvbPDcrKwtTpkzB2LFjUVRUhBkzZjj7sQCA0NBQqFQqswnsWVlZaNKkSY2uTa4RGeInee7vrca74y33dgLAoHahAIC547ugTYD1lXePLd5j83OLynTIKTIskDhyRTrh3HQ+VsMGGpvXIiKi+snhnilBEPDcc89h9erV2Lp1K1q3bm3z/OvXr2PEiBHo3LkzVq5ciZMnT2LYsGHw9vbGhx9+6FSjNRoNevfujaSkJIwbNw6AYcgxKSkJU6dOdeqa5FptG/tLnr89rgt6tmhofD5tRHvMGNkBRWVafLf7Aga1awzAEMKmddXhn4d8cbPQfNVodf48WhXI956/iU2i52euFWDDkaphY/GQoiAIsuhdJSIi13M4TCUmJuKHH37AL7/8goCAAOMcpaCgIPj6+krO1ev1iI+PR8uWLbFixQqo1WpERUVh48aNiI2NRbNmzSz2UhUUFOD06dPG5+fOnUNaWhpCQkIQGWmYXzNz5kwkJCSgT58+6NevHz7++GMUFhYaV/eRewn202DjjCHwUikRFugNP40ambklxtcra1T5adSYMqSt2ftra7u9p7/db3x8ysZ8q1Kt3uLkdyIiqn8cDlOVE8eHDRsmOb5kyRI88cQTkmNKpRLvvfceBg8eDI2maogkOjoamzZtQuPGjS1+xv79+zF8+HDj85kzZwIAEhISsHTpUgDAww8/jGvXrmHOnDnIzMxEjx49sGHDBrNJ6eQ+2odL6075isJKeKBPXTfHTFFpVb0qrV5Adn4J8oq1aBfmb+NdRETk6Zwa5nPEyJEjLR7v2bOn1fcMGzbMrs+ZOnUqh/U8mLeoknnbxg1snFk38kWrBEvKdej3bhIAYPfsEWgS5PqwR0RErlGj1XxEt5OPlwrxXZugsEyH7s2DXd0cSUX0J5dWlcg4lpnHMEVEVI8xTJGsLXist6ubYNEhUckETkMnIqrfGKbIIzgy+nxu7l3IK9Fiyc5z+HjTqdvXKCIiqhdqvJ0MkRx4qarvH4rrHIaf/i8GCoUCQb5eCBHVjXp7nPWaVtU5nV2AV1cfRkZOsdVzBEHAom1nsPP0dac/h4iI5IlhijzC54/2QKi/tKjmPdERxsefPNID/03oi36tq/aQ1Ii2tXmgV3N0CHduVd47vx/DD3su4oWfDBXci8q0mL48FRvSq2pU7T13E3PXH8fE/+5xeBEHERHJG4f5yCP0igzGvtfi8NHGk9h+8hqWPd0fAd5qFJZqUVSmw51dzSvjq0SbHPtqVNgwbQgUCuDrnefx9tqjDrchPcMwj+q/f53DmrQMrEnLwPl5YwBIt7g5c62Q5RSIiDwIwxR5DIVCgRdGdcQLozoajy1+oq/V8wdWbEkT6u8NAFBWhKtLN4sk55luK2NNZV2szLwSyfGiMi1KRXsEXskpZpgiIvIgDFNUb0UE+2LnrFgE+kh/DB7q0wJLd503Pv/X/d2hUSvx3I+pNq9XWRFdPIqXU1SGfu8mSTZHLi7Tmr7VKq1OD7WKo/FERHLGMEX1WrNgX7NjURGB2PvaCJSW65F+JRejuzSBUqmoNkxZ2qpvwdYzkiAFGDZXtseFG4W4+9MdeDymJV6+s5Nd7yEiorrH/+UlsiAswActQvwQ362pcfhvcPtQm+9pYmHLm0Xbz5odsxSmbhSUYuZPadh//qbx2L//PIn8Ui2+2HrG0eYTEVEdYpgistM3k/shqmmg1ddLynW4klOMH/detHmdYgth6p+/HcWqA1fwwMLkGreTiIjqFsMUkZ2USgWOiraUMXXwci4Gzttc7XUqe6YOX87Fom1ncCIzHyez8h1uz497L2L2qkPQ2zE5noiIbh/OmSKqY0XlWgiCgLGf7wAAzF1/3KH3X7xRhNAADWavOgwAGNEpHHFR4RbPPXe9ENl5JejfplHNGk1ERFYxTBE5oEWILy7dNK90bm/5BAC4mlOCUf/ZbvV1rcmEdbHjmXm48+O/0KqRn/FYfmm51fOHf7gVAPDnjCHoEB5gV/uIiMgxHOYjcsDSyf3wwsgO8PeW/n9Im8YN7L7GrwczcCq7wOrrd37yF+75fCf0JpXS564/hjs//gsAcP5GVS0spaVlhCYOizZmlqOrucWsDE9EbothisgBbRv747kR7TG0Q2PJca9arAV1OrsAR6/mIbe4qsdp15nrWLTNfGUgYChWWh3T8gxy8s2u84iZuxn/2nDC1U0hInIKwxSRExo28JI8F4/wffBAd4vvua9XM4c+469TVZsiP/PNfqvnqUzCVNqlHKRdyoFO1KgyrXzD1D9/OwIAWLiNJSCIyD0xTBE5YebIjpLn4iGqB/u0wN7XRkgKgn78cA88F9ve6c+rrK5uiWiLQZRqdRg3fyfGzd+J7PyqbW3sDVPnrhfil7QrtTLkZmvuFxGRJ2GYInJCSAMNekYGAzBssmyaPcICfPDlpN4AgKcGtca4ns3QoqF5tXV73Sgss+u83KKqocHkMzeMj9Mzci3WtzI1/MOtmLY8DWsPXbXr805k5uPsNfP5X/9Ycxi93t6IbJN9ComIPBHDFJGTFj3WGy+N7oiFj/e2+HqXiCAce+tO/GNMZwCAWqXE34a1rfV2lOsFFJZqcTwzD3klVWEq/UpVTaxf0jLw2OI9AICjGXkY89kuHLxR1aV14UYhbhSUGp/vq6jEfjIrH5uPZ1n83IJSLUZ/vB2x/94mGVIEgO92X0ReiRZf7zxf4+9HRCR3LI1A5KSwQB8kDm8HABBgeVjMVyMdntOYTFRvGuSDq7lVvTe/TR2Ef/52BPsv3LK7HVqdHk8s2Yt952/hpdFVw4+3iqS9WSkV17zrU8OKwAs3lJgN4EpOMYZ+sBWh/hrjuZUrCStLOKz6+x1QKhRo1ECDFiGGsgw5ouvfKChFmIXtdBZuO4PCUi3eHtfVavsVCgXMuvaIiNwIe6aIaoG9RchN5yL1imwoee7jpUQDb8f+H6dcp8e+84agtEg0iXt16hWzcwtLtcbHpToFDl3OxW8HMwAA1wuqwpHp91m5/xLGzd+Jwe9vEX1u1Ukbj2Xhwz9OWBxKXLb7AgDz7372WgEe+TLZrFfLloJSrWQuGBGRHLBniqgOiXPDkwNb45khrfH74ar5Sd5qFQJ9vSy807rUiznGx3klWusnAriWXyp5fv+iPRbP+2HPRUktLfHKwkol5VXB6bXV6QAMPXQvje5kdm6rWb8DAL58vDdGdWkCAJi2PA2HrzhW/6rvO5tQXK7DgddHIqSBpvo3EBHVAfZMEdUCe1e/iQtxzhkbhVB/b8nrPl5K+Jms3Pv+6f6YWjGcWMlPNHy4fN8lu9vpSL2pL7dX1bW6USAdMpy96jCe+zHV7D2HqikOOmVZivHxxZtFNs40JwgCiisCXLpJCNPq9A71cFVerzp6vSCZh0ZEZAnDFFEtsPfXeICPtNdJrZTWiPL2UkGtkh7z91bjxdEd8VCf5sZjfVuFONVOW9vY2FIs6oWa8u1+/Lj3Ik5bqOJuqQfLlCAISLlwE/lWQkp2Xgku3jAPWuI2iO9RuU6Pkf/ZjvsW7LI71BaUajHoX1swfbkhEP649yL+l3LZ7Lxnvt2P7m/+aXHFIhFRJQ7zEdUGO9PUpJiW2HH6GkZXDHWZVi/38VJCo5b+P46/j+HH9Jao7MG0uPbYdvJaDRrsvD+PWl7dV+mFnw7afL317HU2X+/3XhIAYHFCH4zoXLWBc4FoCFOBqvt2Misf564XAjD0vHmrrdfkqnTwUg6u5BTjSloxpsV1MG4aPTY6QnL/k45nAwBW7LuE2Xd1rva6RFQ/sWeKqBY82i8SANCvte0eowbeanz/9ABMimllPPbhg9HGxxqVEk8PbiN5T0DF3CXx6jnTiety8r8D5j089hIX+nzqm/2SWlni+WCZecX4OeUyisq0KCmveo89tbQAQJxhd5yu6k0rtzIMas+WPQAkWwDZsvvsDcR9tE3y/YjIfTFMEdWCJwe1xo/PDMCSJ/o6/F7xr2mFQoFmwb5YkzjQeKyyZ6pz00DJ+zxxArZ4KA8Atpww9AwduHgL31WsCgSAGSsO4sWVB/HWb0dRIFqhWGRnmBJPns8RFUS1Fqbs2Xrxt4MZiP7nn/gs6ZT0OxzPxt2f/YXjmVV1vx75cjdOZxfg0a9229Xe2pZTVIZF284gi0VViWoFwxRRLVApFYhp28jhsgaA5RHChn5Vc6t8KyakvzCqI/4+rC3+mD4EgDQQiD02INLhNsjF/4kmqAOGSfA6vYD7vtiFpbvOm53/vwOXJT129oap4rKq0CReTSku9yBmuv+hJZVDhf/eeFJyfPLSfUi/kofE7w/YfH9OURk+2XTK4nyx2vbiykOYu/44Er7ee9s/i6g+YJgicrE72jYCAASLAlTLRg3w2l2d8e8Ho41DTEG+Xnj5zk7o2CQAgDRMTehfFaCaWCieaepxmQauXRaGvTJyiq2eLwhVZRkA+4f5isqqerOOZ+YbH1f2TN002b5Hqaw+TJnOdTOVW2y9bMXlW0WYveow/rPpJO5bsKvaz6qpTccM897E352InMcJ6EQuFhHsi+TZsQg0Wen3zJA2Vt5hMKF/JL7bfREalRIT+0fihz0XAcBiJXKx1+7qjIQBzaG6eQ4T7xqCn1OvYtG2szbf40q2Jrxr9YLJMJ8Wy5LP40pOCV65s6PVuU7WevXKtHos33sRs1YdNm4DBNjXM+Wlsn2Otdd/O5ghKTNxvaDU4nlEJF/smSKSgaZBvg4PEf5jTBS+fLw3DswZKVnB1qiauVR6QYBCoUDPRgJaNWqA2fE1W6U2O968SKdYde2pzttrj9p97tpDV/H6L0ewcNsZsy159HoBG9KvIiOn2GxuVqVhH27FrIrhund+P2Y8/k3yeRzNqJrzpNMDTyxNwfsbjhuvrVba/s9pZTmHLRUrBCt9ajLHCjD0VA2ctxlfiWp9XckpxuId5yRV7J1hqQSEo0q1OiQdy5IEWaL6jGGKyE35eKkwqksT+Hur4S0aYvIVFf0c0SnM7H22ilCOjAo32z+wOmOjI2y+PqBiGLMuLBNNUj97rQCCIODxxXvwxJK9GDA3Cc9+dwBDP9hi99yqStcLynDXp38Z5zMdy1Fg55kb+GLrGej1AsZ/sRNXTIYjdXoBQ0Tb73gplcjOK8Hkpfsk51mapfX+hhO4klOMd9dVBbqHFibj7bVHJcccdfFGEV5Yabt0hT0+/OMEnvpmP/72XUr1JxPVAwxTRB5AHKa8vVQY3D4UgGHSuikfG3WYVAoF1k0b5NBnRwT7YsWUAfj7sLZmrw1uH4rpI9o7dL3a8sr/DuPnlMv469R1bD1xDdkVW+mU6wS751aZqtz+RlSNAdcKSnHQQuX3dYevSqq8F5Rq8bOFshF6C4VGxXO6KstFVIa1FfsuIfbDrViw9YzZ+6z5avtZxH/yF55Y4viEc0EQ8OEfJ/BLWtVej8v3Gqru/3XqumTC/LTlqZi8ZK/dxVOJPAXDFJEHEA/zeakUWPhYbyTPjkVURKDZuQkDW1m9Tp9WDdEuLAAn34k3BjIAeGZwa5uf379NI7x8p/lw37Kn+tdJCYfo5kEWj7/08yGLx7fbUandEuO8J9H0p693nLN47sks6eTu7PxSvL/hhNl5lyxsq7Pn7E3j455vbcSFG4XG5zq9gLPXC/GviiFGSwRBwC3RJPp31x3Dsat5OHu90Op7rEk+cwOfbzmNacvTIAgCSsp1kgr0Qz4w9L4Vl+nwS1oGtpy4hsu3rC8aIPJEDFNEHkC8kkwBBRp4q9E0yBcAsOjx3ojrHI4/pg/B+XljzCa6A8DGGUPwxtgoJNzRyni9cNFE9lnxnTGsY2M00KiwccYQq+34bap5r5aPV/UVyStVBrj7ejbD+XljEBniZ9f7po/sYPdnAMCxq3nVn2RB5VwrcTmqRdstT963t4aTpXIM+aK5SPmlWqufYc3rv6Sj59sbsf/8zepPFtHpzbf6uZpb9T1eWHkQPd/aKKnGDwAzVqTh401VJSFMt0QS0+r00Du4jyKR3HE1H5EHkIQpk99jo7s0MW5fY0378AC0Dw+QHBNXA1ApFVg6uR+0Oj3UNuZUdWsehL8Pa4svRENQtsJU/9Yh2HOu6hf+qKhw/PvBaLMNoKtjTzmI2pB0LBtbj2fhWr7tlXvJZ26gTGv/ptLV8XUgkALAd7sNKzs/STqFZU/1t3muULEgAQDe33Aci7afxaB2ofjuacP7SkXfY9WBKxavsTpVerygRIt3/joKHy8VXhjVwXj9wnJg7PxkKJUKrHt+sMV/S5m5JXh77VEk3NGq2h0FxLadvIZf0zLw5j1RZntgEt1uDFNEHkClVCCqaSBuFpahg0kocpbSQjkAW0Gqkml4UpnUaHr1rk6IahqEbs2D4O+tRttXq/bqCwv0kZR2sFaR3JSfxrGw4axfD2ZUPLJ9Hx79ajfiOptP/nfWsuQL1Z9UQVwjK9iv+iHWn/ZfwsN9DXXHfkkzfD/xFjtlWsfnlz2/PM3Y+xfTthEGtjP0OKbdVOD0NcNQ49XcErSo6HnMyitBXnE52ocH4NXVh7H5eDZ+P3wV5+eNsfszKwuQhvpruI8i1TkO8xF5iN+eG4S/XhlebfFIe9lTqNI0KAGAfzUlHqYMaYtB7UMR5OsFlVKBh/u0AAB89FA04kQbGwOAr50hyZ7NjevapmPZ1Z9kpzI7QyUADKjYKBow1LAqrSYMvfK/w3ivYoWgIFpbOLfimCOfXUk8jCqe7J9bVvXvJTu/aviw/3tJGPmf7biaWyyZtL/leDZSTEpcVOdCDSvI6/WCZAHA7abXC1iw9Qz2OTgkS/LCMEXkIVRKBbwcLGtgix1ZSrKKsNJDfVugQ7i/ZNL6ExVzsSz51wPdcW7uXbivV3OzcCbeBFpsTPemkqFAS+2oTqCP+3fMd3vjD5wwqWJuGn46/mNDtdf5cvtZPL54D7LyqgqGLtp+FkVlWpSW12y4Uvx3WiCaarX95HWcuVYgmag/c8VByUrAyUv34f4Fu8zmWGXnlWDmijQs33vRbAK/eHWk6UR8e8z5NR1Rc/7Aq6sPY9/5m1YLvNaWtYev4l8bjuPBhcm39XPo9mKYIiKLLA3zmbIUYvy91fhzxlC8NibKeOzNe7pg44wh2D17hMXrWKtU3iuyodkcsLjO4Zg/oRdaNaqanO7tVdWODuH+1bYbAPJKtHh6kPVVioPbh2LO3VFWX5eD/FItRn+8HU9/sx/5JeXGMgrO+MvCCsdr+aWSOVPOKCrToVynx8qUy7hYUPWX+UnSKYz49zaM+s9247Hkszdw5pr5ikPTgDjjpzSsSr2CWasOY/D7W3BetEpRHLve+f0Yer69EUkV2+cUlWmN92jf+ZuY8u1+XL4lDWOV881+2HMRDy5MxvTlaU59b3udyS64rdenusEwRUQW2VpJV7mP4KD2je2+XvvwADQJcnyiuGkR0eYNDasU593fDZ2bBmLBxF6SYT4F7OhSg6G37LUxnbH3VcsB75U7O2FYR/u/nyttOpaFbm/+iXavra/V676/4QR2nnG8jIS4g/GzzafQ/rX1eHXNUVwqtO/vxtScX9Jx9lqBsfr7ztPSPRwf/rKqV0dc4mpxRdmK99YdQ35JOXq9vRH3zt8JAHhwYTL+PJqFF36qKmKqs7DKcMORTACG3rB//3kC+87fxPHMPJTr9DiakSfpSdt77iZWp0priS1LPo+nlu6z2sNlz/+0yEFxmQ4/7bskGZ6lKu7fz01Et8WkmFa4klOMYR3NJ1Kv/vtArE69gqcG2q4/VRu81Upj78gDvZtjRpyhDEK7sACsnzbY7Hy9IGDaiPb4RLRNS8/IYKRezDE+f3xAS8y5OwoKhcLqXobeaiXaNPbH5xN6QqsTMH1FmlPt3/HKcIz9bAeGdwwDFNZXxMnR74evOvU+cSapjc2Uf9p/GT/tv4yGfl7YOSvW7HXx8KSlmvJnrhWi25t/AgCOmASgPedu4vU16Xh7XFebQSHxhwPYd/4WPtt8GgAwsX8kvt9zEWO6NcX8ib0AAA8tMoS6zk0D0amJocbb678cAWAotppgYbhb/P8KOr1gcR6iLTq9gA/+OIG+rRpihMmcw9o0b/0xfJN8AR3C/bEmcSBUSoUs5yq6CnumiMgijVqJN8Z2wdAO5r0zrUMbYObIDgjyu/1L0Ct/KQGGOVTVfaYAYMbIDnhvfDfjsS8m9kJU06rrdGgSIJlgnzjcUL29v2gpfuXk97u7R2Bcz2aI72peXqK6nqtlT/VD84Z+2PtaHD56uAfeGdcVHz1keR4YACyfMgBLJ/dFeKBjpSEc0SzY0LPXqUntrPqsS7eKyiWlNCyxp4TVN7vOS54v230BpVodjlyxXn9s33npRPgf9hqGA38/fBXpV3Il87qu5ZtvVn0tv1SyldOG9EwMnLcZB0Qhv9DCxPfqqsn/fvgqFm47g6e+2W/zvJqqXMl6MqsAXd/4AwPnbb6tn+duGKaISNbeu68rvNVKPNqvhV3nV86JGd+zGQa0CcFLozuiaZAv1ol6sbo1k1ZMf2l0Jxz552hJ1fcGGmnHfVeT9wxs28jmxHoAxgBXuTDAT6PGfb2ao01oA+M5O14Zbnwc6u+NYR3DsOfVuOq+ptNi2jbC9peGY03iwNv2GbfT5xU9Q9ZUDtXZCiFv/ma+efbra9Lx9LeWA4mlITrx5W8UliG/pCoI+Xqp8J+NJ7EhPbOq3VtOo/ubfyL9Si5OZuXj2e9ScCWnGJtFG18XlEjD1FNL9+Hhr/Yit8zwffJKyjHl2/1YeyjDeE62SXHYX9KuYPwXO5FRsf1QTlEZDl3Osfi9HFEiWoigFwz7Ver0Asp1ejz9zX4s3Gb/9kaeiMN8RCRr7cICkDZnFHy87Pt/v4JSwy8+X40Ky6fESF5bPmUArtwqRo8WwWbva+CtRpmoGnkDkxIPTw9ujWv5pVha0auhUgIBFlYEvju+K15bnQ4AVrfS2fziMCzcdgYtQ/wQ6FvV06YW9ZbtmhWL+xfsklQgt1e7MH+ctjKx2d9bjchG9lWWl6PqSiXkFJcjt6gcd3/+l0PX/Wm/+b6JxmsWWd8cHADKtXrkFFetGnxwUTKsZbm7P9th9TqjP96OLx/vgwFtQlCm0yOpImilXlJjTspG3NerGf48moU/j2bh7u6GDcbVJsOC0yomzM9edRiLE/pgxL+34UZhGb59sh/KtHr0bxNis6jpzcIypF68hWEdwyRDjiUWSmyUlOuw8WgWNh0z/Hl2qPn+nLaU6/S4mlPi1v8eKzFMEZHs2VtvCoBkKMXUgDaNbL5XXLXctF6Xt1qFN+/pgvWHryIrvxQjO4ejRcOqXwJv3dsFwzuGoUWIH/KKtWjW0NfqKkUAxl884ppG4l9eEcG+aBfmX22Y+t/f7sD9C3ZJjtnqlfESbfWiVFgeFhvQJgTPj2iPCV/tsfnZcnQ9vxS/H76KSzdrb3/Af/9pvqei2GtrDksWPji7z3N+iRaPfrUbicPb4kkL8xEtzbdTiSZdiYcat528hns+34kbFaUhJlUUNRVXt7fkwYW7cOZaId6+twsej2llPG7pOxWX65yeSwgATyzZi52nb2DJ5L6GOYVujMN8RORRarKNS3UFLgFg9d8G4OmOOjzYuxkaB1TNbdLqBGNF778Na4t7oiPs+kxfLxX6tGyIrs0CjfOZxNcUmxTTEltfHGZ8PnNkB4QFmM+vMv3FJx6+7BXZ0Ph4xf9Je+4qLZ8SgzvahpodH9QuVDL3zFFKCEgc1sbp91cyXeEpdq2gFA28nZ8Y3a9VCMZ0ayo5tjLFeq8VYJgAn2nnXoz2mL/lDApKbRcOTfh6L24VlkEjCsdFJsORRy3sQSmubm9JZWmKylWMtvyaliF5bm3Tb2sqV2VOXrLP7fdrZJgiIqpgTxBrHOCNbiGGVVcKhQKTYlqiUQMN7o5uWu17LVEoFFj5bAx+TRxkVnXedKl+z8hgSXkJtUphcahRANC2sWFeVosQXyx7qj/Ozb0Lf708HPGioNC3lf173wGGFY5PDGzl0HvE/hOjw/QR7dArMtjpawDAs8OsDyeVafXGoS5nvBLfEfMn9sI/xrh2S5r8EtthatvJa/ho40lJ72dRNQGs0tlrBTidnY+ZK9Iw+j/bcf+CXWb/1pLP3DBWZbdW1uGttUdtPnfEXjevAM8wRUQe4eU7OwIAXhjZwelrONOr9da9XbHvtTiEBTi/2bJCobC4fc9LFd+pZ2Qw5twdhXujm0mq3HsplRa37xnSPhRLnuiHCf0j8e2T/Y2f0cJG7bBKr97VyeprKqXCWOfLHp880sPi8c8n9MLKZ2Nwft4YjOjk+PDOjLj2eLRfJCb2j8TihD4AgIggn2q3MrJH5d6STw9ug5FRt6/UQHWeX55a7TlZeSX4x5p04/ORogKotsxedRiPfrUHq1Kv4ERWPlIu3ML+8zclw8N6Acaq7OLPqIlvdp232nsl3nbIHXHOFBF5hL8NbYux3SMc+mVfW+zZx9AZfVuF4PCbo+DvrbY4/0qtUkCtUuJf93dDfokWo6KaYH36VUwc0BL+3mpJeQh7KBWGvROt8VIpEdOmEV65sxMaNdDg5f8dsnm9yg2OTUUE+yKiYkjTkXs3uH0o5t3fHQqFAnPvM3w3QRDw0//FoF2YP+5fsMvi8NiANiHYfda+ng/xRt1Bvre/9Ic1Zy1UgjdVptNL/gcgt9j2RPlKlspLPPzlbkyKaWl2/PkfU0UbfFcvv6Tc4gT3ojIt3vjVUHNrfM9maGiyOMPeTc3lij1TROQRKntebE36rs7zI9ojLMAbL45yvnertgX4eFn9TpUruR7uG4mnB7dBZCM//N/Qtk730FQXvtQqw9Dm34a1xUN9paUqFlQUrhTTqJXG0PPPsZaHzSx9M2s1sOZP7GU2r0yhUKBf6xCENNAg1N/y6smFj/W2eNwScZiqrU3DrRnYzvaCiOrU9r6B3yZfMDvmSJACgDkVRUpNiXuexOUgKmn1Aq7mFuPv36e45abPDFNERBVahPhhz6sjMDW2vaubYhd1LW5sPaBNCB7pFyk59tLojpINoa1V554/oZdkLlYljUqJR/tF4tCbozDBzjphACRhUByEAqoJiaaFNY3tcCAU+YrClJfo+/p6qbAmcSAa1mKhWkslOhxRUsNNqG+Hyn0QTZWIetBeWHnQ7PVynR4vrjyIdYcz3XLTZ4YpIiKRmvRs1TXTGkPO6NPSsLpv2gjz3rjE4e0kNb5Ml68/cUcr9IwMRlyU+bynhY/1MvbyBNqoa2TJ6C6GavMatRJ3dm2C98Z3w+KEPtX+3YzuYj7HaUSnMPh6qSQrGm0R1zNTKaseH3pzFHq0CMaE/pGW3mbGnir2wb6We9LslXYpp0bvvx3KdQKW7b6AYyYrCU3nRL1kEqhKynU4k1390KZccc4UEZGbEg9JOeuHZwYgK6/E6uR0pVKBrS8Ox5GMXMSaTBZ/854ukuffPNkPH208iffv746ONdiuZlD7UPz8bIyxmKO9AWbufd3xx5GNxufDOjbG4if6AgA+e7QnPkk6hSU7z0ve88PT/THhv1X1tHxE+82Ja3JVTvy3VfBSzMuk13DnrFhcyy/FrP8dMu5XWN2crJAGGtwsLLN5jtwUl+vwesWE9ccGROKVOzvBT6PG9BXSCfWm5SYKS3XQOVugSwbYM0VE5GaeGtQavSKDMcpCT4yjNGpltav8mgT5YETn8Gp7hoZ2aIxfEgc6FKQsXVKpUKBPqxCHV0iaVpz/1/3djY+D/TR45U7zlYp3tAvF2ucGVX22qLdvVEUPWah/VS+TvfPRTGthRQT5oEeLYDw1qKoYp2mVfbHfnx+EuM5V4XXu+C5Wz5Wr73ZfRLc3/8TQD7Yg3ca+hwCw6ViWxT0N3QXDFBGRm3n97iis+vtAeKtr3jPladqH+QMwbG4dHigNYz5eKov1o7o2C8Krd3XCxw/3kBzv3bIhfps6CBtnDDEeu6OtfZPG1SppSqwMot6i3kRvK3O55twdhS4RQZKex2bB9gXLVX+/w+pEfLEvH7d/Un5NXb5VfTX6XWdu1EFLbh+GKSIichnxNiyPDYjE6C7h6BDu7/T1vn2qH75/uj/usjAhHjDUj2plYS+4KUPaYlzPZmbHuzUPkizjb9PYH989ZX07lkqPDTAvMwAYqsgbrtPA6sT4yhAoDlv2lmnw91bjh2cGIL5rEzzUp7nx+Jy7o4yP+7UKMfa6Ue1gmCIiIpcRD/O9M64bFj1e/URzW5oG+Vqtb1VJWcNFBr1bNpQ8H9AmBC+N7oiWFSGtUQMNHuvfEv1bm1eYD2mgwcE5o7B+2mBJWBKvmqwcaRQPObZo6It7e1S/RVEDbzU6hAdgwWO9JVv/iOe7fVVR6NTabbA2ed5L5T6LM+oawxQREdUrkRZ6phwhXvH31aQ++G9CXyQOb4fNLwzDP+/pgg8fioZSaah/ZUmQnxe81SpJz9SSyX2NjyunYYtXa2pUSnzySE/JdSyVafDXVIWyMNEwp7eozZXlH94c2wXdmwdhxZQBkms0trDfY0M/L7QLc35Rgafjaj4iIqpX5t7XDa+vOYInndxnUKFQ4OCcUdDq9WgkmpyuUiqQcEcryXm2iOe8+YlCUOWiNpXCfDXhg72bY2XKZcR1DsehyzmS64UFeMNf1MM1uksTPNovEj0jg9E0yBfPDm2LBpqqEJdwRysk3NEKWpPq46YrEd+6twuGdwzDl9vPmpU8AAyrLX/Yc9Hmd/V07JkiIiKXebxiC5OYNjWrBu6IpkG++G9CH9xRzXCgLUF+XpIgZUl1ZcAs9RYBgFDRNyUOY5VDfm/e0wWfPtoT/3k4WhJ6kmfHYu1zgySFVVVKw7Y7D/UxFEydFd8Jz40wL0irViklc6q8VEoMaGPoVfv6iT6YFNMKLUL88OLojha/h3iFYuX7HPXOuK5OvU8uGKaIiMhl7mgbip2zYrHsqX6ubkqtq25ulrh8gpdoyE9f0TNlqShrA2817omOQICPlyQ4NQ3ylQzrOUq8p6WXSoFvnuyHTTOHIrZTVfmNIF8vvP+AodzE0A6NjcfFRVlfGm19o+xRVjaOXvJEX0mYdEcMU0RE5FLNgn1rdWscuRDPrbJE3DPlZSE4VbcJtGn5hZoQB7ETmQXwVqvQLsx8VeVDfVog9fWR+HxCT2hUSnQMD5DM/bIUin6dOhBxncPx+t1RWDFlgKR0w7ND22J4pzD4atw7THHOFBER0W0woX9L/HbwKuI6W+6RUYu2qxHv+BziZwgb1vZCrOSlrL0AGt08yPj4eoHt4pmVpSJS54yEt1qJMtGcK2+TALn3tREIC/DBfytWELYI8cP+f4zE+sNXcTwzH1Nj2wGoPnjKHcMUERHRbeDvrcZvourqpsQBwsdLhU8e6YGjV/MwsJ1h/piqmmHCufd3wyOLduOFUeb7KjrKmXIUlRXcxZvABIvqYb0wsoPVKvbx3ZpKNsfOK9YaH7vjkB/DFBERkQv4adT47NGe0AsCAn28cG+PZri3R1Xh0LiocLy77hgaaizvWdcrsiHS/znaavHPuuKlUmLhY71QqpWubjTtpbKlu6hnzI32GjdimCIiInKRsdHWC3G2Dm2A7S8OwZ7tm62e4+ogVenOruYV5033J7SlTWN/LJncF5OX7EO5SakGdyCPvwUiIiIy0zTIB3U1N/ubJ/vBW63Ev+7vVivXc3RSedcIQ+9UuU6AIFjujZMr9kwRERERhnZojPR/jjYr2umoxwZEYs/ZmzZ73SwR92Rp9YJbbV/DMEVEREQAzKufO+Odcc71bHmpq8KTVifAneahM0wRERGRy2lUSjw7tC00KoXbTUJnmCIiIiKXU6uUmBVvvYK6nHECOhEREVENMEwRERER1QDDFBEREVENMEwRERER1QDDFBEREVENMEwRERER1QDDFBEREVENMEwRERER1QDDFBEREVENMEwRERER1QDDFBEREVENMEwRERER1QDDFBEREVENqF3dAHcjCAIAIC8vr1avW15ejqKiIuTl5cHLy6tWr+3JeN+cw/vmPN475/C+OYf3zTmW7lvl7+3K3+O1iWHKQfn5+QCAFi1auLglRERE5Kj8/HwEBQXV6jUVwu2IaB5Mr9cjIyMDAQEBUCgUtXbdvLw8tGjRApcuXUJgYGCtXdfT8b45h/fNebx3zuF9cw7vm3Ms3TdBEJCfn4+IiAgolbU7y4k9Uw5SKpVo3rz5bbt+YGAgf2CcwPvmHN435/HeOYf3zTm8b84xvW+13SNViRPQiYiIiGqAYYqIiIioBhimZMLb2xtvvPEGvL29Xd0Ut8L75hzeN+fx3jmH9805vG/Oqev7xgnoRERERDXAnikiIiKiGmCYIiIiIqoBhikiIiKiGmCYIiIiIqoBhimZmD9/Plq1agUfHx/0798fe/fudXWTXGbu3Lno27cvAgICEBYWhnHjxuHEiROSc0pKSpCYmIhGjRrB398f999/P7KysiTnXLx4EWPGjIGfnx/CwsLw0ksvQavV1uVXcal58+ZBoVBg+vTpxmO8b5ZduXIFjz32GBo1agRfX19069YN+/fvN74uCALmzJmDpk2bwtfXF3FxcTh16pTkGjdv3sTEiRMRGBiI4OBgPPXUUygoKKjrr1JndDodXn/9dbRu3Rq+vr5o27Yt3n77bcm+Z7xvBtu3b8fYsWMREREBhUKBNWvWSF6vrft06NAhDB48GD4+PmjRogXef//92/3Vbitb9628vByvvPIKunXrhgYNGiAiIgKTJk1CRkaG5Bp1dt8Ecrnly5cLGo1G+Prrr4UjR44IzzzzjBAcHCxkZWW5umkuMXr0aGHJkiVCenq6kJaWJtx1111CZGSkUFBQYDzn2WefFVq0aCEkJSUJ+/fvFwYMGCDccccdxte1Wq3QtWtXIS4uTkhNTRXWrVsnhIaGCrNnz3bFV6pze/fuFVq1aiV0795dmDZtmvE475u5mzdvCi1bthSeeOIJYc+ePcLZs2eFP/74Qzh9+rTxnHnz5glBQUHCmjVrhIMHDwr33HOP0Lp1a6G4uNh4zp133ilER0cLu3fvFv766y+hXbt2wqOPPuqKr1Qn3n33XaFRo0bC2rVrhXPnzgkrV64U/P39hU8++cR4Du+bwbp164TXXntNWLVqlQBAWL16teT12rhPubm5Qnh4uDBx4kQhPT1d+PHHHwVfX19h0aJFdfU1a52t+5aTkyPExcUJK1asEI4fPy4kJycL/fr1E3r37i25Rl3dN4YpGejXr5+QmJhofK7T6YSIiAhh7ty5LmyVfGRnZwsAhG3btgmCYPgh8vLyElauXGk859ixYwIAITk5WRAEww+hUqkUMjMzjecsWLBACAwMFEpLS+v2C9Sx/Px8oX379sLGjRuFoUOHGsMU75tlr7zyijBo0CCrr+v1eqFJkybCBx98YDyWk5MjeHt7Cz/++KMgCIJw9OhRAYCwb98+4znr168XFAqFcOXKldvXeBcaM2aM8OSTT0qO3XfffcLEiRMFQeB9s8Y0FNTWffriiy+Ehg0bSn5OX3nlFaFjx463+RvVDUsh1NTevXsFAMKFCxcEQajb+8ZhPhcrKytDSkoK4uLijMeUSiXi4uKQnJzswpbJR25uLgAgJCQEAJCSkoLy8nLJPevUqRMiIyON9yw5ORndunVDeHi48ZzRo0cjLy8PR44cqcPW173ExESMGTNGcn8A3jdrfv31V/Tp0wcPPvggwsLC0LNnT3z11VfG18+dO4fMzEzJfQsKCkL//v0l9y04OBh9+vQxnhMXFwelUok9e/bU3ZepQ3fccQeSkpJw8uRJAMDBgwexY8cOxMfHA+B9s1dt3afk5GQMGTIEGo3GeM7o0aNx4sQJ3Lp1q46+jWvl5uZCoVAgODgYQN3eN2507GLXr1+HTqeT/PICgPDwcBw/ftxFrZIPvV6P6dOnY+DAgejatSsAIDMzExqNxvgDUyk8PByZmZnGcyzd08rXPNXy5ctx4MAB7Nu3z+w13jfLzp49iwULFmDmzJl49dVXsW/fPjz//PPQaDRISEgwfm9L90V838LCwiSvq9VqhISEeOx9mzVrFvLy8tCpUyeoVCrodDq8++67mDhxIgDwvtmptu5TZmYmWrdubXaNytcaNmx4W9ovFyUlJXjllVfw6KOPGjc2rsv7xjBFspaYmIj09HTs2LHD1U2RvUuXLmHatGnYuHEjfHx8XN0ct6HX69GnTx+89957AICePXsiPT0dCxcuREJCgotbJ18//fQTvv/+e/zwww/o0qUL0tLSMH36dERERPC+UZ0qLy/HQw89BEEQsGDBApe0gcN8LhYaGgqVSmW2oiorKwtNmjRxUavkYerUqVi7di22bNmC5s2bG483adIEZWVlyMnJkZwvvmdNmjSxeE8rX/NEKSkpyM7ORq9evaBWq6FWq7Ft2zZ8+umnUKvVCA8P532zoGnTpoiKipIc69y5My5evAig6nvb+hlt0qQJsrOzJa9rtVrcvHnTY+/bSy+9hFmzZuGRRx5Bt27d8Pjjj2PGjBmYO3cuAN43e9XWfaqPP7tAVZC6cOECNm7caOyVAur2vjFMuZhGo0Hv3r2RlJRkPKbX65GUlISYmBgXtsx1BEHA1KlTsXr1amzevNmsC7Z3797w8vKS3LMTJ07g4sWLxnsWExODw4cPS36QKn/QTH9xeooRI0bg8OHDSEtLM/7p06cPJk6caHzM+2Zu4MCBZqU3Tp48iZYtWwIAWrdujSZNmkjuW15eHvbs2SO5bzk5OUhJSTGes3nzZuj1evTv378OvkXdKyoqglIp/RWiUqmg1+sB8L7Zq7buU0xMDLZv347y8nLjORs3bkTHjh09doivMkidOnUKmzZtQqNGjSSv1+l9c2i6Ot0Wy5cvF7y9vYWlS5cKR48eFaZMmSIEBwdLVlTVJ3/729+EoKAgYevWrcLVq1eNf4qKioznPPvss0JkZKSwefNmYf/+/UJMTIwQExNjfL1yif+oUaOEtLQ0YcOGDULjxo09eom/JeLVfILA+2bJ3r17BbVaLbz77rvCqVOnhO+//17w8/MTvvvuO+M58+bNE4KDg4VffvlFOHTokHDvvfdaXLres2dPYc+ePcKOHTuE9u3be9wSf7GEhAShWbNmxtIIq1atEkJDQ4WXX37ZeA7vm0F+fr6QmpoqpKamCgCEjz76SEhNTTWuOquN+5STkyOEh4cLjz/+uJCeni4sX75c8PPzc+vSCLbuW1lZmXDPPfcIzZs3F9LS0iS/K8Qr8+rqvjFMycRnn30mREZGChqNRujXr5+we/duVzfJZQBY/LNkyRLjOcXFxcLf//53oWHDhoKfn58wfvx44erVq5LrnD9/XoiPjxd8fX2F0NBQ4YUXXhDKy8vr+Nu4lmmY4n2z7LfffhO6du0qeHt7C506dRK+/PJLyet6vV54/fXXhfDwcMHb21sYMWKEcOLECck5N27cEB599FHB399fCAwMFCZPnizk5+fX5deoU3l5ecK0adOEyMhIwcfHR2jTpo3w2muvSX6R8b4ZbNmyxeJ/0xISEgRBqL37dPDgQWHQoEGCt7e30KxZM2HevHl19RVvC1v37dy5c1Z/V2zZssV4jbq6bwpBEJWrJSIiIiKHcM4UERERUQ0wTBERERHVAMMUERERUQ0wTBERERHVAMMUERERUQ0wTBERERHVAMMUERERUQ0wTBERERHVAMMUERERUQ0wTBERERHVAMMUERERUQ0wTBERERHVwP8DTGbtstwbx8QAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(losses[20:])\n",
    "plt.yscale(\"log\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "80225343-232f-4388-bc8a-3294bffbb579",
   "metadata": {},
   "outputs": [],
   "source": [
    "with torch.inference_mode():\n",
    "    for x, _ in train_dataset:\n",
    "        x = x.to(device)\n",
    "        xhat, indices_stack = rvq(x.view(x.shape[0], -1))\n",
    "        xhat = xhat.view(x.shape)\n",
    "        xrecs = torch.stack([rvq.decode(indices_stack[:, :k]).view(x.shape) for k in range(1, len(rvq.layers)+1)])\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e4106653-42b6-42e8-bef8-10c9f2c1f32b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "=================================================================\n",
       "Layer (type:depth-idx)                   Param #\n",
       "=================================================================\n",
       "ResidualFSVQ                             --\n",
       "├─ModuleList: 1-1                        --\n",
       "│    └─LFSQ: 2-1                         --\n",
       "│    │    └─FSQ: 3-1                     --\n",
       "│    │    └─Block: 3-2                   267,008\n",
       "│    │    └─Block: 3-3                   1,854,464\n",
       "│    └─LFSQ: 2-2                         --\n",
       "│    │    └─FSQ: 3-4                     --\n",
       "│    │    └─Block: 3-5                   267,008\n",
       "│    │    └─Block: 3-6                   1,854,464\n",
       "│    └─LFSQ: 2-3                         --\n",
       "│    │    └─FSQ: 3-7                     --\n",
       "│    │    └─Block: 3-8                   267,008\n",
       "│    │    └─Block: 3-9                   1,854,464\n",
       "│    └─LFSQ: 2-4                         --\n",
       "│    │    └─FSQ: 3-10                    --\n",
       "│    │    └─Block: 3-11                  267,008\n",
       "│    │    └─Block: 3-12                  1,854,464\n",
       "│    └─LFSQ: 2-5                         --\n",
       "│    │    └─FSQ: 3-13                    --\n",
       "│    │    └─Block: 3-14                  267,008\n",
       "│    │    └─Block: 3-15                  1,854,464\n",
       "│    └─LFSQ: 2-6                         --\n",
       "│    │    └─FSQ: 3-16                    --\n",
       "│    │    └─Block: 3-17                  267,008\n",
       "│    │    └─Block: 3-18                  1,854,464\n",
       "=================================================================\n",
       "Total params: 12,728,832\n",
       "Trainable params: 12,728,832\n",
       "Non-trainable params: 0\n",
       "================================================================="
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torchinfo.summary(rvq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "70d6b8b6-354b-4aec-9df9-2cbfdd4872da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2200x1000 with 28 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nrows = 4\n",
    "\n",
    "fig, axes = plt.subplots(nrows=nrows, ncols=1 + len(xrecs), figsize=(22, 2.5*nrows))\n",
    "x, xhat, xrecs = x.cpu(), xhat.cpu(), xrecs.cpu()\n",
    "\n",
    "for i in range(nrows):\n",
    "    axes[i, 0].imshow(x[i, 0])\n",
    "    for j in range(len(xrecs)):\n",
    "        axes[i, 1+j].imshow(xrecs[j, i, 0])\n",
    "\n",
    "axes[0, 0].set_title(\"Ground Truth\")\n",
    "for j in range(len(xrecs)):\n",
    "    axes[0, 1+j].set_title(f\"Quantizers:{j+1}\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"residual_fsq.png\")"
   ]
  }
 ],
 "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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}