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1891614aa5
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/20515 Needed by the upcoming quantized version of GenerateProposals Reviewed By: dzhulgakov Differential Revision: D14430952 fbshipit-source-id: ea852f04cc4b070f8fbe7a1e6535bba4d5b230fd
237 lines
8.1 KiB
C++
237 lines
8.1 KiB
C++
#include "caffe2/operators/given_tensor_fill_op.h"
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namespace caffe2 {
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REGISTER_CPU_OPERATOR(GivenTensorFill, GivenTensorFillOp<float, CPUContext>);
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REGISTER_CPU_OPERATOR(
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GivenTensorDoubleFill,
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GivenTensorFillOp<double, CPUContext>);
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REGISTER_CPU_OPERATOR(GivenTensorBoolFill, GivenTensorFillOp<bool, CPUContext>);
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REGISTER_CPU_OPERATOR(
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GivenTensorInt16Fill,
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GivenTensorFillOp<int16_t, CPUContext>);
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REGISTER_CPU_OPERATOR(GivenTensorIntFill, GivenTensorFillOp<int, CPUContext>);
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REGISTER_CPU_OPERATOR(
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GivenTensorInt64Fill,
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GivenTensorFillOp<int64_t, CPUContext>);
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REGISTER_CPU_OPERATOR(
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GivenTensorStringFill,
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GivenTensorFillOp<std::string, CPUContext>);
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NO_GRADIENT(GivenTensorFill);
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NO_GRADIENT(GivenTensorDoubleFill);
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NO_GRADIENT(GivenTensorBoolFill);
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NO_GRADIENT(GivenTensorInt16Fill);
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NO_GRADIENT(GivenTensorIntFill);
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NO_GRADIENT(GivenTensorInt64Fill);
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NO_GRADIENT(GivenTensorStringFill);
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OPERATOR_SCHEMA(GivenTensorFill)
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.NumInputs(0, 1)
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.NumOutputs(1)
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.AllowInplace({{0, 0}})
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.SetDoc(R"DOC(
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This op fills an output tensor with the data specified by the *value* and *dtype* arguments. The output tensor shape is specified by the *shape* argument. Beware, when using this argument *value* should have a value for every element of the *output*, as missing values will not be initialized automatically. If *input_as_shape* is set to *true*, then the *input* should be a 1D tensor containing the desired output shape (the dimensions specified in *extra_shape* will also be appended). In this case, the *shape* argument should **not** be set.
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*Note: Do not set the shape argument and pass in an input at the same time.*
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Github Links:
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- https://github.com/caffe2/caffe2/blob/master/caffe2/operators/given_tensor_fill_op.h
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- https://github.com/caffe2/caffe2/blob/master/caffe2/operators/given_tensor_fill_op.cc
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<details>
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<summary> <b>Example</b> </summary>
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**Code**
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```
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workspace.ResetWorkspace()
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op = core.CreateOperator(
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"GivenTensorFill",
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[],
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["out"],
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values=[1., 2., 3.],
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shape=[3],
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)
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workspace.RunOperatorOnce(op)
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print("Out:\n", workspace.FetchBlob("out"))
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```
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**Result**
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```
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Out:
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[1. 2. 3.]
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```
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</details>
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)DOC")
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.Arg(
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"values",
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"*(type depends on dtype, Required=True)* The value of the elements to go in the *output* tensor.",
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true /* required */)
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.Arg(
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"dtype",
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"The data type for the elements of the output tensor. Strictly must be one of the types from DataType enum in TensorProto.")
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.Arg(
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"shape",
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"*(type: [int])* Desired shape of the *output* tensor.")
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.Arg(
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"extra_shape",
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"*(type: [int])* The additional dimensions appended at the end of the *shape* indicated by the input blob. Cannot set the *extra_shape* argument when there is no input blob.")
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.Arg(
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"input_as_shape",
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"*(type: bool; default: False)* set to *True* to use the *input* as shape. First, input must be in CPU context.")
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.Input(
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0,
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"input",
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"(Optional) 1D tensor specifying the shape of the output. Must be used with *input_as_shape=True*")
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.Output(
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0,
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"output",
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"Output tensor with desired dimension filled with specified data. If the shape argument is set, this is the shape specified, and if the *input* exists and *input_as_shape=True*, it is the shape specified by the *input* tensor.")
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.TensorInferenceFunction(FillerTensorInference<>);
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OPERATOR_SCHEMA(GivenTensorDoubleFill)
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.NumInputs(0, 1)
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.NumOutputs(1)
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.AllowInplace({{0, 0}})
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.Arg(
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"values",
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"The value for the elements of the output tensor.",
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true /* required */)
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.Arg(
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"shape",
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"The shape of the output tensor."
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"Cannot set the shape argument and pass in an input at the same time.")
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.Arg(
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"extra_shape",
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"The additional dimensions appended at the end of the shape indicated"
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"by the input blob."
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"Cannot set the extra_shape argument when there is no input blob.")
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.Arg(
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"input_as_shape",
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"1D tensor containing the desired output shape. First input must be in CPU context.")
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.TensorInferenceFunction(
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FillerTensorInference<TensorProto_DataType_DOUBLE>);
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OPERATOR_SCHEMA(GivenTensorBoolFill)
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.NumInputs(0, 1)
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.NumOutputs(1)
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.AllowInplace({{0, 0}})
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.Arg(
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"values",
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"The value for the elements of the output tensor.",
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true /* required */)
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.Arg(
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"shape",
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"The shape of the output tensor."
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"Cannot set the shape argument and pass in an input at the same time.")
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.Arg(
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"extra_shape",
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"The additional dimensions appended at the end of the shape indicated"
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"by the input blob."
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"Cannot set the extra_shape argument when there is no input blob.")
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.Arg(
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"input_as_shape",
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"1D tensor containing the desired output shape. First input must be in CPU context.")
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.TensorInferenceFunction(FillerTensorInference<TensorProto_DataType_BOOL>);
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OPERATOR_SCHEMA(GivenTensorInt16Fill)
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.NumInputs(0, 1)
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.NumOutputs(1)
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.AllowInplace({{0, 0}})
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.Arg(
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"values",
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"The value for the elements of the output tensor.",
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true /* required */)
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.Arg(
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"shape",
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"The shape of the output tensor."
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"Cannot set the shape argument and pass in an input at the same time.")
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.Arg(
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"extra_shape",
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"The additional dimensions appended at the end of the shape indicated"
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"by the input blob."
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"Cannot set the extra_shape argument when there is no input blob.")
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.Arg(
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"input_as_shape",
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"1D tensor containing the desired output shape. First input must be in CPU context.")
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.TensorInferenceFunction(FillerTensorInference<TensorProto_DataType_INT16>);
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OPERATOR_SCHEMA(GivenTensorIntFill)
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.NumInputs(0, 1)
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.NumOutputs(1)
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.AllowInplace({{0, 0}})
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.Arg(
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"values",
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"The value for the elements of the output tensor.",
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true /* required */)
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.Arg(
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"shape",
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"The shape of the output tensor."
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"Cannot set the shape argument and pass in an input at the same time.")
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.Arg(
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"extra_shape",
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"The additional dimensions appended at the end of the shape indicated"
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"by the input blob."
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"Cannot set the extra_shape argument when there is no input blob.")
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.Arg(
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"input_as_shape",
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"1D tensor containing the desired output shape. First input must be in CPU context.")
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.TensorInferenceFunction(FillerTensorInference<TensorProto_DataType_INT32>);
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OPERATOR_SCHEMA(GivenTensorInt64Fill)
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.NumInputs(0, 1)
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.NumOutputs(1)
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.AllowInplace({{0, 0}})
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.Arg(
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"values",
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"The value for the elements of the output tensor.",
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true /* required */)
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.Arg(
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"shape",
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"The shape of the output tensor."
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"Cannot set the shape argument and pass in an input at the same time.")
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.Arg(
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"extra_shape",
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"The additional dimensions appended at the end of the shape indicated"
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"by the input blob."
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"Cannot set the extra_shape argument when there is no input blob.")
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.Arg(
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"input_as_shape",
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"1D tensor containing the desired output shape. First input must be in CPU context.")
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.TensorInferenceFunction(FillerTensorInference<TensorProto_DataType_INT64>);
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OPERATOR_SCHEMA(GivenTensorStringFill)
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.NumInputs(0, 1)
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.NumOutputs(1)
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.AllowInplace({{0, 0}})
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.Arg(
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"values",
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"The value for the elements of the output tensor.",
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true /* required */)
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.Arg(
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"shape",
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"The shape of the output tensor."
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"Cannot set the shape argument and pass in an input at the same time.")
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.Arg(
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"extra_shape",
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"The additional dimensions appended at the end of the shape indicated"
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"by the input blob."
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"Cannot set the extra_shape argument when there is no input blob.")
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.Arg(
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"input_as_shape",
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"1D tensor containing the desired output shape. First input must be in CPU context.")
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.TensorInferenceFunction(
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FillerTensorInference<TensorProto_DataType_STRING>);
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} // namespace caffe2
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