iicd/MeCo
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

add aircraft root

Browse Source
This commit is contained in:
Hanzhang Ma
2024-11-27 16:58:12 +01:00
parent cd80aa277c
commit 4df61fcbb3
1 changed files with 37 additions and 10 deletions
Download Patch File Download Diff File Expand all files Collapse all files

45
correlation/foresight/dataset.py
View File

@@ -25,6 +25,32 @@ import torch
from .imagenet16 import * from .imagenet16 import *
class CUTOUT(object):
def __init__(self, length):
self.length = length
def __repr__(self):
return "{name}(length={length})".format(
name=self.__class__.__name__, **self.__dict__
)
def __call__(self, img):
h, w = img.size(1), img.size(2)
mask = np.ones((h, w), np.float32)
y = np.random.randint(h)
x = np.random.randint(w)
y1 = np.clip(y - self.length // 2, 0, h)
y2 = np.clip(y + self.length // 2, 0, h)
x1 = np.clip(x - self.length // 2, 0, w)
x2 = np.clip(x + self.length // 2, 0, w)
mask[y1:y2, x1:x2] = 0.0
mask = torch.from_numpy(mask)
mask = mask.expand_as(img)
img *= mask
return img
def get_cifar_dataloaders(train_batch_size, test_batch_size, dataset, num_workers, resize=None, datadir='_dataset'): def get_cifar_dataloaders(train_batch_size, test_batch_size, dataset, num_workers, resize=None, datadir='_dataset'):
# print(dataset) # print(dataset)
if 'ImageNet16' in dataset: if 'ImageNet16' in dataset:
@@ -74,7 +100,8 @@ def get_cifar_dataloaders(train_batch_size, test_batch_size, dataset, num_worker
transforms.ToTensor(), transforms.ToTensor(),
transforms.Normalize(mean,std), transforms.Normalize(mean,std),
]) ])
root = '/nfs/data3/hanzhang/MeCo/data' root = '/home/iicd/MeCo/data'
aircraft_dataset_root = '/mnt/Study/DataSet/DataSet/fgvc-aircraft-2013b/fgvc-aircraft-2013b/data'
if dataset == 'cifar10': if dataset == 'cifar10':
train_dataset = CIFAR10(datadir, True, train_transform, download=True) train_dataset = CIFAR10(datadir, True, train_transform, download=True)
@@ -84,18 +111,18 @@ def get_cifar_dataloaders(train_batch_size, test_batch_size, dataset, num_worker
test_dataset = CIFAR100(datadir, False, test_transform, download=True) test_dataset = CIFAR100(datadir, False, test_transform, download=True)
elif dataset == 'aircraft': elif dataset == 'aircraft':
lists = [transforms.RandomCrop(size, padding=pad), transforms.ToTensor(), transforms.Normalize(mean, std)] lists = [transforms.RandomCrop(size, padding=pad), transforms.ToTensor(), transforms.Normalize(mean, std)]
# if resize != None : if resize != None :
# print(resize) print(resize)
# lists += [CUTOUT(resize)] lists += [CUTOUT(resize)]
train_transform = transforms.Compose(lists) train_transform = transforms.Compose(lists)
test_transform = transforms.Compose([transforms.Resize((224,224)), transforms.ToTensor(), transforms.Normalize(mean, std)]) test_transform = transforms.Compose([transforms.Resize((224,224)), transforms.ToTensor(), transforms.Normalize(mean, std)])
train_data = dset.ImageFolder(os.path.join(root, 'train_sorted_images'), train_transform) train_data = dset.ImageFolder(os.path.join(aircraft_dataset_root, 'train_sorted_images'), train_transform)
test_data = dset.ImageFolder(os.path.join(root, 'test_sorted_images'), test_transform) test_data = dset.ImageFolder(os.path.join(aircraft_dataset_root, 'test_sorted_images'), test_transform)
elif dataset == 'oxford': elif dataset == 'oxford':
lists = [transforms.RandomCrop(size, padding=pad), transforms.ToTensor(), transforms.Normalize(mean, std)] lists = [transforms.RandomCrop(size, padding=pad), transforms.ToTensor(), transforms.Normalize(mean, std)]
# if resize != None : if resize != None :
# print(resize) print(resize)
# lists += [CUTOUT(resize)] lists += [CUTOUT(resize)]
train_transform = transforms.Compose(lists) train_transform = transforms.Compose(lists)
test_transform = transforms.Compose([transforms.Resize((224,224)), transforms.ToTensor(), transforms.Normalize(mean, std)]) test_transform = transforms.Compose([transforms.Resize((224,224)), transforms.ToTensor(), transforms.Normalize(mean, std)])