Dassl.pytorch/dassl/data/datasets/da/domainnet.py (42 lines of code) (raw):
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