def _get_data_loader(self, config, names=None):
trs = self.__transformations
if names is None:
train_names, val_names = self._split_data(self.__train_dir,
config.train_percent)
else:
train_names, val_names = names
train_folder = ImageFolder(self.__train_dir,
train_names,
transform=trs['train'])
val_folder = ImageFolder(self.__train_dir,
val_names,
transform=trs['val'])
if not len(train_folder) or not len(val_folder):
raise ValueError('One of the image folders contains zero data, train: %s, val: %s' % \
(len(train_folder), len(val_folder)))
sampler = None
train_loader = torch.utils.data.DataLoader(
train_folder,
batch_size=config.batch_size,
shuffle=True,
sampler=sampler,
num_workers=config.workers,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(val_folder,
batch_size=config.batch_size,
shuffle=True,
num_workers=config.workers,
pin_memory=True)
return train_loader, val_loader
def _get_data_loader(self, config, names=None):
transformations = self.__transform_func()
if names is None:
train_names, val_names = self._split_data(self.__train_dir, config.train_percent)
else:
train_names, val_names = names
loader = self.__get_loader(config)
train_folder = ImageFolder(self.__label_file, self.__train_dir, train_names, transform=transformations['train'],
loader=loader)
val_folder = ImageFolder(self.__label_file, self.__train_dir, val_names, transform=transformations['val'],
loader=loader)
if not len(train_folder) or not len(val_folder):
raise ValueError, 'One of the image folders contains zero data, train: %s, val: %s' % \
(len(train_folder), len(val_folder))
sampler = None
if config.weigh_sample:
sampler = WeightedRandomSampler(train_folder.weights, len(train_folder), replacement=True)
train_loader = torch.utils.data.DataLoader(train_folder, batch_size=config.batch_size, shuffle=True,
sampler=sampler, num_workers=config.workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(val_folder, batch_size=config.batch_size, shuffle=True,
num_workers=config.workers, pin_memory=True)
return train_loader, val_loader
def get_loader(root,
split,
batch_size,
scale_size,
num_workers=2,
shuffle=True):
dataset_name = os.path.basename(root)
image_root = os.path.join(root, 'splits', split)
if dataset_name in ['CelebA']:
dataset = ImageFolder(
root=image_root,
transform=transforms.Compose([
transforms.CenterCrop(160),
transforms.Scale(scale_size),
transforms.ToTensor(),
#transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]))
else:
dataset = ImageFolder(
root=image_root,
transform=transforms.Compose([
transforms.Scale(scale_size),
transforms.ToTensor(),
#transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]))
data_loader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=int(num_workers))
data_loader.shape = [int(num) for num in dataset[0][0].size()]
return data_loader
def __write_submission(self, res, thresholds, output_file=None, detailed_output_file=None):
"""Write final result into csv"""
def sort_arg(filename):
base, number = filename.split('_')
number = int(number[:-4])
return base, number
mapping = {i:k for i, k in enumerate(ImageFolder(self.__label_file, self.__train_dir).classes)}
logger.info(str(mapping))
output_file = output_file or self.__output_file.format(self.__cur_fold)
detailed_output_file = detailed_output_file or self.__detailed_output_file.format(self.__cur_fold)
with open(output_file, 'w') as wf, open(detailed_output_file, 'w') as dwf:
dwf.write(json.dumps({mapping[i]:v for i, v in enumerate(thresholds)}) + '\n')
wf.write('image_name,tags\n')
dwf.write('image_name,probs\n')
for file_name, probs in sorted(res, key=lambda x: sort_arg(x[0])):
name = file_name[:-4]
labels, detailed_enc = [], []
for i, prob in enumerate(probs):
if prob > thresholds[i]:
labels.append(mapping[i])
detailed_enc.append('%s:%.5f' % (mapping[i], prob))
wf.write(','.join([name, ' '.join(labels)]) + '\n')
dwf.write(','.join([name, ' '.join(detailed_enc)]) + '\n')
def test_model(args):
# create model
model = dla.__dict__[args.arch](pretrained=args.pretrained,
pool_size=args.crop_size // 32)
model = torch.nn.DataParallel(model)
# 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)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {} prec {:.03f}) "
.format(args.resume, checkpoint['epoch'], best_prec1))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
data = dataset.get_data(args.data_name)
if data is None:
data = dataset.load_dataset_info(args.data, data_name=args.data_name)
if data is None:
raise ValueError('{} is not pre-defined in dataset.py and info.json '
'does not exist in {}', args.data_name, args.data)
# Data loading code
valdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(mean=data.mean, std=data.std)
if args.crop_10:
t = transforms.Compose([
transforms.Resize(args.scale_size),
transforms.ToTensor(),
normalize])
else:
t = transforms.Compose([
transforms.Resize(args.scale_size),
transforms.CenterCrop(args.crop_size),
transforms.ToTensor(),
normalize])
val_loader = torch.utils.data.DataLoader(
ImageFolder(valdir, t, out_name=args.crop_10),
batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
# define loss function (criterion) and pptimizer
criterion = nn.CrossEntropyLoss()
if args.cuda:
model = model.cuda()
criterion = criterion.cuda()
if args.crop_10:
validate_10(args, val_loader, model,
'{}_i_{}_c_10.txt'.format(args.arch, args.start_epoch))
else:
validate(args, val_loader, model, criterion)
def get_groundtruth(permitted):
folder = ImageFolder(LABEL_FILE, TRAIN_FOLDER_TIF, set(permitted))
truth = map(lambda x: (x[0].split('.')[0], x[1].numpy()), folder.imgs)
truth.sort()
names, data = zip(*truth)
data = np.array(data)
return data
def init_data_loader(config, num_processes=4, path_feature=None):
if os.path.exists(os.path.join(config["batches_dir"],
"class_mapping.json")):
import json
with open(os.path.join(config["batches_dir"], "class_mapping.json"),
"r") as f:
obj = json.loads(f.read())
config["num_labels"] = len(obj)
else:
# The number of labels is the number of dirs in batches_dir
label_dirs = [
p for p in os.listdir(config["batches_dir"])
if os.path.isdir(os.path.join(config["batches_dir"], p))
]
config["num_labels"] = len(label_dirs)
# init dataset
logging.info("Initializing data loader, this might take a while.....")
all_transforms = _init_transforms(config["img_h"], config["img_w"],
config["data_augmentation"])
if path_feature is not None:
train_dataset = ImageFolderWithFeature(config["batches_dir"],
path_feature,
transform=all_transforms)
else:
train_dataset = ImageFolder(config["batches_dir"],
transform=all_transforms)
# init data loader
# configure sampling strategy
class_balanced_sampling = config["tri_loss_params"]["margin"] > 0 or \
config["batch_sampling_params"]["class_balanced"]
if class_balanced_sampling:
logging.info("Using class_balanced sampling strategy.")
# construct data loader
batch_size = config["batch_size"] if (
not class_balanced_sampling) else None
shuffle = not class_balanced_sampling
sampler = TripletSampler(config["batch_sampling_params"], train_dataset) \
if class_balanced_sampling else None
data_loader = torch.utils.data.DataLoader(train_dataset,
shuffle=shuffle,
batch_size=batch_size,
num_workers=num_processes,
batch_sampler=sampler)
# log training set info
count = len(train_dataset)
iterations_per_epoch = len(data_loader)
logging.info(
"[TRAINING SET INFO] number of example: %s, number of labels: %s, "
"iterations_per_epoch: %s" %
(count, config["num_labels"], iterations_per_epoch))
return data_loader
def store_delf_feature(cfg):
# logging
current_time = datetime.datetime.strftime(datetime.datetime.now(),'%Y-%m-%d %H:%M:%S')
log_save_path = os.path.join(cfg.log_root, cfg.stage)
os.makedirs(log_save_path, exist_ok=True)
f = open(log_save_path+'/{}-train-{}.log'.format(current_time, cfg.stage), mode='w', encoding='utf-8')
f.close()
logging.basicConfig(format='%(asctime)s - %(message)s',
datefmt='%d-%b-%y %H:%M:%S',
level=logging.INFO,
filename=log_save_path+'/{}-train.log'.format(current_time))
print('loading dataset...')
dataset = ImageFolder(root=cfg.index_img, transform=transforms.ToTensor())
dataloader = data.DataLoader(dataset=dataset, batch_size=1, shuffle=True, num_workers=2)
print('dataset load done.')
# model construct
print('model construct...')
model = get_delf_feature(cfg.kp_path)
if torch.cuda.is_available():
model = model.cuda()
model.eval()
print('model load done.')
# pca trained params
h5file = h5py.File(os.path.join(cfg.pca_saved, 'pca.h5'), 'r')
pca_mean = copy.deepcopy(h5file['.']['pca_mean'].value)
pca_var = copy.deepcopy(h5file['.']['pca_vars'].value)
pca_matrix = copy.deepcopy(h5file['.']['pca_matrix'].value)
# delf_pca = DelfPCA(pca_n_components=cfg.pca_dims, whitening=True, pca_saved_path=cfg.pca_saved)
delf_features = []
print('delf attention feature extracting...')
pbar = tqdm(dataloader)
for index, (inputs, _, filename) in enumerate(pbar):
if torch.cuda.is_available():
inputs = inputs.cuda()
try:
delf_feature = getDelfFeatureFromMultiScale(stage='delf', inputs=inputs, model=model, filename=filename, \
pca_matrix=pca_matrix, pca_mean = pca_mean, pca_var=pca_var, pca_dims=cfg.pca_dims, rf=cfg.delf_rf, \
stride=cfg.delf_stride, padding=cfg.delf_padding,topk=cfg.topk, scales=cfg.scales, \
iou_thresh=cfg.iou_thres, attn_thres=cfg.atte_thres)
except Exception as e:
print(e)
delf_feature = None
if delf_feature != None:
delf_features.append(delf_feature)
msg = "image name: {}".format(filename[0])
pbar.set_description(desc=msg)
logging.info(msg)
print('delf features get done.')
with open(os.path.join(cfg.delf_saved, 'index.delf'), 'wb') as delf_file:
pickle.dump(delf_features, delf_file, protocol=2)
print('saved DeLF feature at {}'.format(os.path.join(cfg.delf_saved, 'index.delf')))
def filter():
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
dataset = ImageFolder(root=args.icdar_patches, transform=transform)
loader = torch.utils.data.DataLoader(dataset, batch_size=10, shuffle=True)
model = Extractor()
for params in model.parameters():
params.requires_grad = False
acc = clustering(loader=loader, model=model)
def get_loader(root,
split,
labels_per_class,
batch_size,
scale_size,
num_workers=2,
shuffle=True):
image_root = os.path.join(root, 'splits', split)
dataset = ImageFolder(
root=image_root,
transform=transforms.Compose([
transforms.CenterCrop(160),
transforms.Scale(scale_size),
transforms.ToTensor(),
#transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
#transforms.Normalize((0.51, 0.42, 0.37), (0.27, 0.27, 0.27))
])) ## TODO add target_transform=onehot(n_labels)??
if split == 'train':
if labels_per_class != None:
data_loader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=int(num_workers),
sampler=get_sampler(dataset.train_labels.numpy(),
labels_per_class))
else:
data_loader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=int(num_workers))
else:
data_loader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=int(num_workers))
data_loader.shape = [int(num) for num in dataset[0][0].size()]
return data_loader
def main_worker(args):
args.device = torch.device(
'cuda:{}'.format(args.gpu) if torch.cuda.is_available() else 'cpu')
if args.pretrained:
print('=> using pre-trained model')
model = init_encoder_model(args.embed_size, args.pretrained)
else:
print('=> Pretrained model not specified.')
return
model = model.to(args.device)
e_weights = torch.FloatTensor(train_label_emb())
label_model = nn.Embedding.from_pretrained(e_weights)
label_model = label_model.to(args.device)
# Data loading code
print("Initializing Datasets and Dataloaders...")
transformer = transforms.Compose([
transforms.Resize(args.input_size),
transforms.CenterCrop(args.input_size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# Create training and validation datasets
image_datasets = ImageFolder(os.path.join(args.data, 'test'), transformer)
# Create training and validation dataloaders
dataloaders = torch.utils.data.DataLoader(image_datasets,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
print('Evaluating')
validate(dataloaders, model, label_model, args)
print('Finished')
def train(loader, model, clf):
print("============>training<============")
test_dataset = ImageFolder(root="./data/icdar2015/test_patches",
transform=transforms)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=20,
shuffle=True)
pbar = tqdm(total=len(loader))
for cnt, data in enumerate(loader):
pbar.update(1)
im, ann, im_fns = data[0], data[1], data[2]
im = im.cuda()
feat = model(im)
x = feat.cpu().numpy()
y = ann.numpy()
try:
clf.fit(x, y)
except ValueError:
continue
pbar.close()
print("\n")
print("============>testing<============")
test(test_loader, clf)
def train_pca(cfg):
# logging
current_time = datetime.datetime.strftime(datetime.datetime.now(),
'%Y-%m-%d %H:%M:%S')
log_save_path = os.path.join(cfg.log_root, cfg.stage)
os.makedirs(log_save_path, exist_ok=True)
f = open(log_save_path + '/{}-train-pca.log'.format(current_time),
mode='w',
encoding='utf-8')
f.close()
logging.basicConfig(format='%(asctime)s - %(message)s',
datefmt='%d-%b-%y %H:%M:%S',
level=logging.INFO,
filename=log_save_path +
'/{}-train.log'.format(current_time))
print('loading dataset...')
dataset = ImageFolder(root=cfg.index_img, transform=transforms.ToTensor())
dataloader = data.DataLoader(dataset=dataset,
batch_size=1,
shuffle=True,
num_workers=2)
print('dataset load done.')
# model construct
print('model construct...')
model = get_delf_feature(cfg.kp_path)
if torch.cuda.is_available():
model = model.cuda()
model.eval()
print('model load done.')
delf_pca = DelfPCA(pca_n_components=cfg.pca_dims,
pca_saved_path=cfg.pca_saved)
feature_maps = []
print('delf attention feature extracting...')
features_num = 0
pbar = tqdm(dataloader)
for (inputs, _, filename) in pbar:
if torch.cuda.is_available():
inputs = inputs.cuda()
try:
output = getDelfFeatureFromMultiScale(stage='pca', inputs=inputs, model=model, filename=filename, \
pca_matrix=None, pca_var=None, pca_dims=cfg.pca_dims, rf=cfg.delf_rf, \
stride=cfg.delf_stride, padding=cfg.delf_padding,topk=cfg.topk, scales=cfg.scales, \
iou_thresh=cfg.iou_thres, attn_thres=cfg.atte_thres)
except Exception as e:
print(e)
descriptor_np_list = output['descriptor_np_list']
pca_feature = [
descriptor_np_list[i, :]
for i in range(descriptor_np_list.shape[0])
] # 将ndarray变成列表
feature_maps.extend(pca_feature)
curr_fea_nums = descriptor_np_list.shape[0]
features_num += curr_fea_nums
msg = "curr feature nums: {}".format(features_num)
pbar.set_description(desc=msg)
logging.info(filename[0] + "-feature nums: {}".format(curr_fea_nums))
print('features get done. Total feature nums: {}'.format(features_num))
print('start train pca...')
delf_pca(feature_maps)
print('pca trained done. saved at {}'.format(cfg.pca_saved + 'pca.h5'))
def extract(self, input_path, output_path):
'''extract features from single image without batch process.
'''
assert self.mode.lower() in ['pca', 'delf']
batch_timer = AverageMeter()
data_timer = AverageMeter()
since = time.time()
# dataloader.
dataset = ImageFolder(
root = input_path,
transform = transforms.ToTensor())
self.dataloader = torch.utils.data.DataLoader(
dataset = dataset,
batch_size = 1,
shuffle = True,
num_workers = 0)
feature_maps = []
if self.mode.lower() in ['pca']:
# bar = Bar('[{}]{}'.format(self.mode.upper(), self.title), max=len(self.dataloader))
for batch_idx, (inputs, _, filename) in enumerate(self.dataloader):
# image size upper limit.
if not (len(inputs.size()) == 4):
if __DEBUG__:
print('wrong input dimenstion! ({},{})'.format(filename, input.size()))
continue;
if not (inputs.size(2)*inputs.size(3) <= 1200*1200):
if __DEBUG__:
print('passed: image size too large! ({},{})'.format(filename, inputs.size()))
continue;
if not (inputs.size(2) >= 112 and inputs.size(3) >= 112):
if __DEBUG__:
print('passed: image size too small! ({},{})'.format(filename, inputs.size()))
continue;
data_timer.update(time.time() - since)
# prepare inputs
if __is_cuda__():
inputs = __cuda__(inputs)
inputs = __to_var__(inputs)
# get delf feature only for pca calculation.
pca_feature = self.__extract_delf_feature__(inputs.data, filename, mode='pca')
if pca_feature is not None:
feature_maps.extend(pca_feature)
batch_timer.update(time.time() - since)
since = time.time()
# progress
log_msg = ('\n[Extract][Processing:({batch}/{size})] '+ \
'eta: (data:{data:.3f}s),(batch:{bt:.3f}s),(total:{tt:})') \
.format(
batch=batch_idx + 1,
size=len(self.dataloader),
data=data_timer.val,
bt=batch_timer.val,
tt=0)#bar.elapsed_td
print(log_msg)
# bar.next()
print('\nnumber of selected features so far: {}'.format(len(feature_maps)))
if len(feature_maps) >= 10000000: # UPPER LIMIT.
break;
# free GPU cache every.
if batch_idx % 10 == 0:
torch.cuda.empty_cache()
if __DEBUG__:
print('GPU Memory flushed !!!!!!!!!')
# trian PCA.
self.pca(feature_maps)
else:
# bar = Bar('[{}]{}'.format(self.mode.upper(), self.title), max=len(self.dataloader))
assert self.mode.lower() in ['delf']
feature_maps = []
for batch_idx, (inputs, labels, filename) in enumerate(self.dataloader):
# image size upper limit.
if not (len(inputs.size()) == 4):
if __DEBUG__:
print('wrong input dimenstion! ({},{})'.format(filename, input.size()))
continue;
if not (inputs.size(2)*inputs.size(3) <= 1200*1200):
if __DEBUG__:
print('passed: image size too large! ({},{})'.format(filename, inputs.size()))
continue;
if not (inputs.size(2) >= 112 and inputs.size(3) >= 112):
if __DEBUG__:
print('passed: image size too small! ({},{})'.format(filename, inputs.size()))
continue;
data_timer.update(time.time() - since)
# prepare inputs
source_input = inputs
if __is_cuda__():
inputs = __cuda__(inputs)
inputs = __to_var__(inputs)
# get delf everything (score, feature, etc.)
delf_feature = self.__extract_delf_feature__(inputs.data, filename, mode='delf')
# if delf_feature is not None:
# feature_maps.append(delf_feature)
# log.
batch_timer.update(time.time() - since)
since = time.time()
log_msg = ('\n[Extract][Processing:({batch}/{size})] '+ \
'eta: (data:{data:.3f}s),(batch:{bt:.3f}s),(total:{tt:})') \
.format(
batch=batch_idx + 1,
size=len(self.dataloader),
data=data_timer.val,
bt=batch_timer.val,
tt=0)#bar.elapsed_td
print(log_msg)
# bar.next()
# free GPU cache every.
if batch_idx % 10 == 0:
torch.cuda.empty_cache()
if __DEBUG__:
print('GPU Memory flushed !!!!!!!!!')
save_path = "/media/liesmars/SSD1/vearch/plugin/src/streetView/DeLF-pytorch/extract/output"
img_path = "/media/liesmars/SSD1/vearch/plugin/src/streetView/DeLF-pytorch/extract/testdata/_9oMPkzl60LSzvxIlG2FZA(22.279587,114.167117)"
if True:
# for b in range(len(inputs)):
imgname = delf_feature["filename"][0]
# img = source_input.squeeze().numpy()*255
# img = img.astype(np.uint8)
img=cv2.imread(os.path.join(img_path, imgname))
# print(img)
# cv2.imshow('image', img)
# cv2.waitKey(100)
feature_loc = delf_feature["location_np_list"]
for loc in feature_loc:
# cv2.circle(img, )
img = cv2.circle(img,(int(loc[0]),int(loc[1])),2,(0,0,230),-1)
cv2.imwrite(os.path.join(save_path, imgname), img)
print("save")
def __init__(self, params):
self.params = params
# specify the gpu id if using only 1 gpu
if params.ngpu == 1:
os.environ['CUDA_VISIBLE_DEVICES'] = str(params.gpu_id)
try:
os.makedirs(params.outf)
except OSError:
pass
if params.manualSeed is None:
params.manualSeed = random.randint(1, 10000)
print("Random Seed: ", params.manualSeed)
random.seed(params.manualSeed)
torch.manual_seed(params.manualSeed)
if params.cuda:
torch.cuda.manual_seed_all(params.manualSeed)
cudnn.benchmark = True
if torch.cuda.is_available() and not params.cuda:
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda"
)
# datase t
if params.dataset == 'imagenet':
# folder dataset
self.dataset = ImageFolder(
root=params.dataroot,
transform=transforms.Compose([
transforms.Scale(params.imageSize),
transforms.CenterCrop(params.imageSize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]),
classes_idx=(10, 20))
elif params.dataset == 'cifar10':
self.dataset = dset.CIFAR10(root=params.dataroot,
download=True,
transform=transforms.Compose([
transforms.Scale(params.imageSize),
transforms.ToTensor(),
transforms.Normalize(
(0.5, 0.5, 0.5),
(0.5, 0.5, 0.5)),
]))
else:
raise NotImplementedError("No such dataset {}".format(
params.dataset))
assert self.dataset
self.dataloader = torch.utils.data.DataLoader(
self.dataset,
batch_size=params.batchSize,
shuffle=True,
num_workers=int(params.workers))
# some hyper parameters
self.ngpu = int(params.ngpu)
self.nz = int(params.nz)
self.ngf = int(params.ngf)
self.ndf = int(params.ndf)
self.num_classes = int(params.num_classes)
self.nc = 3
# Define the generator and initialize the weights
if params.dataset == 'imagenet':
self.netG = _netG(self.ngpu, self.nz)
else:
self.netG = _netG_CIFAR10(self.ngpu, self.nz)
self.netG.apply(weights_init)
if params.netG != '':
self.netG.load_state_dict(torch.load(params.netG))
print(self.netG)
# Define the discriminator and initialize the weights
if params.dataset == 'imagenet':
self.netD = _netD(self.ngpu, self.num_classes)
else:
self.netD = _netD_CIFAR10(self.ngpu, self.num_classes)
self.netD.apply(weights_init)
if params.netD != '':
self.netD.load_state_dict(torch.load(params.netD))
print(self.netD)
# loss functions
self.dis_criterion = nn.BCELoss()
self.aux_criterion = nn.NLLLoss()
# tensor placeholders
self.input = torch.FloatTensor(params.batchSize, 3, params.imageSize,
params.imageSize)
self.noise = torch.FloatTensor(params.batchSize, self.nz, 1, 1)
self.eval_noise = torch.FloatTensor(params.batchSize, self.nz, 1,
1).normal_(0, 1)
self.dis_label = torch.FloatTensor(params.batchSize)
self.aux_label = torch.LongTensor(params.batchSize)
self.real_label = 1
self.fake_label = 0
# if using cuda
if params.cuda:
self.netD.cuda()
self.netG.cuda()
self.dis_criterion.cuda()
self.aux_criterion.cuda()
self.input, self.dis_label, self.aux_label = self.input.cuda(
), self.dis_label.cuda(), self.aux_label.cuda()
self.noise, self.eval_noise = self.noise.cuda(
), self.eval_noise.cuda()
# define variables
self.input = Variable(self.input)
self.noise = Variable(self.noise)
self.eval_noise = Variable(self.eval_noise)
self.dis_label = Variable(self.dis_label)
self.aux_label = Variable(self.aux_label)
# noise for evaluation
self.eval_noise_ = np.random.normal(0, 1, (params.batchSize, self.nz))
self.eval_label = np.random.randint(0, self.num_classes,
params.batchSize)
self.eval_onehot = np.zeros((params.batchSize, self.num_classes))
self.eval_onehot[np.arange(params.batchSize), self.eval_label] = 1
self.eval_noise_[np.arange(params.batchSize), :self.
num_classes] = self.eval_onehot[np.arange(
params.batchSize)]
self.eval_noise_ = (torch.from_numpy(self.eval_noise_))
self.eval_noise.data.copy_(
self.eval_noise_.view(params.batchSize, self.nz, 1, 1))
# setup optimizer
self.optimizerD = optim.Adam(self.netD.parameters(),
lr=params.lr,
betas=(params.beta1, 0.999))
self.optimizerG = optim.Adam(self.netG.parameters(),
lr=params.lr,
betas=(params.beta1, 0.999))
def main_worker(args):
global best_acc1
args.device = torch.device(
'cuda:{}'.format(args.gpu) if torch.cuda.is_available() else 'cpu')
if args.pretrained:
print('=> using pre-trained model')
model = init_encoder_model(args.embed_size, args.pretrained)
else:
print('=> creating model')
model = init_encoder_model(args.embed_size, args.pretrained)
model = model.to(args.device)
# print("Params to learn:")
# model_params = []
# for name, param in dict(model.named_parameters()).items():
# if name.find("bias") > -1:
# print('Model Layer {} will be trained @ {}'.format(name, args.lr*2))
# model_params.append({'params': param, 'lr': args.lr*2, 'weight_decay': 0})
# else:
# print('Model Layer {} will be trained @ {}'.format(name, args.lr))
# model_params.append({'params': param, 'lr': args.lr, 'weight_decay': args.weight_decay})
e_weights = torch.FloatTensor(train_label_emb())
label_model = nn.Embedding.from_pretrained(e_weights)
label_model = label_model.to(args.device)
optimizer = optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().to(args.device)
# 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)
args.start_epoch = checkpoint['epoch']
best_acc1 = checkpoint['best_acc1']
if args.gpu is not None:
# best_acc1 may be from a checkpoint from a different GPU
best_acc1 = best_acc1.to(args.gpu)
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))
# Data loading code
data_transforms = {
'train':
transforms.Compose([
transforms.RandomResizedCrop(args.input_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'val':
transforms.Compose([
transforms.Resize(args.input_size),
transforms.CenterCrop(args.input_size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
print("Initializing Datasets and Dataloaders...")
# Create training and validation datasets
image_datasets = {
x: ImageFolder(os.path.join(args.data, x), data_transforms[x])
for x in ['train', 'val']
}
# Create training and validation dataloaders
dataloaders_dict = {
x: torch.utils.data.DataLoader(image_datasets[x],
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
for x in ['train', 'val']
}
if args.evaluate:
validate(dataloaders_dict['val'], model, criterion, args)
return
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args)
# train for one epoch
train(dataloaders_dict['train'], model, label_model, criterion,
optimizer, epoch, args)
# evaluate on validation set
acc1 = validate(dataloaders_dict['val'], model, label_model, criterion,
args)
# remember best acc@1 and save checkpoint
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer': optimizer.state_dict(),
}, is_best)
############# DataLoader ######################
if opt.dataset == 'lsun':
dataset = dset.LSUN(db_path=opt.dataroot,
classes=['bedroom_train'],
transform=transforms.Compose([
transforms.Scale(opt.imageSize),
transforms.CenterCrop(opt.imageSize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5)),
]))
elif opt.dataset == 'CelebA':
dataset = ImageFolder(root=opt.dataroot,
transform=transforms.Compose([
transforms.Resize(opt.imageSize),
transforms.CenterCrop(opt.imageSize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5)),
]))
assert dataset
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=opt.batchSize,
shuffle=True,
num_workers=int(opt.workers))
nz = int(opt.nz)
ngf = int(opt.ngf)
ndf = int(opt.ndf)
nc = int(opt.nc)
args = parser.parse_args()
data_dir = os.path.join('.', args.dataset)
print(args.dataset)
train_transform = transforms.Compose([transforms.ToTensor()])
if args.dataset == "cifar10":
train_set = torchvision.datasets.CIFAR10(root=data_dir, train=True, download=True, transform=train_transform)
print(type(train_set.train_data))
print(train_set.train_data.shape)
print(train_set.__getitem__(0)[0].size())
print(train_set.train_data.mean(axis=(0, 1, 2))/255)
print(train_set.train_data.std(axis=(0, 1, 2))/255)
train_set = ImageFolder("cifar10/images/train", transform=train_transform)
image_tensor = torch.stack([x for x, y in train_set])
image_tensor = image_tensor.numpy()
print(image_tensor.shape)
print(train_set.__getitem__(0)[0].size())
print(image_tensor.mean(axis=(0, 2, 3)))
print(image_tensor.std(axis=(0, 2, 3)))
elif args.dataset == "cifar100":
train_set = torchvision.datasets.CIFAR100(root=data_dir, train=True, download=True, transform=train_transform)
print(train_set.train_data.shape)
print(np.mean(train_set.train_data, axis=(0, 1, 2))/255)
print(np.std(train_set.train_data, axis=(0, 1, 2))/255)
elif args.dataset == "mnist":
train_set = torchvision.datasets.MNIST(root=data_dir, train=True, download=True, transform=train_transform)
'val': transforms.Compose(transform_val_list),
'satellite': transforms.Compose(transform_satellite_list)}
train_all = ''
if opt.train_all:
train_all = '_all'
image_datasets = {}
image_datasets['satellite'] = datasets.ImageFolder(os.path.join(data_dir, 'satellite'),
data_transforms['satellite'])
image_datasets['street'] = datasets.ImageFolder(os.path.join(data_dir, 'street'),
data_transforms['train'])
image_datasets['drone'] = datasets.ImageFolder(os.path.join(data_dir, 'drone'),
data_transforms['train'])
image_datasets['google'] = ImageFolder(os.path.join(data_dir, 'google'),
# google contain empty subfolder, so we overwrite the Folder
data_transforms['train'])
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=opt.batchsize,
shuffle=True, num_workers=2, pin_memory=True) # 8 workers may work faster
for x in ['satellite', 'street', 'drone', 'google']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['satellite', 'street', 'drone', 'google']}
class_names = image_datasets['street'].classes
print(dataset_sizes)
use_gpu = torch.cuda.is_available()
######################################################################
# Training the model
# ------------------
#
# Now, let's write a general function to train a model. Here, we will
transforms.Scale(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
data_dir = 'hymenoptera_data'
train_dataset = datasets.ImageFolder(os.path.join(data_dir, 'train'),
transform=transform_train)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True)
train_infl = ImageFolder(os.path.join(data_dir, 'train'),
transform=transform_train)
loader_hess = DataLoader(train_infl,
batch_size=args.batch_size,
shuffle=True,
num_workers=10)
loader_infl = DataLoader(train_infl, batch_size=1, shuffle=False)
val_dataset = ImageFolder(os.path.join(data_dir, 'val'),
transform=transform_val)
val_loader = DataLoader(val_dataset, batch_size=1, shuffle=False)
file_time = time.strftime('%Y-%m-%d-%H:%M:%S')
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
# specify the gpu id if using only 1 gpu
if opt.ngpu == 1:
os.environ['CUDA_VISIBLE_DEVICES'] = str(opt.gpu_id)
if opt.cuda:
torch.cuda.manual_seed_all(opt.manualSeed)
cudnn.benchmark = True
# dataset
dataset = ImageFolder(
root=opt.dataroot,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batchSize,
shuffle=True, num_workers=int(opt.workers))
# model
netVAE = VAE()
criterion = CustomLoss(3e-6)
optimizer = optim.Adam(netVAE.parameters(), lr=0.0001, betas=(0.5, 0.999))
if opt.cuda:
netVAE.cuda()
def make_dir(dir):
try:
os.mkdir(dir)
except Exception as e:
pass
import numpy as np
if __name__ == '__main__':
data_root = config.dataset
landmark_save_root = config.save
landmark_save_dir = landmark_save_root + '/{}'
landmark_save_path = landmark_save_dir + '/{}'
dataset = ImageFolder(root=data_root)
dataloader = data.DataLoader(dataset,
batch_size=4,
shuffle=False,
num_workers=0,
collate_fn=collate_fn,
pin_memory=False)
print('data loaded')
print(len(dataset))
make_dir(landmark_save_root)
maked_label = {}
for i, it in enumerate(dataloader):
for image, label, fname in it:
fname = fname.split('.')[0] + '.png'
def get_dataset(args):
### explicitly set flip = True #######
if args.dataset == "cityscape":
# if 'Det' in args.frame_disc_model or 'Det' in args.video_disc_model or args.frame_det_disc or args.video_det_disc:
clip_file = "/data/linz/proj/Dataset/Cityscape/load_files/int_{}_len_3_max_{}bb_area_3000_extra_panet_lsclip.pkl".format(
int(args.interval), int(args.num_track_per_img))
# if not args.track_gen and args.split == 'val':
# clip_file = "/data/linz/proj/Dataset/Cityscape/load_files/int_{}_len_3_extra_lsclip.pkl".format(int(args.interval))
obj_coord_file = "/data/linz/proj/Dataset/Cityscape/obj_coords/int_{}_len_3_extra_512x1024_max_{}bb_area_3000_panet_lsclip.pkl".format(
int(args.interval), int(args.num_track_per_img))
if args.syn_type == 'extra' and args.vid_length != 1:
clip_file = "/data/linz/proj/Dataset/Cityscape/load_files/int_{}_len_{}_extra_lsclip.pkl".format(
int(args.interval), args.vid_length + 2)
if args.effec_flow:
clip_file = "/data/linz/proj/Dataset/Cityscape/load_files/effec_flow_int_{}_len_3_extra_lsclip.pkl".format(
int(args.interval))
import pickle
with open(clip_file, 'rb') as f:
load_f = pickle.load(f)
if args.split == 'train':
clips_train_file = load_f['train']
elif args.split == 'val':
clips_val_file = load_f['val']
with open(obj_coord_file, 'rb') as f:
load_f = pickle.load(f)
if args.split == 'train':
coords_train_file = load_f['train']
if args.split == 'val':
coords_val_file = load_f['val']
# else:
# coords_val_file = None
crop_size = (args.input_h, args.input_w)
if args.split == 'train':
# train
tfs = []
tfs.append(
transforms.Compose(
[ #transforms.Resize(re_size, interpolation=Image.BILINEAR),
transforms.RandomCrop(crop_size)
]))
# tfs.append(transforms.Compose([ transforms.Resize((150, 300), interpolation=Image.NEAREST),
# transforms.RandomCrop((128, 256)) ]))
tfs.append(
transforms.Compose(
[ #transforms.Resize((150, 300), interpolation=Image.NEAREST),
transforms.RandomCrop(crop_size)
]))
train_dataset = ImageFolder(args,
clips_train_file,
transform=tfs,
bboxes=coords_train_file)
else:
train_dataset = None
if args.split == 'val':
# val
tfs = []
tfs.append(
transforms.Compose(
[ #transforms.Resize(crop_size, interpolation=Image.BILINEAR)
]))
tfs.append(
transforms.Compose(
[ #transforms.Resize((128, 256), interpolation=Image.NEAREST)
]))
val_dataset = ImageFolder(args,
clips_val_file,
transform=tfs,
bboxes=coords_val_file)
else:
val_dataset = None
elif args.dataset == "ucf101":
clip_file = "/data/linz/proj/Dataset/CyclicGen-master/UCF101_test_root_clip.pkl"
with open(clip_file, 'rb') as f:
import pickle
load_f = pickle.load(f)
clips_val_file = load_f['test']
re_size = (256, 256)
crop_size = (256, 256)
train_dataset = None
# val
tfs = []
tfs.append(
transforms.Compose(
[transforms.Resize(crop_size, interpolation=Image.BILINEAR)]))
tfs.append(
transforms.Compose(
[transforms.Resize((256, 256), interpolation=Image.NEAREST)]))
val_dataset = ImageFolder(args, clips_val_file, transform=tfs)
# val_dataset = ImageFolder(args, clips_val_file,transform=None)
elif args.dataset == 'vimeo':
clip_train_file = '/data/linz/proj/Dataset/vimeo_triplet/tri_trainlist.txt'
clip_val_file = '/data/linz/proj/Dataset/vimeo_triplet/tri_testlist.txt'
clips_file = {'train': [], 'val': []}
with open(clip_train_file, 'r') as f:
for line in f:
line = line.strip()
if len(line) < 4:
break
clips_file['train'].append(line)
with open(clip_val_file, 'r') as f:
for line in f:
line = line.strip()
if len(line) < 4:
break
clips_file['val'].append(line)
# crop_size = (128, 224)
# train
tfs = []
tfs.append(
transforms.Compose(
[ #transforms.Resize(re_size, interpolation=Image.BILINEAR),
# transforms.RandomCrop(crop_size)
]))
# tfs.append(transforms.Compose([ transforms.Resize((150, 300), interpolation=Image.NEAREST),
# transforms.RandomCrop((128, 256)) ]))
tfs.append(
transforms.Compose(
[ #transforms.Resize((150, 300), interpolation=Image.NEAREST),
# transforms.RandomCrop(crop_size)
]))
train_dataset = ImageFolder(args, clips_file['train'], transform=tfs)
# val
tfs = []
tfs.append(
transforms.Compose(
[ #transforms.Resize(crop_size, interpolation=Image.BILINEAR)
]))
tfs.append(
transforms.Compose(
[ #transforms.Resize((128, 256), interpolation=Image.NEAREST)
]))
val_dataset = ImageFolder(args, clips_file['val'], transform=tfs)
else:
raise Exception('Invalid dataset %s' % args.dataset)
return train_dataset, val_dataset
def execute(execution_path, writer):
print(cudnn.benchmark)
print(cudnn.deterministic)
if args.dataset == "mnist":
args.number_of_dataset_classes = 10
dataset_path = args.dataset_dir if args.dataset_dir else "../datasets/mnist/images"
normalize = transforms.Normalize(mean=[0.1307], std=[0.3081])
train_transform = transforms.Compose(
[transforms.ToTensor(), normalize])
inference_transform = transforms.Compose([transforms.ToTensor(), normalize])
elif args.dataset == "cifar10":
args.number_of_dataset_classes = 10
dataset_path = args.dataset_dir if args.dataset_dir else "../datasets/cifar10/images"
normalize = transforms.Normalize(mean=[0.491, 0.482, 0.446], std=[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])
elif args.dataset == "cifar100":
args.number_of_dataset_classes = 100
dataset_path = args.dataset_dir if args.dataset_dir else "../datasets/cifar100/images"
normalize = transforms.Normalize(mean=[0.507, 0.486, 0.440], std=[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])
else:
args.number_of_dataset_classes = 1000
dataset_path = args.dataset_dir if args.dataset_dir else "../datasets/imagenet2012/images"
if args.arch.startswith('inception'):
size = (299, 299)
else:
size = (224, 256)
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
train_transform = transforms.Compose(
[transforms.RandomSizedCrop(size[0]), # 224 , 299
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), normalize])
inference_transform = transforms.Compose(
[transforms.Scale(size[1]), # 256
transforms.CenterCrop(size[0]), # 224 , 299
transforms.ToTensor(), normalize])
args.normal_classes = sorted(random.sample(range(0, args.number_of_dataset_classes), args.number_of_model_classes))
print("NORMAL CLASSES:\t", args.normal_classes)
train_path = os.path.join(dataset_path, 'train')
test_path = os.path.join(dataset_path, 'val')
# Creating sets...
train_set = ImageFolder(train_path, transform=train_transform, selected_classes=args.normal_classes,
target_transform=args.normal_classes.index)
val_set = ImageFolder(train_path, transform=inference_transform, selected_classes=args.normal_classes,
target_transform=args.normal_classes.index)
# Preparing train and validation samplers...
total_examples = {}
for index in range(len(train_set)):
_, label = train_set[index]
if label not in total_examples:
total_examples[label] = 1
else:
total_examples[label] += 1
train_indexes = []
val_indexes = []
train_indexes_count = {}
val_indexes_count = {}
indexes_count = {}
for index in range(len(train_set)):
_, label = train_set[index]
if label not in indexes_count:
indexes_count[label] = 1
train_indexes.append(index)
train_indexes_count[label] = 1
val_indexes_count[label] = 0
else:
indexes_count[label] += 1
if indexes_count[label] <= int(total_examples[label] * args.train_set_split):
train_indexes.append(index)
train_indexes_count[label] += 1
else:
val_indexes.append(index)
val_indexes_count[label] += 1
print("TRAIN SET INDEXES TOTALS:", train_indexes_count)
print("VALID SET INDEXES TOTALS:", val_indexes_count)
train_sampler = SubsetRandomSampler(train_indexes)
val_sampler = SubsetRandomSampler(val_indexes)
# Create loaders...
train_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batch_size, num_workers=args.workers,
pin_memory=True, sampler=train_sampler)
val_loader = torch.utils.data.DataLoader(val_set, batch_size=args.batch_size, num_workers=args.workers,
pin_memory=True, sampler=val_sampler)
print("\n$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$")
print("TRAINSET LOADER SIZE: ====>>>> ", len(train_loader.sampler))
print("VALIDSET LOADER SIZE: ====>>>> ", len(val_loader.sampler))
print("$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$")
# Dataset created...
print("\nDATASET:", args.dataset)
# create model
model = create_model()
print("\nMODEL:", model)
best_train_acc1 = 0
best_val_acc1 = 0
if args.train:
###################
# Training...
###################
# define loss function (criterion)...
criterion = nn.CrossEntropyLoss().cuda()
# define optimizer...
optimizer = torch.optim.SGD(model.parameters(), lr=args.original_learning_rate, momentum=args.momentum,
weight_decay=args.weight_decay, nesterov=True)
# define scheduler...
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'max', patience=10, factor=0.2, verbose=True)
print("\n################ TRAINING ################")
best_model_file_path = os.path.join(execution_path, 'best_model.pth.tar')
best_train_acc1, best_val_acc1 = train_val(train_loader, val_loader, model, criterion, optimizer,
scheduler, args.epochs, writer, best_model_file_path)
# save to json file
writer.export_scalars_to_json(os.path.join(execution_path, 'log.json'))
if args.extract_logits:
######################
# Extracting logits...
######################
# Train dataset uses val transform to extract...
train_set = ImageFolder(train_path, transform=inference_transform, selected_classes=args.normal_classes)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=args.batch_size, num_workers=args.workers,
pin_memory=True, sampler=train_sampler)
val_set = ImageFolder(train_path, transform=inference_transform, selected_classes=args.normal_classes)
val_loader = torch.utils.data.DataLoader(val_set, batch_size=args.batch_size, num_workers=args.workers,
pin_memory=True, sampler=val_sampler)
test_set = ImageFolder(test_path, transform=inference_transform)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=args.batch_size, num_workers=args.workers,
pin_memory=True, shuffle=True)
print("\n################ EXTRACTING LOGITS ################")
best_model_file_path = os.path.join(execution_path, 'best_model.pth.tar')
extract_logits_from_file(best_model_file_path, model, args.number_of_model_classes, execution_path,
train_loader, val_loader, test_loader, "best_model")
return best_val_acc1, best_train_acc1
<h1>Results</h1>
{}
'''.format(full_filename, " ".join(html_imgs))
return html
else:
html = '''
<!doctype html>
<title>Upload new File</title>
<h1>Upload new File</h1>
<form method=post enctype=multipart/form-data>
<input type=file name=file>
<input type=submit value=Upload>
</form>
'''
return html
if __name__ == '__main__':
data = ImageFolder('./Flickr', transform=TRANSFORM)
kwargs = {'num_workers': 1, 'pin_memory': True} if CUDA else {}
data_loader = torch.utils.data.DataLoader(data, batch_size=BATCH_SIZE, **kwargs)
search_engine = SearchEngine(data, cuda = CUDA, threshold = THRESHOLD, save_directory = SAVE_DIRECTORY, transform=TRANSFORM)
search_engine.fit(data_loader = data_loader, load_embeddings = True, verbose = True)
app.debug = False
app.run(
host=os.getenv('LISTEN', '0.0.0.0'),
port=int(os.getenv('PORT', '80'))
)
pbar.update(1)
im, ann, im_fns = data[0], data[1], data[2]
im = im.cuda()
feat = model(im)
x = feat.cpu().numpy()
y = ann.numpy()
precision = precision + clf.score(x, y)
pbar.close()
print("\nAverage Precision is {}".format(precision / len(loader)))
if __name__ == '__main__':
icdar_patches = "./data/icdar2015/patches"
dataset = ImageFolder(root=icdar_patches, transform=transforms)
loader = torch.utils.data.DataLoader(dataset, batch_size=20, shuffle=True)
# extractor for deep features
model = Extractor()
model = model.cuda()
for params in model.parameters():
params.requires_grad = False
# vanilla svm
clf = svm.SVC(kernel="rbf", gamma=10)
train(loader=loader, model=model, clf=clf)
cudnn.benchmark = True
if torch.cuda.is_available() and not opt.cuda:
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda"
)
# datase t
if opt.dataset == 'imagenet':
# folder dataset
dataset = ImageFolder(root=opt.dataroot,
transform=transforms.Compose([
transforms.Scale(opt.imageSize),
transforms.CenterCrop(opt.imageSize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5)),
]),
classes_idx=(10, 20))
elif opt.dataset == 'AWA':
# folder dataset
dataset = dset.ImageFolder(root=opt.dataroot,
transform=transforms.Compose([
transforms.Scale(opt.imageSize),
transforms.CenterCrop(opt.imageSize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5)),
]))
elif opt.dataset == 'CUB':
def extract(self, input_path, output_path):
'''extract features from single image without batch process.
'''
assert self.mode.lower() in ['pca', 'delf']
batch_timer = AverageMeter()
data_timer = AverageMeter()
since = time.time()
# dataloader.
dataset = ImageFolder(
root = input_path,
transform = transforms.ToTensor())
self.dataloader = torch.utils.data.DataLoader(
dataset = dataset,
batch_size = 1,
shuffle = True,
num_workers = 0)
feature_maps = []
if self.mode.lower() in ['pca']:
bar = Bar('[{}]{}'.format(self.mode.upper(), self.title), max=len(self.dataloader))
for batch_idx, (inputs, _, filename) in enumerate(self.dataloader):
# image size upper limit.
if not (len(inputs.size()) == 4):
if __DEBUG__:
print('wrong input dimenstion! ({},{})'.format(filename, input.size()))
continue;
if not (inputs.size(2)*inputs.size(3) <= 1200*1200):
if __DEBUG__:
print('passed: image size too large! ({},{})'.format(filename, inputs.size()))
continue;
if not (inputs.size(2) >= 112 and inputs.size(3) >= 112):
if __DEBUG__:
print('passed: image size too small! ({},{})'.format(filename, inputs.size()))
continue;
data_timer.update(time.time() - since)
# prepare inputs
if __is_cuda__():
inputs = __cuda__(inputs)
inputs = __to_var__(inputs)
# get delf feature only for pca calculation.
pca_feature = self.__extract_delf_feature__(inputs.data, filename, mode='pca')
if pca_feature is not None:
feature_maps.extend(pca_feature)
batch_timer.update(time.time() - since)
since = time.time()
# progress
log_msg = ('\n[Extract][Processing:({batch}/{size})] '+ \
'eta: (data:{data:.3f}s),(batch:{bt:.3f}s),(total:{tt:})') \
.format(
batch=batch_idx + 1,
size=len(self.dataloader),
data=data_timer.val,
bt=batch_timer.val,
tt=bar.elapsed_td)
print(log_msg)
bar.next()
print('\nnumber of selected features so far: {}'.format(len(feature_maps)))
if len(feature_maps) >= 10000000: # UPPER LIMIT.
break;
# free GPU cache every.
if batch_idx % 10 == 0:
torch.cuda.empty_cache()
if __DEBUG__:
print('GPU Memory flushed !!!!!!!!!')
# trian PCA.
self.pca(feature_maps)
else:
bar = Bar('[{}]{}'.format(self.mode.upper(), self.title), max=len(self.dataloader))
assert self.mode.lower() in ['delf']
feature_maps = []
for batch_idx, (inputs, labels, filename) in enumerate(self.dataloader):
# image size upper limit.
if not (len(inputs.size()) == 4):
if __DEBUG__:
print('wrong input dimenstion! ({},{})'.format(filename, input.size()))
continue;
if not (inputs.size(2)*inputs.size(3) <= 1200*1200):
if __DEBUG__:
print('passed: image size too large! ({},{})'.format(filename, inputs.size()))
continue;
if not (inputs.size(2) >= 112 and inputs.size(3) >= 112):
if __DEBUG__:
print('passed: image size too small! ({},{})'.format(filename, inputs.size()))
continue;
data_timer.update(time.time() - since)
# prepare inputs
if __is_cuda__():
inputs = __cuda__(inputs)
inputs = __to_var__(inputs)
# get delf everything (score, feature, etc.)
delf_feature = self.__extract_delf_feature__(inputs.data, filename, mode='delf')
if delf_feature is not None:
feature_maps.append(delf_feature)
# log.
batch_timer.update(time.time() - since)
since = time.time()
log_msg = ('\n[Extract][Processing:({batch}/{size})] '+ \
'eta: (data:{data:.3f}s),(batch:{bt:.3f}s),(total:{tt:})') \
.format(
batch=batch_idx + 1,
size=len(self.dataloader),
data=data_timer.val,
bt=batch_timer.val,
tt=bar.elapsed_td)
print(log_msg)
bar.next()
# free GPU cache every.
if batch_idx % 10 == 0:
torch.cuda.empty_cache()
if __DEBUG__:
print('GPU Memory flushed !!!!!!!!!')
# use pickle to save DeLF features.
self.__save_delf_features_to_file__(feature_maps, output_path)
'val': transforms.Compose(transform_val_list),
'satellite': transforms.Compose(transform_satellite_list)
}
train_all = ''
if opt.train_all:
train_all = '_all'
image_datasets = {}
image_datasets['satellite'] = datasets.ImageFolder(
os.path.join(data_dir, 'satellite'), data_transforms['satellite'])
image_datasets['street'] = datasets.ImageFolder(
os.path.join(data_dir, 'street'), data_transforms['train'])
image_datasets['drone'] = datasets.ImageFolder(
os.path.join(data_dir, 'drone3'), data_transforms['train'])
image_datasets['google'] = ImageFolder(os.path.join(data_dir, 'google'),
data_transforms['train'])
dataloaders = {
x:
torch.utils.data.DataLoader(image_datasets[x],
batch_size=opt.batchsize,
shuffle=True,
num_workers=2,
pin_memory=True) # 8 workers may work faster
for x in ['satellite', 'street', 'drone', 'google']
}
dataset_sizes = {
x: len(image_datasets[x])
for x in ['satellite', 'street', 'drone', 'google']
}
class_names = image_datasets['street'].classes
testtransformList.append(transforms.Resize(transResize))
testtransformList.append(transforms.CenterCrop(transCrop))
testtransformList.append(transforms.ToTensor())
testtransformList.append(normalize)
testtransformSequence = transforms.Compose(testtransformList)
confusion_meter = tnt.meter.ConfusionMeter(args.n_class, normalized=True)
if args.use_gpu:
os.environ["CUDA_VISIBLE_DEVICES"] = "{}".format(args.device)
torch.cuda.manual_seed(args.random_seed)
kwargs = {'num_workers': 4, 'pin_memory': True}
if args.is_train:
train_dataset = ImageFolder(os.path.join(args.data_dir, 'train'),
traintransformSequence)
val_dataset = ImageFolder(os.path.join(args.data_dir, 'valid'),
traintransformSequence)
# train_dataset, val_dataset = get_MNIST_train_val_dataset('./data/MNIST')
train_loader, val_loader = get_train_val_loader(
train_dataset,
val_dataset,
val_split=args.val_split,
random_split=args.random_split,
batch_size=args.batch_size,
**kwargs)
args.num_class = train_loader.dataset.num_class
args.num_channels = train_loader.dataset.num_channels
else:
test_dataset = ImageFolder(os.path.join(args.data_dir, 'test'),
