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import json
import os
import torch.backends.cudnn as cudnn
import torch.nn.parallel
from models.models import get_model
from dataloader.data_utils import get_class_weights
from functions import train_one_epoch, test, set_optimizer, save_checkpoint
from models.loss import WL2SP
from utils import Logger, mkdir_p
from utils.args import args_for_train_tl
from models.cross_entropy import CrossEntropyLoss
from dataloader.data import get_trainval_data, get_noisy_student_data
# get all the input variables
args = args_for_train_tl()
state = {k: v for k, v in args._get_kwargs()}
if not os.path.isdir(args.checkpoint):
print("==> Creating checkpoint folder")
mkdir_p(args.checkpoint)
# save all the arguments
args_save_path = args.checkpoint if args.resume == '' else os.path.dirname(args.resume)
with open(os.path.join(args_save_path, 'experiment_args.txt'), 'w') as f:
json.dump(args.__dict__, f, indent=2)
# Use CUDA
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id
use_cuda = torch.cuda.is_available()
# set parameters
best_acc = 0 # best test accuracy
do_save_checkpoint = True
start_epoch = args.start_epoch # start from epoch 0 or last checkpoint epoch
############################################
# Create and Load Model
############################################
model, image_size = get_model(args, num_classes=200, use_pretrained=True, train=True)
model = model.cuda()
model = torch.nn.DataParallel(model)
cudnn.benchmark = True
print(' Total params: %.4fM' % (sum(p.numel() for p in model.parameters()) / 1000000.0))
optimizer = set_optimizer(model, args)
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, 0.94, last_epoch=-1)
noisy_criterion = CrossEntropyLoss()
# pre-trained weights for loss function
pretrained_weights = {}
for n, m in model.named_modules():
if "conv" in n:
try:
pretrained_weights[n] = m.weight
except:
pass
cls_num_list, per_cls_weights = get_class_weights(0.99)
criterion = WL2SP(class_weights=per_cls_weights, pretrained_weights=pretrained_weights)
##############################################
# Load Dataset
##############################################
print('==> Preparing dataset')
noisytrainloader = get_noisy_student_data("u_train_in",
"/root/volume/cvpr/results/noisy_labels/indist_soft_label_inception.csv",
batch_size=32, image_size=image_size)
trainloader, validloader = get_trainval_data(batch_size=args.train_batch, val_batch_size=32, image_size=image_size)
title = '{}-{}-{}-{}'.format(args.dataset, args.model, args.depth, args.norm)
# Resume
if args.resume:
# Load checkpoint.
print('==> Resuming from checkpoint..')
assert os.path.isfile(args.resume), 'Error: no checkpoint directory found!'
args.checkpoint = os.path.dirname(args.resume)
checkpoint = torch.load(args.resume)
best_acc = checkpoint['best_acc']
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title, resume=True)
else:
logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title)
logger.set_names(
['Learning Rate', 'Train Loss', 'Valid Loss', 'Train Acc.', 'Valid Acc.', 'Train Acc.5', 'Valid Acc.5'])
#############################################
# Train and Validate
#############################################
test_flag = False
for epoch in range(start_epoch, args.epochs):
print('\nEpoch: [%d | %d] LR: %f' % (epoch + 1, args.epochs, scheduler.get_lr()[0]))
train_loss, train_acc, train_acc5 = train_one_epoch(trainloader, model, criterion, optimizer, use_cuda=use_cuda,
test_flag=test_flag)
valid_loss, valid_acc, valid_acc5 = test(validloader, model, criterion, use_cuda=use_cuda, test_flag=test_flag)
logger.append([scheduler.get_lr()[0], train_loss, valid_loss, train_acc, valid_acc, train_acc5, valid_acc5])
if epoch >= 10 and epoch % 2 == 0:
train_loss, train_acc, train_acc5 = train_one_epoch(noisytrainloader, model, noisy_criterion, optimizer,
use_cuda=use_cuda, test_flag=test_flag)
valid_loss, valid_acc, valid_acc5 = test(validloader, model, criterion, use_cuda=use_cuda, test_flag=test_flag)
logger.append([scheduler.get_lr()[0], train_loss, valid_loss, train_acc, valid_acc, train_acc5, valid_acc5])
if epoch % 4 == 0:
scheduler.step()
# save model ap
is_best = valid_acc > best_acc
best_loss = max(valid_acc, best_acc)
best_acc = max(valid_acc, best_acc)
if do_save_checkpoint:
save_checkpoint({
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'acc': valid_acc,
'best_acc': best_acc,
'optimizer': optimizer.state_dict(),
}, is_best, checkpoint=args.checkpoint)
###################################
# FineTune on Validation Data ###
###################################
# for fine_epoch in range(args.epochs-20, args.epochs):
# # Deferred Re-balancing Optimization Schedule (https://arxiv.org/pdf/1906.07413.pdf)
# cls_num_list, per_cls_weights = get_class_weights(0.9999)
# criterion = WL2SP(class_weights=per_cls_weights, pretrained_weights=pretrained_weights)
# state['lr'] = args.lr
# print('\nEpoch: [%d | %d] LR: %f' % (fine_epoch + 1, args.epochs, state['lr']))
# train_loss, train_acc, train_acc5 = train_one_epoch(validloader, model, criterion, optimizer, use_cuda=use_cuda)
# valid_loss, valid_acc, valid_acc5 = train_loss, train_acc, train_acc5#test(validloader, model, criterion, use_cuda=use_cuda)
# logger.append([state['lr'], train_loss, valid_loss, train_acc, valid_acc, train_acc5, valid_acc5])
# # log_tensorboard(writer, fine_epoch, train_loss, train_acc, train_acc5, valid_loss, valid_acc, valid_acc5)
#
# # save model ap
# is_best = valid_acc > best_acc
# best_loss = max(valid_acc, best_acc)
# best_acc = max(valid_acc, best_acc)
# if do_save_checkpoint:
# save_checkpoint({
# 'epoch': fine_epoch + 1,
# 'state_dict': model.state_dict(),
# 'acc': valid_acc,
# 'best_acc': best_acc,
# 'optimizer' : optimizer.state_dict(),
# }, is_best, checkpoint=args.checkpoint)
logger.close()
print('Best acc:', best_acc)