class crop_resize(Operation):
"""
Resize the spatial dimensions (last two dimensions) of the first input
according to the bounding boxes specified in the second input, using
bilinear interpolation.
Parameters
----------
x: tensor<[B, C, H, W],T> (Required)
* The input, from which patches (regions of interest) are extracted
and resized using bilinear interpolation.
* Rank ``4``.
roi: tensor<[N,1,4,1,1], T> or tensor<[N,1,5,1,1], T> (Required)
* Regions of interest, or coordinates of the boxes. The above input
represents coordinates of ``N`` boxes.
* The convention to express coordinates depends on the value of the
input ``box_coordinate_mode``.
* Rank ``5``.
* If ``tensor<[N,1,4,1,1], T>``: Resized images are computed for all
``B`` input images.
* If ``tensor<[N,1,5,1,1], T>``: The first element from ``axis=-3``
to be resized is an index. It must be within range ``[0, B)``.
target_height: const<i32> (Optional, Default=1)
* Target height for resizing each patch.
target_width: const<i32> (Optional, Default=1)
* Target width for resizing each patch.
normalized_coordinates : const<bool> (Optional, default=False)
* If true, the bounding box coordinates must be in the
interval ``[0, 1]``. Scaling is based on the input spatial
dimensions: ``(H_in - 1)`` for height and ``(W_in - 1)`` for width.
* If false, the bounding box coordinates must be in the interval
``[0, H_in - 1]`` for height dimensions and ``[0, W_in - 1]`` for
width dimensions.
spatial_scale : const<fp32> (Optional, default=1.0)
* Additional spatial scale that multiplies the bounding box coordinates.
You would use this to implement the RoI Align layer, which typically
uses unnormalized RoI coordinates along with a spatial scale that is
less than or equal to 1.
box_coordinate_mode: const<str> (Optional, default="CORNERS_HEIGHT_FIRST")
* Specifies the convention for specifying the four bounding box
coordinates for an image of size ``(Height, Width)``. The ``(0,0)``
coordinate corresponds to the top-left corner of the image.
* This parameter can take one of four values:
"CORNERS_HEIGHT_FIRST": ``[h_start, w_start, h_end, w_end]``
"CORNERS_WIDTH_FIRST": ``[w_start, h_start, w_end, h_end]``
"CENTER_SIZE_HEIGHT_FIRST": ``[h_center, w_center, box_height, box_width]``
"CENTER_SIZE_WIDTH_FIRST": ``[w_center, h_center, box_width, box_height]``
sampling_mode : const<str> (Optional, default="DEFAULT")
* This parameter can take ``"STRICT_ALIGN_CORNERS"``,
``"ALIGN_CORNERS"``, ``"DEFAULT"``, ``"OFFSET_CORNERS"`` or
``UNALIGN_CORNERS`` as values.
* This same convention is used by the ``resize_bilinear`` op (see
that op for details).
See Also
--------
resize_bilinear
Returns
-------
tensor<[N, B, C, target_height, target_width],T> or tensor<[N, 1, C, target_height, target_width],T>
* Tensor with same type as the input.
* If ``roi : tensor<[N,1,4,1,1], T>``, the output is
``tensor<[N, B, C, target_height, target_width],T>``.
Total crops = ``N*B``; that is, ``N`` crops for each input in the batch.
* If ``roi : tensor<[N,1,5,1,1], T>``, the output is
``tensor<[N, 1, C, target_height, target_width],T>``.
Total crops = ``N``; that is, 1 crop for given input image index
in the batch.
Attributes
----------
T: fp32
"""
input_spec = InputSpec(
x=TensorInputType(),
roi=TensorInputType(),
target_height=IntInputType(const=True, optional=True),
target_width=IntInputType(const=True, optional=True),
normalized_coordinates=BoolInputType(const=True, optional=True),
spatial_scale=FloatInputType(const=True, optional=True),
box_coordinate_mode=StringInputType(const=True, optional=True),
sampling_mode=StringInputType(const=True, optional=True),
)
def default_inputs(self):
return DefaultInputs(
target_height=1,
target_width=1,
normalized_coordinates=False,
spatial_scale=1.,
box_coordinate_mode="CONRNERS_HEIGHT_FIRST",
sampling_mode="DEFAULT",
)
def __init__(self, **kwargs):
super(crop_resize, self).__init__(**kwargs)
def type_inference(self):
if self.x.rank != 4:
raise ValueError(
'input to the "crop_resize" op must be of rank 4. Provided {}'.
format(self.x.rank))
if self.roi.rank != 5:
raise ValueError(
'ROI input to the "crop_resize" op must be of rank 5, provided {}'
.format(self.roi.rank))
if self.sampling_mode.val not in {
"STRICT_ALIGN_CORNERS",
"ALIGN_CORNERS",
"UNALIGN_CORNERS",
"DEFAULT",
"OFFSET_CORNERS",
}:
raise ValueError(
'"crop_resize" op: unrecognized sampling mode "{}"'.format(
self.sampling_mode))
# ret_shape: [N] + [B, C, h_out, w_out]
N, B, C = self.roi.shape[0], self.x.shape[0], self.x.shape[1]
ret_shape = [N, B, C, self.target_height.val, self.target_width.val]
return types.tensor(self.x.dtype, ret_shape)
class upsample_bilinear(Operation):
"""
Upsample the spatial dimensions (last two dimensions) of the input
by scale factors using bilinear interpolation.
The upsample_bilinear operation in MIL corresponds to the recompute_scale_factor=True
mode in the pyorch bilinear interpolation op. That is,
the scale factor is recomputed by the output size.
Note that when the scale_factor_height and scale_factor_width are floating point, this
could result in a different scale factor due to rounding.
Parameters
----------
x: tensor<[\*D, H1, W1],T> (Required)
* Must be at least rank ``3``.
scale_factor_height: const<T2> (Optional, default=1)
* Scale factor for the height dimension (``axis=-2``).
scale_factor_width: const<T2> (Optional, default=1)
* Scale factor for the width dimension (``axis=-1``).
align_corners: const<bool> (Optional, default=True)
* This parameter determines how samples are chosen for bilinear
interpolation. For details, see the Notes section.
Notes
-----
To understand the ``align_corners`` parameter, consider the 1-D case.
You need to sample a grid of pixels whose values are computed using linear
interpolation. This parameter controls how the grid is sampled. If the
input grid is ``[0, Xin-1]`` (corresponding to an input size of ``Xin``),
and if the output size is ``Xout``, then the grid points are sampled in
the following manner:
.. sourcecode:: python
# If align_corners == True:
spacing = (Xin - 1) / (Xout - 1)
grid_point[i] = min(Xin - 1, max(0, i*spacing)), for i=0,1,...,Xout-1
# If align_corners == False:
spacing = Xin / Xout
grid_point[i] = min(Xin - 1, max(0, i*spacing + 0.5*spacing - 0.5)),
... for i=0,1,...,Xout-1
For example:
.. sourcecode:: python
Xin = 2
input_interval = [0,1]
Grid points:
.. sourcecode:: python
[0., 0.1, 0.5, 0.9, 1.] (Xout = 5, align_corners=False)
[0., 0.25, 0.5, 0.75, 1.] (Xout = 5, align_corners=True)
[0., 0., 0.33, 0.67, 1., 1.] (Xout = 6, align_corners=False)
[0., 0.2, 0.4, 0.6, 0.8, 1.] (Xout = 6, align_corners=True)
Note the following similarities:
* ``align_corners=False`` is the same as
``tf.raw_ops.ResizeBilinear(align_corners=False, half_pixel_centers=True)``.
* ``align_corners=True`` is the same as
``tf.raw_ops.ResizeBilinear(align_corners=True, half_pixel_centers=False)``.
Returns
-------
tensor<[\*D, H2, W2],T>
* Tensor with same type as the input.
* ``H2`` = floor(``H1`` * ``scale_factor_height``).
* ``W2`` = floor(``W1`` * ``scale_factor_width``).
Attributes
----------
T: fp32
T2 : fp32 or int32
"""
input_spec = InputSpec(
x=TensorInputType(),
scale_factor_height=IntOrFloatInputType(const=True, optional=True),
scale_factor_width=IntOrFloatInputType(const=True, optional=True),
align_corners=BoolInputType(const=True, optional=True),
)
def default_inputs(self):
return DefaultInputs(
scale_factor_height=1,
scale_factor_width=1,
align_corners=True,
)
def __init__(self, **kwargs):
super(upsample_bilinear, self).__init__(**kwargs)
def type_inference(self):
if self.x.rank < 3:
raise ValueError(
'input to the "upsample_bilinear" op must have rank at least 3'
)
ret_shape = list(self.x.shape)
ret_shape[-1] = np.floor(self.scale_factor_width.val *
ret_shape[-1]) if not is_symbolic(
ret_shape[-1]) else get_new_symbol()
ret_shape[-2] = np.floor(self.scale_factor_height.val *
ret_shape[-2]) if not is_symbolic(
ret_shape[-2]) else get_new_symbol()
return types.tensor(self.x.dtype, ret_shape)
class resample(Operation):
"""
Resample the input image tensor ``x``, at the ``coordinates``.
input. Since the coordinates may not correspond to exact pixels in the
input image, this would require "resampling". sampling_mode determines
the algorithm used for resampling and computing the values.
Parameters
----------
x: tensor<[B, C, H1, W1], T>
* Must be rank ``4``.
coordinates: tensor<[B, H2, W2, 2], U>
* Must be rank ``4``.
* Coordinates are provided in the order (x, y), i.e., (w, h).
* Value of each output location output[b, c, h, w] is calculated by
sampling, from the input image x[b, c, :, :], the pixel at the (x, y)
location corresponding to the length-2 vector: coordinates[b, h, w, :]
* Coordinate (normalized or unnormalized) should be specified according
to ``coordinates_mode``
sampling_mode: const<str>
* Allowed values: "bilinear" , "nearest"
padding_mode: const<str>
* Allowed values: "constant", "border", "reflection", "symmetric"
* Note that following illustration is 1D case for brevity, the op only support 2D image input.
* if ``padding_mode == "constant"``:
the input image is assumed to be padded with the padding_value
E.g., |1, 2, 3| -> |0, 0, 0, 1, 2, 3, 0, 0, 0|
* if ``padding_mode == "border"``:
the input image is assumed to be padded with the values replicated
from the values at the edge. This is also referred to as the
"clamped" or "replication" mode, since the padded values are
clamped to the border values.
E.g., |1, 2, 3| -> |1, 1, 1, 1, 2, 3, 3, 3, 3|
* if ``padding_mode == "reflection"``:
the border values are reflected, *not* including the values at the edge/border
E.g., |1, 2, 3| -> |2, 3, 2, 1, 2, 3, 2, 1, 2|
* if ``padding_mode == "symmetric"``:
values are reflected, including the border/edge values
E.g., |1, 2, 3| -> |3, 2, 1 , 1, 2, 3, 3, 2, 1|
padding_value: const<T>
* To be used only when ``padding_mode == "constant"``, ignored in other cases.
coordinates_mode: const<str>
* allowed values: "unnormalized", "normalized_minus_one_to_one",
"normalized_zero_to_one"
* if ``coordinates_mode == "unnormalized"``, the coordinates input values
are interpreted to be in range [0, W - 1] / [0, H - 1] corresponds to in-image point
* if ``coordinates_mode == "normalized_minus_one_to_one"``, in-image values are [-1, 1]
* if ``coordinates_mode == "normalized_zero_to_one"``, in-image values are [0, 1]
* E.g., if ``coordinates_mode == "normalized_minus_one_to_one"``,
the in range values are [-1, 1]. That is:
* (-1, -1), i.e., (w=-1, h=-1), corresponds to the top-left pixel
* (1, -1), i.e., (w=1, h=-1), corresponds to the top-right pixel
* (-1, 1), i.e., (w=-1, h=1), corresponds to the bottom-left pixel
* (1, 1), i.e., (w=1, h=1), corresponds to the bottom-right pixel
align_corners: const<bool>
* if ``align_corners == True``, the extrema coordinates are corresponding
to the center of the first and last corner pixels.
* if ``align_corners == False``, the extrema coordinates are corresponding
to the edge of the first and last corner pixels.
Returns
-------
tensor<[B, C, H2, W2], T>
Attributes
----------
T: fp32
U: fp32, i32, i64
"""
input_spec = InputSpec(
x=TensorInputType(),
coordinates=TensorInputType(),
sampling_mode=StringInputType(const=True),
padding_mode=StringInputType(const=True),
padding_value=FloatInputType(const=True),
coordinates_mode=StringInputType(const=True),
align_corners=BoolInputType(const=True),
)
def __init__(self, **kwargs):
super(resample, self).__init__(**kwargs)
def type_inference(self):
if self.x.rank != 4:
raise ValueError(
'input "x" to the "resample" op must be a rank 4 tensor. '
"Got rank {} tensor of shape {}".format(
self.x.rank, self.x.shape))
if self.coordinates.rank != 4:
raise ValueError(
'input "coordinates" to the "resample" op must be a rank 4 tensor. '
"Got rank {} tensor of shape {}".format(
self.coordinates.rank, self.coordinates.shape))
input_shape = self.x.shape
coord_shape = self.coordinates.shape
if (not is_symbolic(input_shape[0]) and not is_symbolic(coord_shape[0])
and input_shape[0] != coord_shape[0]):
raise ValueError(
'input "x" and "coordinates" to the "resample" must agree on '
"dimension of batch size: {} vs. {}".format(
input_shape[0], coord_shape[0]))
if not is_symbolic(coord_shape[-1]) and coord_shape[-1] != 2:
raise ValueError(
'input "coordinates" to the "resample" op last dimension must be 2. '
"Got {} for last dimension".format(coord_shape[-1]))
ret_shape = list(input_shape)
ret_shape[2] = coord_shape[1] # Output height
ret_shape[3] = coord_shape[2] # Output width
return types.tensor(self.x.dtype, tuple(ret_shape))
class affine(Operation):
"""
Apply a linear affine transform to the input 2D image tensor. Value at the
(x, y), i.e., (w, h) coordinate of the output, is computed by first computing
the coordinates x’ and y’ with the following equation and then compute the
value at the coordinate (x’,y’) in the input image using either bilinear or
nearest neighbor interpolation. If the (x’, y’) point falls outside the input
image, then padding information is used to compute the value.
* x’ = a0 * x + a1 * y + a2
* y’ = b0 * x + b1 * y + b2
Parameters
----------
x: tensor<[B, C, H1, W1], T>
* Must be rank ``4``.
transform_matrix: tensor<[D, 6], T>
* Must be rank ``2``
* D can be either B or 1.
when D == B, for each batch, there is a separate transform matrix
when D == 1, the same matrix is used for all input batches
for each batch: [a0, a1, a2, b0, b1, b2]
output_height: const<i32>
* Target output height
output_width: const<i32>
* Target output width
sampling_mode: const<str>
* Allowed values: "bilinear"
padding_mode: const<str>
* Allowed values: "constant"
* Note that following illustration is 1D case for brevity, the op only support 2D image input.
* if ``padding_mode == "constant"``:
the input image is assumed to be padded with the padding_value
E.g., |1, 2, 3| -> |0, 0, 0, 1, 2, 3, 0, 0, 0|
padding_value: const<T>
* Currently non-zero values are not supported.
* To be used only when ``padding_mode == "constant"``, ignored in other cases.
coordinates_mode: const<str>
* allowed values: "normalized_minus_one_to_one",
* if ``coordinates_mode == "normalized_minus_one_to_one"``, in-image values are [-1, 1]
* E.g., if ``coordinates_mode == "normalized_minus_one_to_one"``,
the in range values are [-1, 1]. That is:
* (-1, -1), i.e., (w=-1, h=-1), corresponds to the top-left pixel
* (1, -1), i.e., (w=1, h=-1), corresponds to the top-right pixel
* (-1, 1), i.e., (w=-1, h=1), corresponds to the bottom-left pixel
* (1, 1), i.e., (w=1, h=1), corresponds to the bottom-right pixel
align_corners: const<bool>
* Currently align_corners=False is not supported.
* To be used only when ``coordinates_mode != unnormalized``, ignored otherwise.
* if ``align_corners == True``, the extrema coordinates are corresponding
to the center of the first and last corner pixels.
* if ``align_corners == False``, the extrema coordinates are corresponding
to the edge of the first and last corner pixels.
Returns
-------
tensor<[B, C, output_height, output_width], T>
Attributes
----------
T: fp32
"""
input_spec = InputSpec(
x=TensorInputType(),
transform_matrix=TensorInputType(),
output_height=IntInputType(const=True),
output_width=IntInputType(const=True),
sampling_mode=StringInputType(const=True),
padding_mode=StringInputType(const=True),
padding_value=FloatInputType(const=True),
coordinates_mode=StringInputType(const=True),
align_corners=BoolInputType(const=True),
)
def __init__(self, **kwargs):
super(affine, self).__init__(**kwargs)
def type_inference(self):
if self.x.rank != 4:
raise ValueError(
'input "x" to the "affine" op must be a rank 4 tensor. '
"Got rank {} tensor of shape {}".format(
self.x.rank, self.x.shape))
if self.transform_matrix.rank != 2:
raise ValueError(
'input "transform_matrix" to the "affine" op must be a rank 2 tensor. '
"Got rank {} tensor of shape {}".format(
self.transform_matrix.rank, self.transform_matrix.shape))
if self.sampling_mode.val.lower() != "bilinear":
raise NotImplementedError(
'input "sampling_mode" to the "affine" not implemented. '
'Got "{}"'.format(self.sampling_mode.val))
if self.coordinates_mode.val.lower() != "normalized_minus_one_to_one":
raise NotImplementedError(
'input "coordinates_mode" to the "affine" not implemented. '
'Got "{}"'.format(self.coordinates_mode.val))
if self.padding_mode.val.lower(
) != "constant" or self.padding_value.val != 0.0:
raise NotImplementedError(
'input "padding_mode" to the "affine" not implemented. '
'Got "{}" with "padding_value={}"'.format(
self.padding_mode.val, self.padding_value.val))
input_shape = self.x.shape
transform_matrix_shape = self.transform_matrix.shape
if (not is_symbolic(transform_matrix_shape[-1])
and transform_matrix_shape[-1] != 6):
raise ValueError(
'input "transform_matrix" to the "affine" op last dimension must be 6 '
"[a0, a1, a2, b0, b1, b2], "
"Got {} for last dimension".format(transform_matrix_shape[-1]))
ret_shape = list(input_shape)
ret_shape[2] = self.output_height.val
ret_shape[3] = self.output_width.val
return types.tensor(self.x.dtype, tuple(ret_shape))
class matmul(Operation):
"""
Perform N-D batch matrix multiplication with NumPy-style broadcasting
based on the following rules:
Rule 1. If both ``x, y`` are 1-D, return the scalar from the dot product.
Rule 2. If both ``x, y`` are 2-D or higher, perform a broadcast on the batch dimensions
(all dimensions except the last ``2``).
For example:
* ``x.shape == (10, 4, 3)``
* ``y.shape == (5, 10, 3, 2)``
* ``matmul(x, y).shape == (5, 10, 4, 2)``
Conventional matrix multiplication is a special case where both ``x, y`` are
exactly 2-D. For example:
* ``x.shape == (4, 3)``
* ``y.shape == (3, 2)``
* ``matmul(x, y).shape == (4, 2)``
If ``x`` is 1-D, and ``y`` is N-D where ``N >= 2``, ``x`` is first promoted to
matrix ``xm`` by prepending a ``1`` to its dimension, and the resulting ``xm`` is
broadcast to ``y`` following Rule 2 above. After this, remove the inserted dimension.
For example:
* ``x.shape == (4)``
* ``y.shape == (10, 4, 3)``
* ``xm.shape == (1, 4)``
* ``matmul(xm, y).shape == (10, 1, 3)``
* Removing the inserted dimension results in ``matmul(x, y).shape == (10, 3)``.
* Note: ``xm`` and ``matmul(xm, y)`` are for illustration only.
If ``x`` is N-D where ``N >= 2``, and ``y`` is 1-D, ``y`` is first promoted to
matrix ``ym`` by appending a ``1`` to its dimension, and the resulting ``ym`` is
broadcast to ``x`` following Rule 2 above. After this, remove the inserted dimension.
For example:
* ``x.shape == (10, 3, 4)``
* ``y.shape == (4,)``
* ``ym.shape == (4, 1)``
* ``matmul(x, ym).shape == (10, 3, 1)``
* Removing the inserted dimension results in ``matmul(x, y).shape == (10, 3)``.
* Note: ``xm`` and ``matmul(xm, y)`` are for illustration only.
Parameters
----------
x: tensor<[\*,K1], T> (Required)
* ``x`` must be 1-D or higher.
y: tensor<[\*,K2], T> (Required)
* ``y`` must be 1-D or higher.
transpose_x: const bool (Optional)
* Default to ``False``.
* Use ``True`` to transpose the last two dimensions of ``x`` before multiplication.
It has no effect when ``x`` is 1-D.
transpose_y: const bool (Optional)
* Default to ``False``.
* Use ``True`` to transpose the last two dimensions of ``y`` before multiplication.
It has no effect when ``y`` is 1-D.
Returns
-------
tensor<\*, T>
* Scalar or tensor output.
Attributes
----------
T: fp16, fp32, i32
"""
input_spec = InputSpec(
x=TensorInputType(),
y=TensorInputType(),
transpose_x=BoolInputType(const=True, optional=True),
transpose_y=BoolInputType(const=True, optional=True),
)
def default_inputs(self):
return DefaultInputs(
transpose_x=False,
transpose_y=False,
)
def __init__(self, **kwargs):
super().__init__(**kwargs)
def type_inference(self):
x_type = self.x.dtype
x_shape = list(self.x.shape)
y_shape = list(self.y.shape)
x_rank = len(x_shape)
if x_rank == 1 and self.transpose_x.val:
msg = "Op {} (matmul): x is rank 1, but transpose_x is True, which is not allowed."
raise ValueError(msg.format(self.name))
if self.transpose_x.val:
x_shape = list(x_shape)
x_shape[-1], x_shape[-2] = x_shape[-2], x_shape[-1]
x_shape = tuple(x_shape)
if self.transpose_y.val:
y_shape = list(y_shape)
y_shape[-1], y_shape[-2] = y_shape[-2], y_shape[-1]
y_shape = tuple(y_shape)
if not (x_shape[-1] == y_shape[-2] or is_symbolic(x_shape[-1])
or is_symbolic(y_shape[-2])):
msg = "Op {} (matmul): x {}, y {} are not broadcastable"
raise ValueError(msg.format(self.name, self.x.shape, self.y.shape))
if x_rank == 1:
# promote shape of x to rank 2
x_shape = list((1, ) + tuple(x_shape))
ret_shape = list(broadcast_shapes(x_shape[:-2], y_shape[:-2]))
ret_shape += [x_shape[-2], y_shape[-1]]
if x_rank == 1:
# remove the first dimension of the returned shape
return types.tensor(x_type, tuple(ret_shape[1:]))
else:
return types.tensor(x_type, tuple(ret_shape))
@precondition(allow=VALUE)
def value_inference(self):
x = self.x.val
if self.transpose_x.val:
x = np.transpose(x)
y = self.y.val
if self.transpose_y.val:
y = np.transpose(y)
return np.matmul(x, y)