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miunangel
2025-08-16 20:46:31 +08:00
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Dassl.ProGrad.pytorch/dassl/data/datasets/da/__init__.py Normal file
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from .digit5 import Digit5
from .visda17 import VisDA17
from .cifarstl import CIFARSTL
from .office31 import Office31
from .domainnet import DomainNet
from .office_home import OfficeHome
from .mini_domainnet import miniDomainNet

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Dassl.ProGrad.pytorch/dassl/data/datasets/da/cifarstl.py Normal file
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import os.path as osp
from dassl.utils import listdir_nohidden
from ..build import DATASET_REGISTRY
from ..base_dataset import Datum, DatasetBase
@DATASET_REGISTRY.register()
class CIFARSTL(DatasetBase):
"""CIFAR-10 and STL-10.
CIFAR-10:
- 60,000 32x32 colour images.
- 10 classes, with 6,000 images per class.
- 50,000 training images and 10,000 test images.
- URL: https://www.cs.toronto.edu/~kriz/cifar.html.
STL-10:
- 10 classes: airplane, bird, car, cat, deer, dog, horse,
monkey, ship, truck.
- Images are 96x96 pixels, color.
- 500 training images (10 pre-defined folds), 800 test images
per class.
- URL: https://cs.stanford.edu/~acoates/stl10/.
Reference:
- Krizhevsky. Learning Multiple Layers of Features
from Tiny Images. Tech report.
- Coates et al. An Analysis of Single Layer Networks in
Unsupervised Feature Learning. AISTATS 2011.
"""
dataset_dir = "cifar_stl"
domains = ["cifar", "stl"]
def __init__(self, cfg):
root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
self.dataset_dir = osp.join(root, self.dataset_dir)
self.check_input_domains(
cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS
)
train_x = self._read_data(cfg.DATASET.SOURCE_DOMAINS, split="train")
train_u = self._read_data(cfg.DATASET.TARGET_DOMAINS, split="train")
test = self._read_data(cfg.DATASET.TARGET_DOMAINS, split="test")
super().__init__(train_x=train_x, train_u=train_u, test=test)
def _read_data(self, input_domains, split="train"):
items = []
for domain, dname in enumerate(input_domains):
data_dir = osp.join(self.dataset_dir, dname, split)
class_names = listdir_nohidden(data_dir)
for class_name in class_names:
class_dir = osp.join(data_dir, class_name)
imnames = listdir_nohidden(class_dir)
label = int(class_name.split("_")[0])
for imname in imnames:
impath = osp.join(class_dir, imname)
item = Datum(impath=impath, label=label, domain=domain)
items.append(item)
return items

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Dassl.ProGrad.pytorch/dassl/data/datasets/da/digit5.py Normal file
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import random
import os.path as osp
from dassl.utils import listdir_nohidden
from ..build import DATASET_REGISTRY
from ..base_dataset import Datum, DatasetBase
# Folder names for train and test sets
MNIST = {"train": "train_images", "test": "test_images"}
MNIST_M = {"train": "train_images", "test": "test_images"}
SVHN = {"train": "train_images", "test": "test_images"}
SYN = {"train": "train_images", "test": "test_images"}
USPS = {"train": "train_images", "test": "test_images"}
def read_image_list(im_dir, n_max=None, n_repeat=None):
items = []
for imname in listdir_nohidden(im_dir):
imname_noext = osp.splitext(imname)[0]
label = int(imname_noext.split("_")[1])
impath = osp.join(im_dir, imname)
items.append((impath, label))
if n_max is not None:
items = random.sample(items, n_max)
if n_repeat is not None:
items *= n_repeat
return items
def load_mnist(dataset_dir, split="train"):
data_dir = osp.join(dataset_dir, MNIST[split])
n_max = 25000 if split == "train" else 9000
return read_image_list(data_dir, n_max=n_max)
def load_mnist_m(dataset_dir, split="train"):
data_dir = osp.join(dataset_dir, MNIST_M[split])
n_max = 25000 if split == "train" else 9000
return read_image_list(data_dir, n_max=n_max)
def load_svhn(dataset_dir, split="train"):
data_dir = osp.join(dataset_dir, SVHN[split])
n_max = 25000 if split == "train" else 9000
return read_image_list(data_dir, n_max=n_max)
def load_syn(dataset_dir, split="train"):
data_dir = osp.join(dataset_dir, SYN[split])
n_max = 25000 if split == "train" else 9000
return read_image_list(data_dir, n_max=n_max)
def load_usps(dataset_dir, split="train"):
data_dir = osp.join(dataset_dir, USPS[split])
n_repeat = 3 if split == "train" else None
return read_image_list(data_dir, n_repeat=n_repeat)
@DATASET_REGISTRY.register()
class Digit5(DatasetBase):
"""Five digit datasets.
It contains:
- MNIST: hand-written digits.
- MNIST-M: variant of MNIST with blended background.
- SVHN: street view house number.
- SYN: synthetic digits.
- USPS: hand-written digits, slightly different from MNIST.
For MNIST, MNIST-M, SVHN and SYN, we randomly sample 25,000 images from
the training set and 9,000 images from the test set. For USPS which has only
9,298 images in total, we use the entire dataset but replicate its training
set for 3 times so as to match the training set size of other domains.
Reference:
- Lecun et al. Gradient-based learning applied to document
recognition. IEEE 1998.
- Ganin et al. Domain-adversarial training of neural networks.
JMLR 2016.
- Netzer et al. Reading digits in natural images with unsupervised
feature learning. NIPS-W 2011.
"""
dataset_dir = "digit5"
domains = ["mnist", "mnist_m", "svhn", "syn", "usps"]
def __init__(self, cfg):
root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
self.dataset_dir = osp.join(root, self.dataset_dir)
self.check_input_domains(
cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS
)
train_x = self._read_data(cfg.DATASET.SOURCE_DOMAINS, split="train")
train_u = self._read_data(cfg.DATASET.TARGET_DOMAINS, split="train")
test = self._read_data(cfg.DATASET.TARGET_DOMAINS, split="test")
super().__init__(train_x=train_x, train_u=train_u, test=test)
def _read_data(self, input_domains, split="train"):
items = []
for domain, dname in enumerate(input_domains):
func = "load_" + dname
domain_dir = osp.join(self.dataset_dir, dname)
items_d = eval(func)(domain_dir, split=split)
for impath, label in items_d:
item = Datum(
impath=impath,
label=label,
domain=domain,
classname=str(label)
)
items.append(item)
return items

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Dassl.ProGrad.pytorch/dassl/data/datasets/da/domainnet.py Normal file
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import os.path as osp
from ..build import DATASET_REGISTRY
from ..base_dataset import Datum, DatasetBase
@DATASET_REGISTRY.register()
class DomainNet(DatasetBase):
"""DomainNet.
Statistics:
- 6 distinct domains: Clipart, Infograph, Painting, Quickdraw,
Real, Sketch.
- Around 0.6M images.
- 345 categories.
- URL: http://ai.bu.edu/M3SDA/.
Special note: the t-shirt class (327) is missing in painting_train.txt.
Reference:
- Peng et al. Moment Matching for Multi-Source Domain
Adaptation. ICCV 2019.
"""
dataset_dir = "domainnet"
domains = [
"clipart", "infograph", "painting", "quickdraw", "real", "sketch"
]
def __init__(self, cfg):
root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
self.dataset_dir = osp.join(root, self.dataset_dir)
self.split_dir = osp.join(self.dataset_dir, "splits")
self.check_input_domains(
cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS
)
train_x = self._read_data(cfg.DATASET.SOURCE_DOMAINS, split="train")
train_u = self._read_data(cfg.DATASET.TARGET_DOMAINS, split="train")
val = self._read_data(cfg.DATASET.SOURCE_DOMAINS, split="test")
test = self._read_data(cfg.DATASET.TARGET_DOMAINS, split="test")
super().__init__(train_x=train_x, train_u=train_u, val=val, test=test)
def _read_data(self, input_domains, split="train"):
items = []
for domain, dname in enumerate(input_domains):
filename = dname + "_" + split + ".txt"
split_file = osp.join(self.split_dir, filename)
with open(split_file, "r") as f:
lines = f.readlines()
for line in lines:
line = line.strip()
impath, label = line.split(" ")
classname = impath.split("/")[1]
impath = osp.join(self.dataset_dir, impath)
label = int(label)
item = Datum(
impath=impath,
label=label,
domain=domain,
classname=classname
)
items.append(item)
return items

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Dassl.ProGrad.pytorch/dassl/data/datasets/da/mini_domainnet.py Normal file
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import os.path as osp
from ..build import DATASET_REGISTRY
from ..base_dataset import Datum, DatasetBase
@DATASET_REGISTRY.register()
class miniDomainNet(DatasetBase):
"""A subset of DomainNet.
Reference:
- Peng et al. Moment Matching for Multi-Source Domain
Adaptation. ICCV 2019.
- Zhou et al. Domain Adaptive Ensemble Learning.
"""
dataset_dir = "domainnet"
domains = ["clipart", "painting", "real", "sketch"]
def __init__(self, cfg):
root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
self.dataset_dir = osp.join(root, self.dataset_dir)
self.split_dir = osp.join(self.dataset_dir, "splits_mini")
self.check_input_domains(
cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS
)
train_x = self._read_data(cfg.DATASET.SOURCE_DOMAINS, split="train")
train_u = self._read_data(cfg.DATASET.TARGET_DOMAINS, split="train")
test = self._read_data(cfg.DATASET.TARGET_DOMAINS, split="test")
super().__init__(train_x=train_x, train_u=train_u, test=test)
def _read_data(self, input_domains, split="train"):
items = []
for domain, dname in enumerate(input_domains):
filename = dname + "_" + split + ".txt"
split_file = osp.join(self.split_dir, filename)
with open(split_file, "r") as f:
lines = f.readlines()
for line in lines:
line = line.strip()
impath, label = line.split(" ")
classname = impath.split("/")[1]
impath = osp.join(self.dataset_dir, impath)
label = int(label)
item = Datum(
impath=impath,
label=label,
domain=domain,
classname=classname
)
items.append(item)
return items

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Dassl.ProGrad.pytorch/dassl/data/datasets/da/office31.py Normal file
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import os.path as osp
from dassl.utils import listdir_nohidden
from ..build import DATASET_REGISTRY
from ..base_dataset import Datum, DatasetBase
@DATASET_REGISTRY.register()
class Office31(DatasetBase):
"""Office-31.
Statistics:
- 4,110 images.
- 31 classes related to office objects.
- 3 domains: Amazon, Webcam, Dslr.
- URL: https://people.eecs.berkeley.edu/~jhoffman/domainadapt/.
Reference:
- Saenko et al. Adapting visual category models to
new domains. ECCV 2010.
"""
dataset_dir = "office31"
domains = ["amazon", "webcam", "dslr"]
def __init__(self, cfg):
root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
self.dataset_dir = osp.join(root, self.dataset_dir)
self.check_input_domains(
cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS
)
train_x = self._read_data(cfg.DATASET.SOURCE_DOMAINS)
train_u = self._read_data(cfg.DATASET.TARGET_DOMAINS)
test = self._read_data(cfg.DATASET.TARGET_DOMAINS)
super().__init__(train_x=train_x, train_u=train_u, test=test)
def _read_data(self, input_domains):
items = []
for domain, dname in enumerate(input_domains):
domain_dir = osp.join(self.dataset_dir, dname)
class_names = listdir_nohidden(domain_dir)
class_names.sort()
for label, class_name in enumerate(class_names):
class_path = osp.join(domain_dir, class_name)
imnames = listdir_nohidden(class_path)
for imname in imnames:
impath = osp.join(class_path, imname)
item = Datum(
impath=impath,
label=label,
domain=domain,
classname=class_name
)
items.append(item)
return items

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Dassl.ProGrad.pytorch/dassl/data/datasets/da/office_home.py Normal file
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import os.path as osp
from dassl.utils import listdir_nohidden
from ..build import DATASET_REGISTRY
from ..base_dataset import Datum, DatasetBase
@DATASET_REGISTRY.register()
class OfficeHome(DatasetBase):
"""Office-Home.
Statistics:
- Around 15,500 images.
- 65 classes related to office and home objects.
- 4 domains: Art, Clipart, Product, Real World.
- URL: http://hemanthdv.org/OfficeHome-Dataset/.
Reference:
- Venkateswara et al. Deep Hashing Network for Unsupervised
Domain Adaptation. CVPR 2017.
"""
dataset_dir = "office_home"
domains = ["art", "clipart", "product", "real_world"]
def __init__(self, cfg):
root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
self.dataset_dir = osp.join(root, self.dataset_dir)
self.check_input_domains(
cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS
)
train_x = self._read_data(cfg.DATASET.SOURCE_DOMAINS)
train_u = self._read_data(cfg.DATASET.TARGET_DOMAINS)
test = self._read_data(cfg.DATASET.TARGET_DOMAINS)
super().__init__(train_x=train_x, train_u=train_u, test=test)
def _read_data(self, input_domains):
items = []
for domain, dname in enumerate(input_domains):
domain_dir = osp.join(self.dataset_dir, dname)
class_names = listdir_nohidden(domain_dir)
class_names.sort()
for label, class_name in enumerate(class_names):
class_path = osp.join(domain_dir, class_name)
imnames = listdir_nohidden(class_path)
for imname in imnames:
impath = osp.join(class_path, imname)
item = Datum(
impath=impath,
label=label,
domain=domain,
classname=class_name.lower(),
)
items.append(item)
return items

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Dassl.ProGrad.pytorch/dassl/data/datasets/da/visda17.py Normal file
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import os.path as osp
from ..build import DATASET_REGISTRY
from ..base_dataset import Datum, DatasetBase
@DATASET_REGISTRY.register()
class VisDA17(DatasetBase):
"""VisDA17.
Focusing on simulation-to-reality domain shift.
URL: http://ai.bu.edu/visda-2017/.
Reference:
- Peng et al. VisDA: The Visual Domain Adaptation
Challenge. ArXiv 2017.
"""
dataset_dir = "visda17"
domains = ["synthetic", "real"]
def __init__(self, cfg):
root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
self.dataset_dir = osp.join(root, self.dataset_dir)
self.check_input_domains(
cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS
)
train_x = self._read_data("synthetic")
train_u = self._read_data("real")
test = self._read_data("real")
super().__init__(train_x=train_x, train_u=train_u, test=test)
def _read_data(self, dname):
filedir = "train" if dname == "synthetic" else "validation"
image_list = osp.join(self.dataset_dir, filedir, "image_list.txt")
items = []
# There is only one source domain
domain = 0
with open(image_list, "r") as f:
lines = f.readlines()
for line in lines:
line = line.strip()
impath, label = line.split(" ")
classname = impath.split("/")[0]
impath = osp.join(self.dataset_dir, filedir, impath)
label = int(label)
item = Datum(
impath=impath,
label=label,
domain=domain,
classname=classname
)
items.append(item)
return items