def download(self):
archive_path = os.path.join(self.root, 'fc100.zip')
print('Downloading FC100. (160Mb)')
try: # Download from Google Drive first
download_file_from_google_drive(GOOGLE_DRIVE_FILE_ID, archive_path)
archive_file = zipfile.ZipFile(archive_path)
archive_file.extractall(self.root)
os.remove(archive_path)
except zipfile.BadZipFile:
download_file(DROPBOX_LINK, archive_path)
archive_file = zipfile.ZipFile(archive_path)
archive_file.extractall(self.root)
os.remove(archive_path)
def download(self):
if not os.path.exists(self.root):
os.mkdir(self.root)
data_path = os.path.join(self.root, DATA_DIR)
if not os.path.exists(data_path):
os.mkdir(data_path)
tar_path = os.path.join(data_path, os.path.basename(ARCHIVE_URL))
print('Downloading Describable Textures dataset (600Mb)')
download_file(ARCHIVE_URL, tar_path)
tar_file = tarfile.open(tar_path)
tar_file.extractall(data_path)
tar_file.close()
os.remove(tar_path)
def __init__(self,
root,
mode='train',
transform=None,
target_transform=None,
download=False):
super(MiniImagenet, self).__init__()
self.root = os.path.expanduser(root)
if not os.path.exists(self.root):
os.mkdir(self.root)
self.transform = transform
self.target_transform = target_transform
self.mode = mode
self._bookkeeping_path = os.path.join(
self.root, 'mini-imagenet-bookkeeping-' + mode + '.pkl')
if self.mode == 'test':
google_drive_file_id = '1wpmY-hmiJUUlRBkO9ZDCXAcIpHEFdOhD'
dropbox_file_link = 'https://www.dropbox.com/s/ye9jeb5tyz0x01b/mini-imagenet-cache-test.pkl?dl=1'
elif self.mode == 'train':
google_drive_file_id = '1I3itTXpXxGV68olxM5roceUMG8itH9Xj'
dropbox_file_link = 'https://www.dropbox.com/s/9g8c6w345s2ek03/mini-imagenet-cache-train.pkl?dl=1'
elif self.mode == 'validation':
google_drive_file_id = '1KY5e491bkLFqJDp0-UWou3463Mo8AOco'
dropbox_file_link = 'https://www.dropbox.com/s/ip1b7se3gij3r1b/mini-imagenet-cache-validation.pkl?dl=1'
else:
raise ('ValueError', 'Needs to be train, test or validation')
pickle_file = os.path.join(self.root,
'mini-imagenet-cache-' + mode + '.pkl')
try:
if not self._check_exists() and download:
print('Downloading mini-ImageNet --', mode)
download_pkl(google_drive_file_id, self.root, mode)
with open(pickle_file, 'rb') as f:
self.data = pickle.load(f)
except pickle.UnpicklingError:
if not self._check_exists() and download:
print('Download failed. Re-trying mini-ImageNet --', mode)
download_file(dropbox_file_link, pickle_file)
with open(pickle_file, 'rb') as f:
self.data = pickle.load(f)
self.x = torch.from_numpy(self.data["image_data"]).permute(0, 3, 1,
2).float()
self.y = np.ones(len(self.x))
# TODO Remove index_classes from here
self.class_idx = index_classes(self.data['class_dict'].keys())
for class_name, idxs in self.data['class_dict'].items():
for idx in idxs:
self.y[idx] = self.class_idx[class_name]
def get_pretrained_backbone(model, dataset, spec='default', root='~/data', download=False):
"""
[[Source]](https://github.com/learnables/learn2learn/blob/master/learn2learn/vision/models/__init__.py)
**Description**
Returns pretrained backbone for a benchmark dataset.
The returned object is a torch.nn.Module instance.
**Arguments**
* **model** (str) - The name of the model (`cnn4`, `resnet12`, or `wrn28`)
* **dataset** (str) - The name of the benchmark dataset (`mini-imagenet` or `tiered-imagenet`).
* **spec** (str, *optional*, default='default') - Which weight specification to load (`default`).
* **root** (str, *optional*, default='~/data') - Location of the pretrained weights.
* **download** (bool, *optional*, default=False) - Download the pretrained weights if not available?
**Example**
~~~python
backbone = l2l.vision.models.get_pretrained_backbone(
model='resnet12',
dataset='mini-imagenet',
root='~/.data',
download=True,
)
~~~
"""
root = os.path.expanduser(root)
destination_dir = os.path.join(root, 'pretrained_models', dataset)
destination = os.path.join(destination_dir, model + '.pth')
source = _BACKBONE_URLS[dataset][model][spec]
if not os.path.exists(destination) and download:
print(f'Downloading {model} weights for {dataset}.')
os.makedirs(destination_dir, exist_ok=True)
download_file(source, destination)
if model == 'cnn4':
pretrained = CNN4Backbone(channels=3, max_pool=True)
elif model == 'resnet12':
pretrained = ResNet12Backbone(avg_pool=False)
elif model == 'wrn28':
pretrained = WRN28Backbone()
weights = torch.load(destination, map_location='cpu')
pretrained.load_state_dict(weights)
return pretrained
def download(self):
if not os.path.exists(self.root):
os.mkdir(self.root)
data_path = os.path.join(self.root, DATA_DIR)
if not os.path.exists(data_path):
os.mkdir(data_path)
tar_path = os.path.join(data_path, os.path.basename(IMAGES_URL))
print('Downloading VGG Flower102 dataset (330Mb)')
download_file(IMAGES_URL, tar_path)
tar_file = tarfile.open(tar_path)
tar_file.extractall(data_path)
tar_file.close()
os.remove(tar_path)
label_path = os.path.join(data_path, os.path.basename(LABELS_URL))
req = requests.get(LABELS_URL)
with open(label_path, 'wb') as label_file:
label_file.write(req.content)