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main_pseudo1.py
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main_pseudo1.py
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from __future__ import print_function
import args
import os
import sys
import logging
import torch
torch.set_printoptions(profile="full")
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torchvision.utils as vutils
from torch.autograd import Variable
from datetime import datetime
import shutil
import numpy as np
import math
import matplotlib
matplotlib.use('Agg') # For error: _tkinter.TclError: couldn't connect to display "localhost:10.0"
import matplotlib.pyplot as plt
import pickle
sys.path.append('models')
import resnet
import noisy_folder
opt = args.args()
base_dir = '{}/{}_{}_{}_{}_PL_cut{}'.format(opt.save_dir, opt.dataset, opt.model, opt.noise_type, int(opt.noise*100), int(opt.cut*100))
if opt.load_dir:
assert os.path.isdir(opt.load_dir)
opt.save_dir = opt.load_dir
else:
opt.save_dir = '{}_pseudo1'.format(base_dir)
opt.pretrained = '{}/checkpoint_epoch719.pth.tar'.format(base_dir)
try:
os.makedirs(opt.save_dir)
except OSError:
pass
cudnn.benchmark = True
logger = logging.getLogger("ydk_logger")
fileHandler = logging.FileHandler(opt.save_dir+'/train.log')
streamHandler = logging.StreamHandler()
logger.addHandler(fileHandler)
logger.addHandler(streamHandler)
logger.setLevel(logging.INFO)
logger.info(opt)
###################################################################################################
if opt.dataset == 'cifar10_wo_val' : num_classes = 10; in_channels=3
else: logger.info('There exists no data')
##
# Computing mean
trainset = dset.ImageFolder(root='{}/{}/train'.format(opt.dataroot, opt.dataset),
transform=transforms.ToTensor())
trainloader = torch.utils.data.DataLoader(trainset, batch_size=opt.batchSize,
shuffle=False, num_workers=opt.workers)
mean = 0
for i, data in enumerate(trainloader, 0):
imgs, labels = data
mean += torch.from_numpy(np.mean(np.asarray(imgs), axis=(2,3))).sum(0)
mean = mean / len(trainset)
##
transform_train = transforms.Compose(
[
transforms.Resize(opt.imageSize),
transforms.RandomCrop(opt.imageSize, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((mean[0], mean[1], mean[2]), (1.0, 1.0, 1.0))
])
transform_test = transforms.Compose(
[
transforms.Resize(opt.imageSize),
transforms.ToTensor(),
transforms.Normalize((mean[0], mean[1], mean[2]), (1.0, 1.0, 1.0))
])
logger.info(transform_train)
logger.info(transform_test)
with open ('noise/%s/train_labels_n%02d_%s'%(opt.noise_type, opt.noise*000, opt.dataset), 'rb') as fp:
clean_labels = pickle.load(fp)
with open ('noise/%s/train_labels_n%02d_%s'%(opt.noise_type, opt.noise*100, opt.dataset), 'rb') as fp:
noisy_labels = pickle.load(fp)
trainset = noisy_folder.ImageFolder(root='{}/{}/train'.format(opt.dataroot, opt.dataset),
noisy_labels=noisy_labels, transform = transform_train)
testset = dset.ImageFolder(root='{}/{}/test'.format(opt.dataroot, opt.dataset),
transform=transform_test)
clean_labels = list(clean_labels.astype(int))
noisy_labels = list(noisy_labels.astype(int))
inds_noisy = np.asarray([ind for ind in range(len(trainset)) if trainset.imgs[ind][-1] != clean_labels[ind]])
inds_clean = np.delete(np.arange(len(trainset)), inds_noisy)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=opt.batchSize,
shuffle=True, num_workers=opt.workers)
testloader = torch.utils.data.DataLoader(testset, batch_size=opt.batchSize,
shuffle= False, num_workers=opt.workers)
if opt.model == 'resnet34':
net = resnet.resnet34(in_channels=in_channels, num_classes=num_classes)
else: logger.info('no model exists')
##
# output: inds_filt, inds_left
ckpt = torch.load(opt.pretrained)
train_preds_hist = ckpt['train_preds_hist']
preds = train_preds_hist.mean(1)[torch.arange(len(trainset)), np.array(trainset.imgs)[:,1].astype(int)]
nl_ratio = float((preds<opt.cut).sum()) / float(len(preds))
logger.info('nl_ratio: %f'%(nl_ratio))
inds = np.argsort(preds)
inds_filt = inds[:int(len(trainset)*nl_ratio)]
inds_left = inds[int(len(trainset)*nl_ratio):]
logger.info(float( len(np.intersect1d(inds_filt, inds_noisy)) ) / float(len(inds_noisy)))
logger.info(float( len(np.intersect1d(inds_filt, inds_noisy)) ) / float(int(len(trainset)*nl_ratio)))
##
weight = torch.FloatTensor(num_classes).zero_() + 1.
for i in range(num_classes):
weight[i] = (torch.from_numpy(np.array(trainset.imgs)[:,1].astype(int))[inds_left] == i).sum()
weight = 1 / (weight / weight.max())
weight[weight == float("inf")] = 0
criterion = nn.CrossEntropyLoss(weight=weight)
criterion_nll = nn.NLLLoss()
criterion_nr = nn.CrossEntropyLoss(reduce=False)
net .cuda()
criterion .cuda()
criterion_nll.cuda()
criterion_nr .cuda()
optimizer = optim.SGD(net.parameters(),
lr=opt.lr, momentum=opt.momentum, weight_decay=opt.weight_decay)
train_preds = torch.zeros(len(trainset), num_classes) - 1.
num_hist = 10
train_preds_hist = torch.zeros(len(trainset), num_hist, num_classes)
pl_ratio = 0.; nl_ratio = 1.-pl_ratio
train_losses = torch.zeros(len(trainset)) - 1.
if opt.load_dir:
ckpt = torch.load(opt.load_dir+'/'+opt.load_pth)
net .load_state_dict(ckpt['state_dict'])
optimizer .load_state_dict(ckpt['optimizer'])
train_preds_hist = ckpt['train_preds_hist']
pl_ratio = ckpt['pl_ratio']
nl_ratio = ckpt['nl_ratio']
epoch_resume = ckpt['epoch']
logger.info('loading network SUCCESSFUL')
else:
epoch_resume = 0
logger.info('loading network FAILURE')
###################################################################################################
# Start training
best_test_acc = 0.0
for epoch in range(epoch_resume, opt.max_epochs):
train_loss = train_loss_neg = train_acc = 0.0
pl = 0.; nl = 0.;
if epoch in opt.epoch_step:
for param_group in optimizer.param_groups:
param_group['lr'] *= 0.1
opt.lr = param_group['lr']
for i, data in enumerate(trainloader, 0):
net.zero_grad()
imgs, labels, index = data
imgs = Variable(imgs.cuda()); labels = Variable(labels.cuda());
logits = net(imgs)
_, pred = torch.max(logits.data, -1)
acc = float((pred==labels.data).sum())
train_acc += acc
train_loss += imgs.size(0)*criterion(logits, labels).data
labels[ np.intersect1d(np.asarray(index), inds_filt, return_indices=True)[1] ] = -100
loss = criterion(logits, labels)
loss.backward()
optimizer.step()
train_preds[index.cpu()] = F.softmax(logits, -1).cpu().data
train_loss /= len(trainset)
train_acc /= len(trainset)
logger.info('[%6d/%6d] loss: %5f, acc: %5f, lr: %5f'
%(epoch, opt.max_epochs, train_loss, train_acc, opt.lr))
###############################################################################################
if epoch == 0:
for i in range(in_channels): imgs.data[:,i] += mean[i].cuda()
img = vutils.make_grid(imgs.data)
vutils.save_image(img, '%s/x.jpg'%(opt.save_dir))
logger.info('%s/x.jpg saved'%(opt.save_dir))
net.eval()
test_loss = test_acc = 0.0
with torch.no_grad():
for i, data in enumerate(testloader, 0):
imgs, labels = data
imgs = Variable(imgs.cuda()); labels = Variable(labels.cuda())
logits = net(imgs)
loss = criterion(logits, labels)
test_loss += imgs.size(0)*loss.data
_, pred = torch.max(logits.data, -1)
acc = float((pred==labels.data).sum())
test_acc += acc
test_loss /= len(testset)
test_acc /= len(testset)
###############################################################################################
logger.info('\tTESTING...loss: %5f, acc: %5f, best_acc: %5f'
%(test_loss, test_acc, best_test_acc))
net.train()
###############################################################################################
assert train_preds[train_preds<0].nelement() == 0
train_preds_hist[:, epoch%num_hist] = train_preds
train_preds = train_preds*0 - 1.
train_soft_labels = train_preds_hist.mean(1)
label_updated = (torch.max(train_soft_labels, -1)[1]).numpy().tolist()
logger.info('Label update status...noisy: %d/%d, clean: %d/%d'
%((np.array(label_updated)[inds_noisy] != np.array(clean_labels)[inds_noisy]).sum(), len(inds_noisy),
(np.array(label_updated)[inds_clean] != np.array(clean_labels)[inds_clean]).sum(), len(inds_clean)))
###############################################################################################
is_best = test_acc > best_test_acc
best_test_acc = max(test_acc, best_test_acc)
state = ({
'epoch' : epoch,
'state_dict' : net .state_dict(),
'optimizer' : optimizer.state_dict(),
'train_preds_hist': train_preds_hist,
'pl_ratio' : pl_ratio,
'nl_ratio' : nl_ratio,
})
logger.info('saving model...')
fn = os.path.join(opt.save_dir, 'checkpoint.pth.tar')
torch.save(state, fn)
if epoch % 100 == 0 or epoch == opt.max_epochs-1:
fn = os.path.join(opt.save_dir, 'checkpoint_epoch%d.pth.tar'%(epoch))
torch.save(state, fn)
# if is_best:
# logger.info('saving model...')
# fn = os.path.join(opt.save_dir, 'checkpoint.pth.tar')
# torch.save(state, fn)
# fn_best = os.path.join(opt.save_dir, 'model_best.pth.tar')
# logger.info('saving best model...')
# shutil.copyfile(fn, fn_best)
if epoch % 10 == 0:
logger.info('saving histogram...')
plt.hist(train_preds_hist.mean(1)[torch.arange(len(trainset)), np.array(trainset.imgs)[:,1].astype(int)], bins=20, range=(0., 1.), edgecolor='black', color='g')
plt.xlabel('probability'); plt.ylabel('number of data')
plt.grid()
plt.savefig(opt.save_dir+'/histogram_epoch%03d.jpg'%(epoch))
plt.clf()
logger.info('saving separated histogram...')
plt.hist(train_preds_hist.mean(1)[torch.arange(len(trainset))[inds_clean], np.array(trainset.imgs)[:,1].astype(int)[inds_clean]], bins=20, range=(0., 1.), edgecolor='black', alpha=0.5, label='clean')
plt.hist(train_preds_hist.mean(1)[torch.arange(len(trainset))[inds_noisy], np.array(trainset.imgs)[:,1].astype(int)[inds_noisy]], bins=20, range=(0., 1.), edgecolor='black', alpha=0.5, label='noisy')
plt.xlabel('probability'); plt.ylabel('number of data')
plt.grid()
plt.savefig(opt.save_dir+'/histogram_sep_epoch%03d.jpg'%(epoch))
plt.clf()