import logging
import os
from typing import Tuple
import torchvision.transforms as transforms
import torch.nn.functional as F
from torch.utils.data import Dataset
import numpy as np
from PIL import Image
import yaml
from datasets.utils import set_default_from_args
from utils import smart_joint
from utils.conf import base_path
from datasets.utils.continual_dataset import ContinualDataset, fix_class_names_order, store_masked_loaders
from datasets.transforms.denormalization import DeNormalize
from torchvision.transforms.functional import InterpolationMode
from utils.prompt_templates import templates
[docs]
class Resisc45(Dataset):
N_CLASSES = 45
LABELS = [
'airplane',
'airport',
'baseball_diamond',
'basketball_court',
'beach',
'bridge',
'chaparral',
'church',
'circular_farmland',
'cloud',
'commercial_area',
'dense_residential',
'desert',
'forest',
'freeway',
'golf_course',
'ground_track_field',
'harbor',
'industrial_area',
'intersection',
'island',
'lake',
'meadow',
'medium_residential',
'mobile_home_park',
'mountain',
'overpass',
'palace',
'parking_lot',
'railway',
'railway_station',
'rectangular_farmland',
'river',
'roundabout',
'runway',
'sea_ice',
'ship',
'snowberg',
'sparse_residential',
'stadium',
'storage_tank',
'tennis_court',
'terrace',
'thermal_power_station',
'wetland',
]
def __init__(self, root, train=True, transform=None,
target_transform=None, download=False) -> None:
self.root = root
self.train = train
self.transform = transform
self.target_transform = target_transform
self.not_aug_transform = transforms.Compose([
transforms.Resize((224, 224), interpolation=InterpolationMode.BICUBIC),
transforms.ToTensor()]
)
if download:
if os.path.isdir(root) and len(os.listdir(root)) > 0:
logging.info('Download not needed, files already on disk.')
else:
# download from https://people.eecs.berkeley.edu/~hendrycks/imagenet-r.tar
logging.info("Downloading resisc45 dataset...")
ln = 'https://unimore365-my.sharepoint.com/:u:/g/personal/215580_unimore_it/EbxMu5z5HbVIkG9qFCGbg7ABDRZvpBEA8uqVC-Em9HYVug?e=Cfc4Yc'
from onedrivedownloader import download
download(ln, filename=os.path.join(root, 'resisc45.tar.gz'), unzip=True, unzip_path=root, clean=True)
logging.info("Done!")
if self.train:
data_config = yaml.load(open(smart_joint(root, 'resisc45_train.yaml')), Loader=yaml.Loader)
else:
data_config = yaml.load(open(smart_joint(root, 'resisc45_test.yaml')), Loader=yaml.Loader)
self.data = np.array([smart_joint(root, d) for d in data_config['data']])
self.targets = np.array(data_config['targets']).astype(np.int16)
def __len__(self):
return len(self.targets)
def __getitem__(self, index: int) -> Tuple[Image.Image, int, Image.Image]:
"""
Gets the requested element from the dataset.
:param index: index of the element to be returned
:returns: tuple: (image, target) where target is index of the target class.
"""
img, target = self.data[index], self.targets[index]
img = Image.open(img).convert('RGB')
original_img = img.copy()
not_aug_img = self.not_aug_transform(original_img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
if not self.train:
return img, target
if hasattr(self, 'logits'):
return img, target, not_aug_img, self.logits[index]
return img, target, not_aug_img
[docs]
class SequentialResisc45(ContinualDataset):
NAME = 'seq-resisc45'
SETTING = 'class-il'
N_TASKS = 9
N_CLASSES_PER_TASK = 45 // N_TASKS
N_CLASSES = 45
SIZE = (224, 224)
MEAN, STD = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
TRANSFORM = transforms.Compose([
transforms.RandomResizedCrop(SIZE[0], interpolation=InterpolationMode.BICUBIC),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(MEAN, STD),
])
TEST_TRANSFORM = transforms.Compose([
transforms.Resize(size=(256, 256), interpolation=InterpolationMode.BICUBIC),
transforms.CenterCrop(SIZE),
transforms.ToTensor(),
transforms.Normalize(MEAN, STD)
])
[docs]
def get_data_loaders(self):
train_dataset = Resisc45(base_path() + 'NWPU-RESISC45', train=True,
download=True, transform=self.TRANSFORM)
test_dataset = Resisc45(base_path() + 'NWPU-RESISC45', train=False,
download=True, transform=self.TEST_TRANSFORM)
train, test = store_masked_loaders(train_dataset, test_dataset, self)
return train, test
[docs]
def get_class_names(self):
if self.class_names is not None:
return self.class_names
classes = [x.replace('_', ' ') for x in Resisc45.LABELS]
classes = fix_class_names_order(classes, self.args)
self.class_names = classes
return classes
[docs]
@staticmethod
def get_prompt_templates():
return templates['eurosat']
[docs]
@set_default_from_args("backbone")
def get_backbone():
return "vit"
[docs]
@staticmethod
def get_loss():
return F.cross_entropy
[docs]
@set_default_from_args('n_epochs')
def get_epochs(self):
return 30
[docs]
@set_default_from_args('batch_size')
def get_batch_size(self):
return 128