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can train aircraft now

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This commit is contained in:
Mhrooz 2024-10-14 23:19:49 +02:00
parent ef2608bb42
commit c6d53f08ae
2 changed files with 40 additions and 7 deletions
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28
scripts-search/NAS-Bench-201/train-models.sh
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@ -28,16 +28,30 @@ else
mode=cover mode=cover
fi fi
# OMP_NUM_THREADS=4 python ./exps/NAS-Bench-201/main.py \
# --mode ${mode} --save_dir ${save_dir} --max_node 4 \
# --use_less ${use_less} \
# --datasets cifar10 cifar10 cifar100 ImageNet16-120 \
# --splits 1 0 0 0 \
# --xpaths $TORCH_HOME/cifar.python \
# $TORCH_HOME/cifar.python \
# $TORCH_HOME/cifar.python \
# $TORCH_HOME/cifar.python/ImageNet16 \
# --channel 16 --num_cells 5 \
# --workers 4 \
# --srange ${xstart} ${xend} --arch_index ${arch_index} \
# --seeds ${all_seeds}
OMP_NUM_THREADS=4 python ./exps/NAS-Bench-201/main.py \ OMP_NUM_THREADS=4 python ./exps/NAS-Bench-201/main.py \
--mode ${mode} --save_dir ${save_dir} --max_node 4 \ --mode ${mode} --save_dir ${save_dir} --max_node 4 \
--use_less ${use_less} \ --use_less ${use_less} \
--datasets cifar10 cifar10 cifar100 ImageNet16-120 \ --datasets aircraft \
--splits 1 0 0 0 \ --xpaths /lustre/hpe/ws11/ws11.1/ws/xmuhanma-SWAP/train_datasets/datasets/fgvc-aircraft-2013b/data/ \
--xpaths $TORCH_HOME/cifar.python \ --channel 16 \
$TORCH_HOME/cifar.python \ --splits 1 \
$TORCH_HOME/cifar.python \ --num_cells 5 \
$TORCH_HOME/cifar.python/ImageNet16 \
--channel 16 --num_cells 5 \
--workers 4 \ --workers 4 \
--srange ${xstart} ${xend} --arch_index ${arch_index} \ --srange ${xstart} ${xend} --arch_index ${arch_index} \
--seeds ${all_seeds} --seeds ${all_seeds}

19
xautodl/datasets/get_dataset_with_transform.py
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@ -24,6 +24,8 @@ Dataset2Class = {
"ImageNet16-150": 150, "ImageNet16-150": 150,
"ImageNet16-120": 120, "ImageNet16-120": 120,
"ImageNet16-200": 200, "ImageNet16-200": 200,
"aircraft": 100,
"oxford": 102
} }
@ -109,6 +111,12 @@ def get_datasets(name, root, cutout):
elif name.startswith("ImageNet16"): elif name.startswith("ImageNet16"):
mean = [x / 255 for x in [122.68, 116.66, 104.01]] mean = [x / 255 for x in [122.68, 116.66, 104.01]]
std = [x / 255 for x in [63.22, 61.26, 65.09]] std = [x / 255 for x in [63.22, 61.26, 65.09]]
elif name == 'aircraft':
mean = [0.4785, 0.5100, 0.5338]
std = [0.1845, 0.1830, 0.2060]
elif name == 'oxford':
mean = [0.4811, 0.4492, 0.3957]
std = [0.2260, 0.2231, 0.2249]
else: else:
raise TypeError("Unknow dataset : {:}".format(name)) raise TypeError("Unknow dataset : {:}".format(name))
@ -127,6 +135,13 @@ def get_datasets(name, root, cutout):
[transforms.ToTensor(), transforms.Normalize(mean, std)] [transforms.ToTensor(), transforms.Normalize(mean, std)]
) )
xshape = (1, 3, 32, 32) xshape = (1, 3, 32, 32)
elif name.startswith("aircraft") or name.startswith("oxford"):
lists = [transforms.RandomCrop(16, padding=0), transforms.ToTensor(), transforms.Normalize(mean, std)]
if cutout > 0:
lists += [CUTOUT(cutout)]
train_transform = transforms.Compose(lists)
test_transform = transforms.Compose([transforms.Resize((224,224)), transforms.ToTensor(), transforms.Normalize(mean, std)])
xshape = (1, 3, 16, 16)
elif name.startswith("ImageNet16"): elif name.startswith("ImageNet16"):
lists = [ lists = [
transforms.RandomHorizontalFlip(), transforms.RandomHorizontalFlip(),
@ -207,6 +222,10 @@ def get_datasets(name, root, cutout):
root, train=False, transform=test_transform, download=True root, train=False, transform=test_transform, download=True
) )
assert len(train_data) == 50000 and len(test_data) == 10000 assert len(train_data) == 50000 and len(test_data) == 10000
elif name == "aircraft":
train_data = dset.ImageFolder(root='/lustre/hpe/ws11/ws11.1/ws/xmuhanma-SWAP/train_datasets/datasets/fgvc-aircraft-2013b/data/train_sorted_image', transform=train_transform)
test_data = dset.ImageFolder(root='/lustre/hpe/ws11/ws11.1/ws/xmuhanma-SWAP/train_datasets/datasets/fgvc-aircraft-2013b/data/train_sorted_image', transform=test_transform)
elif name.startswith("imagenet-1k"): elif name.startswith("imagenet-1k"):
train_data = dset.ImageFolder(osp.join(root, "train"), train_transform) train_data = dset.ImageFolder(osp.join(root, "train"), train_transform)
test_data = dset.ImageFolder(osp.join(root, "val"), test_transform) test_data = dset.ImageFolder(osp.join(root, "val"), test_transform)