resize¶
dragon.vision.
resize
(
inputs,
sizes=None,
scales=None,
mode='linear',
align_corners=False,
data_format='NCHW',
**kwargs
)[source]¶Resize input via interpolating neighborhoods.
sizes
orscales
will be selected bydata_format
:x, sizes = dragon.ones((1, 2, 3, 4)), (6, 6) a = dragon.vision.resize(x, sizes, data_format='NCHW') # Shape: (1, 2, 6, 6) c = dragon.vision.resize(x, sizes, data_format='NHWC') # Shape: (1, 6, 6, 4)
Use
align_corners
to determine the input coordinates in linear interpolating:# align_corners = False # Use half-pixel transformation scale = float(in_size) / float(out_size) in_coord = (out_coord + 0.5) * scale - 0.5 # align_corners = True # Use align-corners transformation scale = float(in_size - 1) / float(out_size - 1) in_coord = out_coord * scale
- Parameters:
- inputs (dragon.Tensor) – The input tensor.
- sizes (Union[int, Sequence[int], dragon.Tensor], optional) – The output dimensions.
- scales (Union[float, Sequence[float], dragon.Tensor], optional) – The scale along each input dimension.
- mode (str, optional, default='nearest') –
'nearest'
or'linear'
. - align_corners (bool, optional, default=False) – Whether to align corners in linear interpolating.
- data_format (str, optional, default='NCHW') –
'NCHW'
or'NHWC'
.
- Returns:
dragon.Tensor – The output tensor.