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nn_ops.cc
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nn_ops.cc
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/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include <algorithm>
#include <cmath>
#include "tensorflow/core/framework/common_shape_fns.h"
#include "tensorflow/core/framework/kernel_shape_util.h"
#include "tensorflow/core/framework/numeric_op.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/lib/core/bits.h"
#include "tensorflow/core/lib/math/math_util.h"
#include "tensorflow/core/util/mirror_pad_mode.h"
#include "tensorflow/core/util/padding.h"
#include "tensorflow/core/util/tensor_format.h"
namespace tensorflow {
using shape_inference::DimensionHandle;
using shape_inference::InferenceContext;
using shape_inference::ShapeHandle;
namespace {
Status FractionalPoolShapeFn(InferenceContext* c) {
ShapeHandle input;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input));
std::vector<float> pooling_ratio;
TF_RETURN_IF_ERROR(c->GetAttr("pooling_ratio", &pooling_ratio));
if (pooling_ratio.size() != 4) {
return errors::InvalidArgument(
"pooling_ratio field must specify 4 dimensions");
}
std::vector<DimensionHandle> output_dims;
for (int i = 0; i < 4; ++i) {
DimensionHandle d = c->Dim(input, i);
if (c->ValueKnown(d)) {
// This must match the same logic in the kernel function in
// core/kernels/fractional_max_pool_op.cc.
auto val =
static_cast<int64_t>(std::floor(c->Value(d) / pooling_ratio[i]));
if (val < 0) {
return errors::InvalidArgument("Size computed for dim ", i,
" is negative: ", val);
}
output_dims.push_back(c->MakeDim(val));
} else {
output_dims.push_back(c->UnknownDim());
}
}
for (std::size_t i = 0; i < pooling_ratio.size(); ++i) {
if (pooling_ratio[i] < 1) {
return errors::InvalidArgument(
"pooling_ratio cannot be smaller than 1, got: ", pooling_ratio[i]);
}
}
c->set_output(0, c->MakeShape(output_dims));
c->set_output(1, c->Vector(output_dims[1]));
c->set_output(2, c->Vector(output_dims[2]));
return OkStatus();
}
} // namespace
// --------------------------------------------------------------------------
REGISTER_OP("AvgPool")
.Input("value: T")
.Output("output: T")
.Attr("ksize: list(int) >= 4")
.Attr("strides: list(int) >= 4")
.Attr(GetPaddingAttrString())
.Attr(GetConvnetDataFormatAttrString())
.Attr("T: {half, bfloat16, float, double}")
.SetShapeFn(shape_inference::AvgPoolShape);
REGISTER_OP("AvgPoolGrad")
.Input("orig_input_shape: int32")
.Input("grad: T")
.Output("output: T")
.Attr("ksize: list(int) >= 4")
.Attr("strides: list(int) >= 4")
.Attr(GetPaddingAttrString())
.Attr(GetConvnetDataFormatAttrString())
.Attr("T: {half, bfloat16, float, double}")
.SetShapeFn(shape_inference::AvgPoolGradShape);
// --------------------------------------------------------------------------
REGISTER_OP("BatchNormWithGlobalNormalization")
.Input("t: T")
.Input("m: T")
.Input("v: T")
.Input("beta: T")
.Input("gamma: T")
.Output("result: T")
.Attr("T: numbertype")
.Attr("variance_epsilon: float")
.Attr("scale_after_normalization: bool")
.Deprecated(9, "Use tf.nn.batch_normalization()")
.SetShapeFn([](InferenceContext* c) {
ShapeHandle input;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input));
DimensionHandle last_dim = c->Dim(input, 3);
for (int i = 1; i < 5; ++i) { // covers m, v, beta, gamma
ShapeHandle vec;
TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 1, &vec));
TF_RETURN_IF_ERROR(c->Merge(last_dim, c->Dim(vec, 0), &last_dim));
}
ShapeHandle out;
TF_RETURN_IF_ERROR(c->ReplaceDim(input, 3, last_dim, &out));
c->set_output(0, out);
return OkStatus();
});
REGISTER_OP("BatchNormWithGlobalNormalizationGrad")
.Input("t: T")
.Input("m: T")
.Input("v: T")
.Input("gamma: T")
.Input("backprop: T")
.Output("dx: T")
.Output("dm: T")
.Output("dv: T")
.Output("db: T")
.Output("dg: T")
.Attr("T: numbertype")
.Attr("variance_epsilon: float")
.Attr("scale_after_normalization: bool")
.Deprecated(9, "Use tf.nn.batch_normalization()")
.SetShapeFn([](InferenceContext* c) {
ShapeHandle input;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input));
TF_RETURN_IF_ERROR(
c->Merge(input, c->input(4), &input)); // with backprop
DimensionHandle last_dim = c->Dim(input, 3);
for (int i = 1; i < 4; ++i) { // covers m, v, gamma
ShapeHandle vec;
TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 1, &vec));
TF_RETURN_IF_ERROR(c->Merge(last_dim, c->Dim(vec, 0), &last_dim));
}
ShapeHandle dx;
TF_RETURN_IF_ERROR(c->ReplaceDim(input, 3, last_dim, &dx));
c->set_output(0, dx);
ShapeHandle vector_shape = c->Vector(last_dim);
c->set_output(1, vector_shape);
c->set_output(2, vector_shape);
c->set_output(3, vector_shape);
c->set_output(4, vector_shape);
return OkStatus();
});
// --------------------------------------------------------------------------
REGISTER_OP("FusedBatchNorm")
.Input("x: T")
.Input("scale: T")
.Input("offset: T")
.Input("mean: T")
.Input("variance: T")
.Output("y: T")
.Output("batch_mean: T")
.Output("batch_variance: T")
.Output("reserve_space_1: T")
.Output("reserve_space_2: T")
.Attr("T: {float}")
.Attr("epsilon: float = 0.0001")
.Attr("exponential_avg_factor: float = 1.0")
.Attr(GetConvnetDataFormatAttrString())
.Attr("is_training: bool = true")
.SetShapeFn(shape_inference::FusedBatchNormShape);
REGISTER_OP("FusedBatchNormV2")
.Input("x: T")
.Input("scale: U")
.Input("offset: U")
.Input("mean: U")
.Input("variance: U")
.Output("y: T")
.Output("batch_mean: U")
.Output("batch_variance: U")
.Output("reserve_space_1: U")
.Output("reserve_space_2: U")
.Attr("T: {half, bfloat16, float}")
.Attr("U: {float}")
.Attr("epsilon: float = 0.0001")
.Attr("exponential_avg_factor: float = 1.0")
.Attr(GetConvnetDataFormatAttrString())
.Attr("is_training: bool = true")
.SetShapeFn(shape_inference::FusedBatchNormShape);
REGISTER_OP("FusedBatchNormV3")
.Input("x: T")
.Input("scale: U")
.Input("offset: U")
.Input("mean: U")
.Input("variance: U")
.Output("y: T")
.Output("batch_mean: U")
.Output("batch_variance: U")
.Output("reserve_space_1: U")
.Output("reserve_space_2: U")
.Output("reserve_space_3: U")
.Attr("T: {half, bfloat16, float}")
.Attr("U: {bfloat16, float}")
.Attr("epsilon: float = 0.0001")
.Attr("exponential_avg_factor: float = 1.0")
.Attr(GetConvnetDataFormat2D3DAttrString())
.Attr("is_training: bool = true")
.SetShapeFn(shape_inference::FusedBatchNormV3Shape);
REGISTER_OP("_FusedBatchNormEx")
.Input("x: T")
.Input("scale: U")
.Input("offset: U")
.Input("mean: U")
.Input("variance: U")
.Input("side_input: num_side_inputs * T")
.Output("y: T")
.Output("batch_mean: U")
.Output("batch_variance: U")
.Output("reserve_space_1: U")
.Output("reserve_space_2: U")
.Output("reserve_space_3: U")
.Attr("T: {half, float, bfloat16}")
.Attr("U: {float}")
.Attr("epsilon: float = 0.0001")
.Attr("exponential_avg_factor: float = 1.0")
.Attr("num_side_inputs: int >= 0 = 0")
.Attr("activation_mode: string = \"Identity\"")
.Attr(GetConvnetDataFormatAttrString())
.Attr("is_training: bool = true")
.SetShapeFn(shape_inference::FusedBatchNormExShape)
.Doc(R"doc(
Internal FusedBatchNorm operation: reserved for internal use.
Do not invoke this operator directly in Python. A fusion optimization is
expected to create these operators.
)doc");
REGISTER_OP("FusedBatchNormGrad")
.Input("y_backprop: T")
.Input("x: T")
.Input("scale: T")
.Input("reserve_space_1: T")
.Input("reserve_space_2: T")
.Output("x_backprop: T")
.Output("scale_backprop: T")
.Output("offset_backprop: T")
.Output("reserve_space_3: T")
.Output("reserve_space_4: T")
.Attr("T: {float}")
.Attr("epsilon: float = 0.0001")
.Attr(GetConvnetDataFormatAttrString())
.Attr("is_training: bool = true")
.SetShapeFn(shape_inference::FusedBatchNormGradShape);
REGISTER_OP("FusedBatchNormGradV2")
.Input("y_backprop: T")
.Input("x: T")
.Input("scale: float")
.Input("reserve_space_1: U")
.Input("reserve_space_2: U")
.Output("x_backprop: T")
.Output("scale_backprop: U")
.Output("offset_backprop: U")
.Output("reserve_space_3: U")
.Output("reserve_space_4: U")
.Attr("T: {half, bfloat16, float}")
.Attr("U: {float}")
.Attr("epsilon: float = 0.0001")
.Attr(GetConvnetDataFormatAttrString())
.Attr("is_training: bool = true")
.SetShapeFn(shape_inference::FusedBatchNormGradShape);
REGISTER_OP("FusedBatchNormGradV3")
.Input("y_backprop: T")
.Input("x: T")
.Input("scale: float")
.Input("reserve_space_1: U")
.Input("reserve_space_2: U")
.Input("reserve_space_3: U")
.Output("x_backprop: T")
.Output("scale_backprop: U")
.Output("offset_backprop: U")
.Output("reserve_space_4: U")
.Output("reserve_space_5: U")
.Attr("T: {half, bfloat16, float}")
.Attr("U: {float}")
.Attr("epsilon: float = 0.0001")
.Attr(GetConvnetDataFormat2D3DAttrString())
.Attr("is_training: bool = true")
.SetShapeFn(shape_inference::FusedBatchNormGradShape);
REGISTER_OP("_FusedBatchNormGradEx")
.Input("y_backprop: T")
.Input("x: T")
.Input("scale: float")
.Input("reserve_space_1: U")
.Input("reserve_space_2: U")
.Input("reserve_space_3: U")
.Input("offset: float")
.Input("y: T")
.Output("x_backprop: T")
.Output("scale_backprop: U")
.Output("offset_backprop: U")
.Output("reserve_space_4: U")
.Output("reserve_space_5: U")
.Output("side_input_backprop: num_side_inputs * T")
.Attr("T: {half, float}")
.Attr("U: {float}")
.Attr("epsilon: float = 0.0001")
.Attr("num_side_inputs: int >= 0 = 0")
.Attr("activation_mode: string = \"Identity\"")
.Attr(GetConvnetDataFormat2D3DAttrString())
.Attr("is_training: bool = true")
.SetShapeFn(shape_inference::FusedBatchNormGradExShape)
.Doc(R"doc(
Internal FusedBatchNormGrad operation: reserved for internal use.
Do not invoke this operator directly in Python. A fusion optimization is
expected to create these operators.
)doc");
// --------------------------------------------------------------------------
REGISTER_OP("BiasAdd")
.Attr("T: numbertype")
.Input("value: T")
.Input("bias: T")
.Attr(GetConvnetDataFormatAttrString())
.Output("output: T")
.SetShapeFn(shape_inference::BiasAddShape);
// --------------------------------------------------------------------------
REGISTER_OP("BiasAddGrad")
.Attr("T: numbertype")
.Input("out_backprop: T")
.Attr(GetConvnetDataFormatAttrString())
.Output("output: T")
.SetShapeFn(shape_inference::BiasAddGradShape);
// --------------------------------------------------------------------------
REGISTER_OP("BiasAddV1")
.Attr("T: numbertype")
.Input("value: T")
.Input("bias: T")
.Output("output: T")
.SetShapeFn(shape_inference::BiasAddShape);
// --------------------------------------------------------------------------
REGISTER_OP("Conv2D")
.Input("input: T")
.Input("filter: T")
.Output("output: T")
.Attr("T: {half, bfloat16, float, double, int32}")
.Attr("strides: list(int)")
.Attr("use_cudnn_on_gpu: bool = true")
.Attr(GetPaddingAttrStringWithExplicit())
.Attr(GetExplicitPaddingsAttrString())
.Attr(GetConvnetDataFormatAttrString())
.Attr("dilations: list(int) = [1, 1, 1, 1]")
.SetShapeFn(shape_inference::Conv2DShapeWithExplicitPadding);
REGISTER_OP("Conv2DBackpropInput")
.Input("input_sizes: int32")
.Input("filter: T")
.Input("out_backprop: T")
.Output("output: T")
.Attr("T: {half, bfloat16, float, double, int32}")
.Attr("strides: list(int)")
.Attr("use_cudnn_on_gpu: bool = true")
.Attr(GetPaddingAttrStringWithExplicit())
.Attr(GetExplicitPaddingsAttrString())
.Attr(GetConvnetDataFormatAttrString())
.Attr("dilations: list(int) = [1, 1, 1, 1]")
.SetShapeFn(shape_inference::Conv2DBackpropInputShape);
// TODO(jeff): Instead of 'use_cudnn_for_gpu', maybe we should have a
// more general string attribute ('kernel_impl'?) that can be used to
// select among several possible implementations.
REGISTER_OP("Conv2DBackpropFilter")
.Input("input: T")
.Input("filter_sizes: int32")
.Input("out_backprop: T")
.Output("output: T")
.Attr("T: {half, bfloat16, float, double}")
.Attr("strides: list(int)")
.Attr("use_cudnn_on_gpu: bool = true")
.Attr(GetPaddingAttrStringWithExplicit())
.Attr(GetExplicitPaddingsAttrString())
.Attr(GetConvnetDataFormatAttrString())
.Attr("dilations: list(int) = [1, 1, 1, 1]")
.SetShapeFn([](InferenceContext* c) {
ShapeHandle s;
TF_RETURN_IF_ERROR(c->MakeShapeFromShapeTensor(1, &s));
TF_RETURN_IF_ERROR(c->WithRank(s, 4, &s));
c->set_output(0, s);
return OkStatus();
});
REGISTER_OP("_FusedConv2D")
.Input("input: T")
.Input("filter: T")
.Input("args: num_args * T")
.Output("output: T")
.Attr("T: {half, float, double}")
.Attr("num_args: int >= 0")
.Attr("strides: list(int)")
.Attr(GetPaddingAttrStringWithExplicit())
.Attr(GetExplicitPaddingsAttrString())
.Attr(GetConvnetDataFormatAttrString())
.Attr("dilations: list(int) = [1, 1, 1, 1]")
.Attr("use_cudnn_on_gpu: bool = true")
.Attr("fused_ops: list(string) = []")
// Attributes for the FusedBatchNorm ------------------------------------ //
.Attr("epsilon: float = 0.0001")
// Attributes for the LeakyRelu ----------------------------------------- //
.Attr("leakyrelu_alpha: float = 0.2")
// ---------------------------------------------------------------------- //
.SetShapeFn(shape_inference::Conv2DShapeWithExplicitPadding)
.Doc(R"doc(
Performs a convolution followed by a specified series of operations.
The inputs to the convolution are `input` and `filter`. The series of operations
that follows is specified by the `fused_ops` attribute, which is a list of TF op
names specified as strings (e.g. "Relu"). They are performed in order, where the
(first) input to each op is the output of the preceding op. The first input and
the output of each fused_op must be of type T.
Currently supported fused_op combinations are: [X] and [X,A], where X is one of
{"BiasAdd","FusedBatchNorm"} and A is one of {"Elu","Relu","Relu6"}.
* The first input to op X is the Conv2D result, and the additional input(s) to X
are specified by `args`.
* If there is an op A specified, the output of op X is the input to op A, and op
A produces the _FusedConv2D output. Otherwise, op X produces the _FusedConv2D
output.
*NOTE*: Do not invoke this operator directly in Python. Grappler is expected to
create these operators.
)doc");
namespace {
Status CommonFusedConvCalculations(InferenceContext* c, bool has_resize) {
ShapeHandle input;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input));
ShapeHandle resized = input;
int paddings_index = 1;
int filter_index = 2;
if (has_resize) {
paddings_index = 2;
filter_index = 3;
ShapeHandle unused_size;
TF_RETURN_IF_ERROR(c->Merge(c->input(1), c->Vector(2), &unused_size));
const Tensor* size = c->input_tensor(1);
DimensionHandle new_height = c->UnknownDim();
DimensionHandle new_width = c->UnknownDim();
if (size != nullptr) {
new_height = c->MakeDim(size->flat<int32>()(0));
new_width = c->MakeDim(size->flat<int32>()(1));
}
TF_RETURN_IF_ERROR(c->ReplaceDim(resized, 1, new_height, &resized));
TF_RETURN_IF_ERROR(c->ReplaceDim(resized, 2, new_width, &resized));
}
ShapeHandle paddings;
TF_RETURN_IF_ERROR(c->WithRank(c->input(paddings_index), 2, &paddings));
TF_RETURN_IF_ERROR(
c->WithRank(resized, c->Value(c->Dim(paddings, 0)), &resized));
TF_RETURN_IF_ERROR(
c->Merge(paddings, c->Matrix(c->Rank(resized), 2), &paddings));
const Tensor* paddings_t = c->input_tensor(paddings_index);
ShapeHandle padded;
if (paddings_t != nullptr) {
std::vector<DimensionHandle> output_dims;
for (int i = 0; i < 4; ++i) {
DimensionHandle dim = c->Dim(resized, i);
int64_t p0 = static_cast<int64_t>(paddings_t->matrix<int32>()(i, 0));
int64_t p1 = static_cast<int64_t>(paddings_t->matrix<int32>()(i, 1));
if (p0 < 0 || p1 < 0) {
return errors::InvalidArgument("Paddings must be non-negative");
}
TF_RETURN_IF_ERROR(c->Add(dim, p0 + p1, &dim));
output_dims.push_back(dim);
}
padded = c->MakeShape(output_dims);
} else {
padded = c->UnknownShapeOfRank(4);
}
// Work out the convolution's effect with 'padded' as the input.
ShapeHandle filter;
TF_RETURN_IF_ERROR(c->WithRank(c->input(filter_index), 4, &filter));
std::vector<int32> strides;
TF_RETURN_IF_ERROR(c->GetAttr("strides", &strides));
if (strides.size() != 4) {
return errors::InvalidArgument(
"Operation requires the stride attribute to contain 4 values, but ",
"got: ", strides.size());
}
int32_t stride_rows = strides[1];
int32_t stride_cols = strides[2];
DimensionHandle batch_size_dim = c->Dim(padded, 0);
DimensionHandle in_rows_dim = c->Dim(padded, 1);
DimensionHandle in_cols_dim = c->Dim(padded, 2);
DimensionHandle filter_rows_dim = c->Dim(filter, 0);
DimensionHandle filter_cols_dim = c->Dim(filter, 1);
DimensionHandle output_depth_dim = c->Dim(filter, 3);
DimensionHandle unused;
TF_RETURN_IF_ERROR(c->Merge(c->Dim(padded, 3), c->Dim(filter, 2), &unused));
Padding padding;
TF_RETURN_IF_ERROR(c->GetAttr("padding", &padding));
DimensionHandle output_rows, output_cols;
TF_RETURN_IF_ERROR(GetWindowedOutputSizeFromDims(
c, in_rows_dim, filter_rows_dim, stride_rows, padding, &output_rows));
TF_RETURN_IF_ERROR(GetWindowedOutputSizeFromDims(
c, in_cols_dim, filter_cols_dim, stride_cols, padding, &output_cols));
ShapeHandle output_shape = c->MakeShape(
{batch_size_dim, output_rows, output_cols, output_depth_dim});
c->set_output(0, output_shape);
return OkStatus();
}
} // namespace
REGISTER_OP("DataFormatDimMap")
.Input("x: T")
.Output("y: T")
.Attr("T: {int32, int64} = DT_INT32")
.Attr("src_format: string = 'NHWC'")
.Attr("dst_format: string = 'NCHW'")
.SetShapeFn(shape_inference::UnchangedShape);
REGISTER_OP("DataFormatVecPermute")
.Input("x: T")
.Output("y: T")
.Attr("T: {int32, int64} = DT_INT32")
.Attr("src_format: string = 'NHWC'")
.Attr("dst_format: string = 'NCHW'")
.SetShapeFn(shape_inference::UnchangedShape);
REGISTER_OP("FusedResizeAndPadConv2D")
.Input("input: T")
.Input("size: int32")
.Input("paddings: int32")
.Input("filter: T")
.Output("output: T")
.Attr("T: {half, float, double}")
.Attr("resize_align_corners: bool = false")
.Attr(GetMirrorPadModeAttrString())
.Attr("strides: list(int)")
.Attr(GetPaddingAttrString())
.SetShapeFn([](InferenceContext* c) {
return CommonFusedConvCalculations(c, /*has_resize=*/true);
});
REGISTER_OP("FusedPadConv2D")
.Input("input: T")
.Input("paddings: int32")
.Input("filter: T")
.Output("output: T")
.Attr("T: {half, float, double}")
.Attr(GetMirrorPadModeAttrString())
.Attr("strides: list(int)")
.Attr(GetPaddingAttrString())
.SetShapeFn([](InferenceContext* c) {
return CommonFusedConvCalculations(c, /*has_resize=*/false);
});
// --------------------------------------------------------------------------
REGISTER_OP("DepthwiseConv2dNative")
.Input("input: T")
.Input("filter: T")
.Output("output: T")
.Attr("T: {half, bfloat16, float, double}")
.Attr("strides: list(int)")
.Attr(GetPaddingAttrStringWithExplicit())
.Attr(GetExplicitPaddingsAttrString())
.Attr(GetConvnetDataFormatAttrString())
.Attr("dilations: list(int) = [1, 1, 1, 1]")
.SetShapeFn(shape_inference::DepthwiseConv2DNativeShapeWithExplicitPadding);
REGISTER_OP("DepthwiseConv2dNativeBackpropInput")
.Input("input_sizes: int32")
.Input("filter: T")
.Input("out_backprop: T")
.Output("output: T")
.Attr("T: {half, bfloat16, float, double}")
.Attr("strides: list(int)")
.Attr(GetPaddingAttrStringWithExplicit())
.Attr(GetExplicitPaddingsAttrString())
.Attr(GetConvnetDataFormatAttrString())
.Attr("dilations: list(int) = [1, 1, 1, 1]")
.SetShapeFn([](InferenceContext* c) {
ShapeHandle s;
TF_RETURN_IF_ERROR(c->MakeShapeFromShapeTensor(0, &s));
TF_RETURN_IF_ERROR(c->WithRank(s, 4, &s));
c->set_output(0, s);
return OkStatus();
});
REGISTER_OP("DepthwiseConv2dNativeBackpropFilter")
.Input("input: T")
.Input("filter_sizes: int32")
.Input("out_backprop: T")
.Output("output: T")
.Attr("T: {half, bfloat16, float, double}")
.Attr("strides: list(int)")
.Attr(GetPaddingAttrStringWithExplicit())
.Attr(GetExplicitPaddingsAttrString())
.Attr(GetConvnetDataFormatAttrString())
.Attr("dilations: list(int) = [1, 1, 1, 1]")
.SetShapeFn([](InferenceContext* c) {
ShapeHandle s;
TF_RETURN_IF_ERROR(c->MakeShapeFromShapeTensor(1, &s));
TF_RETURN_IF_ERROR(c->WithRank(s, 4, &s));
c->set_output(0, s);
return OkStatus();
});
REGISTER_OP("_FusedDepthwiseConv2dNative")
.Input("input: T")
.Input("filter: T")
.Input("args: num_args * T")
.Output("output: T")
.Attr("T: {half, bfloat16, float, double}")
.Attr("num_args: int >= 0")
.Attr("strides: list(int)")
.Attr(GetPaddingAttrString())
.Attr(GetConvnetDataFormatAttrString())
.Attr("dilations: list(int) = [1, 1, 1, 1]")
.Attr("fused_ops: list(string) = []")
// Attributes for the FusedBatchNorm ------------------------------------ //
.Attr("epsilon: float = 0.0001")
// Attributes for the LeakyRelu ----------------------------------------- //
.Attr("leakyrelu_alpha: float = 0.2")
// ---------------------------------------------------------------------- //
.SetShapeFn(shape_inference::DepthwiseConv2DNativeShape);
// --------------------------------------------------------------------------
REGISTER_OP("Conv3D")
.Input("input: T")
.Input("filter: T")
.Output("output: T")
.Attr("T: {half, bfloat16, float, double}")
.Attr("strides: list(int) >= 5")
.Attr(GetPaddingAttrString())
.Attr(GetConvnet3dDataFormatAttrString())
.Attr("dilations: list(int) = [1, 1, 1, 1, 1]")
.SetShapeFn(shape_inference::Conv3DShape);
REGISTER_OP("Conv3DBackpropInput")
.Input("input: T")
.Input("filter: T")
.Input("out_backprop: T")
.Output("output: T")
.Attr("T: {half, float, double}")
.Attr("strides: list(int) >= 5")
.Attr(GetPaddingAttrString())
.Deprecated(10, "Use Conv3DBackpropInputV2")
.Attr("dilations: list(int) = [1, 1, 1, 1, 1]")
.SetShapeFn([](InferenceContext* c) {
return UnchangedShapeWithRank(c, 5);
});
REGISTER_OP("Conv3DBackpropFilter")
.Input("input: T")
.Input("filter: T")
.Input("out_backprop: T")
.Output("output: T")
.Attr("T: {half, float, double}")
.Attr("strides: list(int) >= 5")
.Attr(GetPaddingAttrString())
.Deprecated(10, "Use Conv3DBackpropFilterV2")
.Attr("dilations: list(int) = [1, 1, 1, 1, 1]")
.SetShapeFn([](InferenceContext* c) {
ShapeHandle out;
TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 5, &out));
c->set_output(0, out);
return OkStatus();
});
REGISTER_OP("Conv3DBackpropInputV2")
.Input("input_sizes: Tshape")
.Input("filter: T")
.Input("out_backprop: T")
.Output("output: T")
.Attr("T: {half, bfloat16, float, double}")
.Attr("strides: list(int) >= 5")
.Attr(GetPaddingAttrString())
.Attr(GetConvnet3dDataFormatAttrString())
.Attr("dilations: list(int) = [1, 1, 1, 1, 1]")
.Attr("Tshape: {int32, int64} = DT_INT32")
.SetShapeFn([](InferenceContext* c) {
ShapeHandle s;
TF_RETURN_IF_ERROR(c->MakeShapeFromShapeTensor(0, &s));
TF_RETURN_IF_ERROR(c->WithRank(s, 5, &s));
c->set_output(0, s);
return OkStatus();
});
REGISTER_OP("Conv3DBackpropFilterV2")
.Input("input: T")
.Input("filter_sizes: int32")
.Input("out_backprop: T")
.Output("output: T")
.Attr("T: {half, bfloat16, float, double}")
.Attr("strides: list(int) >= 5")
.Attr(GetPaddingAttrString())
.Attr(GetConvnet3dDataFormatAttrString())
.Attr("dilations: list(int) = [1, 1, 1, 1, 1]")
.SetShapeFn([](InferenceContext* c) {
ShapeHandle s;
TF_RETURN_IF_ERROR(c->MakeShapeFromShapeTensor(1, &s));
TF_RETURN_IF_ERROR(c->WithRank(s, 5, &s));
c->set_output(0, s);
return OkStatus();
});
// --------------------------------------------------------------------------
REGISTER_OP("AvgPool3D")
.Input("input: T")
.Output("output: T")
.Attr("ksize: list(int) >= 5")
.Attr("strides: list(int) >= 5")
.Attr(GetPaddingAttrString())
.Attr(GetConvnet3dDataFormatAttrString())
.Attr("T: {half, bfloat16, float, double}")
.SetShapeFn(shape_inference::Pool3DShape);
REGISTER_OP("AvgPool3DGrad")
.Input("orig_input_shape: int32")
.Input("grad: T")
.Output("output: T")
.Attr("ksize: list(int) >= 5")
.Attr("strides: list(int) >= 5")
.Attr(GetPaddingAttrString())
.Attr(GetConvnet3dDataFormatAttrString())
.Attr("T: {half, bfloat16, float, double}")
.SetShapeFn(shape_inference::AvgPool3DGradShape);
// --------------------------------------------------------------------------
REGISTER_OP("MaxPool3D")
.Input("input: T")
.Output("output: T")
.Attr("ksize: list(int) >= 5")
.Attr("strides: list(int) >= 5")
.Attr(GetPaddingAttrString())
.Attr(GetConvnet3dDataFormatAttrString())
.Attr("T: {half, bfloat16, float}")
.SetShapeFn(shape_inference::Pool3DShape);
REGISTER_OP("MaxPool3DGrad")
.Input("orig_input: TInput")
.Input("orig_output: TInput")
.Input("grad: T")
.Output("output: T")
.Attr("ksize: list(int) >= 5")
.Attr("strides: list(int) >= 5")
.Attr(GetPaddingAttrString())
.Attr(GetConvnet3dDataFormatAttrString())
.Attr("T: {half, bfloat16, float} = DT_FLOAT")
.Attr("TInput: {half, bfloat16, float} = DT_FLOAT")
.SetShapeFn(shape_inference::MaxPool3DGradShape);
REGISTER_OP("MaxPool3DGradGrad")
.Input("orig_input: T")
.Input("orig_output: T")
.Input("grad: T")
.Output("output: T")
.Attr("ksize: list(int) >= 5 ")
.Attr("strides: list(int) >= 5")
.Attr(GetPaddingAttrString())
.Attr(GetConvnet3dDataFormatAttrString())
.Attr("T: realnumbertype")
.SetShapeFn([](InferenceContext* c) {
TF_RETURN_IF_ERROR(shape_inference::Pool3DShape(c));
ShapeHandle unused;
// Validate 'orig_input' is the same shape as 'grad'
TF_RETURN_IF_ERROR(c->Merge(c->input(0), c->input(2), &unused));
// Validate 'orig_output' is same shape as 'output'
TF_RETURN_IF_ERROR(c->Merge(c->input(1), c->output(0), &unused));
return OkStatus();
});
// --------------------------------------------------------------------------
REGISTER_OP("L2Loss")
.Input("t: T")
.Output("output: T")
.Attr("T: {half, bfloat16, float, double}")
.SetShapeFn(shape_inference::ScalarShape);
// --------------------------------------------------------------------------
REGISTER_OP("LRN")
.Input("input: T")
.Output("output: T")
.Attr("depth_radius: int = 5")
.Attr("bias: float = 1.0")
.Attr("alpha: float = 1.0")
.Attr("beta: float = 0.5")
.Attr("T: {half, bfloat16, float} = DT_FLOAT")
.SetShapeFn([](InferenceContext* c) {
return UnchangedShapeWithRank(c, 4);
});
REGISTER_OP("LRNGrad")
.Input("input_grads: T")
.Input("input_image: T")
.Input("output_image: T")
.Output("output: T")
.Attr("depth_radius: int = 5")
.Attr("bias: float = 1.0")
.Attr("alpha: float = 1.0")
.Attr("beta: float = 0.5")
.Attr("T: {half, bfloat16, float} = DT_FLOAT")
.SetShapeFn([](InferenceContext* c) {
ShapeHandle s;
TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &s)); // input_grads
TF_RETURN_IF_ERROR(c->Merge(s, c->input(1), &s)); // input_image
TF_RETURN_IF_ERROR(c->Merge(s, c->input(2), &s)); // output_image
c->set_output(0, s);
return OkStatus();
});
// --------------------------------------------------------------------------
REGISTER_OP("MaxPool")
.Attr(
"T: {half, bfloat16, float, double, int32, int64, uint8, int16, int8, "
"uint16, qint8} = DT_FLOAT")
.Attr("ksize: list(int) >= 4")
.Attr("strides: list(int) >= 4")
.Attr(GetPaddingAttrStringWithExplicit())
.Attr(GetExplicitPaddingsAttrString())
.Attr("data_format: {'NHWC', 'NCHW', 'NCHW_VECT_C'} = 'NHWC'")
.Input("input: T")
.Output("output: T")
.SetShapeFn(shape_inference::MaxPoolShapeWithExplicitPadding);
REGISTER_OP("MaxPoolV2")
.Attr(
"T: {half, bfloat16, float, double, int32, int64, uint8, int16, int8, "
"uint16, qint8} = DT_FLOAT")
.Attr(GetPaddingAttrString())
.Attr("data_format: {'NHWC', 'NCHW', 'NCHW_VECT_C'} = 'NHWC'")
.Input("input: T")
.Input("ksize: int32")
.Input("strides: int32")
.Output("output: T")
.SetShapeFn([](InferenceContext* c) {
TF_RETURN_IF_ERROR(shape_inference::MaxPoolV2Shape(c, 3));
return OkStatus();
});
REGISTER_OP("MaxPoolGrad")
.Attr("ksize: list(int) >= 4")
.Attr("strides: list(int) >= 4")
.Attr(GetPaddingAttrStringWithExplicit())
.Attr(GetExplicitPaddingsAttrString())
.Attr(GetConvnetDataFormatAttrString())
.Input("orig_input: T")
.Input("orig_output: T")
.Input("grad: T")
.Output("output: T")
.Attr("T: realnumbertype = DT_FLOAT")
.SetShapeFn(shape_inference::MaxPoolGradShape);
REGISTER_OP("MaxPoolGradV2")
.Attr(GetPaddingAttrString())
.Attr(GetConvnetDataFormatAttrString())
.Input("orig_input: T")
.Input("orig_output: T")
.Input("grad: T")
.Input("ksize: int32")
.Input("strides: int32")
.Output("output: T")
.Attr("T: realnumbertype = DT_FLOAT")
.SetShapeFn(shape_inference::MaxPoolGradShape);
// TODO(b/150813181): Implement explicit padding.
REGISTER_OP("MaxPoolGradGrad")
.Attr("ksize: list(int) >= 4")
.Attr("strides: list(int) >= 4")
.Attr(GetPaddingAttrString())
.Attr(GetConvnetDataFormatAttrString())
.Input("orig_input: T")
.Input("orig_output: T")
.Input("grad: T")
.Output("output: T")
.Attr("T: realnumbertype")
.SetShapeFn([](InferenceContext* c) {
TF_RETURN_IF_ERROR(shape_inference::MaxPoolShape(c));
ShapeHandle unused;
// Validate 'orig_input' is the same shape as 'grad'
TF_RETURN_IF_ERROR(c->Merge(c->input(0), c->input(2), &unused));
// Validate 'orig_output' is same shape as 'output'
TF_RETURN_IF_ERROR(c->Merge(c->input(1), c->output(0), &unused));
return OkStatus();
});
REGISTER_OP("MaxPoolGradGradV2")
.Attr(GetPaddingAttrString())
.Attr(GetConvnetDataFormatAttrString())
.Input("orig_input: T")
.Input("orig_output: T")
.Input("grad: T")
.Input("ksize: int32")
.Input("strides: int32")
.Output("output: T")
.Attr("T: realnumbertype")
.SetShapeFn([](InferenceContext* c) {
TF_RETURN_IF_ERROR(shape_inference::MaxPoolV2Shape(c, 5));
ShapeHandle unused;
// Validate 'orig_input' is the same shape as 'grad'
TF_RETURN_IF_ERROR(c->Merge(c->input(0), c->input(2), &unused));
// Validate 'orig_output' is same shape as 'output'
TF_RETURN_IF_ERROR(c->Merge(c->input(1), c->output(0), &unused));
return OkStatus();
});
REGISTER_OP("MaxPoolWithArgmax")
.Attr("ksize: list(int) >= 4")
.Attr("strides: list(int) >= 4")
.Attr("Targmax: {int32, int64} = DT_INT64")
.Attr(GetPaddingAttrString())
.Attr("include_batch_in_index: bool = false")
.Input("input: T")
.Output("output: T")
.Output("argmax: Targmax")
.Attr("T: realnumbertype")
.SetShapeFn([](InferenceContext* c) {
TF_RETURN_IF_ERROR(shape_inference::MaxPoolShape(c));
c->set_output(1, c->output(0));
return OkStatus();
});
REGISTER_OP("MaxPoolGradWithArgmax")
.Attr("ksize: list(int) >= 4")
.Attr("strides: list(int) >= 4")
.Attr(GetPaddingAttrString())
.Attr("include_batch_in_index: bool = false")
.Attr("Targmax: {int32, int64}")
.Input("input: T")
.Input("grad: T")
.Input("argmax: Targmax")
.Output("output: T")
.Attr("T: realnumbertype")
.SetShapeFn([](InferenceContext* c) {
return UnchangedShapeWithRank(c, 4);
});
REGISTER_OP("MaxPoolGradGradWithArgmax")
.Attr("ksize: list(int) >= 4")
.Attr("strides: list(int) >= 4")
.Attr(GetPaddingAttrString())
.Attr("include_batch_in_index: bool = false")
.Attr("Targmax: {int32, int64}")
.Input("input: T")
.Input("grad: T")
.Input("argmax: Targmax")
.Output("output: T")