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