diff --git a/diffusion.ipynb b/diffusion.ipynb index ff5c8a2..54273b7 100644 --- a/diffusion.ipynb +++ b/diffusion.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 4, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -58,22 +58,177 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "class DeviceDataLoader:\n", + " \"\"\"包装一个数据加载器，来把数据移动到另一个设备上\"\"\"\n", + " \"\"\"Wrap a dataloader to move data to a device\"\"\"\n", + "\n", + " def __init__(self, dl, device):\n", + " self.dl = dl\n", + " self.device = device\n", + "\n", + " def __iter__(self):\n", + " \"\"\"在移动到设备后生成一个批次的数据\"\"\"\n", + " \"\"\"Yield a batch of data after moving it to device\"\"\"\n", + " for b in self.dl:\n", + " yield to_device(b, self.device)\n", + "\n", + " def __len__(self):\n", + " \"\"\"批次的数量\"\"\"\n", + " \"\"\"Number of batches\"\"\"\n", + " return len(self.dl)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def get_default_device():\n", + " return torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "def save_images(images, path, **kwargs):\n", + " grid = make_grid(images, **kwargs)\n", + " ndarr = grid.permute(1,2,0).to(\"cpu\").numpy()\n", + " im = Image.fromarray(ndarr)\n", + " im.save(path)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def get(element: torch.Tensor, t: torch.Tensor):\n", + " \"\"\"\n", + " Get value at index position \"t\" in \"element\" and \n", + " reshape it to have the same dimension as a batch of images\n", + "\n", + " 获得在\"element\"中位置\"t\"并且reshape，以和一组照片有相同的维度\n", + " \"\"\"\n", + " ele = element.gather(-1, t)\n", + " return ele.reshape(-1, 1, 1, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "element = torch.tensor([[1,2,3,4,5],\n", + " [2,3,4,5,6],\n", + " [3,4,5,6,7]])\n", + "t = torch.tensor([1,2,0]).unsqueeze(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'get_default_device' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[5], line 4\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdataclasses\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m dataclass\n\u001b[0;32m 3\u001b[0m \u001b[38;5;129;43m@dataclass\u001b[39;49m\n\u001b[1;32m----> 4\u001b[0m \u001b[38;5;28;43;01mclass\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;21;43;01mBaseConfig\u001b[39;49;00m\u001b[43m:\u001b[49m\n\u001b[0;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mDEVICE\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mget_default_device\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mDATASET\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mFlowers\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m#MNIST \"cifar-10\" \"Flowers\"\u001b[39;49;00m\n", - "Cell \u001b[1;32mIn[5], line 5\u001b[0m, in \u001b[0;36mBaseConfig\u001b[1;34m()\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;129m@dataclass\u001b[39m\n\u001b[0;32m 4\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mBaseConfig\u001b[39;00m:\n\u001b[1;32m----> 5\u001b[0m DEVICE \u001b[38;5;241m=\u001b[39m \u001b[43mget_default_device\u001b[49m()\n\u001b[0;32m 6\u001b[0m DATASET \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFlowers\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m#MNIST \"cifar-10\" \"Flowers\"\u001b[39;00m\n\u001b[0;32m 8\u001b[0m \u001b[38;5;66;03m# 记录推断日志信息并保存存档点\u001b[39;00m\n", - "\u001b[1;31mNameError\u001b[0m: name 'get_default_device' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[1, 2, 3, 4, 5],\n", + " [2, 3, 4, 5, 6],\n", + " [3, 4, 5, 6, 7]])\n", + "tensor([[1],\n", + " [2],\n", + " [0]])\n" ] } ], + "source": [ + "print(element)\n", + "print(t)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[[[2]]],\n", + "\n", + "\n", + " [[[4]]],\n", + "\n", + "\n", + " [[[3]]]])\n" + ] + } + ], + "source": [ + "extracted_scores = get(element, t)\n", + "print(extracted_scores)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "def setup_log_directory(config):\n", + " \"\"\"\n", + " Log and Model checkpoint directory Setup\n", + " 记录并且建模目录准备\n", + " \"\"\"\n", + "\n", + " if os.path.isdir(config.root_log_dir):\n", + " # Get all folders numbers in the root_log_dir\n", + " # 在root_log_dir下获得所有文件夹数目\n", + " folder_numbers = [int(folder.replace(\"version_\", \"\")) for folder in os.listdir(config.root_log_dir)]\n", + "\n", + " # Find the latest version number present in the log_dir\n", + " # 找到在log_dir下的最新版本数字\n", + " last_version_number = max(folder_numbers)\n", + "\n", + " # New version name\n", + " version_name = f\"version{last_version_number + 1}\"\n", + "\n", + " else:\n", + " version_name = config.log_dir\n", + "\n", + " # Update the training config default directory\n", + " # 更新训练config默认目录\n", + " log_dir = os.path.join(config.root_log_dir, version_name)\n", + " checkpoint_dir = os.path.join(config.root_checkpoint_dir, version_name) \n", + "\n", + " # Create new directory for saving new experiment version\n", + " # 创建一个新目录来保存新的实验版本\n", + " os.makedirs(log_dir, exist_ok=True)\n", + " os.makedirs(checkpoint_dir, exist_ok=True)\n", + "\n", + " print(f\"Logging at: {log_dir}\")\n", + " print(f\"Model Checkpoint at: {checkpoint_dir}\")\n", + "\n", + " return log_dir, checkpoint_dir\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], "source": [ "from dataclasses import dataclass\n", "\n", @@ -117,7 +272,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.11.8" } }, "nbformat": 4,