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{
"cells": [
{
"attachments": {},
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"source": [
"## 改編ContextUnet及相關代碼，使其首先對二維的情況適用。並於diffusers.Unet2DModel作比較並加以優化。最後再改寫爲3維的情形。\n",
"- 經試用diffusers的Unet2DModel，發現loss從0.3降到0.2但仍然很高，説明存在非Unet2DModel的問題可以優化\n",
"- 改用diffusers的DDMPScheduler和DDPMPipeline后，loss降低至0.1以下，有時甚至可以低至0.004，可見我的代碼問題主要出在DDPM部分。DDPMScheduler部分比較簡短，似乎沒有問題，所以問題應該在DDPMPipeline裏某一部分代碼是我代碼欠缺的。\n",
"- 我在DDPMScheduler部分有一個typo，導致beta_t一直很小，修正后loss從0.2能降低至0.02, 維持在0.1以下\n",
"- 用diffusers的DDPMScheduler似乎效果要好一些，loss總是比我的DDPMScheduler要小一點。儅epoch為19時，前者的loss約0.02，後者loss約0.07。而且前者還支持3維圖像的加噪，不如直接用別人的輪子。但我想知道爲什麽我的loss會高一些。\n",
"- 我意識到別人的DDPMScheduler在sample函數中沒有兼容輸入參數，所以歸根結底還是需要我的DDPMscheduler。不過我可以先用別人的來debug我的ContextUnet.\n",
"- 我需要將我的ContextUnet擴展兼容不同維度的照片，畢竟我本身也需要和原文獻對比完了再拓展到三維的情形\n",
"- 我已將我的ContextUnet轉成了2維的模式，與diffusers.Unet2DModel的loss=0.037相比，我的Unet的loss=0.07。同時我的Unet生成的圖像看上去很奇怪，説明我的Unet也有問題。我需要將代碼退回原Unet，並檢查問題所在。\n",
"- 我將紅移方向的像素的數量限制在了64.以此比較兩個Unet的差別。經比較：\\\n",
"Unet2DModel loss：0.03, 0.0655, 0.05, 0.02, 0.05\\\n",
"ContextUnet loss: 0.1, 0.16, 0.1, 0.2186, 0.06\n",
"- 我把ContextUnet退回到了原作者的版本，結果loss=0.05，輸出的照片也不錯。我主要的改動是改回了他原用的normalization函數，其中還有個參數swish。有時間我可以研究一下具體是哪裏影響了訓練的結果。另外我發現了要想tensorboard的圖綫獨立美觀，需要把他們放在不同的文件夾下\n",
"- 經過驗證，GroupNorm比batchNorm效果要好\n",
"- 已擴展爲接受不同維度的情形\n",
"- 融合cond, guide_w, drop_out這些參數\n",
"- 生成的21cm圖像該暗的地方不夠暗，似乎換成MNIST的數字圖像就沒問題\n",
"- 我用diffusion模型生成MNIST的數字時發現，儘管生成的數據的範圍也存在負數數值，如-0.1,但畫出來的圖像卻是理想的黑色。數據的分佈與21cm的結果的分佈沒多大差別，我現在打算把代碼退回到21cm的情形\n",
"- 我統一了ddpm21cm這個module，能統一實現訓練和生成樣本，但目前有個bug， sample時總是會cuda out of memory，然而單獨resume model並sample就不會。\n",
"- 解決了，問題出在我忘了寫with torch.no_grad():\n",
"- 接下來就是生成800個lightcones，與此同時研究如何計算global signal以及power spectrum\n",
"- 儅訓練圖片的數量達到5000時，生成的圖片與檢測數據的相似程度很高\n",
"- it takes 62 mins to generated 8 images with shape of (64,64,64), which is even slower than simulation, which takes ~5 mins for each image. Besides, the batch_size during training and num of images to be generated are limited to be 2 and 8, respectively.\n",
"- the slowerness can be solved by using multi-GPUs, and the limited-num-of-images can be solved by multi-accuracy, multi-GPUs.\n",
"- In addtion, the performance of DDPM can looks better compared to computation-intensive simulations. "
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"from dataclasses import dataclass\n",
"import h5py\n",
"import torch\n",
"import torch.nn as nn\n",
"from torch.utils.data import DataLoader, Dataset\n",
"# from datasets import Dataset\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import random\n",
"# from abc import ABC, abstractmethod\n",
"import torch.nn.functional as F\n",
"import math\n",
"# from PIL import Image\n",
"import os\n",
"from torch.utils.tensorboard import SummaryWriter\n",
"import copy\n",
"from tqdm.auto import tqdm\n",
"# from torchvision import transforms\n",
"# from diffusers import UNet2DModel#, UNet3DConditionModel\n",
"# from diffusers import DDPMScheduler\n",
"from diffusers.utils import make_image_grid\n",
"import datetime\n",
"from pathlib import Path\n",
"from diffusers.optimization import get_cosine_schedule_with_warmup\n",
"from accelerate import notebook_launcher, Accelerator\n",
"from huggingface_hub import create_repo, upload_folder\n",
"from load_h5 import Dataset4h5\n",
"\n",
"from context_unet import ContextUnet\n",
"\n",
"from huggingface_hub import notebook_login\n",
"\n",
"import torch.multiprocessing as mp\n",
"from torch.utils.data.distributed import DistributedSampler\n",
"from torch.nn.parallel import DistributedDataParallel as DDP\n",
"from torch.distributed import init_process_group, destroy_process_group"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"def ddp_setup(rank: int, world_size: int):\n",
" \"\"\"\n",
" Args:\n",
" rank: Unique identifier of each process\n",
" world_size: Total number of processes\n",
" \"\"\"\n",
" os.environ[\"MASTER_ADDR\"] = \"localhost\"\n",
" os.environ[\"MASTER_PORT\"] = \"12355\"\n",
" torch.cuda.set_device(rank)\n",
" init_process_group(backend=\"nccl\", rank=rank, world_size=world_size)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9bbf7e9db9ce426d9c59d6f6d8e8df29",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"notebook_login()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Add noise:\n",
"\n",
"\\begin{align*}\n",
"x_t &\\sim \\mathcal N\\left(\\sqrt{1-\\beta_t}\\ x_{t-1},\\ \\beta_t \\right) \\\\\n",
"x_t &\\equiv \\sqrt{1-\\beta_t}\\ x_{t-1} + \\sqrt{\\beta_t}\\ \\epsilon\\\\\n",
"\\epsilon &\\sim \\mathcal N(0,1)\\\\\n",
"\\alpha_t & \\equiv 1 - \\beta_t\\\\\n",
"& ...\\\\\n",
"x_t &= \\sqrt{\\bar {\\alpha_t}} x_0 + \\epsilon\\ \\sqrt{1 - \\bar{\\alpha_t}}\\\\\n",
"\\bar {\\alpha_t} &\\equiv \\prod_{i=1}^t \\alpha_i\\\\\n",
"&= \\exp\\left({\\ln{\\prod_{i=1}^t \\alpha_i}}\\right)\\\\\n",
"&= \\exp\\left({\\sum_{i=1}^t\\ln{ \\alpha_i}}\\right)\n",
"\\end{align*}"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"class DDPMScheduler(nn.Module):\n",
" def __init__(self, betas: tuple, num_timesteps: int, img_shape: list, device='cpu'):\n",
" super().__init__()\n",
" \n",
" beta_1, beta_T = betas\n",
" assert 0 < beta_1 <= beta_T <= 1, \"ensure 0 < beta_1 <= beta_T <= 1\"\n",
" self.device = device\n",
" self.num_timesteps = num_timesteps\n",
" self.img_shape = img_shape\n",
" self.beta_t = torch.linspace(beta_1, beta_T, self.num_timesteps) #* (beta_T-beta_1) + beta_1\n",
" self.beta_t = self.beta_t.to(self.device)\n",
"\n",
" # self.drop_prob = drop_prob\n",
" # self.cond = cond\n",
" self.alpha_t = 1 - self.beta_t\n",
" # self.bar_alpha_t = torch.exp(torch.cumsum(torch.log(self.alpha_t), dim=0))\n",
" self.bar_alpha_t = torch.cumprod(self.alpha_t, dim=0)\n",
"\n",
" def add_noise(self, clean_images):\n",
" shape = clean_images.shape\n",
" expand = torch.ones(len(shape)-1, dtype=int)\n",
" # ts_expand = ts.view(ts.shape[0], *expand.tolist())\n",
" # expand = [1 for i in range(len(shape)-1)]\n",
"\n",
" noise = torch.randn_like(clean_images).to(self.device)\n",
" ts = torch.randint(0, self.num_timesteps, (shape[0],)).to(self.device)\n",
" \n",
" # test_expand = test.view(test.shape[0],*expand)\n",
" # extend_dim = [None for i in range(shape.dim()-1)]\n",
" noisy_images = (\n",
" clean_images * torch.sqrt(self.bar_alpha_t[ts]).view(shape[0], *expand.tolist())\n",
" + noise * torch.sqrt(1-self.bar_alpha_t[ts]).view(shape[0], *expand.tolist())\n",
" )\n",
" # print(x_t.shape)\n",
"\n",
" return noisy_images, noise, ts\n",
"\n",
" def sample(self, nn_model, params, device, guide_w = 0):\n",
" n_sample = len(params) #params.shape[0]\n",
" # print(\"params.shape[0], len(params)\", params.shape[0], len(params))\n",
" x_i = torch.randn(n_sample, *self.img_shape).to(device)\n",
" # print(\"x_i.shape =\", x_i.shape)\n",
" # print(\"x_i.shape =\", x_i.shape)\n",
" if guide_w != -1:\n",
" c_i = params\n",
" uncond_tokens = torch.zeros(int(n_sample), params.shape[1]).to(device)\n",
" # uncond_tokens = torch.tensor(np.float32(np.array([0,0]))).to(device)\n",
" # uncond_tokens = uncond_tokens.repeat(int(n_sample),1)\n",
" c_i = torch.cat((c_i, uncond_tokens), 0)\n",
"\n",
" x_i_entire = [] # keep track of generated steps in case want to plot something\n",
" # print(\"self.num_timesteps =\", self.num_timesteps)\n",
" # for i in range(self.num_timesteps, 0, -1):\n",
" # print(f'sampling!!!')\n",
" pbar_sample = tqdm(total=self.num_timesteps)\n",
" pbar_sample.set_description(\"Sampling\")\n",
" for i in reversed(range(0, self.num_timesteps)):\n",
" # print(f'sampling timestep {i:4d}',end='\\r')\n",
" t_is = torch.tensor([i]).to(device)\n",
" t_is = t_is.repeat(n_sample)\n",
"\n",
" z = torch.randn(n_sample, *self.img_shape).to(device) if i > 0 else 0\n",
"\n",
" if guide_w == -1:\n",
" # eps = nn_model(x_i, t_is, return_dict=False)[0]\n",
" eps = nn_model(x_i, t_is)\n",
" # x_i = 1/torch.sqrt(self.alpha_t[i])*(x_i-eps*self.beta_t[i]/torch.sqrt(1-self.bar_alpha_t[i])) + torch.sqrt(self.beta_t[i])*z\n",
" else:\n",
" # double batch\n",
" x_i = x_i.repeat(2, *torch.ones(len(self.img_shape), dtype=int).tolist())\n",
" t_is = t_is.repeat(2)\n",
"\n",
" # split predictions and compute weighting\n",
" # print(\"nn_model input shape\", x_i.shape, t_is.shape, c_i.shape)\n",
" eps = nn_model(x_i, t_is, c_i)\n",
" eps1 = eps[:n_sample]\n",
" eps2 = eps[n_sample:]\n",
" eps = eps1 + guide_w*(eps1 - eps2)\n",
" # eps = (1+guide_w)*eps1 - guide_w*eps2\n",
" x_i = x_i[:n_sample]\n",
" # x_i = 1/torch.sqrt(self.alpha_t[i])*(x_i-eps*self.beta_t[i]/torch.sqrt(1-self.bar_alpha_t[i])) + torch.sqrt(self.beta_t[i])*z\n",
" \n",
" # print(\"x_i.shape =\", x_i.shape)\n",
" x_i = 1/torch.sqrt(self.alpha_t[i])*(x_i-eps*self.beta_t[i]/torch.sqrt(1-self.bar_alpha_t[i])) + torch.sqrt(self.beta_t[i])*z\n",
" \n",
" pbar_sample.update(1)\n",
" # pbar_sample.set_postfix(step=i)\n",
" \n",
" # print(\"x_i.shape =\", x_i.shape)\n",
" # store only part of the intermediate steps\n",
" if i%20==0:# or i==0:# or i<8:\n",
" x_i_entire.append(x_i.detach().cpu().numpy())\n",
" x_i = x_i.detach().cpu().numpy()\n",
" x_i_entire = np.array(x_i_entire)\n",
" return x_i, x_i_entire\n",
"\n",
"\n",
"# ddpm_scheduler = DDPMScheduler((1e-4,0.02),10)\n",
"# noisy_images, noise, ts = ddpm_scheduler.add_noise(images)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"class EMA:\n",
" def __init__(self, beta):\n",
" super().__init__()\n",
" self.beta = beta\n",
" self.step = 0\n",
"\n",
" def update_model_average(self, ma_model, current_model):\n",
" for current_params, ma_params in zip(current_model.parameters(), ma_model.parameters()):\n",
" old_weight, up_weight = ma_params.data, current_params.data\n",
" ma_params.data = self.update_average(old_weight, up_weight)\n",
"\n",
" def update_average(self, old, new):\n",
" if old is None:\n",
" return new\n",
" return old * self.beta + (1 - self.beta) * new\n",
"\n",
" def step_ema(self, ema_model, model):\n",
" self.update_model_average(ema_model, model)\n",
" self.step += 1\n",
"\n",
" def reset_parameters(self, ema_model, model):\n",
" ema_model.load_state_dict(model.state_dict())\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"@dataclass\n",
"class TrainConfig:\n",
" ###########################\n",
" ## hardcoding these here ##\n",
" ###########################\n",
" push_to_hub = True\n",
" hub_model_id = \"Xsmos/ml21cm\"\n",
" hub_private_repo = False\n",
" dataset_name = \"/storage/home/hcoda1/3/bxia34/scratch/LEN128-DIM64-CUB8.h5\"\n",
" device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
" # world_size = torch.cuda.device_count()\n",
" # repeat = 2\n",
"\n",
" # dim = 2\n",
" dim = 3\n",
" stride = (2,2) if dim == 2 else (2,2,1)\n",
" num_image = 2000#32000#20000#15000#7000#25600#3000#10000#1000#10000#5000#2560#800#2560\n",
" batch_size = 2#2#50#20#2#100 # 10\n",
" n_epoch = 10#50#20#20#2#5#25 # 120\n",
" HII_DIM = 28#64\n",
" num_redshift = 4#128#64#512#256#256#64#512#128\n",
" channel = 1\n",
" img_shape = (channel, HII_DIM, num_redshift) if dim == 2 else (channel, HII_DIM, HII_DIM, num_redshift)\n",
"\n",
" ranges_dict = dict(\n",
" params = {\n",
" 0: [4, 6], # ION_Tvir_MIN\n",
" 1: [10, 250], # HII_EFF_FACTOR\n",
" },\n",
" images = {\n",
" 0: [0, 80], # brightness_temp\n",
" }\n",
" )\n",
"\n",
" num_timesteps = 1000#1000 # 1000, 500; DDPM time steps\n",
" # n_sample = 24 # 64, the number of samples in sampling process\n",
" n_param = 2\n",
" guide_w = 0#-1#0#-1#0#-1#0.1#[0,0.1] #[0,0.5,2] strength of generative guidance\n",
" drop_prob = 0#0.28 # only takes effect when guide_w != -1\n",
" ema=True # whether to use ema\n",
" ema_rate=0.995\n",
"\n",
" # seed = 0\n",
" # save_dir = './outputs/'\n",
"\n",
" save_freq = 0#.1 # the period of sampling\n",
" # general parameters for the name and logger \n",
" # device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
" lrate = 1e-4\n",
" lr_warmup_steps = 0#5#00\n",
" output_dir = \"./outputs/\"\n",
" save_name = os.path.join(output_dir, 'model_state')\n",
" # save_freq = 1 #10 # the period of saving model\n",
" # cond = True # if training using the conditional information\n",
" # lr_decay = False #True# if using the learning rate decay\n",
" resume = save_name # if resume from the trained checkpoints\n",
" # params_single = torch.tensor([0.2,0.80000023])\n",
" # params = torch.tile(params_single,(n_sample,1)).to(device)\n",
" # params = params\n",
" # data_dir = './data' # data directory\n",
"\n",
"\n",
" mixed_precision = \"fp16\"\n",
" gradient_accumulation_steps = 1\n",
"\n",
" # date = datetime.datetime.now().strftime(\"%m%d-%H%M\")\n",
" # run_name = f'{date}' # the unique name of each experiment\n",
"\n",
"# config = TrainConfig()\n",
"# print(\"device =\", config.device)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# import os\n",
"# print(os.cpu_count())\n",
"# print(len(os.sched_getaffinity(0)))\n",
"# import torch\n",
"# data = torch.randn((64,64))\n",
"# print(data.dtype)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# @dataclass\n",
"class DDPM21CM:\n",
" def __init__(self, config):\n",
" # config = TrainConfig()\n",
" # date = datetime.datetime.now().strftime(\"%m%d-%H%M\")\n",
" config.run_name = datetime.datetime.now().strftime(\"%m%d-%H%M\") # the unique name of each experiment\n",
" self.config = config\n",
" # dataset = Dataset4h5(config.dataset_name, num_image=config.num_image, HII_DIM=config.HII_DIM, num_redshift=config.num_redshift, drop_prob=config.drop_prob, dim=config.dim)\n",
" # # self.shape_loaded = dataset.images.shape\n",
" # # print(\"shape_loaded =\", self.shape_loaded)\n",
" # self.dataloader = DataLoader(dataset, batch_size=config.batch_size, shuffle=True)\n",
" # del dataset\n",
" self.ddpm = DDPMScheduler(betas=(1e-4, 0.02), num_timesteps=config.num_timesteps, img_shape=config.img_shape, device=config.device)\n",
"\n",
" # initialize the unet\n",
" self.nn_model = ContextUnet(n_param=config.n_param, image_size=config.HII_DIM, dim=config.dim, stride=config.stride)\n",
"\n",
" if config.resume and os.path.exists(config.resume):\n",
" # resume_file = os.path.join(config.output_dir, f\"{config.resume}\")\n",
" self.nn_model.load_state_dict(torch.load(config.resume)['unet_state_dict'])\n",
" print(f\"resumed nn_model from {config.resume}\")\n",
" # nn_model = ContextUnet(n_param=1, image_size=28)\n",
" self.nn_model.train()\n",
" self.nn_model.to(self.ddpm.device)\n",
" # print(\"nn_model.device =\", ddpm.device)\n",
" # number of parameters to be trained\n",
" self.number_of_params = sum(x.numel() for x in self.nn_model.parameters())\n",
" print(f\"Number of parameters for nn_model: {self.number_of_params}\")\n",
"\n",
" # whether to use ema\n",
" if config.ema:\n",
" self.ema = EMA(config.ema_rate)\n",
" if config.resume and os.path.exists(config.resume):\n",
" self.ema_model = ContextUnet(n_param=config.n_param, image_size=config.HII_DIM, dim=config.dim, stride=config.stride).to(config.device)\n",
" self.ema_model.load_state_dict(torch.load(config.resume)['ema_unet_state_dict'])\n",
" print(f\"resumed ema_model from {config.resume}\")\n",
" else:\n",
" self.ema_model = copy.deepcopy(self.nn_model).eval().requires_grad_(False)\n",
"\n",
" self.optimizer = torch.optim.AdamW(self.nn_model.parameters(), lr=config.lrate)\n",
" self.lr_scheduler = get_cosine_schedule_with_warmup(\n",
" optimizer=self.optimizer,\n",
" num_warmup_steps=config.lr_warmup_steps,\n",
" num_training_steps=(int(config.num_image/config.batch_size) * config.n_epoch),\n",
" # num_training_steps=(len(self.dataloader) * config.n_epoch),\n",
" )\n",
"\n",
" self.ranges_dict = config.ranges_dict\n",
"\n",
" def load(self):\n",
" dataset = Dataset4h5(self.config.dataset_name, num_image=self.config.num_image, HII_DIM=self.config.HII_DIM, num_redshift=self.config.num_redshift, drop_prob=self.config.drop_prob, dim=self.config.dim, ranges_dict=self.ranges_dict)\n",
" # self.shape_loaded = dataset.images.shape\n",
" # print(\"shape_loaded =\", self.shape_loaded)\n",
" self.dataloader = DataLoader(dataset, batch_size=self.config.batch_size, shuffle=True, num_workers=len(os.sched_getaffinity(0)), pin_memory=True)\n",
" # del dataset\n",
" # self.accelerate(self.config)\n",
" del dataset\n",
"\n",
" # def accelerate(self):\n",
"\n",
" def train(self):\n",
" ################### \n",
" ## training loop ##\n",
" ###################\n",
" # plot_unet = True\n",
"\n",
" self.load()\n",
" self.accelerator = Accelerator(\n",
" mixed_precision=self.config.mixed_precision,\n",
" gradient_accumulation_steps=self.config.gradient_accumulation_steps,\n",
" log_with=\"tensorboard\",\n",
" project_dir=os.path.join(self.config.output_dir, \"logs\"),\n",
" )\n",
" print(\"self.accelerator.is_main_process:\", self.accelerator.is_main_process)\n",
" if self.accelerator.is_main_process:\n",
" if self.config.output_dir is not None:\n",
" os.makedirs(self.config.output_dir, exist_ok=True)\n",
" if self.config.push_to_hub:\n",
" self.repo_id = create_repo(\n",
" repo_id=self.config.hub_model_id or Path(self.config.output_dir).name, exist_ok=True\n",
" ).repo_id\n",
" self.accelerator.init_trackers(f\"{self.config.run_name}\")\n",
"\n",
" self.nn_model, self.optimizer, self.dataloader, self.lr_scheduler = \\\n",
" self.accelerator.prepare(\n",
" self.nn_model, self.optimizer, self.dataloader, self.lr_scheduler\n",
" )\n",
" \n",
" global_step = 0\n",
" for ep in range(self.config.n_epoch):\n",
" self.ddpm.train()\n",
"\n",
" pbar_train = tqdm(total=len(self.dataloader), disable=not self.accelerator.is_local_main_process)\n",
" pbar_train.set_description(f\"Epoch {ep}\")\n",
" for i, (x, c) in enumerate(self.dataloader):\n",
" with self.accelerator.accumulate(self.nn_model):\n",
" x = x.to(self.config.device)\n",
" xt, noise, ts = self.ddpm.add_noise(x)\n",
" \n",
" if self.config.guide_w == -1:\n",
" noise_pred = self.nn_model(xt, ts)\n",
" else:\n",
" c = c.to(self.config.device)\n",
" noise_pred = self.nn_model(xt, ts, c)\n",
" \n",
" loss = F.mse_loss(noise, noise_pred)\n",
" self.accelerator.backward(loss)\n",
" self.accelerator.clip_grad_norm_(self.nn_model.parameters(), 1)\n",
" self.optimizer.step()\n",
" self.lr_scheduler.step()\n",
" self.optimizer.zero_grad()\n",
"\n",
" # ema update\n",
" if self.config.ema:\n",
" self.ema.step_ema(self.ema_model, self.nn_model)\n",
"\n",
" pbar_train.update(1)\n",
" logs = dict(\n",
" loss=loss.detach().item(),\n",
" lr=self.optimizer.param_groups[0]['lr'],\n",
" step=global_step\n",
" )\n",
" pbar_train.set_postfix(**logs)\n",
"\n",
" self.accelerator.log(logs, step=global_step)\n",
" global_step += 1\n",
"\n",
" # if ep == config.n_epoch-1 or (ep+1)*config.save_freq==1:\n",
" self.save(ep)\n",
"\n",
" del self.nn_model\n",
" if self.config.ema:\n",
" del self.ema_model\n",
" torch.cuda.empty_cache()\n",
"\n",
" def save(self, ep):\n",
" # save model\n",
" if self.accelerator.is_main_process:\n",
" if ep == self.config.n_epoch-1 or (ep+1)*self.config.save_freq==1:\n",
" self.nn_model.eval()\n",
" with torch.no_grad():\n",
" if self.config.push_to_hub:\n",
" upload_folder(\n",
" repo_id = self.repo_id,\n",
" folder_path = \".\",#config.output_dir,\n",
" commit_message = f\"{self.config.run_name}\",\n",
" ignore_patterns = [\"step_*\", \"epoch_*\", \"*.npy\", \"__pycache__\"],\n",
" )\n",
" if self.config.save_name:\n",
" model_state = {\n",
" 'epoch': ep,\n",
" 'unet_state_dict': self.nn_model.state_dict(),\n",
" 'ema_unet_state_dict': self.ema_model.state_dict(),\n",
" }\n",
" torch.save(model_state, self.config.save_name+f\"-N{self.config.num_image}\")\n",
" print('saved model at ' + self.config.save_name+f\"-N{self.config.num_image}\")\n",
" # print('saved model at ' + config.save_dir + f\"model_epoch_{ep}_test_{config.run_name}.pth\")\n",
"\n",
" # def rescale(self, value, type='params', to_ranges=[0,1]):\n",
" # for i, from_ranges in self.ranges_dict[type].items():\n",
" # value[i] = (value[i] - from_ranges[0])/(from_ranges[1]-from_ranges[0]) # normalize\n",
" # value[i] = \n",
" def rescale(self, value, ranges, to: list):\n",
" if value.ndim == 1:\n",
" value = value.view(-1,len(value))\n",
" \n",
" for i in range(np.shape(value)[1]):\n",
" value[:,i] = (value[:,i] - ranges[i][0]) / (ranges[i][1]-ranges[i][0])\n",
" # print(f\"i = {i}, value.min = {value[:,i].min()}, value.max = {value[:,i].max()}\")\n",
" value = value * (to[1]-to[0]) + to[0]\n",
" return value \n",
"\n",
" def sample(self, file, params:torch.tensor=None, repeat=192, ema=False, entire=False):\n",
" # n_sample = params.shape[0]\n",
" \n",
" if params is None:\n",
" params = torch.tensor([0.20000000000000018, 0.5055875000000001])\n",
" params_backup = params.numpy().copy()\n",
" else:\n",
" params_backup = params.numpy().copy()\n",
" params = self.rescale(params, self.ranges_dict['params'], to=[0,1])\n",
"\n",
" print(f\"sampling {repeat} images with normalized params = {params}\")\n",
" params = params.repeat(repeat,1)\n",
" assert params.dim() == 2, \"params must be a 2D torch.tensor\"\n",
" # print(\"params =\", params)\n",
" # print(\"params =\", params)\n",
" # print(\"len(params) =\", len(params))\n",
" # model = self.ema_model if ema else self.nn_model\n",
" # del self.ema_model, self.nn\n",
" # params = torch.tile(params, (n_sample,1)).to(device)\n",
"\n",
" nn_model = ContextUnet(n_param=self.config.n_param, image_size=self.config.HII_DIM, dim=self.config.dim, stride=self.config.stride).to(self.config.device)\n",
" if ema:\n",
" nn_model.load_state_dict(torch.load(file)['ema_unet_state_dict'])\n",
" else:\n",
" nn_model.load_state_dict(torch.load(file)['unet_state_dict'])\n",
" print(f\"nn_model resumed from {file}\")\n",
" # nn_model = ContextUnet(n_param=1, image_size=28)\n",
" # nn_model.train()\n",
" nn_model.to(self.ddpm.device)\n",
" nn_model.eval()\n",
"\n",
" # self.ema_model = ContextUnet(n_param=config.n_param, image_size=config.HII_DIM, dim=config.dim, stride=config.stride).to(config.device)\n",
" # self.ema_model.load_state_dict(torch.load(os.path.join(config.output_dir, f\"{config.resume}\"))['ema_unet_state_dict'])\n",
" # print(f\"resumed ema_model from {config.resume}\")\n",
"\n",
" with torch.no_grad():\n",
" x_last, x_entire = self.ddpm.sample(\n",
" nn_model=nn_model, \n",
" params=params.to(self.config.device), \n",
" device=self.config.device, \n",
" guide_w=self.config.guide_w\n",
" )\n",
"\n",
" # np.save(os.path.join(self.config.output_dir, f\"{self.config.run_name}{'ema' if ema else ''}.npy\"), x_last)\n",
" np.save(os.path.join(self.config.output_dir, f\"Tvir{params_backup[0]}-zeta{params_backup[1]}-N{self.config.num_image}{'ema' if ema else ''}.npy\"), x_last)\n",
"\n",
" if entire:\n",
" np.save(os.path.join(self.config.output_dir, f\"Tvir{params_backup[0]}-zeta{params_backup[1]}-N{self.config.num_image}{'ema' if ema else ''}_entire.npy\"), x_last)\n",
"# print(\"device =\", config.device)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Traceback (most recent call last):\n",
" File \"<string>\", line 1, in <module>\n",
" File \"/storage/home/hcoda1/3/bxia34/.conda/envs/diffusers/lib/python3.9/multiprocessing/spawn.py\", line 116, in spawn_main\n",
" exitcode = _main(fd, parent_sentinel)\n",
" File \"/storage/home/hcoda1/3/bxia34/.conda/envs/diffusers/lib/python3.9/multiprocessing/spawn.py\", line 126, in _main\n",
" self = reduction.pickle.load(from_parent)\n",
"AttributeError: Can't get attribute 'single_main' on <module '__main__' (built-in)>\n"
]
},
{
"ename": "ProcessExitedException",
"evalue": "process 0 terminated with exit code 1",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mProcessExitedException\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[12], line 21\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m__name__\u001b[39m \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m__main__\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m 17\u001b[0m \u001b[39m# torch.multiprocessing.set_start_method(\"spawn\")\u001b[39;00m\n\u001b[1;32m 18\u001b[0m \u001b[39m# args = (config, nn_model, ddpm, optimizer, dataloader, lr_scheduler)\u001b[39;00m\n\u001b[1;32m 19\u001b[0m world_size \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\u001b[39m#torch.cuda.device_count()\u001b[39;00m\n\u001b[0;32m---> 21\u001b[0m mp\u001b[39m.\u001b[39;49mspawn(single_main, args\u001b[39m=\u001b[39;49m(world_size,), nprocs\u001b[39m=\u001b[39;49mworld_size)\n\u001b[1;32m 22\u001b[0m \u001b[39m# notebook_launcher(ddpm21cm.train, num_processes=1, mixed_precision='fp16')\u001b[39;00m\n",
"File \u001b[0;32m/usr/local/pace-apps/manual/packages/pytorch/1.12.0/lib/python3.9/site-packages/torch/multiprocessing/spawn.py:240\u001b[0m, in \u001b[0;36mspawn\u001b[0;34m(fn, args, nprocs, join, daemon, start_method)\u001b[0m\n\u001b[1;32m 236\u001b[0m msg \u001b[39m=\u001b[39m (\u001b[39m'\u001b[39m\u001b[39mThis method only supports start_method=spawn (got: \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m).\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m'\u001b[39m\n\u001b[1;32m 237\u001b[0m \u001b[39m'\u001b[39m\u001b[39mTo use a different start_method use:\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\\t\u001b[39;00m\u001b[39m\\t\u001b[39;00m\u001b[39m'\u001b[39m\n\u001b[1;32m 238\u001b[0m \u001b[39m'\u001b[39m\u001b[39m torch.multiprocessing.start_processes(...)\u001b[39m\u001b[39m'\u001b[39m \u001b[39m%\u001b[39m start_method)\n\u001b[1;32m 239\u001b[0m warnings\u001b[39m.\u001b[39mwarn(msg)\n\u001b[0;32m--> 240\u001b[0m \u001b[39mreturn\u001b[39;00m start_processes(fn, args, nprocs, join, daemon, start_method\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mspawn\u001b[39;49m\u001b[39m'\u001b[39;49m)\n",
"File \u001b[0;32m/usr/local/pace-apps/manual/packages/pytorch/1.12.0/lib/python3.9/site-packages/torch/multiprocessing/spawn.py:198\u001b[0m, in \u001b[0;36mstart_processes\u001b[0;34m(fn, args, nprocs, join, daemon, start_method)\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[39mreturn\u001b[39;00m context\n\u001b[1;32m 197\u001b[0m \u001b[39m# Loop on join until it returns True or raises an exception.\u001b[39;00m\n\u001b[0;32m--> 198\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mnot\u001b[39;00m context\u001b[39m.\u001b[39;49mjoin():\n\u001b[1;32m 199\u001b[0m \u001b[39mpass\u001b[39;00m\n",
"File \u001b[0;32m/usr/local/pace-apps/manual/packages/pytorch/1.12.0/lib/python3.9/site-packages/torch/multiprocessing/spawn.py:149\u001b[0m, in \u001b[0;36mProcessContext.join\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[39mraise\u001b[39;00m ProcessExitedException(\n\u001b[1;32m 141\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mprocess \u001b[39m\u001b[39m%d\u001b[39;00m\u001b[39m terminated with signal \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m\n\u001b[1;32m 142\u001b[0m (error_index, name),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 146\u001b[0m signal_name\u001b[39m=\u001b[39mname\n\u001b[1;32m 147\u001b[0m )\n\u001b[1;32m 148\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 149\u001b[0m \u001b[39mraise\u001b[39;00m ProcessExitedException(\n\u001b[1;32m 150\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mprocess \u001b[39m\u001b[39m%d\u001b[39;00m\u001b[39m terminated with exit code \u001b[39m\u001b[39m%d\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m\n\u001b[1;32m 151\u001b[0m (error_index, exitcode),\n\u001b[1;32m 152\u001b[0m error_index\u001b[39m=\u001b[39merror_index,\n\u001b[1;32m 153\u001b[0m error_pid\u001b[39m=\u001b[39mfailed_process\u001b[39m.\u001b[39mpid,\n\u001b[1;32m 154\u001b[0m exit_code\u001b[39m=\u001b[39mexitcode\n\u001b[1;32m 155\u001b[0m )\n\u001b[1;32m 157\u001b[0m original_trace \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39merror_queues[error_index]\u001b[39m.\u001b[39mget()\n\u001b[1;32m 158\u001b[0m msg \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\\n\u001b[39;00m\u001b[39m-- Process \u001b[39m\u001b[39m%d\u001b[39;00m\u001b[39m terminated with the following error:\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m error_index\n",
"\u001b[0;31mProcessExitedException\u001b[0m: process 0 terminated with exit code 1"
]
}
],
"source": [
"def single_main(rank, world_size):\n",
" config = TrainConfig()\n",
" ddp_setup(rank, world_size)\n",
" \n",
" num_image_list = [100]#[200]#[1600,3200,6400,12800,25600]\n",
" for i, num_image in enumerate(num_image_list):\n",
" config.num_image = num_image\n",
" # config.world_size = world_size\n",
" \n",
" ddpm21cm = DDPM21CM(config)\n",
" print(f\" num_image = {ddpm21cm.config.num_image} \".center(50, '-'))\n",
" print(f\"run_name = {ddpm21cm.config.run_name}\")\n",
" ddpm21cm.train()\n",
"\n",
" \n",
"if __name__ == \"__main__\":\n",
" # torch.multiprocessing.set_start_method(\"spawn\")\n",
" # args = (config, nn_model, ddpm, optimizer, dataloader, lr_scheduler)\n",
" world_size = 1#torch.cuda.device_count()\n",
"\n",
" mp.spawn(single_main, args=(world_size,), nprocs=world_size)\n",
" # notebook_launcher(ddpm21cm.train, num_processes=1, mixed_precision='fp16')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# torch.cuda.set_device(0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n",
"2\n",
"['__name__', '__doc__', '__package__', '__loader__', '__spec__', '__path__', '__file__', '__cached__', '__builtins__', '__annotations__', 'contextlib', 'os', 'torch', 'Device', 'traceback', 'warnings', 'threading', 'List', 'Optional', 'Tuple', 'Union', 'Any', '_utils', '_get_device_index', '_dummy_type', 'classproperty', 'graphs', 'CUDAGraph', 'graph_pool_handle', 'graph', 'make_graphed_callables', 'is_current_stream_capturing', 'streams', 'ExternalStream', 'Stream', 'Event', '_device', '_cudart', '_initialized', '_tls', '_initialization_lock', '_queued_calls', '_is_in_bad_fork', '_device_t', '_LazySeedTracker', '_lazy_seed_tracker', '_CudaDeviceProperties', 'has_magma', 'has_half', 'default_generators', 'is_available', 'is_bf16_supported', '_sleep', '_check_capability', '_check_cubins', 'is_initialized', '_lazy_call', 'DeferredCudaCallError', 'init', '_lazy_init', 'cudart', 'cudaStatus', 'CudaError', 'check_error', 'device', 'device_of', 'set_device', 'get_device_name', 'get_device_capability', 'get_device_properties', 'can_device_access_peer', 'StreamContext', 'stream', 'set_stream', 'device_count', 'get_arch_list', 'get_gencode_flags', 'current_device', 'synchronize', 'ipc_collect', 'current_stream', 'default_stream', 'current_blas_handle', 'set_sync_debug_mode', 'get_sync_debug_mode', 'memory_usage', 'utilization', 'memory', 'caching_allocator_alloc', 'caching_allocator_delete', 'set_per_process_memory_fraction', 'empty_cache', 'memory_stats', 'memory_stats_as_nested_dict', 'reset_accumulated_memory_stats', 'reset_peak_memory_stats', 'reset_max_memory_allocated', 'reset_max_memory_cached', 'memory_allocated', 'max_memory_allocated', 'memory_reserved', 'max_memory_reserved', 'memory_cached', 'max_memory_cached', 'memory_snapshot', 'memory_summary', 'list_gpu_processes', 'mem_get_info', 'random', 'get_rng_state', 'get_rng_state_all', 'set_rng_state', 'set_rng_state_all', 'manual_seed', 'manual_seed_all', 'seed', 'seed_all', 'initial_seed', '_lazy_new', '_CudaBase', 'ByteStorage', 'DoubleStorage', 'FloatStorage', 'HalfStorage', 'LongStorage', 'IntStorage', 'ShortStorage', 'CharStorage', 'BoolStorage', 'BFloat16Storage', 'ComplexDoubleStorage', 'ComplexFloatStorage', 'sparse', 'profiler', 'nvtx', 'amp', 'jiterator', 'ByteTensor', 'CharTensor', 'DoubleTensor', 'FloatTensor', 'IntTensor', 'LongTensor', 'ShortTensor', 'HalfTensor', 'BoolTensor', 'BFloat16Tensor', 'nccl', '_get_device_properties']\n"
]
}
],
"source": [
"print(torch.cuda.is_available())\n",
"print(torch.cuda.device_count())\n",
"print(torch.cuda.__dir__())"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n",
"<class 'torch.cuda.device'>\n",
"Quadro RTX 6000\n",
"0\n",
"(7, 5)\n",
"_CudaDeviceProperties(name='Quadro RTX 6000', major=7, minor=5, total_memory=24212MB, multi_processor_count=72)\n"
]
}
],
"source": [
"print(torch.cuda.is_initialized())\n",
"print(torch.cuda.device)\n",
"print(torch.cuda.get_device_name())\n",
"print(torch.cuda.current_device())\n",
"print(torch.cuda.get_device_capability())\n",
"print(torch.cuda.get_device_properties(torch.cuda.device))\n",
"# print('here')\n",
"# print(torch.cuda.memory_usage())\n",
"# print(torch.cuda.utilization())\n",
"# print(torch.cuda.memory())\n",
"# print('here')\n",
"# print(torch.cuda.memory_summary())"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sampling"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if __name__ == \"__main__\":\n",
" # num_image_list = [1600,3200,6400,12800,25600]\n",
" num_image_list = [1000]\n",
" # num_image_list = [3200,6400,12800,25600]\n",
" # args = (config, nn_model, ddpm, optimizer, dataloader, lr_scheduler)\n",
" repeat = 2\n",
" config = TrainConfig()\n",
" for i, num_image in enumerate(num_image_list):\n",
" config.num_image = num_image\n",
" ddpm21cm = DDPM21CM(config)\n",
"\n",
" ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor([4.4, 131.341]), repeat=repeat)\n",
"\n",
" # ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor((5.6, 19.037)), repeat=repeat)\n",
"\n",
" # ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor((4.699, 30)), repeat=repeat)\n",
"\n",
" # ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor((5.477, 200)), repeat=repeat)\n",
"\n",
" # ddpm21cm.sample(f\"./outputs/model_state-N{num_image}\", params=torch.tensor((4.8, 131.341)), repeat=repeat)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# ls -lth outputs | head"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def plot_grid(samples, c=None, row=1, col=2):\n",
" print(\"samples.shape =\", samples.shape)\n",
" for j in range(samples.shape[4]):\n",
" plt.figure(figsize = (12,6), dpi=400)\n",
" for i in range(len(samples)):\n",
" plt.subplot(row,col,i+1)\n",
" plt.imshow(samples[i,0,:,:,j], cmap='gray')#, vmin=-1, vmax=1)\n",
" plt.xticks([])\n",
" plt.yticks([])\n",
" # plt.suptitle(f\"ION_Tvir_MIN = {c[0][0]}, HII_EFF_FACTOR = {c[0][1]}\")\n",
" # plt.show()\n",
" # plt.suptitle('simulations')\n",
" plt.tight_layout()\n",
" plt.subplots_adjust(wspace=0, hspace=0)\n",
" plt.savefig(f\"test3D-{j:03d}.png\")\n",
" plt.close()\n",
" # plt.show()\n",
" \n",
"data = np.load(\"outputs/Tvir4.400000095367432-zeta131.34100341796875-N1000.npy\")\n",
"# print(data.shape)\n",
"plot_grid(data)\n",
"# plt.imshow(data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# config = TrainConfig()\n",
"# def plot(filename, row=4, col=6):\n",
"# samples = np.load(filename)\n",
"# params = filename.split('guide_w')[-1][:-4]\n",
"# print(\"plotting\", samples.shape, params)\n",
"# plt.figure(figsize = (8,8))\n",
"# for i in range(24):\n",
"# plt.subplot(row,col,i+1)\n",
"# plt.imshow(samples[i,0,:,:], cmap='gray')#, vmin=-1, vmax=1)\n",
"# plt.xticks([])\n",
"# plt.yticks([])\n",
"# # plt.show()\n",
"# plt.suptitle(params)\n",
"# plt.tight_layout()\n",
"# plt.subplots_adjust(wspace=0, hspace=0) \n",
"# plt.show()\n",
"# # plt.savefig('outputs/'+params+'.png')\n",
"# # plt.close()\n",
"# # plt.imshow(images[0,0])\n",
"# # plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# import torch\n",
"# print(torch.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# import torch\n",
"# import os\n",
"\n",
"# def compare_models(num_gpus):\n",
"# model_states = []\n",
" \n",
"# for gpu_id in range(num_gpus):\n",
"# filename = f\"outputs/model_state-N40-device{gpu_id}\"\n",
"# if os.path.exists(filename):\n",
"# state_dict = torch.load(filename, map_location='cpu')\n",
"# model_states.append(state_dict)\n",
"# print(filename)\n",
"# else:\n",
"# print(f\"File {filename} not found!\")\n",
"# return False\n",
" \n",
"# # Compare all model state_dicts\n",
"# print(\"len(model_states) =\", len(model_states))\n",
"# base_state = model_states[0]\n",
"# for state in model_states[1:]:\n",
"# for key in base_state.keys():\n",
"# # print(key, base_state[key], state[key])\n",
"# print(key)\n",
"# print(\"epoch\", base_state['epoch'], state['epoch'])\n",
"\n",
"# print(base_state['unet_state_dict'].keys())\n",
"# for key in base_state['unet_state_dict']:\n",
"# # print(key)\n",
"# if not torch.equal(base_state['unet_state_dict'][key], state['unet_state_dict'][key]):\n",
"# print(\"different\")\n",
"# return \n",
"# # else:\n",
"# print(\"exactly same\")\n",
"\n",
"# # if key == 'epoch':\n",
"# # print(base_state[key], state[key])\n",
"# # else:\n",
"# # print(base_state[key], state[key])\n",
"# # if not torch.equal(base_state[key], state[key]):\n",
"# # # if not (base_state[key] == state[key]):\n",
"# # print(f\"Mismatch found in parameter {key}\")\n",
"# # return False\n",
" \n",
"# # print(\"All models are identical!\")\n",
"# # return True\n",
"\n",
"# if __name__ == \"__main__\":\n",
"# # epoch_to_check = 0 # specify the epoch you want to check\n",
"# num_gpus = torch.cuda.device_count() # specify the number of GPUs used in training\n",
"# compare_models(num_gpus)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"test = np.random.normal(0,1,(800,1,64,64,512))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"12.5"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(test.itemsize*test.size) / 1024/1024/1024"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"del test"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.19"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
|