def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
BaseDataset.__init__(self, opt)
# self.dir_A = os.path.join(opt.dataroot, opt.phase + 'B') # create a path '/path/to/data/trainA'
self.dir_C = os.path.join(opt.dataroot, 'C/' + opt.phase)
self.dir_E = os.path.join(opt.dataroot, 'E/' + opt.phase)
self.dir_M = os.path.join(opt.dataroot, 'M/' + opt.phase)
self.dir_B = os.path.join(
opt.dataroot,
'B/' + opt.phase) # create a path '/path/to/data/trainB'
# self.A_paths = sorted(make_dataset(self.dir_A, opt.max_dataset_size)) # load images from '/path/to/data/trainA'
self.C_paths = sorted(make_dataset(self.dir_C, opt.max_dataset_size))
self.E_paths = sorted(make_dataset(self.dir_E, opt.max_dataset_size))
self.M_paths = sorted(make_dataset(self.dir_M, opt.max_dataset_size))
self.B_paths = sorted(make_dataset(
self.dir_B,
opt.max_dataset_size)) # load images from '/path/to/data/trainB'
# self.A_size = len(self.A_paths) # get the size of dataset B
self.C_size = len(self.C_paths)
self.E_size = len(self.E_paths)
self.M_size = len(self.M_paths)
self.B_size = len(self.B_paths) # get the size of dataset ori
btoA = self.opt.direction == 'BtoA'
input_nc = self.opt.output_nc if btoA else self.opt.input_nc # get the number of channels of input image
output_nc = self.opt.input_nc if btoA else self.opt.output_nc # get the number of channels of output image
# self.transform_A = get_transform(self.opt, grayscale=(input_nc == 1))
self.transform_C = get_transform(self.opt, grayscale=False)
self.transform_E = get_transform(self.opt, grayscale=True)
self.transform_M = get_transform(self.opt, grayscale=True)
self.transform_B = get_transform(self.opt, grayscale=(output_nc == 1))
def __init__(self, root, transform=None):
super(ilab_sup_imgfolder, self).__init__()
self.root = root
self.transform = transform
self.dir_Appe = os.path.join(self.root, 'appearance')
self.Appe_paths = make_dataset(self.dir_Appe)
self.Appe_size = len(self.Appe_paths)
def __init__(self, root, transform=None):
super(ilab_threeswap_imgfolder, self).__init__()
self.root = root
self.transform = transform
self.paths = make_dataset(self.root)
self.C_size = len(self.paths)
self.id_dict = {}
self.bg_dict = {}
self.pose_dict = {}
self.id_cnt = 0
self.bg_cnt = 0
self.pose_cnt = 0
for roots, dirs, files in os.walk('/home2/ilab2M_pose/train_img_c00_10class'):
for file in files:
category = file.split('-')[0]
id = file.split('-')[1]
background = file.split('-')[2]
pose = file.split('-')[3] + file.split('-')[4]
if id not in self.id_dict:
self.id_dict[id] = self.id_cnt
self.id_cnt += 1
if background not in self.bg_dict:
self.bg_dict[background] = self.bg_cnt
self.bg_cnt += 1
if pose not in self.pose_dict:
self.pose_dict[pose] = self.pose_cnt
self.pose_cnt += 1
def __init__(self, root, transform=None):
super(ilab_threeswap_imgfolder, self).__init__()
self.root = root
self.transform = transform
self.idf_dict = {}
self.exp_dict = {}
self.posef_dict = {}
self.idf_cnt = 0
self.exp_cnt = 0
self.posef_cnt = 0
for roots, dirs, files in os.walk('/home2/RaFD/train/data/'):
for file in files:
idf = file.split('_')[0]
expression = file.split('_')[2].split('.')[0]
posef = file.split('_')[1]
if idf not in self.idf_dict:
self.idf_dict[idf] = self.idf_cnt
self.idf_cnt += 1
if expression not in self.exp_dict:
self.exp_dict[expression] = self.exp_cnt
self.exp_cnt += 1
if posef not in self.posef_dict:
self.posef_dict[posef] = self.posef_cnt
self.posef_cnt += 1
print(root)
self.paths = make_dataset(self.root)
file = open('debug.txt', 'w')
file.write(str(self.paths))
file.close()
self.C_size = len(self.paths) - 1
print(self.C_size)
def __init__(self, mode='Train'):
super(SiameseDataset, self).__init__()
self.mode = mode
self.train_dir = './data/photo_parse_train/'
self.test_dir = './data/photo_parse_test/'
self.cari_dir = './data/caricature_parse/'
self.train_path = make_dataset(self.train_dir)
self.train_paths = sorted(self.train_path)
self.train_dict = {}
for img_name in self.train_paths:
person_name = img_name.split('/')[-1]
person_name = person_name[:-11]
if person_name not in self.train_dict.keys():
self.train_dict[person_name] = [img_name]
else:
self.train_dict[person_name].append(img_name)
self.train_size = len(self.train_path)
self.test_path = make_dataset(self.test_dir)
self.test_paths = sorted(self.test_path)
self.test_size = len(self.test_path)
self.cari_path = make_dataset(self.cari_dir)
self.cari_paths = sorted(self.cari_path)
self.cari_dict = {}
for img_name in self.cari_paths:
person_name = img_name.split('/')[-1]
person_name = person_name[:-11]
if person_name not in self.cari_dict.keys():
self.cari_dict[person_name] = [img_name]
else:
self.cari_dict[person_name].append(img_name)
self.cari_size = len(self.cari_path)
transform_list = []
transform_list += [transforms.ToTensor()]
self.transform = transforms.Compose(transform_list)
self.target_transform = self.transform
if self.mode == 'Val':
self.train_size = self.test_size
self.train_path = self.test_path
self.train_paths = self.test_paths
def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
BaseDataset.__init__(self, opt)
self.dir_AB = os.path.join(opt.dataroot, opt.phase) # get the image directory
self.AB_paths = sorted(make_dataset(self.dir_AB, opt.max_dataset_size)) # get image paths
assert(self.opt.load_size >= self.opt.crop_size) # crop_size should be smaller than the size of loaded image
self.input_nc = self.opt.output_nc if self.opt.direction == 'BtoA' else self.opt.input_nc
self.output_nc = self.opt.input_nc if self.opt.direction == 'BtoA' else self.opt.output_nc
def __init__(self, args):
self.args = args
self.data_path = args.data_path
self.dir_AB = os.path.join(args.data_path, args.mode) # ???
self.AB_paths = sorted(make_dataset(self.dir_AB))
assert (agrs.resize_or_crop == 'resize_and_crop')
transform_list = [
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]
self.transform = transforms.Compose(transform_list)
def __init__(self, root, transform=None):
super(ilab_unsup_imgfolder, self).__init__()
self.root = root
self.transform = transform
self.paths = make_dataset(self.root)
self.C_size = len(self.paths)
def __init__(self, dataroot, csv, batch_size=40):
self.batchSize = batch_size
self.root = dataroot
self.frame = pd.read_csv(csv, header=None)
self.image_paths = sorted(make_dataset(self.root))
self.dataset_size = len(self.image_paths)
