transforms.RandomAffine(2, shear=2),
transforms.RandomAffine(2, shear=2),
transforms.RandomAffine(2, shear=2),
#transforms.RandomCrop(224),
transforms.RandomResizedCrop(150,
scale=(0.25, 1.0),
ratio=(0.9, 1.11),
interpolation=2),
transforms.ColorJitter(brightness=0.7,
contrast=0.7,
saturation=0.3,
hue=0.02),
transforms.RandomGrayscale(p=0.75),
QLoss(min_q=2, max_q=60),
transforms.RandomChoice([
transforms.RandomApply((Otsu(), ), p=0.1),
transforms.RandomApply((Sauvola(2, 8), ), p=0.05)
]),
transforms.ToTensor()
])
training = ImageFolder('lines/training', transform=trans)
training.target_transform = tgc.classMap.get_target_transform(
training.class_to_idx)
validation = ImageFolder('lines/validation', transform=None)
validation.target_transform = tgc.classMap.get_target_transform(
validation.class_to_idx)
best_validation = 0
data_loader = torch.utils.data.DataLoader(training,
batch_size=64,
import warnings
from scipy import ndimage
import cv2
from driver import std, mean
from os.path import join
from commons.utils import visualize
transforms.RandomChoice([
transforms.ColorJitter(brightness=0.1),
transforms.ColorJitter(contrast=0.2),
transforms.ColorJitter(saturation=0.1),
transforms.ColorJitter(hue=0.15),
transforms.ColorJitter(brightness=0.1,
contrast=0.1,
saturation=0.1,
hue=0.1),
transforms.ColorJitter(brightness=0.15,
contrast=0.15,
saturation=0.15,
hue=0.15),
transforms.ColorJitter(brightness=0.2,
contrast=0.2,
saturation=0.2,
hue=0.2),
])
def rescale_crop(image, scale, num, mode, output_size=224):
image_list = []
#TODO for cls, it should be h,w.
#TODO but for seg, it could be w,h
h, w = image.size
depth=depth,
latent_size=128,
gpu=0,
seed=27)
dataloader = ProGANDataLoader(
data_path='maua/datasets/flower_pix2pix/B',
prescaled_data=True,
prescaled_data_path='maua/datasets/flowerGAN_prescaled',
transforms=tn.Compose([
tn.Resize(2**depth),
tn.RandomHorizontalFlip(),
tn.RandomVerticalFlip(),
tn.RandomChoice([
tn.RandomRotation([0, 0]),
tn.RandomRotation([90, 90]),
tn.RandomRotation([270, 270])
]),
]))
model.train(dataloader=dataloader,
fade_in=0.75,
save_freq=25,
log_freq=5,
loss="r1-reg",
num_epochs=75)
result = model(
th.randn(1, 128) *
3) # messing with the latent vector has a big effect on output image
save_image(result, 'maua/output/progan_flower1.png')
import torchvision
from torchvision import transforms
image_transforms = {
'train':
transforms.Compose([
transforms.RandomPerspective(distortion_scale=0.2,
p=0.1,
interpolation=3),
transforms.RandomChoice([
transforms.CenterCrop(180),
transforms.CenterCrop(160),
transforms.CenterCrop(140),
transforms.CenterCrop(120),
transforms.Compose(
[transforms.CenterCrop(280),
transforms.Grayscale(3)]),
transforms.Compose([
transforms.CenterCrop(200),
transforms.Grayscale(3),
]),
]),
transforms.Resize((224, 224)),
transforms.ColorJitter(hue=(0.1, 0.2)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'valid':
transforms.Compose([
transforms.RandomPerspective(distortion_scale=0.2,
p=0.1,
if (len(Frames) == self.n):
break
if self.transform is not None:
for i in range(len(Frames)):
Frames[i] = self.transform(Image.fromarray(Frames[i]))
return torch.cat(Frames)
if __name__ == '__main__':
opt = TrainOptions().parse()
tfms = transforms.Compose([
transforms.RandomChoice([
transforms.Resize(opt.loadSize, interpolation=1),
transforms.Resize(opt.loadSize, interpolation=2),
transforms.Resize(opt.loadSize, interpolation=3),
transforms.Resize((opt.loadSize, opt.loadSize), interpolation=1),
transforms.Resize((opt.loadSize, opt.loadSize), interpolation=2),
transforms.Resize((opt.loadSize, opt.loadSize), interpolation=3)
]),
transforms.RandomChoice([
transforms.RandomResizedCrop(opt.fineSize, interpolation=1),
transforms.RandomResizedCrop(opt.fineSize, interpolation=2),
transforms.RandomResizedCrop(opt.fineSize, interpolation=3)
]),
transforms.RandomHorizontalFlip(),
transforms.ToTensor()
])
if ("video" not in opt.name and "synthetic" not in opt.name):
opt.dataroot = './dataset/ilsvrc2012/%s/' % opt.phase
dataset = torchvision.datasets.ImageFolder(opt.dataroot,
def main_worker(gpu, ngpus_per_node, args):
global best_acc1
args.gpu = gpu
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
# create model
print("=> creating model '{}'".format(args.arch))
model = pretrainedmodels.__dict__[args.arch](pretrained=False)
# model.aux_logits=False
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
else:
# DataParallel will divide and allocate batch_size to all available GPUs
if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
# 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)
# accelerate
cudnn.benchmark = True
# set dataset
# set dataset
train_dataset_transformed = TinyImageNetDataset(
'./TinyImageNet',
'./TinyImageNet/train.txt',
transform=transforms.Compose([
transforms.RandomApply(transforms.RandomRotation(20)),
transforms.RandomChoice([
transforms.RandomHorizontalFlip(0.5),
transforms.RandomVerticalFlip(0.5),
transforms.RandomRotation(20)
]),
transforms.RandomChoice([
transforms.ColorJitter(brightness=0.1),
transforms.ColorJitter(contrast=0.1),
transforms.ColorJitter(saturation=0.1)
]),
transforms.RandomGrayscale(0.5),
transforms.ToTensor(),
transforms.RandomErasing(0.5),
transforms.Normalize([0.4802, 0.4481, 0.3975],
[0.2302, 0.2265, 0.2262])
]))
train_dataset_original = TinyImageNetDataset(
'./TinyImageNet',
'./TinyImageNet/train.txt',
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.4802, 0.4481, 0.3975],
[0.2302, 0.2265, 0.2262])
]))
val_dataset = TinyImageNetDataset(
'./TinyImageNet',
'./TinyImageNet/val.txt',
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.4802, 0.4481, 0.3975],
[0.2302, 0.2265, 0.2262])
]))
test_dataset = TinyImageNetDataset(
'./TinyImageNet',
'./TinyImageNet/test.txt',
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.4802, 0.4481, 0.3975],
[0.2302, 0.2265, 0.2262])
]))
train_sampler = None
train_loader_transformed = torch.utils.data.DataLoader(
train_dataset_transformed,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler)
train_loader_original = torch.utils.data.DataLoader(
train_dataset_original,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args)
# train for one epoch
train(train_loader_transformed, model, criterion, optimizer, epoch,
args)
train(train_loader_original, model, criterion, optimizer, epoch, args)
# evaluate on validation set
acc1 = validate(val_loader, model, criterion, args)
# remember best acc@1 and save
best_acc1 = max(acc1, best_acc1)
test(test_loader, model, args)
if using_label_smooth == 'True':
criterion = loss.CrossEntropyLabelSmooth(10, epsilon=0.1)
else:
criterion = nn.CrossEntropyLoss()
#训练中记录三个指标：验证集的最佳acc和对应的loss，验证集上的最低loss
metrics = {'best_acc': 10, 'best_acc_loss': 100, 'best_loss': 100}
train_transform = transforms.Compose([
transforms.Resize((size + 32, size + 32)),
transforms.RandomChoice([
transforms.RandomCrop(size,
padding=1,
pad_if_needed=True,
padding_mode='edge'),
transforms.RandomResizedCrop(size,
scale=(resize_scale, 1.0),
ratio=(0.8, 1.2))
]),
transforms.RandomHorizontalFlip(),
auto_augment.AutoAugment(dataset='CIFAR'), #auto_augment
transforms.ToTensor(),
transforms.RandomErasing(p=erasing_prob),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
val_transform = transforms.Compose([
transforms.Resize((size, size)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
def return_data(args):
# TODO: cnn_datasets return_data
train_dset_dir = args.train_dset_dir
test_dset_dir = args.test_dset_dir
batch_size = args.batch_size
num_workers = args.num_workers
image_size = args.image_size
time_window = args.time_window
trivial_augmentation = bool(args.trivial_augmentation)
sliding_augmentation = bool(args.sliding_augmentation)
transform_list = [transforms.Resize((image_size, image_size))]
if args.channel == 1:
transform_list.append(transforms.Grayscale(num_output_channels=1))
if trivial_augmentation:
trivial_transform_list = [
transforms.ColorJitter(brightness=0.4,
contrast=0.4,
saturation=0.4,
hue=0.1),
transforms.RandomResizedCrop(image_size,
scale=(0.8, 1.0),
ratio=(1, 1)),
RandomNoise(mean=0, std=10),
]
transform_list.append(transforms.RandomChoice(trivial_transform_list))
if sliding_augmentation:
transform_list.append(RandomTimeWindow(time_window=time_window))
else:
transform_list.append(TimeWindow(time_window=time_window))
transform_list.append(transforms.ToTensor())
if args.channel == 1:
transform_list.append(transforms.Normalize([0.5], [0.5]))
else:
transform_list.append(
transforms.Normalize([0.5] * args.channel, [0.5] * args.channel))
print(transform_list)
transform = transforms.Compose(transform_list)
# if args.channel == 1:
# transform = transforms.Compose([
# transforms.Resize((image_size, image_size)),
# transforms.Grayscale(num_output_channels=1),
# TimeWindow(time_window=time_window),
# transforms.ToTensor(),
# transforms.Normalize([0.5], [0.5]),
# ])
# else:
# transform = transforms.Compose([
# transforms.Resize((image_size, image_size)),
# TimeWindow(time_window=time_window),
# transforms.ToTensor(),
# transforms.Normalize([0.5] * args.channel, [0.5] * args.channel),
# ])
train_root = Path(train_dset_dir)
test_root = Path(test_dset_dir)
train_kwargs = {'root': train_root, 'transform': transform}
test_kwargs = {'root': test_root, 'transform': transform}
dset = ImageFolder
train_data = dset(**train_kwargs)
test_data = dset(**test_kwargs)
train_loader = DataLoader(train_data,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
pin_memory=True,
drop_last=True)
test_loader = DataLoader(test_data,
batch_size=252,
shuffle=True,
num_workers=num_workers,
pin_memory=True,
drop_last=True)
data_loader = dict()
data_loader['train'] = train_loader
data_loader['test'] = test_loader
return data_loader
else:
img = Image.open(self.df[index]).convert('RGB')
img = self.transform(img)
return img, torch.from_numpy(np.array(0))
def __len__(self):
return len(self.df)
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_transform = transforms.Compose([
transforms.Resize([299, 299]),
transforms.RandomRotation(15),
transforms.RandomChoice(
[transforms.Resize([256, 256]),
transforms.CenterCrop([256, 256])]),
transforms.ColorJitter(brightness=0.2, contrast=0.2),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), # range [0, 255] -> [0.0,1.0]
normalize,
])
val_transform = transforms.Compose([
transforms.Resize([config.img_size, config.img_size]),
transforms.ToTensor(),
normalize,
])
test_transform = transforms.Compose([
transforms.Resize([config.img_size, config.img_size]),
def __getitem__(self, index):
#prev_time=time.time();
#index2=index%self.listnum;
index2 = index // 5
path = self.path_list[index2][0]
part = self.path_list[index2][1]
path2 = facail_part_path + '/' + part + '/images/' + path + ".jpg"
if (self.choice != None):
image_trans = transforms.Compose([
ToPILImage(),
transforms.RandomChoice(self.choice.augmentation[index % 5]),
#transforms.RandomChoice(self.choice[index//self.listnum]),
Resize(size=(self.choicesize, self.choicesize),
interpolation=Image.NEAREST),
ToTensor(),
LabelUpdata(self.choicesize)
])
else:
image_trans = None
image = Image.open(path2)
if (part == "eye2" or part == "eyebrow2"):
image = image.transpose(Image.FLIP_LEFT_RIGHT)
label = []
#label.append(Image.new('L', (image.size[0],image.size[1]),color=1))
path2 = facail_part_path + '/' + part + '/labels/' + path + '/'
'''
for i in range(2,10):
image2=Image.open(path2+path+"_lbl"+str(i//10)+str(i%10)+".png")
image2=image2.convert('L');
label.append(image2);
sample={'image':image,'label':label,'index':index,'part':self.face_part};
if (image_trans!=None):
sample=image_trans(sample);
sample['label']=torch.cat(tuple(sample['label']),0);
#print("sample['label'].size=",sample['label'].size());
if (self.face_part=="eye"):
if (sample['label'][4].sum()>sample['label'][3].sum()):
sample['label'][3]=sample['label'][4]
sample['label']=sample['label'][[0,3]]
if (self.face_part=="eyebrow"):
if (sample['label'][2].sum()>sample['label'][1].sum()):
sample['label'][1]=sample['label'][2]
sample['label']=sample['label'][[0,1]]
if (self.face_part=="nose"):
sample['label']=sample['label'][[0,5]]
if (self.face_part=="mouth"):
sample['label']=sample['label'][[0,6,7,8]]
'''
if (part == "eye1"):
for i in range(4, 5):
image2 = Image.open(path2 + path + "_lbl" + str(i // 10) +
str(i % 10) + ".png").convert('L')
#image2=image2.convert('L');
label.append(image2)
if (part == "eye2"):
for i in range(5, 6):
image2 = Image.open(path2 + path + "_lbl" + str(i // 10) +
str(i % 10) + ".png").convert('L')
#image2=image2.convert('L');
image2 = image2.transpose(Image.FLIP_LEFT_RIGHT)
label.append(image2)
if (part == "eyebrow1"):
for i in range(2, 3):
image2 = Image.open(path2 + path + "_lbl" + str(i // 10) +
str(i % 10) + ".png").convert('L')
#image2=image2.convert('L');
label.append(image2)
if (part == "eyebrow2"):
for i in range(3, 4):
image2 = Image.open(path2 + path + "_lbl" + str(i // 10) +
str(i % 10) + ".png").convert('L')
#image2=image2.convert('L');
image2 = image2.transpose(Image.FLIP_LEFT_RIGHT)
label.append(image2)
if (part == "nose"):
for i in range(6, 7):
image2 = Image.open(path2 + path + "_lbl" + str(i // 10) +
str(i % 10) + ".png").convert('L')
#image2=image2.convert('L');
label.append(image2)
if (part == "mouth"):
for i in range(7, 10):
image2 = Image.open(path2 + path + "_lbl" + str(i // 10) +
str(i % 10) + ".png").convert('L')
#image2=image2.convert('L');
label.append(image2)
for i in range(len(label)):
label[i] = np.array(label[i])
bg = 255 - np.sum(label, axis=0, keepdims=True)
labels = np.concatenate([bg, label], axis=0) # [L + 1, 64, 64]
labels = np.uint8(labels)
#labels = [TF.to_pil_image(labels[i])for i in range(labels.shape[0])]
#now_time=time.time();
#print("getitem time part1:",now_time-prev_time);
#pre_time=now_time;
sample = {
'image': image,
'label': labels,
'index': index,
'part': self.face_part
}
if (image_trans != None):
sample = image_trans(sample)
sample['label'][0] = torch.sum(
sample['label'][1:sample['label'].shape[0]],
dim=0,
keepdim=True) # 1 x 64 x 64
sample['label'][0] = 1 - sample['label'][0]
'''
sample['label']=torch.cat(tuple(sample['label']),0);#[L,64,64]
lnum=len(sample['label']);
sample['label']=torch.argmax(sample['label'],dim=0,keepdim=False);#[64,64]
sample['label']=sample['label'].unsqueeze(dim=0);#[1,64,64]
sample['label']=torch.zeros(lnum,self.choicesize,self.choicesize).scatter_(0, sample['label'], 255);#[L,64,64]
'''
'''
for l in range(lnum):
for i in range(self.choicesize):
for j in range(self.choicesize):
print(int(sample['label'][l][i][j]),end=' ');
print();
print();
'''
#now_time=time.time();
#print("getitem time part2:",now_time-prev_time);
#pre_time=now_time;
return sample
def get_data(dataset='MNIST', train_augment_angle='0'):
if train_augment_angle == 'all':
transform_augment = [
transforms.RandomAffine(degrees=360, translate=None)
]
elif train_augment_angle == '0':
transform_augment = [
transforms.RandomAffine(degrees=0, translate=None)
]
else:
augment_angle = int(train_augment_angle)
transform_augment = [
transforms.RandomChoice([
transforms.RandomAffine(degrees=(augment_angle * i,
augment_angle * i),
translate=None)
for i in range(int(360 / augment_angle))
])
]
if dataset == 'MNIST':
transform_augment = [*transform_augment]
transform_normalize = [transforms.Normalize((0.1307, ), (0.3081, ))]
transform_train = transforms.Compose(
[*transform_augment,
transforms.ToTensor(), *transform_normalize])
transform_val = transforms.Compose(
[transforms.ToTensor(), *transform_normalize])
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('./../data',
train=True,
download=True,
transform=transform_train),
batch_size=BATCH_SIZE,
shuffle=True) # , num_workers=4)
validation_loader = torch.utils.data.DataLoader(datasets.MNIST(
'./../data', train=False, transform=transform_val),
batch_size=BATCH_SIZE,
shuffle=False)
elif dataset == 'CIFAR10':
transform_augment = [
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(), *transform_augment
]
transform_normalize = transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))
transform_train = transforms.Compose(
[*transform_augment,
transforms.ToTensor(), transform_normalize])
transform_val = transforms.Compose(
[transforms.ToTensor(), transform_normalize])
train_loader = torch.utils.data.DataLoader(
datasets.cifar.CIFAR10('./../data',
train=True,
download=True,
transform=transform_train),
batch_size=BATCH_SIZE,
shuffle=True) # , num_workers=4)
validation_loader = torch.utils.data.DataLoader(datasets.CIFAR10(
'./../data', train=False, transform=transform_val),
batch_size=BATCH_SIZE,
shuffle=False)
else:
raise NotImplementedError
return train_loader, validation_loader
def __getitem__(self, index):
path = self.path_list[index][0]
part = self.path_list[index][1]
path2 = facail_part_path + '/' + part + '/images/' + path + ".jpg"
image = Image.open(path2)
label = []
path2 = facail_part_path + '/' + part + '/labels/' + path + '/'
if (self.choice != None):
image_trans = transforms.Compose([
ToPILImage(),
transforms.RandomChoice(self.choice),
#transforms.RandomChoice(self.choice[index//self.listnum]),
Resize(size=(self.choicesize, self.choicesize),
interpolation=Image.NEAREST),
ToTensor(),
LabelUpdata(self.choicesize)
])
if (part == "eye1"):
for i in range(4, 5):
image2 = Image.open(path2 + path + "_lbl" + str(i // 10) +
str(i % 10) + ".png").convert('L')
#image2=image2.convert('L');
label.append(image2)
if (part == "eye2"):
for i in range(5, 6):
image2 = Image.open(path2 + path + "_lbl" + str(i // 10) +
str(i % 10) + ".png").convert('L')
#image2=image2.convert('L');
#image2=image2.transpose(Image.FLIP_LEFT_RIGHT)
label.append(image2)
if (part == "eyebrow1"):
for i in range(2, 3):
image2 = Image.open(path2 + path + "_lbl" + str(i // 10) +
str(i % 10) + ".png").convert('L')
#image2=image2.convert('L');
label.append(image2)
if (part == "eyebrow2"):
for i in range(3, 4):
image2 = Image.open(path2 + path + "_lbl" + str(i // 10) +
str(i % 10) + ".png").convert('L')
#image2=image2.convert('L');
#image2=image2.transpose(Image.FLIP_LEFT_RIGHT)
label.append(image2)
if (part == "nose"):
for i in range(6, 7):
image2 = Image.open(path2 + path + "_lbl" + str(i // 10) +
str(i % 10) + ".png").convert('L')
#image2=image2.convert('L');
label.append(image2)
if (part == "mouth"):
for i in range(7, 10):
image2 = Image.open(path2 + path + "_lbl" + str(i // 10) +
str(i % 10) + ".png").convert('L')
#image2=image2.convert('L');
label.append(image2)
for i in range(len(label)):
label[i] = np.array(label[i])
bg = 255 - np.sum(label, axis=0, keepdims=True)
labels = np.concatenate([bg, label], axis=0) # [L + 1, 64, 64]
labels = np.uint8(labels)
#labels = [TF.to_pil_image(labels[i])for i in range(labels.shape[0])]
sample = {
'image': image,
'label': labels,
'index': index,
'part': self.face_part
}
if image_trans != None:
sample = image_trans(sample)
sample['label'][0] = torch.sum(
sample['label'][1:sample['label'].shape[0]],
dim=0,
keepdim=True) # 1 x 64 x 64
sample['label'][0] = 1 - sample['label'][0]
#labels_list = torch.cat(sample['label'], dim=0)
'''
for i in range(9):
x3=0;
for x1 in range(64):
for x2 in range(64):
if (labels_list[i][x1][x2]!=0):
x3+=1;
print(print("label"+str(i)+"Num of pixel is "+str(x3)+" After resize"));
'''
return sample['image'], sample['label']
def __getitem__(self, index):
if (self.choice != None):
image_trans = transforms.Compose([
#transforms.RandomChoice(self.choice[index//self.listnum]),
transforms.RandomChoice(self.choice.augmentation[index % 5]),
Resize(size=(self.choicesize, self.choicesize),
interpolation=Image.NEAREST),
ToTensor(),
LabelUpdata(self.choicesize)
])
else:
image_trans = None
#index2=index%self.listnum;
index2 = index // 5
path = self.path_list[index2]
path2 = image_path + '/' + path + ".jpg"
image = Image.open(path2)
label = []
#label.append(Image.new('L', (image.size[0],image.size[1]),color=1))
path2 = label_path + '/' + path + '/'
for i in range(2, 10):
image2 = Image.open(path2 + path + "_lbl" + str(i // 10) +
str(i % 10) + ".png")
image2 = image2.convert('L')
label.append(image2)
for i in range(len(label)):
label[i] = np.array(label[i])
bg = 255 - np.sum(label, axis=0, keepdims=True)
labels = np.concatenate([bg, label], axis=0) # [L + 1, 64, 64]
labels = np.uint8(labels)
labels = [TF.to_pil_image(labels[i]) for i in range(labels.shape[0])]
sample = {"image": image, "label": labels, "index": index}
if (image_trans != None):
sample = image_trans(sample)
sample['label'][0] = torch.sum(
sample['label'][1:sample['label'].shape[0]],
dim=0,
keepdim=True) # 1 x 64 x 64
sample['label'][0] = 1 - sample['label'][0]
'''
f = open("out1.txt", "w")
for i in range(64):
for j in range(64):
print(float(sample['label'][1][i][j]),end=' ',file=f);
print("",file=f);
#input('pause');
'''
lnum = len(sample['label'])
sample['label'] = torch.argmax(sample['label'], dim=0, keepdim=False)
#[64,64]
sample['label'] = sample['label'].unsqueeze(dim=0)
#[1,64,64]
sample['label'] = torch.zeros(lnum, self.choicesize,
self.choicesize).scatter_(
0, sample['label'], 255)
#[L,64,64]
'''
f = open("out2.txt", "w")
for i in range(64):
for j in range(64):
print(float(sample['label'][1][i][j]),end=' ',file=f);
print("",file=f);
input('pause');
'''
return sample
def train(self, params):
if torch.cuda.is_available():
device = 'cuda'
else:
device = 'cpu'
mom_range = params['mom_range']
n_res = params['n_res']
niter = params['niter']
scheduler = params['scheduler']
optimizer_type = params['optimizer']
momentum = params['momentum']
learning_rate = params['learning_rate'].__format__('e')
weight_decay = params['weight_decay'].__format__('e')
weight_decay = float(str(weight_decay)[:1] + str(weight_decay)[-4:])
learning_rate = float(str(learning_rate)[:1] + str(learning_rate)[-4:])
if self.verbose > 1:
print(
"Parameters: \n\t",
'zdim: ' + str(self.n_classes) + "\n\t",
'mom_range: ' + str(mom_range) + "\n\t",
'niter: ' + str(niter) + "\n\t",
'nres: ' + str(n_res) + "\n\t",
'learning_rate: ' + learning_rate.__format__('e') + "\n\t",
'momentum: ' + str(momentum) + "\n\t",
'weight_decay: ' + weight_decay.__format__('e') + "\n\t",
'optimizer_type: ' + optimizer_type + "\n\t",
)
self.modelname = "classif_3dcnn_" \
+ '_bn' + str(self.batchnorm) \
+ '_niter' + str(niter) \
+ '_nres' + str(n_res) \
+ '_momrange' + str(mom_range) \
+ '_momentum' + str(momentum) \
+ '_' + str(optimizer_type) \
+ "_nclasses" + str(self.n_classes) \
+ '_initlr' + learning_rate.__format__('e') \
+ '_wd' + weight_decay.__format__('e') \
+ '_size' + str(self.size)
model = MLP(n_neurons=n_neurons,
n_classes=self.n_classes,
activation=torch.nn.ReLU).to(device)
criterion = nn.CrossEntropyLoss()
if optimizer_type == 'adamw':
optimizer = torch.optim.AdamW(params=model.parameters(),
lr=learning_rate,
weight_decay=weight_decay,
amsgrad=True)
elif optimizer_type == 'sgd':
optimizer = torch.optim.SGD(params=model.parameters(),
lr=learning_rate,
weight_decay=weight_decay,
momentum=momentum)
elif optimizer_type == 'rmsprop':
optimizer = torch.optim.RMSprop(params=model.parameters(),
lr=learning_rate,
weight_decay=weight_decay,
momentum=momentum)
else:
exit('error: no such optimizer type available')
epoch = 0
model = model.to(device)
train_transform = transforms.Compose([
transforms.RandomChoice([XFlip(), YFlip(),
ZFlip()]),
transforms.RandomChoice([Flip90(), Flip180(),
Flip270()]),
ColorJitter3D(.2, .2, .2, .2),
transforms.RandomChoice([
RandomRotation3D(25, 0),
RandomRotation3D(25, 1),
RandomRotation3D(25, 2)
]),
torchvision.transforms.Normalize(mean=(self.mean), std=(self.std)),
Normalize()
])
all_set = MRIDatasetClassifier(self.path,
transform=train_transform,
size=self.size)
spliter = validation_spliter(all_set, cv=self.cross_validation)
print("Training Started on device:", device)
best_losses = []
for cv in range(self.cross_validation):
model.random_init(self.init_func)
best_loss = -1
valid_set, train_set = spliter.__next__()
valid_set.transform = False
train_loader = DataLoader(train_set,
num_workers=0,
shuffle=True,
batch_size=self.batch_size,
pin_memory=False,
drop_last=True)
valid_loader = DataLoader(valid_set,
num_workers=0,
shuffle=True,
batch_size=2,
pin_memory=False,
drop_last=True)
# Get shared output_directory ready
logger = SummaryWriter('logs')
if scheduler == 'ReduceLROnPlateau':
lr_schedule = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer,
factor=0.1,
cooldown=50,
patience=50,
verbose=True,
min_lr=1e-15)
elif scheduler == 'CycleScheduler':
lr_schedule = CycleScheduler(optimizer,
learning_rate,
n_iter=niter * len(train_loader),
momentum=[
max(0.0,
momentum - mom_range),
min(1.0,
momentum + mom_range),
])
losses = {
"train": [],
"valid": [],
}
accuracies = {
"train": [],
"valid": [],
}
shapes = {
"train": len(train_set),
"valid": len(valid_set),
}
early_stop_counter = 0
print("\n\n\nCV:", cv, "/", self.cross_validation,
"\nTrain samples:", len(train_set), "\nValid samples:",
len(valid_set), "\n\n\n")
train_losses = []
train_accuracy = []
valid_losses = []
valid_accuracy = []
for epoch in range(self.epochs):
if early_stop_counter == 200:
if self.verbose > 0:
print('EARLY STOPPING.')
break
best_epoch = False
model.train()
for i, batch in enumerate(train_loader):
# pbar.update(1)
model.zero_grad()
images, targets = batch
images = images.to(device)
targets = targets.to(device)
preds = model(images)
loss = criterion(preds, targets)
loss.backward()
accuracy = sum([
1 if torch.argmax(pred) == target else 0
for (pred, target) in zip(preds, targets)
]) / len(targets)
train_accuracy += [accuracy]
train_losses += [loss.item()]
optimizer.step()
if scheduler == "CycleScheduler":
lr_schedule.step()
logger.add_scalar('training_loss', loss.item(),
i + len(train_loader) * epoch)
del loss
if epoch % self.epochs_per_print == 0:
losses["train"] += [np.mean(train_losses)]
accuracies["train"] += [np.mean(train_accuracy)]
if self.verbose > 1:
print("Epoch: {}:\t"
"Train Loss: {:.5f} , "
"Accuracy: {:.3f} , ".format(
epoch, losses["train"][-1],
accuracies["train"][-1]))
train_losses = []
train_accuracy = []
model.eval()
for i, batch in enumerate(valid_loader):
images, targets = batch
images = images.to(device)
targets = targets.to(device)
preds = model(images)
loss = criterion(preds, targets)
valid_losses += [loss.item()]
accuracy = sum([
1 if torch.argmax(pred) == target else 0
for (pred, target) in zip(preds, targets)
]) / len(targets)
valid_accuracy += [accuracy]
logger.add_scalar('training loss', np.log2(loss.item()),
i + len(train_loader) * epoch)
if scheduler == "ReduceLROnPlateau":
if epoch > 25:
lr_schedule.step(losses["valid"][-1])
mode = 'valid'
if epoch > 10 and epoch % self.epochs_per_print == 0:
if (losses[mode][-1] < best_loss or best_loss == -1) \
and not np.isnan(losses[mode][-1]):
if self.verbose > 1:
print('BEST EPOCH!', losses[mode][-1],
accuracies[mode][-1])
early_stop_counter = 0
best_loss = losses[mode][-1]
best_epoch = True
else:
early_stop_counter += 1
if epoch % self.epochs_per_print == 0:
losses["valid"] += [np.mean(valid_losses)]
accuracies["valid"] += [np.mean(valid_accuracy)]
if self.verbose > 0:
print("Epoch: {}:\t"
"Valid Loss: {:.5f} , "
"Accuracy: {:.3f} ".format(
epoch,
losses["valid"][-1],
accuracies["valid"][-1],
))
if self.verbose > 1:
print("Current LR:", optimizer.param_groups[0]['lr'])
if 'momentum' in optimizer.param_groups[0].keys():
print("Current Momentum:",
optimizer.param_groups[0]['momentum'])
valid_losses = []
valid_accuracy = []
if self.plot_perform:
plot_performance(loss_total=losses,
losses_recon=None,
accuracies=accuracies,
kl_divs=None,
shapes=shapes,
results_path="../figures",
filename="training_loss_trace_" +
self.modelname + '.jpg')
if self.verbose > 0:
print('BEST LOSS :', best_loss)
best_losses += [best_loss]
return best_losses
])
# color changes
brightness = 10
contrast = 10
saturation = 10
hue = 0.25
color_transform = transforms.Compose([
transforms.ColorJitter(brightness=brightness,
contrast=contrast,
saturation=saturation,
hue=hue),
transforms.ToTensor(),
])
# allows to chose randomly from the different transformations
transform_list = transforms.RandomChoice(
[rotation_transform, hoz_transform, vert_transform, color_transform])
# ## Loading the data
# loading the custom dataset
dataset = CofgaDataset(csv_file='dataset/train_preprocessed.csv',
root_dir='dataset/root/train/resized/',
transform=transform_list)
print("Total number of images: ", len(dataset))
COFGA_headers = pd.read_csv('dataset/train_preprocessed.csv')
COFGA_labels = COFGA_headers.columns.tolist()
COFGA_labels.pop(0)
# Data Uplaod
print('\n[Phase 1] : Data Preparation')
torch.manual_seed(2809)
gaussian_transforms = [
transforms.Lambda(lambda x: ndimage.gaussian_filter(x, sigma=0)),
transforms.Lambda(lambda x: ndimage.gaussian_filter(x, sigma=1)),
transforms.Lambda(lambda x: ndimage.gaussian_filter(x, sigma=2)),
transforms.Lambda(lambda x: ndimage.gaussian_filter(x, sigma=5)),
transforms.Lambda(lambda x: ndimage.gaussian_filter(x, sigma=10))
]
transform_train_noise = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(cf.mean['cifar100'], cf.std['cifar100']),
transforms.RandomChoice(gaussian_transforms),
#transforms.ToTensor()
])
transform_train_clean = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(cf.mean['cifar100'], cf.std['cifar100']),
]) # meanstd transformation
transform_test_noise = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(cf.mean['cifar100'], cf.std['cifar100']),
transforms.RandomChoice(gaussian_transforms),
#transforms.ToTensor()
def train(args):
# Get hardware device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Check if weights and biases integration is enabled.
if args.wandb == 1:
import wandb
wandb.init(entity='surajpai',
project='FacialEmotionRecognition',
config=vars(args))
# Get the dataset with "Training" usage.
dataset = FER2013Dataset(args.data_path, "Training")
# Randomly split the dataset into train and validation based on the specified train_split argument
train_dataset, validation_dataset = torch.utils.data.random_split(
dataset, [
int(len(dataset) * args.train_split),
len(dataset) - int(len(dataset) * args.train_split)
])
logging.info(
'Samples in the training set: {}\n Samples in the validation set: {} \n\n'
.format(len(train_dataset), len(validation_dataset)))
# Get class weights as inverse of frequencies from class occurences in the dataset.
dataset_summary = dataset.get_summary_statistics()
class_weights = (1 / dataset_summary["class_occurences"])
class_weights = torch.Tensor(class_weights /
np.sum(class_weights)).to(device)
# Train loader and validation loader initialized with batch_size as specified and randomly shuffled
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
pin_memory=True)
val_loader = DataLoader(validation_dataset,
batch_size=args.batch_size,
shuffle=True,
pin_memory=True)
# Model initialization
model = torch.nn.DataParallel(Model(args.model_config)).to(device)
# Set torch optimizer
optimizer = torch.optim.Adam(model.parameters(), )
# Get loss for training the network from the utils get_loss function
criterion = get_loss(args, class_weights)
bestLoss = -1000
# Create metric logger object
metrics = Metrics(upload=args.wandb)
# Define augmentation transforms, if --augment is enabled
if args.augment == 1:
transform = transforms.RandomChoice([
transforms.RandomHorizontalFlip(p=0.75),
transforms.RandomAffine(15,
translate=(0.1, 0.1),
scale=(1.2, 1.2),
shear=15),
transforms.ColorJitter()
])
# Start iterating over the total number of epochs set by epochs argument
for n_epoch in range(args.epochs):
# Reset running metrics at the beginning of each epoch.
metrics.reset()
# Utils logger
logging.info(' Starting Epoch: {}/{} \n'.format(n_epoch, args.epochs))
'''
TRAINING
'''
# Model in train mode for batch-norm and dropout related ops.
model.train()
# Iterate over each batch in the train loader
for idx, batch in enumerate(tqdm(train_loader)):
# Reset gradients
optimizer.zero_grad()
# Apply augmentation transforms, if --augment is enabled
if args.augment == 1 and n_epoch % 2 == 0:
batch = apply_transforms(batch, transform)
# Move the batch to the device, needed explicitly if GPU is present
image, target = batch["image"].to(device), batch["emotion"].to(
device)
# Run a forward pass over images from the batch
out = model(image)
# Calculate loss based on the criterion set
loss = criterion(out, target)
# Backward pass from the final loss
loss.backward()
# Update the optimizer
optimizer.step()
# Update metrics for this batch
metrics.update_train({
"loss": loss.item(),
"predicted": out,
"ground_truth": target
})
'''
VALIDATION
'''
logging.info(' Validating on the validation split ... \n \n')
# Model in eval mode.
model.eval()
# Set no grad to disable gradient saving.
with torch.no_grad():
# Iterate over each batch in the val loader
for idx, batch in enumerate(val_loader):
# Move the batch to the device, needed explicitly if GPU is present
image, target = batch["image"].to(device), batch["emotion"].to(
device)
# Forward pass
out = model(image)
# Calculate loss based on the criterion set
loss = criterion(out, target)
# Metrics and sample predictions updated for validation batch
metrics.update_val({
"loss": loss.item(),
"predicted": out,
"ground_truth": target,
"image": image,
"class_mapping": dataset.get_class_mapping()
})
# Display metrics at the end of each epoch
metrics.display()
# Weight Checkpointing to save the best model on validation loss
save_path = "./saved_models/{}.pth.tar".format(
args.model_config.split('/')[-1].split('.')[0])
bestLoss = min(bestLoss, metrics.metric_dict["loss@val"])
is_best = (bestLoss == metrics.metric_dict["loss@val"])
save_checkpoint(
{
'epoch': n_epoch,
'state_dict': model.state_dict(),
'bestLoss': bestLoss,
'optimizer': optimizer.state_dict(),
}, is_best, save_path)
# After training is completed, if weights and biases is enabled, visualize filters and upload final model.
if args.wandb == 1:
visualize_filters(model.modules())
wandb.save(save_path)
# Get report from the metrics logger
train_report, val_report = metrics.get_report()
# Save the report to csv files
train_report.to_csv("{}_trainreport.csv".format(
save_path.rstrip(".pth.tar")))
val_report.to_csv("{}_valreport.csv".format(save_path.rstrip(".pth.tar")))
# Just normalization for validation
transform_options = [
#transforms.ColorJitter(brightness=0.5, contrast=0.5, hue=0.5)
#transforms.RandomRotation(degrees=[-30, 30])
#transforms.GaussianBlur(kernel_size=5)
transforms.RandomAffine(0, shear=20)
]
data_transforms = {
'train':
transforms.Compose([
# transforms.RandomResizedCrop(IMG_SIZE)
# ,
#transforms.RandomHorizontalFlip()
# ,
transforms.RandomApply([transforms.RandomChoice(transform_options)],
p=1.0)
]),
'val':
transforms.Compose([
transforms.Resize((IMG_SIZE, IMG_SIZE)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
img_path = "data/training_data/" + "000001.jpg"
img = Image.open(img_path).convert("RGB")
img = data_transforms["train"](img)
img.show()
img.save("RandomAffine.png")
def load_transforms(name):
"""Load data transformations.
Note:
- Gaussian Blur is defined at the bottom of this file.
"""
_name = name.lower()
if _name == "default":
transform = transforms.Compose([
transforms.RandomCrop(32, padding=8),
transforms.RandomHorizontalFlip(),
transforms.ToTensor()
])
elif _name == "transfer":
transform = transforms.Compose(
[transforms.CenterCrop(32),
transforms.ToTensor()])
elif _name == "cifar":
transform = transforms.Compose([
transforms.RandomResizedCrop(32),
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomApply(
[transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)], p=0.8),
transforms.RandomGrayscale(p=0.2),
transforms.ToTensor()
])
elif _name == "mnist":
transform = transforms.Compose([
transforms.RandomChoice([
transforms.RandomAffine((-90, 90)),
transforms.RandomAffine(0, translate=(0.2, 0.4)),
transforms.RandomAffine(0, scale=(0.8, 1.1)),
transforms.RandomAffine(0, shear=(-20, 20))
]),
GaussianBlur(kernel_size=3),
transforms.ToTensor()
])
elif _name == "stl10":
transform = transforms.Compose([
transforms.RandomResizedCrop(96),
transforms.RandomHorizontalFlip(),
transforms.RandomApply(
[transforms.ColorJitter(0.8, 0.8, 0.8, 0.2)], p=0.8),
transforms.RandomGrayscale(p=0.2),
GaussianBlur(kernel_size=9),
transforms.ToTensor()
])
elif _name == "fashionmnist" or _name == "fmnist":
transform = transforms.Compose([
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomRotation((-90, 90)),
transforms.RandomChoice([
transforms.RandomAffine((-90, 90)),
transforms.RandomAffine(0, translate=(0.2, 0.4)),
transforms.RandomAffine(0, scale=(0.8, 1.1)),
transforms.RandomAffine(0, shear=(-20, 20))
]),
GaussianBlur(kernel_size=3),
transforms.ToTensor()
])
elif _name == 'deca_train':
transform = transforms.Compose([
transforms.Resize(72),
transforms.RandomCrop(64),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
])
elif _name == 'deca_test':
transform = transforms.Compose([
transforms.Resize(72),
transforms.CenterCrop(64),
transforms.ToTensor(),
])
elif _name == "test":
transform = transforms.ToTensor()
else:
raise NameError("{} not found in transform loader".format(name))
return transform
########## 001 Data Transforms #####################
image_trans = {
'train':
transforms.Compose([
# transfer the input image into gray scale
#transforms.Grayscale(num_output_channels=1),
# resize the input image into the predefined scale
#transforms.Resize((img_h, img_w), interpolation=PIL.Image.BICUBIC), # (h, w)
# random choose one of the predefined transforms (in the list) when performing the training process
#transforms.Lambda(lambda img : head_center(img)),
transforms.Lambda(lambda img: pad_img(img, img_w)),
transforms.RandomChoice([
transforms.RandomHorizontalFlip(),
#transforms.ColorJitter(brightness=0.3, contrast=0.3, saturation=0.3, hue=0.3),
#transforms.Lambda(lambda img : centralize(img,0.4,0.4,0.5,0.5)),
#transforms.RandomRotation(30, resample=False, expand=False, center=None)
]),
#transforms.Lambda(lambda img : verticalize(img)),
# transfer the type of input image into tensor style
transforms.ToTensor(),
]),
'valid':
transforms.Compose([
#transforms.Grayscale(num_output_channels=1),
#transforms.Resize((img_h, img_w), interpolation=PIL.Image.BICUBIC),
#transforms.Lambda(lambda img : centralize(img,0.4,0.4,0.4,0.3)),
#transforms.Lambda(lambda img : head_center(img)),
transforms.Lambda(lambda img: pad_img(img, img_w)),
transforms.RandomChoice([
"""**Define Data Preprocessing**"""
# Define transforms for training phase
train_transform = tr.Compose([tr.Resize(256), # Resizes short size of the PIL image to 256
tr.CenterCrop(224), # Crops a central square patch of the image 224
# because torchvision's AlexNet needs a 224x224 input! Remember this when
# applying different transformations, otherwise you get an error
# /======================================================================================\
# 4.A: Data Augmentation
# ----------------------------------------------------------------------------------------
# tr.RandomHorizontalFlip(),
# tr.RandomPerspective(distortion_scale=0.2),
# tr.RandomRotation(degrees=10),
# ----------------------------------------------------------------------------------------
tr.RandomChoice([tr.RandomHorizontalFlip(),
tr.RandomPerspective(distortion_scale=0.2),
tr.RandomRotation(degrees=10)]),
# \======================================================================================/
tr.ToTensor(), # Turn PIL Image to torch.Tensor
# /======================================================================================\
# Normalizes tensor with mean and standard deviation
# ----------------------------------------------------------------------------------------
# Till 3.A:
# tr.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
# ----------------------------------------------------------------------------------------
# From 3.B on:
tr.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
# \======================================================================================/
])
# Define transforms for the evaluation phase
eval_transform = tr.Compose([tr.Resize(256),
def __init__(self, mdlParams, indSet):
"""
Args:
mdlParams (dict): Configuration for loading
indSet (string): Indicates train, val, test
"""
# Mdlparams
self.mdlParams = mdlParams
# Number of classes
self.numClasses = mdlParams['numClasses']
# Model input size
self.input_size = (np.int32(mdlParams['input_size'][0]),np.int32(mdlParams['input_size'][1]))
# Whether or not to use ordered cropping
self.orderedCrop = mdlParams['orderedCrop']
# Number of crops for multi crop eval
self.multiCropEval = mdlParams['multiCropEval']
# Whether during training same-sized crops should be used
self.same_sized_crop = mdlParams['same_sized_crops']
# Only downsample
self.only_downsmaple = mdlParams.get('only_downsmaple',False)
# Potential class balancing option
self.balancing = mdlParams['balance_classes']
# Whether data should be preloaded
self.preload = mdlParams['preload']
# Potentially subtract a mean
self.subtract_set_mean = mdlParams['subtract_set_mean']
# Potential switch for evaluation on the training set
self.train_eval_state = mdlParams['trainSetState']
# Potential setMean to deduce from channels
self.setMean = mdlParams['setMean'].astype(np.float32)
# Current indSet = 'trainInd'/'valInd'/'testInd'
self.indices = mdlParams[indSet]
self.indSet = indSet
# feature scaling for meta
if mdlParams.get('meta_features',None) is not None and mdlParams['scale_features']:
self.feature_scaler = mdlParams['feature_scaler_meta']
if self.balancing == 3 and indSet == 'trainInd':
# Sample classes equally for each batch
# First, split set by classes
not_one_hot = np.argmax(mdlParams['labels_array'],1)
self.class_indices = []
for i in range(mdlParams['numClasses']):
self.class_indices.append(np.where(not_one_hot==i)[0])
# Kick out non-trainind indices
self.class_indices[i] = np.setdiff1d(self.class_indices[i],mdlParams['valInd'])
# And test indices
if 'testInd' in mdlParams:
self.class_indices[i] = np.setdiff1d(self.class_indices[i],mdlParams['testInd'])
# Now sample indices equally for each batch by repeating all of them to have the same amount as the max number
indices = []
max_num = np.max([len(x) for x in self.class_indices])
# Go thourgh all classes
for i in range(mdlParams['numClasses']):
count = 0
class_count = 0
max_num_curr_class = len(self.class_indices[i])
# Add examples until we reach the maximum
while(count < max_num):
# Start at the beginning, if we are through all available examples
if class_count == max_num_curr_class:
class_count = 0
indices.append(self.class_indices[i][class_count])
count += 1
class_count += 1
print("Largest class",max_num,"Indices len",len(indices))
print("Intersect val",np.intersect1d(indices,mdlParams['valInd']),"Intersect Testind",np.intersect1d(indices,mdlParams['testInd']))
# Set labels/inputs
self.labels = mdlParams['labels_array'][indices,:]
self.im_paths = np.array(mdlParams['im_paths'])[indices].tolist()
# Normal train proc
if self.same_sized_crop:
cropping = transforms.RandomCrop(self.input_size)
elif self.only_downsmaple:
cropping = transforms.Resize(self.input_size)
else:
cropping = transforms.RandomResizedCrop(self.input_size[0])
# All transforms
self.composed = transforms.Compose([
cropping,
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.ColorJitter(brightness=32. / 255.,saturation=0.5),
transforms.ToTensor(),
transforms.Normalize(torch.from_numpy(self.setMean).float(),torch.from_numpy(np.array([1.,1.,1.])).float())
])
elif self.orderedCrop and (indSet == 'valInd' or self.train_eval_state == 'eval' or indSet == 'testInd'):
# Also flip on top
if mdlParams.get('eval_flipping',0) > 1:
# Complete labels array, only for current indSet, repeat for multiordercrop
inds_rep = np.repeat(mdlParams[indSet], mdlParams['multiCropEval']*mdlParams['eval_flipping'])
self.labels = mdlParams['labels_array'][inds_rep,:]
# meta
if mdlParams.get('meta_features',None) is not None:
self.meta_data = mdlParams['meta_array'][inds_rep,:]
# Path to images for loading, only for current indSet, repeat for multiordercrop
self.im_paths = np.array(mdlParams['im_paths'])[inds_rep].tolist()
print("len im path",len(self.im_paths))
if self.mdlParams.get('var_im_size',False):
self.cropPositions = np.tile(mdlParams['cropPositions'][mdlParams[indSet],:,:],(1,mdlParams['eval_flipping'],1))
self.cropPositions = np.reshape(self.cropPositions,[mdlParams['multiCropEval']*mdlParams['eval_flipping']*mdlParams[indSet].shape[0],2])
#self.cropPositions = np.repeat(self.cropPositions, (mdlParams['eval_flipping'],1))
#print("CP examples",self.cropPositions[:50,:])
else:
self.cropPositions = np.tile(mdlParams['cropPositions'], (mdlParams['eval_flipping']*mdlParams[indSet].shape[0],1))
# Flip states
if mdlParams['eval_flipping'] == 2:
self.flipPositions = np.array([0,1])
elif mdlParams['eval_flipping'] == 3:
self.flipPositions = np.array([0,1,2])
elif mdlParams['eval_flipping'] == 4:
self.flipPositions = np.array([0,1,2,3])
self.flipPositions = np.repeat(self.flipPositions, mdlParams['multiCropEval'])
self.flipPositions = np.tile(self.flipPositions, mdlParams[indSet].shape[0])
print("Crop positions shape",self.cropPositions.shape,"flip pos shape",self.flipPositions.shape)
print("Flip example",self.flipPositions[:30])
else:
# Complete labels array, only for current indSet, repeat for multiordercrop
inds_rep = np.repeat(mdlParams[indSet], mdlParams['multiCropEval'])
self.labels = mdlParams['labels_array'][inds_rep,:]
# meta
if mdlParams.get('meta_features',None) is not None:
self.meta_data = mdlParams['meta_array'][inds_rep,:]
# Path to images for loading, only for current indSet, repeat for multiordercrop
self.im_paths = np.array(mdlParams['im_paths'])[inds_rep].tolist()
print("len im path",len(self.im_paths))
# Set up crop positions for every sample
if self.mdlParams.get('var_im_size',False):
self.cropPositions = np.reshape(mdlParams['cropPositions'][mdlParams[indSet],:,:],[mdlParams['multiCropEval']*mdlParams[indSet].shape[0],2])
#print("CP examples",self.cropPositions[:50,:])
else:
self.cropPositions = np.tile(mdlParams['cropPositions'], (mdlParams[indSet].shape[0],1))
print("CP",self.cropPositions.shape)
#print("CP Example",self.cropPositions[0:len(mdlParams['cropPositions']),:])
# Set up transforms
self.norm = transforms.Normalize(np.float32(self.mdlParams['setMean']),np.float32(self.mdlParams['setStd']))
self.trans = transforms.ToTensor()
elif indSet == 'valInd' or indSet == 'testInd':
if self.multiCropEval == 0:
if self.only_downsmaple:
self.cropping = transforms.Resize(self.input_size)
else:
self.cropping = transforms.Compose([transforms.CenterCrop(np.int32(self.input_size[0]*1.5)),transforms.Resize(self.input_size)])
# Complete labels array, only for current indSet
self.labels = mdlParams['labels_array'][mdlParams[indSet],:]
# meta
if mdlParams.get('meta_features',None) is not None:
self.meta_data = mdlParams['meta_array'][mdlParams[indSet],:]
# Path to images for loading, only for current indSet
self.im_paths = np.array(mdlParams['im_paths'])[mdlParams[indSet]].tolist()
else:
# Deterministic processing
if self.mdlParams.get('deterministic_eval',False):
total_len_per_im = mdlParams['numCropPositions']*len(mdlParams['cropScales'])*mdlParams['cropFlipping']
# Actual transforms are functionally applied at forward pass
self.cropPositions = np.zeros([total_len_per_im,3])
ind = 0
for i in range(mdlParams['numCropPositions']):
for j in range(len(mdlParams['cropScales'])):
for k in range(mdlParams['cropFlipping']):
self.cropPositions[ind,0] = i
self.cropPositions[ind,1] = mdlParams['cropScales'][j]
self.cropPositions[ind,2] = k
ind += 1
# Complete labels array, only for current indSet, repeat for multiordercrop
print("crops per image",total_len_per_im)
self.cropPositions = np.tile(self.cropPositions, (mdlParams[indSet].shape[0],1))
inds_rep = np.repeat(mdlParams[indSet], total_len_per_im)
self.labels = mdlParams['labels_array'][inds_rep,:]
# meta
if mdlParams.get('meta_features',None) is not None:
self.meta_data = mdlParams['meta_array'][inds_rep,:]
# Path to images for loading, only for current indSet, repeat for multiordercrop
self.im_paths = np.array(mdlParams['im_paths'])[inds_rep].tolist()
else:
self.cropping = transforms.RandomResizedCrop(self.input_size[0],scale=(mdlParams.get('scale_min',0.08),1.0))
# Complete labels array, only for current indSet, repeat for multiordercrop
inds_rep = np.repeat(mdlParams[indSet], mdlParams['multiCropEval'])
self.labels = mdlParams['labels_array'][inds_rep,:]
# meta
if mdlParams.get('meta_features',None) is not None:
self.meta_data = mdlParams['meta_array'][inds_rep,:]
# Path to images for loading, only for current indSet, repeat for multiordercrop
self.im_paths = np.array(mdlParams['im_paths'])[inds_rep].tolist()
print(len(self.im_paths))
# Set up transforms
self.norm = transforms.Normalize(np.float32(self.mdlParams['setMean']),np.float32(self.mdlParams['setStd']))
self.trans = transforms.ToTensor()
else:
all_transforms = []
# Normal train proc
if self.same_sized_crop:
all_transforms.append(transforms.RandomCrop(self.input_size))
elif self.only_downsmaple:
all_transforms.append(transforms.Resize(self.input_size))
else:
all_transforms.append(transforms.RandomResizedCrop(self.input_size[0],scale=(mdlParams.get('scale_min',0.08),1.0)))
if mdlParams.get('flip_lr_ud',False):
all_transforms.append(transforms.RandomHorizontalFlip())
all_transforms.append(transforms.RandomVerticalFlip())
# Full rot
if mdlParams.get('full_rot',0) > 0:
if mdlParams.get('scale',False):
all_transforms.append(transforms.RandomChoice([transforms.RandomAffine(mdlParams['full_rot'], scale=mdlParams['scale'], shear=mdlParams.get('shear',0), resample=Image.NEAREST),
transforms.RandomAffine(mdlParams['full_rot'],scale=mdlParams['scale'],shear=mdlParams.get('shear',0), resample=Image.BICUBIC),
transforms.RandomAffine(mdlParams['full_rot'],scale=mdlParams['scale'],shear=mdlParams.get('shear',0), resample=Image.BILINEAR)]))
else:
all_transforms.append(transforms.RandomChoice([transforms.RandomRotation(mdlParams['full_rot'], resample=Image.NEAREST),
transforms.RandomRotation(mdlParams['full_rot'], resample=Image.BICUBIC),
transforms.RandomRotation(mdlParams['full_rot'], resample=Image.BILINEAR)]))
# Color distortion
if mdlParams.get('full_color_distort') is not None:
all_transforms.append(transforms.ColorJitter(brightness=mdlParams.get('brightness_aug',32. / 255.),saturation=mdlParams.get('saturation_aug',0.5), contrast = mdlParams.get('contrast_aug',0.5), hue = mdlParams.get('hue_aug',0.2)))
else:
all_transforms.append(transforms.ColorJitter(brightness=32. / 255.,saturation=0.5))
# Autoaugment
if self.mdlParams.get('autoaugment',False):
all_transforms.append(AutoAugment())
# Cutout
if self.mdlParams.get('cutout',0) > 0:
all_transforms.append(Cutout_v0(n_holes=1,length=self.mdlParams['cutout']))
# Normalize
all_transforms.append(transforms.ToTensor())
all_transforms.append(transforms.Normalize(np.float32(self.mdlParams['setMean']),np.float32(self.mdlParams['setStd'])))
# All transforms
self.composed = transforms.Compose(all_transforms)
# Complete labels array, only for current indSet
self.labels = mdlParams['labels_array'][mdlParams[indSet],:]
# meta
if mdlParams.get('meta_features',None) is not None:
self.meta_data = mdlParams['meta_array'][mdlParams[indSet],:]
# Path to images for loading, only for current indSet
self.im_paths = np.array(mdlParams['im_paths'])[mdlParams[indSet]].tolist()
# Potentially preload
if self.preload:
self.im_list = []
for i in range(len(self.im_paths)):
self.im_list.append(Image.open(self.im_paths[i]))
random_transforms = [
transforms.RandomApply([], p=0),
transforms.CenterCrop(48),
transforms.ColorJitter(brightness=5),
transforms.ColorJitter(saturation=5),
transforms.ColorJitter(contrast=5),
transforms.ColorJitter(hue=0.4),
transforms.RandomRotation(15),
transforms.RandomAffine(0, translate=(0.2, 0.2), resample=Image.BICUBIC),
transforms.RandomAffine(0, shear=20, resample=Image.BICUBIC),
transforms.RandomAffine(0, scale=(0.8, 1.2), resample=Image.BICUBIC),
]
train_data_transforms = transforms.Compose([
transforms.Resize((64, 64)),
transforms.RandomChoice(random_transforms),
transforms.Resize((48, 48)),
CLAHE(),
transforms.ToTensor(),
transforms.Normalize((0.3337, 0.3064, 0.3171), (0.2672, 0.2564, 0.2629))
])
data_transforms = transforms.Compose([
transforms.Resize((48, 48)),
# CLAHE(),
transforms.ToTensor(),
transforms.Normalize((0.3337, 0.3064, 0.3171), (0.2672, 0.2564, 0.2629))
])
if self.use_gpu: targets = targets.cuda()
targets = (1 -
self.epsilon) * targets + self.epsilon / self.num_classes
loss = (-targets * log_probs).mean(0).sum()
return loss
xent = CrossEntropyLabelSmooth(num_classes=2)
medianBlur = MedianBlur(3)
data_transforms = {
'train':
transforms.Compose([
transforms.Scale(299),
transforms.RandomRotation(15),
transforms.RandomChoice(
[transforms.RandomResizedCrop(224),
transforms.CenterCrop(224)]),
#transforms.RandomResizedCrop(224),
#transforms.Resize([ 299, 299]),
#transforms.CenterCrop(224),
transforms.RandomChoice(
[transforms.RandomHorizontalFlip(), medianBlur]),
transforms.ColorJitter(brightness=0.2,
contrast=0.2,
saturation=0.2,
hue=0.2),
transforms.ToTensor(), # range [0, 255] -> [0.0,1.0]
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'validation':
transforms.Compose([
import torchvision.transforms as transforms
from util import util
if __name__ == '__main__':
opt = TrainOptions().parse()
dataset = torchvision.datasets.ImageFolder(
opt.dataroot,
transform=transforms.Compose([
transforms.RandomChoice([
transforms.Resize(opt.loadSize, interpolation=1),
transforms.Resize(opt.loadSize, interpolation=2),
transforms.Resize(opt.loadSize, interpolation=3),
transforms.Resize((opt.loadSize, opt.loadSize),
interpolation=1),
transforms.Resize((opt.loadSize, opt.loadSize),
interpolation=2),
transforms.Resize((opt.loadSize, opt.loadSize),
interpolation=3)
]),
transforms.RandomChoice([
transforms.RandomResizedCrop(opt.fineSize, interpolation=1),
transforms.RandomResizedCrop(opt.fineSize, interpolation=2),
transforms.RandomResizedCrop(opt.fineSize, interpolation=3)
]),
transforms.RandomChoice([
transforms.ColorJitter(brightness=.05,
contrast=.05,
saturation=.05,
hue=.05),
def main(args):
# Model settings
model = ResNet()
if args.cuda:
model = model.cuda()
optimizer = optim.Adam(model.parameters(), args.lr, weight_decay=args.wd)
if args.ckpt > 0:
ckpt_name = 'resnet152'
if args.poison:
ckpt_name += '-poison'
ckpt_name += '-' + str(args.ckpt) + '.pkl'
ckpt_path = os.path.join('./ckpt', ckpt_name)
print('Loading checkpoint from {}'.format(ckpt_path))
dct = torch.load(ckpt_path)
model.load_state_dict(dct['model'])
optimizer.load_state_dict(dct['optim'])
# Data loader settings
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Resize((64, 64)),
transforms.Normalize((.5, .5, .5), (.5, .5, .5)),
])
aug_transform = transforms.Compose([
transforms.RandomChoice([
# do nothing
transforms.Compose([]),
# horizontal flip
transforms.RandomHorizontalFlip(1.),
# random crop
transforms.RandomResizedCrop(64),
# rotate
transforms.RandomRotation(30)
]),
transforms.ToTensor(),
transforms.Resize((64, 64)),
transforms.Normalize((.5, .5, .5), (.5, .5, .5)),
])
task_dir = '/data/csnova1/benchmarks/%s' % args.task
poison_dir = '/data/csnova1/poison'
poison_config = get_poison_config()
if args.task == "cifar10":
Loader = CIFAR10Loader
PoisonedILoader = PoisonedCIFAR10Loader
train_loader = Loader(root=task_dir,
batch_size=args.batch_size,
split='train',
transform=aug_transform)
test_loader = PoisonedILoader(root=task_dir,
poison_root=poison_dir,
poison_config=poison_config,
poison_num=6,
batch_size=args.batch_size,
split="val",
transform=transform)
# Start
if args.run == "train":
train(args, train_loader, model, optimizer)
elif args.run == "test":
evaluate(args, test_loader, model)
T_0=5,
T_mult=2)
normMean = [0.5964188, 0.4566936, 0.3908954]
normStd = [0.2590655, 0.2314241, 0.2269535]
train_transformer = transforms.Compose([
transforms.RandomChoice([
transforms.ColorJitter(brightness=[0.8, 1.2],
contrast=[0.8, 1.2],
saturation=[0.8, 1.2],
hue=[-0.2, 0.2]),
transforms.Compose(
[transforms.Resize((150, 150)),
transforms.Resize((200, 200))]),
transforms.Compose([
transforms.RandomResizedCrop(150,
scale=(0.78, 1),
ratio=(0.90, 1.10),
interpolation=2),
transforms.Resize((200, 200))
]),
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomRotation((-20, 20),
resample=False,
expand=False,
center=None),
]),
# transforms.RandomHorizontalFlip(p=0.5),
# transforms.RandomRotation(degrees=(5, 10)),
transforms.ToTensor(),
transforms.Normalize(normMean, normStd)
])
batch_size = args.batch_size
num_epoch = args.epochs
lr = args.learning_rate
wd = args.weight_decay
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
summary = SummaryWriter('./runs/')
train_transforms = transforms.Compose([
transforms.Resize((args.image_size, args.image_size)),
transforms.RandomHorizontalFlip(),
transforms.RandomChoice([
transforms.ColorJitter(0.2, 0.2, 0.2, 0.2),
transforms.RandomResizedCrop(224),
transforms.RandomAffine(degrees=15,
translate=(0.2, 0.2),
scale=(0.8, 1.2),
shear=15,
resample=Image.BILINEAR)
]),
transforms.ToTensor(),
transforms.Normalize((0.4452, 0.4457, 0.4464),
(0.2592, 0.2596, 0.2600)),
])
test_transforms = transforms.Compose([
transforms.Resize((args.image_size, args.image_size)),
transforms.ToTensor(),
transforms.Normalize((0.4452, 0.4457, 0.4464),
(0.2592, 0.2596, 0.2600)),
])
from yolo.config import IOU_THRESHOLD, TENSORBOARD_PATH
from tensorboardX import SummaryWriter
from datetime import datetime
import time
from torch.optim import SGD, RMSprop, Adam
from torch.optim.lr_scheduler import StepLR
from yolo.utils.evaluate.metter import AverageMeter
general_transform = Compose(
[Resize((448, 448)),
RandomHorizontalFlip(0.3),
XyToCenter()])
transform = transforms.Compose([
transforms.RandomChoice([
transforms.ColorJitter(hue=.3, saturation=.2),
transforms.RandomGrayscale(p=0.3),
]),
transforms.ToTensor()
])
val_general_transform = Compose([Resize((448, 448)), XyToCenter()])
val_transform = transforms.Compose([transforms.ToTensor()])
# +
batch_size = 32
ds = OpenImage('/data/data/OpenImage/',
'train',
general_transform=general_transform,
transform=transform)
def ContrastivePredictiveCodingAugmentations(img):
# We use transformations as traceable
# https://arxiv.org/pdf/1805.09501.pdf
pool = [
transforms.RandomRotation( # Rotation
30,
resample=False,
expand=False,
center=None,
fill=None),
transforms.RandomAffine( # Shearing
0,
translate=None,
scale=None,
shear=30,
resample=False,
fillcolor=0),
transforms.RandomAffine( # Translate
0,
translate=(0.3, 0.3),
scale=None,
shear=None,
resample=False,
fillcolor=0),
transforms.Lambda(lambda x: imo.autocontrast(x)), # Autocontrast
transforms.Lambda(lambda x: imo.invert(x)), # Invert
transforms.Lambda(lambda x: imo.equalize(x)), # Equalize
transforms.Lambda(lambda x: imo.solarize(x)), # Solarize
transforms.Lambda(lambda x: imo.posterize(
x, bits=int(np.random.randint(4, 8) + 1))), # Posterize
transforms.Lambda(
lambda x: ime.Color(x).enhance(np.random.uniform())), # Color
transforms.Lambda(lambda x: ime.Brightness(x).enhance(
np.random.uniform())), # Brightness
transforms.Lambda(lambda x: ime.Contrast(x).enhance(np.random.uniform(
))), # Contrast
transforms.Lambda(lambda x: ime.Sharpness(x).enhance(np.random.uniform(
))), # Sharpness
transforms.Compose( # Set black
[
transforms.ToTensor(),
transforms.RandomErasing(1.0),
transforms.ToPILImage()
])
]
# 1.
t1 = transforms.RandomChoice(pool)
t2 = transforms.RandomChoice(pool)
t3 = transforms.RandomChoice(pool)
img = t3(t2(t1(img)))
# https://www.nature.com/articles/s41591-018-0107-6
# 2. Only elastic def, no shearing as this is part of pool as well as hist changes
if np.random.uniform() < 0.2:
img = elastic_transform(img, sigma=10)
# 3. In pool
# 4.
if np.random.uniform() < 0.25:
img = transforms.functional.to_grayscale(img, num_output_channels=3)
return img