code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
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def resized_crop(
img: Tensor,
top: int,
left: int,
height: int,
width: int,
size: List[int],
interpolation: InterpolationMode=InterpolationMode.BILINEAR) -> Tensor:
"""Crop the given image and resize it to desired size.
If the image is paddle Tensor, it i... | Crop the given image and resize it to desired size.
If the image is paddle Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
Args:
img (PIL Image or Tensor): Image to be cropped. (0,0) denotes the top left corner of the image.
top (i... | resized_crop | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_paddle/paddlevision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_paddle/paddlevision/transforms/functional.py | Apache-2.0 |
def get_params(img: Tensor, scale: List[float],
ratio: List[float]) -> Tuple[int, int, int, int]:
"""Get parameters for ``crop`` for a random sized crop.
Args:
img (PIL Image or Tensor): Input image.
scale (list): range of scale of the origin size cropped
... | Get parameters for ``crop`` for a random sized crop.
Args:
img (PIL Image or Tensor): Input image.
scale (list): range of scale of the origin size cropped
ratio (list): range of aspect ratio of the origin aspect ratio cropped
Returns:
tuple: params (i, j... | get_params | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_paddle/paddlevision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_paddle/paddlevision/transforms/transforms.py | Apache-2.0 |
def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be cropped and resized.
Returns:
PIL Image or Tensor: Randomly cropped and resized image.
"""
i, j, h, w = self.get_params(img, self.scale, self.ratio)
return F.resized_crop(img... |
Args:
img (PIL Image or Tensor): Image to be cropped and resized.
Returns:
PIL Image or Tensor: Randomly cropped and resized image.
| forward | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_paddle/paddlevision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_paddle/paddlevision/transforms/transforms.py | Apache-2.0 |
def accuracy_torch(output, target, topk=(1, )):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pr... | Computes the accuracy over the k top predictions for the specified values of k | accuracy_torch | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/metric.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/metric.py | Apache-2.0 |
def synchronize_between_processes(self):
"""
Warning: does not synchronize the deque!
"""
if not is_dist_avail_and_initialized():
return
t = torch.tensor(
[self.count, self.total], dtype=torch.float64, device='cuda')
dist.barrier()
dist.all... |
Warning: does not synchronize the deque!
| synchronize_between_processes | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/utils.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/utils.py | Apache-2.0 |
def accuracy(output, target, topk=(1, )):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(... | Computes the accuracy over the k top predictions for the specified values of k | accuracy | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/utils.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/utils.py | Apache-2.0 |
def average_checkpoints(inputs):
"""Loads checkpoints from inputs and returns a model with averaged weights. Original implementation taken from:
https://github.com/pytorch/fairseq/blob/a48f235636557b8d3bc4922a6fa90f3a0fa57955/scripts/average_checkpoints.py#L16
Args:
inputs (List[str]): An iterable of... | Loads checkpoints from inputs and returns a model with averaged weights. Original implementation taken from:
https://github.com/pytorch/fairseq/blob/a48f235636557b8d3bc4922a6fa90f3a0fa57955/scripts/average_checkpoints.py#L16
Args:
inputs (List[str]): An iterable of string paths of checkpoints to load fro... | average_checkpoints | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/utils.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/utils.py | Apache-2.0 |
def store_model_weights(model,
checkpoint_path,
checkpoint_key='model',
strict=True):
"""
This method can be used to prepare weights files for new models. It receives as
input a model architecture and a checkpoint from the training scri... |
This method can be used to prepare weights files for new models. It receives as
input a model architecture and a checkpoint from the training script and produces
a file with the weights ready for release.
Examples:
from torchvision import models as M
# Classification
model = M... | store_model_weights | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/utils.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/utils.py | Apache-2.0 |
def has_file_allowed_extension(filename: str,
extensions: Tuple[str, ...]) -> bool:
"""Checks if a file is an allowed extension.
Args:
filename (string): path to a file
extensions (tuple of strings): extensions to consider (lowercase)
Returns:
bool: T... | Checks if a file is an allowed extension.
Args:
filename (string): path to a file
extensions (tuple of strings): extensions to consider (lowercase)
Returns:
bool: True if the filename ends with one of given extensions
| has_file_allowed_extension | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/datasets/folder.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/datasets/folder.py | Apache-2.0 |
def find_classes(directory: str) -> Tuple[List[str], Dict[str, int]]:
"""Finds the class folders in a dataset.
See :class:`DatasetFolder` for details.
"""
classes = sorted(
entry.name for entry in os.scandir(directory) if entry.is_dir())
if not classes:
raise FileNotFoundError(
... | Finds the class folders in a dataset.
See :class:`DatasetFolder` for details.
| find_classes | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/datasets/folder.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/datasets/folder.py | Apache-2.0 |
def make_dataset(
directory: str,
class_to_idx: Optional[Dict[str, int]]=None,
extensions: Optional[Tuple[str, ...]]=None,
is_valid_file: Optional[Callable[[str], bool]]=None, ) -> List[Tuple[
str, int]]:
"""Generates a list of samples of a form (path_to_sample, class).
... | Generates a list of samples of a form (path_to_sample, class).
See :class:`DatasetFolder` for details.
Note: The class_to_idx parameter is here optional and will use the logic of the ``find_classes`` function
by default.
| make_dataset | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/datasets/folder.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/datasets/folder.py | Apache-2.0 |
def make_dataset(
directory: str,
class_to_idx: Dict[str, int],
extensions: Optional[Tuple[str, ...]]=None,
is_valid_file: Optional[Callable[[str], bool]]=None, ) -> List[
Tuple[str, int]]:
"""Generates a list of samples of a form (path_to_sample, ... | Generates a list of samples of a form (path_to_sample, class).
This can be overridden to e.g. read files from a compressed zip file instead of from the disk.
Args:
directory (str): root dataset directory, corresponding to ``self.root``.
class_to_idx (Dict[str, int]): Dictionary... | make_dataset | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/datasets/folder.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/datasets/folder.py | Apache-2.0 |
def __getitem__(self, index: int) -> Tuple[Any, Any]:
"""
Args:
index (int): Index
Returns:
tuple: (sample, target) where target is class_index of the target class.
"""
path, target = self.samples[index]
sample = self.loader(path)
if self.... |
Args:
index (int): Index
Returns:
tuple: (sample, target) where target is class_index of the target class.
| __getitem__ | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/datasets/folder.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/datasets/folder.py | Apache-2.0 |
def __init__(
self,
inverted_residual_setting: List[InvertedResidualConfig],
last_channel: int,
num_classes: int=1000,
block: Optional[Callable[..., nn.Module]]=None,
norm_layer: Optional[Callable[..., nn.Module]]=None,
dropout: float=0... |
MobileNet V3 main class
Args:
inverted_residual_setting (List[InvertedResidualConfig]): Network structure
last_channel (int): The number of channels on the penultimate layer
num_classes (int): Number of classes
block (Optional[Callable[..., nn.Module]]):... | __init__ | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/models/mobilenet_v3_torch.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/models/mobilenet_v3_torch.py | Apache-2.0 |
def mobilenet_v3_large(pretrained: bool=False,
progress: bool=True,
**kwargs: Any) -> MobileNetV3:
"""
Constructs a large MobileNetV3 architecture from
`"Searching for MobileNetV3" <https://arxiv.org/abs/1905.02244>`_.
Args:
pretrained (bool): If Tr... |
Constructs a large MobileNetV3 architecture from
`"Searching for MobileNetV3" <https://arxiv.org/abs/1905.02244>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
| mobilenet_v3_large | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/models/mobilenet_v3_torch.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/models/mobilenet_v3_torch.py | Apache-2.0 |
def mobilenet_v3_small(pretrained: bool=False,
progress: bool=True,
**kwargs: Any) -> MobileNetV3:
"""
Constructs a small MobileNetV3 architecture from
`"Searching for MobileNetV3" <https://arxiv.org/abs/1905.02244>`_.
Args:
pretrained (bool): If Tr... |
Constructs a small MobileNetV3 architecture from
`"Searching for MobileNetV3" <https://arxiv.org/abs/1905.02244>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
| mobilenet_v3_small | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/models/mobilenet_v3_torch.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/models/mobilenet_v3_torch.py | Apache-2.0 |
def _make_divisible(v: float, divisor: int,
min_value: Optional[int]=None) -> int:
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be seen here:
https://github.com/tensorflow/models/blob/master/r... |
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be seen here:
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
| _make_divisible | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/models/_utils.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/models/_utils.py | Apache-2.0 |
def get_params(transform_num: int) -> Tuple[int, Tensor, Tensor]:
"""Get parameters for autoaugment transformation
Returns:
params required by the autoaugment transformation
"""
policy_id = torch.randint(transform_num, (1, )).item()
probs = torch.rand((2, ))
... | Get parameters for autoaugment transformation
Returns:
params required by the autoaugment transformation
| get_params | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/autoaugment.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/autoaugment.py | Apache-2.0 |
def forward(self, img: Tensor):
"""
img (PIL Image or Tensor): Image to be transformed.
Returns:
PIL Image or Tensor: AutoAugmented image.
"""
fill = self.fill
if isinstance(img, Tensor):
if isinstance(fill, (int, float)):
fill... |
img (PIL Image or Tensor): Image to be transformed.
Returns:
PIL Image or Tensor: AutoAugmented image.
| forward | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/autoaugment.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/autoaugment.py | Apache-2.0 |
def to_tensor(pic):
"""Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
This function does not support torchscript.
See :class:`~torchvision.transforms.ToTensor` for more details.
Args:
pic (PIL Image or numpy.ndarray): Image to be converted to tensor.
Returns:
Tensor: Conv... | Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
This function does not support torchscript.
See :class:`~torchvision.transforms.ToTensor` for more details.
Args:
pic (PIL Image or numpy.ndarray): Image to be converted to tensor.
Returns:
Tensor: Converted image.
| to_tensor | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def pil_to_tensor(pic):
"""Convert a ``PIL Image`` to a tensor of the same type.
This function does not support torchscript.
See :class:`~torchvision.transforms.PILToTensor` for more details.
Args:
pic (PIL Image): Image to be converted to tensor.
Returns:
Tensor: Converted image.... | Convert a ``PIL Image`` to a tensor of the same type.
This function does not support torchscript.
See :class:`~torchvision.transforms.PILToTensor` for more details.
Args:
pic (PIL Image): Image to be converted to tensor.
Returns:
Tensor: Converted image.
| pil_to_tensor | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def convert_image_dtype(image: torch.Tensor,
dtype: torch.dtype=torch.float) -> torch.Tensor:
"""Convert a tensor image to the given ``dtype`` and scale the values accordingly
This function does not support PIL Image.
Args:
image (torch.Tensor): Image to be converted
... | Convert a tensor image to the given ``dtype`` and scale the values accordingly
This function does not support PIL Image.
Args:
image (torch.Tensor): Image to be converted
dtype (torch.dtype): Desired data type of the output
Returns:
Tensor: Converted image
.. note::
W... | convert_image_dtype | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def to_pil_image(pic, mode=None):
"""Convert a tensor or an ndarray to PIL Image. This function does not support torchscript.
See :class:`~torchvision.transforms.ToPILImage` for more details.
Args:
pic (Tensor or numpy.ndarray): Image to be converted to PIL Image.
mode (`PIL.Image mode`_):... | Convert a tensor or an ndarray to PIL Image. This function does not support torchscript.
See :class:`~torchvision.transforms.ToPILImage` for more details.
Args:
pic (Tensor or numpy.ndarray): Image to be converted to PIL Image.
mode (`PIL.Image mode`_): color space and pixel depth of input dat... | to_pil_image | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def normalize(tensor: Tensor,
mean: List[float],
std: List[float],
inplace: bool=False) -> Tensor:
"""Normalize a float tensor image with mean and standard deviation.
This transform does not support PIL Image.
.. note::
This transform acts out of place by d... | Normalize a float tensor image with mean and standard deviation.
This transform does not support PIL Image.
.. note::
This transform acts out of place by default, i.e., it does not mutates the input tensor.
See :class:`~torchvision.transforms.Normalize` for more details.
Args:
tensor ... | normalize | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def resize(img: Tensor,
size: List[int],
interpolation: InterpolationMode=InterpolationMode.BILINEAR,
max_size: Optional[int]=None,
antialias: Optional[bool]=None) -> Tensor:
r"""Resize the input image to the given size.
If the image is torch Tensor, it is expected
... | Resize the input image to the given size.
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
.. warning::
The output image might be different depending on its type: when downsampling, the interpolation of PIL images
... | resize | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def pad(img: Tensor,
padding: List[int],
fill: int=0,
padding_mode: str="constant") -> Tensor:
r"""Pad the given image on all sides with the given "pad" value.
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means at most 2 leading dimensions for mod... | Pad the given image on all sides with the given "pad" value.
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means at most 2 leading dimensions for mode reflect and symmetric,
at most 3 leading dimensions for mode edge,
and an arbitrary number of leading dimensions for ... | pad | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def crop(img: Tensor, top: int, left: int, height: int, width: int) -> Tensor:
"""Crop the given image at specified location and output size.
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
If image size is smaller than o... | Crop the given image at specified location and output size.
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
If image size is smaller than output size along any edge, image is padded with 0 and then cropped.
Args:
... | crop | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def center_crop(img: Tensor, output_size: List[int]) -> Tensor:
"""Crops the given image at the center.
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
If image size is smaller than output size along any edge, image is pa... | Crops the given image at the center.
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
If image size is smaller than output size along any edge, image is padded with 0 and then center cropped.
Args:
img (PIL Image ... | center_crop | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def resized_crop(
img: Tensor,
top: int,
left: int,
height: int,
width: int,
size: List[int],
interpolation: InterpolationMode=InterpolationMode.BILINEAR) -> Tensor:
"""Crop the given image and resize it to desired size.
If the image is torch Tensor, it is... | Crop the given image and resize it to desired size.
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
Notably used in :class:`~torchvision.transforms.RandomResizedCrop`.
Args:
img (PIL Image or Tensor): Image to be... | resized_crop | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def hflip(img: Tensor) -> Tensor:
"""Horizontally flip the given image.
Args:
img (PIL Image or Tensor): Image to be flipped. If img
is a Tensor, it is expected to be in [..., H, W] format,
where ... means it can have an arbitrary number of leading
dimensions.
R... | Horizontally flip the given image.
Args:
img (PIL Image or Tensor): Image to be flipped. If img
is a Tensor, it is expected to be in [..., H, W] format,
where ... means it can have an arbitrary number of leading
dimensions.
Returns:
PIL Image or Tensor: Hor... | hflip | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def _get_perspective_coeffs(startpoints: List[List[int]],
endpoints: List[List[int]]) -> List[float]:
"""Helper function to get the coefficients (a, b, c, d, e, f, g, h) for the perspective transforms.
In Perspective Transform each pixel (x, y) in the original image gets transformed... | Helper function to get the coefficients (a, b, c, d, e, f, g, h) for the perspective transforms.
In Perspective Transform each pixel (x, y) in the original image gets transformed as,
(x, y) -> ( (ax + by + c) / (gx + hy + 1), (dx + ey + f) / (gx + hy + 1) )
Args:
startpoints (list of list of ints... | _get_perspective_coeffs | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def perspective(img: Tensor,
startpoints: List[List[int]],
endpoints: List[List[int]],
interpolation: InterpolationMode=InterpolationMode.BILINEAR,
fill: Optional[List[float]]=None) -> Tensor:
"""Perform perspective transform of the given image.
If... | Perform perspective transform of the given image.
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
Args:
img (PIL Image or Tensor): Image to be transformed.
startpoints (list of list of ints): List containing ... | perspective | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def vflip(img: Tensor) -> Tensor:
"""Vertically flip the given image.
Args:
img (PIL Image or Tensor): Image to be flipped. If img
is a Tensor, it is expected to be in [..., H, W] format,
where ... means it can have an arbitrary number of leading
dimensions.
Ret... | Vertically flip the given image.
Args:
img (PIL Image or Tensor): Image to be flipped. If img
is a Tensor, it is expected to be in [..., H, W] format,
where ... means it can have an arbitrary number of leading
dimensions.
Returns:
PIL Image or Tensor: Verti... | vflip | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def five_crop(
img: Tensor,
size: List[int]) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
"""Crop the given image into four corners and the central crop.
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
... | Crop the given image into four corners and the central crop.
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
.. Note::
This transform returns a tuple of images and there may be a
mismatch in the number of inpu... | five_crop | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def ten_crop(img: Tensor, size: List[int],
vertical_flip: bool=False) -> List[Tensor]:
"""Generate ten cropped images from the given image.
Crop the given image into four corners and the central crop plus the
flipped version of these (horizontal flipping is used by default).
If the image is... | Generate ten cropped images from the given image.
Crop the given image into four corners and the central crop plus the
flipped version of these (horizontal flipping is used by default).
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading... | ten_crop | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def adjust_brightness(img: Tensor, brightness_factor: float) -> Tensor:
"""Adjust brightness of an image.
Args:
img (PIL Image or Tensor): Image to be adjusted.
If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
where ... means it can have an arbitrary n... | Adjust brightness of an image.
Args:
img (PIL Image or Tensor): Image to be adjusted.
If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
where ... means it can have an arbitrary number of leading dimensions.
brightness_factor (float): How much to ad... | adjust_brightness | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def adjust_contrast(img: Tensor, contrast_factor: float) -> Tensor:
"""Adjust contrast of an image.
Args:
img (PIL Image or Tensor): Image to be adjusted.
If img is torch Tensor, it is expected to be in [..., 3, H, W] format,
where ... means it can have an arbitrary number of le... | Adjust contrast of an image.
Args:
img (PIL Image or Tensor): Image to be adjusted.
If img is torch Tensor, it is expected to be in [..., 3, H, W] format,
where ... means it can have an arbitrary number of leading dimensions.
contrast_factor (float): How much to adjust the c... | adjust_contrast | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def adjust_saturation(img: Tensor, saturation_factor: float) -> Tensor:
"""Adjust color saturation of an image.
Args:
img (PIL Image or Tensor): Image to be adjusted.
If img is torch Tensor, it is expected to be in [..., 3, H, W] format,
where ... means it can have an arbitrary ... | Adjust color saturation of an image.
Args:
img (PIL Image or Tensor): Image to be adjusted.
If img is torch Tensor, it is expected to be in [..., 3, H, W] format,
where ... means it can have an arbitrary number of leading dimensions.
saturation_factor (float): How much to a... | adjust_saturation | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def adjust_hue(img: Tensor, hue_factor: float) -> Tensor:
"""Adjust hue of an image.
The image hue is adjusted by converting the image to HSV and
cyclically shifting the intensities in the hue channel (H).
The image is then converted back to original image mode.
`hue_factor` is the amount of shift... | Adjust hue of an image.
The image hue is adjusted by converting the image to HSV and
cyclically shifting the intensities in the hue channel (H).
The image is then converted back to original image mode.
`hue_factor` is the amount of shift in H channel and must be in the
interval `[-0.5, 0.5]`.
... | adjust_hue | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def adjust_gamma(img: Tensor, gamma: float, gain: float=1) -> Tensor:
r"""Perform gamma correction on an image.
Also known as Power Law Transform. Intensities in RGB mode are adjusted
based on the following equation:
.. math::
I_{\text{out}} = 255 \times \text{gain} \times \left(\frac{I_{\text... | Perform gamma correction on an image.
Also known as Power Law Transform. Intensities in RGB mode are adjusted
based on the following equation:
.. math::
I_{\text{out}} = 255 \times \text{gain} \times \left(\frac{I_{\text{in}}}{255}\right)^{\gamma}
See `Gamma Correction`_ for more details.
... | adjust_gamma | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def affine(img: Tensor,
angle: float,
translate: List[int],
scale: float,
shear: List[float],
interpolation: InterpolationMode=InterpolationMode.NEAREST,
fill: Optional[List[float]]=None,
resample: Optional[int]=None,
fillcolor: Opt... | Apply affine transformation on the image keeping image center invariant.
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
Args:
img (PIL Image or Tensor): image to transform.
angle (number): rotation angle in ... | affine | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def to_grayscale(img, num_output_channels=1):
"""Convert PIL image of any mode (RGB, HSV, LAB, etc) to grayscale version of image.
This transform does not support torch Tensor.
Args:
img (PIL Image): PIL Image to be converted to grayscale.
num_output_channels (int): number of channels of th... | Convert PIL image of any mode (RGB, HSV, LAB, etc) to grayscale version of image.
This transform does not support torch Tensor.
Args:
img (PIL Image): PIL Image to be converted to grayscale.
num_output_channels (int): number of channels of the output image. Value can be 1 or 3. Default is 1.
... | to_grayscale | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def rgb_to_grayscale(img: Tensor, num_output_channels: int=1) -> Tensor:
"""Convert RGB image to grayscale version of image.
If the image is torch Tensor, it is expected
to have [..., 3, H, W] shape, where ... means an arbitrary number of leading dimensions
Note:
Please, note that this method s... | Convert RGB image to grayscale version of image.
If the image is torch Tensor, it is expected
to have [..., 3, H, W] shape, where ... means an arbitrary number of leading dimensions
Note:
Please, note that this method supports only RGB images as input. For inputs in other color spaces,
plea... | rgb_to_grayscale | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def erase(img: Tensor,
i: int,
j: int,
h: int,
w: int,
v: Tensor,
inplace: bool=False) -> Tensor:
""" Erase the input Tensor Image with given value.
This transform does not support PIL Image.
Args:
img (Tensor Image): Tensor image of size ... | Erase the input Tensor Image with given value.
This transform does not support PIL Image.
Args:
img (Tensor Image): Tensor image of size (C, H, W) to be erased
i (int): i in (i,j) i.e coordinates of the upper left corner.
j (int): j in (i,j) i.e coordinates of the upper left corner.
... | erase | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def gaussian_blur(img: Tensor,
kernel_size: List[int],
sigma: Optional[List[float]]=None) -> Tensor:
"""Performs Gaussian blurring on the image by given kernel.
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of ... | Performs Gaussian blurring on the image by given kernel.
If the image is torch Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions.
Args:
img (PIL Image or Tensor): Image to be blurred
kernel_size (sequence of ints or int): Gaussian ke... | gaussian_blur | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def invert(img: Tensor) -> Tensor:
"""Invert the colors of an RGB/grayscale image.
Args:
img (PIL Image or Tensor): Image to have its colors inverted.
If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
where ... means it can have an arbitrary number of l... | Invert the colors of an RGB/grayscale image.
Args:
img (PIL Image or Tensor): Image to have its colors inverted.
If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
where ... means it can have an arbitrary number of leading dimensions.
If img is P... | invert | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def posterize(img: Tensor, bits: int) -> Tensor:
"""Posterize an image by reducing the number of bits for each color channel.
Args:
img (PIL Image or Tensor): Image to have its colors posterized.
If img is torch Tensor, it should be of type torch.uint8 and
it is expected to be i... | Posterize an image by reducing the number of bits for each color channel.
Args:
img (PIL Image or Tensor): Image to have its colors posterized.
If img is torch Tensor, it should be of type torch.uint8 and
it is expected to be in [..., 1 or 3, H, W] format, where ... means
... | posterize | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def solarize(img: Tensor, threshold: float) -> Tensor:
"""Solarize an RGB/grayscale image by inverting all pixel values above a threshold.
Args:
img (PIL Image or Tensor): Image to have its colors inverted.
If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
... | Solarize an RGB/grayscale image by inverting all pixel values above a threshold.
Args:
img (PIL Image or Tensor): Image to have its colors inverted.
If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
where ... means it can have an arbitrary number of leading... | solarize | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def adjust_sharpness(img: Tensor, sharpness_factor: float) -> Tensor:
"""Adjust the sharpness of an image.
Args:
img (PIL Image or Tensor): Image to be adjusted.
If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
where ... means it can have an arbitrary ... | Adjust the sharpness of an image.
Args:
img (PIL Image or Tensor): Image to be adjusted.
If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
where ... means it can have an arbitrary number of leading dimensions.
sharpness_factor (float): How much to ... | adjust_sharpness | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def autocontrast(img: Tensor) -> Tensor:
"""Maximize contrast of an image by remapping its
pixels per channel so that the lowest becomes black and the lightest
becomes white.
Args:
img (PIL Image or Tensor): Image on which autocontrast is applied.
If img is torch Tensor, it is expec... | Maximize contrast of an image by remapping its
pixels per channel so that the lowest becomes black and the lightest
becomes white.
Args:
img (PIL Image or Tensor): Image on which autocontrast is applied.
If img is torch Tensor, it is expected to be in [..., 1 or 3, H, W] format,
... | autocontrast | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def equalize(img: Tensor) -> Tensor:
"""Equalize the histogram of an image by applying
a non-linear mapping to the input in order to create a uniform
distribution of grayscale values in the output.
Args:
img (PIL Image or Tensor): Image on which equalize is applied.
If img is torch ... | Equalize the histogram of an image by applying
a non-linear mapping to the input in order to create a uniform
distribution of grayscale values in the output.
Args:
img (PIL Image or Tensor): Image on which equalize is applied.
If img is torch Tensor, it is expected to be in [..., 1 or 3... | equalize | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/functional.py | Apache-2.0 |
def get_params(img: Tensor,
output_size: Tuple[int, int]) -> Tuple[int, int, int, int]:
"""Get parameters for ``crop`` for a random crop.
Args:
img (PIL Image or Tensor): Image to be cropped.
output_size (tuple): Expected output size of the crop.
Retu... | Get parameters for ``crop`` for a random crop.
Args:
img (PIL Image or Tensor): Image to be cropped.
output_size (tuple): Expected output size of the crop.
Returns:
tuple: params (i, j, h, w) to be passed to ``crop`` for random crop.
| get_params | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be cropped.
Returns:
PIL Image or Tensor: Cropped image.
"""
if self.padding is not None:
img = F.pad(img, self.padding, self.fill, self.padding_mode)
width, height... |
Args:
img (PIL Image or Tensor): Image to be cropped.
Returns:
PIL Image or Tensor: Cropped image.
| forward | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be flipped.
Returns:
PIL Image or Tensor: Randomly flipped image.
"""
if torch.rand(1) < self.p:
return F.hflip(img)
return img |
Args:
img (PIL Image or Tensor): Image to be flipped.
Returns:
PIL Image or Tensor: Randomly flipped image.
| forward | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be flipped.
Returns:
PIL Image or Tensor: Randomly flipped image.
"""
if torch.rand(1) < self.p:
return F.vflip(img)
return img |
Args:
img (PIL Image or Tensor): Image to be flipped.
Returns:
PIL Image or Tensor: Randomly flipped image.
| forward | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be Perspectively transformed.
Returns:
PIL Image or Tensor: Randomly transformed image.
"""
fill = self.fill
if isinstance(img, Tensor):
if isinstance(fill, (int, f... |
Args:
img (PIL Image or Tensor): Image to be Perspectively transformed.
Returns:
PIL Image or Tensor: Randomly transformed image.
| forward | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def get_params(width: int, height: int, distortion_scale: float) -> Tuple[
List[List[int]], List[List[int]]]:
"""Get parameters for ``perspective`` for a random perspective transform.
Args:
width (int): width of the image.
height (int): height of the image.
... | Get parameters for ``perspective`` for a random perspective transform.
Args:
width (int): width of the image.
height (int): height of the image.
distortion_scale (float): argument to control the degree of distortion and ranges from 0 to 1.
Returns:
List ... | get_params | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def get_params(img: Tensor, scale: List[float],
ratio: List[float]) -> Tuple[int, int, int, int]:
"""Get parameters for ``crop`` for a random sized crop.
Args:
img (PIL Image or Tensor): Input image.
scale (list): range of scale of the origin size cropped
... | Get parameters for ``crop`` for a random sized crop.
Args:
img (PIL Image or Tensor): Input image.
scale (list): range of scale of the origin size cropped
ratio (list): range of aspect ratio of the origin aspect ratio cropped
Returns:
tuple: params (i, j... | get_params | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be cropped and resized.
Returns:
PIL Image or Tensor: Randomly cropped and resized image.
"""
i, j, h, w = self.get_params(img, self.scale, self.ratio)
return F.resized_crop(img... |
Args:
img (PIL Image or Tensor): Image to be cropped and resized.
Returns:
PIL Image or Tensor: Randomly cropped and resized image.
| forward | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def forward(self, tensor: Tensor) -> Tensor:
"""
Args:
tensor (Tensor): Tensor image to be whitened.
Returns:
Tensor: Transformed image.
"""
shape = tensor.shape
n = shape[-3] * shape[-2] * shape[-1]
if n != self.transformation_matrix.shap... |
Args:
tensor (Tensor): Tensor image to be whitened.
Returns:
Tensor: Transformed image.
| forward | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def get_params(
brightness: Optional[List[float]],
contrast: Optional[List[float]],
saturation: Optional[List[float]],
hue: Optional[List[float]]) -> Tuple[Tensor, Optional[
float], Optional[float], Optional[float], Optional[float]]:
"""Get the par... | Get the parameters for the randomized transform to be applied on image.
Args:
brightness (tuple of float (min, max), optional): The range from which the brightness_factor is chosen
uniformly. Pass None to turn off the transformation.
contrast (tuple of float (min, max), ... | get_params | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Input image.
Returns:
PIL Image or Tensor: Color jittered image.
"""
fn_idx, brightness_factor, contrast_factor, saturation_factor, hue_factor = \
self.get_params(self.brightness, se... |
Args:
img (PIL Image or Tensor): Input image.
Returns:
PIL Image or Tensor: Color jittered image.
| forward | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def get_params(degrees: List[float]) -> float:
"""Get parameters for ``rotate`` for a random rotation.
Returns:
float: angle parameter to be passed to ``rotate`` for random rotation.
"""
angle = float(
torch.empty(1).uniform_(float(degrees[0]), float(degrees[1]))... | Get parameters for ``rotate`` for a random rotation.
Returns:
float: angle parameter to be passed to ``rotate`` for random rotation.
| get_params | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be rotated.
Returns:
PIL Image or Tensor: Rotated image.
"""
fill = self.fill
if isinstance(img, Tensor):
if isinstance(fill, (int, float)):
fill = [... |
Args:
img (PIL Image or Tensor): Image to be rotated.
Returns:
PIL Image or Tensor: Rotated image.
| forward | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def get_params(degrees: List[float],
translate: Optional[List[float]],
scale_ranges: Optional[List[float]],
shears: Optional[List[float]],
img_size: List[int]) -> Tuple[float, Tuple[int, int], float,
... | Get parameters for affine transformation
Returns:
params to be passed to the affine transformation
| get_params | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def forward(self, img):
"""
img (PIL Image or Tensor): Image to be transformed.
Returns:
PIL Image or Tensor: Affine transformed image.
"""
fill = self.fill
if isinstance(img, Tensor):
if isinstance(fill, (int, float)):
fill = ... |
img (PIL Image or Tensor): Image to be transformed.
Returns:
PIL Image or Tensor: Affine transformed image.
| forward | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be converted to grayscale.
Returns:
PIL Image or Tensor: Randomly grayscaled image.
"""
num_output_channels = F._get_image_num_channels(img)
if torch.rand(1) < self.p:
... |
Args:
img (PIL Image or Tensor): Image to be converted to grayscale.
Returns:
PIL Image or Tensor: Randomly grayscaled image.
| forward | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def get_params(img: Tensor,
scale: Tuple[float, float],
ratio: Tuple[float, float],
value: Optional[List[float]]=None) -> Tuple[int, int, int,
int, Tensor]:
"""Get parameters for ``erase`` for... | Get parameters for ``erase`` for a random erasing.
Args:
img (Tensor): Tensor image to be erased.
scale (sequence): range of proportion of erased area against input image.
ratio (sequence): range of aspect ratio of erased area.
value (list, optional): erasing val... | get_params | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def forward(self, img):
"""
Args:
img (Tensor): Tensor image to be erased.
Returns:
img (Tensor): Erased Tensor image.
"""
if torch.rand(1) < self.p:
# cast self.value to script acceptable type
if isinstance(self.value, (int, floa... |
Args:
img (Tensor): Tensor image to be erased.
Returns:
img (Tensor): Erased Tensor image.
| forward | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def forward(self, img: Tensor) -> Tensor:
"""
Args:
img (PIL Image or Tensor): image to be blurred.
Returns:
PIL Image or Tensor: Gaussian blurred image
"""
sigma = self.get_params(self.sigma[0], self.sigma[1])
return F.gaussian_blur(img, self.ker... |
Args:
img (PIL Image or Tensor): image to be blurred.
Returns:
PIL Image or Tensor: Gaussian blurred image
| forward | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be inverted.
Returns:
PIL Image or Tensor: Randomly color inverted image.
"""
if torch.rand(1).item() < self.p:
return F.invert(img)
return img |
Args:
img (PIL Image or Tensor): Image to be inverted.
Returns:
PIL Image or Tensor: Randomly color inverted image.
| forward | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be posterized.
Returns:
PIL Image or Tensor: Randomly posterized image.
"""
if torch.rand(1).item() < self.p:
return F.posterize(img, self.bits)
return img |
Args:
img (PIL Image or Tensor): Image to be posterized.
Returns:
PIL Image or Tensor: Randomly posterized image.
| forward | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be solarized.
Returns:
PIL Image or Tensor: Randomly solarized image.
"""
if torch.rand(1).item() < self.p:
return F.solarize(img, self.threshold)
return img |
Args:
img (PIL Image or Tensor): Image to be solarized.
Returns:
PIL Image or Tensor: Randomly solarized image.
| forward | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be sharpened.
Returns:
PIL Image or Tensor: Randomly sharpened image.
"""
if torch.rand(1).item() < self.p:
return F.adjust_sharpness(img, self.sharpness_factor)
ret... |
Args:
img (PIL Image or Tensor): Image to be sharpened.
Returns:
PIL Image or Tensor: Randomly sharpened image.
| forward | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be autocontrasted.
Returns:
PIL Image or Tensor: Randomly autocontrasted image.
"""
if torch.rand(1).item() < self.p:
return F.autocontrast(img)
return img |
Args:
img (PIL Image or Tensor): Image to be autocontrasted.
Returns:
PIL Image or Tensor: Randomly autocontrasted image.
| forward | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def forward(self, img):
"""
Args:
img (PIL Image or Tensor): Image to be equalized.
Returns:
PIL Image or Tensor: Randomly equalized image.
"""
if torch.rand(1).item() < self.p:
return F.equalize(img)
return img |
Args:
img (PIL Image or Tensor): Image to be equalized.
Returns:
PIL Image or Tensor: Randomly equalized image.
| forward | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step1-5/mobilenetv3_ref/torchvision/transforms/transforms.py | Apache-2.0 |
def synchronize_between_processes(self):
"""
Warning: does not synchronize the deque!
"""
t = paddle.to_tensor([self.count, self.total], dtype='float64')
t = t.numpy().tolist()
self.count = int(t[0])
self.total = t[1] |
Warning: does not synchronize the deque!
| synchronize_between_processes | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/utils.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/utils.py | Apache-2.0 |
def accuracy(output, target, topk=(1, )):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with paddle.no_grad():
maxk = max(topk)
batch_size = target.shape[0]
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.e... | Computes the accuracy over the k top predictions for the specified values of k | accuracy | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/utils.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/utils.py | Apache-2.0 |
def __init__(self, args):
"""
Args:
args: Parameters generated using argparser.
Returns: None
"""
super().__init__()
self.args = args
# init inference engine
self.predictor, self.config, self.input_tensor, self.output_tensor = self.load_predi... |
Args:
args: Parameters generated using argparser.
Returns: None
| __init__ | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/deploy/inference_python/infer.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/deploy/inference_python/infer.py | Apache-2.0 |
def load_predictor(self, model_file_path, params_file_path):
"""load_predictor
initialize the inference engine
Args:
model_file_path: inference model path (*.pdmodel)
model_file_path: inference parmaeter path (*.pdiparams)
Return:
predictor: Predicto... | load_predictor
initialize the inference engine
Args:
model_file_path: inference model path (*.pdmodel)
model_file_path: inference parmaeter path (*.pdiparams)
Return:
predictor: Predictor created using Paddle Inference.
config: Configuration of t... | load_predictor | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/deploy/inference_python/infer.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/deploy/inference_python/infer.py | Apache-2.0 |
def preprocess(self, img_path):
"""preprocess
Preprocess to the input.
Args:
img_path: Image path.
Returns: Input data after preprocess.
"""
with open(img_path, "rb") as f:
img = Image.open(f)
img = img.convert("RGB")
img = s... | preprocess
Preprocess to the input.
Args:
img_path: Image path.
Returns: Input data after preprocess.
| preprocess | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/deploy/inference_python/infer.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/deploy/inference_python/infer.py | Apache-2.0 |
def postprocess(self, x):
"""postprocess
Postprocess to the inference engine output.
Args:
x: Inference engine output.
Returns: Output data after argmax.
"""
x = x.flatten()
class_id = x.argmax()
prob = x[class_id]
return class_id, p... | postprocess
Postprocess to the inference engine output.
Args:
x: Inference engine output.
Returns: Output data after argmax.
| postprocess | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/deploy/inference_python/infer.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/deploy/inference_python/infer.py | Apache-2.0 |
def run(self, x):
"""run
Inference process using inference engine.
Args:
x: Input data after preprocess.
Returns: Inference engine output
"""
self.input_tensor.copy_from_cpu(x)
self.predictor.run()
output = self.output_tensor.copy_to_cpu()
... | run
Inference process using inference engine.
Args:
x: Input data after preprocess.
Returns: Inference engine output
| run | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/deploy/inference_python/infer.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/deploy/inference_python/infer.py | Apache-2.0 |
def infer_main(args):
"""infer_main
Main inference function.
Args:
args: Parameters generated using argparser.
Returns:
class_id: Class index of the input.
prob: : Probability of the input.
"""
inference_engine = InferenceEngine(args)
# init benchmark
if args.... | infer_main
Main inference function.
Args:
args: Parameters generated using argparser.
Returns:
class_id: Class index of the input.
prob: : Probability of the input.
| infer_main | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/deploy/inference_python/infer.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/deploy/inference_python/infer.py | Apache-2.0 |
def has_file_allowed_extension(filename: str,
extensions: Tuple[str, ...]) -> bool:
"""Checks if a file is an allowed extension.
Args:
filename (string): path to a file
extensions (tuple of strings): extensions to consider (lowercase)
Returns:
bool: T... | Checks if a file is an allowed extension.
Args:
filename (string): path to a file
extensions (tuple of strings): extensions to consider (lowercase)
Returns:
bool: True if the filename ends with one of given extensions
| has_file_allowed_extension | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/paddlevision/datasets/folder.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/paddlevision/datasets/folder.py | Apache-2.0 |
def find_classes(directory: str) -> Tuple[List[str], Dict[str, int]]:
"""Finds the class folders in a dataset.
See :class:`DatasetFolder` for details.
"""
classes = sorted(
entry.name for entry in os.scandir(directory) if entry.is_dir())
if not classes:
raise FileNotFoundError(
... | Finds the class folders in a dataset.
See :class:`DatasetFolder` for details.
| find_classes | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/paddlevision/datasets/folder.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/paddlevision/datasets/folder.py | Apache-2.0 |
def make_dataset(
directory: str,
class_to_idx: Optional[Dict[str, int]]=None,
extensions: Optional[Tuple[str, ...]]=None,
is_valid_file: Optional[Callable[[str], bool]]=None, ) -> List[Tuple[
str, int]]:
"""Generates a list of samples of a form (path_to_sample, class).
... | Generates a list of samples of a form (path_to_sample, class).
See :class:`DatasetFolder` for details.
Note: The class_to_idx parameter is here optional and will use the logic of the ``find_classes`` function
by default.
| make_dataset | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/paddlevision/datasets/folder.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/paddlevision/datasets/folder.py | Apache-2.0 |
def make_dataset(
directory: str,
class_to_idx: Dict[str, int],
extensions: Optional[Tuple[str, ...]]=None,
is_valid_file: Optional[Callable[[str], bool]]=None, ) -> List[
Tuple[str, int]]:
"""Generates a list of samples of a form (path_to_sample, ... | Generates a list of samples of a form (path_to_sample, class).
This can be overridden to e.g. read files from a compressed zip file instead of from the disk.
Args:
directory (str): root dataset directory, corresponding to ``self.root``.
class_to_idx (Dict[str, int]): Dictionary... | make_dataset | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/paddlevision/datasets/folder.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/paddlevision/datasets/folder.py | Apache-2.0 |
def __getitem__(self, index: int) -> Tuple[Any, Any]:
"""
Args:
index (int): Index
Returns:
tuple: (sample, target) where target is class_index of the target class.
"""
path, target = self.samples[index]
sample = self.loader(path)
if self.... |
Args:
index (int): Index
Returns:
tuple: (sample, target) where target is class_index of the target class.
| __getitem__ | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/paddlevision/datasets/folder.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/paddlevision/datasets/folder.py | Apache-2.0 |
def alexnet(pretrained: bool=False, **kwargs: Any) -> AlexNet:
r"""AlexNet model architecture from the
`"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.
The required minimum input size of the model is 63x63.
Args:
pretrained (str): Pre-trained parameters of the model on ImageNet
... | AlexNet model architecture from the
`"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.
The required minimum input size of the model is 63x63.
Args:
pretrained (str): Pre-trained parameters of the model on ImageNet
| alexnet | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/paddlevision/models/alexnet.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/paddlevision/models/alexnet.py | Apache-2.0 |
def _make_divisible(v: float, divisor: int,
min_value: Optional[int]=None) -> int:
"""
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be seen here:
https://github.com/tensorflow/models/blob/master/r... |
This function is taken from the original tf repo.
It ensures that all layers have a channel number that is divisible by 8
It can be seen here:
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
| _make_divisible | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/paddlevision/models/mobilenet_v3.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/paddlevision/models/mobilenet_v3.py | Apache-2.0 |
def __init__(
self,
inverted_residual_setting: List[InvertedResidualConfig],
last_channel: int,
num_classes: int=1000,
block: Optional[Callable[..., nn.Layer]]=None,
norm_layer: Optional[Callable[..., nn.Layer]]=None,
dropout: float=0.2... |
MobileNet V3 main class
Args:
inverted_residual_setting (List[InvertedResidualConfig]): Network structure
last_channel (int): The number of channels on the penultimate layer
num_classes (int): Number of classes
block (Optional[Callable[..., nn.Layer]]): ... | __init__ | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/paddlevision/models/mobilenet_v3.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/paddlevision/models/mobilenet_v3.py | Apache-2.0 |
def mobilenet_v3_large(pretrained: bool=False,
progress: bool=True,
**kwargs: Any) -> MobileNetV3:
"""
Constructs a large MobileNetV3 architecture from
`"Searching for MobileNetV3" <https://arxiv.org/abs/1905.02244>`_.
Args:
pretrained (bool): If Tr... |
Constructs a large MobileNetV3 architecture from
`"Searching for MobileNetV3" <https://arxiv.org/abs/1905.02244>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
| mobilenet_v3_large | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/paddlevision/models/mobilenet_v3.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/paddlevision/models/mobilenet_v3.py | Apache-2.0 |
def mobilenet_v3_small(pretrained: bool=False,
progress: bool=True,
**kwargs: Any) -> MobileNetV3:
"""
Constructs a small MobileNetV3 architecture from
`"Searching for MobileNetV3" <https://arxiv.org/abs/1905.02244>`_.
Args:
pretrained (bool): If Tr... |
Constructs a small MobileNetV3 architecture from
`"Searching for MobileNetV3" <https://arxiv.org/abs/1905.02244>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
| mobilenet_v3_small | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/paddlevision/models/mobilenet_v3.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/paddlevision/models/mobilenet_v3.py | Apache-2.0 |
def get_params(transform_num: int) -> Tuple[int, Tensor, Tensor]:
"""Get parameters for autoaugment transformation
Returns:
params required by the autoaugment transformation
"""
policy_id = int(paddle.randint(low=0, high=transform_num, shape=(1, )))
probs = paddle.ra... | Get parameters for autoaugment transformation
Returns:
params required by the autoaugment transformation
| get_params | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/paddlevision/transforms/autoaugment.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/paddlevision/transforms/autoaugment.py | Apache-2.0 |
def forward(self, img: Tensor):
"""
img (PIL Image or Tensor): Image to be transformed.
Returns:
PIL Image or Tensor: AutoAugmented image.
"""
fill = self.fill
if isinstance(img, Tensor):
if isinstance(fill, (int, float)):
fill... |
img (PIL Image or Tensor): Image to be transformed.
Returns:
PIL Image or Tensor: AutoAugmented image.
| forward | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/paddlevision/transforms/autoaugment.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/paddlevision/transforms/autoaugment.py | Apache-2.0 |
def to_tensor(pic):
"""Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
See :class:`~paddlevision.transforms.ToTensor` for more details.
Args:
pic (PIL Image or numpy.ndarray): Image to be converted to tensor.
Returns:
Tensor: Converted image.
"""
if not (F_pil._is_pil_... | Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
See :class:`~paddlevision.transforms.ToTensor` for more details.
Args:
pic (PIL Image or numpy.ndarray): Image to be converted to tensor.
Returns:
Tensor: Converted image.
| to_tensor | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/paddlevision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/paddlevision/transforms/functional.py | Apache-2.0 |
def normalize(tensor: Tensor,
mean: List[float],
std: List[float],
inplace: bool=False) -> Tensor:
"""Normalize a float tensor image with mean and standard deviation.
This transform does not support PIL Image.
.. note::
This transform acts out of place by d... | Normalize a float tensor image with mean and standard deviation.
This transform does not support PIL Image.
.. note::
This transform acts out of place by default, i.e., it does not mutates the input tensor.
See :class:`~paddlevision.transforms.Normalize` for more details.
Args:
tensor... | normalize | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/paddlevision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/paddlevision/transforms/functional.py | Apache-2.0 |
def resize(img: Tensor,
size: List[int],
interpolation: InterpolationMode=InterpolationMode.BILINEAR,
max_size: Optional[int]=None,
antialias: Optional[bool]=None) -> Tensor:
r"""Resize the input image to the given size.
If the image is paddle Tensor, it is expected
... | Resize the input image to the given size.
If the image is paddle Tensor, it is expected
to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
.. warning::
The output image might be different depending on its type: when downsampling, the interpolation of PIL images
... | resize | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/paddlevision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/paddlevision/transforms/functional.py | Apache-2.0 |
def pad(img: Tensor,
padding: List[int],
fill: int=0,
padding_mode: str="constant") -> Tensor:
r"""Pad the given image on all sides with the given "pad" value.
If the image is paddle Tensor, it is expected
to have [..., H, W] shape, where ... means at most 2 leading dimensions for mo... | Pad the given image on all sides with the given "pad" value.
If the image is paddle Tensor, it is expected
to have [..., H, W] shape, where ... means at most 2 leading dimensions for mode reflect and symmetric,
at most 3 leading dimensions for mode edge,
and an arbitrary number of leading dimensions for... | pad | python | PaddlePaddle/models | tutorials/mobilenetv3_prod/Step6/paddlevision/transforms/functional.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/mobilenetv3_prod/Step6/paddlevision/transforms/functional.py | Apache-2.0 |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves Python code examples from Django repository that contain 'django' in the code, which helps identify Django-specific code snippets but provides limited analytical insights beyond basic filtering.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.