Diffusion_Generative.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "from inspect import isfunction\n",
    "from functools import partial\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm.auto import tqdm\n",
    "from einops import rearrange\n",
    "\n",
    "import torch\n",
    "from torch import nn, einsum\n",
    "import torch.nn.functional as F\n",
    "from torchvision.transforms import Compose, ToTensor, Lambda, ToPILImage, CenterCrop, Resize,Normalize\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import DataLoader\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean=torch.tensor([0.4850, 0.4560, 0.4060])\n",
    "std=torch.tensor([0.2290, 0.2240, 0.2250])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from unet_v2 import UNet\n",
    "model=UNet(3,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UNet(\n",
       "  (time_embed): SinusoidalPositionEmbeddings()\n",
       "  (image_embed): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (inc): DoubleConv(\n",
       "    (double_conv): Sequential(\n",
       "      (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): SiLU()\n",
       "      (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (5): SiLU()\n",
       "    )\n",
       "  )\n",
       "  (down1): Down(\n",
       "    (maxpool_conv): Sequential(\n",
       "      (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "      (1): DoubleConv(\n",
       "        (double_conv): Sequential(\n",
       "          (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): SiLU()\n",
       "          (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (5): SiLU()\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (down2): Down(\n",
       "    (maxpool_conv): Sequential(\n",
       "      (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "      (1): DoubleConv(\n",
       "        (double_conv): Sequential(\n",
       "          (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): SiLU()\n",
       "          (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (5): SiLU()\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (down3): Down(\n",
       "    (maxpool_conv): Sequential(\n",
       "      (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "      (1): DoubleConv(\n",
       "        (double_conv): Sequential(\n",
       "          (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): SiLU()\n",
       "          (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (5): SiLU()\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (down4): Down(\n",
       "    (maxpool_conv): Sequential(\n",
       "      (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "      (1): DoubleConv(\n",
       "        (double_conv): Sequential(\n",
       "          (0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): SiLU()\n",
       "          (3): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (4): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (5): SiLU()\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (up1): Up(\n",
       "    (up): ConvTranspose2d(1024, 512, kernel_size=(2, 2), stride=(2, 2))\n",
       "    (conv): DoubleConv(\n",
       "      (double_conv): Sequential(\n",
       "        (0): Conv2d(1024, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): SiLU()\n",
       "        (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (5): SiLU()\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (up2): Up(\n",
       "    (up): ConvTranspose2d(512, 256, kernel_size=(2, 2), stride=(2, 2))\n",
       "    (conv): DoubleConv(\n",
       "      (double_conv): Sequential(\n",
       "        (0): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): SiLU()\n",
       "        (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (5): SiLU()\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (up3): Up(\n",
       "    (up): ConvTranspose2d(256, 128, kernel_size=(2, 2), stride=(2, 2))\n",
       "    (conv): DoubleConv(\n",
       "      (double_conv): Sequential(\n",
       "        (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): SiLU()\n",
       "        (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (5): SiLU()\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (up4): Up(\n",
       "    (up): ConvTranspose2d(128, 64, kernel_size=(2, 2), stride=(2, 2))\n",
       "    (conv): DoubleConv(\n",
       "      (double_conv): Sequential(\n",
       "        (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): SiLU()\n",
       "        (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (5): SiLU()\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (outc): OutConv(\n",
       "    (conv): Conv2d(64, 3, kernel_size=(1, 1), stride=(1, 1))\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x=torch.rand((16,3,128,128))\n",
    "#t=torch.rand((16,))\n",
    "#print(t.shape)\n",
    "#t=F.interpolate(t,(128,128),mode=\"nearest\")\n",
    "#print(x.shape)\n",
    "#print(t.shape)\n",
    "#z=model(x,t)\n",
    "#z.shape\n",
    "#t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def losses(noise,predicted_noise, loss_type=\"l1\"):\n",
    "    if loss_type == 'l1':\n",
    "        loss = F.l1_loss(noise, predicted_noise)\n",
    "    elif loss_type == 'l2':\n",
    "        loss = F.mse_loss(noise, predicted_noise)\n",
    "    elif loss_type == \"huber\":\n",
    "        loss = F.smooth_l1_loss(noise, predicted_noise)\n",
    "    else:\n",
    "        raise NotImplementedError()\n",
    "\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration huggan--flowers-102-categories-2ab3d0588f2a8da7\n",
      "Found cached dataset parquet (/home/ubuntu/.cache/huggingface/datasets/huggan___parquet/huggan--flowers-102-categories-2ab3d0588f2a8da7/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8fdbe4ed4da44a59a0b21dc889b3e97c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#data_name=\"huggan/CelebA-faces\"\n",
    "data_name=\"huggan/flowers-102-categories\"\n",
    "dataset = load_dataset(\"huggan/flowers-102-categories\")\n",
    "image_size = 128\n",
    "channels = 3\n",
    "batch_size = 24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision import transforms\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "# define image transformations (e.g. using torchvision)\n",
    "transform = Compose([\n",
    "            Resize(image_size,antialias=True),\n",
    "            CenterCrop(image_size),\n",
    "            transforms.RandomHorizontalFlip(),\n",
    "            transforms.ToTensor(),\n",
    "            Normalize(mean, std)\n",
    "            #transforms.Lambda(lambda t: (t * 2) - 1)\n",
    "])\n",
    "\n",
    "# define function\n",
    "def transforms(examples):\n",
    "    examples[\"pixel_values\"] = [transform(image.convert(\"RGB\")) for image in examples[\"image\"]]\n",
    "    del examples[\"image\"]\n",
    "    return examples\n",
    "\n",
    "transformed_dataset = dataset.with_transform(transforms)\n",
    "#transformed_dataset = dataset.with_transform(transforms).remove_columns(\"labels\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "postprocess = Compose([\n",
    "     Lambda(lambda t: (t.cpu() * std.reshape(3,1,1))+mean.reshape(3,1,1)),\n",
    "     Lambda(lambda t: t.permute(1, 2, 0)), # CHW to HWC\n",
    "     Lambda(lambda t: t * 255.),\n",
    "     Lambda(lambda t: t.cpu().numpy().astype(np.uint8)),\n",
    "     ToPILImage(),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_len=int(len(transformed_dataset[\"train\"])*0.9)\n",
    "val_len=len(transformed_dataset[\"train\"])-train_len\n",
    "train_set, val_set = torch.utils.data.random_split(transformed_dataset[\"train\"],[train_len,val_len])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataloader\n",
    "train_dataloader = DataLoader(train_set, batch_size=batch_size, shuffle=True,drop_last=True)\n",
    "val_dataloader = DataLoader(val_set, batch_size=batch_size, shuffle=False,drop_last=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['pixel_values'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, torch.Size([24, 3, 128, 128]), tensor(-2.1179), tensor(2.6400))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch = next(iter(train_dataloader))\n",
    "print(batch.keys()),batch[\"pixel_values\"].shape,batch[\"pixel_values\"].min(),batch[\"pixel_values\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff88c02aee0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "timg=postprocess(batch[\"pixel_values\"][7])\n",
    "plt.imshow(timg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def cos_diffusion_schedule(diffusion_times):\n",
    "    # diffusion times -> angles\n",
    "    min_signal_rate = torch.tensor(0.02).to(diffusion_times)\n",
    "    max_signal_rate = torch.tensor(0.95).to(diffusion_times)\n",
    "    start_angle = torch.acos(max_signal_rate)\n",
    "    end_angle = torch.acos(min_signal_rate)\n",
    "    diffusion_angles = start_angle + diffusion_times * (end_angle - start_angle)\n",
    "    # angles -> signal and noise rates\n",
    "    signal_rates = torch.cos(diffusion_angles).to(diffusion_times)\n",
    "    noise_rates = torch.sin(diffusion_angles).to(diffusion_times)\n",
    "    # note that their squared sum is always: sin^2(x) + cos^2(x) = 1\n",
    "    return noise_rates, signal_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def lin_diffusion_schedule(diffusion_times):\n",
    "    # diffusion times -> angles\n",
    "    min_signal_rate = torch.tensor(0.02).to(diffusion_times)\n",
    "    max_signal_rate = torch.tensor(0.999).to(diffusion_times)\n",
    "    diffusion_angles = min_signal_rate + diffusion_times * (max_signal_rate - min_signal_rate)\n",
    "    # angles -> signal and noise rates\n",
    "    signal_rates = 1.0-diffusion_angles\n",
    "    noise_rates = diffusion_angles\n",
    "    return noise_rates, signal_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model = ResNet50(n_classes=channels,time_dim=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UNet(\n",
       "  (time_embed): SinusoidalPositionEmbeddings()\n",
       "  (image_embed): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (inc): DoubleConv(\n",
       "    (double_conv): Sequential(\n",
       "      (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): SiLU()\n",
       "      (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (5): SiLU()\n",
       "    )\n",
       "  )\n",
       "  (down1): Down(\n",
       "    (maxpool_conv): Sequential(\n",
       "      (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "      (1): DoubleConv(\n",
       "        (double_conv): Sequential(\n",
       "          (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): SiLU()\n",
       "          (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (5): SiLU()\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (down2): Down(\n",
       "    (maxpool_conv): Sequential(\n",
       "      (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "      (1): DoubleConv(\n",
       "        (double_conv): Sequential(\n",
       "          (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): SiLU()\n",
       "          (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (5): SiLU()\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (down3): Down(\n",
       "    (maxpool_conv): Sequential(\n",
       "      (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "      (1): DoubleConv(\n",
       "        (double_conv): Sequential(\n",
       "          (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): SiLU()\n",
       "          (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (5): SiLU()\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (down4): Down(\n",
       "    (maxpool_conv): Sequential(\n",
       "      (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "      (1): DoubleConv(\n",
       "        (double_conv): Sequential(\n",
       "          (0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (2): SiLU()\n",
       "          (3): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (4): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (5): SiLU()\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (up1): Up(\n",
       "    (up): ConvTranspose2d(1024, 512, kernel_size=(2, 2), stride=(2, 2))\n",
       "    (conv): DoubleConv(\n",
       "      (double_conv): Sequential(\n",
       "        (0): Conv2d(1024, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): SiLU()\n",
       "        (3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (5): SiLU()\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (up2): Up(\n",
       "    (up): ConvTranspose2d(512, 256, kernel_size=(2, 2), stride=(2, 2))\n",
       "    (conv): DoubleConv(\n",
       "      (double_conv): Sequential(\n",
       "        (0): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): SiLU()\n",
       "        (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (5): SiLU()\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (up3): Up(\n",
       "    (up): ConvTranspose2d(256, 128, kernel_size=(2, 2), stride=(2, 2))\n",
       "    (conv): DoubleConv(\n",
       "      (double_conv): Sequential(\n",
       "        (0): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): SiLU()\n",
       "        (3): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (5): SiLU()\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (up4): Up(\n",
       "    (up): ConvTranspose2d(128, 64, kernel_size=(2, 2), stride=(2, 2))\n",
       "    (conv): DoubleConv(\n",
       "      (double_conv): Sequential(\n",
       "        (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): SiLU()\n",
       "        (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (5): SiLU()\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (outc): OutConv(\n",
       "    (conv): Conv2d(64, 3, kernel_size=(1, 1), stride=(1, 1))\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def reverse_diffusion(model,initial_noise, diffusion_steps,device=\"cuda\"):\n",
    "    # reverse diffusion = sampling\n",
    "    #print(next(model.parameters()).device)\n",
    "    num_images = initial_noise.shape[0]\n",
    "    step_size = 1.0 / diffusion_steps\n",
    "    # important line:\n",
    "    # at the first sampling step, the \"noisy image\" is pure noise\n",
    "    # but its signal rate is assumed to be nonzero (min_signal_rate)\n",
    "    next_noisy_images = initial_noise\n",
    "    all_pred_imgs=[]\n",
    "    for step in tqdm(range(diffusion_steps)):\n",
    "        noisy_images = next_noisy_images\n",
    "        # separate the current noisy image to its components\n",
    "        diffusion_times = torch.ones((num_images,1,1,1)).to(device) - step * step_size\n",
    "        diffusion_times=diffusion_times.to(initial_noise)\n",
    "        noise_rates, signal_rates = cos_diffusion_schedule(diffusion_times)\n",
    "        pred_noises, pred_images = model.denoise(noisy_images, noise_rates, signal_rates,training=False)\n",
    "        all_pred_imgs.append(pred_images)\n",
    "        # remix the predicted components using the next signal and noise rates\n",
    "        next_diffusion_times = diffusion_times - step_size\n",
    "        next_noise_rates, next_signal_rates = cos_diffusion_schedule(next_diffusion_times)\n",
    "        next_noisy_images = (next_signal_rates * pred_images + next_noise_rates * pred_noises)\n",
    "        # this new noisy image will be used in the next step\n",
    "        #next_noisy_images=pred_images\n",
    "    # Last step\n",
    "    #diffusion_times = torch.zeros((num_images,1,1,1)).to(device)\n",
    "    #diffusion_times=diffusion_times.to(initial_noise)\n",
    "    #noise_rates, signal_rates = lin_diffusion_schedule(diffusion_times)\n",
    "    #pred_noises, pred_images = model.denoise(pred_images, noise_rates, signal_rates,training=False)\n",
    "    \n",
    "    return pred_images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def generate(model,num_images, diffusion_steps,device=\"cuda\"):\n",
    "    # noise -> images -> denormalized images\n",
    "    initial_noise =torch.randn((num_images,channels, image_size, image_size)).to(device)\n",
    "    #initial_noise = tf.random.normal(shape=(num_images, image_size, image_size, 3))\n",
    "    generated_images = reverse_diffusion(model,initial_noise, diffusion_steps)\n",
    "    ims=[]\n",
    "    for i in range(generated_images.shape[0]):\n",
    "        ims.append(postprocess(generated_images[i]))\n",
    "    return ims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_images(images,num_rows=3, num_cols=6):\n",
    "    plt.figure(figsize=(num_cols * 2.0, num_rows * 2.0))\n",
    "    for row in range(num_rows):\n",
    "        for col in range(num_cols):\n",
    "            index = row * num_cols + col\n",
    "            plt.subplot(num_rows, num_cols, index + 1)\n",
    "            plt.imshow(images[index])\n",
    "            plt.axis(\"off\")\n",
    "    plt.tight_layout()\n",
    "    #plt.show()\n",
    "    return plt\n",
    "   #plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pytorch_lightning as pl\n",
    "from ema_pytorch import EMA\n",
    "\n",
    "class Diffusion(pl.LightningModule):\n",
    "\n",
    "    def __init__(self, model, lr, weight_decay):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.model = model\n",
    "        self.lr = lr\n",
    "        self.weight_decay = weight_decay\n",
    "        \n",
    "        self.ema_network = EMA (\n",
    "        self.model,\n",
    "        beta = 0.999,              # exponential moving average factor\n",
    "        update_after_step = 1,    # only after this number of .update() calls will it start updating\n",
    "        update_every = 1,          # how often to actually update, to save on compute (updates every 10th .update() call)\n",
    "        )\n",
    "        self.gidx=1\n",
    "    \n",
    "    \n",
    "    def denoise(self,noisy_images, noise_rates, signal_rates,training=True):\n",
    "        # predict noise component and calculate the image component using it\n",
    "        if training:\n",
    "            network = self.model\n",
    "        else:\n",
    "            network = self.ema_network\n",
    "        pred_noises = network(noisy_images, noise_rates.reshape((noise_rates.shape[0]),)**2)\n",
    "        pred_images = (noisy_images - noise_rates * pred_noises) / signal_rates\n",
    "        return pred_noises, pred_images\n",
    "    \n",
    "    \n",
    "    def forward(self, noisy_images, noisy_rates):\n",
    "        outputs = self.model(noisy_images, noisy_rates)\n",
    "        return outputs\n",
    "    \n",
    "    def losses(self,noise,predicted_noise, loss_type=\"l1\"):\n",
    "        if loss_type == 'l1':\n",
    "            loss = F.l1_loss(noise, predicted_noise)\n",
    "        elif loss_type == 'l2':\n",
    "            loss = F.mse_loss(noise, predicted_noise)\n",
    "        elif loss_type == \"huber\":\n",
    "            loss = F.smooth_l1_loss(noise, predicted_noise)\n",
    "        else:\n",
    "            raise NotImplementedError()\n",
    "        return loss\n",
    "    \n",
    "    @torch.no_grad()\n",
    "    def get_noise(self,images):\n",
    "        noises = torch.randn((batch_size,channels, image_size, image_size)).to(images)\n",
    "        diffusion_times = torch.rand((batch_size,1,1,1)).to(images) \n",
    "        noise_rates, signal_rates = cos_diffusion_schedule(diffusion_times)\n",
    "        noisy_images = signal_rates * images + noise_rates * noises\n",
    "        return noises,noise_rates,noisy_images,signal_rates\n",
    "        \n",
    "     \n",
    "    def common_step(self, batch, batch_idx):\n",
    "        images = batch[\"pixel_values\"]\n",
    "        noises,noise_rates, noisy_images,signal_rates=self.get_noise(images)\n",
    "        pred_noises, pred_images = self.denoise(noisy_images, noise_rates, signal_rates)\n",
    "        noise_loss = self.losses(noises, pred_noises)\n",
    "        image_loss = self.losses(images, pred_images)\n",
    "        return noise_loss,{\"NoiseLoss\":noise_loss,\"ImageLoss\":image_loss}\n",
    "    \n",
    "    def training_step(self, batch, batch_idx):\n",
    "        loss, loss_dict = self.common_step(batch, batch_idx)     \n",
    "        # logs metrics for each training_step,\n",
    "        # and the average across the epoch\n",
    "        self.log(\"training_loss\", loss)\n",
    "        for k,v in loss_dict.items():\n",
    "            self.log(\"train_\" + k, v.item())\n",
    "        self.ema_network.update()\n",
    "        return loss\n",
    "    \n",
    "    @torch.no_grad()\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        loss, loss_dict = self.common_step(batch, batch_idx)     \n",
    "        self.log(\"validation_loss\", loss)\n",
    "        for k,v in loss_dict.items():\n",
    "            self.log(\"validation_\" + k, v.item())\n",
    "        return loss\n",
    "    \n",
    "    def validation_epoch_end(self,validation_step_outputs):\n",
    "        self.eval()\n",
    "        images=generate(self,24,200,device=\"cuda\")\n",
    "        rows=len(images)//4\n",
    "        cols=len(images)//rows\n",
    "        plt=plot_images(images,rows,cols)\n",
    "        plt.savefig(os.path.join(\"./lightning_logs/simple_unet\",f\"{self.gidx}\"+\"_res.png\"))\n",
    "        self.train()\n",
    "        self.gidx+=1\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        optimizer = torch.optim.AdamW(self.model.parameters(), lr=self.lr,weight_decay=self.weight_decay)\n",
    "        return optimizer\n",
    "\n",
    "    def train_dataloader(self):\n",
    "        return train_dataloader\n",
    "\n",
    "    def val_dataloader(self):\n",
    "        return val_dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "DModel = Diffusion(model=model, lr=1e-3, weight_decay=1e-5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load pretrained model\n",
    "#ckpt_file_path=\"lightning_logs/LogitsLastLayer/checkpoints/flowers_diffusion-epoch=38-validation_loss=0.0600.ckpt\"\n",
    "#model_sd=torch.load(ckpt_file_path)[\"state_dict\"]\n",
    "#DModel.load_state_dict(model_sd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start tensorboard.\n",
    "#%load_ext tensorboard\n",
    "#%tensorboard --logdir ./lightning_logs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pytorch_lightning import Trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pytorch_lightning.callbacks import ModelCheckpoint\n",
    "checkpoint_callback = ModelCheckpoint(\n",
    "    save_top_k=2,\n",
    "    monitor=\"validation_loss\",\n",
    "    mode=\"min\",\n",
    "    save_last=True,\n",
    "    filename=\"flowers_diffusion-{epoch:02d}-{validation_loss:.4f}\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "logger = pl.loggers.TensorBoardLogger(save_dir='.',version='simple_unet',name='lightning_logs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name        | Type | Params\n",
      "-------------------------------------\n",
      "0 | model       | UNet | 31.1 M\n",
      "1 | ema_network | EMA  | 62.1 M\n",
      "-------------------------------------\n",
      "31.1 M    Trainable params\n",
      "31.1 M    Non-trainable params\n",
      "62.1 M    Total params\n",
      "248.590   Total estimated model params size (MB)\n"
     ]
    },
    {
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       "version_major": 2,
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       "Validation sanity check: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/data_loading.py:105: UserWarning: The dataloader, val dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
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     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/data_loading.py:105: UserWarning: The dataloader, train dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
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