From 85c82ab569835706b5d687f4ecbec303fa888f76 Mon Sep 17 00:00:00 2001 From: wangwl Date: Tue, 23 Dec 2025 01:45:54 +0000 Subject: [PATCH 1/3] add SWL --- .../SWL/WSL-Images/CODE_OF_CONDUCT.md | 5 + .../SWL/WSL-Images/CONTRIBUTING.md | 35 + .../Classification/SWL/WSL-Images/LICENSE | 399 ++++++++++ .../Classification/SWL/WSL-Images/README.md | 38 + .../Classification/SWL/WSL-Images/hubconf.py | 78 ++ .../build-in/Classification/SWL/coverage.txt | 3 + PyTorch/build-in/Classification/SWL/run | 1 + .../Classification/SWL/weloTrainStep.py | 692 ++++++++++++++++++ PyTorch/build-in/Classification/SWL/wsl.py | 253 +++++++ .../build-in/Classification/SWL/wsl_loss.jpg | Bin 0 -> 35863 bytes .../build-in/Classification/SWL/wsl_loss.txt | 29 + 11 files changed, 1533 insertions(+) create mode 100644 PyTorch/build-in/Classification/SWL/WSL-Images/CODE_OF_CONDUCT.md create mode 100644 PyTorch/build-in/Classification/SWL/WSL-Images/CONTRIBUTING.md create mode 100644 PyTorch/build-in/Classification/SWL/WSL-Images/LICENSE create mode 100644 PyTorch/build-in/Classification/SWL/WSL-Images/README.md create mode 100644 PyTorch/build-in/Classification/SWL/WSL-Images/hubconf.py create mode 100644 PyTorch/build-in/Classification/SWL/coverage.txt create mode 100644 PyTorch/build-in/Classification/SWL/run create mode 100644 PyTorch/build-in/Classification/SWL/weloTrainStep.py create mode 100644 PyTorch/build-in/Classification/SWL/wsl.py create mode 100644 PyTorch/build-in/Classification/SWL/wsl_loss.jpg create mode 100644 PyTorch/build-in/Classification/SWL/wsl_loss.txt diff --git a/PyTorch/build-in/Classification/SWL/WSL-Images/CODE_OF_CONDUCT.md b/PyTorch/build-in/Classification/SWL/WSL-Images/CODE_OF_CONDUCT.md new file mode 100644 index 000000000..0f7ad8bfc --- /dev/null +++ b/PyTorch/build-in/Classification/SWL/WSL-Images/CODE_OF_CONDUCT.md @@ -0,0 +1,5 @@ +# Code of Conduct + +Facebook has adopted a Code of Conduct that we expect project participants to adhere to. +Please read the [full text](https://code.fb.com/codeofconduct/) +so that you can understand what actions will and will not be tolerated. diff --git a/PyTorch/build-in/Classification/SWL/WSL-Images/CONTRIBUTING.md b/PyTorch/build-in/Classification/SWL/WSL-Images/CONTRIBUTING.md new file mode 100644 index 000000000..47c825fbb --- /dev/null +++ b/PyTorch/build-in/Classification/SWL/WSL-Images/CONTRIBUTING.md @@ -0,0 +1,35 @@ +# Contributing to WSL-Images +We want to make contributing to this project as easy and transparent as possible. + +## Our Development Process +Minor changes and improvements will be released on an ongoing basis. Larger changes (e.g., changesets implementing a new benchmark) will be released on a more periodic basis. + +## Pull Requests +We actively welcome your pull requests. + +1. Fork the repo and create your branch from `master`. +2. If you've added code that should be tested, add tests. +3. If you've changed APIs, update the documentation. +4. Ensure the test suite passes. +5. Make sure your code lints. +6. If you haven't already, complete the Contributor License Agreement ("CLA"). + +## Contributor License Agreement ("CLA") +In order to accept your pull request, we need you to submit a CLA. You only need +to do this once to work on any of Facebook's open source projects. + +Complete your CLA here: + +## Issues +We use GitHub issues to track public bugs. Please ensure your description is +clear and has sufficient instructions to be able to reproduce the issue. Follow +the template provided [here](.github/issue_template.md) when opening issues. + +## Coding Style +* 4 spaces for indentation rather than tabs +* 80 character line length +* 80 character line length + +## License +By contributing to WSL-Images, you agree that your contributions will be licensed +under the LICENSE file in the root directory of this source tree. diff --git a/PyTorch/build-in/Classification/SWL/WSL-Images/LICENSE b/PyTorch/build-in/Classification/SWL/WSL-Images/LICENSE new file mode 100644 index 000000000..f2915d90a --- /dev/null +++ b/PyTorch/build-in/Classification/SWL/WSL-Images/LICENSE @@ -0,0 +1,399 @@ +Attribution-NonCommercial 4.0 International + +======================================================================= + +Creative Commons Corporation ("Creative Commons") is not a law firm and +does not provide legal services or legal advice. Distribution of +Creative Commons public licenses does not create a lawyer-client or +other relationship. 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For +the avoidance of doubt, this paragraph does not form part of the +public licenses. + +Creative Commons may be contacted at creativecommons.org. diff --git a/PyTorch/build-in/Classification/SWL/WSL-Images/README.md b/PyTorch/build-in/Classification/SWL/WSL-Images/README.md new file mode 100644 index 000000000..a43144e08 --- /dev/null +++ b/PyTorch/build-in/Classification/SWL/WSL-Images/README.md @@ -0,0 +1,38 @@ +## WSL-Images + +This project provides models pre-trained in weakly-supervised fashion on **940 million** public images with 1.5K hashtags matching with 1000 ImageNet1K synsets, followed by fine-tuning on ImageNet1K dataset. Please refer to "Exploring the Limits of Weakly Supervised Pretraining" (https://arxiv.org/abs/1805.00932) presented at ECCV 2018 for the details of model training. + +We are providing 4 models with different capacities. + +| Model | #Parameters | FLOPS | Top-1 Acc. | Top-5 Acc. | +| ------------------ | :---------: | :---: | :--------: | :--------: | +| ResNeXt-101 32x8d | 88M | 16B | 82.2 | 96.4 | +| ResNeXt-101 32x16d | 193M | 36B | 84.2 | 97.2 | +| ResNeXt-101 32x32d | 466M | 87B | 85.1 | 97.5 | +| ResNeXt-101 32x48d | 829M | 153B | 85.4 | 97.6 | + +Our models significantly improve the training accuracy on ImageNet compared to training from scratch. **We achieve state-of-the-art accuracy of 85.4% on ImageNet with our ResNext-101 32x48d model.** + +## Loading models with torch.hub +The models are available with [torch.hub](https://pytorch.org/docs/stable/hub.html). +As an example, to load the ResNext-101 32x16d model, simply run: + +``` +model = torch.hub.load('facebookresearch/WSL-Images', 'resnext101_32x16d_wsl') +``` +Please refer to [torch.hub](https://pytorch.org/docs/stable/hub.html) to see a full example of using the model to classify an image. + +## Citing WSL-Images + +If you use the WSL-Images models, please cite the following publication. +``` +@inproceedings{wslimageseccv2018, + title={Exploring the Limits of Weakly Supervised Pretraining}, + author={Dhruv Kumar Mahajan and Ross B. Girshick and Vignesh Ramanathan and Kaiming He and Manohar Paluri and Yixuan Li and Ashwin Bharambe and Laurens van der Maaten}, + booktitle={ECCV}, + year={2018} +} +``` + +## License +WSL-Images models are released under the CC-BY-NC 4.0 license. See [LICENSE](LICENSE) for additional details. diff --git a/PyTorch/build-in/Classification/SWL/WSL-Images/hubconf.py b/PyTorch/build-in/Classification/SWL/WSL-Images/hubconf.py new file mode 100644 index 000000000..d28f8adaa --- /dev/null +++ b/PyTorch/build-in/Classification/SWL/WSL-Images/hubconf.py @@ -0,0 +1,78 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +# Optional list of dependencies required by the package +dependencies = ['torch', 'torchvision'] + +from torch.hub import load_state_dict_from_url +from torchvision.models.resnet import ResNet, Bottleneck + + +model_urls = { + 'resnext101_32x8d': 'https://download.pytorch.org/models/ig_resnext101_32x8-c38310e5.pth', + 'resnext101_32x16d': 'https://download.pytorch.org/models/ig_resnext101_32x16-c6f796b0.pth', + 'resnext101_32x32d': 'https://download.pytorch.org/models/ig_resnext101_32x32-e4b90b00.pth', + 'resnext101_32x48d': 'https://download.pytorch.org/models/ig_resnext101_32x48-3e41cc8a.pth', +} + + +def _resnext(arch, block, layers, pretrained, progress, **kwargs): + model = ResNet(block, layers, **kwargs) + state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) + model.load_state_dict(state_dict) + return model + + +def resnext101_32x8d_wsl(progress=True, **kwargs): + """Constructs a ResNeXt-101 32x8 model pre-trained on weakly-supervised data + and finetuned on ImageNet from Figure 5 in + `"Exploring the Limits of Weakly Supervised Pretraining" `_ + + Args: + progress (bool): If True, displays a progress bar of the download to stderr. + """ + kwargs['groups'] = 32 + kwargs['width_per_group'] = 8 + return _resnext('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], True, progress, **kwargs) + + +def resnext101_32x16d_wsl(progress=True, **kwargs): + """Constructs a ResNeXt-101 32x16 model pre-trained on weakly-supervised data + and finetuned on ImageNet from Figure 5 in + `"Exploring the Limits of Weakly Supervised Pretraining" `_ + + Args: + progress (bool): If True, displays a progress bar of the download to stderr. + """ + kwargs['groups'] = 32 + kwargs['width_per_group'] = 16 + return _resnext('resnext101_32x16d', Bottleneck, [3, 4, 23, 3], True, progress, **kwargs) + + +def resnext101_32x32d_wsl(progress=True, **kwargs): + """Constructs a ResNeXt-101 32x32 model pre-trained on weakly-supervised data + and finetuned on ImageNet from Figure 5 in + `"Exploring the Limits of Weakly Supervised Pretraining" `_ + + Args: + progress (bool): If True, displays a progress bar of the download to stderr. + """ + kwargs['groups'] = 32 + kwargs['width_per_group'] = 32 + return _resnext('resnext101_32x32d', Bottleneck, [3, 4, 23, 3], True, progress, **kwargs) + + +def resnext101_32x48d_wsl(progress=True, **kwargs): + """Constructs a ResNeXt-101 32x48 model pre-trained on weakly-supervised data + and finetuned on ImageNet from Figure 5 in + `"Exploring the Limits of Weakly Supervised Pretraining" `_ + + Args: + progress (bool): If True, displays a progress bar of the download to stderr. + """ + kwargs['groups'] = 32 + kwargs['width_per_group'] = 48 + return _resnext('resnext101_32x48d', Bottleneck, [3, 4, 23, 3], True, progress, **kwargs) diff --git a/PyTorch/build-in/Classification/SWL/coverage.txt b/PyTorch/build-in/Classification/SWL/coverage.txt new file mode 100644 index 000000000..3aa0a123b --- /dev/null +++ b/PyTorch/build-in/Classification/SWL/coverage.txt @@ -0,0 +1,3 @@ +all api: ['_amp_foreach_non_finite_check_and_unscale_', '_amp_update_scale_', '_copy_from', '_has_compatible_shallow_copy_type', '_local_scalar_dense', '_log_softmax', '_log_softmax_backward_data', '_pin_memory', '_reshape_alias', 'add_', 'addmm', 'as_strided', 'as_strided_', 'convolution', 'convolution_backward', 'copy_stride', 'div', 'eq', 'fill_', 'fused_sgd', 'is_pinned', 'linear', 'max_pool2d', 'maxpool2d_backward', 'maxpool2d_forward', 'mean', 'mm', 'mul', 'mul_', 'native_batch_norm', 'native_batch_norm_backward', 'nll_loss_backward', 'nll_loss_forward', 'reciprocal', 'relu_', 'set_', 'sum', 'threshold_backward', 'topk_out', 'view', 'zero_'], total: 41 +fallback op: [], total: 0 +coverage rate: 100.00% diff --git a/PyTorch/build-in/Classification/SWL/run b/PyTorch/build-in/Classification/SWL/run new file mode 100644 index 000000000..fbac492b0 --- /dev/null +++ b/PyTorch/build-in/Classification/SWL/run @@ -0,0 +1 @@ +bash ../sdaaTest.sh wsl 8 0 diff --git a/PyTorch/build-in/Classification/SWL/weloTrainStep.py b/PyTorch/build-in/Classification/SWL/weloTrainStep.py new file mode 100644 index 000000000..13297c11b --- /dev/null +++ b/PyTorch/build-in/Classification/SWL/weloTrainStep.py @@ -0,0 +1,692 @@ +#!/usr/bin/env python3 +# coding: utf-8 + +import os +import random +import sys +import time +import json +import argparse +from collections import OrderedDict +from pathlib import Path +import numpy as np +import pandas as pd +from tqdm import tqdm +import importlib + +os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" # 强烈推荐在 shell/最顶端设置 +os.environ["PYTHONHASHSEED"] = "12345" +os.environ["OMP_NUM_THREADS"] = "1" +os.environ["MKL_NUM_THREADS"] = "1" + +def ensure_cublas_workspace(config=":4096:8"): + """ + 尝试为 cuBLAS 设置可复现 workspace。强烈建议在主脚本入口处（import torch 之前） + 通过 export 设置该 env。此函数会在运行时设置，但如果 torch 已经被 import， + 则可能为时已晚——函数会打印提醒。 + """ + already = os.environ.get("CUBLAS_WORKSPACE_CONFIG") + if already: + print(f"[seed_utils] CUBLAS_WORKSPACE_CONFIG 已存在：{already}") + else: + os.environ["CUBLAS_WORKSPACE_CONFIG"] = config + print(f"[seed_utils] 已设置 CUBLAS_WORKSPACE_CONFIG={config} （注意：请在 import torch 前设置以保证生效）") + +def set_global_seed(seed: int = 42, set_threads: bool = True): + """ + 统一随机性设置。注意：若希望完全发挥效果，请在主脚本入口（import torch 之前） + 先调用 ensure_cublas_workspace(...) 或在 shell 中 export CUBLAS_WORKSPACE_CONFIG。 + """ + ensure_cublas_workspace() # 会设置 env 并提醒 + os.environ["PYTHONHASHSEED"] = str(seed) + + if set_threads: + os.environ["OMP_NUM_THREADS"] = "1" + os.environ["MKL_NUM_THREADS"] = "1" + + random.seed(seed) + np.random.seed(seed) + + # 现在导入 torch（晚导入以便前面 env 生效） + import torch + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + # 强制确定性（如果存在不确定性算子，PyTorch 会报错并提示） + try: + torch.use_deterministic_algorithms(True) + except Exception as e: + print("[seed_utils] 设置 deterministic 模式时出错：", e) + print("[seed_utils] 请确认 CUBLAS_WORKSPACE_CONFIG 已在 import torch 之前设置。") + + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + if set_threads: + torch.set_num_threads(1) + torch.set_num_interop_threads(1) + + print(f"[seed_utils] 全局 seed 已设置为 {seed}") + +set_global_seed(2025) + +""" +通用训练模版（优先从本地导入 Model -> 支持 DDP / 单卡，AMP，resume，日志，checkpoint） +保存为 train_template_localmodel.py +""" +import torch +import torch.nn as nn +import torch.optim as optim +import torch.backends.cudnn as cudnn +import torchvision.transforms as transforms +import torchvision.datasets as datasets +import torchvision.models as tv_models + +import torch.distributed as dist +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.data import DataLoader +from torch.utils.data.distributed import DistributedSampler + +from torch.sdaa import amp +# from torch.cuda import amp + + +# ---------------------------- +# Helper utilities (self-contained) +# ---------------------------- +class AverageMeter(object): + def __init__(self, name='Meter', fmt=':.4f'): + self.name = name + self.fmt = fmt + self.reset() + def reset(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / max(1, self.count) + def __str__(self): + fmtstr = '{name} {val' + self.fmt + '} (avg {avg' + self.fmt + '})' + return fmtstr.format(name=self.name, val=self.val, avg=self.avg) + +def accuracy(output, target, topk=(1,)): + """Computes the precision@k for the specified values of k + 返回一个 list，每个元素是 tensor（百分比形式） + """ + with torch.no_grad(): + maxk = max(topk) + batch_size = target.size(0) + + # output: (N, C) -> pred: (maxk, N) + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() # (maxk, N) + correct = pred.eq(target.view(1, -1).expand_as(pred)) # (maxk, N) bool + + res = [] + for k in topk: + # 把前 k 行展平后求和（返回 0-dim tensor），随后换算为百分比 + correct_k = correct[:k].reshape(-1).float().sum() # 注意：不传 keepdim + # 乘以 100.0 / batch_size，保持返回 tensor（和之前代码兼容） + res.append(correct_k.mul_(100.0 / batch_size)) + return res + +def save_checkpoint(state, is_best, save_dir, filename='checkpoint.pth'): + save_path = os.path.join(save_dir, filename) + torch.save(state, save_path) + if is_best: + best_path = os.path.join(save_dir, 'model_best.pth') + torch.save(state, best_path) + +def set_seed(seed, deterministic=False): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + if deterministic: + cudnn.deterministic = True + cudnn.benchmark = False + else: + cudnn.deterministic = False + cudnn.benchmark = True + +# ---------------------------- +# Argument parser +# ---------------------------- +def parse_args(): + parser = argparse.ArgumentParser(description='Generic PyTorch training template (DDP/AMP) with LocalModel priority') + parser.add_argument('--name', default='run', type=str, help='experiment name (log/checkpoints dir)') + parser.add_argument('--seed', default=42, type=int, help='random seed') + parser.add_argument('--arch', default='None', type=str, help='model name') + parser.add_argument('--deterministic', action='store_true', help='set cudnn deterministic (may be slower)') + parser.add_argument('--dataset', default='cifar10', choices=['cifar10','cifar100','imagenet','custom'], help='which dataset') + parser.add_argument('--datapath', default='./data', type=str, help='dataset root / imagenet root / custom root') + parser.add_argument('--imagenet_dir', default='./imagenet', type=str, help='if dataset=imagenet, path to imagenet root') + parser.add_argument('--custom_eval_dir', default=None, help='if dataset=custom, provide val dir') + parser.add_argument('--num_workers', default=4, type=int, help='dataloader workers per process') + parser.add_argument('--epochs', default=200, type=int) + parser.add_argument('--steps', default=0, type=int, help='max steps to run (if >0, training will stop when global_step reaches this).') + parser.add_argument('--batch_size', default=128, type=int) + parser.add_argument('--model_name', default='resnet18', help='torchvision model name or python path e.g. mypkg.mymodule.Model (used if no local Model)') + parser.add_argument('--num_classes', default=None, type=int, help='override num classes (auto-detect for common sets)') + parser.add_argument('--pretrained', action='store_true', help='use torchvision pretrained weights when available') + parser.add_argument('--optimizer', default='sgd', choices=['sgd','adam','adamw'], help='optimizer') + parser.add_argument('--lr', '--learning_rate', default=0.1, type=float) + parser.add_argument('--momentum', default=0.9, type=float) + parser.add_argument('--weight_decay', default=5e-4, type=float) + parser.add_argument('--nesterov', action='store_true') + parser.add_argument('--scheduler', default='multistep', choices=['multistep','step','cosine','none'], help='lr scheduler') + parser.add_argument('--milestones', default='100,150', type=str, help='milestones for multistep (comma sep)') + parser.add_argument('--step_size', default=30, type=int, help='step size for StepLR or cosine max epochs') + parser.add_argument('--gamma', default=0.1, type=float) + parser.add_argument('--scheduler_step_per_batch', action='store_true', help='call scheduler.step() per batch (for some schedulers)') + parser.add_argument('--resume', default='', type=str, help='path to checkpoint to resume from') + parser.add_argument('--start_epoch', default=0, type=int) + parser.add_argument('--print_freq', default=100, type=int) + parser.add_argument('--save_freq', default=10, type=int, help='save checkpoint every N epochs (rank0 only)') + parser.add_argument('--amp', action='store_true', default = True,help='use automatic mixed precision (AMP)') + parser.add_argument('--grad_accum_steps', default=1, type=int, help='gradient accumulation steps') + parser.add_argument('--local_rank', default=None, type=int, help='local rank passed by torchrun (if any). Use -1 or None for non-distributed') + parser.add_argument('--cutmix_prob', default=0.0, type=float) + parser.add_argument('--beta', default=1.0, type=float) + parser.add_argument('--seed_sampler', default=False, action='store_true', help='set sampler epoch seeds to make deterministic distributed shuffling') + args = parser.parse_args() + args.milestones = [int(x) for x in args.milestones.split(',')] if args.milestones else [] + return args + +# ---------------------------- +# build model (优先 LocalModel) +# ---------------------------- +def build_model_with_local_priority(args, device=None): + """ + 用参数 args.arch 作为模块名导入 Model() + 如果模块不存在或没有 Model 类，则报错停止。 + """ + try: + # 动态导入模块，比如 args.arch = "rexnet" + mod = importlib.import_module(args.arch) + Model = getattr(mod, "Model") # 从模块中获取 Model 类 + except Exception as e: + raise RuntimeError( + f"无法导入模型模块 '{args.arch}' 或未找到类 Model。" + f"\n错误信息：{e}" + ) + + # 解析数据集类别数 + if args.dataset == 'cifar10': + num_classes = 10 + elif args.dataset == 'cifar100': + num_classes = 100 + else: + print(f"[ERROR] 不支持的数据集类型：{args.dataset}，无法确定类别数。程序终止。") + sys.exit(1) + + + # 实例化 + try: + model = Model(num_classes) + except Exception as e: + raise RuntimeError( + f"Model() 实例化失败，请检查模型构造函数。\n错误信息：{e}" + ) + + return model + +# ---------------------------- +# Data loader factory +# ---------------------------- +def build_dataloaders(args, rank, world_size): + if args.dataset == 'cifar10' or args.dataset == 'cifar100': + mean = (0.4914, 0.4822, 0.4465) + std = (0.2470, 0.2435, 0.2616) if args.dataset == 'cifar10' else (0.2023, 0.1994, 0.2010) + # train_transform = transforms.Compose([ + # transforms.RandomCrop(32, padding=4), + # transforms.RandomHorizontalFlip(), + # transforms.ToTensor(), + # transforms.Normalize(mean, std), + # ]) + # test_transform = transforms.Compose([ + # transforms.ToTensor(), + # transforms.Normalize(mean, std), + # ]) + + train_transform = transforms.Compose([ # 2025/12/3 从visformer模型开始 + transforms.Resize(256), # 先放大到 256 + transforms.RandomCrop(224), # 再随机裁剪为 224（更符合 ImageNet 风格增强） + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize(mean, std), + ]) + test_transform = transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + transforms.Normalize(mean, std), + ]) + root = args.datapath + if args.dataset == 'cifar10': + train_set = datasets.CIFAR10(root=root, train=True, download=False, transform=train_transform) + val_set = datasets.CIFAR10(root=root, train=False, download=False, transform=test_transform) + num_classes = 10 + else: + train_set = datasets.CIFAR100(root=root, train=True, download=False, transform=train_transform) + val_set = datasets.CIFAR100(root=root, train=False, download=False, transform=test_transform) + num_classes = 100 + + elif args.dataset == 'imagenet': + train_dir = os.path.join(args.imagenet_dir, 'train') + val_dir = os.path.join(args.imagenet_dir, 'val') + train_transform = transforms.Compose([ + transforms.RandomResizedCrop(224), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225)), + ]) + test_transform = transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225)), + ]) + train_set = datasets.ImageFolder(train_dir, train_transform) + val_set = datasets.ImageFolder(val_dir, test_transform) + num_classes = args.num_classes or 1000 + + elif args.dataset == 'custom': + train_dir = os.path.join(args.datapath, 'train') + val_dir = args.custom_eval_dir or os.path.join(args.datapath, 'val') + train_transform = transforms.Compose([ + transforms.RandomResizedCrop(224), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + ]) + test_transform = transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + ]) + train_set = datasets.ImageFolder(train_dir, train_transform) + val_set = datasets.ImageFolder(val_dir, test_transform) + num_classes = len(train_set.classes) + else: + raise ValueError("Unknown dataset") + + if dist.is_initialized() and world_size > 1: + train_sampler = DistributedSampler(train_set, num_replicas=world_size, rank=rank, shuffle=True) + else: + train_sampler = None + + train_loader = DataLoader(train_set, + batch_size=args.batch_size, + shuffle=(train_sampler is None), + num_workers=args.num_workers, + pin_memory=True, + sampler=train_sampler, + drop_last=False) + val_loader = DataLoader(val_set, + batch_size=args.batch_size, + shuffle=False, + num_workers=args.num_workers, + pin_memory=True) + + return train_loader, val_loader, num_classes, train_sampler + +# ---------------------------- +# Train & validate +# ---------------------------- +def train_one_epoch(args, epoch, model, criterion, optimizer, train_loader, device, scaler, scheduler=None, train_sampler=None, global_step_start=0, max_global_steps=None): + """ + 现在支持：若 max_global_steps 非 None，则当 global_step 达到该值时提前退出 + 返回: epoch_summary_dict, step_logs_list, global_step_end + step_logs_list: list of dicts with per-step info (for logging to CSV if需要) + """ + batch_time = AverageMeter('Time') + data_time = AverageMeter('Data') + losses = AverageMeter('Loss') + top1 = AverageMeter('Acc@1') + top5 = AverageMeter('Acc@5') + + model.train() + end = time.time() + optimizer.zero_grad() + + iters = len(train_loader) + step_logs = [] + global_step = global_step_start + + for i, (images, targets) in enumerate(train_loader): + # check global steps limit + if (max_global_steps is not None) and (global_step >= max_global_steps): + break + + data_time.update(time.time() - end) + images = images.to(device, non_blocking=True) + targets = targets.to(device, non_blocking=True) + + if args.amp: + with amp.autocast(): + outputs = model(images) + loss = criterion(outputs, targets) / args.grad_accum_steps + else: + outputs = model(images) + loss = criterion(outputs, targets) / args.grad_accum_steps + + if args.amp: + scaler.scale(loss).backward() + else: + loss.backward() + + # 每当累积步满足 grad_accum_steps 就 step + if (i + 1) % args.grad_accum_steps == 0: + if args.amp: + scaler.step(optimizer) + scaler.update() + else: + optimizer.step() + optimizer.zero_grad() + if scheduler is not None and args.scheduler_step_per_batch: + scheduler.step() + + with torch.no_grad(): + acc1, acc5 = accuracy(outputs, targets, topk=(1,5)) + losses.update(loss.item() * args.grad_accum_steps, images.size(0)) + top1.update(acc1.item(), images.size(0)) + top5.update(acc5.item(), images.size(0)) + + batch_time.update(time.time() - end) + end = time.time() + + # increment global step AFTER processing this batch + global_step += 1 + + # per-step print (controlled by print_freq) + if ((global_step % args.print_freq == 0) or (i == iters - 1)) and ((dist.get_rank() if dist.is_initialized() else 0) == 0): + lr = optimizer.param_groups[0]['lr'] + print(f"Epoch[{epoch}]:step[{i+1}/{iters}] step_train_loss {losses.val:.4f} acc1 {top1.val:.2f} acc5 {top5.val:.2f}") + + # collect per-step log + step_logs.append({ + 'epoch': epoch, + 'batch_idx': i, + 'global_step': global_step, + 'lr': optimizer.param_groups[0]['lr'], + 'loss': losses.val, + 'loss_avg': losses.avg, + 'acc1': top1.val, + 'acc1_avg': top1.avg, + 'acc5': top5.val, + 'acc5_avg': top5.avg, + 'time': batch_time.val + }) + + # if reached max_global_steps inside epoch, break (handled at loop start next iter) + if (max_global_steps is not None) and (global_step >= max_global_steps): + if (dist.get_rank() if dist.is_initialized() else 0) == 0: + print(f"[Info] 达到 max_global_steps={max_global_steps}，将在 epoch 内提前停止。") + break + + # --- flush remaining grads if needed (handle gradient accumulation leftovers) --- + processed_batches = global_step - global_step_start # 实际处理的 batch 数 + if args.grad_accum_steps > 1 and (processed_batches % args.grad_accum_steps) != 0: + # only step if there are gradients + grads_present = any((p.grad is not None and p.requires_grad) for p in model.parameters()) + if grads_present: + if args.amp: + try: + scaler.step(optimizer) + scaler.update() + except Exception as e: + # 防御性：若 scaler.step 因某些原因失败，尝试普通 step（只在极端情况下） + print("[Warning] scaler.step 失败，尝试普通 optimizer.step():", e) + optimizer.step() + else: + optimizer.step() + optimizer.zero_grad() + if scheduler is not None and args.scheduler_step_per_batch: + scheduler.step() + if (dist.get_rank() if dist.is_initialized() else 0) == 0: + print(f"[Info] flushed remaining gradients after early stop (processed_batches={processed_batches}, grad_accum={args.grad_accum_steps}).") + + if scheduler is not None and not args.scheduler_step_per_batch: + scheduler.step() + + return OrderedDict([('loss', losses.avg), ('acc1', top1.avg), ('acc5', top5.avg)]), step_logs, global_step + +def validate(args, model, val_loader, criterion, device, max_batches=None): + """ + Validate on the val_loader. + If max_batches is not None, only process up to that many batches (useful for quick checks). + Returns an OrderedDict with loss/acc1/acc5 (averaged over processed samples). + """ + losses = AverageMeter('Loss') + top1 = AverageMeter('Acc@1') + top5 = AverageMeter('Acc@5') + + model.eval() + processed_batches = 0 + processed_samples = 0 + with torch.no_grad(): + for i, (images, targets) in enumerate(tqdm(val_loader)): + images = images.to(device, non_blocking=True) + targets = targets.to(device, non_blocking=True) + outputs = model(images) + loss = criterion(outputs, targets) + acc1, acc5 = accuracy(outputs, targets, topk=(1,5)) + batch_n = images.size(0) + losses.update(loss.item(), batch_n) + top1.update(acc1.item(), batch_n) + top5.update(acc5.item(), batch_n) + + processed_batches += 1 + processed_samples += batch_n + + if (max_batches is not None) and (processed_batches >= max_batches): + break + + # 如果没处理任何样本，避免除0（不太可能，但防御性） + if processed_samples == 0: + return OrderedDict([('loss', 0.0), ('acc1', 0.0), ('acc5', 0.0)]) + return OrderedDict([('loss', losses.avg), ('acc1', top1.avg), ('acc5', top5.avg)]) + +# ---------------------------- +# Main +# ---------------------------- +def main(): + args = parse_args() + + # handle local_rank from env if not provided + local_rank_env = os.environ.get('LOCAL_RANK', None) + if args.local_rank is None and local_rank_env is not None: + args.local_rank = int(local_rank_env) + + distributed = (args.local_rank is not None and args.local_rank != -1) + if distributed: + dist.init_process_group(backend='nccl', init_method='env://') + rank = dist.get_rank() + world_size = dist.get_world_size() + else: + rank = 0 + world_size = 1 + + if distributed: + torch.cuda.set_device(args.local_rank) + device = torch.device('cuda', args.local_rank) + else: + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + set_seed(args.seed + (rank if distributed else 0), deterministic=args.deterministic) + + save_dir = os.path.join('models', args.name) + if rank == 0: + os.makedirs(save_dir, exist_ok=True) + with open(os.path.join(save_dir, 'args.json'), 'w') as f: + json.dump(vars(args), f, indent=2) + if distributed: + dist.barrier() + + train_loader, val_loader, auto_num_classes, train_sampler = build_dataloaders(args, rank, world_size) + if args.num_classes is None: + args.num_classes = auto_num_classes + + # 使用本地 Model 优先（LocalModel 已在文件顶部尝试导入） + model = build_model_with_local_priority(args, device) + model.to(device) + + if distributed: + model = DDP(model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True) + + criterion = nn.CrossEntropyLoss().to(device) + params = [p for p in model.parameters() if p.requires_grad] + if args.optimizer == 'sgd': + optimizer = optim.SGD(params, lr=args.lr, momentum=args.momentum, + weight_decay=args.weight_decay, nesterov=args.nesterov) + elif args.optimizer == 'adam': + optimizer = optim.Adam(params, lr=args.lr, weight_decay=args.weight_decay) + elif args.optimizer == 'adamw': + optimizer = optim.AdamW(params, lr=args.lr, weight_decay=args.weight_decay) + else: + raise ValueError('Unknown optimizer') + + scheduler = None + if args.scheduler == 'multistep': + scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=args.milestones, gamma=args.gamma) + elif args.scheduler == 'step': + scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=args.step_size, gamma=args.gamma) + elif args.scheduler == 'cosine': + scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs) + elif args.scheduler == 'none': + scheduler = None + + scaler = amp.GradScaler() if args.amp else None + + start_epoch = args.start_epoch + best_acc = 0.0 + if args.resume: + if os.path.isfile(args.resume): + ckpt = torch.load(args.resume, map_location='cpu') + model_state = ckpt.get('state_dict', ckpt) + if isinstance(model, DDP): + model.module.load_state_dict(model_state) + else: + model.load_state_dict(model_state) + if 'optimizer' in ckpt: + optimizer.load_state_dict(ckpt['optimizer']) + start_epoch = ckpt.get('epoch', start_epoch) + best_acc = ckpt.get('best_acc', best_acc) + print(f"=> resumed from {args.resume}, start_epoch={start_epoch}") + else: + print(f"=> resume path {args.resume} not found") + + log_columns = ['epoch', 'lr', 'loss', 'acc1', 'acc5', 'val_loss', 'val_acc1', 'val_acc5'] + log_df = pd.DataFrame(columns=log_columns) + # step-level log + step_log_columns = ['epoch', 'batch_idx', 'global_step', 'lr', 'loss', 'loss_avg', 'acc1', 'acc1_avg', 'acc5', 'acc5_avg', 'time'] + step_log_df = pd.DataFrame(columns=step_log_columns) + + total_epochs = args.epochs + # global_step计数器（训练过程中跨epoch持续） + global_step = 0 + + epoch = start_epoch + # loop until either epoch criteria or step criteria met + while True: + if train_sampler is not None: + if args.seed_sampler: + train_sampler.set_epoch(epoch + args.seed) + else: + train_sampler.set_epoch(epoch) + + if rank == 0: + print(f"==== Epoch {epoch}/{total_epochs - 1} ====") + + # 如果传入了 args.steps (>0)，则把剩余允许的 step 数传给 train_one_epoch， + # 否则 max_global_steps=None（按整 epoch 执行完） + if args.steps and args.steps > 0: + max_global_steps = args.steps + else: + max_global_steps = None + + train_log, step_logs, global_step = train_one_epoch( + args, epoch, model, criterion, optimizer, train_loader, device, scaler, + scheduler, train_sampler, global_step_start=global_step, max_global_steps=max_global_steps + ) + + # 如果启用了按 steps 的模式且已经达到上限，标记需要在做一次验证后退出 + if max_global_steps is not None and global_step >= max_global_steps: + if rank == 0: + print(f"[Main] 达到 max_global_steps={max_global_steps}（global_step={global_step}），将在完成验证后退出训练。") + # 我们不 return 立刻退出；后面的 validate / 保存逻辑会执行一次，然后 main 返回/结束 + end_due_to_steps = True + else: + end_due_to_steps = False + + # 验证并记录 epoch 级别日志（如果在 step 模式下很可能在中间某个 epoch 提前结束，但我们仍做一次 validate） + val_log = validate(args, model, val_loader, criterion, device, args.batch_size) + current_lr = optimizer.param_groups[0]['lr'] + + if rank == 0: + # epoch summary print, 格式与示例对齐 + print(f"Epoch[{epoch}]: epoch_train_loss {train_log['loss']:.4f} acc1 {train_log['acc1']:.2f} acc5 {train_log['acc5']:.2f} | " + f"val_loss {val_log['loss']:.4f} acc1 {val_log['acc1']:.2f} acc5 {val_log['acc5']:.2f} lr {current_lr:.6f}") + row = { + 'epoch': epoch, + 'lr': current_lr, + 'loss': train_log['loss'], + 'acc1': train_log['acc1'], + 'acc5': train_log['acc5'], + 'val_loss': val_log['loss'], + 'val_acc1': val_log['acc1'], + 'val_acc5': val_log['acc5'], + } + new_row_df = pd.DataFrame([row]) + log_df = pd.concat([log_df, new_row_df], ignore_index=True) + log_df.to_csv(os.path.join(save_dir, 'log.csv'), index=False) + + is_best = val_log['acc1'] > best_acc + if is_best: + best_acc = val_log['acc1'] + if (epoch % args.save_freq == 0) or is_best or ( (max_global_steps is None) and (epoch == total_epochs - 1) ) : + state = { + 'epoch': epoch, + 'state_dict': model.module.state_dict() if isinstance(model, DDP) else model.state_dict(), + 'best_acc': best_acc, + 'optimizer': optimizer.state_dict(), + 'args': vars(args) + } + save_checkpoint(state, is_best, save_dir, filename=f'checkpoint_epoch_{epoch}.pth') + + # 如果是因为 steps 模式达到上限，则在完成 validation / 保存后退出训练 + if end_due_to_steps: + if rank == 0: + print(f"[Main] 已在 steps 模式下完成最后一次验证并保存，训练结束（global_step={global_step}）。") + break + + # increment epoch + epoch += 1 + + # stopping conditions: + # 1) if steps mode enabled and reached steps -> stop + if args.steps and args.steps > 0: + if global_step >= args.steps: + if rank == 0: + print(f"[Main] 已达到指定 steps={args.steps}（global_step={global_step}），训练结束。") + break + + # 2) if steps not used, stop when epoch >= epochs + else: + if epoch >= total_epochs: + if rank == 0: + print(f"[Main] 已达到指定 epochs={total_epochs}（epoch={epoch}），训练结束。") + break + + if dist.is_initialized(): + dist.barrier() + if rank == 0: + print("Training finished. Best val acc1: {:.2f}".format(best_acc)) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/PyTorch/build-in/Classification/SWL/wsl.py b/PyTorch/build-in/Classification/SWL/wsl.py new file mode 100644 index 000000000..895f35531 --- /dev/null +++ b/PyTorch/build-in/Classification/SWL/wsl.py @@ -0,0 +1,253 @@ +# resnext_wsl_single_file.py +import torch +import torch.nn as nn +from torch.hub import load_state_dict_from_url + +# ============================ +# 1. WSL 预训练权重 URL +# ============================ +model_urls = { + 'resnext101_32x8d': 'https://download.pytorch.org/models/ig_resnext101_32x8-c38310e5.pth', + 'resnext101_32x16d': 'https://download.pytorch.org/models/ig_resnext101_32x16-c6f796b0.pth', + 'resnext101_32x32d': 'https://download.pytorch.org/models/ig_resnext101_32x32-e4b90b00.pth', + 'resnext101_32x48d': 'https://download.pytorch.org/models/ig_resnext101_32x48-3e41cc8a.pth', +} + +# ============================ +# 2. 基础工具 +# ============================ +def conv3x3(in_planes, out_planes, stride=1, groups=1): + return nn.Conv2d( + in_planes, out_planes, + kernel_size=3, stride=stride, + padding=1, groups=groups, bias=False + ) + + +def conv1x1(in_planes, out_planes, stride=1): + return nn.Conv2d( + in_planes, out_planes, + kernel_size=1, stride=stride, bias=False + ) + +# ============================ +# 3. Bottleneck（ResNeXt 核心） +# ============================ +class Bottleneck(nn.Module): + expansion = 4 + + def __init__( + self, + inplanes, + planes, + stride=1, + downsample=None, + groups=1, + width_per_group=64 + ): + super().__init__() + + width = int(planes * (width_per_group / 64.)) * groups + + self.conv1 = conv1x1(inplanes, width) + self.bn1 = nn.BatchNorm2d(width) + + self.conv2 = conv3x3( + width, width, + stride=stride, + groups=groups + ) + self.bn2 = nn.BatchNorm2d(width) + + self.conv3 = conv1x1(width, planes * self.expansion) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x + + out = self.relu(self.bn1(self.conv1(x))) + out = self.relu(self.bn2(self.conv2(out))) + out = self.bn3(self.conv3(out)) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + return out + +# ============================ +# 4. ResNet / ResNeXt 主体 +# ============================ +class ResNet(nn.Module): + def __init__( + self, + block, + layers, + num_classes=1000, + groups=1, + width_per_group=64 + ): + super().__init__() + + self.inplanes = 64 + self.groups = groups + self.width_per_group = width_per_group + + self.conv1 = nn.Conv2d( + 3, 64, + kernel_size=7, + stride=2, + padding=3, + bias=False + ) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d( + kernel_size=3, stride=2, padding=1 + ) + + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.fc = nn.Linear(512 * block.expansion, num_classes) + + self._init_weights() + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + conv1x1(self.inplanes, planes * block.expansion, stride), + nn.BatchNorm2d(planes * block.expansion), + ) + + layers = [] + layers.append( + block( + self.inplanes, planes, + stride=stride, + downsample=downsample, + groups=self.groups, + width_per_group=self.width_per_group, + ) + ) + self.inplanes = planes * block.expansion + + for _ in range(1, blocks): + layers.append( + block( + self.inplanes, planes, + groups=self.groups, + width_per_group=self.width_per_group, + ) + ) + + return nn.Sequential(*layers) + + def _init_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def forward(self, x): + x = self.relu(self.bn1(self.conv1(x))) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.fc(x) + return x + +# ============================ +# 5. 构造 ResNeXt-101 WSL +# ============================ +def _resnext(arch, groups, width_per_group, progress=True, **kwargs): + model = ResNet( + Bottleneck, + [3, 4, 23, 3], + groups=groups, + width_per_group=width_per_group, + **kwargs + ) + state_dict = load_state_dict_from_url( + model_urls[arch], + progress=progress + ) + model.load_state_dict(state_dict) + return model + +def resnext101_32x8d(num_classes=1000): + return ResNet( + Bottleneck, + layers=[3, 4, 23, 3], + num_classes=num_classes, + groups=32, + width_per_group=8, + ) + + +def resnext101_32x16d(num_classes=1000): + return ResNet( + Bottleneck, + layers=[3, 4, 23, 3], + num_classes=num_classes, + groups=32, + width_per_group=16, + ) + + +def resnext101_32x32d(num_classes=1000): + return ResNet( + Bottleneck, + layers=[3, 4, 23, 3], + num_classes=num_classes, + groups=32, + width_per_group=32, + ) + + +def resnext101_32x48d(num_classes=1000): + return ResNet( + Bottleneck, + layers=[3, 4, 23, 3], + num_classes=num_classes, + groups=32, + width_per_group=48, + ) + + +def Model(num_classes=1000, variant="32x8d"): + """ + variant: + - "32x8d" + - "32x16d" + - "32x32d" + - "32x48d" + """ + if variant == "32x8d": + return resnext101_32x8d(num_classes=num_classes) + elif variant == "32x16d": + return resnext101_32x16d(num_classes=num_classes) + elif variant == "32x32d": + return resnext101_32x32d(num_classes=num_classes) + elif variant == "32x48d": + return resnext101_32x48d(num_classes=num_classes) + else: + raise ValueError(f"Unknown variant: {variant}") diff --git a/PyTorch/build-in/Classification/SWL/wsl_loss.jpg b/PyTorch/build-in/Classification/SWL/wsl_loss.jpg new file mode 100644 index 0000000000000000000000000000000000000000..6d478cfdf564af63db6cdd1b82ea09e96185632e GIT binary patch literal 35863 zcmeFa1zcQPmOWfJL4yW|011Qwg1ZKXK;bS44u!h}3P^wi4G?X}lA+JRzJ3!H8}}|g zA@Ti(q^#_m+`RmP!lJ6`n%X*OeM4hgdq-zicTaEM=-BuKZ1U^W^tYwumDRQNjm@ot z!=vMq)3fu7%OCAR0-*e*kPIN*^HH_!Z zMD$#Nn8Z(FGAmo}FmS8ylbE`UV39KNe0y;4qiH|2>_679puekSe`(miwQCxHgMx&( zc_;(`alqLnb5;P>zskR^!N2;z|Cv4@kpBA9;|UQ4^mz6UHjb{RZ)Ob{i#je8DCMs> z#Qk7aVmr5XDgVbdXoXUYb`2%>Edmz(^#kWBaxeT@|zI`!e}D zG15jF&BSBJW~HTMr4P_nGjC$;%^KXWVTPEuT>h3!DJLa+RJQZ1`@8Xl+xl*oK%3LI z7sY6uC8du(lcOa_2(3dE6Wbt%0_O|xw!4r`9?iWllf$PXQnvtO`|D1Cn49n>DSS~`b&7)y+f7Ix%%4?-1;kpAFXs+>j(7JpSu}oz}ZKZ;F zi*^K|ln~$dN?>po=K#Rt>@or}BSx$19vlMfewW+Wx`sV`-x46cj}&>a7vWjNPfO6u z1a{^jvfYYC-ErjKHK%9N{_2@puU1b`zewez>^x6t>5Km=_zlyS6xR(lM&$glXW?*? 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z=BDKGcGTGI=h3Zdh>n1Cavv4rNXobqaLABB;TJs}r@pypu*5lGid*_NykR zIG1K`jxI7TI_FNwd&ST;Jj)vwS>Yb`NQ>hRzauC(DmbY8i^9W(rx>8lLirC(J-eVt zRDv!1;u>?`_2=a&q}dH*c^t8|csTB}=V-XmNcQmUix>5VDrNE3cBDTHuK&;cr2q5h z%VF(#x~VO->Ccbjk3BOzSGQmMg4x_H%q7y$N3-jdqjnz7Kw%Dz(f|qa0!x!Fqj@*Y z&=KzPncPKxl96_&F%kompE#XZjlt*yWD}|SSDZR;4Ry~n^~XYOyHByDgiWP=RH&6Y zwY*KH#I)gl^j!sg=K|fF+0rtslR)*~R1+&6dFY@Y-t5$1(kfKTIrPuk9O$`jI z%hw8925Le-Tth2x^1l6UQ0qk`4l6b*!m<@jC6(%vf<(xY!E(5>ofbj|>!VZ$b71vu5Zfzwo{u$yJK*}dnk4ne znC;a**#z5>ZU^rCx%$1&I>48iKk6t$>?#KqVm{zuIeaVw6E(G}2g2U&+;=bn`(4Vh ztrCIbxgqcNQ4{G-WLwjCsC&4fXA;} Date: Wed, 7 Jan 2026 05:55:48 +0000 Subject: [PATCH 2/3] fix: cleanup code and update --- .../SWL/WSL-Images/CODE_OF_CONDUCT.md | 5 - .../SWL/WSL-Images/CONTRIBUTING.md | 35 -- .../Classification/SWL/WSL-Images/LICENSE | 399 ------------------ .../Classification/SWL/WSL-Images/README.md | 38 -- .../Classification/SWL/WSL-Images/hubconf.py | 78 ---- .../build-in/Classification/SWL/coverage.txt | 3 - PyTorch/build-in/Classification/SWL/readme | 65 +++ .../Classification/SWL/requirements_exact.txt | 89 ++++ PyTorch/build-in/Classification/SWL/run | 1 - .../build-in/Classification/SWL/wsl_loss.jpg | Bin 35863 -> 0 bytes .../build-in/Classification/SWL/wsl_loss.txt | 29 -- 11 files changed, 154 insertions(+), 588 deletions(-) delete mode 100644 PyTorch/build-in/Classification/SWL/WSL-Images/CODE_OF_CONDUCT.md delete mode 100644 PyTorch/build-in/Classification/SWL/WSL-Images/CONTRIBUTING.md delete mode 100644 PyTorch/build-in/Classification/SWL/WSL-Images/LICENSE delete mode 100644 PyTorch/build-in/Classification/SWL/WSL-Images/README.md delete mode 100644 PyTorch/build-in/Classification/SWL/WSL-Images/hubconf.py delete mode 100644 PyTorch/build-in/Classification/SWL/coverage.txt create mode 100644 PyTorch/build-in/Classification/SWL/readme create mode 100644 PyTorch/build-in/Classification/SWL/requirements_exact.txt delete mode 100644 PyTorch/build-in/Classification/SWL/run delete mode 100644 PyTorch/build-in/Classification/SWL/wsl_loss.jpg delete mode 100644 PyTorch/build-in/Classification/SWL/wsl_loss.txt diff --git a/PyTorch/build-in/Classification/SWL/WSL-Images/CODE_OF_CONDUCT.md b/PyTorch/build-in/Classification/SWL/WSL-Images/CODE_OF_CONDUCT.md deleted file mode 100644 index 0f7ad8bfc..000000000 --- a/PyTorch/build-in/Classification/SWL/WSL-Images/CODE_OF_CONDUCT.md +++ /dev/null @@ -1,5 +0,0 @@ -# Code of Conduct - -Facebook has adopted a Code of Conduct that we expect project participants to adhere to. -Please read the [full text](https://code.fb.com/codeofconduct/) -so that you can understand what actions will and will not be tolerated. diff --git a/PyTorch/build-in/Classification/SWL/WSL-Images/CONTRIBUTING.md b/PyTorch/build-in/Classification/SWL/WSL-Images/CONTRIBUTING.md deleted file mode 100644 index 47c825fbb..000000000 --- a/PyTorch/build-in/Classification/SWL/WSL-Images/CONTRIBUTING.md +++ /dev/null @@ -1,35 +0,0 @@ -# Contributing to WSL-Images -We want to make contributing to this project as easy and transparent as possible. - -## Our Development Process -Minor changes and improvements will be released on an ongoing basis. Larger changes (e.g., changesets implementing a new benchmark) will be released on a more periodic basis. - -## Pull Requests -We actively welcome your pull requests. - -1. Fork the repo and create your branch from `master`. -2. If you've added code that should be tested, add tests. -3. If you've changed APIs, update the documentation. -4. Ensure the test suite passes. -5. Make sure your code lints. -6. If you haven't already, complete the Contributor License Agreement ("CLA"). - -## Contributor License Agreement ("CLA") -In order to accept your pull request, we need you to submit a CLA. You only need -to do this once to work on any of Facebook's open source projects. - -Complete your CLA here: - -## Issues -We use GitHub issues to track public bugs. Please ensure your description is -clear and has sufficient instructions to be able to reproduce the issue. Follow -the template provided [here](.github/issue_template.md) when opening issues. - -## Coding Style -* 4 spaces for indentation rather than tabs -* 80 character line length -* 80 character line length - -## License -By contributing to WSL-Images, you agree that your contributions will be licensed -under the LICENSE file in the root directory of this source tree. diff --git a/PyTorch/build-in/Classification/SWL/WSL-Images/LICENSE b/PyTorch/build-in/Classification/SWL/WSL-Images/LICENSE deleted file mode 100644 index f2915d90a..000000000 --- a/PyTorch/build-in/Classification/SWL/WSL-Images/LICENSE +++ /dev/null @@ -1,399 +0,0 @@ -Attribution-NonCommercial 4.0 International - -======================================================================= - -Creative Commons Corporation ("Creative Commons") is not a law firm and -does not provide legal services or legal advice. Distribution of -Creative Commons public licenses does not create a lawyer-client or -other relationship. 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For -the avoidance of doubt, this paragraph does not form part of the -public licenses. - -Creative Commons may be contacted at creativecommons.org. diff --git a/PyTorch/build-in/Classification/SWL/WSL-Images/README.md b/PyTorch/build-in/Classification/SWL/WSL-Images/README.md deleted file mode 100644 index a43144e08..000000000 --- a/PyTorch/build-in/Classification/SWL/WSL-Images/README.md +++ /dev/null @@ -1,38 +0,0 @@ -## WSL-Images - -This project provides models pre-trained in weakly-supervised fashion on **940 million** public images with 1.5K hashtags matching with 1000 ImageNet1K synsets, followed by fine-tuning on ImageNet1K dataset. Please refer to "Exploring the Limits of Weakly Supervised Pretraining" (https://arxiv.org/abs/1805.00932) presented at ECCV 2018 for the details of model training. - -We are providing 4 models with different capacities. - -| Model | #Parameters | FLOPS | Top-1 Acc. | Top-5 Acc. | -| ------------------ | :---------: | :---: | :--------: | :--------: | -| ResNeXt-101 32x8d | 88M | 16B | 82.2 | 96.4 | -| ResNeXt-101 32x16d | 193M | 36B | 84.2 | 97.2 | -| ResNeXt-101 32x32d | 466M | 87B | 85.1 | 97.5 | -| ResNeXt-101 32x48d | 829M | 153B | 85.4 | 97.6 | - -Our models significantly improve the training accuracy on ImageNet compared to training from scratch. **We achieve state-of-the-art accuracy of 85.4% on ImageNet with our ResNext-101 32x48d model.** - -## Loading models with torch.hub -The models are available with [torch.hub](https://pytorch.org/docs/stable/hub.html). -As an example, to load the ResNext-101 32x16d model, simply run: - -``` -model = torch.hub.load('facebookresearch/WSL-Images', 'resnext101_32x16d_wsl') -``` -Please refer to [torch.hub](https://pytorch.org/docs/stable/hub.html) to see a full example of using the model to classify an image. - -## Citing WSL-Images - -If you use the WSL-Images models, please cite the following publication. -``` -@inproceedings{wslimageseccv2018, - title={Exploring the Limits of Weakly Supervised Pretraining}, - author={Dhruv Kumar Mahajan and Ross B. Girshick and Vignesh Ramanathan and Kaiming He and Manohar Paluri and Yixuan Li and Ashwin Bharambe and Laurens van der Maaten}, - booktitle={ECCV}, - year={2018} -} -``` - -## License -WSL-Images models are released under the CC-BY-NC 4.0 license. See [LICENSE](LICENSE) for additional details. diff --git a/PyTorch/build-in/Classification/SWL/WSL-Images/hubconf.py b/PyTorch/build-in/Classification/SWL/WSL-Images/hubconf.py deleted file mode 100644 index d28f8adaa..000000000 --- a/PyTorch/build-in/Classification/SWL/WSL-Images/hubconf.py +++ /dev/null @@ -1,78 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -# Optional list of dependencies required by the package -dependencies = ['torch', 'torchvision'] - -from torch.hub import load_state_dict_from_url -from torchvision.models.resnet import ResNet, Bottleneck - - -model_urls = { - 'resnext101_32x8d': 'https://download.pytorch.org/models/ig_resnext101_32x8-c38310e5.pth', - 'resnext101_32x16d': 'https://download.pytorch.org/models/ig_resnext101_32x16-c6f796b0.pth', - 'resnext101_32x32d': 'https://download.pytorch.org/models/ig_resnext101_32x32-e4b90b00.pth', - 'resnext101_32x48d': 'https://download.pytorch.org/models/ig_resnext101_32x48-3e41cc8a.pth', -} - - -def _resnext(arch, block, layers, pretrained, progress, **kwargs): - model = ResNet(block, layers, **kwargs) - state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) - model.load_state_dict(state_dict) - return model - - -def resnext101_32x8d_wsl(progress=True, **kwargs): - """Constructs a ResNeXt-101 32x8 model pre-trained on weakly-supervised data - and finetuned on ImageNet from Figure 5 in - `"Exploring the Limits of Weakly Supervised Pretraining" `_ - - Args: - progress (bool): If True, displays a progress bar of the download to stderr. - """ - kwargs['groups'] = 32 - kwargs['width_per_group'] = 8 - return _resnext('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], True, progress, **kwargs) - - -def resnext101_32x16d_wsl(progress=True, **kwargs): - """Constructs a ResNeXt-101 32x16 model pre-trained on weakly-supervised data - and finetuned on ImageNet from Figure 5 in - `"Exploring the Limits of Weakly Supervised Pretraining" `_ - - Args: - progress (bool): If True, displays a progress bar of the download to stderr. - """ - kwargs['groups'] = 32 - kwargs['width_per_group'] = 16 - return _resnext('resnext101_32x16d', Bottleneck, [3, 4, 23, 3], True, progress, **kwargs) - - -def resnext101_32x32d_wsl(progress=True, **kwargs): - """Constructs a ResNeXt-101 32x32 model pre-trained on weakly-supervised data - and finetuned on ImageNet from Figure 5 in - `"Exploring the Limits of Weakly Supervised Pretraining" `_ - - Args: - progress (bool): If True, displays a progress bar of the download to stderr. - """ - kwargs['groups'] = 32 - kwargs['width_per_group'] = 32 - return _resnext('resnext101_32x32d', Bottleneck, [3, 4, 23, 3], True, progress, **kwargs) - - -def resnext101_32x48d_wsl(progress=True, **kwargs): - """Constructs a ResNeXt-101 32x48 model pre-trained on weakly-supervised data - and finetuned on ImageNet from Figure 5 in - `"Exploring the Limits of Weakly Supervised Pretraining" `_ - - Args: - progress (bool): If True, displays a progress bar of the download to stderr. - """ - kwargs['groups'] = 32 - kwargs['width_per_group'] = 48 - return _resnext('resnext101_32x48d', Bottleneck, [3, 4, 23, 3], True, progress, **kwargs) diff --git a/PyTorch/build-in/Classification/SWL/coverage.txt b/PyTorch/build-in/Classification/SWL/coverage.txt deleted file mode 100644 index 3aa0a123b..000000000 --- a/PyTorch/build-in/Classification/SWL/coverage.txt +++ /dev/null @@ -1,3 +0,0 @@ -all api: ['_amp_foreach_non_finite_check_and_unscale_', '_amp_update_scale_', '_copy_from', '_has_compatible_shallow_copy_type', '_local_scalar_dense', '_log_softmax', '_log_softmax_backward_data', '_pin_memory', '_reshape_alias', 'add_', 'addmm', 'as_strided', 'as_strided_', 'convolution', 'convolution_backward', 'copy_stride', 'div', 'eq', 'fill_', 'fused_sgd', 'is_pinned', 'linear', 'max_pool2d', 'maxpool2d_backward', 'maxpool2d_forward', 'mean', 'mm', 'mul', 'mul_', 'native_batch_norm', 'native_batch_norm_backward', 'nll_loss_backward', 'nll_loss_forward', 'reciprocal', 'relu_', 'set_', 'sum', 'threshold_backward', 'topk_out', 'view', 'zero_'], total: 41 -fallback op: [], total: 0 -coverage rate: 100.00% diff --git a/PyTorch/build-in/Classification/SWL/readme b/PyTorch/build-in/Classification/SWL/readme new file mode 100644 index 000000000..10da84230 --- /dev/null +++ b/PyTorch/build-in/Classification/SWL/readme @@ -0,0 +1,65 @@ +```markdown +## 1. 模型链接 +- 原始仓库链接： +https://github.com/huggingface/pytorch-image-models?tab=readme-ov-file#models + +## 2. 快速开始 + +使用本模型执行训练的主要流程如下： + +1. **基础环境安装**：介绍训练前需要完成的基础环境检查和安装。 +2. **获取数据集**：介绍如何获取训练所需的数据集。 +3. **构建环境**：介绍如何构建模型运行所需要的环境。 +4. **启动训练**：介绍如何运行训练。 + +### 2.1 基础环境安装 + +请参考主仓库的基础环境安装章节，完成训练前的基础环境检查和安装（如驱动、固件等）。 + +### 2.2 准备数据集 + +#### 2.2.1 获取数据集 + +训练使用 **CIFAR-100** 数据集。该数据集为开源数据集，包含 100 个类别的 60000 张彩色图像。 + +#### 2.2.2 处理数据集 + +请确保数据集已下载并解压。根据训练脚本的默认配置，建议将数据集存放在模型目录的上级 `data` 目录中（即 `../data`），或者根据实际路径修改训练命令中的 `--datapath` 参数。 + +### 2.3 构建环境 + +所使用的环境下需包含 PyTorch 框架虚拟环境。 + +1. 执行以下命令，启动虚拟环境（根据实际环境名称修改）： + + ```bash + conda activate torch_env_py310 + +``` + +2. 安装 Python 依赖。确保已安装项目所需的依赖包： +```bash +pip install -r requirements_exact.txt + +``` + + + +### 2.4 启动训练 + +1. 在构建好的环境中，进入模型训练脚本所在目录。 + +2. 运行训练。该模型支持单机单卡训练。 +执行以下命令启动训练（使用 CIFAR-100 数据集，Batch Size 为 128）： +```bash +python weloTrainStep.py \ + --name train \ + --arch swl \ + --print_freq 1 \ + --steps 100 \ + --dataset cifar100 \ + --datapath ../data \ + --batch_size 16 \ + --epochs 100 + +``` diff --git a/PyTorch/build-in/Classification/SWL/requirements_exact.txt b/PyTorch/build-in/Classification/SWL/requirements_exact.txt new file mode 100644 index 000000000..7394b3319 --- /dev/null +++ b/PyTorch/build-in/Classification/SWL/requirements_exact.txt @@ -0,0 +1,89 @@ +addict==2.4.0 +aliyun-python-sdk-core==2.16.0 +aliyun-python-sdk-kms==2.16.5 +anyio==4.11.0 +astunparse==1.6.3 +certifi==2024.12.14 +cffi==2.0.0 +charset-normalizer==3.4.1 +click==8.3.1 +colorama==0.4.6 +contourpy==1.3.2 +crcmod==1.7 +cryptography==46.0.3 +cycler==0.12.1 +einops==0.8.1 +exceptiongroup==1.3.1 +filelock==3.14.0 +fonttools==4.60.1 +fsspec==2024.12.0 +future @ file:///croot/future_1730902796226/work +git-filter-repo==2.47.0 +h11==0.16.0 +hf-xet==1.2.0 +httpcore==1.0.9 +httpx==0.28.1 +huggingface_hub==1.1.5 +idna==3.10 +inplace-abn @ git+https://github.com/mapillary/inplace_abn.git@b50bfe9c7cd7116a3ab091a352b48d6ba5ee701c +Jinja2==3.1.5 +jmespath==0.10.0 +joblib==1.5.2 +kiwisolver==1.4.9 +Markdown==3.10 +markdown-it-py==4.0.0 +MarkupSafe==3.0.2 +matplotlib==3.10.7 +mdurl==0.1.2 +mmdet==3.3.0 +mmengine==0.10.7 +model-index==0.1.11 +mpmath==1.3.0 +networkx==3.4.2 +numpy==1.23.5 +opencv-python==4.12.0.88 +opendatalab==0.0.10 +openmim==0.3.9 +openxlab==0.1.3 +ordered-set==4.1.0 +oss2==2.17.0 +packaging @ file:///croot/packaging_1734472117206/work +pandas==2.3.3 +pillow==11.1.0 +platformdirs==4.5.1 +pycocotools==2.0.11 +pycparser @ file:///tmp/build/80754af9/pycparser_1636541352034/work +pycryptodome==3.23.0 +Pygments==2.19.2 +pyparsing==3.2.5 +python-dateutil==2.9.0.post0 +pytz==2023.4 +PyYAML @ file:///croot/pyyaml_1728657952215/work +requests==2.28.2 +rich==13.4.2 +safetensors==0.7.0 +scikit-learn==1.7.2 +scipy==1.15.3 +shapely==2.1.2 +shellingham==1.5.4 +six @ file:///tmp/build/80754af9/six_1644875935023/work +sniffio==1.3.1 +sympy==1.13.3 +tabulate==0.9.0 +termcolor==3.2.0 +terminaltables==3.1.10 +threadpoolctl==3.6.0 +timm==1.0.22 +tomli==2.3.0 +torch @ file:///apps/torch-2.4.0a0%2Bgit4451b0e-cp310-cp310-linux_x86_64.whl#sha256=2e472c916044cac5a1a0e0d8b0e12bb943d8522b24ff826c8014dd444dccd378 +torch_sdaa @ file:///apps/torch_sdaa-2.0.0-cp310-cp310-linux_x86_64.whl#sha256=5aa57889b002e1231fbf806642e1353bfa016297bc25178396e89adc2b1f92e7 +torchaudio @ file:///apps/torchaudio-2.0.2%2Bda3eb8d-cp310-cp310-linux_x86_64.whl#sha256=46525c02fb7eaa8dafea860428de3d01e437ba8d6ff2cc228d7c71975ac4054b +torchdata @ file:///apps/torchdata-0.6.1%2Be1feeb2-py3-none-any.whl#sha256=aa2dc1a7732ea68adfad186978049bf68cc1afdbbdd1e17a8024227ab770e433 +torchtext @ file:///apps/torchtext-0.15.2a0%2B4571036-cp310-cp310-linux_x86_64.whl#sha256=7e42c684ba366f97b59ec37488bf95e416cce3892b6589200d2b3ad159ee5788 +torchvision @ file:///apps/torchvision-0.15.1a0%2B42759b1-cp310-cp310-linux_x86_64.whl#sha256=4b904db2d50102415536bc764bbc31c669b90b1b014f90964e9eccaadb2fd9eb +tqdm==4.65.2 +typer-slim==0.20.0 +typing_extensions==4.15.0 +tzdata==2025.2 +urllib3==1.26.20 +yapf==0.43.0 diff --git a/PyTorch/build-in/Classification/SWL/run b/PyTorch/build-in/Classification/SWL/run deleted file mode 100644 index fbac492b0..000000000 --- a/PyTorch/build-in/Classification/SWL/run +++ /dev/null @@ -1 +0,0 @@ -bash ../sdaaTest.sh wsl 8 0 diff --git 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(100%) diff --git a/PyTorch/build-in/Classification/SWL/readme b/PyTorch/build-in/Classification/SWL/readme.md similarity index 100% rename from PyTorch/build-in/Classification/SWL/readme rename to PyTorch/build-in/Classification/SWL/readme.md diff --git a/PyTorch/build-in/Classification/SWL/requirements_exact.txt b/PyTorch/build-in/Classification/SWL/requirements.txt similarity index 100% rename from PyTorch/build-in/Classification/SWL/requirements_exact.txt rename to PyTorch/build-in/Classification/SWL/requirements.txt