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-rw-r--r--tensorflow/contrib/cloud/kernels/bigquery_table_accessor.cc28
-rw-r--r--tensorflow/contrib/fused_conv/ops/fused_conv2d_bias_activation_op.cc7
-rw-r--r--tensorflow/contrib/tensor_forest/kernels/v4/input_data.cc2
-rw-r--r--tensorflow/core/framework/op_gen_lib.cc29
-rw-r--r--tensorflow/core/framework/op_kernel.cc7
-rw-r--r--tensorflow/core/kernels/summary_kernels.cc2
-rw-r--r--tensorflow/core/ops/data_flow_ops.cc19
-rw-r--r--tensorflow/core/ops/image_ops.cc36
-rw-r--r--tensorflow/core/ops/training_ops.cc42
-rw-r--r--tensorflow/core/profiler/internal/tfprof_timeline.h1
-rw-r--r--tensorflow/core/profiler/internal/tfprof_utils.cc3
11 files changed, 0 insertions, 176 deletions
diff --git a/tensorflow/contrib/cloud/kernels/bigquery_table_accessor.cc b/tensorflow/contrib/cloud/kernels/bigquery_table_accessor.cc
index deb324634b..1bfd27305d 100644
--- a/tensorflow/contrib/cloud/kernels/bigquery_table_accessor.cc
+++ b/tensorflow/contrib/cloud/kernels/bigquery_table_accessor.cc
@@ -18,7 +18,6 @@ limitations under the License.
#include "tensorflow/core/lib/strings/numbers.h"
namespace tensorflow {
-
namespace {
constexpr size_t kBufferSize = 1024 * 1024; // In bytes.
@@ -40,33 +39,6 @@ Status ParseJson(StringPiece json, Json::Value* result) {
return Status::OK();
}
-string ColumnTypeToString(BigQueryTableAccessor::ColumnType enum_type) {
- switch (enum_type) {
- case BigQueryTableAccessor::ColumnType::kRecord:
- return "RECORD";
- case BigQueryTableAccessor::ColumnType::kString:
- return "STRING";
- case BigQueryTableAccessor::ColumnType::kBytes:
- return "BYTES";
- case BigQueryTableAccessor::ColumnType::kInteger:
- return "INTEGER";
- case BigQueryTableAccessor::ColumnType::kFloat:
- return "FLOAT";
- case BigQueryTableAccessor::ColumnType::kBoolean:
- return "BOOLEAN";
- case BigQueryTableAccessor::ColumnType::kTimestamp:
- return "TIMESTAMP";
- case BigQueryTableAccessor::ColumnType::kDate:
- return "DATE";
- case BigQueryTableAccessor::ColumnType::kTime:
- return "TIME";
- case BigQueryTableAccessor::ColumnType::kDatetime:
- return "DATETIME";
- case BigQueryTableAccessor::ColumnType::kNone:
- return "NONE";
- }
-}
-
Status ParseColumnType(const string& type,
BigQueryTableAccessor::ColumnType* enum_type) {
if (type == "RECORD") {
diff --git a/tensorflow/contrib/fused_conv/ops/fused_conv2d_bias_activation_op.cc b/tensorflow/contrib/fused_conv/ops/fused_conv2d_bias_activation_op.cc
index 6a56237f67..bafd1d5941 100644
--- a/tensorflow/contrib/fused_conv/ops/fused_conv2d_bias_activation_op.cc
+++ b/tensorflow/contrib/fused_conv/ops/fused_conv2d_bias_activation_op.cc
@@ -25,13 +25,6 @@ limitations under the License.
namespace tensorflow {
-namespace {
-// Return the string containing the list of valid activation modes, that can be
-// used as an Attr() in REGISTER_OP.
-string GetAllActivationModeAttrString() { return "activation_mode: {'Relu'}"; }
-
-} // namespace
-
// --------------------------------------------------------------------------
// TODO(pauldonnelly): Add support for double inputs and scales to this Op,
diff --git a/tensorflow/contrib/tensor_forest/kernels/v4/input_data.cc b/tensorflow/contrib/tensor_forest/kernels/v4/input_data.cc
index 14cb19d36f..bf0fb92450 100644
--- a/tensorflow/contrib/tensor_forest/kernels/v4/input_data.cc
+++ b/tensorflow/contrib/tensor_forest/kernels/v4/input_data.cc
@@ -21,8 +21,6 @@ namespace tensorflow {
namespace tensorforest {
namespace {
-const int32 SPARSE_DEFAULT = 0;
-
bool DecideInequalityTest(const decision_trees::InequalityTest& test,
float value) {
float bias = test.threshold().float_value();
diff --git a/tensorflow/core/framework/op_gen_lib.cc b/tensorflow/core/framework/op_gen_lib.cc
index e78b6ab5d9..870bbb141b 100644
--- a/tensorflow/core/framework/op_gen_lib.cc
+++ b/tensorflow/core/framework/op_gen_lib.cc
@@ -266,35 +266,6 @@ static void StringReplace(const string& from, const string& to, string* s) {
*s = str_util::Join(split, to.c_str());
}
-static void RenameInDocs(const string& from, const string& to, OpDef* op_def) {
- const string from_quoted = strings::StrCat("`", from, "`");
- const string to_quoted = strings::StrCat("`", to, "`");
- for (int i = 0; i < op_def->input_arg_size(); ++i) {
- if (!op_def->input_arg(i).description().empty()) {
- StringReplace(from_quoted, to_quoted,
- op_def->mutable_input_arg(i)->mutable_description());
- }
- }
- for (int i = 0; i < op_def->output_arg_size(); ++i) {
- if (!op_def->output_arg(i).description().empty()) {
- StringReplace(from_quoted, to_quoted,
- op_def->mutable_output_arg(i)->mutable_description());
- }
- }
- for (int i = 0; i < op_def->attr_size(); ++i) {
- if (!op_def->attr(i).description().empty()) {
- StringReplace(from_quoted, to_quoted,
- op_def->mutable_attr(i)->mutable_description());
- }
- }
- if (!op_def->summary().empty()) {
- StringReplace(from_quoted, to_quoted, op_def->mutable_summary());
- }
- if (!op_def->description().empty()) {
- StringReplace(from_quoted, to_quoted, op_def->mutable_description());
- }
-}
-
static void RenameInDocs(const string& from, const string& to,
ApiDef* api_def) {
const string from_quoted = strings::StrCat("`", from, "`");
diff --git a/tensorflow/core/framework/op_kernel.cc b/tensorflow/core/framework/op_kernel.cc
index aee3a0afbc..16bf5c256f 100644
--- a/tensorflow/core/framework/op_kernel.cc
+++ b/tensorflow/core/framework/op_kernel.cc
@@ -943,13 +943,6 @@ Status FindKernelRegistration(const DeviceType& device_type,
return Status::OK();
}
-Status FindKernelRegistration(const DeviceType& device_type, const Node& node,
- const KernelRegistration** reg,
- bool* was_attr_mismatch) {
- return FindKernelRegistration(device_type, node.def(), reg,
- was_attr_mismatch);
-}
-
} // namespace
// TODO(irving): Change const NodeDef& to const Node&
diff --git a/tensorflow/core/kernels/summary_kernels.cc b/tensorflow/core/kernels/summary_kernels.cc
index da3644779d..d317a8d33d 100644
--- a/tensorflow/core/kernels/summary_kernels.cc
+++ b/tensorflow/core/kernels/summary_kernels.cc
@@ -278,8 +278,6 @@ class WriteAudioSummaryOp : public OpKernel {
private:
int max_outputs_;
- bool has_sample_rate_attr_;
- float sample_rate_attr_;
};
REGISTER_KERNEL_BUILDER(Name("WriteAudioSummary").Device(DEVICE_CPU),
WriteAudioSummaryOp);
diff --git a/tensorflow/core/ops/data_flow_ops.cc b/tensorflow/core/ops/data_flow_ops.cc
index 12c27c7984..4f946fb3ca 100644
--- a/tensorflow/core/ops/data_flow_ops.cc
+++ b/tensorflow/core/ops/data_flow_ops.cc
@@ -171,29 +171,10 @@ Status TwoElementVectorInputsAndScalarOutputs(InferenceContext* c) {
return Status::OK();
}
-Status ScalarAndTwoElementVectorInputsAndScalarOutputs(InferenceContext* c) {
- ShapeHandle handle;
- DimensionHandle unused_handle;
- TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &handle));
- for (int i = 1; i < c->num_inputs(); ++i) {
- TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 1, &handle));
- TF_RETURN_IF_ERROR(c->WithValue(c->Dim(handle, 0), 2, &unused_handle));
- }
- for (int i = 0; i < c->num_outputs(); ++i) {
- c->set_output(i, c->Scalar());
- }
- return Status::OK();
-}
-
Status TwoElementOutput(InferenceContext* c) {
c->set_output(0, c->Vector(2));
return Status::OK();
}
-
-Status ScalarOutput(InferenceContext* c) {
- c->set_output(0, c->Scalar());
- return Status::OK();
-}
} // namespace
REGISTER_OP("RandomShuffleQueue")
diff --git a/tensorflow/core/ops/image_ops.cc b/tensorflow/core/ops/image_ops.cc
index 7484ebb078..ef2ac267cc 100644
--- a/tensorflow/core/ops/image_ops.cc
+++ b/tensorflow/core/ops/image_ops.cc
@@ -25,42 +25,6 @@ using shape_inference::ShapeHandle;
namespace {
-const char kDecodeJpegCommonDocStr[] = R"doc(
-The attr `channels` indicates the desired number of color channels for the
-decoded image.
-
-Accepted values are:
-
-* 0: Use the number of channels in the JPEG-encoded image.
-* 1: output a grayscale image.
-* 3: output an RGB image.
-
-If needed, the JPEG-encoded image is transformed to match the requested number
-of color channels.
-
-The attr `ratio` allows downscaling the image by an integer factor during
-decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than
-downscaling the image later.
-
-)doc";
-
-const char kDecodeJpegCommonParamsDocStr[] = R"doc(
-channels: Number of color channels for the decoded image.
-ratio: Downscaling ratio.
-fancy_upscaling: If true use a slower but nicer upscaling of the
- chroma planes (yuv420/422 only).
-try_recover_truncated: If true try to recover an image from truncated input.
-acceptable_fraction: The minimum required fraction of lines before a truncated
- input is accepted.
-dct_method: string specifying a hint about the algorithm used for
- decompression. Defaults to "" which maps to a system-specific
- default. Currently valid values are ["INTEGER_FAST",
- "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal
- jpeg library changes to a version that does not have that specific
- option.)
-image: 3-D with shape `[height, width, channels]`..
-)doc";
-
// Sets output[0] to shape [batch_dim,height,width,channel_dim], where
// height and width come from the size_tensor.
Status SetOutputToSizedImage(InferenceContext* c, DimensionHandle batch_dim,
diff --git a/tensorflow/core/ops/training_ops.cc b/tensorflow/core/ops/training_ops.cc
index e8d03877c9..6ce9595fb6 100644
--- a/tensorflow/core/ops/training_ops.cc
+++ b/tensorflow/core/ops/training_ops.cc
@@ -22,48 +22,6 @@ using shape_inference::DimensionHandle;
using shape_inference::InferenceContext;
using shape_inference::ShapeHandle;
-const char kAddSignCommonDocStr[] = R"doc(
-Update '*var' according to the AddSign update.
-
-m_t <- beta1 * m_{t-1} + (1 - beta1) * g
-update <- (alpha + sign_decay * sign(g) *sign(m)) * g
-variable <- variable - lr_t * update
-
-var: Should be from a Variable().
-m: Should be from a Variable().
-lr: Scaling factor. Must be a scalar.
-sign_decay: Must be a scalar.
-alpha: Must be a scalar.
-beta: Must be a scalar.
-grad: The gradient.
-)doc";
-
-const char kPowerSignCommonDocStr[] = R"doc(
-Update '*var' according to the AddSign update.
-
-m_t <- beta1 * m_{t-1} + (1 - beta1) * g
-update <- exp(logbase * sign_decay * sign(g) * sign(m_t)) * g
-variable <- variable - lr_t * update
-
-var: Should be from a Variable().
-m: Should be from a Variable().
-lr: Scaling factor. Must be a scalar.
-logbase: Must be a scalar.
-sign_decay: Must be a scalar.
-beta: Must be a scalar.
-grad: The gradient.
-)doc";
-
-const char kOutDocStr[] = R"doc(
-out: Same as "var".
-)doc";
-
-const char kLockDocStr[] = R"doc(
-use_locking: If `True`, updating of the var and m tensors is
- protected by a lock; otherwise the behavior is undefined, but may exhibit less
- contention.
-)doc";
-
static ShapeHandle ShapeOrHandleShape(InferenceContext* c, int input) {
auto* handle_data = c->input_handle_shapes_and_types(input);
if (handle_data != nullptr && !handle_data->empty() &&
diff --git a/tensorflow/core/profiler/internal/tfprof_timeline.h b/tensorflow/core/profiler/internal/tfprof_timeline.h
index 4428ab571f..651ad3f0c1 100644
--- a/tensorflow/core/profiler/internal/tfprof_timeline.h
+++ b/tensorflow/core/profiler/internal/tfprof_timeline.h
@@ -178,7 +178,6 @@ class Timeline {
int64 step_;
const string outfile_;
int64 next_pid_ = 0;
- int64 allocator_pid_ = -1;
MemoryTracker mem_tracker_;
ChromeTraceFormatter chrome_formatter_;
std::map<string, int64> device_pids_;
diff --git a/tensorflow/core/profiler/internal/tfprof_utils.cc b/tensorflow/core/profiler/internal/tfprof_utils.cc
index 2813bb46fa..7712ebd926 100644
--- a/tensorflow/core/profiler/internal/tfprof_utils.cc
+++ b/tensorflow/core/profiler/internal/tfprof_utils.cc
@@ -355,9 +355,6 @@ static const char* const kOpTypes =
static const char* const kScope =
"scope: The nodes in the model graph are organized by their names, which "
"is hierarchical like filesystem.";
-static const char* const kGraph =
- "graph: The nodes in the model graph are organized by their operation "
- "input and output.";
static const char* const kCode =
"code: When python trace is available, the nodes are python lines and "
"their are organized by the python call stack.";
generated by cgit v1.2.3 (git 2.39.1) at 2024-06-27 07:29:54 +0000