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@ -80,6 +80,8 @@ def interpolate(x,
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The input must be a 3-D Tensor of the shape (num_batches, channels, in_w)
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or 4-D (num_batches, channels, in_h, in_w), or a 5-D Tensor of the shape
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(num_batches, channels, in_d, in_h, in_w) or (num_batches, in_d, in_h, in_w, channels),
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Where in_w is width of the input tensor, in_h is the height of the input tensor,
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in_d is the depth of the intput tensor.
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and the resizing only applies on the three dimensions(depth, height and width).
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Supporting resample methods:
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@ -88,6 +90,7 @@ def interpolate(x,
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'trilinear' : Trilinear interpolation
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'nearest' : Nearest neighbor interpolation
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'bicubic' : Bicubic interpolation
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'area': Area interpolation
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Linear interpolation is the method of using a line connecting two known quantities
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to determine the value of an unknown quantity between the two known quantities.
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@ -114,6 +117,12 @@ def interpolate(x,
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smoother than corresponding surfaces obtained by bilinear interpolation or
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nearest-neighbor interpolation.
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Area interpolation is to perform area interpolation
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in both the 3rd dimension(in height direction) , the 4th dimension(in width
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direction) and the 5th dimension(in depth direction) on input tensor. Set to
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area will directly call `paddle.nn.functional.adaptive_avg_pool1d` or
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`paddle.nn.functional.adaptive_avg_pool2d` or `paddle.nn.functional.adaptive_avg_pool3d`.
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Example:
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.. code-block:: text
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@ -207,11 +216,11 @@ def interpolate(x,
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when input is a 4-D Tensor and is (out_d, out_h, out_w) when input is a 5-D Tensor.
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Default: None. If a list, each element can be an integer or a Tensor Variable of shape: [1].
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If a Tensor Variable, its dimensions size should be a 1.
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scale_factor (float|Tensor|list|None): The multiplier for the input height or width. At
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least one of :attr:`out_shape` or :attr:`scale_factor` must be set.
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And :attr:`out_shape` has a higher priority than :attr:`scale_factor`.Has to match input size if it is a list.
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scale_factor (float|Tensor|list|tuple|None): The multiplier for the input height or width. At
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least one of :attr:`size` or :attr:`scale_factor` must be set.
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And :attr:`size` has a higher priority than :attr:`scale_factor`.Has to match input size if it is either a list or a tuple or a Tensor.
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Default: None.
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mode (str): The resample method. It supports 'linear', 'nearest', 'bilinear',
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mode (str): The resample method. It supports 'linear', 'area', 'nearest', 'bilinear',
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'bicubic' and 'trilinear' currently. Default: 'nearest'
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align_corners(bool) : An optional bool, If True, the centers of the 4 corner pixels of the
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input and output tensors are aligned, preserving the values at the
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@ -235,7 +244,7 @@ def interpolate(x,
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Raises:
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TypeError: size should be a list or tuple or Tensor.
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ValueError: The 'mode' of image_resize can only be 'linear', 'bilinear',
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'trilinear', 'bicubic', or 'nearest' currently.
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'trilinear', 'bicubic', 'area' or 'nearest' currently.
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ValueError: 'linear' only support 3-D tensor.
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ValueError: 'bilinear', 'bicubic' and 'nearest' only support 4-D tensor.
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ValueError: 'trilinear' only support 5-D tensor.
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@ -283,10 +292,11 @@ def interpolate(x,
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'TRILINEAR',
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'NEAREST',
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'BICUBIC',
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'AREA',
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]
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if resample not in resample_methods:
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raise ValueError(
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"The 'resample' of image_resize can only be 'linaer', 'bilinear', 'trilinear', "
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"The 'resample' of image_resize can only be 'area', 'linear', 'bilinear', 'trilinear', "
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" 'bicubic' or 'nearest' currently.")
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if resample in ['LINEAR'] and len(x.shape) != 3:
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@ -310,8 +320,17 @@ def interpolate(x,
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raise ValueError(
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"align_corners option can only be set with the interpolating modes: linear | bilinear | bicubic | trilinear"
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)
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if resample == 'AREA' and len(x.shape) == 3:
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return paddle.nn.functional.adaptive_avg_pool1d(x, size)
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if resample == 'AREA' and len(x.shape) == 4:
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return paddle.nn.functional.adaptive_avg_pool2d(x, size)
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if resample == 'AREA' and len(x.shape) == 5:
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return paddle.nn.functional.adaptive_avg_pool3d(x, size)
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helper = LayerHelper('{}_interp_v2'.format(resample_type), **locals())
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dtype = helper.input_dtype()
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dtype = helper.input_dtype(input_param_name='x')
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if len(x.shape) == 3 and data_format not in ['NCW', 'NWC']:
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raise ValueError(
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"Got wrong value for param `data_format`: " + data_format +
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@ -349,14 +368,15 @@ def interpolate(x,
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out_shape = size
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scale = scale_factor
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if out_shape is not None and scale is not None:
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raise ValueError("Only one of size or scale_factor should be defined.")
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if out_shape is not None:
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if isinstance(out_shape, Variable):
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out_shape.stop_gradient = True
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inputs['OutSize'] = out_shape
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else:
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if not (_is_list_or_turple_(out_shape)):
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raise TypeError(
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"out_shape should be a list or tuple or Variable.")
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raise TypeError("size should be a list or tuple or Variable.")
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# Validate the shape
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contain_var = False
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for dim_idx, dim_size in enumerate(out_shape):
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@ -388,7 +408,7 @@ def interpolate(x,
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if len(x.shape) == 3:
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if len(out_shape) != 1:
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raise ValueError(
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"out_shape length should be 2 for input 3-D tensor")
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"size length should be 2 for input 3-D tensor")
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if contain_var:
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attrs['out_w'] = size_list[0]
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else:
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@ -396,7 +416,7 @@ def interpolate(x,
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attrs['out_w'] = out_shape[0]
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if len(x.shape) == 4:
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if len(out_shape) != 2:
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raise ValueError("out_shape length should be 2 for "
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raise ValueError("size length should be 2 for "
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"input 4-D tensor.")
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if contain_var:
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attrs['out_h'] = size_list[0]
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@ -407,7 +427,7 @@ def interpolate(x,
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attrs['out_w'] = out_shape[1]
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if len(x.shape) == 5:
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if len(out_shape) != 3:
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raise ValueError("out_shape length should be 3 for "
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raise ValueError("size length should be 3 for "
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"input 5-D tensor.")
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if contain_var:
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attrs['out_d'] = size_list[0]
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@ -430,7 +450,7 @@ def interpolate(x,
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for i in range(len(x.shape) - 2):
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scale_list.append(scale)
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attrs['scale'] = list(map(float, scale_list))
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elif isinstance(scale, list):
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elif isinstance(scale, list) or isinstance(scale, float):
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if len(scale) != len(x.shape) - 2:
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raise ValueError("scale_shape length should be {} for "
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"input {}-D tensor.".format(
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@ -441,7 +461,8 @@ def interpolate(x,
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attrs['scale'] = list(map(float, scale))
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else:
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raise TypeError(
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"Attr(scale)'s type should be float, int, list or Tensor.")
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"Attr(scale)'s type should be float, int, list, tuple, or Tensor."
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)
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if in_dygraph_mode():
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attr_list = []
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@ -480,9 +501,12 @@ def upsample(x,
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name=None):
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"""
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This op resizes a batch of images.
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The input must be a 3-D Tensor of the shape (num_batches, channels, in_w)
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or 4-D (num_batches, channels, in_h, in_w), or a 5-D Tensor of the shape
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(num_batches, channels, in_d, in_h, in_w) or (num_batches, in_d, in_h, in_w, channels),
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Where in_w is width of the input tensor, in_h is the height of the input tensor,
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in_d is the depth of the intput tensor.
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and the resizing only applies on the three dimensions(depth, height and width).
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Supporting resample methods:
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@ -507,12 +531,21 @@ def upsample(x,
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data points on a two-dimensional regular grid. The interpolated surface is
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smoother than corresponding surfaces obtained by bilinear interpolation or
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nearest-neighbor interpolation.
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Trilinear interpolation is an extension of linear interpolation for
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interpolating functions of three variables (e.g. D-direction,
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H-direction and W-direction in this op) on a rectilinear 3D grid.
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The linear interpolation is performed on three directions.
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align_corners and align_mode are optional parameters,the calculation method
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of interpolation can be selected by them.
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Area interpolation is to perform area interpolation
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in both the 3rd dimension(in height direction) , the 4th dimension(in width
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direction) and the 5th dimension(in depth direction) on input tensor. Set to
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area will directly call `paddle.nn.functional.adaptive_avg_pool1d` or
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`paddle.nn.functional.adaptive_avg_pool2d` or `paddle.nn.functional.adaptive_avg_pool3d`.
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Example:
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.. code-block:: text
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For scale_factor:
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@ -605,9 +638,10 @@ def upsample(x,
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when input is a 4-D Tensor and is (out_d, out_h, out_w) when input is a 5-D Tensor.
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Default: None. If a list, each element can be an integer or a Tensor Variable of shape: [1].
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If a Tensor Variable, its dimensions size should be a 1.
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scale_factor (float|Tensor|list|None): The multiplier for the input height or width. At
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least one of :attr:`out_shape` or :attr:`scale_factor` must be set.
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And :attr:`out_shape` has a higher priority than :attr:`scale_factor`.
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scale_factor (float|Tensor|list|tuple|None): The multiplier for the input height or width. At
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least one of :attr:`size` or :attr:`scale_factor` must be set.
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And :attr:`size` has a higher priority than :attr:`scale_factor`.Has to match input size if
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it is either a list or a tuple or a Tensor.
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Default: None.
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mode (str): The resample method. It supports 'linear', 'nearest', 'bilinear',
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'bicubic' and 'trilinear' currently. Default: 'nearest'
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xiaoting
5 years ago
committed by
GitHub