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color_cnn_downsample.py
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color_cnn_downsample.py
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import os
os.environ['OMP_NUM_THREADS'] = '1'
import argparse
import sys
import shutil
from distutils.dir_util import copy_tree
import datetime
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets
import color_distillation.utils.transforms as T
from color_distillation import models
from color_distillation.loss.label_smooth import LSR_loss
from color_distillation.models.color_cnn import ColorCNN
from color_distillation.trainer import CNNTrainer
from color_distillation.utils.load_checkpoint import checkpoint_loader
from color_distillation.utils.draw_curve import draw_curve
from color_distillation.utils.logger import Logger
from color_distillation.utils.image_utils import img_color_denormalize
def main():
# settings
parser = argparse.ArgumentParser(description='ColorCNN down sample')
parser.add_argument('--num_colors', type=int, default=None)
parser.add_argument('--alpha', type=float, default=1, help='multiplier of regularization terms')
parser.add_argument('--beta', type=float, default=0, help='multiplier of regularization terms')
parser.add_argument('--gamma', type=float, default=0, help='multiplier of reconstruction loss')
parser.add_argument('--color_jitter', type=float, default=1)
parser.add_argument('--color_norm', type=float, default=4, help='normalizer for color palette')
parser.add_argument('--label_smooth', type=float, default=0.0)
parser.add_argument('--soften', type=float, default=1, help='soften coefficient for softmax')
parser.add_argument('-d', '--dataset', type=str, default='cifar10',
choices=['cifar10', 'cifar100', 'stl10', 'svhn', 'imagenet', 'tiny200'])
parser.add_argument('-a', '--arch', type=str, default='vgg16', choices=models.names())
parser.add_argument('-j', '--num_workers', type=int, default=4)
parser.add_argument('-b', '--batch_size', type=int, default=128, metavar='N',
help='input batch size for training (default: 128)')
parser.add_argument('--epochs', type=int, default=60, metavar='N', help='number of epochs to train (default: 10)')
parser.add_argument('--lr', type=float, default=1e-2, metavar='LR', help='learning rate (default: 0.1)')
parser.add_argument('--weight_decay', type=float, default=5e-4)
parser.add_argument('--momentum', type=float, default=0.5, metavar='M', help='SGD momentum (default: 0.5)')
parser.add_argument('--log_interval', type=int, default=100, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--backbone', type=str, default='unet', choices=['unet', 'dncnn'])
parser.add_argument('--resume', type=str, default=None)
parser.add_argument('--train_classifier', action='store_true')
parser.add_argument('--visualize', action='store_true')
parser.add_argument('--seed', type=int, default=None, help='random seed (default: None)')
args = parser.parse_args()
# seed
if args.seed is not None:
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
else:
torch.backends.cudnn.benchmark = True
# dataset
data_path = os.path.expanduser('~/Data/') + args.dataset
if args.dataset == 'svhn':
H, W, C = 32, 32, 3
num_class = 10
normalize = T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
train_trans = T.Compose([T.ToTensor(), normalize, ])
test_trans = T.Compose([T.ToTensor(), normalize, ])
denormalizer = img_color_denormalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
train_set = datasets.SVHN(data_path, split='train', download=True, transform=train_trans)
test_set = datasets.SVHN(data_path, split='test', download=True, transform=test_trans)
elif args.dataset == 'cifar10' or args.dataset == 'cifar100':
H, W, C = 32, 32, 3
num_class = 10 if args.dataset == 'cifar10' else 100
normalize = T.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
train_trans = T.Compose([T.RandomCrop(32, padding=4), T.RandomHorizontalFlip(), T.ToTensor(), normalize, ])
test_trans = T.Compose([T.ToTensor(), normalize, ])
denormalizer = img_color_denormalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
if args.dataset == 'cifar10':
train_set = datasets.CIFAR10(data_path, train=True, download=True, transform=train_trans)
test_set = datasets.CIFAR10(data_path, train=False, download=True, transform=test_trans)
else:
train_set = datasets.CIFAR100(data_path, train=True, download=True, transform=train_trans)
test_set = datasets.CIFAR100(data_path, train=False, download=True, transform=test_trans)
elif args.dataset == 'imagenet':
H, W, C = 224, 224, 3
num_class = 1000
normalize = T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
train_trans = T.Compose([T.RandomResizedCrop(224), T.RandomHorizontalFlip(), T.ToTensor(), normalize, ])
test_trans = T.Compose([T.Resize(256), T.CenterCrop(224), T.ToTensor(), normalize, ])
denormalizer = img_color_denormalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
train_set = datasets.ImageNet(data_path, split='train', transform=train_trans)
test_set = datasets.ImageNet(data_path, split='val', transform=test_trans)
elif args.dataset == 'stl10':
H, W, C = 96, 96, 3
num_class = 10
# smaller batch size
args.batch_size = 32
normalize = T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
train_trans = T.Compose([T.RandomCrop(96, padding=12), T.RandomHorizontalFlip(), T.ToTensor(), normalize, ])
test_trans = T.Compose([T.ToTensor(), normalize, ])
denormalizer = img_color_denormalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
train_set = datasets.STL10(data_path, split='train', download=True, transform=train_trans)
test_set = datasets.STL10(data_path, split='test', download=True, transform=test_trans)
elif args.dataset == 'tiny200':
H, W, C = 64, 64, 3
num_class = 200
normalize = T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
train_trans = T.Compose([T.RandomCrop(64, padding=8), T.RandomHorizontalFlip(), T.ToTensor(), normalize, ])
test_trans = T.Compose([T.ToTensor(), normalize, ])
denormalizer = img_color_denormalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
train_set = datasets.ImageFolder(data_path + '/train', transform=train_trans)
test_set = datasets.ImageFolder(data_path + '/val', transform=test_trans)
else:
raise Exception
# network specific setting
if args.arch == 'alexnet':
if 'cifar' not in args.dataset:
args.color_norm = 1
train_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batch_size, shuffle=True,
num_workers=args.num_workers, pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batch_size, shuffle=False,
num_workers=args.num_workers, pin_memory=True)
logdir = 'logs/colorcnn/{}/{}/{}colors/{}'.format(args.dataset, args.arch,
'full_' if args.num_colors is None else args.num_colors,
datetime.datetime.today().strftime('%Y-%m-%d_%H-%M-%S'))
if args.resume is None:
os.makedirs(logdir, exist_ok=True)
copy_tree('./color_distillation', logdir + '/scripts/color_distillation')
for script in os.listdir('.'):
if script.split('.')[-1] == 'py':
dst_file = os.path.join(logdir, 'scripts', os.path.basename(script))
shutil.copyfile(script, dst_file)
sys.stdout = Logger(os.path.join(logdir, 'log.txt'), )
print('Settings:')
print(vars(args))
# model
classifier = models.create(args.arch, num_class, not args.train_classifier).cuda()
if not args.train_classifier:
if args.dataset != 'imagenet':
resume_fname = 'logs/grid/{}/{}/full_colors'.format(args.dataset, args.arch) + '/model.pth'
classifier.load_state_dict(torch.load(resume_fname))
classifier.eval()
for param in classifier.parameters():
param.requires_grad = False
model = ColorCNN(args.backbone, args.num_colors, args.soften, args.color_norm, args.color_jitter).cuda()
optimizer = optim.SGD(list(model.parameters()) + list(classifier.parameters()),
lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, 20, 1)
# scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=args.lr,
# steps_per_epoch=len(train_loader), epochs=args.epochs)
# loss
if args.label_smooth:
criterion = LSR_loss(args.label_smooth)
else:
criterion = nn.CrossEntropyLoss()
# draw curve
x_epoch = []
train_loss_s = []
train_prec_s = []
og_test_loss_s = []
og_test_prec_s = []
trainer = CNNTrainer(model, criterion, args.num_colors, classifier, denormalizer, args.alpha, args.beta, args.gamma)
# learn
if args.resume is None:
# print('Testing...')
# trainer.test(test_loader)
for epoch in range(1, args.epochs + 1):
print('Training...')
train_loss, train_prec = trainer.train(epoch, train_loader, optimizer, args.log_interval, scheduler)
print('Testing...')
og_test_loss, og_test_prec = trainer.test(test_loader)
x_epoch.append(epoch)
train_loss_s.append(train_loss)
train_prec_s.append(train_prec)
og_test_loss_s.append(og_test_loss)
og_test_prec_s.append(og_test_prec)
draw_curve(os.path.join(logdir, 'learning_curve.jpg'), x_epoch, train_loss_s, train_prec_s,
og_test_loss_s, og_test_prec_s)
# save
torch.save(model.state_dict(), os.path.join(logdir, 'ColorCNN.pth'))
else:
resume_dir = 'logs/colorcnn/{}/{}/{}colors/'.format(
args.dataset, args.arch, 'full_' if args.num_colors is None else args.num_colors) + args.resume
resume_fname = resume_dir + '/ColorCNN.pth'
model.load_state_dict(torch.load(resume_fname))
model.eval()
print('Test loaded model...')
trainer.test(test_loader, args.visualize)
if __name__ == '__main__':
main()