281 lines
7.6 KiB
Plaintext
281 lines
7.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"import gc\n",
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"import os\n",
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"import cv2\n",
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"import math\n",
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"import base64\n",
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"import random\n",
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"import numpy as np\n",
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"from PIL import Image \n",
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"from tqdm import tqdm\n",
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"from datetime import datetime\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"from torch.cuda import amp\n",
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"import torch.nn.functional as F\n",
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"from torch.optim import Adam, AdamW\n",
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"from torch.utils.data import Dataset, DataLoader\n",
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"\n",
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"import torchvision\n",
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"import torchvision.transforms as TF\n",
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"import torchvision.datasets as datasets\n",
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"from torchvision.utils import make_grid\n",
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"\n",
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"from torchmetrics import MeanMetric\n",
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"\n",
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"from IPython.display import display, HTML, clear_output\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Helper functions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"def to_device(data, device):\n",
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" \"\"\"将张量移动到选择的设备\"\"\"\n",
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" \"\"\"Move tensor(s) to chosen device\"\"\"\n",
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" if isinstance(data, (list, tuple)):\n",
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" return [to_device(x, device) for x in data]\n",
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" return data.to(device, non_blocking=true)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"class DeviceDataLoader:\n",
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" \"\"\"包装一个数据加载器，来把数据移动到另一个设备上\"\"\"\n",
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" \"\"\"Wrap a dataloader to move data to a device\"\"\"\n",
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"\n",
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" def __init__(self, dl, device):\n",
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" self.dl = dl\n",
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" self.device = device\n",
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"\n",
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" def __iter__(self):\n",
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" \"\"\"在移动到设备后生成一个批次的数据\"\"\"\n",
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" \"\"\"Yield a batch of data after moving it to device\"\"\"\n",
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" for b in self.dl:\n",
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" yield to_device(b, self.device)\n",
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"\n",
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" def __len__(self):\n",
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" \"\"\"批次的数量\"\"\"\n",
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" \"\"\"Number of batches\"\"\"\n",
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" return len(self.dl)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_default_device():\n",
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" return torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"def save_images(images, path, **kwargs):\n",
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" grid = make_grid(images, **kwargs)\n",
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" ndarr = grid.permute(1,2,0).to(\"cpu\").numpy()\n",
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" im = Image.fromarray(ndarr)\n",
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" im.save(path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get(element: torch.Tensor, t: torch.Tensor):\n",
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" \"\"\"\n",
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" Get value at index position \"t\" in \"element\" and \n",
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" reshape it to have the same dimension as a batch of images\n",
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"\n",
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" 获得在\"element\"中位置\"t\"并且reshape，以和一组照片有相同的维度\n",
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" \"\"\"\n",
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" ele = element.gather(-1, t)\n",
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" return ele.reshape(-1, 1, 1, 1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"element = torch.tensor([[1,2,3,4,5],\n",
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" [2,3,4,5,6],\n",
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" [3,4,5,6,7]])\n",
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"t = torch.tensor([1,2,0]).unsqueeze(1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tensor([[1, 2, 3, 4, 5],\n",
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" [2, 3, 4, 5, 6],\n",
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" [3, 4, 5, 6, 7]])\n",
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"tensor([[1],\n",
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" [2],\n",
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" [0]])\n"
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]
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}
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],
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"source": [
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"print(element)\n",
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"print(t)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tensor([[[[2]]],\n",
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"\n",
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"\n",
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" [[[4]]],\n",
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"\n",
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"\n",
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" [[[3]]]])\n"
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]
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}
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],
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"source": [
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"extracted_scores = get(element, t)\n",
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"print(extracted_scores)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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"def setup_log_directory(config):\n",
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" \"\"\"\n",
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" Log and Model checkpoint directory Setup\n",
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" 记录并且建模目录准备\n",
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" \"\"\"\n",
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"\n",
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" if os.path.isdir(config.root_log_dir):\n",
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" # Get all folders numbers in the root_log_dir\n",
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" # 在root_log_dir下获得所有文件夹数目\n",
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" folder_numbers = [int(folder.replace(\"version_\", \"\")) for folder in os.listdir(config.root_log_dir)]\n",
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"\n",
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" # Find the latest version number present in the log_dir\n",
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" # 找到在log_dir下的最新版本数字\n",
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" last_version_number = max(folder_numbers)\n",
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"\n",
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" # New version name\n",
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" version_name = f\"version{last_version_number + 1}\"\n",
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"\n",
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" else:\n",
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" version_name = config.log_dir\n",
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"\n",
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" # Update the training config default directory\n",
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" # 更新训练config默认目录\n",
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" log_dir = os.path.join(config.root_log_dir, version_name)\n",
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" checkpoint_dir = os.path.join(config.root_checkpoint_dir, version_name) \n",
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"\n",
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" # Create new directory for saving new experiment version\n",
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" # 创建一个新目录来保存新的实验版本\n",
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" os.makedirs(log_dir, exist_ok=True)\n",
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" os.makedirs(checkpoint_dir, exist_ok=True)\n",
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"\n",
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" print(f\"Logging at: {log_dir}\")\n",
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" print(f\"Model Checkpoint at: {checkpoint_dir}\")\n",
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"\n",
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" return log_dir, checkpoint_dir\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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"from dataclasses import dataclass\n",
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"\n",
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"@dataclass\n",
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"class BaseConfig:\n",
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" DEVICE = get_default_device()\n",
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" DATASET = \"Flowers\" #MNIST \"cifar-10\" \"Flowers\"\n",
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"\n",
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" # 记录推断日志信息并保存存档点\n",
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" root_log_dir = os.path.join(\"Logs_Checkpoints\", \"Inference\")\n",
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" root_checkpoint_dir = os.path.join(\"Logs_Checkpoints\",\"checkpoints\")\n",
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"\n",
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" #目前的日志和存档点目录\n",
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" log_dir = \"version_0\"\n",
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" checkpoint_dir = \"version_0\"\n",
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"\n",
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"@dataclass\n",
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"class TrainingConfig:\n",
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" TIMESTEPS = 1000\n",
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" IMG_SHAPE = (1,32,32) if BaseConfig.DATASET == \"MNIST\" else (3,32,32)\n",
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" NUM_EPOCHS = 800\n",
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" BATCH_SIZE = 32\n",
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" LR = 2e-4\n",
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" NUM_WORKERS = 2"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "DLML",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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