code stringlengths 3 6.57k |
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self._trim_window() |
on_append(self, value, time) |
CounterValue(DictMixin) |
__init__(self, manager, keys) |
__getitem__(self, key) |
len(key) |
available_keys.append(_key) |
len(available_keys) |
len(available_keys) |
CounterValue(self.manager, key) |
CounterValue(self.manager, key) |
__len__(self) |
len(self.keys() |
__iter__(self) |
iter(self.keys() |
__contains__(self, key) |
self.keys() |
keys(self) |
set() |
len(self._keys) |
len(self._keys) |
result.add(key[0] if key else '__value__') |
to_dict(self, get_value=None) |
iteritems(self) |
isinstance(value, BaseCounter) |
getattr(value, get_value) |
value.to_dict(get_value) |
CounterManager(DictMixin) |
__init__(self, cls=TimebaseAverageWindowCounter) |
event(self, key, value=1) |
isinstance(key, six.string_types) |
isinstance(key, tuple) |
self.cls() |
event(value) |
value(self, key, value=1) |
isinstance(key, six.string_types) |
isinstance(key, tuple) |
self.cls() |
value(value) |
trim(self) |
list(iteritems(self.counters) |
value.empty() |
__getitem__(self, key) |
len(key) |
available_keys.append(_key) |
len(available_keys) |
len(available_keys) |
CounterValue(self, key) |
CounterValue(self, key) |
__iter__(self) |
iter(self.keys() |
__len__(self) |
len(self.keys() |
keys(self) |
set() |
result.add(key[0] if key else () |
to_dict(self, get_value=None) |
self.trim() |
iteritems(self) |
isinstance(value, BaseCounter) |
getattr(value, get_value) |
value.to_dict(get_value) |
dump(self, filename) |
open(filename, 'wb') |
cPickle.dump(self.counters, fp) |
logging.error("can't dump counter to file: %s" % filename) |
load(self, filename) |
open(filename) |
cPickle.load(fp) |
logging.debug("can't load counter from file: %s" % filename) |
logging.get_logger(__name__) |
ViTFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin) |
do_resize (:obj:`bool`, `optional`, defaults to :obj:`True`) |
size (:obj:`int` or :obj:`Tuple(int) |
be (width, height) |
to (size, size) |
resample (:obj:`int`, `optional`, defaults to :obj:`PIL.Image.BILINEAR`) |
do_normalize (:obj:`bool`, `optional`, defaults to :obj:`True`) |
image_mean (:obj:`List[int]`, defaults to :obj:`[0.5, 0.5, 0.5]`) |
image_std (:obj:`List[int]`, defaults to :obj:`[0.5, 0.5, 0.5]`) |
super() |
__init__(**kwargs) |
image(s) |
images (:obj:`PIL.Image.Image`, :obj:`np.ndarray`, :obj:`torch.Tensor`, :obj:`List[PIL.Image.Image]`, :obj:`List[np.ndarray]`, :obj:`List[torch.Tensor]`) |
shape (C, H, W) |
return_tensors (:obj:`str` or :class:`~transformers.file_utils.TensorType`, `optional`, defaults to :obj:`'np'`) |
isinstance(images, (Image.Image, np.ndarray) |
is_torch_tensor(images) |
isinstance(images, (list, tuple) |
len(images) |
isinstance(images[0], (Image.Image, np.ndarray) |
is_torch_tensor(images[0]) |
isinstance(images, (list, tuple) |
and (isinstance(images[0], (Image.Image, np.ndarray) |
is_torch_tensor(images[0]) |
transformations (resizing + normalization) |
self.resize(image=image, size=self.size, resample=self.resample) |
self.normalize(image=image, mean=self.image_mean, std=self.image_std) |
BatchFeature(data=data, tensor_type=return_tensors) |
read_val() |
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