gaussian2S_2model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Using device: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Using device: \u001b[1;36m1\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "from torch import nn, optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from tqdm.notebook import tqdm\n",
    "from scipy.stats import norm\n",
    "import plotly.graph_objects as go\n",
    "from torch.optim.lr_scheduler import CosineAnnealingLR, CosineAnnealingWarmRestarts, StepLR, OneCycleLR\n",
    "from pathlib import Path\n",
    "import matplotlib.animation as animation\n",
    "from sklearn.datasets import *\n",
    "\n",
    "\n",
    "\n",
    "from rich import print\n",
    "import imageio\n",
    "\n",
    "\n",
    "from utils.Models import MLP\n",
    "from utils.Datasets import BBdataset\n",
    "from utils.utils import *\n",
    "seed = 233\n",
    "torch.manual_seed(seed)\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed_all(seed)\n",
    "np.random.seed(seed)\n",
    "# os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1\"\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "device = 1\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "\n",
    "experiment_name = \"gaussian2S_2model\"  # 你可以根据需要动态设置这个变量\n",
    "# log_dir = Path('experiments') / experiment_name / 'test' / time.strftime(\"%Y-%m-%d/%H_%M_%S/\")\n",
    "log_dir = Path('experiments') / experiment_name\n",
    "log_dir.mkdir(parents=True, exist_ok=True)\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成初始和目标密度样本\n",
    "def gen_data(m=4, num_samples=1000):\n",
    "    mus = [(m, m), (m, -m), (-m, m), (-m, -m)]\n",
    "    source_dist = np.random.multivariate_normal([0, 0], [[1, 0], [0, 1]], num_samples)\n",
    "    target_dist = make_s_curve(n_samples=num_samples, noise=0.01)[0][:, ::2] * 2\n",
    "    return source_dist, target_dist\n",
    "\n",
    "# 生成二维Brownian bridge\n",
    "def gen_bridge_2d(x, y, ts, T, num_samples):\n",
    "    sigma = 1\n",
    "    bridge = torch.zeros((ts.shape[0], num_samples, 2))\n",
    "    drift = torch.zeros((ts.shape[0], num_samples, 2))\n",
    "    bridge[0] = x\n",
    "    for i in range(len(ts) - 1):\n",
    "        dt = ts[i+1] - ts[i]\n",
    "        dydt = (y - bridge[i]) / (T - ts[i])\n",
    "        drift[i, :] = dydt\n",
    "        diffusion = sigma * torch.sqrt(dt) * torch.randn(num_samples, 2)\n",
    "        bridge[i+1] = bridge[i] + dydt * dt\n",
    "        bridge[i+1, :] += diffusion\n",
    "    return bridge, drift\n",
    "\n",
    "# 主函数\n",
    "def gen_2d_data(m=4, num_samples=1000, epsilon=0.001, T=1):\n",
    "    if not isinstance(num_samples, int):\n",
    "        num_samples = int(num_samples)\n",
    "    source_dist, target_dist = gen_data(m, num_samples)\n",
    "        \n",
    "    epsilon1 = 0.001\n",
    "    epsilon2 = 0.0001\n",
    "\n",
    "    t1 = torch.arange(0, 0.91, epsilon1)\n",
    "    t2 = torch.arange(0.91, T, epsilon2)\n",
    "    ts = torch.concatenate((t1, t2))\n",
    "    \n",
    "    source_dist = torch.Tensor(source_dist)\n",
    "    target_dist = torch.Tensor(target_dist)\n",
    "    bridge, drift = gen_bridge_2d(source_dist, target_dist, torch.concatenate((t1, t2)), T=T, num_samples=num_samples)\n",
    "    \n",
    "    a_min = torch.min(t2)\n",
    "    a_max = torch.max(t2)\n",
    "    t2 = (t2 - a_min) / (a_max - a_min)\n",
    "    return t1, t2, ts, bridge, drift, source_dist, target_dist\n",
    "\n",
    "t1, t2, ts, bridge, drift, source_sample, target_sample = gen_2d_data(num_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_source_and_target(sour, targ, left_title=\"Source distribution\", right_title=\"Target distribution\", save_path=None):\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    axs[0].scatter(*sour.T, c='#D27685', s=10, alpha=0.6)\n",
    "    axs[1].scatter(*targ.T, c='#37306B', s=10, alpha=0.6)\n",
    "    axs[0].set_title(left_title)\n",
    "    axs[1].set_title(right_title)\n",
    "    axs[0].grid(True, alpha=0.3)\n",
    "    axs[1].grid(True, alpha=0.3)\n",
    "    axs[0].set_xlim(-8, 8)\n",
    "    axs[0].set_ylim(-8, 8)\n",
    "    axs[1].set_xlim(-8, 8)\n",
    "    axs[1].set_ylim(-8, 8)\n",
    "    fig.show()\n",
    "    if save_path is not None:\n",
    "        fig.savefig(save_path)\n",
    "\n",
    "plot_source_and_target(source_sample, target_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_gaussian2d(bridge, colors=True):\n",
    "    def get_color(point):\n",
    "        x, y = point\n",
    "        if x > 0 and y > 0:\n",
    "            return 0\n",
    "        elif x > 0 and y < 0:\n",
    "            return 1\n",
    "        elif x < 0 and y > 0:\n",
    "            return 2\n",
    "        else:\n",
    "            return 3\n",
    "\n",
    "    # 生成数据\n",
    "    color_list = ['#37306B', '#66347F', '#9E4784', '#D27685']  # 对应于目标均值的颜色\n",
    "\n",
    "    # 创建3D图形\n",
    "    fig = plt.figure(figsize=(10, 8))\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "    # 为每个样本绘制bridge\n",
    "    for i in range(bridge.shape[1]):\n",
    "        x = bridge[:, i, 0].numpy()  # X坐标\n",
    "        y = bridge[:, i, 1].numpy()  # Y坐标\n",
    "        z = ts.numpy()               # 时间作为Z坐标\n",
    "        c = color_list[get_color((x[-1], y[-1]))] if colors else '#D27685'\n",
    "        ax.plot(x, y, z, color=c, label=f'Sample {i+1}', alpha=0.3)\n",
    "\n",
    "    # 添加标签和图例\n",
    "    ax.set_xlabel('X')\n",
    "    ax.set_ylabel('Y')\n",
    "    ax.set_zlabel('Time')\n",
    "\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_gaussian2d_gif(bridge):\n",
    "    def get_color(point):\n",
    "        x, y = point\n",
    "        if x > 0 and y > 0:\n",
    "            return 0\n",
    "        elif x > 0 and y < 0:\n",
    "            return 1\n",
    "        elif x < 0 and y > 0:\n",
    "            return 2\n",
    "        else:\n",
    "            return 3\n",
    "    colors = ['b', 'g', 'r', 'c']  # 对应于目标均值的颜色\n",
    "    fig, ax = plt.subplots(figsize=(10, 8))\n",
    "    ax.set_xlabel('X')\n",
    "    ax.set_ylabel('Y')\n",
    "    ax.set_xlim(-10, 10)\n",
    "    ax.set_ylim(-10, 10)\n",
    "    # 定义更新函数\n",
    "    def update(frame):\n",
    "        ax.clear()\n",
    "        ax.set_xlabel('X')\n",
    "        ax.set_ylabel('Y')\n",
    "        ax.set_xlim(-10, 10)\n",
    "        ax.set_ylim(-10, 10)\n",
    "        for i in range(bridge.shape[1]):\n",
    "            x = bridge[:frame + 1, i, 0].numpy()  # 注意：frame + 1\n",
    "            y = bridge[:frame + 1, i, 1].numpy()  # 注意：frame + 1\n",
    "            if x.size > 0 and y.size > 0:  # 确保x和y不为空\n",
    "                color_index = get_color((x[-1], y[-1]))\n",
    "                ax.plot(x, y, color=colors[color_index], alpha=0.5)\n",
    "                ax.scatter(x[-1], y[-1], color=colors[color_index], alpha=1, s=10)\n",
    "    # 创建动画\n",
    "    ani = animation.FuncAnimation(fig, update, frames=len(ts), interval=100)\n",
    "    # 保存为GIF需要花费6分钟左右\n",
    "    ani.save(log_dir / 'brownian_bridge.gif', writer='imagemagick')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">====================\n",
       "</pre>\n"
      ],
      "text/plain": [
       "====================\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">torch.Size</span><span style=\"font-weight: bold\">([</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1810</span><span style=\"font-weight: bold\">])</span>\n",
       "torch.float32 cpu\n",
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       "\u001b[1;35mtorch.Size\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m1810\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m\n",
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">torch.Size</span><span style=\"font-weight: bold\">([</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1810</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12000</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"font-weight: bold\">])</span>\n",
       "torch.float32 cpu\n",
       "</pre>\n"
      ],
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       "\u001b[1;35mtorch.Size\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m1810\u001b[0m, \u001b[1;36m12000\u001b[0m, \u001b[1;36m2\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m\n",
       "torch.float32 cpu\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">torch.Size</span><span style=\"font-weight: bold\">([</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1810</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12000</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"font-weight: bold\">])</span>\n",
       "torch.float32 cpu\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1;35mtorch.Size\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m1810\u001b[0m, \u001b[1;36m12000\u001b[0m, \u001b[1;36m2\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m\n",
       "torch.float32 cpu\n"
      ]
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">torch.Size</span><span style=\"font-weight: bold\">([</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12000</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"font-weight: bold\">])</span>\n",
       "torch.float32 cpu\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1;35mtorch.Size\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m12000\u001b[0m, \u001b[1;36m2\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m\n",
       "torch.float32 cpu\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">torch.Size</span><span style=\"font-weight: bold\">([</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12000</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"font-weight: bold\">])</span>\n",
       "torch.float32 cpu\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1;35mtorch.Size\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m12000\u001b[0m, \u001b[1;36m2\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m\n",
       "torch.float32 cpu\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([10908000, 1, 7])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_samples = 12000\n",
    "t1, t2, ts, bridge, drift, source_sample, target_sample = gen_2d_data(num_samples=num_samples)\n",
    "print_debug(ts, bridge, drift, source_sample, target_sample)\n",
    "\n",
    "# split data\n",
    "start1 = torch.cat(torch.split(source_sample.repeat(len(t1)-1, 1, 1), 1, dim=1), dim=0)\n",
    "start2 = torch.cat(torch.split(source_sample.repeat(len(t2)-1, 1, 1), 1, dim=1), dim=0)\n",
    "\n",
    "times1 = t1[:len(t1)-1].repeat(num_samples, 1).reshape(-1, 1, 1)\n",
    "times2 = t2[:len(t2)-1].repeat(num_samples, 1).reshape(-1, 1, 1)\n",
    "\n",
    "bridge1 = bridge[:len(t1), :, :]\n",
    "bridge2 = bridge[len(t1):, :, :]\n",
    "\n",
    "positions1 = torch.cat(torch.split(bridge1[:-1, :], 1, dim=1), dim=0)\n",
    "positions2 = torch.cat(torch.split(bridge2[:-1, :], 1, dim=1), dim=0)\n",
    "\n",
    "drift1 = drift[:len(t1), :, :]\n",
    "drift2 = drift[len(t1):, :, :]\n",
    "\n",
    "scores1 = torch.cat(torch.split(drift1[:-1, :], 1, dim=1), dim=0)\n",
    "scores2 = torch.cat(torch.split(drift2[:-1, :], 1, dim=1), dim=0)\n",
    "\n",
    "\n",
    "# start = torch.cat(torch.split(source_sample.repeat(len(ts)-1, 1, 1), 1, dim=1), dim=0)\n",
    "# times = ts[:len(ts)-1].repeat(num_samples, 1).reshape(-1, 1, 1)\n",
    "# positions = torch.cat(torch.split(bridge[:-1, :], 1, dim=1), dim=0)\n",
    "# scores = torch.cat(torch.split(drift[:-1, :], 1, dim=1), dim=0)\n",
    "\n",
    "# print_debug(start1, times1, positions1, scores1)\n",
    "raw_data1 = torch.concat([start1, times1, positions1, scores1], dim=-1)\n",
    "raw_data2 = torch.concat([start2, times2, positions2, scores2], dim=-1)\n",
    "raw_data1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">==========<span style=\"color: #808000; text-decoration-color: #808000\">model</span>==========\n",
       "</pre>\n"
      ],
      "text/plain": [
       "==========\u001b[33mmodel\u001b[0m==========\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">batch_szi<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-weight: bold\">e:1000</span>00, channel:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, length:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "batch_szi\u001b[1;92me:1000\u001b[0m00, channel:\u001b[1;36m1\u001b[0m, length:\u001b[1;36m7\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">MLP</span><span style=\"font-weight: bold\">(</span>\n",
       "  <span style=\"font-weight: bold\">(</span>fcin<span style=\"font-weight: bold\">)</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Linear</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">in_features</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span>, <span style=\"color: #808000; text-decoration-color: #808000\">out_features</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #808000; text-decoration-color: #808000\">bias</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"font-weight: bold\">)</span>\n",
       "  <span style=\"font-weight: bold\">(</span>fcs<span style=\"font-weight: bold\">)</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">ModuleList</span><span style=\"font-weight: bold\">(</span>\n",
       "    <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span><span style=\"font-weight: bold\">)</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Linear</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">in_features</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #808000; text-decoration-color: #808000\">out_features</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #808000; text-decoration-color: #808000\">bias</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"font-weight: bold\">)</span>\n",
       "    <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"font-weight: bold\">)</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">BatchNorm1d</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #808000; text-decoration-color: #808000\">eps</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1e-05</span>, <span style=\"color: #808000; text-decoration-color: #808000\">momentum</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.1</span>, <span style=\"color: #808000; text-decoration-color: #808000\">affine</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, <span style=\"color: #808000; text-decoration-color: #808000\">track_running_stats</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"font-weight: bold\">)</span>\n",
       "    <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"font-weight: bold\">)</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Linear</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">in_features</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #808000; text-decoration-color: #808000\">out_features</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #808000; text-decoration-color: #808000\">bias</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"font-weight: bold\">)</span>\n",
       "    <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span><span style=\"font-weight: bold\">)</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">BatchNorm1d</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #808000; text-decoration-color: #808000\">eps</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1e-05</span>, <span style=\"color: #808000; text-decoration-color: #808000\">momentum</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.1</span>, <span style=\"color: #808000; text-decoration-color: #808000\">affine</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, <span style=\"color: #808000; text-decoration-color: #808000\">track_running_stats</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"font-weight: bold\">)</span>\n",
       "    <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span><span style=\"font-weight: bold\">)</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Linear</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">in_features</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #808000; text-decoration-color: #808000\">out_features</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #808000; text-decoration-color: #808000\">bias</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"font-weight: bold\">)</span>\n",
       "    <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span><span style=\"font-weight: bold\">)</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">BatchNorm1d</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #808000; text-decoration-color: #808000\">eps</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1e-05</span>, <span style=\"color: #808000; text-decoration-color: #808000\">momentum</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.1</span>, <span style=\"color: #808000; text-decoration-color: #808000\">affine</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, <span style=\"color: #808000; text-decoration-color: #808000\">track_running_stats</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"font-weight: bold\">)</span>\n",
       "    <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span><span style=\"font-weight: bold\">)</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Linear</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">in_features</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #808000; text-decoration-color: #808000\">out_features</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #808000; text-decoration-color: #808000\">bias</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"font-weight: bold\">)</span>\n",
       "    <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7</span><span style=\"font-weight: bold\">)</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">BatchNorm1d</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #808000; text-decoration-color: #808000\">eps</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1e-05</span>, <span style=\"color: #808000; text-decoration-color: #808000\">momentum</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.1</span>, <span style=\"color: #808000; text-decoration-color: #808000\">affine</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span>, <span style=\"color: #808000; text-decoration-color: #808000\">track_running_stats</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"font-weight: bold\">)</span>\n",
       "  <span style=\"font-weight: bold\">)</span>\n",
       "  <span style=\"font-weight: bold\">(</span>fcout<span style=\"font-weight: bold\">)</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Linear</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">in_features</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #808000; text-decoration-color: #808000\">out_features</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>, <span style=\"color: #808000; text-decoration-color: #808000\">bias</span>=<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-style: italic\">True</span><span style=\"font-weight: bold\">)</span>\n",
       "  <span style=\"font-weight: bold\">(</span>relu<span style=\"font-weight: bold\">)</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">ReLU</span><span style=\"font-weight: bold\">()</span>\n",
       "<span style=\"font-weight: bold\">)</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1;35mMLP\u001b[0m\u001b[1m(\u001b[0m\n",
       "  \u001b[1m(\u001b[0mfcin\u001b[1m)\u001b[0m: \u001b[1;35mLinear\u001b[0m\u001b[1m(\u001b[0m\u001b[33min_features\u001b[0m=\u001b[1;36m5\u001b[0m, \u001b[33mout_features\u001b[0m=\u001b[1;36m256\u001b[0m, \u001b[33mbias\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m\n",
       "  \u001b[1m(\u001b[0mfcs\u001b[1m)\u001b[0m: \u001b[1;35mModuleList\u001b[0m\u001b[1m(\u001b[0m\n",
       "    \u001b[1m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m)\u001b[0m: \u001b[1;35mLinear\u001b[0m\u001b[1m(\u001b[0m\u001b[33min_features\u001b[0m=\u001b[1;36m256\u001b[0m, \u001b[33mout_features\u001b[0m=\u001b[1;36m256\u001b[0m, \u001b[33mbias\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m\n",
       "    \u001b[1m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m: \u001b[1;35mBatchNorm1d\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m256\u001b[0m, \u001b[33meps\u001b[0m=\u001b[1;36m1e\u001b[0m\u001b[1;36m-05\u001b[0m, \u001b[33mmomentum\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.1\u001b[0m, \u001b[33maffine\u001b[0m=\u001b[3;92mTrue\u001b[0m, \u001b[33mtrack_running_stats\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m\n",
       "    \u001b[1m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1m)\u001b[0m: \u001b[1;35mLinear\u001b[0m\u001b[1m(\u001b[0m\u001b[33min_features\u001b[0m=\u001b[1;36m256\u001b[0m, \u001b[33mout_features\u001b[0m=\u001b[1;36m256\u001b[0m, \u001b[33mbias\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m\n",
       "    \u001b[1m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m: \u001b[1;35mBatchNorm1d\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m256\u001b[0m, \u001b[33meps\u001b[0m=\u001b[1;36m1e\u001b[0m\u001b[1;36m-05\u001b[0m, \u001b[33mmomentum\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.1\u001b[0m, \u001b[33maffine\u001b[0m=\u001b[3;92mTrue\u001b[0m, \u001b[33mtrack_running_stats\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m\n",
       "    \u001b[1m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1m)\u001b[0m: \u001b[1;35mLinear\u001b[0m\u001b[1m(\u001b[0m\u001b[33min_features\u001b[0m=\u001b[1;36m256\u001b[0m, \u001b[33mout_features\u001b[0m=\u001b[1;36m256\u001b[0m, \u001b[33mbias\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m\n",
       "    \u001b[1m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1m)\u001b[0m: \u001b[1;35mBatchNorm1d\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m256\u001b[0m, \u001b[33meps\u001b[0m=\u001b[1;36m1e\u001b[0m\u001b[1;36m-05\u001b[0m, \u001b[33mmomentum\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.1\u001b[0m, \u001b[33maffine\u001b[0m=\u001b[3;92mTrue\u001b[0m, \u001b[33mtrack_running_stats\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m\n",
       "    \u001b[1m(\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1m)\u001b[0m: \u001b[1;35mLinear\u001b[0m\u001b[1m(\u001b[0m\u001b[33min_features\u001b[0m=\u001b[1;36m256\u001b[0m, \u001b[33mout_features\u001b[0m=\u001b[1;36m256\u001b[0m, \u001b[33mbias\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m\n",
       "    \u001b[1m(\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1m)\u001b[0m: \u001b[1;35mBatchNorm1d\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m256\u001b[0m, \u001b[33meps\u001b[0m=\u001b[1;36m1e\u001b[0m\u001b[1;36m-05\u001b[0m, \u001b[33mmomentum\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.1\u001b[0m, \u001b[33maffine\u001b[0m=\u001b[3;92mTrue\u001b[0m, \u001b[33mtrack_running_stats\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m\n",
       "  \u001b[1m)\u001b[0m\n",
       "  \u001b[1m(\u001b[0mfcout\u001b[1m)\u001b[0m: \u001b[1;35mLinear\u001b[0m\u001b[1m(\u001b[0m\u001b[33min_features\u001b[0m=\u001b[1;36m256\u001b[0m, \u001b[33mout_features\u001b[0m=\u001b[1;36m2\u001b[0m, \u001b[33mbias\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m\n",
       "  \u001b[1m(\u001b[0mrelu\u001b[1m)\u001b[0m: \u001b[1;35mReLU\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n",
       "\u001b[1m)\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochs = 50\n",
    "batch_size = 100000\n",
    "lr = 1e-4\n",
    "\n",
    "# 组合成data\n",
    "train_ds1 = BBdataset(raw_data1)\n",
    "train_dl1 = DataLoader(train_ds1, batch_size=batch_size, shuffle=True, num_workers=20)\n",
    "\n",
    "train_ds2 = BBdataset(raw_data2)\n",
    "train_dl2 = DataLoader(train_ds2, batch_size=batch_size, shuffle=True, num_workers=20)\n",
    "\n",
    "model1 = MLP(input_dim=5, output_dim=2, hidden_layers=4, hidden_dim=256).to(device)\n",
    "model2 = MLP(input_dim=5, output_dim=2, hidden_layers=4, hidden_dim=256).to(device)\n",
    "loss_fn = nn.MSELoss()\n",
    "loss_list1 = []\n",
    "loss_list2 = []\n",
    "print('='*10+'model'+'='*10)\n",
    "print(\"batch_szie:{}, channel:{}, length:{}\".format(*next(train_dl1.__iter__()).shape)) # batch_szie, channel, length\n",
    "print(model1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8ad6a50877774b7992ab2bdcb784837a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training (lr: X)  (loss= X):   0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def train(model, train_dl, optimizer, scheduler, loss_fn):\n",
    "    losses = 0\n",
    "    for training_data in train_dl:\n",
    "        training_data = training_data.to(device)\n",
    "        # x: (batch_size, channel, length-2), y: (batch_size, channel, 2)\n",
    "        x, y = training_data[:, 0, :-2], training_data[:, 0, -2:] # 不同模型要改\n",
    "        # print(x.shape)\n",
    "        x = x.to(device)\n",
    "        y = y.to(device)\n",
    "\n",
    "        pred = model(x)\n",
    "        loss = loss_fn(pred, y)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        if scheduler is not None:\n",
    "            scheduler.step()\n",
    "        optimizer.zero_grad()\n",
    "        losses += loss.item() / len(train_dl)\n",
    "        \n",
    "    return losses\n",
    "from torch.optim.lr_scheduler import OneCycleLR\n",
    "\n",
    "model1.train()\n",
    "model1.to(device)\n",
    "optimizer1 = torch.optim.Adam(model1.parameters(), lr=1e-6)\n",
    "\n",
    "model2.train()\n",
    "model2.to(device)\n",
    "optimizer2 = torch.optim.Adam(model2.parameters(), lr=1e-6)\n",
    "\n",
    "epoch_iterator = tqdm(range(epochs), desc=\"Training (lr: X)  (loss= X)\", dynamic_ncols=True)\n",
    "for e in epoch_iterator:\n",
    "    now_loss1 = train(model1, train_dl1, optimizer1, None, loss_fn)\n",
    "    now_loss2 = train(model2, train_dl2, optimizer2, None, loss_fn)\n",
    "    loss_list1.append(now_loss1)\n",
    "    loss_list2.append(now_loss2)\n",
    "    cur_lr = optimizer1.param_groups[-1]['lr']\n",
    "    # cur_lr = optimizer1.param_groups[-1]['lr']\n",
    "    epoch_iterator.set_description(\"Training (lr: %2.5f)  (loss1=%2.5f) (loss2=%2.5f)\" % (cur_lr, now_loss1, now_loss2))\n",
    "    \n",
    "epoch_iterator.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "for e in epoch_iterator:\n",
    "    # now_loss1 = train(model1, train_dl1, optimizer1, None, loss_fn)\n",
    "    now_loss2 = train(model2, train_dl2, optimizer2, None, loss_fn)\n",
    "    # loss_list1.append(now_loss1)\n",
    "    loss_list2.append(now_loss2)\n",
    "    cur_lr = optimizer1.param_groups[-1]['lr']\n",
    "    # cur_lr = optimizer1.param_groups[-1]['lr']\n",
    "    epoch_iterator.set_description(\"Training (lr: %2.5f) (loss2=%2.5f)\" % (cur_lr, now_loss2))\n",
    "    \n",
    "epoch_iterator.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model1.state_dict(), log_dir / 'model1.pth')\n",
    "torch.save(model2.state_dict(), log_dir / 'model2.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vH/L+1hIdrqy3NgwAAF5GsXCzvPQ4nd8zWS0uU39fdcDqOAAAeBXFwgNOPHq6YE2h6hqbLU4DAID3UCw84OK8VGUnRaqqvkkLNxy2Og4AAF5DsfCAEJuhmWO7S5LmrTwgl4tHTwEAwYFi4SHXjspWbLhde0prtHhLsdVxAADwCoqFh8RFhOqW8a1zLZ76cJdauGoBAAgCFAsPumV8D8VF2LW7tEaLNh2xOg4AAB5HsfCg+MhQ/WhCriTp6Q93q7nFZXEiAAA8i2LhYTeN666EqFDtK6vVO19y1QIAENgoFh4WGxGq2y5ovWrxzEdctQAABDaKhRfMHNtdSdFhOnCsjnUtAAABrcPF4vDhw/r+97+v5ORkRUZGatCgQVq7dq0nsgWM6HC7bp94/KrFx7vVxFULAECA6lCxqKio0Lhx4xQaGqrFixdr27ZtevLJJ5WYmOipfAHjxvO6q0tMuArL6/WvdYesjgMAgEfYO3LyY489puzsbM2bN6/ttR49erg9VCCKDAvRHRf21G8WbdMfP96jq4ZnKczOnSgAQGDp0G+2d955RyNHjtT06dOVmpqqYcOG6bnnnjvjxzidTjkcjnZHsJoxpptSY8N1uLJer60ttDoOAABu16FisW/fPj377LPq3bu33n//fd1xxx26++679eKLL572Y+bMmaP4+Pi2Izs7+5xD+6uI0BDNuqiXJOnPn+xRQ1OLxYkAAHAvwzTNs15rOiwsTCNHjtTnn3/e9trdd9+t/Px8rVq16pQf43Q65XQ62/7tcDiUnZ2tqqoqxcXFnUN0/9TQ1KKLfr9MRVUNevi7AzTz/O5WRwIA4Bs5HA7Fx8d/4+/vDl2xyMjIUP/+/du91q9fPxUUFJz2Y8LDwxUXF9fuCGZctQAABLIOFYtx48Zp586d7V7btWuXcnJy3Boq0F07MltdEyJVWu3US1+cvpQBAOBvOlQs7rvvPq1evVq/+93vtGfPHr388sv661//qlmzZnkqX0AKs9v044tbr1o8u2yP6hqbLU4EAIB7dKhYjBo1SgsXLtQrr7yigQMH6je/+Y2eeuopzZgxw1P5AtbVI7KUnRSpsppG/XP1QavjAADgFh2avOkOZzv5Ixi8vrZQP31jk5Kiw/Tpzy5STHiHlhUBAMBrPDJ5E+41bVhX9egSrfLaRj27bI/VcQAAOGcUCwvZQ2z6xdQ8SdJzn+3XoYo6ixMBAHBuKBYWm9I/TWNzk9XY7NKji3dYHQcAgHNCsbCYYRj6n+/0k2FIizYVad3BcqsjAQDQaRQLHzAgM17XjWxd6vzX726Ty+XV+bQAALgNxcJH3D+lr2LC7fryUJXe2njY6jgAAHQKxcJHpMSG686LekqSHl+yk0WzAAB+iWLhQ24Z10NZiZEqdjRo7vJ9VscBAKDDKBY+JCI0RP/17X6SpLmf7lVRVb3FiQAA6BiKhY+ZOjBdo7snqaHJpceX7PzmDwAAwIdQLHyMYRj65Xf6yzCkhRsOa0NBhdWRAAA4axQLHzQoK15XD8+SJP1m0TZ5eTsXAAA6jWLho356aV9FhYVofUGl3t1UZHUcAADOCsXCR6XFReiOia2Pnz763nY1NLVYnAgAgG9GsfBhP7ogV5nxETpS1aDnP+PxUwCA76NY+LCI0BD9/Pjup/+3bK9KHA0WJwIA4MwoFj7uu0MyNbxbguoaW/Trd7dZHQcAgDOiWPg4wzD0mysHKsRm6N+bi7R0W4nVkQAAOC2KhR8YkBmvH03IlST98q0tqm5osjgRAACnRrHwE/dO7q2c5CgVOxr0xPusyAkA8E0UCz8RERqi300bJEn6x+qDWneQFTkBAL6HYuFHxvXqomtGZMk0pQff3KTGZpfVkQAAaIdi4Wf++9v9lBwdpl0lNfrL8r1WxwEAoB2KhZ9JjA7Try7vL0n608d7tKe0xuJEAAB8hWLhh747JFMX9U1RY4tLD765SS4Xm5QBAHwDxcIPGYah304bpKiwEOUfqNAr+QVWRwIAQBLFwm91TYjUA1P6SpIefW8Hy30DAHwCxcKPzTy/u4ZkJ6ja2ayH3t5qdRwAACgW/izEZujRqwbJbjO0ZGux3t9abHUkAECQo1j4uX4Zcbrtgtblvn/19hY5WO4bAGAhikUAuHtSb3VPjlKJw6lHFm23Og4AIIhRLAJARGiIHrt6sAxDenVtoZZs4ZYIAMAaFIsAMSY3ue2WyINvbuIpEQCAJSgWAeT+S/pqQGacKuqa9MDrX7JwFgDA6ygWASTMbtPT1w9VuN2mz3aX6cVVB6yOBAAIMhSLANMrNVb/fVk/SdKcxTu0s7ja4kQAgGBCsQhAN56X07qXSLNL9yzYIGdzi9WRAABBgmIRgAzD0OPXDFFydJh2FFfr9+/vtDoSACBIUCwCVEpsuB67erAk6bnP9mvlnjKLEwEAggHFIoBN7p+m743pJkm6/7UvVVnXaHEiAECgo1gEuP+5rJ9yu0Sr2NGg/1q4WabJI6gAAM+hWAS4qDC7nrp+qOw2Q+9tLta/1h+2OhIAIIBRLILA4KwE3XdJH0nSQ29vUcGxOosTAQACFcUiSNw+sadGdU9UbWOL7l6wQY3NLqsjAQACEMUiSITYDP3h2qGKi7BrY2Gl5ixmF1QAgPtRLIJIdlKUnrx2qCRp3soDWrTpiLWBAAABh2IRZC7pn6bbJ/aUJP38jU3ae7TG4kQAgEBCsQhCD0zpozE9klTb2KI7/rlOdY3NVkcCAAQIikUQsofY9MfvDVNKbLh2ldTofxZuYX0LAIBbUCyCVGpshP54wzCF2Ay9ueGwXllTaHUkAEAAoFgEsfNyk/XTS/tKkma/s1WbD1VZnAgA4O8oFkHutgm5mtwvTY0tLt358jpV1TVZHQkA4McoFkHOZjP05PQhyk6KVGF5ve5/faNcLuZbAAA6h2IBxUeF6tkZIxRmt+nD7aWa++k+qyMBAPwUxQKSpIFd4/XwdwdIkp54f4dW7T1mcSIAgD+iWKDN9aOyddXwrnKZ0o9f2aDDlfVWRwIA+BmKBdoYhqFHrhykvPRYldU49cMX16rWyeJZAICzR7FAO5FhIXp+5kh1iQnT9iKH7lmwUS1M5gQAnCWKBU6SlRilv/5g5PHJnCV6fMkOqyMBAPwExQKnNLxbop64ZrAkae6n+/TaWlbmBAB8M4oFTuuKoV1198W9JEn/vXCzVu/jSREAwJlRLHBG907uo8sGZaipxdTt/1yng8dqrY4EAPBhFAuckc1m6PfTh2hwVrwq65p0y/x8VdWz7DcA4NQoFvhGkWEhev4HI5UeF6G9R2t118vr1dzisjoWAMAHUSxwVlLjIvT8zJGKDA3RZ7vL9OtF26yOBADwQRQLnLWBXeP1v9cNlST9fdVB/X3VAUvzAAB8D8UCHfKtgen62bf6SpIefnebPtxWYnEiAIAvoVigw+6Y2FPTR2SpxWVq1svrlX+g3OpIAAAfQbFAhxmGod9dNUiT8lLlbHbp1vn52lHssDoWAMAHdKhYzJ49W4ZhtDvy8vI8lQ0+LDTEpj99b7hG5iTK0dCsH/xtjQrL66yOBQCwWIevWAwYMEBFRUVtx4oVKzyRC34gMixEf5s5Sn3SYlRa7dTMF9boWI3T6lgAAAt1uFjY7Xalp6e3HV26dPFELviJ+KhQ/f2WMeqaEKl9ZbW6eX6+athqHQCCVoeLxe7du5WZmanc3FzNmDFDBQUFZzzf6XTK4XC0OxBY0uMj9PdbRyspOkybDlXp9n+sk7O5xepYAAALdKhYjBkzRvPnz9eSJUv07LPPav/+/ZowYYKqq6tP+zFz5sxRfHx825GdnX3OoeF7eqbEaN5NoxQVFqIVe8p0/2tfyuUyrY4FAPAywzTNTv/0r6ysVE5Ojv7whz/o1ltvPeU5TqdTTudX990dDoeys7NVVVWluLi4zn5q+KhPdx3VrS/mq6nF1MyxOZr93QEyDMPqWACAc+RwOBQfH/+Nv7/P6XHThIQE9enTR3v27DntOeHh4YqLi2t3IHBd0CdFv58+RIYhvbjqoP748em/NwAAgeecikVNTY327t2rjIwMd+VBALhiaFc99J3+kqQ/LN2l+Sv3W5wIAOAtHSoWDzzwgJYvX64DBw7o888/17Rp0xQSEqIbbrjBU/ngp24a10N3X9xLkjT73W36x+qDFicCAHiDvSMnHzp0SDfccIOOHTumlJQUjR8/XqtXr1ZKSoqn8sGP3XdJHzmbXZr76T798q0tstsM3TC6m9WxAAAe1KFisWDBAk/lQAAyDEO/mJqnZpepv63Yrwff3KwQw9C1o3gyCAACFXuFwKMMw9D/XNZPN53fXZL08zc36V/rDlkbCgDgMRQLeJxhGHro8v76/nndZJrSA298qbc2HLY6FgDAAygW8ArDMPTr7w7UDaNby8VPXtuod788YnUsAICbUSzgNTaboUeuHKhrR2bJZUr3vrpR720usjoWAMCNKBbwKpvN0KNXDdbVw7PU4jJ19ysbtGRLsdWxAABuQrGA19lshh6/ZrCuHJqpZpepu15er/e3Ui4AIBBQLGCJEJuh308fosuHtJaLO19az4ROAAgAFAtYxh5i0/9eO6Tttsh9r21khU4A8HMUC1jKHmLTE9cM1syxOTJN6ZdvbdH/LWPjMgDwVxQLWM5mMzT7uwN010Wte4s8vmSnHluyQ6ZpWpwMANBRFAv4BMMw9MClffXg1DxJ0rPL9upXb2+Vy0W5AAB/QrGAT/l/E3vqkWkDZRjSP1Yf1AOvf6nmFpfVsQAAZ4liAZ8zY0yOnrpuqEJsht7ccFh3vrRezuYWq2MBAM4CxQI+6YqhXTX3+yMUZrfpg20lunX+WtU1NlsdCwDwDSgW8FmT+6dp/s2jFBUWohV7yvS9577QsRqn1bEAAGdAsYBPO79nF730wzFKiArVxsJKXf3s5zpQVmt1LADAaVAs4POGdUvUG7efr6zESB04Vqernv1cGwoqrI4FADgFigX8Qq/UGL155/ka2DVO5bWNuuG51Vq6rcTqWACA/0CxgN9IjY3Qq7eN1YV9U9TQ5NL/+8dalgAHAB9DsYBfiQ636/kfjNT1o7LlOr4E+GNLdrCQFgD4CIoF/I49xKY5Vw3STy7pI6l1lc6fvLZRjc0spAUAVqNYwC8ZhqG7J/XWE9cMlt1m6K2NRzTzhTWqqm+yOhoABDWKBfza9JHZeuGmUYoOC9Gqfcc0/S+fq+BYndWxACBoUSzg9y7ok6LXbh+r1Nhw7Sqp0Xf/vEIr95RZHQsAghLFAgFhQGa83rlrvIZkJ6iyrkk/eGGN5q3cz9brAOBlFAsEjPT4CL1623m6alhXtbhMPfzuNv3sjU1sYAYAXkSxQECJCA3Rk9cO0f9c1k82Q3p93SFd/9fVKnU0WB0NAIICxQIBxzAM/XBCrl68ZbTiI0O1oaBSl/9phTYWVlodDQACHsUCAWtC7xS9PWuceqfGqMTh1LVzV+lf6w5ZHQsAAhrFAgGte5dovXnn+ZrcL02NzS7d//qX+u2ibWpuYTEtAPAEigUCXmxEqP564wjdfXEvSdLzK/bre89/wbwLAPAAigWCgs1m6CdT+urZGcMVE27Xmv3l+vYzK7Rq7zGrowFAQKFYIKhMHZShd+4ap7z0WJXVODXj+dV6dtleNjEDADehWCDo5KbEaOGd43TV8K5ymdJjS3botn+sVVUd+4wAwLmiWCAoRYaF6MnpQzTnqkEKs9v04fZSfedPn2nL4SqrowGAX6NYIGgZhqEbRnfTm3ecr+ykSBWW1+uqZz/Xy18UsBQ4AHQSxQJBb2DXeC26a0LbI6n/tXCz7n/tS9U1NlsdDQD8DsUCkBQfFarnfjBCv5iaJ5shvbnhsL7zzApujQBAB1EsgOMMw9DtE3vq5R+dp/S4CO0rq9W0/1up5z/bx1MjAHCWKBbAfzgvN1mL75mgKf3T1NRi6rf/3q6Z89aotJoFtQDgm1AsgFNIjA7T3BtH6JFpAxURatNnu8s09anP9MmOUqujAYBPo1gAp2EYhmaMydG7d41XXnqsjtU26ub5+Zr9zlY1NLVYHQ8AfBLFAvgGvdNi9dascbp5XHdJ0vzPD+jKP6/U7pJqa4MBgA+iWABnISI0RA9dPkDzbhql5Ogw7Siu1nf+uELzV+5nYicAfA3FAuiAi/JStfjeCZrQu4uczS7Nfnebrn9utQ6U1VodDQB8AsUC6KDU2Ai9ePNo/ebKgYoKC9Ga/eX61tOf6oUVXL0AAIoF0Ak2m6Ebz8vR+/deoLG5yWpocunXi7bpur+u0n6uXgAIYhQL4BxkJ0XppR+O0W+vHKjosBDlH6jQt576VM9/tk8tXL0AEIQoFsA5stkMff+8HL1/3wUa36t17sVv/71d185dpb1Ha6yOBwBeRbEA3CQrMUr/uHW05lw1SDHhdq07WKFvP/2Z/rJ8r5paXFbHAwCvoFgAbnRiK/b377ug7cmRRxfv0OV/XKF1ByusjgcAHkexADyga0Kk/n7LaD1xzWAlRoVqR3G1rvnL5/rvhZtVVd9kdTwA8BiKBeAhhmFo+shsfXT/hbpmRJZMU3rpiwJNenK53t54WKbJ5E4AgYdiAXhYUnSYfj99iBbcdp56pkSrrMapexZs1A9eWKODx3g0FUBgoVgAXnJebrLeu2eC7r+kj8LsrTumTvnfT/Wnj3ersZnJnQACA8UC8KJwe4h+PKm33r/3q0dTf//BLk19+lN9uuuo1fEA4JxRLAAL9OgSrX/cOlpPXTdUXWLCtPdorX7wwhr96O9ruT0CwK8ZppdnkDkcDsXHx6uqqkpxcXHe/NSAT6qqb9LTH+7W31cdULPLVFiITT+c0EOzLuql6HC71fEAQNLZ//6mWAA+Yk9ptR5+d5s+210mSUqLC9eDU/vpiqGZMgzD4nQAgh3FAvBDpmlq6bYS/fbf21VQXidJGpGTqNmXD9CgrHiL0wEIZhQLwI81NLXobyv2608f71F9U4sMQ7p2RLbun9JHqXERVscDEIQoFkAAKK5q0KOLt+utjUckSZGhIfrRhB66bWJPxTD/AoAXUSyAALL2QLkeeW+7NhRUSpKSo8N096TeumF0N4XZebgLgOdRLIAAY5qmlmwp1uPv79T+stZHUrsnR+mnl+bp24PSmeAJwKMoFkCAampxaUF+oZ7+cJfKaholSUOzE/Tg1DyNyU22OB2AQEWxAAJcjbNZz326T899tk91jS2SpEl5qfrJlD4akMkTJADci2IBBInS6gY9/eFuLcgvVIur9X/nKf3TdPek3hrYlYIBwD0oFkCQ2Xu0Rk9/uFvvbjqiE/9XX9I/TfdQMAC4AcUCCFJ7Sqv1zEd72hWMyf3SdO9kCgaAzjvb39/n9Jzao48+KsMwdO+9957LfwaAG/VKjdUzNwzT0vsu0BVDM2UzpA+3l+g7f1yhH76Yr82HqqyOCCCAdbpY5Ofna+7cuRo8eLA78wBwk16psXr6+mH64L6JurKtYJTq8j+t0E3z1mjtgXKrIwIIQJ0qFjU1NZoxY4aee+45JSYmnvFcp9Mph8PR7gDgPb1SY/TU9cO09CcTddWwrrIZ0rKdR3XNX1bpurmr9Nnuo/LyHVEAAaxTxWLWrFm67LLLNHny5G88d86cOYqPj287srOzO/MpAZyjnikx+sN1Q/Xx/Rfq+lHZCg0x9MX+ct34tzW68v8+19JtJXK5KBgAzk2HJ28uWLBAjzzyiPLz8xUREaELL7xQQ4cO1VNPPXXK851Op5xOZ9u/HQ6HsrOzmbwJWOxIZb3++uk+LcgvUEOTS5KUlx6rOy/qpcsGZSjExkqeAL7ikcmbhYWFuueee/TSSy8pIuLsdlgMDw9XXFxcuwOA9TITIjX7uwO04ucX644LWzc121Fcrbtf2aBJTy7TK2sK1NDUYnVMAH6mQ1cs3nrrLU2bNk0hISFtr7W0tMgwDNlsNjmdznbvnQqPmwK+qaquSS+uOqAXVu5XZV2TJCkpOkzfPy9HN56Xo5TYcIsTArCSR9axqK6u1sGDB9u9dvPNNysvL08///nPNXDgQLcFA2CNWmezXllToHkrD+hwZb0kKSzEpiuHZerW8bnqmx5rcUIAVvDaAlnfNMeis8EAWKu5xaX3t5bo+RX72rZrl6QJvbvo1vE9NLFPCjuqAkHkbH9/272YCYAfsYfYdNngDF02OEPrDlbohRX7tXhLkT7bXabPdpepV2qMbhnXQ1cOy1RUGD9KALRiSW8AZ62wvE4vfn5AC/ILVeNsliTFhtt19Ygsff+8HPVKjbE4IQBPYa8QAB5T3dCkV/ML9c/VB3XgWF3b6+f3TNYPxuZocr802UPOaccAAD6GYgHA41wuUyv2lOnvqw7q4x0lOrG+VnpchL43ppuuH5Wt1LizezQdgG+jWADwqkMVdXr5iwK9ml+oY7WNkiS7zdClA9J1w+huOr9nsmwsugX4LYoFAEs4m1u0ZEux/r7qoNYdrGh7PTspUteNzNY1I7KVHs9VDMDfUCwAWG7rkSotWFOotzYeVnVD62RPmyFdnJeq60Z100V9U5iLAfgJigUAn1Hf2KL3Nhfp1fxCrfnadu2pseGaPjJL143spm7JURYmBPBNKBYAfNKe0hq9trZQ/1p3qG0uhiSN7pGka4ZnaeqgdMVGhFqYEMCpUCwA+LTGZpc+2l6iBfmF+nT3UZ34SRQRatOlA9J11fAsje/VhV1WAR9BsQDgN4qq6rVww2H9a90h7T1a2/Z6amy4pg3rqquGZ7FHCWAxigUAv2OapjYdqtK/1h/SO18eadtlVZIGdo3TFUO6auqgdGUlMh8D8DaKBQC/1tjs0sc7SvWv9Yf0yY5SNbu++lE1NDtBlw3K0LcHZ6hrQqSFKYHgQbEAEDDKaxu1aNMR/XtTkdYcKNfXf2oN69ZaMqYOomQAnkSxABCQSh0NWrK1WIs2FSn/NCXjWwO5XQK4G8UCQMArdTRo8ZZi/XtTkfIPti8ZQ7LiNXVQhr49MIM1MgA3oFgACColjgYt3lykxVuKT7pdMiAzTt8elKGpA9OVm8LW7kBnUCwABK3S6gZ9sLVEi7cUadXeY/ravE/lpcfq0gHpmtwvTQMy49gYDThLFAsAkHSsxqml20r03pZifb6nrN3TJamx4bo4L1UX56VqfO8uigqzW5gU8G0UCwD4D1V1TVq6vUQfbivRZ7uPqraxpe29MLtNY3OTNalfa9Fg8ifQHsUCAM7A2dyiNfvL9dH2Un20o0SF5fXt3u+bFqsL81J0cd9UDc9JVCi7sCLIUSwA4CyZpqm9R2v04fZSfby9VGsPlreblxEbYdcFvVN0Yd8UXdg3VSmx4daFBSxCsQCATqqsa9TyXUe1bOdRLdtZqoqvLS0uSYOz4nVh31Rd1DdFg7MS2CgNQYFiAQBu0OIy9eWhSi3bUapPdh7V5sNV7d6Pi7Dr/J5dNK53F43v1UXdk6NkGBQNBB6KBQB4QGl1g5bvPKpPdpbqs91lqm5obvd+14RIjeuVrHG9uuj8nl24bYKAQbEAAA9rbnFp8+EqrdxTphV7yrTuYIWaWtr/SM1Lj9X5PbvovNwkje6RpISoMIvSAueGYgEAXlbX2Kz8AxWtRWN3mbYVOdq9bxhSv/Q4nZebrPNykzSmR7Lio0ItSgt0DMUCACxWVuPUqr3HtHpf67H3aG27979eNMbkJmlU9yQlRXNFA76JYgEAPqa0ukFf7Cs/bdGQpJ4p0Rrdo7VkjOqepKzESCaDwidQLADAx50oGqv2HdPaA+XaVVJz0jnpcREa1SNJo7onalT3JPVJi+XxVliCYgEAfqaitlFrD1Yo/0C51uwv15bDVe32NpGkmHC7hnVL0PBuiRqRk6ih3RIUF8E8DXgexQIA/Fx9Y4s2FFYof39r2dhQUNFufxOpdZ5G37RYDc9J1IhuiRqek8haGvAIigUABJgWl6mdxdVaV1Ch9QcrtO5ghQrK6046LzEqVEOyEzQ0O0HDuiVqaFYCT5/gnFEsACAIlFY3aP3BSq0vqNDaA+XacsShxmbXSefldok+XjQSNDQ7UX3SYxRuD7EgMfwVxQIAglBjs0vbixzaUFChjYWV2lhYqQPHTr6qYbcZ6p0Wq/4ZcRqQ2Xr0y4xjvgZOi2IBAJAkldc26svCSm04XjS+LKxUVX3TKc/tlhTVVjT6Z8apf0a80uLCmbMBigUA4NRM09ThynptO+LQ1uPH9iKHDlfWn/L8xKhQ9c+MU7/042UjM049U2IUGmLzcnJYiWIBAOiQitpGbStyHC8cVdpeVK09R2vU4jr510RYiE2902LULyNOeemx6pcRp34ZcawcGsAoFgCAc9bQ1KLdJTXaXuRoLR1FDm0/4lC1s/mU56fGhisvI0790mOVl9FaOHK7xCjMztUNf0exAAB4hGmaOlRR31oyihzaUVStHcWOU04SlVonivboEq0+abHqnRajvmmx6p0Wq+7JUbJzO8VvUCwAAF5V62zWzpJq7Siqbi0cxa2l43RXN8JCbMpNiVbvtFj1SY1Rr9QY9UyNUffkaK5w+CCKBQDAcqZp6khVg3aVVGt3SbV2Ftdod2m1dpfUqL6p5ZQfE2Iz1C0pSj1TYtQzNVq9UloLR8+UGMVH8jisVSgWAACf5XK1Ppmyq6RaO0uqtaekRnuP1mjv0VrVnOYKhyQlRYepe3KUuidHKyc5Wt27tP69e3I0q4t6GMUCAOB3TNNUicOpvUdrtKe0pt2fJQ7nGT82MSpUOcnRyk2Jbr3akRKjXqnR6pbErRV3oFgAAAJKdUOTDh6r08FjdTpwrFYHymrb/l5affrS8dWtla8KR05ylLKTopQWF8E29GeJYgEACBq1zua2krHv+C2VvUdrtLe05qQdYb8uNMRQ14RIZSe1Fo3sxChlJ0Ue/zNKiVGhrDp63Nn+/rZ7MRMAAB4RHW5vWxX0607cWmktG18VjoPH6nSksl5NLaYOHKs77aOyMeF2ZSdFqVtSpLqdKB9JUeqWFKWsxEg2cjsFrlgAAIJSc4tLxY4GFZbXq7CiTofK61RYUa/C8joVlNed8faKJBmGlBYboeykSGUlthaN7ON/ZiVGKSMhIqCWPedWCAAA56ChqUWHKupUWF6vguNlo6C8rq141J3hFosk2QwpIz5SXRMj1TUhUpkJEcpMiFRmwol/Ryom3H9uHHArBACAcxARGqJeqbHqlRp70numaaq8tlEF5XU6XFmvwvJ6Haqo06GKr/50Nrt0uLL+tJu7SVJchL1d2chK/KqIZCVGqUtMmN/N8aBYAADQQYZhKDkmXMkx4RrWLfGk910uU2W1Th06fmulqKpBRyrrjx8NOlJVr8q6JjkamuUortaO4upTfp5wu+1rRSNSmfGRSouPUHpchNLjW4/YcLtPlQ+KBQAAbmazGUqNjVBqbISGn6J4SK1PshRV1etwZYMOV9TrcGWdDlfU61BF61WOYkeDnM0u7Ttaq31Ha0/7uaLCQr4qGnERSouP0A/H91ByTLinvrwzolgAAGCB6HD7aW+1SFJjs0vFVQ2tt1YqWwtHcVW9ih3O1j+rGuRoaFZdY4v2ldVqX9lX5ePm87t76as4GcUCAAAfFGa3qVtylLolR532nLrGZhVXNajY0aASR4OKq1pLh1VXKySKBQAAfisqzK7clBjlpsRYHaVN4DxgCwAALEexAAAAbkOxAAAAbkOxAAAAbkOxAAAAbkOxAAAAbkOxAAAAbkOxAAAAbkOxAAAAbkOxAAAAbkOxAAAAbkOxAAAAbkOxAAAAbuP13U1N05QkORwOb39qAADQSSd+b5/4PX46Xi8W1dXVkqTs7Gxvf2oAAHCOqqurFR8ff9r3DfObqoebuVwuHTlyRLGxsTIM46w+xuFwKDs7W4WFhYqLi/NwQjDe3sV4exfj7V2Mt3d5crxN01R1dbUyMzNls51+JoXXr1jYbDZlZWV16mPj4uL4xvQixtu7GG/vYry9i/H2Lk+N95muVJzA5E0AAOA2FAsAAOA2flEswsPD9dBDDyk8PNzqKEGB8fYuxtu7GG/vYry9yxfG2+uTNwEAQODyiysWAADAP1AsAACA21AsAACA21AsAACA21AsAACA2/h8sfjzn/+s7t27KyIiQmPGjNGaNWusjhQwPv30U11++eXKzMyUYRh666232r1vmqZ+9atfKSMjQ5GRkZo8ebJ2795tTVg/N2fOHI0aNUqxsbFKTU3VlVdeqZ07d7Y7p6GhQbNmzVJycrJiYmJ09dVXq6SkxKLE/u3ZZ5/V4MGD21YfHDt2rBYvXtz2PmPtWY8++qgMw9C9997b9hpj7j6zZ8+WYRjtjry8vLb3rR5rny4Wr776qn7yk5/ooYce0vr16zVkyBBdeumlKi0ttTpaQKitrdWQIUP05z//+ZTvP/7443rmmWf0l7/8RV988YWio6N16aWXqqGhwctJ/d/y5cs1a9YsrV69WkuXLlVTU5OmTJmi2tratnPuu+8+vfvuu3r99de1fPlyHTlyRFdddZWFqf1XVlaWHn30Ua1bt05r167VxRdfrCuuuEJbt26VxFh7Un5+vubOnavBgwe3e50xd68BAwaoqKio7VixYkXbe5aPtenDRo8ebc6aNavt3y0tLWZmZqY5Z84cC1MFJknmwoUL2/7tcrnM9PR084knnmh7rbKy0gwPDzdfeeUVCxIGltLSUlOSuXz5ctM0W8c2NDTUfP3119vO2b59uynJXLVqlVUxA0piYqL5/PPPM9YeVF1dbfbu3dtcunSpOXHiRPOee+4xTZPvb3d76KGHzCFDhpzyPV8Ya5+9YtHY2Kh169Zp8uTJba/ZbDZNnjxZq1atsjBZcNi/f7+Ki4vbjX98fLzGjBnD+LtBVVWVJCkpKUmStG7dOjU1NbUb77y8PHXr1o3xPkctLS1asGCBamtrNXbsWMbag2bNmqXLLrus3dhKfH97wu7du5WZmanc3FzNmDFDBQUFknxjrL2+u+nZKisrU0tLi9LS0tq9npaWph07dliUKngUFxdL0inH/8R76ByXy6V7771X48aN08CBAyW1jndYWJgSEhLanct4d97mzZs1duxYNTQ0KCYmRgsXLlT//v21ceNGxtoDFixYoPXr1ys/P/+k9/j+dq8xY8Zo/vz56tu3r4qKivTwww9rwoQJ2rJli0+Mtc8WCyBQzZo1S1u2bGl3TxTu17dvX23cuFFVVVV64403NHPmTC1fvtzqWAGpsLBQ99xzj5YuXaqIiAir4wS8qVOntv198ODBGjNmjHJycvTaa68pMjLSwmStfPZWSJcuXRQSEnLSTNaSkhKlp6dblCp4nBhjxt+97rrrLi1atEiffPKJsrKy2l5PT09XY2OjKisr253PeHdeWFiYevXqpREjRmjOnDkaMmSInn76acbaA9atW6fS0lINHz5cdrtddrtdy5cv1zPPPCO73a60tDTG3IMSEhLUp08f7dmzxye+v322WISFhWnEiBH66KOP2l5zuVz66KOPNHbsWAuTBYcePXooPT293fg7HA598cUXjH8nmKapu+66SwsXLtTHH3+sHj16tHt/xIgRCg0NbTfeO3fuVEFBAePtJi6XS06nk7H2gEmTJmnz5s3auHFj2zFy5EjNmDGj7e+MuefU1NRo7969ysjI8I3vb69MEe2kBQsWmOHh4eb8+fPNbdu2mbfddpuZkJBgFhcXWx0tIFRXV5sbNmwwN2zYYEoy//CHP5gbNmwwDx48aJqmaT766KNmQkKC+fbbb5ubNm0yr7jiCrNHjx5mfX29xcn9zx133GHGx8eby5YtM4uKitqOurq6tnNuv/12s1u3bubHH39srl271hw7dqw5duxYC1P7r1/84hfm8uXLzf3795ubNm0yf/GLX5iGYZgffPCBaZqMtTd8/akQ02TM3en+++83ly1bZu7fv99cuXKlOXnyZLNLly5maWmpaZrWj7VPFwvTNM0//vGPZrdu3cywsDBz9OjR5urVq62OFDA++eQTU9JJx8yZM03TbH3k9Je//KWZlpZmhoeHm5MmTTJ37txpbWg/dapxlmTOmzev7Zz6+nrzzjvvNBMTE82oqChz2rRpZlFRkXWh/dgtt9xi5uTkmGFhYWZKSoo5adKktlJhmoy1N/xnsWDM3ee6664zMzIyzLCwMLNr167mddddZ+7Zs6ftfavH2jBN0/TOtREAABDofHaOBQAA8D8UCwAA4DYUCwAA4DYUCwAA4DYUCwAA4DYUCwAA4DYUCwAA4DYUCwAA4DYUCwAA4DYUCwAA4DYUCwAA4Db/H+Lc6u4jFZBwAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, len(loss_list1)+1), loss_list1)\n",
    "plt.title('Loss')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(range(1, len(loss_list2)+1), loss_list2)\n",
    "plt.title('Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_num_samples = 4000\n",
    "_,_,test_ts, test_bridge, test_drift, test_source_sample, test_target_sample = gen_2d_data(num_samples=test_num_samples)\n",
    "# print_debug(test_ts, test_bridge, test_drift, test_source_sample, test_target_sample)\n",
    "pred_bridge = torch.zeros_like(test_bridge)\n",
    "pred_bridge[0, :] = test_source_sample\n",
    "model1.eval()\n",
    "model2.eval()\n",
    "# model.to(device)\n",
    "sigma=1\n",
    "with torch.no_grad():\n",
    "    for i in range(len(test_ts) - 1):\n",
    "        dt = (test_ts[i+1] - test_ts[i])\n",
    "        test_source_sample_reshaped = test_source_sample.reshape(-1, 1, test_source_sample.shape[-1])\n",
    "        test_ts_reshaped = test_ts[i].reshape(-1, 1, 1).repeat(test_num_samples, 1, 1)\n",
    "        pred_bridge_reshaped = pred_bridge[i].reshape(-1, 1, pred_bridge.shape[-1])\n",
    "        x = torch.concat([test_source_sample_reshaped, test_ts_reshaped, pred_bridge_reshaped], axis=-1)\n",
    "        if i <= len(t1):\n",
    "            dydt = model1(x[:,0,:].to(device)).cpu()\n",
    "        else:\n",
    "            dydt = model2(x[:,0,:].to(device)).cpu()\n",
    "        # print_debug(dydt)\n",
    "        diffusion = sigma * torch.sqrt(dt) * torch.randn(test_num_samples, 2)\n",
    "        pred_bridge[i+1, :] = pred_bridge[i, :] + dydt * dt + diffusion\n",
    "        \n",
    "        \n",
    "        \n",
    "        # dt = ts[i+1] - ts[i]\n",
    "        # dydt = (y - bridge[i]) / (T - ts[i])\n",
    "        # drift[i, :] = dydt\n",
    "        # diffusion = sigma * torch.sqrt(dt) * torch.randn(num_samples, 2)\n",
    "        # bridge[i+1] = bridge[i] + dydt * dt + diffusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# draw_gaussian2d(bridge, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# draw_gaussian2d(pred_bridge, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_source_and_target(pred_bridge[0, :, :], pred_bridge[-1, :, :], left_title=\"Source Sample\", right_title=\"Predict Sample\", save_path=log_dir / 'pred_bridge.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_source_and_target(test_bridge[0, :, :], test_bridge[-1, :, :], save_path=log_dir / 'gt_test_bridge.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5b73731ec4a7498a95fffe48fd9f4bb6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "save_gif_frame(pred_bridge, log_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}