add diffusion.ipynb
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Hanzhang ma
125
diffusion.ipynb
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125
diffusion.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 4,
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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": null,
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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": 5,
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'get_default_device' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
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"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",
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"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",
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"\u001b[1;31mNameError\u001b[0m: name 'get_default_device' is not defined"
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]
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}
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],
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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": "3.11.0"
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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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