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data.py
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from typing import Any
import numpy as np
import ignite.distributed as idist
import torchvision
# import torchvision.transforms as T
from torchvision import transforms
from torch.utils.data import DataLoader, Subset
def setup_data(config: Any, is_test = False, few_shot_num = None):
"""Download datasets and create dataloaders
Parameters
----------
config: needs to contain `data_path`, `train_batch_size`, `eval_batch_size`, and `num_workers`
"""
local_rank = idist.get_local_rank()
normalize = transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
)
train_transform = transforms.Compose(
[
transforms.Resize((256, 256)),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]
)
test_transform = transforms.Compose(
[
transforms.Resize((256, 256)),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
]
)
if local_rank > 0:
# Ensure that only rank 0 download the dataset
idist.barrier()
if config.dataset.name == "cifar10":
if is_test:
raise NotImplementedError("Cifar10 test not implemented.")
train_dataset = torchvision.datasets.CIFAR10(
root=config.data_path,
train=True,
download=False,
transform=train_transform,
)
val_dataset = torchvision.datasets.CIFAR10(
root=config.data_path,
train=False,
download=False,
transform=test_transform,
)
else:
name = config.dataset.name
name = name.replace("-", "")
print(name)
# # # https://python3-cookbook.readthedocs.io/zh_CN/latest/c09/p23_executing_code_with_local_side_effects.html
# loc = locals()
# exec(f"from dataloader/{ablation_method} import train")
# train = loc["train"]
# dataset_module = __import__(f"dataloader.{config.dataset.name}") #!! can't !! https://docs.python.org/3/library/importlib.html#importlib.__import__
import importlib
dataset_module = importlib.import_module(f"dataloader.{name}")
# https://stackoverflow.com/a/58671549
namespace = vars(dataset_module)
public = (name for name in namespace if name[:1] != "_")
# https://stackoverflow.com/questions/9542738/find-a-value-in-a-list
# matches = (x for x in lst if x > 6)
# matches = filter(fulfills_some_condition, lst)
matches = [n for n in public if name.lower() == n.lower()]
print(matches)
assert len(matches) == 1
dataset_cls = getattr(dataset_module, matches[0])
# from dataloader.officehome import OfficeHome
# from dataloader.visda2017 import VisDA2017
# from dataloader.domainnet import DomainNet
# if config.dataset.name == "office-home":
# dataset_cls = OfficeHome
# elif config.dataset.name == "visda2017":
# dataset_cls = VisDA2017
# elif config.dataset.name == "domainnet":
# dataset_cls = DomainNet
# else:
# raise NotImplementedError(f"{config.dataset.name} not implemented.")
train_dataset = dataset_cls(
config.dataset.root, config.dataset.domain, transform=train_transform
)
val_dataset = dataset_cls(
config.dataset.root, config.dataset.domain, transform=test_transform
)
assert len(train_dataset) == len(val_dataset)
if is_test: # few-shot on target domain
if few_shot_num is None:
raise ValueError("few_shot_num should be specified for test dataset.")
# cnt = [ [] for _ in range(val_dataset.num_classes) ] # CAN'T DO THIS, because task has been divied
cnt = [ [] for _ in range(config.dataset.num_classes) ]
indecies = np.random.permutation(len(val_dataset.targets))
# for i, v in enumerate(val_dataset.targets):
for i in indecies:
v = val_dataset.targets[i]
if len(cnt[v]) < few_shot_num:
cnt[v].append(i)
for i in cnt:
assert len(i) == few_shot_num
# turn cnt into numpy array and flatten it
train_indices = np.array(cnt).flatten()
val_indices = np.array([i for i in range(len(val_dataset)) if i not in train_indices])
np.random.shuffle(val_indices) # np.random.randint() or sample
if config.val_sample_num is not None:
val_indices = val_indices[:config.val_sample_num]
# print(val_indices[:100])
# print(val_indices[:100])
# for i in val_indices[:100]:
# print(val_dataset.targets[i], end=" ")
# from collections import Counter
# cnt = Counter(val_dataset.targets[i] for i in val_indices[:1000])
# print(cnt)
# cnt = Counter(val_dataset.targets)
# print(cnt)
# exit(0)
train_dataset = Subset(train_dataset, train_indices)
val_dataset = Subset(val_dataset, val_indices)
else:
# split the dataset with indices
indices = np.random.permutation(len(train_dataset))
num_train = int(len(train_dataset) * config.data.train_ratio)
train_dataset = Subset(train_dataset, indices[:num_train])
val_dataset = Subset(val_dataset, indices[num_train:])
if local_rank == 0:
# Ensure that only rank 0 download the dataset
idist.barrier()
# dataloader_train = idist.auto_dataloader(
dataloader_train = DataLoader(
train_dataset,
batch_size=config.train_batch_size,
shuffle=False,
# shuffle=True,
num_workers=config.num_workers,
)
dataloader_eval = DataLoader(
val_dataset,
batch_size=config.eval_batch_size,
shuffle=False,
num_workers=config.num_workers,
)
return dataloader_train, dataloader_eval