A PyTorch implementation for paper Unsupervised Domain Adaptation by Backpropagation
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"""Dataset setting and data loader for Office."""
import torch
from torchvision import datasets, transforms
import torch.utils.data as data
import os
import params
def get_office(train, category):
"""Get Office datasets loader."""
# image pre-processing
pre_process = transforms.Compose([transforms.Resize(params.office_image_size),
transforms.ToTensor(),
transforms.Normalize(
mean=params.imagenet_dataset_mean,
std=params.imagenet_dataset_mean)])
# datasets and data_loader
office_dataset = datasets.ImageFolder(
os.path.join(params.dataset_root, 'office', category, 'images'),
transform=pre_process)
office_dataloader = torch.utils.data.DataLoader(
dataset=office_dataset,
batch_size=params.batch_size,
shuffle=True,
num_workers=8)
return office_dataloader