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# to you under the Apache License, Version 2.0 (the
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#
# http://www.apache.org/licenses/LICENSE-2.0
#
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# software distributed under the License is distributed on an
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# KIND, either express or implied. See the License for the
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# pylint: disable=invalid-name, too-many-lines
"""Neural network operations."""
from tvm.relay import expr
from ...expr import Constant, Expr, const
from ..dyn.nn import _make as _dyn_make
from . import _make
from .utils import get_pad_tuple1d, get_pad_tuple2d, get_pad_tuple3d
def conv1d(
data,
weight,
strides=1,
padding=0,
dilation=1,
groups=1,
channels=None,
kernel_size=None,
data_layout="NCW",
kernel_layout="OIW",
out_layout="",
out_dtype="",
):
r"""1D convolution.
This operator takes the weight as the convolution kernel
and convolves it with data to produce an output.
In the default case, where the data_layout is `NCW`
and kernel_layout is `OIW`, conv1d takes in
a data Tensor with shape `(batch_size, in_channels, width)`,
and a weight Tensor with shape `(channels, in_channels, kernel_size)`
to produce an output Tensor with the following rule:
.. math::
\mbox{out}[b, c, w] = \sum_{dw, k}
\mbox{data}[b, k, \mbox{strides}[0] * w + dw] *
\mbox{weight}[c, k, dw]
Padding and dilation are applied to data and weight respectively before the computation.
This operator accepts data layout specification.
Semantically, the operator will convert the layout to the canonical layout
(`NCW` for data and `OIW` for weight), perform the computation,
then convert to the out_layout.
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
weight : tvm.relay.Expr
The weight expressions.
strides : Optional[int, Tuple[int]]
The strides of convolution.
padding : Optional[int, Tuple[int]]
The padding of convolution on both sides of the input before convolution.
dilation : Optional[int, Tuple[int]]
Specifies the dilation rate to be used for dilated convolution.
groups : Optional[int]
Currently unused for 1D convolution.
channels : Optional[int]
Number of output channels of this convolution.
kernel_size : Optional[int, Tuple[int]]
The spatial dimension of the convolution kernel.
data_layout : Optional[str]
Layout of the input.
kernel_layout : Optional[str]
Layout of the weight.
out_layout : Optional[str]
Layout of the output, by default, out_layout is the same as data_layout
out_dtype : Optional[str]
Specifies the output data type for mixed precision conv2d.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
if isinstance(kernel_size, int):
kernel_size = (kernel_size,)
if isinstance(strides, int):
strides = (strides,)
if isinstance(dilation, int):
dilation = (dilation,)
padding = get_pad_tuple1d(padding)
return _make.conv1d(
data,
weight,
strides,
padding,
dilation,
groups,
channels,
kernel_size,
data_layout,
kernel_layout,
out_layout,
out_dtype,
)
def conv2d(
data,
weight,
strides=(1, 1),
padding=(0, 0),
dilation=(1, 1),
groups=1,
channels=None,
kernel_size=None,
data_layout="NCHW",
kernel_layout="OIHW",
out_layout="",
out_dtype="",
):
r"""2D convolution.
This operator takes the weight as the convolution kernel
and convolves it with data to produce an output.
In the default case, where the data_layout is `NCHW`
and kernel_layout is `OIHW`, conv2d takes in
a data Tensor with shape `(batch_size, in_channels, height, width)`,
and a weight Tensor with shape `(channels, in_channels, kernel_size[0], kernel_size[1])`
to produce an output Tensor with the following rule:
.. math::
\mbox{out}[b, c, y, x] = \sum_{dy, dx, k}
\mbox{data}[b, k, \mbox{strides}[0] * y + dy, \mbox{strides}[1] * x + dx] *
\mbox{weight}[c, k, dy, dx]
Padding and dilation are applied to data and weight respectively before the computation.
This operator accepts data layout specification.
Semantically, the operator will convert the layout to the canonical layout
(`NCHW` for data and `OIHW` for weight), perform the computation,
then convert to the out_layout.
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
weight : tvm.relay.Expr
The weight expressions.
strides : Optional[int, Tuple[int]]
The strides of convolution.
padding : Optional[int, Tuple[int]]
The padding of convolution on both sides of inputs before convolution.
dilation : Optional[int, Tuple[int]]
Specifies the dilation rate to be used for dilated convolution.
groups : Optional[int]
Number of groups for grouped convolution.
channels : Optional[int]
Number of output channels of this convolution.
kernel_size : Optional[int, Tuple[int]]
The spatial of the convolution kernel.
data_layout : Optional[str]
Layout of the input.
kernel_layout : Optional[str]
Layout of the weight.
out_layout : Optional[str]
Layout of the output, by default, out_layout is the same as data_layout
out_dtype : Optional[str]
Specifies the output data type for mixed precision conv2d.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
if isinstance(kernel_size, int):
kernel_size = (kernel_size, kernel_size)
if isinstance(strides, int):
strides = (strides, strides)
if isinstance(dilation, int):
dilation = (dilation, dilation)
# TODO enforce 4-way padding in topi/nn/conv2d after #4644 merged
# convert 2-way padding to 4-way padding
padding = get_pad_tuple2d(padding)
return _make.conv2d(
data,
weight,
strides,
padding,
dilation,
groups,
channels,
kernel_size,
data_layout,
kernel_layout,
out_layout,
out_dtype,
)
def conv3d(
data,
weight,
strides=(1, 1, 1),
padding=(0, 0, 0),
dilation=(1, 1, 1),
groups=1,
channels=None,
kernel_size=None,
data_layout="NCDHW",
kernel_layout="OIDHW",
out_layout="",
out_dtype="",
):
r"""3D convolution.
This operator takes the weight as the convolution kernel
and convolves it with data to produce an output.
In the default case, where the data_layout is `NCDHW`
and kernel_layout is `OIDHW`, conv3d takes in
a data Tensor with shape `(batch_size, in_channels, depth, height, width)`,
and a weight Tensor with shape `(channels, in_channels, kernel_size[0], kernel_size[1],
kernel_size[2])` to produce an output Tensor with the following rule:
.. math::
\mbox{out}[b, c, z, y, x] = \sum_{dz, dy, dx, k}
\mbox{data}[b, k, \mbox{strides}[0] * z + dz, \mbox{strides}[1] * y + dy,
\mbox{strides}[2] * x + dx] * \mbox{weight}[c, k, dz, dy, dx]
Padding and dilation are applied to data and weight respectively before the computation.
This operator accepts data layout specification.
Semantically, the operator will convert the layout to the canonical layout
(`NCDHW` for data and `OIDHW` for weight), perform the computation,
then convert to the out_layout.
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
weight : tvm.relay.Expr
The weight expressions.
strides : Optional[Tuple[int]]
The strides of convolution.
padding : Optional[int, Tuple[int]]
The padding of convolution on both sides of inputs before convolution.
dilation : Optional[int, Tuple[int]]
Specifies the dilation rate to be used for dilated convolution.
groups : Optional[int]
Number of groups for grouped convolution.
channels : Optional[int]
Number of output channels of this convolution.
kernel_size : Optional[int, Tuple[int]]
The spatial of the convolution kernel.
data_layout : Optional[str]
Layout of the input.
kernel_layout : Optional[str]
Layout of the weight.
out_layout : Optional[str]
Layout of the output, by default, out_layout is the same as data_layout
out_dtype : Optional[str]
Specifies the output data type for mixed precision conv2d.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
if isinstance(kernel_size, int):
kernel_size = (kernel_size, kernel_size, kernel_size)
if isinstance(strides, int):
strides = (strides, strides, strides)
if isinstance(dilation, int):
dilation = (dilation, dilation, dilation)
padding = get_pad_tuple3d(padding)
return _make.conv3d(
data,
weight,
strides,
padding,
dilation,
groups,
channels,
kernel_size,
data_layout,
kernel_layout,
out_layout,
out_dtype,
)
def contrib_conv3d_winograd_without_weight_transform(
data,
weight,
tile_size,
strides=(1, 1, 1),
padding=(0, 0, 0),
dilation=(1, 1, 1),
groups=1,
channels=None,
kernel_size=None,
data_layout="NCDHW",
kernel_layout="OIDHW",
out_layout="",
out_dtype="",
):
r"""3D convolution with winograd algorithm.
The basic parameters are the same as the ones in vanilla conv3d.
It assumes the weight is pre-transformed by nn.contrib_conv3d_winograd_weight_transform
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
weight : tvm.relay.Expr
The weight expressions.
tile_size : int
The Tile size of winograd. E.g. 2 for F(2x2x2, 3x3x3) and 4 for F(4x4x4, 3x3x3)
strides : tuple of int, optional
The strides of convolution.
padding : tuple of int, optional
The padding of convolution on both sides of inputs before convolution.
dilation : tuple of int, optional
Specifies the dilation rate to be used for dilated convolution.
groups : int, optional
Number of groups for grouped convolution.
channels : int, optional
Number of output channels of this convolution.
kernel_size : tuple of int, optional
The spatial of the convolution kernel.
data_layout : str, optional
Layout of the input.
kernel_layout : str, optional
Layout of the weight.
out_layout : str, optional
Layout of the output, by default, out_layout is the same as data_layout
out_dtype : str, optional
Specifies the output data type for mixed precision conv2d.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
# convert 3-way padding to 6-way padding
padding = get_pad_tuple3d(padding)
return _make.contrib_conv3d_winograd_without_weight_transform(
data,
weight,
tile_size,
strides,
padding,
dilation,
groups,
channels,
kernel_size,
data_layout,
kernel_layout,
out_layout,
out_dtype,
)
def conv3d_transpose(
data,
weight,
strides=(1, 1, 1),
padding=(0, 0, 0),
dilation=(1, 1, 1),
groups=1,
channels=None,
kernel_size=None,
data_layout="NCDHW",
kernel_layout="OIDHW",
out_layout="",
output_padding=(0, 0, 0),
out_dtype="",
):
r"""3D transpose convolution.
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
weight : tvm.relay.Expr
The weight expressions.
strides : Optional[Tuple[int]]
The strides of convolution.
padding : Optional[int, Tuple[int]]
The padding of convolution on both sides of inputs before convolution.
dilation : Optional[int, Tuple[int]]
Specifies the dilation rate to be used for dilated convolution.
groups : Optional[int]
Number of groups for grouped convolution.
channels : Optional[int]
Number of output channels of this convolution.
kernel_size : Optional[int, Tuple[int]]
The spatial of the convolution kernel.
data_layout : Optional[str]
Layout of the input.
kernel_layout : Optional[str]
Layout of the weight.
out_layout : Optional[str]
Layout of the output, by default, out_layout is the same as data_layout
out_dtype : Optional[str]
Specifies the output data type for mixed precision conv3d.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
if isinstance(kernel_size, int):
kernel_size = (kernel_size, kernel_size, kernel_size)
if isinstance(strides, int):
strides = (strides, strides, strides)
if isinstance(dilation, int):
dilation = (dilation, dilation, dilation)
padding = get_pad_tuple3d(padding)
return _make.conv3d_transpose(
data,
weight,
strides,
padding,
dilation,
groups,
channels,
kernel_size,
data_layout,
kernel_layout,
out_layout,
output_padding,
out_dtype,
)
def conv2d_transpose(
data,
weight,
strides=(1, 1),
padding=(0, 0),
dilation=(1, 1),
groups=1,
channels=None,
kernel_size=None,
data_layout="NCHW",
kernel_layout="OIHW",
out_layout="",
output_padding=(0, 0),
out_dtype="",
):
"""Two dimensional transposed convolution operator.
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
weight : tvm.relay.Expr
The weight expressions.
strides : Tuple[int], optional
The strides of convolution.
padding : Tuple[int], optional
The padding of convolution on both sides of inputs.
dilation : Tuple[int], optional
Specifies the dilation rate to be used for dilated convolution.
channels : int, optional
Number of output channels of this convolution.
kernel_size : tuple of int, optional
The spatial of the convolution kernel.
groups : int, optional
Number of groups for grouped convolution.
data_layout : str, optional
Layout of the input.
kernel_layout : str, optional
Layout of the weight.
out_layout : Optional[str]
Layout of the output, by default, out_layout is the same as data_layout
output_padding : Tuple[int], optional
Used to disambiguate the output shape.
out_dtype : str, optional
Specifies the output data type for mixed precision conv2d.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
# convert 2-way padding to 4-way padding
padding = get_pad_tuple2d(padding)
return _make.conv2d_transpose(
data,
weight,
strides,
padding,
dilation,
groups,
channels,
kernel_size,
data_layout,
kernel_layout,
out_layout,
output_padding,
out_dtype,
)
def conv1d_transpose(
data,
weight,
strides=(1,),
padding=(0,),
dilation=(1,),
groups=1,
channels=None,
kernel_size=None,
data_layout="NCW",
kernel_layout="OIW",
out_layout="",
output_padding=(0,),
out_dtype="",
):
"""One dimensional transposed convolution operator.
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
weight : tvm.relay.Expr
The weight expressions.
strides : Tuple[int], optional
The strides of convolution.
padding : Tuple[int], optional
The padding of convolution on both sides of inputs.
dilation : Tuple[int], optional
Specifies the dilation rate to be used for dilated convolution.
channels : int, optional
Number of output channels of this convolution.
kernel_size : tuple of int, optional
The spatial of the convolution kernel.
groups : int, optional
Number of groups for grouped convolution.
data_layout : str, optional
Layout of the input.
kernel_layout : str, optional
Layout of the weight.
out_layout : Optional[str]
Layout of the output, by default, out_layout is the same as data_layout
output_padding : Tuple[int], optional
Used to disambiguate the output shape.
out_dtype : str, optional
Specifies the output data type for mixed precision conv2d.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
return _make.conv1d_transpose(
data,
weight,
strides,
padding,
dilation,
groups,
channels,
kernel_size,
data_layout,
kernel_layout,
out_layout,
output_padding,
out_dtype,
)
def softmax(data, axis=-1):
r"""Computes softmax.
.. math:: \text{softmax}(x)_i = \frac{exp(x_i)}{\sum_j exp(x_j)}
.. note::
This operator can be optimized away for inference.
Parameters
----------
data: tvm.relay.Expr
The input data to the operator.
axis: int, optional
The axis to sum over when computing softmax
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
return _make.softmax(data, axis)
def fast_softmax(data, axis=-1):
r"""Computes softmax.
Use approximation to compute exponent for faster speed.
.. math:: \text{softmax}(x)_i = \frac{exp(x_i)}{\sum_j exp(x_j)}
.. note::
This operator can be optimized away for inference.
Parameters
----------
data: tvm.relay.Expr
The input data to the operator.
axis: int, optional
The axis to sum over when computing softmax
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
return _make.fast_softmax(data, axis)
def log_softmax(data, axis=-1):
r"""Computes log softmax.
.. math::
\text{log_softmax}(x)_i = \log \frac{exp(x_i)}{\sum_j exp(x_j)}
.. note::
This operator can be optimized away for inference.
Parameters
----------
data: tvm.relay.Expr
The input data to the operator.
axis: int, optional
The axis to sum over when computing log softmax
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
return _make.log_softmax(data, axis)
def max_pool1d(data, pool_size=(1,), strides=(1,), padding=(0,), layout="NCW", ceil_mode=False):
r"""1D maximum pooling operator.
This operator takes data as input and does 1D max value calculation
with in pool_size sized window by striding defined by stride.
In the default case, where the data_layout is `NCW`
a data Tensor with shape `(batch_size, channels, width)`,
to produce an output Tensor.
The ceil_mode is used to take ceil or floor while computing out shape.
count_include_pad indicates including or excluding padded input values in computation.
This operator accepts data layout specification.
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
pool_size : int or tuple of int, optional
The size of window for pooling.
strides : int or tuple of int, optional
The strides of pooling.
padding : int or tuple of int, optional
The padding for pooling.
layout : str, optional
Layout of the input.
ceil_mode : bool, optional
To enable or disable ceil while pooling.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
if isinstance(pool_size, int):
pool_size = (pool_size,)
if isinstance(strides, int):
strides = (strides,)
padding = get_pad_tuple1d(padding)
return _make.max_pool1d(data, pool_size, strides, padding, layout, ceil_mode)
def max_pool2d(
data, pool_size=(1, 1), strides=(1, 1), padding=(0, 0), layout="NCHW", ceil_mode=False
):
r"""2D maximum pooling operator.
This operator takes data as input and does 2D max value calculation
with in pool_size sized window by striding defined by stride
In the default case, where the data_layout is `NCHW`
a data Tensor with shape `(batch_size, in_channels, height, width)`,
to produce an output Tensor with the following rule:
with data of shape (b, c, h, w) and pool_size (kh, kw)
.. math::
\mbox{out}(b, c, y, x) = \max_{m=0, \ldots, kh-1} \max_{n=0, \ldots, kw-1}
\mbox{data}(b, c, \mbox{stride}[0] * y + m, \mbox{stride}[1] * x + n)
Padding is applied to data before the computation.
ceil_mode is used to take ceil or floor while computing out shape.
This operator accepts data layout specification.
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
pool_size : int or tuple of int, optional
The size of window for pooling.
strides : tuple of int, optional
The strides of pooling.
padding : tuple of int, optional
The padding for pooling.
layout : str, optional
Layout of the input.
ceil_mode : bool, optional
To enable or disable ceil while pooling.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
if isinstance(pool_size, int):
pool_size = (pool_size, pool_size)
if isinstance(strides, int):
strides = (strides, strides)
padding = get_pad_tuple2d(padding)
return _make.max_pool2d(data, pool_size, strides, padding, layout, ceil_mode)
def max_pool3d(
data, pool_size=(1, 1, 1), strides=(1, 1, 1), padding=(0, 0, 0), layout="NCDHW", ceil_mode=False
):
r"""3D maximum pooling operator.
This operator takes data as input and does 3D max value calculation
with in pool_size sized window by striding defined by stride.
In the default case, where the data_layout is `NCDHW`
a data Tensor with shape `(batch_size, channels, depth, height, width)`,
to produce an output Tensor.
The ceil_mode is used to take ceil or floor while computing out shape.
count_include_pad indicates including or excluding padded input values in computation.
This operator accepts data layout specification.
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
pool_size : int or tuple of int, optional
The size of window for pooling.
strides : tuple of int, optional
The strides of pooling.
padding : tuple of int, optional
The padding for pooling.
layout : str, optional
Layout of the input.
ceil_mode : bool, optional
To enable or disable ceil while pooling.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
if isinstance(pool_size, int):
pool_size = (pool_size, pool_size, pool_size)
if isinstance(strides, int):
strides = (strides, strides, strides)
padding = get_pad_tuple3d(padding)
return _make.max_pool3d(data, pool_size, strides, padding, layout, ceil_mode)
def avg_pool1d(
data,
pool_size=(1,),
strides=(1,),
padding=(0,),
layout="NCW",
ceil_mode=False,
count_include_pad=False,
):
r"""1D average pooling operator.
This operator takes data as input and does 1D average value calculation
with in pool_size sized window by striding defined by stride
In the default case, where the data_layout is `NCW`
a data Tensor with shape `(batch_size, channels, width)`,
to produce an output Tensor.
The ceil_mode is used to take ceil or floor while computing out shape.
count_include_pad indicates including or excluding padded input values in computation.
This operator accepts data layout specification.
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
pool_size : int or tuple of int, optional
The size of window for pooling.
strides : int or tuple of int, optional
The strides of pooling.
padding : int or tuple of int, optional
The padding for pooling.
layout : str, optional
Layout of the input.
ceil_mode : bool, optional
To enable or disable ceil while pooling.
count_include_pad : bool, optional
To include padding to compute the average.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
if isinstance(pool_size, int):
pool_size = (pool_size,)
if isinstance(strides, int):
strides = (strides,)
padding = get_pad_tuple1d(padding)
return _make.avg_pool1d(data, pool_size, strides, padding, layout, ceil_mode, count_include_pad)
def avg_pool2d(
data,
pool_size=(1, 1),
strides=(1, 1),
padding=(0, 0),
layout="NCHW",
ceil_mode=False,
count_include_pad=False,
):
r"""2D average pooling operator.
This operator takes data as input and does 2D average value calculation
with in pool_size sized window by striding defined by stride
In the default case, where the data_layout is `NCHW`
a data Tensor with shape `(batch_size, in_channels, height, width)`,
to produce an output Tensor with the following rule:
with data of shape (b, c, h, w), pool_size (kh, kw)
.. math::
\mbox{out}(b, c, y, x) = \frac{1}{kh * kw} \sum_{m=0}^{kh-1} \sum_{n=0}^{kw-1}
\mbox{data}(b, c, \mbox{stride}[0] * y + m, \mbox{stride}[1] * x + n)
Padding is applied to data before the computation.
ceil_mode is used to take ceil or floor while computing out shape.
count_include_pad indicates including or excluding padded input values in computation.
This operator accepts data layout specification.
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
pool_size : int or tuple of int, optional
The size of window for pooling.
strides : tuple of int, optional
The strides of pooling.
padding : tuple of int, optional
The padding for pooling.
layout : str, optional
Layout of the input.
ceil_mode : bool, optional
To enable or disable ceil while pooling.
count_include_pad : bool, optional
To include padding to compute the average.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
if isinstance(pool_size, int):
pool_size = (pool_size, pool_size)
if isinstance(strides, int):
strides = (strides, strides)
padding = get_pad_tuple2d(padding)
return _make.avg_pool2d(data, pool_size, strides, padding, layout, ceil_mode, count_include_pad)
def avg_pool3d(
data,
pool_size=(1, 1, 1),
strides=(1, 1, 1),
padding=(0, 0, 0),
layout="NCDHW",
ceil_mode=False,
count_include_pad=False,
):
r"""3D average pooling operator.
This operator takes data as input and does 3D average value calculation
with in pool_size sized window by striding defined by stride
In the default case, where the data_layout is `NCDHW`
a data Tensor with shape `(batch_size, channels, depth, height, width)`,
to produce an output Tensor.
The ceil_mode is used to take ceil or floor while computing out shape.
count_include_pad indicates including or excluding padded input values in computation.
This operator accepts data layout specification.
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
pool_size : int or tuple of int, optional
The size of window for pooling.
strides : tuple of int, optional
The strides of pooling.
padding : tuple of int, optional
The padding for pooling.
layout : str, optional
Layout of the input.
ceil_mode : bool, optional
To enable or disable ceil while pooling.
count_include_pad : bool, optional
To include padding to compute the average.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
if isinstance(pool_size, int):
pool_size = (pool_size, pool_size, pool_size)
if isinstance(strides, int):
strides = (strides, strides, strides)
padding = get_pad_tuple3d(padding)
return _make.avg_pool3d(data, pool_size, strides, padding, layout, ceil_mode, count_include_pad)
def max_pool2d_grad(
out_grad, data, pool_size=(1, 1), strides=(1, 1), padding=(0, 0), layout="NCHW", ceil_mode=False
):
r"""Gradient of 2D maximum pooling operator.
This operator takes out_grad and data as input and calculates gradient of max_pool2d.
Parameters
----------
out_grad : tvm.relay.Expr
The output gradient
data : tvm.relay.Expr
The input data to the operator.
pool_size : int or tuple of int, optional
The size of window for pooling.
strides : tuple of int, optional
The strides of pooling.
padding : tuple of int, optional
The padding for pooling.
layout : str, optional
Layout of the input.
ceil_mode : bool, optional
To enable or disable ceil while pooling.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
return _make.max_pool2d_grad(out_grad, data, pool_size, strides, padding, layout, ceil_mode)
def avg_pool2d_grad(
out_grad,
data,
pool_size=(1, 1),
strides=(1, 1),
padding=(0, 0),
layout="NCHW",
ceil_mode=False,
count_include_pad=False,
):
r"""Gradient of 2D average pooling operator.
This operator takes out_grad and data as input and calculates gradient of avg_pool2d.
Parameters
----------
out_grad : tvm.relay.Expr
The output gradient
data : tvm.relay.Expr
The input data to the operator.
pool_size : int or tuple of int, optional
The size of window for pooling.
strides : tuple of int, optional
The strides of pooling.
padding : tuple of int, optional
The padding for pooling.
layout : str, optional
Layout of the input.
ceil_mode : bool, optional
To enable or disable ceil while pooling.
count_include_pad : bool, optional
To include padding to compute the average.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
return _make.avg_pool2d_grad(
out_grad, data, pool_size, strides, padding, layout, ceil_mode, count_include_pad
)
def global_max_pool2d(data, layout="NCHW"):
r"""2D global maximum pooling operator.
This operator takes data as input and does 2D max value calculation
across each window represented by WxH.
In the default case, where the data_layout is `NCHW`
a data Tensor with shape `(batch_size, in_channels, height, width)`,
to produce an output Tensor with the following rule:
with data of shape (b, c, h, w)
.. math::
\mbox{out}(b, c, 1, 1) = \max_{m=0, \ldots, h} \max_{n=0, \ldots, w}
\mbox{data}(b, c, m, n)
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
layout : str, optional
Layout of the input.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
return _make.global_max_pool2d(data, layout)
def global_avg_pool2d(data, layout="NCHW"):
r"""2D global average pooling operator.
This operator takes data as input and does 2D average value calculation
across each window represented by WxH.
In the default case, where the data_layout is `NCHW`
a data Tensor with shape `(batch_size, in_channels, height, width)`,
to produce an output Tensor with the following rule:
with data of shape (b, c, h, w)
.. math::
\mbox{out}(b, c, 1, 1) = \frac{1}{h * w} \sum_{m=0}^{h-1} \sum_{n=0}^{w-1}
\mbox{data}(b, c, m, n)
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
layout : str, optional
Layout of the input.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
return _make.global_avg_pool2d(data, layout)
def upsampling(
data, scale_h=1, scale_w=1, layout="NCHW", method="nearest_neighbor", align_corners=False
):
"""Upsampling.
This operator takes data as input and does 2D scaling to the given scale factor.
In the default case, where the data_layout is `NCHW`
with data of shape (n, c, h, w)
out will have a shape (n, c, h*scale_h, w*scale_w)
method indicates the algorithm to be used while calculating the out value
and method can be one of ("bilinear", "nearest_neighbor", "bicubic")
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
scale_h : tvm.relay.Expr or int or float
The scale factor for height upsampling.
scale_w : tvm.relay.Expr or int or float
The scale factor for width upsampling.
layout : str, optional
Layout of the input.
method : str, optional
Scale method to used [nearest_neighbor, bilinear, bicubic].
align_corners : bool, optional
Whether to keep corners in proper place.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
if isinstance(scale_h, Constant):
scale_h = scale_h.data.asnumpy().item()
if isinstance(scale_w, Constant):
scale_w = scale_w.data.asnumpy().item()
if isinstance(scale_h, Expr) or isinstance(scale_w, Expr):
if not isinstance(scale_h, Expr):
scale_h = const(scale_h, "float64")
if not isinstance(scale_w, Expr):
scale_w = const(scale_w, "float64")
return _dyn_make.upsampling(data, scale_h, scale_w, layout, method, align_corners)
return _make.upsampling(data, scale_h, scale_w, layout, method, align_corners)
def upsampling3d(
data,
scale_d=1,
scale_h=1,
scale_w=1,
layout="NCDHW",
method="nearest_neighbor",
coordinate_transformation_mode="half_pixel",
):
"""3D Upsampling.
This operator takes data as input and does 3D scaling to the given scale factor.
In the default case, where the data_layout is `NCDHW`
with data of shape (n, c, d, h, w)
out will have a shape (n, c, d*scale_d, h*scale_h, w*scale_w)
method indicates the algorithm to be used while calculating the out value
and method can be one of ("trilinear", "nearest_neighbor")
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
scale_d : tvm.relay.Expr
The scale factor for depth upsampling.
scale_h : tvm.relay.Expr
The scale factor for height upsampling.
scale_w : tvm.relay.Expr
The scale factor for width upsampling.
layout : str, optional
Layout of the input.
method : str, optional
Scale method to used [nearest_neighbor, trilinear].
coordinate_transformation_mode: string, optional
Describes how to transform the coordinate in the resized tensor
to the coordinate in the original tensor.
Refer to the ONNX Resize operator specification for details.
Available options are "half_pixel", "align_corners" and "asymmetric".
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
if isinstance(scale_d, Constant):
scale_d = scale_d.data.asnumpy().item()
if isinstance(scale_h, Constant):
scale_h = scale_h.data.asnumpy().item()
if isinstance(scale_w, Constant):
scale_w = scale_w.data.asnumpy().item()
if isinstance(scale_d, Expr) or isinstance(scale_h, Expr) or isinstance(scale_w, Expr):
if not isinstance(scale_d, Expr):
scale_d = const(scale_d, "float64")
if not isinstance(scale_h, Expr):
scale_h = const(scale_h, "float64")
if not isinstance(scale_w, Expr):
scale_w = const(scale_w, "float64")
return _dyn_make.upsampling3d(
data, scale_d, scale_h, scale_w, layout, method, coordinate_transformation_mode
)
return _make.upsampling3d(
data, scale_d, scale_h, scale_w, layout, method, coordinate_transformation_mode
)
def batch_flatten(data):
"""BatchFlatten.
This operator flattens all the dimensions except for the batch dimension.
which results a 2D output.
For data with shape ``(d1, d2, ..., dk)``
batch_flatten(data) returns reshaped output of shape ``(d1, d2*...*dk)``.
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
Returns
-------
result : tvm.relay.Expr
The Flattened result.
"""
return _make.batch_flatten(data)
def bias_add(data, bias, axis=1):
"""add_bias operator.
Add 1D bias to the axis of data.
This function is a special case of add which allows
inference of shape of the bias from data.
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
bias : tvm.relay.Expr
The bias to be added.
axis : int, optional
The axis to add the bias.
Returns
-------
result : tvm.relay.Expr
The final result.
"""
return _make.bias_add(data, bias, axis)
def dense(data, weight, units=None, out_dtype=""):
"""Dense operator.
Applies a linear transformation
.. math::
`Y = X * W^T`
Parameters
----------
data : tvm.relay.Expr
The input data to the operator,
of shape `(d_1, d_2, ..., d_n, units_in)`.
weight : tvm.relay.Expr
The weight expressions, 2-D matrix,
of shape `(units, units_in)`.
units : int, optional
Number of hidden units of the dense transformation.
out_dtype : str, optional
Specifies the output data type for mixed precision dense,
of shape `(d_1, d_2, ..., d_n, units)`.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
return _make.dense(data, weight, units, out_dtype)
def contrib_dense_pack(data, weight, units=None, out_dtype=""):
"""Dense operator.
Applies a linear transformation
.. math::
`Y = X * W^T`
Parameters
----------
data : tvm.relay.Expr
The input data to the operator,
of shape `(d_1, d_2, ..., d_n, units_in)`.
weight : tvm.relay.Expr
The transformed weight expressions, 3-D matrix,
of shape `(units // pack_weight_tile, units_in, pack_weight_tile)`.
units : int, optional
Number of hidden units of the dense transformation.
out_dtype : str, optional
Specifies the output data type for mixed precision dense,
of shape `(d_1, d_2, ..., d_n, units)`.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
return _make.contrib_dense_pack(data, weight, units, out_dtype)
def fifo_buffer(data, buffer, axis):
"""FIFO buffer to enable computation reuse in CNNs with sliding indow input
Compute equivalent of
.. code-block:: python
concat(buffer, data, axis=axis)
.slice_axis(axis=axis,
begin=data.shape[axis],
end=data.shape[axis]+buffer.shape[axis])
Useful for
* Encoding explicit re-use of computation in convolution ops operated on a sliding window input
* Implementing a FIFO queue to cache intermediate results, e.g. as in Fast WaveNet.
Parameters
----------
data : tvm.relay.Expr
The input data
buffer : tvm.relay.Expr
Previous value of the FIFO buffer
axis : int
Specify which axis should be used for buffering
Returns
-------
result : tvm.relay.Expr
Updated value for the buffer
"""
return _make.fifo_buffer(data, buffer, axis)
def relu(data):
"""Rectified linear unit.
.. math::
out = max(x, 0)
Parameters
----------
data : tvm.relay.Expr
The input data
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
return _make.relu(data)
def leaky_relu(data, alpha=0.01):
"""This operator takes data as input and does Leaky version
of a Rectified Linear Unit.
.. math::
`y = x > 0 ? x : alpha * x`
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
alpha : float
Slope coefficient for the negative half axis.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
return _make.leaky_relu(data, alpha)
def prelu(data, alpha, axis=1):
"""This operator takes data as input and does Leaky version
of a Rectified Linear Unit.
.. math::
y = x > 0 ? x : alpha * x
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
alpha : tvm.relay.Expr
Slope coefficient for the negative half axis.
axis : int, optional
Specify which shape axis the channel is specified.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
return _make.prelu(data, alpha, axis)
def pad(data, pad_width, pad_value=0, pad_mode="constant"):
r"""Padding
This operator takes in a tensor and pads each axis by the specified
widths using the specified value.
Parameters
----------
data: tvm.relay.Expr
The input data to the operator
pad_width: tuple of <tuple of <int>>, or tvm.relay.Expr, required
Number of values padded to the edges of each axis, in the format
of ((before_1, after_1), ..., (before_N, after_N))
pad_value: float, or tvm.relay.Expr, optional, default=0
The value used for padding
pad_mode: 'constant', 'edge', 'reflect'
'constant' pads with constant_value pad_value
'edge' pads using the edge values of the input array
'reflect' pads by reflecting values with respect to the edge
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
if isinstance(pad_width, Constant):
pad_width = [list(i) for i in pad_width.data.asnumpy()]
if not isinstance(pad_value, Expr):
pad_value = const(pad_value)
if isinstance(pad_width, Expr):
return _dyn_make.pad(data, pad_width, pad_value, pad_mode)
return _make.pad(data, pad_width, pad_value, pad_mode)
def dilate(data, strides, dilation_value=0.0):
"""Dilate data with given dilation value (0 by default).
Parameters
----------
data : tvm.relay.Expr
n-D, can be any layout.
strides : tuple of <int>
Dilation stride on each dimension, 1 means no dilation.
dilation_value : int/float, optional
Value used to dilate the input.
Returns
-------
Output : tvm.relay.Expr
The computed result
"""
return _make.dilate(data, strides, dilation_value)
def mirror_pad(data, pad_width, mode="SYMMETRIC"):
r"""MirrorPadding
This operator takes in a tensor and pads each axis by the specified
widths using mirroring of the border pixels.
Parameters
----------
data: tvm.relay.Expr
The input data to the operator
pad_width: tuple of <tuple of <int>>, required
Number of values padded to the edges of each axis, in the format
of ((before_1, after_1), ..., (before_N, after_N))
mode: string, optional, default='SYMMETRIC'
What type of mirroring to use, must be SYMMETRIC or REFLECT.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
return _make.mirror_pad(data, pad_width, mode)
def lrn(data, size=5, axis=1, bias=2, alpha=0.00001, beta=0.75):
"""This operator takes data as input and does local response normalization.
Normalize the input in a local region across or within feature maps.
Each input value is divided by (data / (bias + (alpha * sum_data ^2 /size))^beta)
where n is the size of each local region, and the sum is taken over the region
centered at that value (zero padding is added where necessary).
.. math::
(data / (bias + (alpha * sum_data ^2 /size))^beta)
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
size : int, optional
The size of the local region to be considered for normalization.
axis : int, optional
Input data layout channel axis. Default value is 1 for NCHW format
bias : float, optional
The offset parameter to avoid dividing by 0.
alpha : float, optional
The scaling parameter.
beta : float, optional
The exponent parameter.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
return _make.lrn(data, size, axis, alpha, beta, bias)
def l2_normalize(data, eps, axis=None):
"""Perform L2 normalization on the input data
.. math::
y(i, j) = x(i, j) / sqrt(max(sum(x^2), eps))
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
eps : float
epsilon value
axis : list of int, optional
axis over the normalization applied
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
return _make.l2_normalize(data, eps, axis)
def dropout(data, rate=0.5):
"""Applies the dropout operation to the input array.
During training, each element of the input is set to zero with
probability ``p``. The whole array is rescaled by ``1/(1-p)``
to keep the expected sum of the input unchanged.
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
rate : float, optional (default=0.5)
The probability for an element to be reset to 0.
Returns
-------
result : tvm.relay.Expr
The result of dropout
"""
return expr.TupleWrapper(dropout_raw(data, rate), 2)[0]
def dropout_raw(data, rate=0.5):
"""Applies the dropout operation to the input array.
During training, each element of the input is set to zero with
probability ``p``. The whole array is rescaled by ``1/(1-p)``
to keep the expected sum of the input unchanged.
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
rate : float, optional (default=0.5)
The probability for an element to be reset to 0.
Returns
-------
result : tvm.relay.Expr
The result of dropout
"""
return _make.dropout(data, rate)
def batch_norm(
data, gamma, beta, moving_mean, moving_var, axis=1, epsilon=1e-5, center=True, scale=True
):
r"""
Batch normalization layer (Ioffe and Szegedy, 2014).
Normalizes the input at each batch, i.e. applies a transformation
that maintains the mean activation close to 0 and the activation
standard deviation close to 1.
.. math::
data\_mean[i] = mean(data[:,i,:,...]) \\
data\_var[i] = var(data[:,i,:,...])
Then compute the normalized output, which has the same shape as input, as following:
.. math::
out[:,i,:,...] = \frac{data[:,i,:,...] - data\_mean[i]}{\sqrt{data\_var[i]+\epsilon}}
* gamma[i] + beta[i]
Both *mean* and *var* returns a scalar by treating the input as a vector.
Assume the input has size *k* on axis 1, then both ``gamma`` and ``beta``
have shape *(k,)*.
Besides the inputs and the outputs, this operator accepts two auxiliary
states, ``moving_mean`` and ``moving_var``, which are *k*-length
vectors. They are global statistics for the whole dataset, which are updated by
.. code:: python
moving_mean = moving_mean * momentum + data_mean * (1 - momentum)
moving_var = moving_var * momentum + data_var * (1 - momentum)
The parameter ``axis`` specifies which axis of the input shape denotes
the 'channel' (separately normalized groups). The default is 1.
Specifying -1 sets the channel axis to be the last item in the input shape.
.. note::
This operator can be optimized away for inference.
Parameters
----------
data : tvm.relay.Expr
Input to which batch_norm will be applied.
gamma : tvm.relay.Expr
The gamma scale factor.
beta : tvm.relay.Expr
The beta offset factor.
moving_mean : tvm.relay.Expr
Running mean of input,
moving_var : tvm.relay.Expr
Running variance of input.
axis : int, optional, default=1
Specify along which shape axis the channel is specified.
epsilon : double, optional, default=1e-5
Small float added to variance to avoid dividing by zero.
center : boolean, optional, default=True
If True, add offset of beta to normalized tensor, If False,
beta is ignored.
scale : boolean, optional, default=True
If true, multiply by gamma. If False, gamma is not used.
When the next layer is piecewise linear (also e.g. nn.relu),
this can be disabled since the scaling will be done by the next layer.
Returns
-------
result : relay.Tuple([tvm.relay.Expr, tvm.relay.Expr, tvm.relay.Expr])
Tuple of normed data (same shape as input),
new running mean (k-length vector),
and new running variance (k-length vector)
"""
result = _make.batch_norm(
data, gamma, beta, moving_mean, moving_var, axis, epsilon, center, scale
)
return expr.TupleWrapper(result, 3)
def instance_norm(data, gamma, beta, axis=1, epsilon=1e-5, center=True, scale=True):
r"""
Instance Normalization (Ulyanov and et al., 2016)
Applies instance normalization to the n-dimensional input array.
.. math::
out = \frac{data - mean(data)}{\sqrt{var(data)+\epsilon}}
* gamma + beta
The instance normalization is similar to batch normalization, but unlike
batch normalization, the mean and var are calculated per-dimension
separately for each object(instance) in a mini-batch, not over a batch.
And the same normalization is applied both at test and train time.
Assume the input has size *k* on axis 1, then both ``gamma`` and ``beta``
have shape *(k,)*.
The parameter ``axis`` specifies which axis of the input shape denotes
the 'channel'. The default is 1. Specifying -1 sets the channel axis
to be the last item in the input shape.
.. note::
This operator can be optimized away for inference.
Parameters
----------
data : tvm.relay.Expr
Input to which instance_norm will be applied.
gamma : tvm.relay.Expr
The gamma scale factor.
beta : tvm.relay.Expr
The beta offset factor.
axis : int, optional, default=1
Specify along which shape axis the channel is specified.
epsilon : double, optional, default=1e-5
Small float added to variance to avoid dividing by zero.
center : boolean, optional, default=True
If True, add offset of beta to normalized tensor, If False,
beta is ignored.
scale : boolean, optional, default=True
If True, multiply by gamma. If False, gamma is not used.
Returns
-------
result : tvm.relay.Expr
The normalized data.
.. _`Instance Normalization: The Missing Ingredient for Fast Stylization`:
https://arxiv.org/abs/1607.08022
"""
return _make.instance_norm(data, gamma, beta, axis, epsilon, center, scale)
def layer_norm(data, gamma, beta, axis=-1, epsilon=1e-5, center=True, scale=True):
r"""
Layer normalization (Lei Ba and et al., 2016).
Applies layer normalization to the n-dimensional input array.
This operator takes an n-dimensional input array and normalizes
the input using the given axis:
.. math::
out = \frac{data - mean(data, axis)}{\sqrt{var(data, axis)+\epsilon}}
* gamma + beta
Unlike batch normalization, the mean and var are computed along the channel dimension.
Assume the input has size k on axis 1, then both gamma and beta have shape (k,).
.. note::
This operator can be optimized away for inference.
Parameters
----------
data : tvm.relay.Expr
Input to which layer_norm will be applied.
gamma : tvm.relay.Expr
The gamma scale factor.
beta : tvm.relay.Expr
The beta offset factor.
axis : int, optional, default=-1
The axis that should be normalized, typically the axis of the channels.
epsilon : double, optional, default=1e-5
Small float added to variance to avoid dividing by zero.
center : boolean, optional, default=True
If True, add offset of beta to normalized tensor, If False,
beta is ignored.
scale : boolean, optional, default=True
If True, multiply by gamma. If False, gamma is not used.
Returns
-------
result : tvm.relay.Expr
The normalized data.
"""
return _make.layer_norm(data, gamma, beta, axis, epsilon, center, scale)
def group_norm(data, gamma, beta, num_groups, axis=1, epsilon=1e-5, center=True, scale=True):
r"""
Group normalization normalizes over group of channels for each training examples.
We can say that, Group Norm is in between Instance Norm and Layer Norm. When we put
all the channels into a single group, group normalization becomes Layer normalization.
And, when we put each channel into different groups it becomes Instance normalization
https://arxiv.org/pdf/1803.08494.pdf
Applies group normalization to the n-dimensional input array by seperating the input channels
into 'num_groups' groups, each containing 'num_channels / num_groups' channels.
The mean and standard-deviation are calculated separately over the each group. gamma and
beta are learnable per-channel affine transform parameter vectors of size num_channels.
.. math::
out = \frac{data - mean(data, axis)}{\sqrt{var(data, axis)+\epsilon}}
* gamma + beta
Unlike batch normalization, the mean and var are computed along a group of channels.
If the input has size k on axis 1, then both gamma and beta have shape (k,).
.. note::
This operator can be optimized away for inference.
Parameters
----------
data : tvm.relay.Expr
Input to which group_norm will be applied.
gamma : tvm.relay.Expr
The gamma scale factor.
beta : tvm.relay.Expr
The beta offset factor.
num_groups : int
The number of groups to separate the channels into.
axis : int, optional, default=1
The axis of the channels.
epsilon : double, optional, default=1e-5
Small float added to variance to avoid dividing by zero.
center : boolean, optional, default=True
If True, add offset of beta to normalized tensor, If False,
beta is ignored.
scale : boolean, optional, default=True
If True, multiply by gamma. If False, gamma is not used.
Returns
-------
result : tvm.relay.Expr
The normalized data.
"""
return _make.group_norm(data, gamma, beta, num_groups, axis, epsilon, center, scale)
def batch_matmul(x, y, out_dtype=""):
r"""
Computes batch matrix multiplication of `x` and `y` when `x` and `y` are data
in batch.
.. math::
\mbox{batch_matmul}(x, y)[i, :, :] = \mbox{matmul}(x[i, :, :], y[i, :, :]^T)
Parameters
----------
x : tvm.relay.Expr
The first input.
y : tvm.relay.Expr
The second input.
out_dtype : str, optional
Specifies the output data type for mixed precision batch matmul
Returns
-------
result: tvm.relay.Expr
The computed result.
"""
return _make.batch_matmul(x, y, out_dtype)
# pylint: disable=no-else-return,inconsistent-return-statements
def sparse_dense(dense_mat, sparse_mat, sparse_lhs=False):
r"""
Computes the matrix multiplication of `dense_mat` and `sparse_mat`, where `dense_mat` is
a dense matrix and `sparse_mat` is a sparse (either BSR or CSR) namedtuple with
fields `data`, `indices`, and `indptr`.
\if sparse_lhs=False:
.. math::
\mbox{sparse_dense}(dense_mat, sparse_mat)[m, n]
= \mbox{matmul}(D, \mbox{as_dense}(S)^T)[m, n]
\if sparse_lhs=True:
.. math::
\mbox{sparse_dense}(dense_mat, sparse_mat)[m, n]
= \mbox{matmul}(\mbox{as_dense}(S), (D)^T)[m, n]
where `as_dense` returns dense equivalent of the given S(sparse matrix)
while performing matmul with given D(dense matrix).
See
https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html
and
https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.sparse.bsr_matrix.html
for more detail on the sparse matrix representation.
Parameters
----------
dense_mat : tvm.relay.Expr
The input dense matrix for the matrix multiplication
sparse_mat : Union[namedtuple, Tuple[ndarray, ndarray, ndarray]].
The input sparse matrix for the matrix multiplication.
sparse_lhs : bool, optional
Indicates whether lhs or rhs matrix is sparse. Default value is False.
Returns
-------
result: tvm.relay.Expr
The computed result.
"""
if hasattr(sparse_mat, "indices"):
return _make.sparse_dense(
dense_mat, sparse_mat.data, sparse_mat.indices, sparse_mat.indptr, sparse_lhs
)
else:
return _make.sparse_dense(
dense_mat, sparse_mat[0], sparse_mat[1], sparse_mat[2], sparse_lhs
)
def sparse_transpose(x):
r"""
Computes the fast matrix transpose of x,
where x is a sparse tensor in CSR format (represented as a namedtuple
with fields `data`, `indices`, and `indptr`).
** Currently only support Square Matrices **
.. math::
\mbox{sparse_transpose}(x)[n, n] = (x^T)[n, n]
Please refer to https://github.com/scipy/scipy/blob/v1.3.0/scipy/sparse/csr.py
for the algorithm implemented in this operator.
Parameters
----------
x : Union[namedtuple, Tuple[ndarray, ndarray, ndarray]].
The sparse weight matrix for the fast matrix transpose.
Returns
-------
result : relay.Tuple([tvm.relay.Expr, tvm.relay.Expr, tvm.relay.Expr])
Tuple of output sparse tensor (same shape and format as input),
i.e. if CSR then output is in ([data, indices, indptr]) form
"""
if hasattr(x, "indices"):
return expr.TupleWrapper(_make.sparse_transpose(x.data, x.indices, x.indptr), 3)
return expr.TupleWrapper(_make.sparse_transpose(x[0], x[1], x[2]), 3)
# pylint: disable=no-else-return,inconsistent-return-statements
def sparse_add(dense_mat, sparse_mat):
r"""
Computes the matrix addition of `dense_mat` and `sparse_mat`, where `dense_mat` is
a dense matrix and `sparse_mat` is a sparse (CSR) namedtuple with
fields `data`, `indices`, and `indptr`.
.. math::
\mbox{sparse_add}(dense_mat, sparse_mat)[m, n] = \mbox{add}(\mbox{as_dense}(S), (D))[m, n]
where `as_dense` returns dense equivalent of the given S(sparse matrix)
while performing addition with given D(dense matrix).
Parameters
----------
dense_mat : tvm.relay.Expr
The input dense matrix for the matrix addition
sparse_mat : Union[namedtuple, Tuple[ndarray, ndarray, ndarray]].
The input sparse matrix(CSR) for the matrix addition.
Returns
-------
result: tvm.relay.Expr
The computed result.
Examples
-------
.. code-block:: python
dense_data = [[ 3., 4., 4. ]
[ 4., 2., 5. ]]
sparse_data = [4., 8.]
sparse_indices =[0, 2]
sparse_indptr =[0, 1, 2]
output = relay.sparse_add(dense_data, sparse_data, sparse_indices, sparse_indptr)
output = [[ 7., 4., 4. ]
[ 4., 2., 13. ]]
"""
if hasattr(sparse_mat, "indices"):
return _make.sparse_add(dense_mat, sparse_mat.data, sparse_mat.indices, sparse_mat.indptr)
else:
return _make.sparse_add(dense_mat, sparse_mat[0], sparse_mat[1], sparse_mat[2])
def contrib_conv2d_winograd_without_weight_transform(
data,
weight,
tile_size,
strides=(1, 1),
padding=(0, 0),
dilation=(1, 1),
groups=1,
channels=None,
kernel_size=None,
data_layout="NCHW",
kernel_layout="OIHW",
out_layout="",
out_dtype="",
):
r"""2D convolution with winograd algorithm.
The basic parameters are the same as the ones in vanilla conv2d.
It assumes the weight is pre-transformed by nn.contrib_conv2d_winograd_weight_transform
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
weight : tvm.relay.Expr
The weight expressions.
tile_size : int
The Tile size of winograd. E.g. 2 for F(2x2, 3x3) and 4 for F(4x4, 3x3)
strides : tuple of int, optional
The strides of convolution.
padding : tuple of int, optional
The padding of convolution on both sides of inputs before convolution.
dilation : tuple of int, optional
Specifies the dilation rate to be used for dilated convolution.
groups : int, optional
Number of groups for grouped convolution.
channels : int, optional
Number of output channels of this convolution.
kernel_size : tuple of int, optional
The spatial of the convolution kernel.
data_layout : str, optional
Layout of the input.
kernel_layout : str, optional
Layout of the weight.
out_layout : str, optional
Layout of the output, by default, out_layout is the same as data_layout
out_dtype : str, optional
Specifies the output data type for mixed precision conv2d.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
# convert 2-way padding to 4-way padding
padding = get_pad_tuple2d(padding)
return _make.contrib_conv2d_winograd_without_weight_transform(
data,
weight,
tile_size,
strides,
padding,
dilation,
groups,
channels,
kernel_size,
data_layout,
kernel_layout,
out_layout,
out_dtype,
)
def contrib_conv2d_gemm_without_weight_transform(
data,
weight,
strides=(1, 1),
padding=(0, 0),
dilation=(1, 1),
groups=1,
channels=None,
kernel_size=None,
data_layout="NCHW",
kernel_layout="OIHW",
out_layout="",
out_dtype="",
):
r"""2D convolution with gemm algorithm.
The basic parameters are the same as the ones in vanilla conv2d.
It assumes the weight is pre-transformed by nn.contrib_conv2d_gemm_weight_transform
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
weight : tvm.relay.Expr
The weight expressions.
strides : tuple of int, optional
The strides of convolution.
padding : tuple of int, optional
The padding of convolution on both sides of inputs before convolution.
dilation : tuple of int, optional
Specifies the dilation rate to be used for dilated convolution.
groups : int, optional
Number of groups for grouped convolution.
channels : int, optional
Number of output channels of this convolution.
kernel_size : tuple of int, optional
The spatial of the convolution kernel.
data_layout : str, optional
Layout of the input.
kernel_layout : str, optional
Layout of the weight.
out_layout : str, optional
Layout of the output, by default, out_layout is the same as data_layout
out_dtype : str, optional
Specifies the output data type for mixed precision conv2d.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
# convert 2-way padding to 4-way padding
padding = get_pad_tuple2d(padding)
return _make.contrib_conv2d_gemm_without_weight_transform(
data,
weight,
strides,
padding,
dilation,
groups,
channels,
kernel_size,
data_layout,
kernel_layout,
out_layout,
out_dtype,
)
def contrib_conv2d_nchwc(
data,
kernel,
strides=(1, 1),
padding=(0, 0),
dilation=(1, 1),
groups=1,
channels=None,
kernel_size=None,
data_layout="NCHW8c",
kernel_layout="OIHW",
out_layout="",
out_dtype="",
):
r"""Variant of 2D convolution.
This operator takes the weight as the convolution kernel
and convolves it with data to produce an output, following a specialized
NCHWc data layout.
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
kernel : tvm.relay.Expr
The kernel expressions.
strides : tuple of int, optional
The strides of convolution.
padding : tuple of int, optional
The padding of convolution on both sides of inputs before convolution.
dilation : tuple of int, optional
Specifies the dilation rate to be used for dilated convolution.
groups : int, optional
Number of groups for grouped convolution.
channels : int, optional
Number of output channels of this convolution.
kernel_size : tuple of int, optional
The spatial of the convolution kernel.
data_layout : str, optional
Layout of the input.
kernel_layout : str, optional
Layout of the weight.
out_layout : str, optional
Layout of the output, by default, out_layout is the same as data_layout
out_dtype : str, optional
Specifies the output data type for mixed precision conv2d.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
# convert 2-way padding to 4-way padding
padding = get_pad_tuple2d(padding)
return _make.contrib_conv2d_NCHWc(
data,
kernel,
strides,
padding,
dilation,
groups,
channels,
kernel_size,
data_layout,
kernel_layout,
out_layout,
out_dtype,
)
def contrib_depthwise_conv2d_nchwc(
data,
kernel,
strides=(1, 1),
padding=(0, 0),
dilation=(1, 1),
groups=1,
channels=None,
kernel_size=None,
data_layout="NCHW8c",
kernel_layout="OIHW",
out_layout="",
out_dtype="",
):
r"""Variant of 2D depthwise convolution.
This operator takes the weight as the depthwise convolution kernel
and depthwise convolves it with data to produce an output, following a specialized
NCHWc data layout.
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
kernel : tvm.relay.Expr
The kernel expressions.
strides : tuple of int, optional
The strides of convolution.
padding : tuple of int, optional
The padding of convolution on both sides of inputs before convolution.
dilation : tuple of int, optional
Specifies the dilation rate to be used for dilated convolution.
groups : int, optional
Number of groups for grouped convolution.
channels : int, optional
Number of output channels of this convolution.
kernel_size : tuple of int, optional
The spatial of the convolution kernel.
data_layout : str, optional
Layout of the input.
kernel_layout : str, optional
Layout of the weight.
out_layout : str, optional
Layout of the output, by default, out_layout is the same as data_layout
out_dtype : str, optional
Specifies the output data type for mixed precision conv2d.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
# convert 2-way padding to 4-way padding
padding = get_pad_tuple2d(padding)
return _make.contrib_depthwise_conv2d_NCHWc(
data,
kernel,
strides,
padding,
dilation,
groups,
channels,
kernel_size,
data_layout,
kernel_layout,
out_layout,
out_dtype,
)
def contrib_conv2d_winograd_weight_transform(weight, tile_size):
r"""Weight Transformation part for 2D convolution with winograd algorithm.
We separate this as a single op to enable pre-compute for inference.
Use this together with nn.contrib_conv2d_winograd_without_weight_transform
Parameters
----------
weight : tvm.relay.Expr
The weight expressions.
tile_size : int
The Tile size of winograd. E.g. 2 for F(2x2, 3x3) and 4 for F(4x4, 3x3)
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
return _make.contrib_conv2d_winograd_weight_transform(weight, tile_size)
def contrib_conv2d_gemm_weight_transform(weights, tile_rows, tile_cols):
r"""Weight Transformation part for 2D convolution with gemm algorithm.
We separate this as a single op to enable pre-compute for inference.
Use this together with nn.contrib_conv2d_gemm_without_weight_transform
Parameters
----------
weights : tvm.relay.Expr
The weight expressions.
tile_rows: int
Tile rows of the weight transformation for ConvGemm.
tile_cols: int
Tile columns of the weight transformation for ConvGemm.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
return _make.contrib_conv2d_gemm_weight_transform(weights, tile_rows, tile_cols)
def contrib_conv3d_winograd_weight_transform(weight, tile_size):
r"""Weight Transformation part for 3D convolution with winograd algorithm.
We separate this as a single op to enable pre-compute for inference.
Use this together with nn.contrib_conv3d_winograd_without_weight_transform
Parameters
----------
weight : tvm.relay.Expr
The weight expressions.
tile_size : int
The Tile size of winograd. E.g. 2 for F(2x2x2, 3x3x3) and 4 for F(4x4x4, 3x3x3)
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
return _make.contrib_conv3d_winograd_weight_transform(weight, tile_size)
def contrib_conv2d_winograd_nnpack_weight_transform(weight, convolution_algorithm, out_dtype=""):
r"""Weight Transformation part for 2D convolution with winograd algorithm.
We separate this as a single op to enable pre-compute for inference.
Use this together with nn.contrib_conv2d_winograd_without_weight_transform
Parameters
----------
weight : tvm.relay.Expr
The weight expressions.
convolution_algorithm : int
The Tile size of winograd. E.g. 2 for F(2x2, 3x3) and 4 for F(4x4, 3x3)
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
return _make.contrib_conv2d_winograd_nnpack_weight_transform(
weight, convolution_algorithm, out_dtype
)
def deformable_conv2d(
data,
offset,
weight,
strides=(1, 1),
padding=(0, 0),
dilation=(1, 1),
deformable_groups=1,
groups=1,
channels=None,
kernel_size=None,
data_layout="NCHW",
kernel_layout="OIHW",
out_layout="",
out_dtype="",
):
r"""Deformable 2d convolution.
The deformable convolution operation is described in https://arxiv.org/abs/1703.06211
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
offset : tvm.relay.Expr
The offset expressions.
weight : tvm.relay.Expr
The weight expressions.
strides : tuple of int, optional
The strides of convolution.
padding : tuple of int, optional
The padding of convolution on both sides of inputs before convolution.
dilation : tuple of int, optional
Specifies the dilation rate to be used for dilated convolution.
deformable_groups : int, optional
Number of deformable groups.
groups : int, optional
Number of groups for grouped convolution.
channels : int, optional
Number of output channels of this convolution.
kernel_size : tuple of int, optional
The spatial of the convolution kernel.
data_layout : str, optional
Layout of the input.
kernel_layout : str, optional
Layout of the weight.
out_layout : str, optional
Layout of the output, by default, out_layout is the same as data_layout
out_dtype : str, optional
Specifies the output data type for mixed precision conv2d.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
# convert 2-way padding to 4-way padding
padding = get_pad_tuple2d(padding)
return _make.deformable_conv2d(
data,
offset,
weight,
strides,
padding,
dilation,
deformable_groups,
groups,
channels,
kernel_size,
data_layout,
kernel_layout,
out_layout,
out_dtype,
)
def bitpack(data, bits=1, pack_axis=1, bit_axis=2, pack_type="uint32", name="BitPack"):
"""Tensor packing for bitserial operations.
The values along the input tensor's pack_axis are quantized
and packed together into the specified pack_type in a new bit axis.
For example, consider bitpacking with data to be a tensor with shape `[1, 64, 128, 128]`,
pack_axis=1, bit_axis=4, pack_type=uint8, and bits=2. The output in this case will
be of shape `[1, 8, 128, 128, 2]`. The dimension of axis 1 has been reduced by a factor
of 8 since each value is packed into an 8-bit uint8. Axis 4 is now two bitplanes
representing the quantized value of the incoming data. The output tensor is now
ready to be used in a bitserial operation.
Parameters
----------
data : tvm.relay.expr
The incoming tensor to be packed.
bits : int
Number of bits that should be packed.
pack_axis : int
Axis that should be decomposed and packed.
bit_axis : int
New axis containing bitplane.
pack_type : str
Datatype to pack bits into.
name : str, optional
Name of the operation.
Returns
-------
result : tvm.relay.Expr
The packed tensor.
"""
return _make.bitpack(data, bits, pack_axis, bit_axis, pack_type, name)
def bitserial_conv2d(
data,
weight,
strides=(1, 1),
padding=(0, 0),
channels=None,
kernel_size=(3, 3),
activation_bits=1,
weight_bits=1,
data_layout="NCHW",
kernel_layout="OIHW",
pack_dtype="uint32",
out_dtype="int16",
unipolar=True,
):
r"""2D convolution using bitserial computation.
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
weight : tvm.relay.Expr
The weight expressions.
strides : tuple of int, optional
The strides of convolution.
padding : tuple of int, optional
The padding of convolution on both sides of inputs before convolution.
channels : int, optional
Number of output channels of this convolution.
kernel_size : tuple of int, optional
The spatial of the convolution kernel.
activation_bits : int
Number of bits to pack for activations.
weight_bits : int
Number of bits to pack for weights.
data_layout : str, optional
Layout of the input.
kernel_layout : str, optional
Layout of the kernel
pack_dtype: str, optional
Datatype to pack bits into.
out_dtype : str, optional
Specifies the output data type for mixed precision conv2d.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
# convert 2-way padding to 4-way padding
padding = get_pad_tuple2d(padding)
return _make.bitserial_conv2d(
data,
weight,
strides,
padding,
channels,
kernel_size,
activation_bits,
weight_bits,
data_layout,
kernel_layout,
pack_dtype,
out_dtype,
unipolar,
)
def bitserial_dense(
data,
weight,
units=None,
data_bits=1,
weight_bits=1,
pack_dtype="uint32",
out_dtype="int16",
unipolar=True,
):
"""Bitserial Dense operator.
Applies matrix multiplication of two quantized matrices
using a fast bitserial algorithm.
.. math::
`Y = X * W`
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
weight : tvm.relay.Expr
The weight expressions.
units : int, optional
Number of hidden units of the dense transformation.
data_bits : int
Number of bits incoming tensor should be packed with.
weight_bits : int
Number of bits weight tensor should be packed with.
pack_dtype : str, optional
Datatype to pack individual bits into before computation.
out_dtype : str, optional
Specifies the output data type for mixed precision dense.
unipolar : bool, optional
Whether to use unipolar or bipolar quantization for inputs.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
return _make.bitserial_dense(
data, weight, units, data_bits, weight_bits, pack_dtype, out_dtype, unipolar
)
def cross_entropy(predictions, targets):
"""CrossEntropy without logits.
Parameters
----------
predictions : tvm.relay.Expr
The predictions.
targets : tvm.relay.Expr
The targets.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
return _make.cross_entropy(predictions, targets)
def cross_entropy_with_logits(predictions, targets):
"""CrossEntropy with logits.
Parameters
----------
predictions : tvm.relay.Expr
The predictions.
targets : tvm.relay.Expr
The targets.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
return _make.cross_entropy_with_logits(predictions, targets)
def depth_to_space(data, block_size, layout="NCHW", mode="DCR"):
"""Convert channels into spatial blocks.
Parameters
----------
data : tvm.relay.Expr
Input data with channels divisible by block_size**2
block_size : int
Size of blocks to convert channels into.
layout : string
One of NCHW or NHWC, indicates channel axis.
mode : string
One of DCR or CDR, indicates which order channels
are accessed in.
Returns
-------
result : tvm.relay.Expr
Tensor with shape [in_batch, in_channel / block_size * block_size,
in_height * block_size, in_width * block_size]
"""
return _make.depth_to_space(data, block_size, layout, mode)
def space_to_depth(data, block_size, layout="NCHW"):
"""Convert spatial blocks into channels.
Parameters
----------
data : tvm.relay.Expr
Input data with spatial dimensions divisible by block_size
block_size : int
Size of blocks to decompose into channels.
layout : string
One of NCHW or NHWC, indicates channel axis.
Returns
-------
result : tvm.relay.Expr
Tensor with shape [in_batch, in_channel * block_size * block_size,
in_height / block_size, in_width / block_size]
"""
return _make.space_to_depth(data, block_size, layout)
def adaptive_max_pool2d(data, output_size=None, layout="NCHW"):
r"""2D adaptive max pooling operator. This operator is experimental.
This operator takes data as input and does 2D max value calculation
across each window represented by WxH.
In the default case, where the data_layout is `NCHW`
a data Tensor with shape `(batch_size, in_channels, height, width)`,
to produce an output Tensor with shape
(batch_size, in_channels, output_height, output_width).
The pooling kernel and stride sizes are automatically chosen for
desired output sizes.
For output_size:
If this argument is not provided, input height and width will be used
as output height and width.
If a single integer is provided for output_size, the output size is
(N x C x output_size x output_size) for any input (NCHW).
If a tuple of integers (height, width) are provided for output_size,
the output size is (N x C x height x width) for any input (NCHW).
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
output_size : tuple of int. optional
Output height and width.
layout : str, optional
Layout of the input.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
output_size = [] or output_size
return _make.adaptive_max_pool2d(data, output_size, layout)
def adaptive_avg_pool2d(data, output_size=None, layout="NCHW"):
r"""2D adaptive average pooling operator. This operator is experimental.
This operator takes data as input and does 2D average value calculation
across each window represented by WxH.
In the default case, where the data_layout is `NCHW`
a data Tensor with shape `(batch_size, in_channels, height, width)`,
to produce an output Tensor with shape
(batch_size, in_channels, output_height, output_width).
The pooling kernel and stride sizes are automatically chosen for
desired output sizes.
For output_size:
If this argument is not provided, input height and width will be used
as output height and width.
If a single integer is provided for output_size, the output size is
(N x C x output_size x output_size) for any input (NCHW).
If a tuple of integers (height, width) are provided for output_size,
the output size is (N x C x height x width) for any input (NCHW).
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
output_size : tuple of int. optional
Output height and width.
layout : str, optional
Layout of the input.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
output_size = [] or output_size
return _make.adaptive_avg_pool2d(data, output_size, layout)
def adaptive_max_pool3d(data, output_size=None, layout="NCDHW"):
r"""3D adaptive max pooling operator. This operator is experimental.
This operator takes data as input and does 3D max value calculation
across each window represented by DxWxH.
In the default case, where the data_layout is `NCDHW`
a data Tensor with shape `(batch_size, in_channels, depth, height, width)`,
to produce an output Tensor with shape
(batch_size, in_channels, output_depth, output_height, output_width).
The pooling kernel and stride sizes are automatically chosen for
desired output sizes.
For output_size:
If this argument is not provided, input depth, height and width will be used
as output depth, height and width.
If a single integer is provided for output_size, the output size is
(N x C x output_size x output_size x output_size) for any input (NCDHW).
If a tuple of integers (depth, height, width) are provided for output_size,
the output size is (N x C x depth x height x width) for any input (NCDHW).
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
output_size : tuple of int. optional
Output height and width.
layout : str, optional
Layout of the input.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
output_size = [] or output_size
return _make.adaptive_max_pool3d(data, output_size, layout)
def adaptive_avg_pool3d(data, output_size=None, layout="NCDHW"):
r"""3D adaptive avg pooling operator. This operator is experimental.
This operator takes data as input and does 3D avg value calculation
across each window represented by DxWxH.
In the default case, where the data_layout is `NCDHW`
a data Tensor with shape `(batch_size, in_channels, depth, height, width)`,
to produce an output Tensor with shape
(batch_size, in_channels, output_depth, output_height, output_width).
The pooling kernel and stride sizes are automatically chosen for
desired output sizes.
For output_size:
If this argument is not provided, input depth, height and width will be used
as output depth, height and width.
If a single integer is provided for output_size, the output size is
(N x C x output_size x output_size x output_size) for any input (NCDHW).
If a tuple of integers (depth, height, width) are provided for output_size,
the output size is (N x C x depth x height x width) for any input (NCDHW).
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
output_size : tuple of int. optional
Output height and width.
layout : str, optional
Layout of the input.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
output_size = [] or output_size
return _make.adaptive_avg_pool3d(data, output_size, layout)
def global_max_pool3d(data, layout="NCDHW"):
r"""3D global maximum pooling operator.
This operator takes data as input and does 3D max value calculation
across each window represented by DxWxH.
In the default case, where the data_layout is `NCDHW`
a data Tensor with shape `(batch_size, in_channels, depth, height, width)`,
to produce an output Tensor with the following rule:
with data of shape (b, c, d, h, w)
.. math::
\mbox{out}(b, c, 1, 1, 1) = \max_{l=0, \ldots, d}, \max_{m=0, \ldots, h},
\max_{n=0, \ldots, w} \mbox{data}(b, c, l, m, n)
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
layout : str, optional
Layout of the input.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
output_size = [1, 1, 1]
return _make.adaptive_max_pool3d(data, output_size, layout)
def global_avg_pool3d(data, layout="NCDHW"):
r"""3D global average pooling operator.
This operator takes data as input and does 3D average value calculation
across each window represented by DxWxH.
In the default case, where the data_layout is `NCDHW`
a data Tensor with shape `(batch_size, in_channels, depth, height, width)`,
to produce an output Tensor with the following rule:
with data of shape (b, c, d, h, w)
.. math::
\mbox{out}(b, c, 1, 1, 1) = \frac{1}{d * h * w} \sum_{l=0}^{d-1} \sum_{m=0}^{h-1}
\sum_{n=0}^{w-1} \mbox{data}(b, c, l, m, n)
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
layout : str, optional
Layout of the input.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
output_size = [1, 1, 1]
return _make.adaptive_avg_pool3d(data, output_size, layout)
def correlation(
data1, data2, kernel_size, max_displacement, stride1, stride2, padding, is_multiply, layout
):
r"""Applies correlation to inputs.
The correlation layer performs multiplicative patch comparisons between two feature maps.
Given two multi-channel feature maps :math:`f_{1}, f_{2}`, with :math:`w`, :math:`h`, and
:math:`c` being their width, height, and number of channels, the correlation layer lets the
network compare each patch from :math:`f_{1}` with each patch from :math:`f_{2}`.
For now we consider only a single comparison of two patches. The 'correlation' of two patches
centered at :math:`x_{1}` in the first map and :math:`x_{2}` in the second map is then defined
as:
.. math::
c(x_{1}, x_{2}) = \sum_{o \in [-k,k] \times [-k,k]} <f_{1}(x_{1} + o), f_{2}(x_{2} + o)>
for a square patch of size :math:`K:=2k+1`.
Note that the equation above is identical to one step of a convolution in neural networks, but
instead of convolving data with a filter, it convolves data with other data. For this
reason, it has no training weights.
Computing :math:`c(x_{1}, x_{2})` involves :math:`c * K^{2}` multiplications. Comparing all
patch combinations involves :math:`w^{2}*h^{2}` such computations.
Given a maximum displacement :math:`d`, for each location :math:`x_{1}` it computes
correlations :math:`c(x_{1}, x_{2})` only in a neighborhood of size :math:`D:=2d+1`,
by limiting the range of :math:`x_{2}`. We use strides :math:`s_{1}, s_{2}`, to quantize
:math:`x_{1}` globally and to quantize :math:`x_{2}` within the neighborhood
centered around :math:`x_{1}`.
The final output is defined by the following expression:
.. math::
out[n, q, i, j] = c(x_{i, j}, x_{q})
where :math:`i` and :math:`j` enumerate spatial locations in :math:`f_{1}`, and :math:`q`
denotes the :math:`q^{th}` neighborhood of :math:`x_{i,j}`.
Parameters
----------
data1 : tvm.te.Tensor
4-D with shape [batch, channel, height, width]
data2 : tvm.te.Tensor
4-D with shape [batch, channel, height, width]
kernel_size: int
Kernel size for correlation, must be an odd number
max_displacement: int
Max displacement of Correlation
stride1: int
Stride for data1
stride2: int
Stride for data2 within the neightborhood centered around data1
padding : int or a list/tuple of 2 or 4 ints
Padding size, or
[pad_height, pad_width] for 2 ints, or
[pad_top, pad_left, pad_bottom, pad_right] for 4 ints
is_multiply: bool
operation type is either multiplication or substraction
layout: str
layout of data1, data2 and the output
Returns
-------
Output : tvm.te.Tensor
4-D with shape [batch, out_channel, out_height, out_width]
"""
if isinstance(padding, int):
padding = (padding, padding)
return _make.correlation(
data1, data2, kernel_size, max_displacement, stride1, stride2, padding, is_multiply, layout
)
def space_to_batch_nd(data, block_shape, paddings, pad_value=0):
r"""Divide spatial dimensions of the data into a grid of blocks
and interleave them into batch dim.
Parameters
----------
data : tvm.te.Tensor
N-D with shape [batch, spatial_shape, remaining_shape]
block_shape : relay.Expr
1-D of size [M] where M is number of spatial dims, specifies block size
for each spatial dimension.
paddings : relay.Expr
2-D of shape [M, 2] where M is number of spatial dims, specifies
[before, after] paddings for each spatial dimension.
pad_value : float, or relay.Expr, optional, default=0
The value used for padding.
Returns
-------
result : relay.Expr
N-D Tensor with shape
[in_batch * prod(block_shape),
padded_data[1] / block_shape[0], ..., padded_data[M] / block_shape[M-1],
remaining_shape]
"""
return _make.space_to_batch_nd(data, block_shape, paddings, pad_value)
def batch_to_space_nd(data, block_shape, crops):
r"""Reshape the batch dimension into spatial dimensions.
Parameters
----------
data : tvm.te.Tensor
N-D with shape [batch, spatial_shape, remaining_shape]
block_shape : relay.Expr
1-D of size [M] where M is number of spatial dims, specifies block size
for each spatial dimension.
crops : relay.Expr
2-D of shape [M, 2] where M is number of spatial dims, specifies
[begin, end] crop size for each spatial dimension.
Returns
-------
result : relay.Expr
N-D Tensor with shape
[batch / prod(block_shape),
in_shape[1] * block_shape[0] - crops[0,0] - crops[0,1], ...,
in_shape[M] * block_shape[M-1] - crops[M-1, 0] - crops[M-1, 1],
remaining_shape]
"""
return _make.batch_to_space_nd(data, block_shape, crops)