{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network Helper\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import inspect\n",
    "import torch\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def exists(x):\n",
    "    return x is not None\n",
    "\n",
    "def default(val, d):\n",
    "    if exists(val):\n",
    "        return val\n",
    "    return d() if inspect.isfunction(d) else d\n",
    "\n",
    "class Residual(nn.Module):\n",
    "    def __init__(self, fn):\n",
    "        super().__init__()\n",
    "        self.fn = fn\n",
    "\n",
    "    def forward(self, x, *args, **kwargs):\n",
    "        return self.fn(x, *args, **kwargs) + x\n",
    "\n",
    "# 上采样(反卷积)\n",
    "def Upsample(dim):\n",
    "    return nn.ConvTranspose2d(dim, dim, 4, 2, 1)\n",
    "\n",
    "def Downsample(dim):\n",
    "    return nn.Conv2d(dim, dim, 4, 2 ,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Positional embedding\n",
    "\n",
    "目的是让网络知道\n",
    "当前是哪一个step. \n",
    "ddpm采用正弦位置编码\n",
    "\n",
    "输入是shape为(batch_size, 1)的tensor, batch中每一个sample所处的t,并且将这个tensor转换为shape为(batch_size, dim)的tensor.\n",
    "这个tensor会被加到每一个残差模块中.\n",
    "\n",
    "总之就是将$t$编码为embedding,和原本的输入一起进入网络,让网络“知道”当前的输入属于哪个step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SinusolidalPositionEmbedding(nn.Module):\n",
    "    def __init__(self, dim):\n",
    "        super().__init__()\n",
    "        self.dim = dim\n",
    "\n",
    "    def forward(self, time):\n",
    "        device = time.device\n",
    "        half_dim = self.dim // 2\n",
    "        embeddings = math.log(10000) / (half_dim - 1)\n",
    "        embeddings = torch.exp(torch.arange(half_dim, device=device) * -embeddings)\n",
    "        embeddings = time[:, :, None] * embeddings[None, None, :]\n",
    "        embeddings = torch.cat((embeddings.sin(), embeddings.cos()), dim=-1)\n",
    "        return embeddings\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ResNet/ConvNeXT block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Block(nn.Module):\n",
    "    def __init__(self, dim, dim_out, groups = 8):\n",
    "        super().__init__()\n",
    "        self.proj = nn.Conv2d(dim, dim_out, 3, padding=1)\n",
    "        self.norm = nn.GroupNorm(groups, dim_out)\n",
    "        self.act = nn.SiLU()\n",
    "    \n",
    "    def forward(self, x, scale_shift = None):\n",
    "        x = self.proj(x)\n",
    "        x = self.norm(x)\n",
    "\n",
    "        if exists(scale_shift):\n",
    "            scale, shift = scale_shift\n",
    "            x = x * (scale + 1) + shift\n",
    "\n",
    "        x = self.act(x)\n",
    "        return x\n",
    "\n",
    "class ResnetBlock(nn.Module):\n",
    "    def __init__(self, dim, dim_out, *, time_emb_dim = None, groups = 8):\n",
    "        super().__init__()\n",
    "        self.mlp = (\n",
    "            nn.Sequential(\n",
    "                nn.SiLU(), \n",
    "                nn.Linear(time_emb_dim, dim_out)\n",
    "            )\n",
    "            if exists(time_emb_dim) else None\n",
    "        )\n",
    "        self.block1 = Block(dim, dim_out, groups=groups)\n",
    "        self.block2 = Block(dim_out, dim_out=dim_out, groups=groups)\n",
    "        self.res_conv = nn.Conv2d(dim, dim_out, 1) if dim != dim_out else nn.Identity()\n",
    "    \n",
    "    def forward(self, x, time_emb = None):\n",
    "        h = self.block1(x)\n",
    "\n",
    "        if exists(self.mlp) and exists(time_emb):\n",
    "            time_emb = self.mlp(time_emb)\n",
    "            h = rearrange(time_emb, 'b n -> b () n') + h\n",
    "\n",
    "        h = self.block2(h)\n",
    "        return h + self.res_conv(x)\n",
    "    \n",
    "    "
   ]
  }
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