tf.raw_ops.FusedResizeAndPadConv2D
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Performs a resize and padding as a preprocess during a convolution.
tf.raw_ops.FusedResizeAndPadConv2D(
input,
size,
paddings,
filter,
mode,
strides,
padding,
resize_align_corners=False,
name=None
)
It's often possible to do spatial transformations more efficiently as part of
the packing stage of a convolution, so this op allows for an optimized
implementation where these stages are fused together. This prevents the need to
write out the intermediate results as whole tensors, reducing memory pressure,
and we can get some latency gains by merging the transformation calculations.
The data_format attribute for Conv2D isn't supported by this op, and defaults to
'NHWC' order.
Internally this op uses a single per-graph scratch buffer, which means that it
will block if multiple versions are being run in parallel. This is because this
operator is primarily an optimization to minimize memory usage.
Args |
|---|
input
|
A Tensor. Must be one of the following types: half, float32, float64.
4-D with shape [batch, in_height, in_width, in_channels].
|
size
|
A Tensor of type int32.
A 1-D int32 Tensor of 2 elements: new_height, new_width. The
new size for the images.
|
paddings
|
A Tensor of type int32.
A two-column matrix specifying the padding sizes. The number of
rows must be the same as the rank of input.
|
filter
|
A Tensor. Must have the same type as input. 4-D with shape
[filter_height, filter_width, in_channels, out_channels].
|
mode
|
A string from: "REFLECT", "SYMMETRIC".
|
strides
|
A list of ints.
1-D of length 4. The stride of the sliding window for each dimension
of input. Must be in the same order as the dimension specified with format.
|
padding
|
A string from: "SAME", "VALID".
The type of padding algorithm to use.
|
resize_align_corners
|
An optional bool. Defaults to False.
If true, the centers of the 4 corner pixels of the input and output tensors are
aligned, preserving the values at the corner pixels. Defaults to false.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor. Has the same type as input.
|
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Last updated 2023-10-06 UTC.
[null,null,["Last updated 2023-10-06 UTC."],[],[],null,["# tf.raw_ops.FusedResizeAndPadConv2D\n\n\u003cbr /\u003e\n\nPerforms a resize and padding as a preprocess during a convolution.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.raw_ops.FusedResizeAndPadConv2D`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/FusedResizeAndPadConv2D)\n\n\u003cbr /\u003e\n\n tf.raw_ops.FusedResizeAndPadConv2D(\n input,\n size,\n paddings,\n filter,\n mode,\n strides,\n padding,\n resize_align_corners=False,\n name=None\n )\n\nIt's often possible to do spatial transformations more efficiently as part of\nthe packing stage of a convolution, so this op allows for an optimized\nimplementation where these stages are fused together. This prevents the need to\nwrite out the intermediate results as whole tensors, reducing memory pressure,\nand we can get some latency gains by merging the transformation calculations.\nThe data_format attribute for Conv2D isn't supported by this op, and defaults to\n'NHWC' order.\nInternally this op uses a single per-graph scratch buffer, which means that it\nwill block if multiple versions are being run in parallel. This is because this\noperator is primarily an optimization to minimize memory usage.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `input` | A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`. 4-D with shape `[batch, in_height, in_width, in_channels]`. |\n| `size` | A `Tensor` of type `int32`. A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The new size for the images. |\n| `paddings` | A `Tensor` of type `int32`. A two-column matrix specifying the padding sizes. The number of rows must be the same as the rank of `input`. |\n| `filter` | A `Tensor`. Must have the same type as `input`. 4-D with shape `[filter_height, filter_width, in_channels, out_channels]`. |\n| `mode` | A `string` from: `\"REFLECT\", \"SYMMETRIC\"`. |\n| `strides` | A list of `ints`. 1-D of length 4. The stride of the sliding window for each dimension of `input`. Must be in the same order as the dimension specified with format. |\n| `padding` | A `string` from: `\"SAME\", \"VALID\"`. The type of padding algorithm to use. |\n| `resize_align_corners` | An optional `bool`. Defaults to `False`. If true, the centers of the 4 corner pixels of the input and output tensors are aligned, preserving the values at the corner pixels. Defaults to false. |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `Tensor`. Has the same type as `input`. ||\n\n\u003cbr /\u003e"]]
