From 24eb692670d7a8c6cdb0cc1c799d5f940e02637a Mon Sep 17 00:00:00 2001 From: "Sean (Seok-Won) Yi" Date: Wed, 25 Oct 2023 15:52:31 +0900 Subject: [PATCH 1/6] New pre-commit related files. --- .flake8 | 5 +++++ .isort.cfg | 4 ++++ .pre-commit-config.yaml | 27 +++++++++++++++++++++++++++ 3 files changed, 36 insertions(+) create mode 100644 .flake8 create mode 100644 .isort.cfg create mode 100644 .pre-commit-config.yaml diff --git a/.flake8 b/.flake8 new file mode 100644 index 00000000..f1d91c99 --- /dev/null +++ b/.flake8 @@ -0,0 +1,5 @@ +[flake8] +exclude = .venv, .idea, .pytest_cache, __pycache__, .git, .scripts/*, logs/*, docker/*, build/* +ignore = E501, E203, W503 +per-file-ignores = */__init__.py: F401 +max-line-length = 88 \ No newline at end of file diff --git a/.isort.cfg b/.isort.cfg new file mode 100644 index 00000000..bd2a5fd4 --- /dev/null +++ b/.isort.cfg @@ -0,0 +1,4 @@ +[settings] +multi_line_output=3 +lines_after_imports=2 +sections=FUTURE,STDLIB,THIRDPARTY,FIRSTPARTY,LOCALFOLDER diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..be27aba1 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,27 @@ +# See https://pre-commit.com for more information +# See https://pre-commit.com/hooks.html for more hooks +repos: + - repo: https://github.com/PyCQA/isort + rev: 5.12.0 + hooks: + - id: isort + language_version: python3.10 + args: ["--profile", "black"] + - repo: https://github.com/ambv/black + rev: 23.3.0 + hooks: + - id: black + language_version: python3.10 + - repo: https://github.com/pycqa/flake8 + rev: 6.0.0 + hooks: + - id: flake8 + language_version: python3.10 + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.4.0 + hooks: + - id: check-yaml + - id: debug-statements + - id: trailing-whitespace +default_language_version: + python: python3.10 From e4a43afb442581fc52a4cbb97c0447738d4d2d40 Mon Sep 17 00:00:00 2001 From: "Sean (Seok-Won) Yi" Date: Wed, 25 Oct 2023 15:52:53 +0900 Subject: [PATCH 2/6] Initial pre-commit. --- README.md | 22 ++-- losses.py | 36 +++--- main_ce.py | 283 ++++++++++++++++++++++------------------ main_linear.py | 193 ++++++++++++++++------------ main_supcon.py | 284 ++++++++++++++++++++++++----------------- networks/resnet_big.py | 74 +++++++---- pyproject.toml | 15 +++ util.py | 43 ++++--- 8 files changed, 554 insertions(+), 396 deletions(-) create mode 100644 pyproject.toml diff --git a/README.md b/README.md index 4ac5357f..c4c10da7 100644 --- a/README.md +++ b/README.md @@ -3,9 +3,9 @@

-This repo covers an reference implementation for the following papers in PyTorch, using CIFAR as an illustrative example: -(1) Supervised Contrastive Learning. [Paper](https://arxiv.org/abs/2004.11362) -(2) A Simple Framework for Contrastive Learning of Visual Representations. [Paper](https://arxiv.org/abs/2002.05709) +This repo covers an reference implementation for the following papers in PyTorch, using CIFAR as an illustrative example: +(1) Supervised Contrastive Learning. [Paper](https://arxiv.org/abs/2004.11362) +(2) A Simple Framework for Contrastive Learning of Visual Representations. [Paper](https://arxiv.org/abs/2002.05709) ## Update @@ -40,32 +40,32 @@ Results on CIFAR-10: | |Arch | Setting | Loss | Accuracy(%) | |----------|:----:|:---:|:---:|:---:| | SupCrossEntropy | ResNet50 | Supervised | Cross Entropy | 95.0 | -| SupContrast | ResNet50 | Supervised | Contrastive | 96.0 | +| SupContrast | ResNet50 | Supervised | Contrastive | 96.0 | | SimCLR | ResNet50 | Unsupervised | Contrastive | 93.6 | Results on CIFAR-100: | |Arch | Setting | Loss | Accuracy(%) | |----------|:----:|:---:|:---:|:---:| | SupCrossEntropy | ResNet50 | Supervised | Cross Entropy | 75.3 | -| SupContrast | ResNet50 | Supervised | Contrastive | 76.5 | +| SupContrast | ResNet50 | Supervised | Contrastive | 76.5 | | SimCLR | ResNet50 | Unsupervised | Contrastive | 70.7 | Results on ImageNet (Stay tuned): | |Arch | Setting | Loss | Accuracy(%) | |----------|:----:|:---:|:---:|:---:| | SupCrossEntropy | ResNet50 | Supervised | Cross Entropy | - | -| SupContrast | ResNet50 | Supervised | Contrastive | 79.1 (MoCo trick) | +| SupContrast | ResNet50 | Supervised | Contrastive | 79.1 (MoCo trick) | | SimCLR | ResNet50 | Unsupervised | Contrastive | - | ## Running -You might use `CUDA_VISIBLE_DEVICES` to set proper number of GPUs, and/or switch to CIFAR100 by `--dataset cifar100`. +You might use `CUDA_VISIBLE_DEVICES` to set proper number of GPUs, and/or switch to CIFAR100 by `--dataset cifar100`. **(1) Standard Cross-Entropy** ``` python main_ce.py --batch_size 1024 \ --learning_rate 0.8 \ --cosine --syncBN \ ``` -**(2) Supervised Contrastive Learning** +**(2) Supervised Contrastive Learning** Pretraining stage: ``` python main_supcon.py --batch_size 1024 \ @@ -84,7 +84,7 @@ python main_linear.py --batch_size 512 \ --learning_rate 5 \ --ckpt /path/to/model.pth ``` -**(3) SimCLR** +**(3) SimCLR** Pretraining stage: ``` python main_supcon.py --batch_size 1024 \ @@ -104,7 +104,7 @@ python main_linear.py --batch_size 512 \ On custom dataset: ``` python main_supcon.py --batch_size 1024 \ - --learning_rate 0.5 \ + --learning_rate 0.5 \ --temp 0.1 --cosine \ --dataset path \ --data_folder ./path \ @@ -115,7 +115,7 @@ python main_supcon.py --batch_size 1024 \ The `--data_folder` must be of form ./path/label/xxx.png folowing https://pytorch.org/docs/stable/torchvision/datasets.html#torchvision.datasets.ImageFolder convension. -and +and ## t-SNE Visualization **(1) Standard Cross-Entropy** diff --git a/losses.py b/losses.py index 17117d42..c58c4a34 100644 --- a/losses.py +++ b/losses.py @@ -2,17 +2,15 @@ Author: Yonglong Tian (yonglong@mit.edu) Date: May 07, 2020 """ -from __future__ import print_function - import torch -import torch.nn as nn +from torch import nn class SupConLoss(nn.Module): """Supervised Contrastive Learning: https://arxiv.org/pdf/2004.11362.pdf. It also supports the unsupervised contrastive loss in SimCLR""" - def __init__(self, temperature=0.07, contrast_mode='all', - base_temperature=0.07): + + def __init__(self, temperature=0.07, contrast_mode="all", base_temperature=0.07): super(SupConLoss, self).__init__() self.temperature = temperature self.contrast_mode = contrast_mode @@ -31,44 +29,44 @@ def forward(self, features, labels=None, mask=None): Returns: A loss scalar. """ - device = (torch.device('cuda') - if features.is_cuda - else torch.device('cpu')) + device = torch.device("cuda") if features.is_cuda else torch.device("cpu") if len(features.shape) < 3: - raise ValueError('`features` needs to be [bsz, n_views, ...],' - 'at least 3 dimensions are required') + raise ValueError( + "`features` needs to be [bsz, n_views, ...]," + "at least 3 dimensions are required" + ) if len(features.shape) > 3: features = features.view(features.shape[0], features.shape[1], -1) batch_size = features.shape[0] if labels is not None and mask is not None: - raise ValueError('Cannot define both `labels` and `mask`') + raise ValueError("Cannot define both `labels` and `mask`") elif labels is None and mask is None: mask = torch.eye(batch_size, dtype=torch.float32).to(device) elif labels is not None: labels = labels.contiguous().view(-1, 1) if labels.shape[0] != batch_size: - raise ValueError('Num of labels does not match num of features') + raise ValueError("Num of labels does not match num of features") mask = torch.eq(labels, labels.T).float().to(device) else: mask = mask.float().to(device) contrast_count = features.shape[1] contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0) - if self.contrast_mode == 'one': + if self.contrast_mode == "one": anchor_feature = features[:, 0] anchor_count = 1 - elif self.contrast_mode == 'all': + elif self.contrast_mode == "all": anchor_feature = contrast_feature anchor_count = contrast_count else: - raise ValueError('Unknown mode: {}'.format(self.contrast_mode)) + raise ValueError("Unknown mode: {}".format(self.contrast_mode)) # compute logits anchor_dot_contrast = torch.div( - torch.matmul(anchor_feature, contrast_feature.T), - self.temperature) + torch.matmul(anchor_feature, contrast_feature.T), self.temperature + ) # for numerical stability logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True) logits = anchor_dot_contrast - logits_max.detach() @@ -80,7 +78,7 @@ def forward(self, features, labels=None, mask=None): torch.ones_like(mask), 1, torch.arange(batch_size * anchor_count).view(-1, 1).to(device), - 0 + 0, ) mask = mask * logits_mask @@ -92,7 +90,7 @@ def forward(self, features, labels=None, mask=None): mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1) # loss - loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos + loss = -(self.temperature / self.base_temperature) * mean_log_prob_pos loss = loss.view(anchor_count, batch_size).mean() return loss diff --git a/main_ce.py b/main_ce.py index 29573d30..3a826ce1 100644 --- a/main_ce.py +++ b/main_ce.py @@ -1,99 +1,115 @@ -from __future__ import print_function - +import argparse +import math import os import sys -import argparse import time -import math import tensorboard_logger as tb_logger import torch -import torch.backends.cudnn as cudnn -from torchvision import transforms, datasets +from torch.backends import cudnn +from torchvision import datasets, transforms -from util import AverageMeter -from util import adjust_learning_rate, warmup_learning_rate, accuracy -from util import set_optimizer, save_model from networks.resnet_big import SupCEResNet - -try: - import apex - from apex import amp, optimizers -except ImportError: - pass +from util import ( + AverageMeter, + accuracy, + adjust_learning_rate, + save_model, + set_optimizer, + warmup_learning_rate, +) def parse_option(): - parser = argparse.ArgumentParser('argument for training') - - parser.add_argument('--print_freq', type=int, default=10, - help='print frequency') - parser.add_argument('--save_freq', type=int, default=50, - help='save frequency') - parser.add_argument('--batch_size', type=int, default=256, - help='batch_size') - parser.add_argument('--num_workers', type=int, default=16, - help='num of workers to use') - parser.add_argument('--epochs', type=int, default=500, - help='number of training epochs') + parser = argparse.ArgumentParser("argument for training") + + parser.add_argument("--print_freq", type=int, default=10, help="print frequency") + parser.add_argument("--save_freq", type=int, default=50, help="save frequency") + parser.add_argument("--batch_size", type=int, default=256, help="batch_size") + parser.add_argument( + "--num_workers", type=int, default=16, help="num of workers to use" + ) + parser.add_argument( + "--epochs", type=int, default=500, help="number of training epochs" + ) # optimization - parser.add_argument('--learning_rate', type=float, default=0.2, - help='learning rate') - parser.add_argument('--lr_decay_epochs', type=str, default='350,400,450', - help='where to decay lr, can be a list') - parser.add_argument('--lr_decay_rate', type=float, default=0.1, - help='decay rate for learning rate') - parser.add_argument('--weight_decay', type=float, default=1e-4, - help='weight decay') - parser.add_argument('--momentum', type=float, default=0.9, - help='momentum') + parser.add_argument( + "--learning_rate", type=float, default=0.2, help="learning rate" + ) + parser.add_argument( + "--lr_decay_epochs", + type=str, + default="350,400,450", + help="where to decay lr, can be a list", + ) + parser.add_argument( + "--lr_decay_rate", type=float, default=0.1, help="decay rate for learning rate" + ) + parser.add_argument("--weight_decay", type=float, default=1e-4, help="weight decay") + parser.add_argument("--momentum", type=float, default=0.9, help="momentum") # model dataset - parser.add_argument('--model', type=str, default='resnet50') - parser.add_argument('--dataset', type=str, default='cifar10', - choices=['cifar10', 'cifar100'], help='dataset') + parser.add_argument("--model", type=str, default="resnet50") + parser.add_argument( + "--dataset", + type=str, + default="cifar10", + choices=["cifar10", "cifar100"], + help="dataset", + ) # other setting - parser.add_argument('--cosine', action='store_true', - help='using cosine annealing') - parser.add_argument('--syncBN', action='store_true', - help='using synchronized batch normalization') - parser.add_argument('--warm', action='store_true', - help='warm-up for large batch training') - parser.add_argument('--trial', type=str, default='0', - help='id for recording multiple runs') + parser.add_argument("--cosine", action="store_true", help="using cosine annealing") + parser.add_argument( + "--syncBN", action="store_true", help="using synchronized batch normalization" + ) + parser.add_argument( + "--warm", action="store_true", help="warm-up for large batch training" + ) + parser.add_argument( + "--trial", type=str, default="0", help="id for recording multiple runs" + ) opt = parser.parse_args() # set the path according to the environment - opt.data_folder = './datasets/' - opt.model_path = './save/SupCon/{}_models'.format(opt.dataset) - opt.tb_path = './save/SupCon/{}_tensorboard'.format(opt.dataset) + opt.data_folder = "./datasets/" + opt.model_path = "./save/SupCon/{}_models".format(opt.dataset) + opt.tb_path = "./save/SupCon/{}_tensorboard".format(opt.dataset) - iterations = opt.lr_decay_epochs.split(',') + iterations = opt.lr_decay_epochs.split(",") opt.lr_decay_epochs = list([]) for it in iterations: opt.lr_decay_epochs.append(int(it)) - opt.model_name = 'SupCE_{}_{}_lr_{}_decay_{}_bsz_{}_trial_{}'.\ - format(opt.dataset, opt.model, opt.learning_rate, opt.weight_decay, - opt.batch_size, opt.trial) + opt.model_name = "SupCE_{}_{}_lr_{}_decay_{}_bsz_{}_trial_{}".format( + opt.dataset, + opt.model, + opt.learning_rate, + opt.weight_decay, + opt.batch_size, + opt.trial, + ) if opt.cosine: - opt.model_name = '{}_cosine'.format(opt.model_name) + opt.model_name = "{}_cosine".format(opt.model_name) # warm-up for large-batch training, if opt.batch_size > 256: opt.warm = True if opt.warm: - opt.model_name = '{}_warm'.format(opt.model_name) + opt.model_name = "{}_warm".format(opt.model_name) opt.warmup_from = 0.01 opt.warm_epochs = 10 if opt.cosine: - eta_min = opt.learning_rate * (opt.lr_decay_rate ** 3) - opt.warmup_to = eta_min + (opt.learning_rate - eta_min) * ( - 1 + math.cos(math.pi * opt.warm_epochs / opt.epochs)) / 2 + eta_min = opt.learning_rate * (opt.lr_decay_rate**3) + opt.warmup_to = ( + eta_min + + (opt.learning_rate - eta_min) + * (1 + math.cos(math.pi * opt.warm_epochs / opt.epochs)) + / 2 + ) else: opt.warmup_to = opt.learning_rate @@ -105,64 +121,73 @@ def parse_option(): if not os.path.isdir(opt.save_folder): os.makedirs(opt.save_folder) - if opt.dataset == 'cifar10': + if opt.dataset == "cifar10": opt.n_cls = 10 - elif opt.dataset == 'cifar100': + elif opt.dataset == "cifar100": opt.n_cls = 100 else: - raise ValueError('dataset not supported: {}'.format(opt.dataset)) + raise ValueError("dataset not supported: {}".format(opt.dataset)) return opt def set_loader(opt): # construct data loader - if opt.dataset == 'cifar10': + if opt.dataset == "cifar10": mean = (0.4914, 0.4822, 0.4465) std = (0.2023, 0.1994, 0.2010) - elif opt.dataset == 'cifar100': + elif opt.dataset == "cifar100": mean = (0.5071, 0.4867, 0.4408) std = (0.2675, 0.2565, 0.2761) else: - raise ValueError('dataset not supported: {}'.format(opt.dataset)) + raise ValueError("dataset not supported: {}".format(opt.dataset)) normalize = transforms.Normalize(mean=mean, std=std) - train_transform = transforms.Compose([ - transforms.RandomResizedCrop(size=32, scale=(0.2, 1.)), - transforms.RandomHorizontalFlip(), - transforms.ToTensor(), - normalize, - ]) - - val_transform = transforms.Compose([ - transforms.ToTensor(), - normalize, - ]) - - if opt.dataset == 'cifar10': - train_dataset = datasets.CIFAR10(root=opt.data_folder, - transform=train_transform, - download=True) - val_dataset = datasets.CIFAR10(root=opt.data_folder, - train=False, - transform=val_transform) - elif opt.dataset == 'cifar100': - train_dataset = datasets.CIFAR100(root=opt.data_folder, - transform=train_transform, - download=True) - val_dataset = datasets.CIFAR100(root=opt.data_folder, - train=False, - transform=val_transform) + train_transform = transforms.Compose( + [ + transforms.RandomResizedCrop(size=32, scale=(0.2, 1.0)), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + normalize, + ] + ) + + val_transform = transforms.Compose( + [ + transforms.ToTensor(), + normalize, + ] + ) + + if opt.dataset == "cifar10": + train_dataset = datasets.CIFAR10( + root=opt.data_folder, transform=train_transform, download=True + ) + val_dataset = datasets.CIFAR10( + root=opt.data_folder, train=False, transform=val_transform + ) + elif opt.dataset == "cifar100": + train_dataset = datasets.CIFAR100( + root=opt.data_folder, transform=train_transform, download=True + ) + val_dataset = datasets.CIFAR100( + root=opt.data_folder, train=False, transform=val_transform + ) else: raise ValueError(opt.dataset) train_sampler = None train_loader = torch.utils.data.DataLoader( - train_dataset, batch_size=opt.batch_size, shuffle=(train_sampler is None), - num_workers=opt.num_workers, pin_memory=True, sampler=train_sampler) + train_dataset, + batch_size=opt.batch_size, + shuffle=(train_sampler is None), + num_workers=opt.num_workers, + pin_memory=True, + sampler=train_sampler, + ) val_loader = torch.utils.data.DataLoader( - val_dataset, batch_size=256, shuffle=False, - num_workers=8, pin_memory=True) + val_dataset, batch_size=256, shuffle=False, num_workers=8, pin_memory=True + ) return train_loader, val_loader @@ -225,13 +250,21 @@ def train(train_loader, model, criterion, optimizer, epoch, opt): # print info if (idx + 1) % opt.print_freq == 0: - print('Train: [{0}][{1}/{2}]\t' - 'BT {batch_time.val:.3f} ({batch_time.avg:.3f})\t' - 'DT {data_time.val:.3f} ({data_time.avg:.3f})\t' - 'loss {loss.val:.3f} ({loss.avg:.3f})\t' - 'Acc@1 {top1.val:.3f} ({top1.avg:.3f})'.format( - epoch, idx + 1, len(train_loader), batch_time=batch_time, - data_time=data_time, loss=losses, top1=top1)) + print( + "Train: [{0}][{1}/{2}]\t" + "BT {batch_time.val:.3f} ({batch_time.avg:.3f})\t" + "DT {data_time.val:.3f} ({data_time.avg:.3f})\t" + "loss {loss.val:.3f} ({loss.avg:.3f})\t" + "Acc@1 {top1.val:.3f} ({top1.avg:.3f})".format( + epoch, + idx + 1, + len(train_loader), + batch_time=batch_time, + data_time=data_time, + loss=losses, + top1=top1, + ) + ) sys.stdout.flush() return losses.avg, top1.avg @@ -266,14 +299,20 @@ def validate(val_loader, model, criterion, opt): end = time.time() if idx % opt.print_freq == 0: - print('Test: [{0}/{1}]\t' - 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' - 'Loss {loss.val:.4f} ({loss.avg:.4f})\t' - 'Acc@1 {top1.val:.3f} ({top1.avg:.3f})'.format( - idx, len(val_loader), batch_time=batch_time, - loss=losses, top1=top1)) - - print(' * Acc@1 {top1.avg:.3f}'.format(top1=top1)) + print( + "Test: [{0}/{1}]\t" + "Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t" + "Loss {loss.val:.4f} ({loss.avg:.4f})\t" + "Acc@1 {top1.val:.3f} ({top1.avg:.3f})".format( + idx, + len(val_loader), + batch_time=batch_time, + loss=losses, + top1=top1, + ) + ) + + print(" * Acc@1 {top1.avg:.3f}".format(top1=top1)) return losses.avg, top1.avg @@ -301,33 +340,33 @@ def main(): time1 = time.time() loss, train_acc = train(train_loader, model, criterion, optimizer, epoch, opt) time2 = time.time() - print('epoch {}, total time {:.2f}'.format(epoch, time2 - time1)) + print("epoch {}, total time {:.2f}".format(epoch, time2 - time1)) # tensorboard logger - logger.log_value('train_loss', loss, epoch) - logger.log_value('train_acc', train_acc, epoch) - logger.log_value('learning_rate', optimizer.param_groups[0]['lr'], epoch) + logger.log_value("train_loss", loss, epoch) + logger.log_value("train_acc", train_acc, epoch) + logger.log_value("learning_rate", optimizer.param_groups[0]["lr"], epoch) # evaluation loss, val_acc = validate(val_loader, model, criterion, opt) - logger.log_value('val_loss', loss, epoch) - logger.log_value('val_acc', val_acc, epoch) + logger.log_value("val_loss", loss, epoch) + logger.log_value("val_acc", val_acc, epoch) if val_acc > best_acc: best_acc = val_acc if epoch % opt.save_freq == 0: save_file = os.path.join( - opt.save_folder, 'ckpt_epoch_{epoch}.pth'.format(epoch=epoch)) + opt.save_folder, "ckpt_epoch_{epoch}.pth".format(epoch=epoch) + ) save_model(model, optimizer, opt, epoch, save_file) # save the last model - save_file = os.path.join( - opt.save_folder, 'last.pth') + save_file = os.path.join(opt.save_folder, "last.pth") save_model(model, optimizer, opt, opt.epochs, save_file) - print('best accuracy: {:.2f}'.format(best_acc)) + print("best accuracy: {:.2f}".format(best_acc)) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/main_linear.py b/main_linear.py index 1b5e7960..579a5eef 100644 --- a/main_linear.py +++ b/main_linear.py @@ -1,101 +1,110 @@ -from __future__ import print_function - -import sys import argparse -import time import math +import sys +import time import torch -import torch.backends.cudnn as cudnn +from torch.backends import cudnn from main_ce import set_loader -from util import AverageMeter -from util import adjust_learning_rate, warmup_learning_rate, accuracy -from util import set_optimizer -from networks.resnet_big import SupConResNet, LinearClassifier - -try: - import apex - from apex import amp, optimizers -except ImportError: - pass +from networks.resnet_big import LinearClassifier, SupConResNet +from util import ( + AverageMeter, + accuracy, + adjust_learning_rate, + set_optimizer, + warmup_learning_rate, +) def parse_option(): - parser = argparse.ArgumentParser('argument for training') - - parser.add_argument('--print_freq', type=int, default=10, - help='print frequency') - parser.add_argument('--save_freq', type=int, default=50, - help='save frequency') - parser.add_argument('--batch_size', type=int, default=256, - help='batch_size') - parser.add_argument('--num_workers', type=int, default=16, - help='num of workers to use') - parser.add_argument('--epochs', type=int, default=100, - help='number of training epochs') + parser = argparse.ArgumentParser("argument for training") + + parser.add_argument("--print_freq", type=int, default=10, help="print frequency") + parser.add_argument("--save_freq", type=int, default=50, help="save frequency") + parser.add_argument("--batch_size", type=int, default=256, help="batch_size") + parser.add_argument( + "--num_workers", type=int, default=16, help="num of workers to use" + ) + parser.add_argument( + "--epochs", type=int, default=100, help="number of training epochs" + ) # optimization - parser.add_argument('--learning_rate', type=float, default=0.1, - help='learning rate') - parser.add_argument('--lr_decay_epochs', type=str, default='60,75,90', - help='where to decay lr, can be a list') - parser.add_argument('--lr_decay_rate', type=float, default=0.2, - help='decay rate for learning rate') - parser.add_argument('--weight_decay', type=float, default=0, - help='weight decay') - parser.add_argument('--momentum', type=float, default=0.9, - help='momentum') + parser.add_argument( + "--learning_rate", type=float, default=0.1, help="learning rate" + ) + parser.add_argument( + "--lr_decay_epochs", + type=str, + default="60,75,90", + help="where to decay lr, can be a list", + ) + parser.add_argument( + "--lr_decay_rate", type=float, default=0.2, help="decay rate for learning rate" + ) + parser.add_argument("--weight_decay", type=float, default=0, help="weight decay") + parser.add_argument("--momentum", type=float, default=0.9, help="momentum") # model dataset - parser.add_argument('--model', type=str, default='resnet50') - parser.add_argument('--dataset', type=str, default='cifar10', - choices=['cifar10', 'cifar100'], help='dataset') + parser.add_argument("--model", type=str, default="resnet50") + parser.add_argument( + "--dataset", + type=str, + default="cifar10", + choices=["cifar10", "cifar100"], + help="dataset", + ) # other setting - parser.add_argument('--cosine', action='store_true', - help='using cosine annealing') - parser.add_argument('--warm', action='store_true', - help='warm-up for large batch training') + parser.add_argument("--cosine", action="store_true", help="using cosine annealing") + parser.add_argument( + "--warm", action="store_true", help="warm-up for large batch training" + ) - parser.add_argument('--ckpt', type=str, default='', - help='path to pre-trained model') + parser.add_argument( + "--ckpt", type=str, default="", help="path to pre-trained model" + ) opt = parser.parse_args() # set the path according to the environment - opt.data_folder = './datasets/' + opt.data_folder = "./datasets/" - iterations = opt.lr_decay_epochs.split(',') + iterations = opt.lr_decay_epochs.split(",") opt.lr_decay_epochs = list([]) for it in iterations: opt.lr_decay_epochs.append(int(it)) - opt.model_name = '{}_{}_lr_{}_decay_{}_bsz_{}'.\ - format(opt.dataset, opt.model, opt.learning_rate, opt.weight_decay, - opt.batch_size) + opt.model_name = "{}_{}_lr_{}_decay_{}_bsz_{}".format( + opt.dataset, opt.model, opt.learning_rate, opt.weight_decay, opt.batch_size + ) if opt.cosine: - opt.model_name = '{}_cosine'.format(opt.model_name) + opt.model_name = "{}_cosine".format(opt.model_name) # warm-up for large-batch training, if opt.warm: - opt.model_name = '{}_warm'.format(opt.model_name) + opt.model_name = "{}_warm".format(opt.model_name) opt.warmup_from = 0.01 opt.warm_epochs = 10 if opt.cosine: - eta_min = opt.learning_rate * (opt.lr_decay_rate ** 3) - opt.warmup_to = eta_min + (opt.learning_rate - eta_min) * ( - 1 + math.cos(math.pi * opt.warm_epochs / opt.epochs)) / 2 + eta_min = opt.learning_rate * (opt.lr_decay_rate**3) + opt.warmup_to = ( + eta_min + + (opt.learning_rate - eta_min) + * (1 + math.cos(math.pi * opt.warm_epochs / opt.epochs)) + / 2 + ) else: opt.warmup_to = opt.learning_rate - if opt.dataset == 'cifar10': + if opt.dataset == "cifar10": opt.n_cls = 10 - elif opt.dataset == 'cifar100': + elif opt.dataset == "cifar100": opt.n_cls = 100 else: - raise ValueError('dataset not supported: {}'.format(opt.dataset)) + raise ValueError("dataset not supported: {}".format(opt.dataset)) return opt @@ -106,8 +115,8 @@ def set_model(opt): classifier = LinearClassifier(name=opt.model, num_classes=opt.n_cls) - ckpt = torch.load(opt.ckpt, map_location='cpu') - state_dict = ckpt['model'] + ckpt = torch.load(opt.ckpt, map_location="cpu") + state_dict = ckpt["model"] if torch.cuda.is_available(): if torch.cuda.device_count() > 1: @@ -125,7 +134,7 @@ def set_model(opt): model.load_state_dict(state_dict) else: - raise NotImplementedError('This code requires GPU') + raise NotImplementedError("This code requires GPU") return model, classifier, criterion @@ -173,13 +182,21 @@ def train(train_loader, model, classifier, criterion, optimizer, epoch, opt): # print info if (idx + 1) % opt.print_freq == 0: - print('Train: [{0}][{1}/{2}]\t' - 'BT {batch_time.val:.3f} ({batch_time.avg:.3f})\t' - 'DT {data_time.val:.3f} ({data_time.avg:.3f})\t' - 'loss {loss.val:.3f} ({loss.avg:.3f})\t' - 'Acc@1 {top1.val:.3f} ({top1.avg:.3f})'.format( - epoch, idx + 1, len(train_loader), batch_time=batch_time, - data_time=data_time, loss=losses, top1=top1)) + print( + "Train: [{0}][{1}/{2}]\t" + "BT {batch_time.val:.3f} ({batch_time.avg:.3f})\t" + "DT {data_time.val:.3f} ({data_time.avg:.3f})\t" + "loss {loss.val:.3f} ({loss.avg:.3f})\t" + "Acc@1 {top1.val:.3f} ({top1.avg:.3f})".format( + epoch, + idx + 1, + len(train_loader), + batch_time=batch_time, + data_time=data_time, + loss=losses, + top1=top1, + ) + ) sys.stdout.flush() return losses.avg, top1.avg @@ -215,14 +232,20 @@ def validate(val_loader, model, classifier, criterion, opt): end = time.time() if idx % opt.print_freq == 0: - print('Test: [{0}/{1}]\t' - 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' - 'Loss {loss.val:.4f} ({loss.avg:.4f})\t' - 'Acc@1 {top1.val:.3f} ({top1.avg:.3f})'.format( - idx, len(val_loader), batch_time=batch_time, - loss=losses, top1=top1)) - - print(' * Acc@1 {top1.avg:.3f}'.format(top1=top1)) + print( + "Test: [{0}/{1}]\t" + "Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t" + "Loss {loss.val:.4f} ({loss.avg:.4f})\t" + "Acc@1 {top1.val:.3f} ({top1.avg:.3f})".format( + idx, + len(val_loader), + batch_time=batch_time, + loss=losses, + top1=top1, + ) + ) + + print(" * Acc@1 {top1.avg:.3f}".format(top1=top1)) return losses.avg, top1.avg @@ -245,19 +268,23 @@ def main(): # train for one epoch time1 = time.time() - loss, acc = train(train_loader, model, classifier, criterion, - optimizer, epoch, opt) + loss, acc = train( + train_loader, model, classifier, criterion, optimizer, epoch, opt + ) time2 = time.time() - print('Train epoch {}, total time {:.2f}, accuracy:{:.2f}'.format( - epoch, time2 - time1, acc)) + print( + "Train epoch {}, total time {:.2f}, accuracy:{:.2f}".format( + epoch, time2 - time1, acc + ) + ) # eval for one epoch loss, val_acc = validate(val_loader, model, classifier, criterion, opt) if val_acc > best_acc: best_acc = val_acc - print('best accuracy: {:.2f}'.format(best_acc)) + print("best accuracy: {:.2f}".format(best_acc)) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/main_supcon.py b/main_supcon.py index ea6a625f..7689a669 100644 --- a/main_supcon.py +++ b/main_supcon.py @@ -1,119 +1,151 @@ -from __future__ import print_function - +import argparse +import math import os import sys -import argparse import time -import math import tensorboard_logger as tb_logger import torch -import torch.backends.cudnn as cudnn -from torchvision import transforms, datasets +from torch.backends import cudnn +from torchvision import datasets, transforms -from util import TwoCropTransform, AverageMeter -from util import adjust_learning_rate, warmup_learning_rate -from util import set_optimizer, save_model -from networks.resnet_big import SupConResNet from losses import SupConLoss - -try: - import apex - from apex import amp, optimizers -except ImportError: - pass +from networks.resnet_big import SupConResNet +from util import ( + AverageMeter, + TwoCropTransform, + adjust_learning_rate, + save_model, + set_optimizer, + warmup_learning_rate, +) def parse_option(): - parser = argparse.ArgumentParser('argument for training') - - parser.add_argument('--print_freq', type=int, default=10, - help='print frequency') - parser.add_argument('--save_freq', type=int, default=50, - help='save frequency') - parser.add_argument('--batch_size', type=int, default=256, - help='batch_size') - parser.add_argument('--num_workers', type=int, default=16, - help='num of workers to use') - parser.add_argument('--epochs', type=int, default=1000, - help='number of training epochs') + parser = argparse.ArgumentParser("argument for training") + + parser.add_argument("--print_freq", type=int, default=10, help="print frequency") + parser.add_argument("--save_freq", type=int, default=50, help="save frequency") + parser.add_argument("--batch_size", type=int, default=256, help="batch_size") + parser.add_argument( + "--num_workers", type=int, default=16, help="num of workers to use" + ) + parser.add_argument( + "--epochs", type=int, default=1000, help="number of training epochs" + ) # optimization - parser.add_argument('--learning_rate', type=float, default=0.05, - help='learning rate') - parser.add_argument('--lr_decay_epochs', type=str, default='700,800,900', - help='where to decay lr, can be a list') - parser.add_argument('--lr_decay_rate', type=float, default=0.1, - help='decay rate for learning rate') - parser.add_argument('--weight_decay', type=float, default=1e-4, - help='weight decay') - parser.add_argument('--momentum', type=float, default=0.9, - help='momentum') + parser.add_argument( + "--learning_rate", type=float, default=0.05, help="learning rate" + ) + parser.add_argument( + "--lr_decay_epochs", + type=str, + default="700,800,900", + help="where to decay lr, can be a list", + ) + parser.add_argument( + "--lr_decay_rate", type=float, default=0.1, help="decay rate for learning rate" + ) + parser.add_argument("--weight_decay", type=float, default=1e-4, help="weight decay") + parser.add_argument("--momentum", type=float, default=0.9, help="momentum") # model dataset - parser.add_argument('--model', type=str, default='resnet50') - parser.add_argument('--dataset', type=str, default='cifar10', - choices=['cifar10', 'cifar100', 'path'], help='dataset') - parser.add_argument('--mean', type=str, help='mean of dataset in path in form of str tuple') - parser.add_argument('--std', type=str, help='std of dataset in path in form of str tuple') - parser.add_argument('--data_folder', type=str, default=None, help='path to custom dataset') - parser.add_argument('--size', type=int, default=32, help='parameter for RandomResizedCrop') + parser.add_argument("--model", type=str, default="resnet50") + parser.add_argument( + "--dataset", + type=str, + default="cifar10", + choices=["cifar10", "cifar100", "path"], + help="dataset", + ) + parser.add_argument( + "--mean", type=str, help="mean of dataset in path in form of str tuple" + ) + parser.add_argument( + "--std", type=str, help="std of dataset in path in form of str tuple" + ) + parser.add_argument( + "--data_folder", type=str, default=None, help="path to custom dataset" + ) + parser.add_argument( + "--size", type=int, default=32, help="parameter for RandomResizedCrop" + ) # method - parser.add_argument('--method', type=str, default='SupCon', - choices=['SupCon', 'SimCLR'], help='choose method') + parser.add_argument( + "--method", + type=str, + default="SupCon", + choices=["SupCon", "SimCLR"], + help="choose method", + ) # temperature - parser.add_argument('--temp', type=float, default=0.07, - help='temperature for loss function') + parser.add_argument( + "--temp", type=float, default=0.07, help="temperature for loss function" + ) # other setting - parser.add_argument('--cosine', action='store_true', - help='using cosine annealing') - parser.add_argument('--syncBN', action='store_true', - help='using synchronized batch normalization') - parser.add_argument('--warm', action='store_true', - help='warm-up for large batch training') - parser.add_argument('--trial', type=str, default='0', - help='id for recording multiple runs') + parser.add_argument("--cosine", action="store_true", help="using cosine annealing") + parser.add_argument( + "--syncBN", action="store_true", help="using synchronized batch normalization" + ) + parser.add_argument( + "--warm", action="store_true", help="warm-up for large batch training" + ) + parser.add_argument( + "--trial", type=str, default="0", help="id for recording multiple runs" + ) opt = parser.parse_args() # check if dataset is path that passed required arguments - if opt.dataset == 'path': - assert opt.data_folder is not None \ - and opt.mean is not None \ - and opt.std is not None + if opt.dataset == "path": + assert ( + opt.data_folder is not None and opt.mean is not None and opt.std is not None + ) # set the path according to the environment if opt.data_folder is None: - opt.data_folder = './datasets/' - opt.model_path = './save/SupCon/{}_models'.format(opt.dataset) - opt.tb_path = './save/SupCon/{}_tensorboard'.format(opt.dataset) + opt.data_folder = "./datasets/" + opt.model_path = "./save/SupCon/{}_models".format(opt.dataset) + opt.tb_path = "./save/SupCon/{}_tensorboard".format(opt.dataset) - iterations = opt.lr_decay_epochs.split(',') + iterations = opt.lr_decay_epochs.split(",") opt.lr_decay_epochs = list([]) for it in iterations: opt.lr_decay_epochs.append(int(it)) - opt.model_name = '{}_{}_{}_lr_{}_decay_{}_bsz_{}_temp_{}_trial_{}'.\ - format(opt.method, opt.dataset, opt.model, opt.learning_rate, - opt.weight_decay, opt.batch_size, opt.temp, opt.trial) + opt.model_name = "{}_{}_{}_lr_{}_decay_{}_bsz_{}_temp_{}_trial_{}".format( + opt.method, + opt.dataset, + opt.model, + opt.learning_rate, + opt.weight_decay, + opt.batch_size, + opt.temp, + opt.trial, + ) if opt.cosine: - opt.model_name = '{}_cosine'.format(opt.model_name) + opt.model_name = "{}_cosine".format(opt.model_name) # warm-up for large-batch training, if opt.batch_size > 256: opt.warm = True if opt.warm: - opt.model_name = '{}_warm'.format(opt.model_name) + opt.model_name = "{}_warm".format(opt.model_name) opt.warmup_from = 0.01 opt.warm_epochs = 10 if opt.cosine: - eta_min = opt.learning_rate * (opt.lr_decay_rate ** 3) - opt.warmup_to = eta_min + (opt.learning_rate - eta_min) * ( - 1 + math.cos(math.pi * opt.warm_epochs / opt.epochs)) / 2 + eta_min = opt.learning_rate * (opt.lr_decay_rate**3) + opt.warmup_to = ( + eta_min + + (opt.learning_rate - eta_min) + * (1 + math.cos(math.pi * opt.warm_epochs / opt.epochs)) + / 2 + ) else: opt.warmup_to = opt.learning_rate @@ -130,48 +162,58 @@ def parse_option(): def set_loader(opt): # construct data loader - if opt.dataset == 'cifar10': + if opt.dataset == "cifar10": mean = (0.4914, 0.4822, 0.4465) std = (0.2023, 0.1994, 0.2010) - elif opt.dataset == 'cifar100': + elif opt.dataset == "cifar100": mean = (0.5071, 0.4867, 0.4408) std = (0.2675, 0.2565, 0.2761) - elif opt.dataset == 'path': + elif opt.dataset == "path": mean = eval(opt.mean) std = eval(opt.std) else: - raise ValueError('dataset not supported: {}'.format(opt.dataset)) + raise ValueError("dataset not supported: {}".format(opt.dataset)) normalize = transforms.Normalize(mean=mean, std=std) - train_transform = transforms.Compose([ - transforms.RandomResizedCrop(size=opt.size, scale=(0.2, 1.)), - transforms.RandomHorizontalFlip(), - transforms.RandomApply([ - transforms.ColorJitter(0.4, 0.4, 0.4, 0.1) - ], p=0.8), - transforms.RandomGrayscale(p=0.2), - transforms.ToTensor(), - normalize, - ]) - - if opt.dataset == 'cifar10': - train_dataset = datasets.CIFAR10(root=opt.data_folder, - transform=TwoCropTransform(train_transform), - download=True) - elif opt.dataset == 'cifar100': - train_dataset = datasets.CIFAR100(root=opt.data_folder, - transform=TwoCropTransform(train_transform), - download=True) - elif opt.dataset == 'path': - train_dataset = datasets.ImageFolder(root=opt.data_folder, - transform=TwoCropTransform(train_transform)) + train_transform = transforms.Compose( + [ + transforms.RandomResizedCrop(size=opt.size, scale=(0.2, 1.0)), + transforms.RandomHorizontalFlip(), + transforms.RandomApply([transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)], p=0.8), + transforms.RandomGrayscale(p=0.2), + transforms.ToTensor(), + normalize, + ] + ) + + if opt.dataset == "cifar10": + train_dataset = datasets.CIFAR10( + root=opt.data_folder, + transform=TwoCropTransform(train_transform), + download=True, + ) + elif opt.dataset == "cifar100": + train_dataset = datasets.CIFAR100( + root=opt.data_folder, + transform=TwoCropTransform(train_transform), + download=True, + ) + elif opt.dataset == "path": + train_dataset = datasets.ImageFolder( + root=opt.data_folder, transform=TwoCropTransform(train_transform) + ) else: raise ValueError(opt.dataset) train_sampler = None train_loader = torch.utils.data.DataLoader( - train_dataset, batch_size=opt.batch_size, shuffle=(train_sampler is None), - num_workers=opt.num_workers, pin_memory=True, sampler=train_sampler) + train_dataset, + batch_size=opt.batch_size, + shuffle=(train_sampler is None), + num_workers=opt.num_workers, + pin_memory=True, + sampler=train_sampler, + ) return train_loader @@ -219,13 +261,12 @@ def train(train_loader, model, criterion, optimizer, epoch, opt): features = model(images) f1, f2 = torch.split(features, [bsz, bsz], dim=0) features = torch.cat([f1.unsqueeze(1), f2.unsqueeze(1)], dim=1) - if opt.method == 'SupCon': + if opt.method == "SupCon": loss = criterion(features, labels) - elif opt.method == 'SimCLR': + elif opt.method == "SimCLR": loss = criterion(features) else: - raise ValueError('contrastive method not supported: {}'. - format(opt.method)) + raise ValueError("contrastive method not supported: {}".format(opt.method)) # update metric losses.update(loss.item(), bsz) @@ -241,12 +282,19 @@ def train(train_loader, model, criterion, optimizer, epoch, opt): # print info if (idx + 1) % opt.print_freq == 0: - print('Train: [{0}][{1}/{2}]\t' - 'BT {batch_time.val:.3f} ({batch_time.avg:.3f})\t' - 'DT {data_time.val:.3f} ({data_time.avg:.3f})\t' - 'loss {loss.val:.3f} ({loss.avg:.3f})'.format( - epoch, idx + 1, len(train_loader), batch_time=batch_time, - data_time=data_time, loss=losses)) + print( + "Train: [{0}][{1}/{2}]\t" + "BT {batch_time.val:.3f} ({batch_time.avg:.3f})\t" + "DT {data_time.val:.3f} ({data_time.avg:.3f})\t" + "loss {loss.val:.3f} ({loss.avg:.3f})".format( + epoch, + idx + 1, + len(train_loader), + batch_time=batch_time, + data_time=data_time, + loss=losses, + ) + ) sys.stdout.flush() return losses.avg @@ -275,22 +323,22 @@ def main(): time1 = time.time() loss = train(train_loader, model, criterion, optimizer, epoch, opt) time2 = time.time() - print('epoch {}, total time {:.2f}'.format(epoch, time2 - time1)) + print("epoch {}, total time {:.2f}".format(epoch, time2 - time1)) # tensorboard logger - logger.log_value('loss', loss, epoch) - logger.log_value('learning_rate', optimizer.param_groups[0]['lr'], epoch) + logger.log_value("loss", loss, epoch) + logger.log_value("learning_rate", optimizer.param_groups[0]["lr"], epoch) if epoch % opt.save_freq == 0: save_file = os.path.join( - opt.save_folder, 'ckpt_epoch_{epoch}.pth'.format(epoch=epoch)) + opt.save_folder, "ckpt_epoch_{epoch}.pth".format(epoch=epoch) + ) save_model(model, optimizer, opt, epoch, save_file) # save the last model - save_file = os.path.join( - opt.save_folder, 'last.pth') + save_file = os.path.join(opt.save_folder, "last.pth") save_model(model, optimizer, opt, opt.epochs, save_file) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/networks/resnet_big.py b/networks/resnet_big.py index 4f4e37df..edd5e481 100644 --- a/networks/resnet_big.py +++ b/networks/resnet_big.py @@ -5,8 +5,8 @@ Adapted from: https://github.com/bearpaw/pytorch-classification """ import torch -import torch.nn as nn -import torch.nn.functional as F +from torch import nn +from torch.functional as F class BasicBlock(nn.Module): @@ -15,16 +15,26 @@ class BasicBlock(nn.Module): def __init__(self, in_planes, planes, stride=1, is_last=False): super(BasicBlock, self).__init__() self.is_last = is_last - self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.conv1 = nn.Conv2d( + in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False + ) self.bn1 = nn.BatchNorm2d(planes) - self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) + self.conv2 = nn.Conv2d( + planes, planes, kernel_size=3, stride=1, padding=1, bias=False + ) self.bn2 = nn.BatchNorm2d(planes) self.shortcut = nn.Sequential() if stride != 1 or in_planes != self.expansion * planes: self.shortcut = nn.Sequential( - nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), - nn.BatchNorm2d(self.expansion * planes) + nn.Conv2d( + in_planes, + self.expansion * planes, + kernel_size=1, + stride=stride, + bias=False, + ), + nn.BatchNorm2d(self.expansion * planes), ) def forward(self, x): @@ -47,16 +57,26 @@ def __init__(self, in_planes, planes, stride=1, is_last=False): self.is_last = is_last self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) - self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.conv2 = nn.Conv2d( + planes, planes, kernel_size=3, stride=stride, padding=1, bias=False + ) self.bn2 = nn.BatchNorm2d(planes) - self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False) + self.conv3 = nn.Conv2d( + planes, self.expansion * planes, kernel_size=1, bias=False + ) self.bn3 = nn.BatchNorm2d(self.expansion * planes) self.shortcut = nn.Sequential() if stride != 1 or in_planes != self.expansion * planes: self.shortcut = nn.Sequential( - nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), - nn.BatchNorm2d(self.expansion * planes) + nn.Conv2d( + in_planes, + self.expansion * planes, + kernel_size=1, + stride=stride, + bias=False, + ), + nn.BatchNorm2d(self.expansion * planes), ) def forward(self, x): @@ -77,8 +97,9 @@ def __init__(self, block, num_blocks, in_channel=3, zero_init_residual=False): super(ResNet, self).__init__() self.in_planes = 64 - self.conv1 = nn.Conv2d(in_channel, 64, kernel_size=3, stride=1, padding=1, - bias=False) + self.conv1 = nn.Conv2d( + in_channel, 64, kernel_size=3, stride=1, padding=1, bias=False + ) self.bn1 = nn.BatchNorm2d(64) self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) @@ -88,7 +109,7 @@ def __init__(self, block, num_blocks, in_channel=3, zero_init_residual=False): for m in self.modules(): if isinstance(m, nn.Conv2d): - nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) @@ -141,15 +162,16 @@ def resnet101(**kwargs): model_dict = { - 'resnet18': [resnet18, 512], - 'resnet34': [resnet34, 512], - 'resnet50': [resnet50, 2048], - 'resnet101': [resnet101, 2048], + "resnet18": [resnet18, 512], + "resnet34": [resnet34, 512], + "resnet50": [resnet50, 2048], + "resnet101": [resnet101, 2048], } class LinearBatchNorm(nn.Module): """Implements BatchNorm1d by BatchNorm2d, for SyncBN purpose""" + def __init__(self, dim, affine=True): super(LinearBatchNorm, self).__init__() self.dim = dim @@ -164,21 +186,21 @@ def forward(self, x): class SupConResNet(nn.Module): """backbone + projection head""" - def __init__(self, name='resnet50', head='mlp', feat_dim=128): + + def __init__(self, name="resnet50", head="mlp", feat_dim=128): super(SupConResNet, self).__init__() model_fun, dim_in = model_dict[name] self.encoder = model_fun() - if head == 'linear': + if head == "linear": self.head = nn.Linear(dim_in, feat_dim) - elif head == 'mlp': + elif head == "mlp": self.head = nn.Sequential( nn.Linear(dim_in, dim_in), nn.ReLU(inplace=True), - nn.Linear(dim_in, feat_dim) + nn.Linear(dim_in, feat_dim), ) else: - raise NotImplementedError( - 'head not supported: {}'.format(head)) + raise NotImplementedError("head not supported: {}".format(head)) def forward(self, x): feat = self.encoder(x) @@ -188,7 +210,8 @@ def forward(self, x): class SupCEResNet(nn.Module): """encoder + classifier""" - def __init__(self, name='resnet50', num_classes=10): + + def __init__(self, name="resnet50", num_classes=10): super(SupCEResNet, self).__init__() model_fun, dim_in = model_dict[name] self.encoder = model_fun() @@ -200,7 +223,8 @@ def forward(self, x): class LinearClassifier(nn.Module): """Linear classifier""" - def __init__(self, name='resnet50', num_classes=10): + + def __init__(self, name="resnet50", num_classes=10): super(LinearClassifier, self).__init__() _, feat_dim = model_dict[name] self.fc = nn.Linear(feat_dim, num_classes) diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..3e7e0d08 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,15 @@ +[tool.poetry] +name = "supervised-contrastive-loss" +version = "0.1.0" +description = "" +authors = ["Sean (Seok-Won) Yi "] +readme = "README.md" +packages = [{include = "supervised_contrastive_loss"}] + +[tool.poetry.dependencies] +python = "^3.10" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/util.py b/util.py index b6323530..0e277756 100644 --- a/util.py +++ b/util.py @@ -1,13 +1,15 @@ from __future__ import print_function import math + import numpy as np import torch -import torch.optim as optim +from torch import optime class TwoCropTransform: """Create two crops of the same image""" + def __init__(self, transform): self.transform = transform @@ -17,6 +19,7 @@ def __call__(self, x): class AverageMeter(object): """Computes and stores the average and current value""" + def __init__(self): self.reset() @@ -53,43 +56,47 @@ def accuracy(output, target, topk=(1,)): def adjust_learning_rate(args, optimizer, epoch): lr = args.learning_rate if args.cosine: - eta_min = lr * (args.lr_decay_rate ** 3) - lr = eta_min + (lr - eta_min) * ( - 1 + math.cos(math.pi * epoch / args.epochs)) / 2 + eta_min = lr * (args.lr_decay_rate**3) + lr = ( + eta_min + (lr - eta_min) * (1 + math.cos(math.pi * epoch / args.epochs)) / 2 + ) else: steps = np.sum(epoch > np.asarray(args.lr_decay_epochs)) if steps > 0: - lr = lr * (args.lr_decay_rate ** steps) + lr = lr * (args.lr_decay_rate**steps) for param_group in optimizer.param_groups: - param_group['lr'] = lr + param_group["lr"] = lr def warmup_learning_rate(args, epoch, batch_id, total_batches, optimizer): if args.warm and epoch <= args.warm_epochs: - p = (batch_id + (epoch - 1) * total_batches) / \ - (args.warm_epochs * total_batches) + p = (batch_id + (epoch - 1) * total_batches) / ( + args.warm_epochs * total_batches + ) lr = args.warmup_from + p * (args.warmup_to - args.warmup_from) for param_group in optimizer.param_groups: - param_group['lr'] = lr + param_group["lr"] = lr def set_optimizer(opt, model): - optimizer = optim.SGD(model.parameters(), - lr=opt.learning_rate, - momentum=opt.momentum, - weight_decay=opt.weight_decay) + optimizer = optim.SGD( + model.parameters(), + lr=opt.learning_rate, + momentum=opt.momentum, + weight_decay=opt.weight_decay, + ) return optimizer def save_model(model, optimizer, opt, epoch, save_file): - print('==> Saving...') + print("==> Saving...") state = { - 'opt': opt, - 'model': model.state_dict(), - 'optimizer': optimizer.state_dict(), - 'epoch': epoch, + "opt": opt, + "model": model.state_dict(), + "optimizer": optimizer.state_dict(), + "epoch": epoch, } torch.save(state, save_file) del state From f20e1257d995b0fa22c1461a095a9466c6f8cce0 Mon Sep 17 00:00:00 2001 From: "Sean (Seok-Won) Yi" Date: Wed, 25 Oct 2023 16:06:41 +0900 Subject: [PATCH 3/6] Poetry files. --- poetry.lock | 569 +++++++++++++++++++++++++++++++++++++++++++++++++ pyproject.toml | 5 +- 2 files changed, 572 insertions(+), 2 deletions(-) create mode 100644 poetry.lock diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 00000000..bc072cc7 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,569 @@ +# This file is automatically @generated by Poetry 1.4.0 and should not be changed by hand. + +[[package]] +name = "certifi" +version = "2023.7.22" +description = "Python package for providing Mozilla's CA Bundle." +category = "main" +optional = false +python-versions = ">=3.6" +files = [ + {file = "certifi-2023.7.22-py3-none-any.whl", hash = "sha256:92d6037539857d8206b8f6ae472e8b77db8058fec5937a1ef3f54304089edbb9"}, + {file = "certifi-2023.7.22.tar.gz", hash = "sha256:539cc1d13202e33ca466e88b2807e29f4c13049d6d87031a3c110744495cb082"}, +] + +[[package]] +name = "charset-normalizer" +version = "3.3.1" +description = "The Real First Universal Charset Detector. 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[tool.poetry.dependencies] python = "^3.10" - +torch = {url = "https://download.pytorch.org/whl/cu118/torch-2.0.0%2Bcu118-cp310-cp310-linux_x86_64.whl"} +torchvision = {url = "https://download.pytorch.org/whl/cu118/torchvision-0.15.0%2Bcu118-cp310-cp310-linux_x86_64.whl"} [build-system] requires = ["poetry-core"] From 477e37043a6a193f84e955d4f4096475da4484a2 Mon Sep 17 00:00:00 2001 From: "Sean (Seok-Won) Yi" Date: Wed, 25 Oct 2023 16:06:55 +0900 Subject: [PATCH 4/6] Fixed erroneous import statement. --- networks/resnet_big.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/networks/resnet_big.py b/networks/resnet_big.py index edd5e481..2299e410 100644 --- a/networks/resnet_big.py +++ b/networks/resnet_big.py @@ -6,7 +6,7 @@ """ import torch from torch import nn -from torch.functional as F +from torch.nn import functional as F class BasicBlock(nn.Module): From fc9996624191685c9145ea630092588dafd3342f Mon Sep 17 00:00:00 2001 From: "Sean (Seok-Won) Yi" Date: Wed, 25 Oct 2023 16:11:57 +0900 Subject: [PATCH 5/6] Removed TensorBoard and change logger. --- main_ce.py | 26 +++++--------------------- main_supcon.py | 20 ++------------------ 2 files changed, 7 insertions(+), 39 deletions(-) diff --git a/main_ce.py b/main_ce.py index 3a826ce1..5c0f1cb1 100644 --- a/main_ce.py +++ b/main_ce.py @@ -4,7 +4,6 @@ import sys import time -import tensorboard_logger as tb_logger import torch from torch.backends import cudnn from torchvision import datasets, transforms @@ -61,9 +60,6 @@ def parse_option(): # other setting parser.add_argument("--cosine", action="store_true", help="using cosine annealing") - parser.add_argument( - "--syncBN", action="store_true", help="using synchronized batch normalization" - ) parser.add_argument( "--warm", action="store_true", help="warm-up for large batch training" ) @@ -76,7 +72,6 @@ def parse_option(): # set the path according to the environment opt.data_folder = "./datasets/" opt.model_path = "./save/SupCon/{}_models".format(opt.dataset) - opt.tb_path = "./save/SupCon/{}_tensorboard".format(opt.dataset) iterations = opt.lr_decay_epochs.split(",") opt.lr_decay_epochs = list([]) @@ -113,10 +108,6 @@ def parse_option(): else: opt.warmup_to = opt.learning_rate - opt.tb_folder = os.path.join(opt.tb_path, opt.model_name) - if not os.path.isdir(opt.tb_folder): - os.makedirs(opt.tb_folder) - opt.save_folder = os.path.join(opt.model_path, opt.model_name) if not os.path.isdir(opt.save_folder): os.makedirs(opt.save_folder) @@ -196,10 +187,6 @@ def set_model(opt): model = SupCEResNet(name=opt.model, num_classes=opt.n_cls) criterion = torch.nn.CrossEntropyLoss() - # enable synchronized Batch Normalization - if opt.syncBN: - model = apex.parallel.convert_syncbn_model(model) - if torch.cuda.is_available(): if torch.cuda.device_count() > 1: model = torch.nn.DataParallel(model) @@ -329,9 +316,6 @@ def main(): # build optimizer optimizer = set_optimizer(opt, model) - # tensorboard - logger = tb_logger.Logger(logdir=opt.tb_folder, flush_secs=2) - # training routine for epoch in range(1, opt.epochs + 1): adjust_learning_rate(opt, optimizer, epoch) @@ -343,14 +327,14 @@ def main(): print("epoch {}, total time {:.2f}".format(epoch, time2 - time1)) # tensorboard logger - logger.log_value("train_loss", loss, epoch) - logger.log_value("train_acc", train_acc, epoch) - logger.log_value("learning_rate", optimizer.param_groups[0]["lr"], epoch) + print(f"epoch {epoch} train_loss: {loss}") + print(f"\ttrain_acc: {train_acc}") + print(f"\tlearning_rate {optimizer.param_groups[0]['lr']}") # evaluation loss, val_acc = validate(val_loader, model, criterion, opt) - logger.log_value("val_loss", loss, epoch) - logger.log_value("val_acc", val_acc, epoch) + print(f"epoch {epoch} val_loss: {loss}") + print(f"\tval_acc: {val_acc}") if val_acc > best_acc: best_acc = val_acc diff --git a/main_supcon.py b/main_supcon.py index 7689a669..1372f184 100644 --- a/main_supcon.py +++ b/main_supcon.py @@ -4,7 +4,6 @@ import sys import time -import tensorboard_logger as tb_logger import torch from torch.backends import cudnn from torchvision import datasets, transforms @@ -88,9 +87,6 @@ def parse_option(): # other setting parser.add_argument("--cosine", action="store_true", help="using cosine annealing") - parser.add_argument( - "--syncBN", action="store_true", help="using synchronized batch normalization" - ) parser.add_argument( "--warm", action="store_true", help="warm-up for large batch training" ) @@ -110,7 +106,6 @@ def parse_option(): if opt.data_folder is None: opt.data_folder = "./datasets/" opt.model_path = "./save/SupCon/{}_models".format(opt.dataset) - opt.tb_path = "./save/SupCon/{}_tensorboard".format(opt.dataset) iterations = opt.lr_decay_epochs.split(",") opt.lr_decay_epochs = list([]) @@ -149,10 +144,6 @@ def parse_option(): else: opt.warmup_to = opt.learning_rate - opt.tb_folder = os.path.join(opt.tb_path, opt.model_name) - if not os.path.isdir(opt.tb_folder): - os.makedirs(opt.tb_folder) - opt.save_folder = os.path.join(opt.model_path, opt.model_name) if not os.path.isdir(opt.save_folder): os.makedirs(opt.save_folder) @@ -222,10 +213,6 @@ def set_model(opt): model = SupConResNet(name=opt.model) criterion = SupConLoss(temperature=opt.temp) - # enable synchronized Batch Normalization - if opt.syncBN: - model = apex.parallel.convert_syncbn_model(model) - if torch.cuda.is_available(): if torch.cuda.device_count() > 1: model.encoder = torch.nn.DataParallel(model.encoder) @@ -312,9 +299,6 @@ def main(): # build optimizer optimizer = set_optimizer(opt, model) - # tensorboard - logger = tb_logger.Logger(logdir=opt.tb_folder, flush_secs=2) - # training routine for epoch in range(1, opt.epochs + 1): adjust_learning_rate(opt, optimizer, epoch) @@ -326,8 +310,8 @@ def main(): print("epoch {}, total time {:.2f}".format(epoch, time2 - time1)) # tensorboard logger - logger.log_value("loss", loss, epoch) - logger.log_value("learning_rate", optimizer.param_groups[0]["lr"], epoch) + print(f"epoch {epoch} loss: {loss}") + print(f"learning_rate: {optimizer.param_groups[0]['lr']}") if epoch % opt.save_freq == 0: save_file = os.path.join( From b85b0fa94851a84a70173d1d0b20dd83df61bd52 Mon Sep 17 00:00:00 2001 From: "Sean (Seok-Won) Yi" Date: Wed, 25 Oct 2023 16:12:26 +0900 Subject: [PATCH 6/6] Fixed erroneous import name. --- util.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/util.py b/util.py index 0e277756..436156a4 100644 --- a/util.py +++ b/util.py @@ -4,7 +4,7 @@ import numpy as np import torch -from torch import optime +from torch import optim class TwoCropTransform: