def __init__(self, options):
self.device = options.device
self.enc_path = options.enc_path
self.content_size = options.content_size
self.enc_iter = options.enc_iter
if not os.path.exists(os.path.join(self.enc_path, 'codes')):
os.makedirs(os.path.join(self.enc_path, 'codes'))
transforms = []
if options.crop_size is not None:
transforms.append(T.CenterCrop(options.crop_size))
transforms.append(T.Resize(options.image_size))
transforms.append(T.ToTensor())
transforms.append(T.Normalize((0.5, 0.5, 0.5, 0), (0.5, 0.5, 0.5, 1)))
self.dataset = ImageFolder(options.data_root,
transform=T.Compose(transforms))
self.dataloader = torch.utils.data.DataLoader(
self.dataset,
batch_size=options.batch_size,
num_workers=options.nloader)
self.data_iter = iter(self.dataloader)
self.enc = models.Encoder(options.image_size, options.image_size,
options.enc_features, options.enc_blocks,
options.enc_adain_features,
options.enc_adain_blocks,
options.content_size)
self.enc.to(self.device)
self.enc.load_state_dict(
torch.load(os.path.join(self.enc_path, 'models',
'{0}_enc.pt'.format(self.enc_iter)),
map_location=self.device))
def main(args):
args.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('device: {}'.format(args.device))
# create model
model = ConvNet(cfg.NUM_CLASSES).to(args.device)
# define loss function (criterion)
criterion = nn.CrossEntropyLoss().to(args.device)
# load checkpoint
if args.model_weight:
if os.path.isfile(args.model_weight):
print("=> loading checkpoint '{}'".format(args.model_weight))
checkpoint = torch.load(args.model_weight, map_location=args.device)
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}'".format(args.model_weight))
else:
print("=> no checkpoint found at '{}'".format(args.model_weight))
# Data loading code
test_dataset = ImageFolder(cfg.TEST_PATH, mode='test')
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
# Evaluate on test dataset
validate(test_loader, model, criterion, args)
def __init__(self, options):
super(ClassifierTrainer, self).__init__(options, copy_keys=copy_keys)
transforms = []
if options.crop_size is not None:
transforms.append(T.CenterCrop(options.crop_size))
transforms.append(T.Resize(options.image_size))
transforms.append(T.CenterCrop(options.image_size))
transforms.append(T.ToTensor())
image_transforms = transforms + [
T.Normalize((0.5, 0.5, 0.5, 0), (0.5, 0.5, 0.5, 1))
]
self.dataset = ImageFolder(options.data_root,
transform=T.Compose(image_transforms))
self.dataloader = torch.utils.data.DataLoader(
self.dataset,
batch_size=options.batch_size,
shuffle=True,
drop_last=True,
num_workers=options.nloader)
self.data_iter = iter(self.dataloader)
self.cla = models.ClassifierOrDiscriminator(
options.image_size, options.image_size, options.cla_features,
options.cla_blocks, options.cla_adain_features,
options.cla_adain_blocks, self.nclass)
self.cla.to(self.device)
self.cla_optim = optim.Adam(self.cla.parameters(),
lr=self.lr,
eps=1e-4)
self.add_model('cla', self.cla, self.cla_optim)
if self.load_path is not None:
self.load(options.load_iter)
def detect_img(img_path,
model,
device,
detect_size,
class_names,
conf_thres,
nms_thres,
output_dir,
save_plot=True):
"""
Using the given trained model to detect veins on the image (specified by img_path).
- model: yolo loaded with checkpoint weights
- img_path: absolute path to the image
- detect_size: input image size of the model
- class_names: a list of target class names
"""
print(f'\nPerforming object detection on ---> {img_path} \t')
FloatTensor = torch.cuda.FloatTensor if device.type == 'cuda' else torch.FloatTensor
img_np = np.array(Image.open(img_path))
img = ImageFolder(folder_path='',
img_size=detect_size).preprocess(img_path)
img = img.unsqueeze(0).type(FloatTensor)
begin_time = time.time()
with torch.no_grad():
outputs = model(img)
detections_list = non_max_suppression(outputs, conf_thres, nms_thres)
end_time = time.time()
# print(f'detections: {detections}')
inference_time = end_time - begin_time
print(f'inference_time: {inference_time}s')
detections_rescaled = None
if detections_list[0] is not None:
# it is a list due to the implementation of non_max_suppression that deal with batch samples
detections_rescaled = rescale_boxes(detections_list[0].clone(),
detect_size, img_np.shape[:2])
plot_img_and_bbox(img_path, detections_rescaled, detect_size,
class_names, output_dir, save_plot)
else:
print('No veins detected on this image!')
return detections_list, detections_rescaled, inference_time
def process_options(options):
if options.cmd in ['encode', 'stage2']:
if options.enc_path is None:
raise ValueError('encoder path must be specified')
if options.cmd in ['stage1', 'classifier', 'stage2', 'gan']:
options.save_path = options.save_path or options.load_path
if options.save_path is None:
raise ValueError('save path must be specified')
if options.augment is not None:
options.augment = options.augment == 'true'
if options.cla_fake is not None:
options.cla_fake = options.cla_fake == 'true'
if options.load_path is not None:
with open(os.path.join(options.load_path, 'options')) as file:
saved_options = json.load(file)
for key in load_keys:
options.__dict__[key] = saved_options[key]
for key in override_keys:
if options.__dict__[key] is None:
options.__dict__[key] = saved_options[key]
options.load_iter = options.load_iter or 'last'
else:
if options.cmd in ['encode', 'stage2']:
with open(os.path.join(options.enc_path, 'options')) as file:
enc_options = json.load(file)
print('using encoder structure from stage 1')
for key in enc_keys + ['data_root', 'image_size', 'content_size']:
options.__dict__[key] = enc_options[key]
if options.cmd == 'stage2':
if not options.reset_gen:
print('using generator structure from stage 1')
for key in gen_keys:
options.__dict__[key] = enc_options[key]
if not (options.reset_gen and options.reset_sty):
print('using length of style code from stage 1')
options.style_size = enc_options['style_size']
if options.cmd == 'stage2' and options.cla_path is not None:
with open(os.path.join(options.cla_path, 'options')) as file:
cla_options = json.load(file)
if cla_options['image_size'] != options.image_size:
raise ValueError('image size of stage 1 networks and classifier 2 does not match')
if not options.reset_dis:
print('using discriminator structure from pre-trained classifier')
for key1, key2 in zip(dis_keys, cla_keys):
options.__dict__[key1] = cla_options[key2]
if not options.reset_cla:
print('using classifier structure from pre-trained classifier')
for key in cla_keys:
options.__dict__[key] = cla_options[key]
for key, value in stage_defaults[options.cmd].items():
if options.__dict__[key] is None:
options.__dict__[key] = value
for key, value in defaults.items():
if options.__dict__[key] is None:
options.__dict__[key] = value
dataset = ImageFolder(options.data_root, transform = T.ToTensor())
if options.image_size is None:
if options.crop_size is None:
print('image size not specified, using image size of dataset')
options.image_size = dataset[0][0].size(1)
else:
print('image size not specified, using crop size')
options.image_size = options.crop_size
options.nclass = dataset.get_nclass()
if options.style_size is None:
print('style size not specified, using defaults')
options.style_size = min(max(min(options.nclass // 4, 256), 16), options.nclass)
if options.image_size % 2 != 0:
raise ValueError('image size must be an even integer')
options.vis_row = options.visualize_size[0]
options.vis_col = options.visualize_size[1]
for min_size, max_size, features, content_size, batch_size, vis_col in default_sizes:
if min_size <= options.image_size <= max_size:
options.features = options.features or features
options.mlp_features = options.mlp_features or features[-1]
if options.blocks is None:
options.blocks = (['cc'] + ['cbc'] * (len(options.features) - 2) + ['f'])
options.gen_blocks = options.gen_blocks or (['tc'] + ['cbc'] * (len(options.features) - 2) + ['f'])
options.content_size = options.content_size or content_size
options.batch_size = options.batch_size or (batch_size * 2 if options.cmd in ['classifier', 'gan', 'encode'] else batch_size)
options.vis_col = options.vis_col or (vis_col * 2 if options.cmd in ['gan'] else vis_col)
if options.content_size is None:
raise ValueError('content size not specified and failed to set defaults')
if options.batch_size is None:
raise ValueError('batch size not specified and failed to set defaults')
options.vis_col = options.vis_col or (10 if options.image_size < 16 else 2)
options.vis_row = options.vis_row or (options.vis_col if options.cmd in ['gan'] else options.vis_col * 2)
options.content_dropout = options.content_dropout or (1 - 2 / options.content_size)
options.style_dropout = options.style_dropout or (1 - 2 / options.style_size)
if options.cmd == 'stage1':
for key1, key2 in zip(enc_keys, net_keys):
if options.__dict__[key1] is None:
options.__dict__[key1] = options.__dict__[key2]
if options.__dict__[key1] is None:
raise ValueError('encoder structure incomplete and failed to set defaults')
if options.cmd in ['stage1', 'stage2', 'gan']:
for key1, key2 in zip(gen_keys, net_keys):
if options.__dict__[key1] is None:
options.__dict__[key1] = options.__dict__[key2]
if options.__dict__[key1] is None:
raise ValueError('generator structure incomplete and failed to set defaults')
if options.cmd == 'classifier' or (options.cmd == 'stage1' and not options.mlp):
for key1, key2 in zip(cla_keys, net_keys):
if options.__dict__[key1] is None:
options.__dict__[key1] = options.__dict__[key2]
if options.__dict__[key1] is None:
raise ValueError('classifier structure incomplete and failed to set defaults')
if options.cmd in ['stage2', 'gan']:
for key1, key2 in zip(dis_keys, net_keys):
if options.__dict__[key1] is None:
options.__dict__[key1] = options.__dict__[key2]
if options.__dict__[key1] is None:
raise ValueError('discriminator structure incomplete and failed to set defaults')
if options.cmd in ['stage1', 'classifier', 'stage2', 'gan']:
save_options = {}
for key in load_keys + override_keys:
save_options[key] = options.__dict__[key]
if not os.path.exists(options.save_path):
os.makedirs(options.save_path)
with open(os.path.join(options.save_path, 'options'), 'w') as file:
json.dump(save_options, file)
options.augment_options = types.SimpleNamespace()
for key in augment_keys:
options.augment_options.__dict__[key] = options.__dict__[key]
return options
def __init__(self, options):
super(GANTrainer, self).__init__(options,
subfolders=['samples'],
copy_keys=copy_keys)
transforms = []
if options.crop_size is not None:
transforms.append(T.CenterCrop(options.crop_size))
transforms.append(T.Resize(options.image_size))
transforms.append(T.ToTensor())
transforms.append(T.Normalize((0.5, 0.5, 0.5, 0), (0.5, 0.5, 0.5, 1)))
self.dataset = ImageFolder(options.data_root,
transform=T.Compose(transforms))
self.dataloader = torch.utils.data.DataLoader(
self.dataset,
batch_size=options.batch_size,
shuffle=True,
drop_last=True,
num_workers=options.nloader)
self.data_iter = iter(self.dataloader)
self.gen = models.Generator(options.image_size, options.image_size,
options.gen_features, options.gen_blocks,
options.gen_adain_features,
options.gen_adain_blocks,
options.content_size)
self.gen.to(self.device)
self.gen_optim = optim.RMSprop(self.gen.parameters(),
lr=self.lr,
eps=1e-4,
alpha=0.9)
self.add_model('gen', self.gen, self.gen_optim)
self.dis = models.ClassifierOrDiscriminator(options.image_size,
options.image_size,
options.dis_features,
options.dis_blocks,
options.dis_adain_features,
options.dis_adain_blocks)
self.dis.to(self.device)
self.dis_optim = optim.RMSprop(self.dis.parameters(),
lr=self.lr,
eps=1e-4,
alpha=0.9)
self.add_model('dis', self.dis, self.dis_optim)
if self.load_path is not None:
self.vis_codes = torch.load(os.path.join(self.load_path, 'samples',
'codes.pt'),
map_location=self.device)
self.load(options.load_iter)
else:
self.vis_codes = gaussian_noise(options.vis_row * options.vis_col,
options.content_size).to(
self.device)
self.state.dis_total_batches = 0
if self.save_path != self.load_path:
torch.save(self.vis_codes,
os.path.join(self.save_path, 'samples', 'codes.pt'))
self.add_periodic_func(self.visualize_fixed, options.visualize_iter)
self.visualize_fixed()
self.loss_avg_factor = 0.9
'crop_size': 380
}
joint_transform = joint_transforms.Compose([
joint_transforms.RandomCrop(args['crop_size']),
joint_transforms.RandomHorizontallyFlip(),
joint_transforms.RandomRotate(10),
])
img_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
target_transform = transforms.ToTensor()
train_set = ImageFolder(duts_train_path, joint_transform, img_transform,
target_transform)
train_loader = DataLoader(train_set,
batch_size=args['train_batch_size'],
num_workers=12,
shuffle=True,
drop_last=True)
criterionBCE = nn.BCELoss().cuda()
def main():
exp_name = 'dpnet'
train(exp_name)
def train(exp_name):
from datasets import ImageFolder
import torchvision.transforms as transforms
from PIL import Image
from itertools import izip
rawroot = '/mnt/Data1/Water_Real'
outroot = './results'
outname = 'concat'
datasets = []
# input images
datasets.append(
ImageFolder(rawroot,
transform=transforms.Compose(
[transforms.Resize(256),
transforms.CenterCrop(256)]),
return_path=True))
# results images
for exp_name in [
'warp_L1', 'warp_L1VGG', 'color_L1VGG', 'color_L1VGGAdv',
'both_L1VGGAdv'
]:
datasets.append(
ImageFolder(os.path.join(outroot, '%s_test' % exp_name),
return_path=True))
# concat and save each image
for i, imgs in enumerate(izip(*datasets)):
name = imgs[0][-1]
'momentum': 0.9,
'snapshot': ''
}
joint_transform = joint_transforms.Compose([
joint_transforms.RandomCrop(300),
joint_transforms.RandomHorizontallyFlip(),
joint_transforms.RandomRotate(10)
])
img_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
target_transform = transforms.ToTensor()
train_set = ImageFolder(msra10k_path, joint_transform, img_transform,
target_transform)
train_loader = DataLoader(train_set,
batch_size=args['train_batch_size'],
num_workers=12,
shuffle=True)
criterion = nn.BCEWithLogitsLoss().cuda()
log_path = os.path.join(ckpt_path, exp_name,
str(datetime.datetime.now()) + '.txt')
def main():
net = R3Net().cuda().train()
optimizer = optim.SGD([{
'params': [
joint_transforms.RandomCrop(448),
joint_transforms.RandomHorizontallyFlip(),
joint_transforms.RandomRotate(10)
])
img_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
depth_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
target_transform = transforms.ToTensor()
to_pil = transforms.ToPILImage()
train_set = ImageFolder(dutsk_path, args['status'],joint_transform, img_transform, target_transform,depth_transform)
train_loader = DataLoader(train_set, batch_size=args['train_batch_size'], num_workers=12, shuffle=True)
criterion = nn.BCEWithLogitsLoss().cuda()
log_path = os.path.join(ckpt_path, exp_name, str(datetime.datetime.now()) + '.txt')
def main():
net = R3Net(num_class=1).cuda().train()
# net = nn.DataParallel(net,device_ids=[0,1])
optimizer = optim.SGD([
{'params': [param for name, param in net.named_parameters() if name[-4:] == 'bias'],
'lr': 2 * args['lr']},
{'params': [param for name, param in net.named_parameters() if name[-4:] != 'bias'],
'lr': args['lr'], 'weight_decay': args['weight_decay']}
def select_worst_images(args, model, full_train_loader, device):
print("Selecting images for next epoch training...")
model.eval()
gts = []
paths = []
losses = []
micro_batch_size = args.batch_size // args.batch_split
precrop, crop = bit_hyperrule.get_resolution_from_dataset(args.dataset)
if args.input_channels == 3:
train_tx = tv.transforms.Compose([
tv.transforms.Resize((precrop, precrop)),
tv.transforms.RandomCrop((crop, crop)),
tv.transforms.RandomHorizontalFlip(),
tv.transforms.ToTensor(),
tv.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
elif args.input_channels == 2:
train_tx = tv.transforms.Compose([
tv.transforms.Resize((precrop, precrop)),
tv.transforms.RandomCrop((crop, crop)),
tv.transforms.RandomHorizontalFlip(),
tv.transforms.ToTensor(),
tv.transforms.Normalize((0.5, 0.5), (0.5, 0.5)),
])
elif args.input_channels == 1:
train_tx = tv.transforms.Compose([
tv.transforms.Resize((precrop, precrop)),
tv.transforms.RandomCrop((crop, crop)),
tv.transforms.RandomHorizontalFlip(),
tv.transforms.ToTensor(),
tv.transforms.Normalize((0.5), (0.5)),
])
pbar = enumerate(full_train_loader)
pbar = tqdm.tqdm(pbar, total=len(full_train_loader))
for b, (path, x, y) in pbar:
with torch.no_grad():
x = x.to(device, non_blocking=True)
y = y.to(device, non_blocking=True)
# compute output, measure accuracy and record loss.
logits = model(x)
paths.extend(path)
gts.extend(y.cpu().numpy())
c = torch.nn.CrossEntropyLoss(reduction='none')(logits, y)
losses.extend(
c.cpu().numpy().tolist()) # Also ensures a sync point.
# measure elapsed time
end = time.time()
gts = np.array(gts)
losses = np.array(losses)
losses[np.argsort(losses)[int(losses.shape[0] *
(1.0 - args.noise)):]] = 0.0 #
#paths_ = np.array(paths)[np.where(losses > np.median(losses))[0]]
#gts_ = gts[np.where(losses > np.median(losses))[0]]
selection_idx = int(args.data_fraction * losses.shape[0])
paths_ = np.array(paths)[np.argsort(losses)[-selection_idx:]]
gts_ = gts[np.argsort(losses)[-selection_idx:]]
smart_train_set = ImageFolder(paths_, gts_, train_tx, crop)
smart_train_loader = torch.utils.data.DataLoader(
smart_train_set,
batch_size=micro_batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=False)
return smart_train_set, smart_train_loader
plt.show()
x = SynthData(256, n=1)
"""
for i in range(100):
x.step(steps=3)
plt.imshow(x.u[0].numpy())
plt.title('%d %f %f %f'%(i,x.u[0].min(), x.u[0].mean(), x.u[0].max()))
plt.draw()
plt.pause(.1)
"""
from datasets import ImageFolder
import torchvision.transforms as transforms
img_dir = '/mnt/Data1/ImageNet/val'
data = ImageFolder(img_dir,
transform=transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(256),
transforms.ToTensor(),
]))
img = data[10][0]
for i in range(100):
img1 = x(img)[0]
plt.imshow(img1.permute(1, 2, 0))
plt.draw()
plt.pause(.1)
image_transform = transforms.Compose([
transforms.Resize(int(imsize * 76 / 64)),
transforms.RandomCrop(imsize),
transforms.RandomHorizontalFlip()
])
if cfg.DATA_DIR.find('lsun') != -1:
from datasets import LSUNClass
dataset = LSUNClass('%s/%s_%s_lmdb' %
(cfg.DATA_DIR, cfg.DATASET_NAME, split_dir),
base_size=cfg.TREE.BASE_SIZE,
transform=image_transform)
elif cfg.DATA_DIR.find('imagenet') != -1:
from datasets import ImageFolder
dataset = ImageFolder(cfg.DATA_DIR,
split_dir='train',
custom_classes=CLASS_DIC[cfg.DATASET_NAME],
base_size=cfg.TREE.BASE_SIZE,
transform=image_transform)
elif cfg.DATA_DIR.find('xray_ct_scan') != -1:
from datasets import ImageFolder
dataset = ImageFolder(
cfg.DATA_DIR,
split_dir='train',
custom_classes=["Covid-19", "No_findings", "Pneumonia"],
base_size=cfg.TREE.BASE_SIZE,
transform=image_transform)
elif cfg.GAN.B_CONDITION: # text to image task
from datasets import TextDataset
dataset = TextDataset(cfg.DATA_DIR,
split_dir,
base_size=cfg.TREE.BASE_SIZE,
def main(args):
best_acc1 = 0
os.makedirs('checkpoints', exist_ok=True)
args.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('device: {}'.format(args.device))
# create model
model = ConvNet(cfg.NUM_CLASSES).to(args.device)
#model.apply(weights_init_normal)
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().to(args.device)
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume, map_location=args.device)
args.start_epoch = checkpoint['epoch']
best_acc1 = checkpoint['best_acc1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
# Data loading code
train_dataset = ImageFolder(cfg.TRAIN_PATH)
val_dataset = ImageFolder(cfg.VAL_PATH)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
logger = Logger('./logs')
for epoch in range(args.start_epoch, args.epochs):
# train for one epoch
adjust_learning_rate(optimizer, epoch, args)
train_loss, train_acc = train(train_loader, model, criterion,
optimizer, epoch, args)
# evaluate on validation set
val_loss, val_acc = validate(val_loader, model, criterion, args)
# remember best acc@1 and save checkpoint
is_best = val_acc > best_acc1
best_acc1 = max(val_acc, best_acc1)
# log
info = {
'train_loss': float(train_loss),
'train_acc': float(train_acc),
'val_loss': float(val_loss),
'val_acc': float(val_acc)
}
for tag, value in info.items():
logger.scalar_summary(tag, value, epoch)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer': optimizer.state_dict(),
}, is_best)
def __init__(self, options):
super(Stage1Trainer, self).__init__(options,
subfolders=['reconstructions'],
copy_keys=copy_keys)
transforms = []
if options.crop_size is not None:
transforms.append(T.CenterCrop(options.crop_size))
transforms.append(T.Resize(options.image_size))
transforms.append(T.ToTensor())
image_transforms = transforms + [
T.Normalize((0.5, 0.5, 0.5, 0), (0.5, 0.5, 0.5, 1))
]
image_set = ImageFolder(options.data_root,
transform=T.Compose(image_transforms))
if options.weight_root is not None:
self.has_weight = True
weight_transforms = transforms + [lambda x: x[0]]
weight_set = ImageFolder(options.weight_root,
transform=T.Compose(weight_transforms))
self.dataset = ParallelDataset(image_set, weight_set)
else:
self.has_weight = False
self.dataset = image_set
self.dataloader = torch.utils.data.DataLoader(
self.dataset,
batch_size=options.batch_size,
shuffle=True,
drop_last=True,
num_workers=options.nloader)
self.data_iter = iter(self.dataloader)
self.enc = models.Encoder(options.image_size, options.image_size,
options.enc_features, options.enc_blocks,
options.enc_adain_features,
options.enc_adain_blocks,
options.content_size)
self.enc.to(self.device)
self.enc_optim = optim.Adam(self.enc.parameters(),
lr=self.lr,
eps=1e-4)
self.add_model('enc', self.enc, self.enc_optim)
self.gen = models.TwoPartNestedDropoutGenerator(
options.image_size, options.image_size, options.gen_features,
options.gen_blocks, options.gen_adain_features,
options.gen_adain_blocks, options.content_size, options.style_size)
self.gen.to(self.device)
self.gen_optim = optim.Adam(self.gen.parameters(),
lr=self.lr,
eps=1e-4)
self.add_model('gen', self.gen, self.gen_optim)
if self.mlp:
self.cla = models.MLPClassifier(options.content_size,
options.mlp_features,
options.mlp_layers, self.nclass)
else:
self.cla = models.ClassifierOrDiscriminator(
options.image_size, options.image_size, options.cla_features,
options.cla_blocks, options.cla_adain_features,
options.cla_adain_blocks, self.nclass)
self.cla.to(self.device)
self.cla_optim = optim.Adam(self.cla.parameters(),
lr=self.lr,
eps=1e-4)
self.add_model('cla', self.cla, self.cla_optim)
self.sty = models.NormalizedStyleBank(self.nclass, options.style_size,
image_set.get_class_freq())
self.sty.to(self.device)
self.sty_optim = optim.Adam(self.sty.parameters(),
lr=self.sty_lr,
eps=1e-8)
self.add_model('sty', self.sty, self.sty_optim)
if self.load_path is not None:
self.vis_images = torch.load(os.path.join(self.load_path,
'reconstructions',
'images.pt'),
map_location=self.device)
self.vis_labels = torch.load(os.path.join(self.load_path,
'reconstructions',
'labels.pt'),
map_location=self.device)
if self.has_weight:
self.vis_weights = torch.load(os.path.join(
self.load_path, 'reconstructions', 'weights.pt'),
map_location=self.device)
self.load(options.load_iter)
else:
vis_images = []
vis_labels = []
if self.has_weight:
vis_weights = []
vis_index = random.sample(range(len(image_set)),
options.vis_row * options.vis_col)
for k in vis_index:
image, label = image_set[k]
vis_images.append(image)
vis_labels.append(label)
if self.has_weight:
weight, _ = weight_set[k]
vis_weights.append(weight)
self.vis_images = torch.stack(vis_images, dim=0).to(self.device)
self.vis_labels = one_hot(
torch.tensor(vis_labels, dtype=torch.int32),
self.nclass).to(self.device)
if self.has_weight:
self.vis_weights = torch.stack(vis_weights,
dim=0).to(self.device)
if self.save_path != self.load_path:
torch.save(
self.vis_images,
os.path.join(self.save_path, 'reconstructions', 'images.pt'))
torch.save(
self.vis_labels,
os.path.join(self.save_path, 'reconstructions', 'labels.pt'))
save_image(
self.vis_images.add(1).div(2),
os.path.join(self.save_path, 'reconstructions', 'target.png'),
self.vis_col)
if self.has_weight:
torch.save(
self.vis_weights,
os.path.join(self.save_path, 'reconstructions',
'weights.pt'))
save_image(
self.vis_weights.unsqueeze(1),
os.path.join(self.save_path, 'reconstructions',
'weight.png'), self.vis_col)
self.add_periodic_func(self.visualize_fixed, options.visualize_iter)
self.visualize_fixed()
self.con_drop_prob = torch.Tensor(options.content_size)
for i in range(options.content_size):
self.con_drop_prob[i] = options.content_dropout**i
self.sty_drop_prob = torch.Tensor(options.style_size)
for i in range(options.style_size):
self.sty_drop_prob[i] = options.style_dropout**i
'snapshot': '',
'epoch': 60
}
joint_transform = joint_transforms.Compose([
joint_transforms.RandomCrop(300),
joint_transforms.RandomHorizontallyFlip(),
joint_transforms.RandomRotate(10)
])
img_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
target_transform = transforms.ToTensor()
train_set = ImageFolder(kaist_path, joint_transform, img_transform,
target_transform)
train_loader = DataLoader(train_set,
batch_size=args['train_batch_size'],
num_workers=6,
shuffle=True)
criterion = nn.BCEWithLogitsLoss().cuda()
log_path = os.path.join(ckpt_path, exp_name,
str(datetime.datetime.now()) + '.txt')
def main():
net = R3Net().cuda().train()
optimizer = optim.SGD([{
'params': [
def mktrainval(args, logger):
"""Returns train and validation datasets."""
precrop, crop = bit_hyperrule.get_resolution_from_dataset(args.dataset)
if args.input_channels == 3:
train_tx = tv.transforms.Compose([
tv.transforms.Resize((precrop, precrop)),
tv.transforms.RandomCrop((crop, crop)),
tv.transforms.RandomHorizontalFlip(),
tv.transforms.ToTensor(),
tv.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
val_tx = tv.transforms.Compose([
tv.transforms.Resize((crop, crop)),
tv.transforms.ToTensor(),
tv.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
elif args.input_channels == 2:
train_tx = tv.transforms.Compose([
tv.transforms.Resize((precrop, precrop)),
tv.transforms.RandomCrop((crop, crop)),
tv.transforms.RandomHorizontalFlip(),
tv.transforms.ToTensor(),
tv.transforms.Normalize((0.5, 0.5), (0.5, 0.5)),
])
val_tx = tv.transforms.Compose([
tv.transforms.Resize((crop, crop)),
tv.transforms.ToTensor(),
tv.transforms.Normalize((0.5, 0.5), (0.5, 0.5)),
])
elif args.input_channels == 1:
train_tx = tv.transforms.Compose([
tv.transforms.Resize((precrop, precrop)),
tv.transforms.RandomCrop((crop, crop)),
tv.transforms.RandomHorizontalFlip(),
tv.transforms.ToTensor(),
tv.transforms.Normalize((0.5), (0.5)),
])
val_tx = tv.transforms.Compose([
tv.transforms.Resize((crop, crop)),
tv.transforms.ToTensor(),
tv.transforms.Normalize((0.5), (0.5)),
])
if args.dataset == "cifar10":
train_set = tv.datasets.CIFAR10(args.datadir,
transform=train_tx,
train=True,
download=True)
valid_set = tv.datasets.CIFAR10(args.datadir,
transform=val_tx,
train=False,
download=True)
elif args.dataset == "cifar100":
train_set = tv.datasets.CIFAR100(args.datadir,
transform=train_tx,
train=True,
download=True)
valid_set = tv.datasets.CIFAR100(args.datadir,
transform=val_tx,
train=False,
download=True)
elif args.dataset == "imagenet2012":
folder_path = pjoin(args.datadir, "train")
files = sorted(glob.glob("%s/*/*.*" % folder_path))
#labels = [int(file.split("/")[-2]) for file in files]
labels = [class_dict[file.split("/")[-2]] for file in files]
train_set = ImageFolder(files, labels, train_tx, crop)
folder_path = pjoin(args.datadir, "val")
files = sorted(glob.glob("%s/*/*.*" % folder_path))
#labels = [int(file.split("/")[-2]) for file in files]
labels = [class_dict[file.split("/")[-2]] for file in files]
valid_set = ImageFolder(files, labels, val_tx, crop)
#train_set = tv.datasets.ImageFolder(pjoin(args.datadir, "train"), train_tx)
#valid_set = tv.datasets.ImageFolder(pjoin(args.datadir, "val"), val_tx)
else:
raise ValueError(f"Sorry, we have not spent time implementing the "
f"{args.dataset} dataset in the PyTorch codebase. "
f"In principle, it should be easy to add :)")
if args.examples_per_class is not None:
logger.info(
f"Looking for {args.examples_per_class} images per class...")
indices = fs.find_fewshot_indices(train_set, args.examples_per_class)
train_set = torch.utils.data.Subset(train_set, indices=indices)
logger.info(f"Using a training set with {len(train_set)} images.")
logger.info(f"Using a validation set with {len(valid_set)} images.")
micro_batch_size = args.batch_size // args.batch_split
micro_batch_size_val = 4 * micro_batch_size
valid_loader = torch.utils.data.DataLoader(valid_set,
batch_size=micro_batch_size_val,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
drop_last=False)
if micro_batch_size <= len(train_set):
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=micro_batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=False)
train_loader_val = torch.utils.data.DataLoader(
train_set,
batch_size=micro_batch_size_val,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=False)
else:
# In the few-shot cases, the total dataset size might be smaller than the batch-size.
# In these cases, the default sampler doesn't repeat, so we need to make it do that
# if we want to match the behaviour from the paper.
train_loader = torch.utils.data.DataLoader(
train_set,
batch_size=micro_batch_size,
num_workers=args.workers,
pin_memory=True,
sampler=torch.utils.data.RandomSampler(
train_set, replacement=True, num_samples=micro_batch_size))
return train_set, valid_set, train_loader, valid_loader, train_loader_val
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--dataroot',
default='/mnt/Data1',
help='path to images')
parser.add_argument('--workers',
default=4,
type=int,
help='number of data loading workers (default: 4)')
parser.add_argument('--batch-size',
default=16,
type=int,
help='mini-batch size')
parser.add_argument('--outroot',
default='./results',
help='path to save the results')
parser.add_argument('--exp-name',
default='test',
help='name of expirement')
parser.add_argument('--load',
default='',
help='name of pth to load weights from')
parser.add_argument('--freeze-cc-net',
dest='freeze_cc_net',
action='store_true',
help='dont train the color corrector net')
parser.add_argument('--freeze-warp-net',
dest='freeze_warp_net',
action='store_true',
help='dont train the warp net')
parser.add_argument('--test',
dest='test',
action='store_true',
help='only test the network')
parser.add_argument(
'--synth-data',
dest='synth_data',
action='store_true',
help='use synthetic data instead of tank data for training')
parser.add_argument('--epochs',
default=3,
type=int,
help='number of epochs to train for')
parser.add_argument('--no-warp-net',
dest='warp_net',
action='store_false',
help='do not include warp net in the model')
parser.add_argument('--warp-net-downsample',
default=3,
type=int,
help='number of downsampling layers in warp net')
parser.add_argument('--no-color-net',
dest='color_net',
action='store_false',
help='do not include color net in the model')
parser.add_argument('--color-net-downsample',
default=3,
type=int,
help='number of downsampling layers in color net')
parser.add_argument(
'--no-color-net-skip',
dest='color_net_skip',
action='store_false',
help='dont use u-net skip connections in the color net')
parser.add_argument(
'--dim',
default=32,
type=int,
help='initial feature dimension (doubled at each downsampling layer)')
parser.add_argument('--n-res',
default=8,
type=int,
help='number of residual blocks')
parser.add_argument('--norm',
default='gn',
type=str,
help='type of normalization layer')
parser.add_argument(
'--denormalize',
dest='denormalize',
action='store_true',
help='denormalize output image by input image mean/var')
parser.add_argument(
'--weight-X-L1',
default=1.,
type=float,
help='weight of L1 reconstruction loss after color corrector net')
parser.add_argument('--weight-Y-L1',
default=1.,
type=float,
help='weight of L1 reconstruction loss after warp net')
parser.add_argument('--weight-Y-VGG',
default=1.,
type=float,
help='weight of perceptual loss after warp net')
parser.add_argument(
'--weight-Z-L1',
default=1.,
type=float,
help='weight of L1 reconstruction loss after color net')
parser.add_argument('--weight-Z-VGG',
default=.5,
type=float,
help='weight of perceptual loss after color net')
parser.add_argument('--weight-Z-Adv',
default=0.2,
type=float,
help='weight of adversarial loss after color net')
args = parser.parse_args()
# set random seed for consistent fixed batch
torch.manual_seed(8)
# set weights of losses of intermediate outputs to zero if not necessary
if not args.warp_net:
args.weight_Y_L1 = 0
args.weight_Y_VGG = 0
if not args.color_net:
args.weight_Z_L1 = 0
args.weight_Z_VGG = 0
args.weight_Z_Adv = 0
# datasets
train_dir_1 = os.path.join(args.dataroot, 'Water', 'train')
train_dir_2 = os.path.join(args.dataroot, 'ImageNet', 'train')
val_dir_1 = os.path.join(args.dataroot, 'Water', 'test')
val_dir_2 = os.path.join(args.dataroot, 'ImageNet', 'test')
test_dir = os.path.join(args.dataroot, 'Water_Real')
if args.synth_data:
train_data = ImageFolder(train_dir_2,
transform=transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5]),
synthdata.SynthData(224,
n=args.batch_size),
]))
else:
train_data = PairedImageFolder(
train_dir_1,
train_dir_2,
transform=transforms.Compose([
pairedtransforms.RandomResizedCrop(224),
pairedtransforms.RandomHorizontalFlip(),
pairedtransforms.ToTensor(),
pairedtransforms.Normalize(mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5]),
]))
val_data = PairedImageFolder(val_dir_1,
val_dir_2,
transform=transforms.Compose([
pairedtransforms.Resize(256),
pairedtransforms.CenterCrop(256),
pairedtransforms.ToTensor(),
pairedtransforms.Normalize(
mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5]),
]))
test_data = ImageFolder(test_dir,
transform=transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(256),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5]),
]),
return_path=True)
train_loader = torch.utils.data.DataLoader(train_data,
batch_size=args.batch_size,
num_workers=args.workers,
pin_memory=True,
shuffle=True)
val_loader = torch.utils.data.DataLoader(val_data,
batch_size=args.batch_size,
num_workers=args.workers,
pin_memory=True,
shuffle=True)
test_loader = torch.utils.data.DataLoader(test_data,
batch_size=args.batch_size,
num_workers=args.workers,
pin_memory=True,
shuffle=False)
# fixed test batch for visualization during training
fixed_batch = iter(val_loader).next()[0]
# model
model = networks.Model(args)
model.cuda()
# load weights from checkpoint
if args.test and not args.load:
args.load = args.exp_name
if args.load:
model.load_state_dict(torch.load(
os.path.join(args.outroot, '%s_net.pth' % args.load)),
strict=args.test)
# create outroot if necessary
if not os.path.exists(args.outroot):
os.makedirs(args.outroot)
# if args.test only run test script
if args.test:
test(test_loader, model, args)
return
# main training loop
for epoch in range(args.epochs):
train(train_loader, model, fixed_batch, epoch, args)
torch.save(model.state_dict(),
os.path.join(args.outroot, '%s_net.pth' % args.exp_name))
test(test_loader, model, args)
def main(args):
writer = SummaryWriter("./logs/{0}".format(args.output_folder))
save_filename = "./models/{0}".format(args.output_folder)
if args.dataset in ["mnist", "fashion-mnist", "cifar10"]:
transform = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]
)
if args.dataset == "mnist":
# Define the train & test datasets
train_dataset = datasets.MNIST(
args.data_folder, train=True, download=True, transform=transform
)
test_dataset = datasets.MNIST(
args.data_folder, train=False, transform=transform
)
num_channels = 1
elif args.dataset == "fashion-mnist":
# Define the train & test datasets
train_dataset = datasets.FashionMNIST(
args.data_folder, train=True, download=True, transform=transform
)
test_dataset = datasets.FashionMNIST(
args.data_folder, train=False, transform=transform
)
num_channels = 1
elif args.dataset == "cifar10":
# Define the train & test datasets
train_dataset = datasets.CIFAR10(
args.data_folder, train=True, download=True, transform=transform
)
test_dataset = datasets.CIFAR10(
args.data_folder, train=False, transform=transform
)
num_channels = 3
valid_dataset = test_dataset
elif args.dataset == "miniimagenet":
transform = transforms.Compose(
[
transforms.RandomResizedCrop(128),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]
)
# Define the train, valid & test datasets
train_dataset = MiniImagenet(
args.data_folder, train=True, download=True, transform=transform
)
valid_dataset = MiniImagenet(
args.data_folder, valid=True, download=True, transform=transform
)
test_dataset = MiniImagenet(
args.data_folder, test=True, download=True, transform=transform
)
num_channels = 3
else:
transform = transforms.Compose(
[
transforms.RandomResizedCrop(args.image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]
)
# Define the train, valid & test datasets
train_dataset = ImageFolder(
os.path.join(args.data_folder, "train"), transform=transform
)
valid_dataset = ImageFolder(
os.path.join(args.data_folder, "val"), transform=transform
)
test_dataset = valid_dataset
num_channels = 3
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
)
valid_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=args.batch_size,
shuffle=False,
drop_last=True,
num_workers=args.num_workers,
pin_memory=True,
)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=16, shuffle=True)
# Fixed images for Tensorboard
fixed_images, _ = next(iter(test_loader))
fixed_grid = make_grid(fixed_images, nrow=8, range=(-1, 1), normalize=True)
save_image(fixed_grid, "true.png")
writer.add_image("original", fixed_grid, 0)
model = VectorQuantizedVAE(num_channels, args.hidden_size, args.k).to(args.device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
# Generate the samples first once
reconstruction = generate_samples(fixed_images, model, args)
grid = make_grid(reconstruction.cpu(), nrow=8, range=(-1, 1), normalize=True)
save_image(grid, "rec.png")
writer.add_image("reconstruction", grid, 0)
best_loss = -1
for epoch in range(args.num_epochs):
train(train_loader, model, optimizer, args, writer)
loss, _ = test(valid_loader, model, args, writer)
print(epoch, "test loss: ", loss)
reconstruction = generate_samples(fixed_images, model, args)
grid = make_grid(reconstruction.cpu(), nrow=8, range=(-1, 1), normalize=True)
save_image(grid, "rec.png")
writer.add_image("reconstruction", grid, epoch + 1)
if (epoch == 0) or (loss < best_loss):
best_loss = loss
with open("{0}/best.pt".format(save_filename), "wb") as f:
torch.save(model.state_dict(), f)
with open("{0}/model_{1}.pt".format(save_filename, epoch + 1), "wb") as f:
torch.save(model.state_dict(), f)
def train():
from benchmark import calc_fid, extract_feature_from_generator_fn, load_patched_inception_v3, real_image_loader, image_generator, image_generator_perm
import lpips
from config import IM_SIZE_GAN, BATCH_SIZE_GAN, NFC, NBR_CLS, DATALOADER_WORKERS, EPOCH_GAN, ITERATION_AE, GAN_CKECKPOINT
from config import SAVE_IMAGE_INTERVAL, SAVE_MODEL_INTERVAL, LOG_INTERVAL, SAVE_FOLDER, TRIAL_NAME, DATA_NAME, MULTI_GPU
from config import FID_INTERVAL, FID_BATCH_NBR, PRETRAINED_AE_PATH
from config import data_root_colorful, data_root_sketch_1, data_root_sketch_2, data_root_sketch_3
real_features = None
inception = load_patched_inception_v3().cuda()
inception.eval()
percept = lpips.PerceptualLoss(model='net-lin', net='vgg', use_gpu=True)
saved_image_folder = saved_model_folder = None
log_file_path = None
if saved_image_folder is None:
saved_image_folder, saved_model_folder = make_folders(
SAVE_FOLDER, 'GAN_' + TRIAL_NAME)
log_file_path = saved_image_folder + '/../gan_log.txt'
log_file = open(log_file_path, 'w')
log_file.close()
dataset = PairedMultiDataset(data_root_colorful,
data_root_sketch_1,
data_root_sketch_2,
data_root_sketch_3,
im_size=IM_SIZE_GAN,
rand_crop=True)
print('the dataset contains %d images.' % len(dataset))
dataloader = iter(
DataLoader(dataset,
BATCH_SIZE_GAN,
sampler=InfiniteSamplerWrapper(dataset),
num_workers=DATALOADER_WORKERS,
pin_memory=True))
from datasets import ImageFolder
from datasets import trans_maker_augment as trans_maker
dataset_rgb = ImageFolder(data_root_colorful, trans_maker(512))
dataset_skt = ImageFolder(data_root_sketch_3, trans_maker(512))
net_ae = AE(nfc=NFC, nbr_cls=NBR_CLS)
if PRETRAINED_AE_PATH is None:
PRETRAINED_AE_PATH = 'train_results/' + 'AE_' + TRIAL_NAME + '/models/%d.pth' % ITERATION_AE
else:
from config import PRETRAINED_AE_ITER
PRETRAINED_AE_PATH = PRETRAINED_AE_PATH + '/models/%d.pth' % PRETRAINED_AE_ITER
net_ae.load_state_dicts(PRETRAINED_AE_PATH)
net_ae.cuda()
net_ae.eval()
RefineGenerator = None
if DATA_NAME == 'celeba':
from models import RefineGenerator_face as RefineGenerator
elif DATA_NAME == 'art' or DATA_NAME == 'shoe':
from models import RefineGenerator_art as RefineGenerator
net_ig = RefineGenerator(nfc=NFC, im_size=IM_SIZE_GAN).cuda()
net_id = Discriminator(nc=3).cuda(
) # we use the patch_gan, so the im_size for D should be 512 even if training image size is 1024
if MULTI_GPU:
net_ae = nn.DataParallel(net_ae)
net_ig = nn.DataParallel(net_ig)
net_id = nn.DataParallel(net_id)
net_ig_ema = copy_G_params(net_ig)
opt_ig = optim.Adam(net_ig.parameters(), lr=2e-4, betas=(0.5, 0.999))
opt_id = optim.Adam(net_id.parameters(), lr=2e-4, betas=(0.5, 0.999))
if GAN_CKECKPOINT is not None:
ckpt = torch.load(GAN_CKECKPOINT)
net_ig.load_state_dict(ckpt['ig'])
net_id.load_state_dict(ckpt['id'])
net_ig_ema = ckpt['ig_ema']
opt_ig.load_state_dict(ckpt['opt_ig'])
opt_id.load_state_dict(ckpt['opt_id'])
## create a log file
losses_g_img = AverageMeter()
losses_d_img = AverageMeter()
losses_mse = AverageMeter()
losses_rec_s = AverageMeter()
losses_rec_ae = AverageMeter()
fixed_skt = fixed_rgb = fixed_perm = None
fid = [[0, 0]]
for epoch in range(EPOCH_GAN):
for iteration in tqdm(range(10000)):
rgb_img, skt_img_1, skt_img_2, skt_img_3 = next(dataloader)
rgb_img = rgb_img.cuda()
rd = random.randint(0, 3)
if rd == 0:
skt_img = skt_img_1.cuda()
elif rd == 1:
skt_img = skt_img_2.cuda()
else:
skt_img = skt_img_3.cuda()
if iteration == 0:
fixed_skt = skt_img_3[:8].clone().cuda()
fixed_rgb = rgb_img[:8].clone()
fixed_perm = true_randperm(fixed_rgb.shape[0], 'cuda')
### 1. train D
gimg_ae, style_feats = net_ae(skt_img, rgb_img)
g_image = net_ig(gimg_ae, style_feats)
pred_r = net_id(rgb_img)
pred_f = net_id(g_image.detach())
loss_d = d_hinge_loss(pred_r, pred_f)
net_id.zero_grad()
loss_d.backward()
opt_id.step()
loss_rec_ae = F.mse_loss(gimg_ae, rgb_img) + F.l1_loss(
gimg_ae, rgb_img)
losses_rec_ae.update(loss_rec_ae.item(), BATCH_SIZE_GAN)
### 2. train G
pred_g = net_id(g_image)
loss_g = g_hinge_loss(pred_g)
if DATA_NAME == 'shoe':
loss_mse = 10 * (F.l1_loss(g_image, rgb_img) +
F.mse_loss(g_image, rgb_img))
else:
loss_mse = 10 * percept(
F.adaptive_avg_pool2d(g_image, output_size=256),
F.adaptive_avg_pool2d(rgb_img, output_size=256)).sum()
losses_mse.update(loss_mse.item() / BATCH_SIZE_GAN, BATCH_SIZE_GAN)
loss_all = loss_g + loss_mse
if DATA_NAME == 'shoe':
### the grey image reconstruction
perm = true_randperm(BATCH_SIZE_GAN)
img_ae_perm, style_feats_perm = net_ae(skt_img, rgb_img[perm])
gimg_grey = net_ig(img_ae_perm, style_feats_perm)
gimg_grey = gimg_grey.mean(dim=1, keepdim=True)
real_grey = rgb_img.mean(dim=1, keepdim=True)
loss_rec_grey = F.mse_loss(gimg_grey, real_grey)
loss_all += 10 * loss_rec_grey
net_ig.zero_grad()
loss_all.backward()
opt_ig.step()
for p, avg_p in zip(net_ig.parameters(), net_ig_ema):
avg_p.mul_(0.999).add_(p.data, alpha=0.001)
### 3. logging
losses_g_img.update(pred_g.mean().item(), BATCH_SIZE_GAN)
losses_d_img.update(pred_r.mean().item(), BATCH_SIZE_GAN)
if iteration % SAVE_IMAGE_INTERVAL == 0: #show the current images
with torch.no_grad():
backup_para_g = copy_G_params(net_ig)
load_params(net_ig, net_ig_ema)
gimg_ae, style_feats = net_ae(fixed_skt, fixed_rgb)
gmatch = net_ig(gimg_ae, style_feats)
gimg_ae_perm, style_feats = net_ae(fixed_skt,
fixed_rgb[fixed_perm])
gmismatch = net_ig(gimg_ae_perm, style_feats)
gimg = torch.cat([
F.interpolate(fixed_rgb, IM_SIZE_GAN),
F.interpolate(fixed_skt.repeat(1, 3, 1, 1),
IM_SIZE_GAN), gmatch,
F.interpolate(gimg_ae, IM_SIZE_GAN), gmismatch,
F.interpolate(gimg_ae_perm, IM_SIZE_GAN)
])
vutils.save_image(
gimg,
f'{saved_image_folder}/img_iter_{epoch}_{iteration}.jpg',
normalize=True,
range=(-1, 1))
del gimg
make_matrix(
dataset_rgb, dataset_skt, net_ae, net_ig, 5,
f'{saved_image_folder}/img_iter_{epoch}_{iteration}_matrix.jpg'
)
load_params(net_ig, backup_para_g)
if iteration % LOG_INTERVAL == 0:
log_msg = 'Iter: [{0}/{1}] G: {losses_g_img.avg:.4f} D: {losses_d_img.avg:.4f} MSE: {losses_mse.avg:.4f} Rec: {losses_rec_s.avg:.5f} FID: {fid:.4f}'.format(
epoch,
iteration,
losses_g_img=losses_g_img,
losses_d_img=losses_d_img,
losses_mse=losses_mse,
losses_rec_s=losses_rec_s,
fid=fid[-1][0])
print(log_msg)
print('%.5f' % (losses_rec_ae.avg))
if log_file_path is not None:
log_file = open(log_file_path, 'a')
log_file.write(log_msg + '\n')
log_file.close()
losses_g_img.reset()
losses_d_img.reset()
losses_mse.reset()
losses_rec_s.reset()
losses_rec_ae.reset()
if iteration % SAVE_MODEL_INTERVAL == 0 or iteration + 1 == 10000:
print('Saving history model')
torch.save(
{
'ig': net_ig.state_dict(),
'id': net_id.state_dict(),
'ae': net_ae.state_dict(),
'ig_ema': net_ig_ema,
'opt_ig': opt_ig.state_dict(),
'opt_id': opt_id.state_dict(),
}, '%s/%d.pth' % (saved_model_folder, epoch))
if iteration % FID_INTERVAL == 0 and iteration > 1:
print("calculating FID ...")
fid_batch_images = FID_BATCH_NBR
if real_features is None:
if os.path.exists('%s_fid_feats.npy' % (DATA_NAME)):
real_features = pickle.load(
open('%s_fid_feats.npy' % (DATA_NAME), 'rb'))
else:
real_features = extract_feature_from_generator_fn(
real_image_loader(dataloader,
n_batches=fid_batch_images),
inception)
real_mean = np.mean(real_features, 0)
real_cov = np.cov(real_features, rowvar=False)
pickle.dump(
{
'feats': real_features,
'mean': real_mean,
'cov': real_cov
}, open('%s_fid_feats.npy' % (DATA_NAME), 'wb'))
real_features = pickle.load(
open('%s_fid_feats.npy' % (DATA_NAME), 'rb'))
sample_features = extract_feature_from_generator_fn(
image_generator(dataset,
net_ae,
net_ig,
n_batches=fid_batch_images),
inception,
total=fid_batch_images)
cur_fid = calc_fid(sample_features,
real_mean=real_features['mean'],
real_cov=real_features['cov'])
sample_features_perm = extract_feature_from_generator_fn(
image_generator_perm(dataset,
net_ae,
net_ig,
n_batches=fid_batch_images),
inception,
total=fid_batch_images)
cur_fid_perm = calc_fid(sample_features_perm,
real_mean=real_features['mean'],
real_cov=real_features['cov'])
fid.append([cur_fid, cur_fid_perm])
print('fid:', fid)
if log_file_path is not None:
log_file = open(log_file_path, 'a')
log_msg = 'fid: %.5f, %.5f' % (fid[-1][0], fid[-1][1])
log_file.write(log_msg + '\n')
log_file.close()
from datasets import ImageFolder
import cfg
parser = argparse.ArgumentParser(description='PyTorch RPC Predicting')
parser.add_argument('-i', '--image', metavar='PATH', type=str, help='path to image')
parser.add_argument('-w', '--model_weight', default=cfg.WEIGHTS_PATH, type=str, metavar='PATH',
help='path to latest checkpoint')
args = parser.parse_args()
print(args)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('device:{}'.format(device))
# classes' name
dataset = ImageFolder(cfg.VAL_PATH)
class_to_idx = dataset.class_to_idx
idx_to_class = dict(zip(class_to_idx.values(), class_to_idx.keys()))
print(idx_to_class)
# load data
def load_image(imgPath):
img = Image.open(imgPath).convert('RGB')
trans = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
return trans(img)
'momentum': 0.9,
'snapshot': ''
}
joint_transform = joint_transforms.Compose([
joint_transforms.RandomCrop(300),
joint_transforms.RandomHorizontallyFlip(),
joint_transforms.RandomRotate(10)
])
img_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
target_transform = transforms.ToTensor()
train_set = ImageFolder(cvpr2014_trainning_path, joint_transform,
img_transform, target_transform)
train_loader = DataLoader(train_set,
batch_size=args['train_batch_size'],
num_workers=12,
shuffle=True)
criterion = nn.BCEWithLogitsLoss().cuda()
log_path = os.path.join(ckpt_path, exp_name,
str(datetime.datetime.now()) + '.txt')
def main():
net = BR2Net().cuda().train()
optimizer = optim.SGD([{
'params': [
'snapshot': ''
}
##########################data augmentation###############################
joint_transform = joint_transforms.Compose([
joint_transforms.RandomCrop(384, 384),
joint_transforms.RandomHorizontallyFlip(),
joint_transforms.RandomRotate(10)
])
img_transform = transforms.Compose([
transforms.ColorJitter(0.1, 0.1, 0.1),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
target_transform = transforms.ToTensor()
##########################################################################
train_set = ImageFolder(train_data, joint_transform, img_transform,
target_transform)
train_loader = DataLoader(train_set,
batch_size=args['train_batch_size'],
num_workers=12,
shuffle=True)
criterion = nn.BCEWithLogitsLoss().cuda()
criterion_BCE = nn.BCELoss().cuda()
criterion_MAE = nn.L1Loss().cuda()
criterion_MSE = nn.MSELoss().cuda()
log_path = os.path.join(ckpt_path, exp_name,
str(datetime.datetime.now()) + '.txt')
def main():
model = RGBD_sal()
net = model.cuda().train()
def execute(experiment):
print('EXECUTE')
print(experiment['parameters'])
time.sleep(3)
parameters = experiment['parameters'].copy()
raw_results = experiment['raw_results']
args.experiment_path = os.path.join("artifacts", parameters['dataset'], parameters['architecture'])
######################################
# Initial configuration...
######################################
float_parameters = [
'test_set_split',
'reduce_train_set',
'validation_set_split',
'sgd_lr',
'sgd_momentum',
'sgd_weight_decay',
'sgd_dampening',
'adam_lr',
'adam_beta1',
'adam_beta2',
'adam_eps',
'adam_weight_decay',
'rmsprop_lr',
'rmsprop_momentum',
'rmsprop_alpha',
'rmsprop_eps',
'rmsprop_weight_decay',
'adagrad_lr',
'adagrad_learning_decay',
'adagrad_weight_decay',
'adagrad_initial_acumulator',
'tas_alpha',
'tas_beta',
'tas_gamma']
int_parameters = [
'epochs',
'batch_size',
'executions',
'base_seed']
bool_parameters = [
'do_validation_set',
'combine_datasets',
'sgd_nesterov',
'adam_amsgrad',
'rmsprop_centered']
for float_parameter in float_parameters:
if float_parameter in parameters.keys():
parameters[float_parameter] = float(parameters[float_parameter])
for int_parameter in int_parameters:
if int_parameter in parameters.keys():
parameters[int_parameter] = int(parameters[int_parameter])
for bool_parameter in bool_parameters:
if bool_parameter in parameters.keys():
parameters[bool_parameter] = parameters[bool_parameter] == 'True'
# Using seeds...
random.seed(parameters['base_seed'])
numpy.random.seed(parameters['base_seed'])
torch.manual_seed(parameters['base_seed'])
torch.cuda.manual_seed(parameters['base_seed'])
args.execution_seed = parameters['base_seed'] + args.execution
print("EXECUTION SEED:", args.execution_seed)
print(args.dataset)
# Configuring args and dataset...
if args.dataset == "mnist":
args.number_of_dataset_classes = 10
args.number_of_model_classes = args.number_of_dataset_classes
normalize = transforms.Normalize((0.1307,), (0.3081,))
train_transform = transforms.Compose(
[transforms.ToTensor(), normalize])
inference_transform = transforms.Compose([transforms.ToTensor(), normalize])
dataset_path = args.dataset_dir if args.dataset_dir else "datasets/mnist"
train_set = torchvision.datasets.MNIST(root=dataset_path, train=True, download=True, transform=train_transform)
test_set = torchvision.datasets.MNIST(root=dataset_path, train=False, download=True, transform=inference_transform)
elif args.dataset == "cifar10":
args.number_of_dataset_classes = 10
args.number_of_model_classes = args.number_of_dataset_classes
normalize = transforms.Normalize((0.491, 0.482, 0.446), (0.247, 0.243, 0.261))
train_transform = transforms.Compose(
[transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), normalize])
inference_transform = transforms.Compose([transforms.ToTensor(), normalize])
dataset_path = args.dataset_dir if args.dataset_dir else "datasets/cifar10"
train_set = torchvision.datasets.CIFAR10(root=dataset_path, train=True, download=True, transform=train_transform)
test_set = torchvision.datasets.CIFAR10(root=dataset_path, train=False, download=True, transform=inference_transform)
elif args.dataset == "cifar100":
args.number_of_dataset_classes = 100
args.number_of_model_classes = args.number_of_dataset_classes
normalize = transforms.Normalize((0.507, 0.486, 0.440), (0.267, 0.256, 0.276))
train_transform = transforms.Compose(
[transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), normalize])
inference_transform = transforms.Compose([transforms.ToTensor(), normalize])
dataset_path = args.dataset_dir if args.dataset_dir else "datasets/cifar100"
train_set = torchvision.datasets.CIFAR100(root=dataset_path, train=True, download=True, transform=train_transform)
test_set = torchvision.datasets.CIFAR100(root=dataset_path, train=False, download=True, transform=inference_transform)
else:
args.number_of_dataset_classes = 1000
args.number_of_model_classes = args.number_of_model_classes if args.number_of_model_classes else 1000
if args.arch.startswith('inception'):
size = (299, 299)
else:
size = (224, 256)
normalize = transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
train_transform = transforms.Compose(
[transforms.RandomResizedCrop(size[0]), # 224 , 299
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), normalize])
inference_transform = transforms.Compose(
[transforms.Resize(size[1]), # 256
transforms.CenterCrop(size[0]), # 224 , 299
transforms.ToTensor(), normalize])
dataset_path = args.dataset_dir if args.dataset_dir else "/mnt/ssd/imagenet_scripts/2012/images"
train_path = os.path.join(dataset_path, 'train')
val_path = os.path.join(dataset_path, 'val')
train_set = ImageFolder(train_path, transform=train_transform)
test_set = ImageFolder(val_path, transform=inference_transform)
# Preparing paths...
# TODO make execution path an input
args.execution_path = 'results'
if not os.path.exists(args.execution_path):
os.makedirs(args.execution_path)
######################################
# Preparing data...
######################################
if parameters['combine_datasets']:
complete_dataset = torch.utils.data.ConcatDataset((train_set, test_set))
train_set, test_set = torch.utils.data.random_split(complete_dataset,
[round((1 - parameters['test_set_split'])*len(complete_dataset)),
round(parameters['test_set_split']*len(complete_dataset))])
if parameters['do_validation_set']:
train_set, validation_set = torch.utils.data.random_split(train_set,
[round((1 - parameters['validation_set_split'])*len(complete_dataset)),
round(parameters['validation_set_split']*len(complete_dataset))])
if parameters['reduce_train_set'] != 1.0:
train_set, _ = torch.utils.data.random_split(train_set, [round(parameters['reduce_train_set'] * len(train_set)), round((1 - parameters['reduce_train_set']) * len(train_set))])
test_set, _ = torch.utils.data.random_split(test_set, [round(parameters['reduce_train_set'] * len(test_set)), round((1 - parameters['reduce_train_set']) * len(test_set))])
# TODO make shuffle a general parameter
train_loader = DataLoader(train_set,
batch_size=parameters['batch_size'],
num_workers=args.workers,
shuffle=True)
test_loader = DataLoader(test_set,
batch_size=parameters['batch_size'],
num_workers=args.workers,
shuffle=True)
print("\n$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$")
print("TRAINSET LOADER SIZE: ====>>>> ", len(train_loader.sampler))
print("TESTSET LOADER SIZE: ====>>>> ", len(test_loader.sampler))
print("$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$")
# Dataset created...
print("\nDATASET:", args.dataset)
# create model
torch.manual_seed(args.execution_seed)
torch.cuda.manual_seed(args.execution_seed)
print("=> creating model '{}'".format(parameters['architecture']))
# model = create_model()
model = models.__dict__[parameters['architecture']](num_classes=args.number_of_model_classes)
model.cuda()
print("\nMODEL:", model)
torch.manual_seed(args.base_seed)
torch.cuda.manual_seed(args.base_seed)
#########################################
# Training...
#########################################
# define loss function (criterion)...
criterion = nn.CrossEntropyLoss().cuda()
# define optimizer..
if parameters['training_method'] == 'sgd':
optimizer = torch.optim.SGD(model.parameters(),
lr=parameters['sgd_lr'],
momentum=parameters['sgd_momentum'],
weight_decay=parameters['sgd_weight_decay'],
nesterov=parameters['sgd_nesterov'])
elif parameters['training_method'] == 'adam':
print('****************AMSGRAD*************')
print(parameters['adam_amsgrad'])
time.sleep(3)
optimizer = torch.optim.Adam(model.parameters(),
lr=parameters['adam_lr'],
betas=[parameters['adam_beta1'], parameters['adam_beta2']],
eps=parameters['adam_eps'],
weight_decay=parameters['adam_weight_decay'],
amsgrad=parameters['adam_amsgrad'])
elif parameters['training_method'] == 'adagrad':
optimizer = torch.optim.Adagrad(model.parameters(),
lr=parameters['adagrad_lr'],
lr_decay=parameters['adagrad_learning_decay'],
weight_decay=parameters['adagrad_weight_decay'])
elif parameters['training_method'] == 'rmsprop':
optimizer = torch.optim.RMSprop(model.parameters(),
lr=parameters['rmsprop_lr'],
momentum=parameters['rmsprop_momentum'],
alpha=parameters['rmsprop_alpha'],
eps=parameters['rmsprop_eps'],
centered=parameters['rmsprop_centered'],
weight_decay=parameters['rmsprop_weight_decay'])
#optimizer = torch.optim.Adam(model.parameters(), lr=0.01) # , weight_decay=5e-4)
# define scheduler...
#scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', patience=10, factor=0.2, verbose=True,
#
# threshold=0.05, threshold_mode='rel')
print(parameters)
if parameters['learning_method'] == 'tas':
alpha = parameters['tas_alpha']
beta = parameters['tas_beta']
gamma = parameters['tas_gamma']
our_lambda = lambda epoch: (1 - gamma)/(1 + math.exp(alpha*(epoch/parameters['epochs']-beta))) + gamma
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=our_lambda)
if parameters['learning_method'] == 'fixed_interval':
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=parameters['fixed_interval_rate'],
gamma=parameters['fixed_interval_period'])
if parameters['learning_method'] == 'fixed_epochs':
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
milestones=parameters['fixed_epochs_milestones'],
gamma=parameters['fixed_epochs_rate'])
if parameters['learning_method'] == 'constant':
scheduler = None
# model.initialize_parameters() ####### It works for AlexNet_, LeNet and VGG...
# initialize_parameters(model)
print("\n################ TRAINING ################")
best_model_file_path = os.path.join(args.execution_path, 'best_model.pth.tar')
best_train_acc1, best_val_acc1, final_train_loss = \
train_val(parameters, raw_results, train_loader, test_loader, model, criterion, optimizer,
scheduler, best_model_file_path)
# save to json file
return best_train_acc1, best_val_acc1, final_train_loss, raw_results
def main(config):
result_name = '{}_{}_{}way_{}shot'.format(
config['data_name'],
config['arch']['base_model'],
config['general']['way_num'],
config['general']['shot_num'],
)
save_path = os.path.join(config['general']['save_root'], result_name)
if not os.path.exists(save_path):
os.mkdir(save_path)
fout_path = os.path.join(save_path, 'train_info.txt')
fout_file = open(fout_path, 'a+')
with open(os.path.join(save_path, 'config.json'), 'w') as handle:
json.dump(config, handle, indent=4, sort_keys=True)
print_func(config, fout_file)
train_trsfms = transforms.Compose([
transforms.Resize((config['general']['image_size'],
config['general']['image_size'])),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std),
])
val_trsfms = transforms.Compose([
transforms.Resize((config['general']['image_size'],
config['general']['image_size'])),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std),
])
model = ALTNet(**config['arch'])
print_func(model, fout_file)
optimizer = optim.Adam(model.parameters(), lr=config['train']['optim_lr'])
if config['train']['lr_scheduler']['name'] == 'StepLR':
lr_scheduler = optim.lr_scheduler.StepLR(
optimizer=optimizer, **config['train']['lr_scheduler']['args'])
elif config['train']['lr_scheduler']['name'] == 'MultiStepLR':
lr_scheduler = optim.lr_scheduler.MultiStepLR(
optimizer=optimizer, **config['train']['lr_scheduler']['args'])
else:
raise RuntimeError
if config['train']['loss']['name'] == 'CrossEntropyLoss':
criterion = nn.CrossEntropyLoss(**config['train']['loss']['args'])
else:
raise RuntimeError
device, _ = prepare_device(config['n_gpu'])
model = model.to(device)
criterion = criterion.to(device)
best_val_prec1 = 0
best_test_prec1 = 0
for epoch_index in range(config['train']['epochs']):
print_func('{} Epoch {} {}'.format('=' * 35, epoch_index, '=' * 35),
fout_file)
train_dataset = ImageFolder(
data_root=config['general']['data_root'],
mode='train',
episode_num=config['train']['episode_num'],
way_num=config['general']['way_num'],
shot_num=config['general']['shot_num'],
query_num=config['general']['query_num'],
transform=train_trsfms,
)
val_dataset = ImageFolder(
data_root=config['general']['data_root'],
mode='val',
episode_num=config['test']['episode_num'],
way_num=config['general']['way_num'],
shot_num=config['general']['shot_num'],
query_num=config['general']['query_num'],
transform=val_trsfms,
)
test_dataset = ImageFolder(
data_root=config['general']['data_root'],
mode='test',
episode_num=config['test']['episode_num'],
way_num=config['general']['way_num'],
shot_num=config['general']['shot_num'],
query_num=config['general']['query_num'],
transform=val_trsfms,
)
print_func(
'The num of the train_dataset: {}'.format(len(train_dataset)),
fout_file)
print_func('The num of the val_dataset: {}'.format(len(val_dataset)),
fout_file)
print_func('The num of the test_dataset: {}'.format(len(test_dataset)),
fout_file)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=config['train']['batch_size'],
shuffle=True,
num_workers=config['general']['workers_num'],
drop_last=True,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=config['test']['batch_size'],
shuffle=True,
num_workers=config['general']['workers_num'],
drop_last=True,
pin_memory=True)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=config['test']['batch_size'],
shuffle=True,
num_workers=config['general']['workers_num'],
drop_last=True,
pin_memory=True)
# train for 5000 episodes in each epoch
print_func('============ Train on the train set ============',
fout_file)
train(train_loader, model, criterion, optimizer, epoch_index, device,
fout_file, config['general']['image2level'],
config['general']['print_freq'])
print_func('============ Validation on the val set ============',
fout_file)
val_prec1 = validate(val_loader, model, criterion, epoch_index, device,
fout_file, config['general']['image2level'],
config['general']['print_freq'])
print_func(
' * Prec@1 {:.3f} Best Prec1 {:.3f}'.format(
val_prec1, best_val_prec1), fout_file)
print_func('============ Testing on the test set ============',
fout_file)
test_prec1 = validate(test_loader, model, criterion, epoch_index,
device, fout_file,
config['general']['image2level'],
config['general']['print_freq'])
print_func(
' * Prec@1 {:.3f} Best Prec1 {:.3f}'.format(
test_prec1, best_test_prec1), fout_file)
if val_prec1 > best_val_prec1:
best_val_prec1 = val_prec1
best_test_prec1 = test_prec1
save_model(model,
save_path,
config['data_name'],
epoch_index,
is_best=True)
if epoch_index % config['general'][
'save_freq'] == 0 and epoch_index != 0:
save_model(model,
save_path,
config['data_name'],
epoch_index,
is_best=False)
lr_scheduler.step()
print_func('............Training is end............', fout_file)
image_transform = transforms.Compose([
transforms.Scale(int(imsize * 76 / 64)),
transforms.RandomCrop(imsize),
transforms.RandomHorizontalFlip()
])
if cfg.DATA_DIR.find('lsun') != -1:
from datasets import LSUNClass
dataset = LSUNClass('%s/%s_%s_lmdb' %
(cfg.DATA_DIR, cfg.DATASET_NAME, split_dir),
base_size=cfg.TREE.BASE_SIZE,
transform=image_transform)
elif cfg.DATA_DIR.find('imagenet') != -1:
from datasets import ImageFolder
dataset = ImageFolder(cfg.DATA_DIR,
split_dir='train',
custom_classes=CLASS_DIC[cfg.DATASET_NAME],
base_size=cfg.TREE.BASE_SIZE,
transform=image_transform)
elif cfg.GAN.B_CONDITION: # text to image task
if cfg.DATASET_NAME == 'birds':
from datasets import TextDataset
dataset = TextDataset(cfg.DATA_DIR,
split_dir,
base_size=cfg.TREE.BASE_SIZE,
transform=image_transform)
elif cfg.DATASET_NAME == 'flowers':
from datasets import FlowersDataset
dataset = FlowersDataset(cfg.DATA_DIR,
split_dir,
base_size=cfg.TREE.BASE_SIZE,
transform=image_transform)
def main(result_path, epoch_num):
config = json.load(open(os.path.join(result_path, 'config.json')))
fout_path = os.path.join(result_path, 'test_info.txt')
fout_file = open(fout_path, 'a+')
print_func(config, fout_file)
trsfms = transforms.Compose([
transforms.Resize((config['general']['image_size'],
config['general']['image_size'])),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std),
])
model = ALTNet(**config['arch'])
print_func(model, fout_file)
state_dict = torch.load(
os.path.join(result_path,
'{}_best_model.pth'.format(config['data_name'])))
model.load_state_dict(state_dict)
if config['train']['loss']['name'] == 'CrossEntropyLoss':
criterion = nn.CrossEntropyLoss(**config['train']['loss']['args'])
else:
raise RuntimeError
device, _ = prepare_device(config['n_gpu'])
model = model.to(device)
criterion = criterion.to(device)
total_accuracy = 0.0
total_h = np.zeros(epoch_num)
total_accuracy_vector = []
for epoch_idx in range(epoch_num):
test_dataset = ImageFolder(
data_root=config['general']['data_root'],
mode='test',
episode_num=600,
way_num=config['general']['way_num'],
shot_num=config['general']['shot_num'],
query_num=config['general']['query_num'],
transform=trsfms,
)
print_func('The num of the test_dataset: {}'.format(len(test_dataset)),
fout_file)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=config['test']['batch_size'],
shuffle=True,
num_workers=config['general']['workers_num'],
drop_last=True,
pin_memory=True)
print_func('============ Testing on the test set ============',
fout_file)
_, accuracies = validate(test_loader, model, criterion, epoch_idx,
device, fout_file,
config['general']['image2level'],
config['general']['print_freq'])
test_accuracy, h = mean_confidence_interval(accuracies)
print_func("Test Accuracy: {}\t h: {}".format(test_accuracy, h[0]),
fout_file)
total_accuracy += test_accuracy
total_accuracy_vector.extend(accuracies)
total_h[epoch_idx] = h
aver_accuracy, _ = mean_confidence_interval(total_accuracy_vector)
print_func(
'Aver Accuracy: {:.3f}\t Aver h: {:.3f}'.format(
aver_accuracy, total_h.mean()), fout_file)
print_func('............Testing is end............', fout_file)
def __init__(self, options):
super(Stage2Trainer, self).__init__(options, subfolders = ['samples', 'reconstructions'], copy_keys = copy_keys)
transforms = []
if options.crop_size is not None:
transforms.append(T.CenterCrop(options.crop_size))
transforms.append(T.Resize(options.image_size))
transforms.append(T.ToTensor())
transforms.append(T.Normalize((0.5, 0.5, 0.5, 0), (0.5, 0.5, 0.5, 1)))
image_set = ImageFolder(options.data_root, transform = T.Compose(transforms))
enc_codes = torch.load(os.path.join(options.enc_path, 'codes', '{0}_codes.pt'.format(options.enc_iter)))
code_set = torch.utils.data.TensorDataset(enc_codes[:, 0], enc_codes[:, 1])
self.dataset = ParallelDataset(image_set, code_set)
self.dataloader = torch.utils.data.DataLoader(self.dataset, batch_size = options.batch_size, shuffle = True, drop_last = True, num_workers = options.nloader)
self.data_iter = iter(self.dataloader)
enc_stats = torch.load(os.path.join(options.enc_path, 'codes', '{0}_stats.pt'.format(options.enc_iter)))
self.con_full_mean = enc_stats['full_mean']
self.con_full_std = enc_stats['full_std']
self.con_eigval = enc_stats['eigval']
self.con_eigvec = enc_stats['eigvec']
self.dim_weight = enc_stats['dim_weight']
if self.con_weight > 0:
self.enc = models.Encoder(options.image_size, options.image_size, options.enc_features, options.enc_blocks, options.enc_adain_features, options.enc_adain_blocks, options.content_size)
self.enc.to(self.device)
self.enc.load_state_dict(torch.load(os.path.join(options.enc_path, 'models', '{0}_enc.pt'.format(options.enc_iter)), map_location = self.device))
self.gen = models.TwoPartNestedDropoutGenerator(options.image_size, options.image_size, options.gen_features, options.gen_blocks, options.gen_adain_features, options.gen_adain_blocks, options.content_size, options.style_size)
self.gen.to(self.device)
if (self.load_path is None) and not options.reset_gen:
self.gen.load_state_dict(torch.load(os.path.join(options.enc_path, 'models', '{0}_gen.pt'.format(options.enc_iter)), map_location = self.device))
self.gen_optim = optim.RMSprop(self.gen.parameters(), lr = self.lr, eps = 1e-4)
self.add_model('gen', self.gen, self.gen_optim)
self.cla = models.ClassifierOrDiscriminator(options.image_size, options.image_size, options.cla_features, options.cla_blocks, options.cla_adain_features, options.cla_adain_blocks, self.nclass)
self.cla.to(self.device)
if (self.load_path is None) and (options.cla_path is not None) and not options.reset_cla:
self.cla.load_state_dict(torch.load(os.path.join(options.cla_path, 'models', '{0}_cla.pt'.format(options.cla_iter)), map_location = self.device))
self.cla_optim = optim.RMSprop(self.cla.parameters(), lr = self.lr, eps = 1e-4)
self.add_model('cla', self.cla, self.cla_optim)
self.dis = models.ClassifierOrDiscriminator(options.image_size, options.image_size, options.dis_features, options.dis_blocks, options.dis_adain_features, options.dis_adain_blocks, self.nclass)
self.dis.to(self.device)
if (self.load_path is None) and (options.cla_path is not None) and not options.reset_dis:
self.dis.load_state_dict(torch.load(os.path.join(options.cla_path, 'models', '{0}_cla.pt'.format(options.cla_iter)), map_location = self.device))
self.dis.convert()
self.dis_optim = optim.RMSprop(self.dis.parameters(), lr = self.lr, eps = 1e-4)
self.add_model('dis', self.dis, self.dis_optim)
self.sty = models.NormalizedStyleBank(self.nclass, options.style_size, image_set.get_class_freq())
self.sty.to(self.device)
if (self.load_path is None) and not options.reset_sty:
self.sty.load_state_dict(torch.load(os.path.join(options.enc_path, 'models', '{0}_sty.pt'.format(options.enc_iter)), map_location = self.device))
self.sty_optim = optim.Adam(self.sty.parameters(), lr = self.sty_lr, eps = 1e-8)
self.add_model('sty', self.sty, self.sty_optim)
if self.load_path is not None:
rec_images = torch.load(os.path.join(self.load_path, 'reconstructions', 'images.pt'), map_location = self.device)
self.rec_codes = torch.load(os.path.join(self.load_path, 'reconstructions', 'codes.pt'), map_location = self.device)
self.rec_labels = torch.load(os.path.join(self.load_path, 'reconstructions', 'labels.pt'), map_location = self.device)
self.vis_codes = torch.load(os.path.join(self.load_path, 'samples', 'codes.pt'), map_location = self.device)
self.vis_style_noise = torch.load(os.path.join(self.load_path, 'samples', 'style_noise.pt'), map_location = self.device)
self.load(options.load_iter)
else:
rec_images = []
rec_codes = []
rec_labels = []
rec_index = random.sample(range(len(self.dataset)), options.vis_row * options.vis_col)
for k in rec_index:
image, label, code, _ = self.dataset[k]
rec_images.append(image)
rec_codes.append(code)
rec_labels.append(label)
rec_images = torch.stack(rec_images, dim = 0)
self.rec_codes = torch.stack(rec_codes, dim = 0).to(self.device)
self.rec_labels = one_hot(torch.tensor(rec_labels, dtype = torch.int32), self.nclass).to(self.device)
self.vis_codes = self.noise_to_con_code(gaussian_noise(options.vis_row * options.vis_col, options.content_size)).to(self.device)
self.vis_style_noise = gaussian_noise(options.vis_row * options.vis_col, options.style_size).to(self.device)
self.state.dis_total_batches = 0
if self.save_path != self.load_path:
torch.save(rec_images, os.path.join(self.save_path, 'reconstructions', 'images.pt'))
torch.save(self.rec_codes, os.path.join(self.save_path, 'reconstructions', 'codes.pt'))
torch.save(self.rec_labels, os.path.join(self.save_path, 'reconstructions', 'labels.pt'))
torch.save(self.vis_codes, os.path.join(self.save_path, 'samples', 'codes.pt'))
torch.save(self.vis_style_noise, os.path.join(self.save_path, 'samples', 'style_noise.pt'))
save_image(rec_images.add(1).div(2), os.path.join(self.save_path, 'reconstructions', 'target.png'), self.vis_col)
self.add_periodic_func(self.visualize_fixed, options.visualize_iter)
self.visualize_fixed()
self.loss_avg_factor = 0.9
self.sty_drop_prob = torch.Tensor(options.style_size)
for i in range(options.style_size):
self.sty_drop_prob[i] = options.style_dropout ** i
detect_queue = deque([[
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff
],
[
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff
],
[
0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0xff
]])
# Get Dataloader using ImageFolder
os.makedirs(save_path, exist_ok=True)
dataloader = DataLoader(ImageFolder(image_folder, img_size=img_size),
batch_size=batch_size,
shuffle=False,
num_workers=8)
# Extracts class labels from file
classes = load_classes('tl.names')
# Initiate model
#model = myNet(num_classes, anchors, input_size, True, is_train = True).cuda()
#model.load_state_dict(torch.load('./weight/final_param.pkl'),False)
model = torch.load('./model_0914_17.pkl', map_location='cuda:0')
model.set_test()
cuda = torch.cuda.is_available()
if cuda:
