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train_isd.py
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train_isd.py
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import builtins
import os
import sys
import time
import argparse
import socket
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
from torchvision import transforms, datasets
from PIL import ImageFilter
import numpy as np
from util import adjust_learning_rate, AverageMeter
import models.resnet as resnet
from models.alexnet import AlexNet
from tools import get_logger
def parse_option():
parser = argparse.ArgumentParser('argument for training')
parser.add_argument('data', type=str, help='path to dataset')
parser.add_argument('--dataset', type=str, default='imagenet',
choices=['imagenet', 'imagenet100'],
help='use full or subset of the dataset')
parser.add_argument('--debug', action='store_true', help='whether in debug mode or not')
parser.add_argument('--print_freq', type=int, default=100, help='print frequency')
parser.add_argument('--save_freq', type=int, default=2, help='save frequency')
parser.add_argument('--batch_size', type=int, default=256, help='batch_size')
parser.add_argument('--num_workers', type=int, default=12, help='num of workers to use')
parser.add_argument('--epochs', type=int, default=130, help='number of training epochs')
# optimization
parser.add_argument('--learning_rate', type=float, default=0.01, help='learning rate')
parser.add_argument('--lr_decay_epochs', type=str, default='90,120', 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('--cos', action='store_true',
help='whether to cosine learning rate or not')
parser.add_argument('--weight_decay', type=float, default=1e-4, help='weight decay')
parser.add_argument('--sgd_momentum', type=float, default=0.9, help='SGD momentum')
# model definition
parser.add_argument('--arch', type=str, default='alexnet',
choices=['alexnet' , 'resnet18' , 'resnet50', 'mobilenet'])
# isd loss function
parser.add_argument('--queue_size', type=int, default=128000)
parser.add_argument('--temp', type=float, default=0.02)
parser.add_argument('--momentum', type=float, default=0.999)
# GPU setting
parser.add_argument('--gpu', default=None, type=int, help='GPU id to use.')
parser.add_argument('--checkpoint_path', default='output/', type=str,
help='where to save checkpoints. ')
parser.add_argument('--resume', default='', type=str,
help='path to latest checkpoint (default: none)')
opt = parser.parse_args()
iterations = opt.lr_decay_epochs.split(',')
opt.lr_decay_epochs = list([])
for it in iterations:
opt.lr_decay_epochs.append(int(it))
return opt
# Extended version of ImageFolder to return index of image too.
class ImageFolderEx(datasets.ImageFolder) :
def __getitem__(self, index):
sample, target = super(ImageFolderEx, self).__getitem__(index)
return index, sample, target
class KLD(nn.Module):
def forward(self, inputs, targets):
inputs = F.log_softmax(inputs, dim=1)
targets = F.softmax(targets, dim=1)
return F.kl_div(inputs, targets, reduction='batchmean')
def get_mlp(inp_dim, hidden_dim, out_dim):
mlp = nn.Sequential(
nn.Linear(inp_dim, hidden_dim),
nn.BatchNorm1d(hidden_dim),
nn.ReLU(inplace=True),
nn.Linear(hidden_dim, out_dim),
)
return mlp
class ISD(nn.Module):
def __init__(self, arch, K=65536, m=0.999, T=0.07):
super(ISD, self).__init__()
self.K = K
self.m = m
self.T = T
# create encoders and projection layers
if 'resnet' in arch:
# both encoders should have same arch
self.encoder_q = resnet.__dict__[arch]()
self.encoder_k = resnet.__dict__[arch]()
# save output embedding dimensions
# assuming that both encoders have same dim
feat_dim = self.encoder_q.fc.in_features
out_dim = feat_dim
##### prediction layer ####
# 1. have a prediction layer for q with BN
self.predict_q = nn.Sequential(
nn.Linear(feat_dim, feat_dim, bias=False),
nn.BatchNorm1d(feat_dim),
nn.ReLU(inplace=True),
nn.Linear(feat_dim, feat_dim, bias=True),
)
##### projection layers ####
# 1. no projection layers for encoders
self.encoder_k.fc = nn.Sequential()
self.encoder_q.fc = nn.Sequential()
else:
raise ValueError('arch not found: {}'.format(arch))
# copy query encoder weights to key encoder
for param_q, param_k in zip(self.encoder_q.parameters(), self.encoder_k.parameters()):
param_k.data.copy_(param_q.data)
param_k.requires_grad = False
# setup queue
self.register_buffer('queue', torch.randn(self.K, out_dim))
# normalize the queue
self.queue = nn.functional.normalize(self.queue, dim=0)
# setup the queue pointer
self.register_buffer('queue_ptr', torch.zeros(1, dtype=torch.long))
@torch.no_grad()
def _momentum_update_key_encoder(self):
for param_q, param_k in zip(self.encoder_q.parameters(), self.encoder_k.parameters()):
param_k.data = param_k.data * self.m + param_q.data * (1. - self.m)
@torch.no_grad()
def data_parallel(self):
self.encoder_q = torch.nn.DataParallel(self.encoder_q)
self.encoder_k = torch.nn.DataParallel(self.encoder_k)
self.predict_q = torch.nn.DataParallel(self.predict_q)
@torch.no_grad()
def _dequeue_and_enqueue(self, keys):
batch_size = keys.shape[0]
ptr = int(self.queue_ptr)
assert self.K % batch_size == 0
# replace the keys at ptr (dequeue and enqueue)
self.queue[ptr:ptr + batch_size] = keys
ptr = (ptr + batch_size) % self.K # move pointer
self.queue_ptr[0] = ptr
def forward(self, im_q, im_k):
# compute query features
feat_q = self.encoder_q(im_q)
# compute prediction queries
q = self.predict_q(feat_q)
q = nn.functional.normalize(q, dim=1)
# compute key features
with torch.no_grad():
# update the key encoder
self._momentum_update_key_encoder()
# shuffle keys
shuffle_ids, reverse_ids = get_shuffle_ids(im_k.shape[0])
im_k = im_k[shuffle_ids]
# forward through the key encoder
k = self.encoder_k(im_k)
k = nn.functional.normalize(k, dim=1)
# undo shuffle
k = k[reverse_ids]
# calculate similarities
queue = self.queue.clone().detach()
sim_q = torch.mm(q, queue.t())
sim_k = torch.mm(k, queue.t())
# scale the similarities with temperature
sim_q /= self.T
sim_k /= self.T
# dequeue and enqueue
self._dequeue_and_enqueue(k)
return sim_q, sim_k
def get_shuffle_ids(bsz):
"""generate shuffle ids for ShuffleBN"""
forward_inds = torch.randperm(bsz).long().cuda()
backward_inds = torch.zeros(bsz).long().cuda()
value = torch.arange(bsz).long().cuda()
backward_inds.index_copy_(0, forward_inds, value)
return forward_inds, backward_inds
class TwoCropsTransform:
"""Take two random crops of one image as the query and key."""
def __init__(self, base_transform):
self.base_transform = base_transform
def __call__(self, x):
q = self.base_transform(x)
k = self.base_transform(x)
return [q, k]
class GaussianBlur(object):
"""Gaussian blur augmentation in SimCLR https://arxiv.org/abs/2002.05709"""
def __init__(self, sigma=[.1, 2.]):
self.sigma = sigma
def __call__(self, x):
sigma = random.uniform(self.sigma[0], self.sigma[1])
x = x.filter(ImageFilter.GaussianBlur(radius=sigma))
return x
# Create train loader
def get_train_loader(opt):
traindir = os.path.join(opt.data, 'train')
image_size = 224
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
normalize = transforms.Normalize(mean=mean, std=std)
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([GaussianBlur([.1, 2.])], p=0.5),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize
]
train_dataset = ImageFolderEx(
traindir,
TwoCropsTransform(transforms.Compose(augmentation))
)
if opt.dataset == 'imagenet100':
subset_classes(train_dataset, num_classes=100)
print('==> train dataset')
print(train_dataset)
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=opt.batch_size, shuffle=True,
num_workers=opt.num_workers, pin_memory=True, drop_last=True)
return train_loader
def main():
args = parse_option()
os.makedirs(args.checkpoint_path, exist_ok=True)
if not args.debug:
os.environ['PYTHONBREAKPOINT'] = '0'
logger = get_logger(
logpath=os.path.join(args.checkpoint_path, 'logs'),
filepath=os.path.abspath(__file__)
)
def print_pass(*args):
logger.info(*args)
builtins.print = print_pass
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
print(args)
train_loader = get_train_loader(args)
isd = ISD(args.arch, K=args.queue_size, m=args.momentum, T=args.temp)
isd.data_parallel()
isd = isd.cuda()
print(isd)
criterion = KLD().cuda()
params = [p for p in isd.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params,
lr=args.learning_rate,
momentum=args.sgd_momentum,
weight_decay=args.weight_decay)
cudnn.benchmark = True
args.start_epoch = 1
if args.resume:
print('==> resume from checkpoint: {}'.format(args.resume))
ckpt = torch.load(args.resume)
print('==> resume from epoch: {}'.format(ckpt['epoch']))
isd.load_state_dict(ckpt['state_dict'], strict=True)
optimizer.load_state_dict(ckpt['optimizer'])
args.start_epoch = ckpt['epoch'] + 1
# routine
for epoch in range(args.start_epoch, args.epochs + 1):
adjust_learning_rate(epoch, args, optimizer)
print("==> training...")
time1 = time.time()
loss = train_student(epoch, train_loader, isd, criterion, optimizer, args)
time2 = time.time()
print('epoch {}, total time {:.2f}'.format(epoch, time2 - time1))
# saving the model
if epoch % args.save_freq == 0:
print('==> Saving...')
state = {
'opt': args,
'state_dict': isd.state_dict(),
'optimizer': optimizer.state_dict(),
'epoch': epoch,
}
save_file = os.path.join(args.checkpoint_path, 'ckpt_epoch_{epoch}.pth'.format(epoch=epoch))
torch.save(state, save_file)
# help release GPU memory
del state
torch.cuda.empty_cache()
def train_student(epoch, train_loader, isd, criterion, optimizer, opt):
"""
one epoch training for CompReSS
"""
isd.train()
batch_time = AverageMeter()
data_time = AverageMeter()
loss_meter = AverageMeter()
end = time.time()
for idx, (indices, (im_q, im_k), _) in enumerate(train_loader):
data_time.update(time.time() - end)
im_q = im_q.cuda(non_blocking=True)
im_k = im_k.cuda(non_blocking=True)
# ===================forward=====================
sim_q, sim_k = isd(im_q=im_q, im_k=im_k)
loss = criterion(inputs=sim_q, targets=sim_k)
# ===================backward=====================
optimizer.zero_grad()
loss.backward()
optimizer.step()
# ===================meters=====================
loss_meter.update(loss.item(), im_q.size(0))
torch.cuda.synchronize()
batch_time.update(time.time() - end)
end = time.time()
# 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'.format(
epoch, idx + 1, len(train_loader), batch_time=batch_time,
data_time=data_time, loss=loss_meter))
sys.stdout.flush()
return loss_meter.avg
def subset_classes(dataset, num_classes=10):
np.random.seed(1234)
all_classes = sorted(dataset.class_to_idx.items(), key=lambda x: x[1])
subset_classes = [all_classes[i] for i in np.random.permutation(len(all_classes))[:num_classes]]
subset_classes = sorted(subset_classes, key=lambda x: x[1])
dataset.classes_to_idx = {c: i for i, (c, _) in enumerate(subset_classes)}
dataset.classes = [c for c, _ in subset_classes]
orig_to_new_inds = {orig_ind: new_ind for new_ind, (_, orig_ind) in enumerate(subset_classes)}
dataset.samples = [(p, orig_to_new_inds[i]) for p, i in dataset.samples if i in orig_to_new_inds]
if __name__ == '__main__':
main()