The relationship between contrastive accuracy and linear evaluation performance? #8
Comments
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Hi @WeihongM, you may already understand this, but in case not: the contrastive task and the linear evaluation task are different tasks. The contrastive task is to classify between matching and unmatching views, e.g., "do these two crops come from the same image?" The linear evaluation task is typically something different, like "which of the 10 cifar classes does this image belong to?" The transfer task is often harder than the contrastive task so you could do very well on the contrastive task while performing poorly on the transfer task. |
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Hello, @phillipi thanks for your reply. |
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It's possible that for character classification, the views/augmentations you are using are not the best ones. For example, you wouldn't want to use color jitter if the characters are black and white. If you have enough supervised examples, then it will be hard to beat training a supervised net from scratch. The self-supervised learning should help the most when you don't have many labeled exmples to learn from. Mileage definitely varies between different tasks. |
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Hello, @phillipi |
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Right, color jitter is probably degenerate on black and white images. Other augmentations like cropping should still be fine. |
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I will try it. Thanks for your advice. |
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Hello, I'm in SCAU next door to you. I'm also studying unsupervised learning recently. I'd like to ask you about it. I've already sent it to your email address. Could you have a look？thx |
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Hello, This python file is for reference. |
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能加你微信吗老哥，有问题可以再向你请教
…------------------ 原始邮件 ------------------
发件人: "HobbitLong/PyContrast" <[email protected]>;
发送时间: 2020年9月16日(星期三) 上午9:50
收件人: "HobbitLong/PyContrast"<[email protected]>;
抄送: "326"<[email protected]>;"Comment"<[email protected]>;
主题: Re: [HobbitLong/PyContrast] The relationship between contrastive accuracy and linear evaluation performance? (#8)
This python file is for reference.
`
#!/usr/bin/env python
Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import builtins
import math
import os
import random
import shutil
import time
import warnings
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
from moco.net import Net
import moco.loader
import moco.builder
model_names = sorted(name for name in models.dict
if name.islower() and not name.startswith("__")
and callable(models.dict[name]))
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('data', metavar='DIR',
help='path to dataset')
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet50',
choices=model_names,
help='model architecture: ' +
' | '.join(model_names) +
' (default: resnet50)')
parser.add_argument('-j', '--workers', default=32, type=int, metavar='N',
help='number of data loading workers (default: 32)')
parser.add_argument('--epochs', default=200, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=256, type=int,
metavar='N',
help='mini-batch size (default: 256), this is the total '
'batch size of all GPUs on the current node when '
'using Data Parallel or Distributed Data Parallel')
parser.add_argument('--lr', '--learning-rate', default=0.03, type=float,
metavar='LR', help='initial learning rate', dest='lr')
parser.add_argument('--schedule', default=[120, 160], nargs='*', type=int,
help='learning rate schedule (when to drop lr by 10x)')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
help='momentum of SGD solver')
parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,
metavar='W', help='weight decay (default: 1e-4)',
dest='weight_decay')
parser.add_argument('-p', '--print-freq', default=10, type=int,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('--world-size', default=-1, type=int,
help='number of nodes for distributed training')
parser.add_argument('--rank', default=-1, type=int,
help='node rank for distributed training')
parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='nccl', type=str,
help='distributed backend')
parser.add_argument('--seed', default=None, type=int,
help='seed for initializing training. ')
parser.add_argument('--gpu', default=None, type=int,
help='GPU id to use.')
parser.add_argument('--multiprocessing-distributed', action='store_true',
help='Use multi-processing distributed training to launch '
'N processes per node, which has N GPUs. This is the '
'fastest way to use PyTorch for either single node or '
'multi node data parallel training')
moco specific configs:
parser.add_argument('--moco-dim', default=128, type=int,
help='feature dimension (default: 128)')
parser.add_argument('--moco-k', default=65536, type=int,
help='queue size; number of negative keys (default: 65536)')
parser.add_argument('--moco-m', default=0.999, type=float,
help='moco momentum of updating key encoder (default: 0.999)')
parser.add_argument('--moco-t', default=0.07, type=float,
help='softmax temperature (default: 0.07)')
options for moco v2
parser.add_argument('--mlp', action='store_true',
help='use mlp head')
parser.add_argument('--aug-plus', action='store_true',
help='use moco v2 data augmentation')
parser.add_argument('--cos', action='store_true',
help='use cosine lr schedule')
def main():
args = parser.parse_args()
if args.seed is not None: random.seed(args.seed) torch.manual_seed(args.seed) cudnn.deterministic = True warnings.warn('You have chosen to seed training. ' 'This will turn on the CUDNN deterministic setting, ' 'which can slow down your training considerably! ' 'You may see unexpected behavior when restarting ' 'from checkpoints.') if args.gpu is not None: warnings.warn('You have chosen a specific GPU. This will completely ' 'disable data parallelism.') if args.dist_url == "env://" and args.world_size == -1: args.world_size = int(os.environ["WORLD_SIZE"]) args.distributed = args.world_size > 1 or args.multiprocessing_distributed ngpus_per_node = torch.cuda.device_count() if args.multiprocessing_distributed: # Since we have ngpus_per_node processes per node, the total world_size # needs to be adjusted accordingly args.world_size = ngpus_per_node * args.world_size # Use torch.multiprocessing.spawn to launch distributed processes: the # main_worker process function mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args)) else: # Simply call main_worker function main_worker(args.gpu, ngpus_per_node, args)
def main_worker(gpu, ngpus_per_node, args):
args.gpu = gpu
# suppress printing if not master if args.multiprocessing_distributed and args.gpu != 0: def print_pass(*args): pass builtins.print = print_pass if args.gpu is not None: print("Use GPU: {} for training".format(args.gpu)) if args.distributed: if args.dist_url == "env://" and args.rank == -1: args.rank = int(os.environ["RANK"]) if args.multiprocessing_distributed: # For multiprocessing distributed training, rank needs to be the # global rank among all the processes args.rank = args.rank * ngpus_per_node + gpu dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank) # create model print("=> creating model '{}'".format(args.arch)) model = moco.builder.MoCo( # models.__dict__[args.arch], Net, args.moco_dim, args.moco_k, args.moco_m, args.moco_t, args.mlp) print(model) if args.distributed: # For multiprocessing distributed, DistributedDataParallel constructor # should always set the single device scope, otherwise, # DistributedDataParallel will use all available devices. if args.gpu is not None: torch.cuda.set_device(args.gpu) model.cuda(args.gpu) # When using a single GPU per process and per # DistributedDataParallel, we need to divide the batch size # ourselves based on the total number of GPUs we have args.batch_size = int(args.batch_size / ngpus_per_node) args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node) model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) else: model.cuda() # DistributedDataParallel will divide and allocate batch_size to all # available GPUs if device_ids are not set model = torch.nn.parallel.DistributedDataParallel(model) elif args.gpu is not None: torch.cuda.set_device(args.gpu) model = model.cuda(args.gpu) # comment out the following line for debugging raise NotImplementedError("Only DistributedDataParallel is supported.") else: # AllGather implementation (batch shuffle, queue update, etc.) in # this code only supports DistributedDataParallel. raise NotImplementedError("Only DistributedDataParallel is supported.") # define loss function (criterion) and optimizer criterion = nn.CrossEntropyLoss().cuda(args.gpu) optimizer = torch.optim.SGD(model.parameters(), args.lr, momentum=args.momentum, weight_decay=args.weight_decay) # optionally resume from a checkpoint if args.resume: if os.path.isfile(args.resume): print("=> loading checkpoint '{}'".format(args.resume)) if args.gpu is None: checkpoint = torch.load(args.resume) else: # Map model to be loaded to specified single gpu. loc = 'cuda:{}'.format(args.gpu) checkpoint = torch.load(args.resume, map_location=loc) args.start_epoch = checkpoint['epoch'] model.load_state_dict(checkpoint['state_dict']) optimizer.load_state_dict(checkpoint['optimizer']) print("=> loaded checkpoint '{}' (epoch {})" .format(args.resume, checkpoint['epoch'])) else: print("=> no checkpoint found at '{}'".format(args.resume)) cudnn.benchmark = True # Data loading code # traindir = os.path.join(args.data, 'train') # normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], # std=[0.229, 0.224, 0.225]) normalize = transforms.Normalize(mean=[0.1307,], std=[0.3081,]) if args.aug_plus: # MoCo v2's aug: similar to SimCLR https://arxiv.org/abs/2002.05709 augmentation = [ # transforms.RandomResizedCrop(224, scale=(0.2, 1.)), transforms.RandomApply([ transforms.ColorJitter(0.4, 0.4, 0.4, 0.1) # not strengthened ], p=0.8), transforms.RandomGrayscale(p=0.2), transforms.RandomApply([moco.loader.GaussianBlur([.1, 2.])], p=0.5), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize ] else: # MoCo v1's aug: the same as InstDisc https://arxiv.org/abs/1805.01978 augmentation = [ # transforms.RandomResizedCrop(224, scale=(0.2, 1.)), transforms.RandomGrayscale(p=0.2), transforms.ColorJitter(0.4, 0.4, 0.4, 0.4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize ] # train_dataset = datasets.ImageFolder( # traindir, # moco.loader.TwoCropsTransform(transforms.Compose(augmentation))) train_dataset = datasets.MNIST(root = "./data/", transform=moco.loader.TwoCropsTransform(transforms.Compose(augmentation)), train = True, download = True) if args.distributed: train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) else: train_sampler = None train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None), num_workers=args.workers, pin_memory=True, sampler=train_sampler, drop_last=True) for epoch in range(args.start_epoch, args.epochs): if args.distributed: train_sampler.set_epoch(epoch) adjust_learning_rate(optimizer, epoch, args) # train for one epoch train(train_loader, model, criterion, optimizer, epoch, args) if not args.multiprocessing_distributed or (args.multiprocessing_distributed and args.rank % ngpus_per_node == 0): save_checkpoint({ 'epoch': epoch + 1, 'arch': args.arch, 'state_dict': model.state_dict(), 'optimizer' : optimizer.state_dict(), }, is_best=False, filename='checkpoint_{:04d}.pth.tar'.format(epoch))
def train(train_loader, model, criterion, optimizer, epoch, args):
batch_time = AverageMeter('Time', ':6.3f')
data_time = AverageMeter('Data', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
top1 = AverageMeter('Acc@1', ':6.2f')
top5 = AverageMeter('Acc@5', ':6.2f')
progress = ProgressMeter(
len(train_loader),
[batch_time, data_time, losses, top1, top5],
prefix="Epoch: [{}]".format(epoch))
# switch to train mode model.train() end = time.time() for i, (images, _) in enumerate(train_loader): # measure data loading time data_time.update(time.time() - end) if args.gpu is not None: images[0] = images[0].cuda(args.gpu, non_blocking=True) images[1] = images[1].cuda(args.gpu, non_blocking=True) # compute output output, target = model(im_q=images[0], im_k=images[1]) loss = criterion(output, target) # acc1/acc5 are (K+1)-way contrast classifier accuracy # measure accuracy and record loss acc1, acc5 = accuracy(output, target, topk=(1, 5)) losses.update(loss.item(), images[0].size(0)) top1.update(acc1[0], images[0].size(0)) top5.update(acc5[0], images[0].size(0)) # compute gradient and do SGD step optimizer.zero_grad() loss.backward() optimizer.step() # measure elapsed time batch_time.update(time.time() - end) end = time.time() if i % args.print_freq == 0: progress.display(i)
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
torch.save(state, filename)
if is_best:
shutil.copyfile(filename, 'model_best.pth.tar')
class AverageMeter(object):
"""Computes and stores the average and current value"""
def init(self, name, fmt=':f'):
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 / self.count def __str__(self): fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})' return fmtstr.format(**self.__dict__)
class ProgressMeter(object):
def init(self, num_batches, meters, prefix=""):
self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
self.meters = meters
self.prefix = prefix
def display(self, batch): entries = [self.prefix + self.batch_fmtstr.format(batch)] entries += [str(meter) for meter in self.meters] print('\t'.join(entries)) def _get_batch_fmtstr(self, num_batches): num_digits = len(str(num_batches // 1)) fmt = '{:' + str(num_digits) + 'd}' return '[' + fmt + '/' + fmt.format(num_batches) + ']'
def adjust_learning_rate(optimizer, epoch, args):
"""Decay the learning rate based on schedule"""
lr = args.lr
if args.cos: # cosine lr schedule
lr *= 0.5 * (1. + math.cos(math.pi * epoch / args.epochs))
else: # stepwise lr schedule
for milestone in args.schedule:
lr *= 0.1 if epoch >= milestone else 1.
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def accuracy(output, target, topk=(1,)):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True) pred = pred.t() correct = pred.eq(target.view(1, -1).expand_as(pred)) res = [] for k in topk: correct_k = correct[:k].view(-1).float().sum(0, keepdim=True) res.append(correct_k.mul_(100.0 / batch_size)) return res
if name == 'main':
main()
`
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大佬我遇到了一个错误，No module named 'moco.net'，pip install找不到这个库 |
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Hello, @HobbitLong
I have succeeded in training with cifar-10 dataset. And now I train with these methods on the custom dataset.
For example, when I apply MoCo method, I change the length of the queue and other parameters. I find that it is very hard to judge the convergence of the model.
The contrastive accuracy can reach a very high level(nearly 95%), and the loss value is nearly stable. However, when I use linear evaluation, the top-1 accuracy is only 50%. I find the contrastive accuracy rate has not direct relationship with the linear evaluation performance.
This means, Sometimes when I use larger queue, and the contrastive accuracy is smaller than the smaller queue length. However, the performance on linear evaluation is that the larger queue is better than a smaller queue.
Can you give me some reason that why the contrastive accuracy is so high but the linear evaluation performance is low value? Or Is there anything that can help me solve this problem.
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