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Commit 52402169 by Ting PAN
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Clean the torch operators

1 parent d0fa332c
Inline Side-by-side
Showing with 2568 additions and 2256 deletions
Docs/api/python/contents/core/tensor.rst
......@@ -47,7 +47,7 @@ Override
List Brief
============================== =============================================================================
`Tensor.__add__`_ x.__add__(y) <=> x + y
`Tensor.__radd__`_ x.__radd__(y) <=> y + x
`Tensor.__radd__`_ x.__radd__(y) <=> y + x
`Tensor.__sub__`_ x.__sub__(y) <=> x - y
`Tensor.__rsub__`_ x.__rsub__(y) <=> y - x
`Tensor.__mul__`_ x.__mul__(y) <=> x * y
......@@ -55,7 +55,12 @@ List Brief
`Tensor.__div__`_ x.__div__(y) <=> x / y
`Tensor.__rdiv__`_ x.__rdiv__(y) <=> y / x
`Tensor.__neg__`_ x.__neg__() <=> -x
`Tensor.__str__`_ Return the information(name/shape).
`Tensor.__gt__`_ x.__gt__() <=> x > y
`Tensor.__ge__`_ x.__ge__() <=> x >= y
`Tensor.__lt__`_ x.__lt__() <=> x < y
`Tensor.__le__`_ x.__le__() <=> x <= y
`Tensor.__eq__`_ x.__eq__() <=> x == y
`Tensor.__repr__`_ Return the information(name/shape).
`Tensor.__getitem__`_ Return a Tensor with specific indices.
`Tensor.__call__`_ Return the expressions for displaying.
============================== =============================================================================
......@@ -70,6 +75,24 @@ API Reference
:members:
.. automethod:: __init__
.. automethod:: __add__
.. automethod:: __radd__
.. automethod:: __sub__
.. automethod:: __rsub__
.. automethod:: __mul__
.. automethod:: __rmul__
.. automethod:: __div__
.. automethod:: __rdiv__
.. automethod:: __neg__
.. automethod:: __gt__
.. automethod:: __ge__
.. automethod:: __lt__
.. automethod:: __le__
.. automethod:: __eq__
.. automethod:: __eq__
.. automethod:: __repr__
.. automethod:: __getitem__
.. automethod:: __call__
.. _Tensor.Variable: #dragon.core.tensor.Tensor.Variable
.. _Tensor.Placeholder: #dragon.core.tensor.Tensor.Placeholder
......@@ -90,8 +113,12 @@ API Reference
.. _Tensor.__div__: #dragon.core.tensor.Tensor.__div__
.. _Tensor.__rdiv__: #dragon.core.tensor.Tensor.__rdiv__
.. _Tensor.__neg__: #dragon.core.tensor.Tensor.__neg__
.. _Tensor.__str__: #dragon.core.tensor.Tensor.__str__
.. _Tensor.__getattr__: #dragon.core.tensor.Tensor.__getattr__
.. _Tensor.__gt__: #dragon.core.tensor.Tensor.__gt__
.. _Tensor.__ge__: #dragon.core.tensor.Tensor.__ge__
.. _Tensor.__lt__: #dragon.core.tensor.Tensor.__lt__
.. _Tensor.__le__: #dragon.core.tensor.Tensor.__le__
.. _Tensor.__eq__: #dragon.core.tensor.Tensor.__eq__
.. _Tensor.__repr__: #dragon.core.tensor.Tensor.__repr__
.. _Tensor.__getitem__: #dragon.core.tensor.Tensor.__getitem__
.. _Tensor.__call__: #dragon.core.tensor.Tensor.__call__
......
Docs/api/python/contents/operators.rst
......@@ -11,7 +11,7 @@ Common
operators/data
operators/initializer
operators/arithmetic
operators/ndarray
operators/array
operators/control_flow
operators/misc
operators/mpi
......
Docs/api/python/contents/operators/array.rst 0 → 100644
============
:mod:`Array`
============
.. toctree::
:hidden:
.. automodule:: dragon.operators.array
:members:
.. _ops.Reduce(*args, **kwargs): #dragon.operators.array.Reduce
\ No newline at end of file
Docs/api/python/contents/operators/ndarray.rst deleted 100644 → 0
==============
:mod:`NDArray`
==============
.. toctree::
:hidden:
.. automodule:: dragon.operators.ndarray
:members:
.. _ops.Reduce(*args, **kwargs): #dragon.operators.ndarray.Reduce
\ No newline at end of file
Docs/api/python/contents/ops.rst
......@@ -129,8 +129,8 @@ List Brief
`L2Norm`_ L2 Normalization. `[Liu et.al, 2015] <https://arxiv.org/abs/1506.04579>`_.
================== ======================================================================
NDArray
-------
Array
-----
=============== ======================================================================
List Brief
=============== ======================================================================
......@@ -157,6 +157,7 @@ List Brief
`ExpandDims`_ Expand the new dimension with size 1 to specific axis.
`Shape`_ Get the dynamic shape of a Tensor.
`Arange`_ Return evenly spaced values within a given interval.
`Multinomial`_ Return indices sampled from the multinomial distribution.
=============== ======================================================================
Control Flow
......@@ -167,7 +168,9 @@ List Brief
`Copy`_ Copy A to B.
`Equal`_ *Equal* Comparing between A and B.
`Less`_ *Less* Comparing between A and B.
`LessEqual`_ *LessEqual* Comparing between A and B.
`Greater`_ *Greater* Comparing between A and B.
`GreaterEqual`_ *GreaterEqual* Comparing between A and B.
=============== ======================================================================
Misc
......@@ -277,34 +280,37 @@ List Brief
.. _InstanceNorm: operators/norm.html#dragon.operators.norm.InstanceNorm
.. _L2Norm: operators/norm.html#dragon.operators.norm.L2Norm
.. _Gather: operators/ndarray.html#dragon.operators.ndarray.Gather
.. _Crop: operators/ndarray.html#dragon.operators.ndarray.Crop
.. _Reduce: operators/ndarray.html#dragon.operators.ndarray.Reduce
.. _Sum: operators/ndarray.html#dragon.operators.ndarray.Sum
.. _Mean: operators/ndarray.html#dragon.operators.ndarray.Mean
.. _Max: operators/ndarray.html#dragon.operators.ndarray.Max
.. _ArgMax: operators/ndarray.html#dragon.operators.ndarray.ArgMax
.. _Min: operators/ndarray.html#dragon.operators.ndarray.Min
.. _ArgMin: operators/ndarray.html#dragon.operators.ndarray.ArgMin
.. _Slice: operators/ndarray.html#dragon.operators.ndarray.Slice
.. _Stack: operators/ndarray.html#dragon.operators.ndarray.Stack
.. _Concat: operators/ndarray.html#dragon.operators.ndarray.Concat
.. _Transpose: operators/ndarray.html#dragon.operators.ndarray.Transpose
.. _Repeat: operators/ndarray.html#dragon.operators.ndarray.Repeat
.. _Tile: operators/ndarray.html#dragon.operators.ndarray.Tile
.. _Pad: operators/ndarray.html#dragon.operators.ndarray.Pad
.. _OneHot: operators/ndarray.html#dragon.operators.ndarray.OneHot
.. _Flatten: operators/ndarray.html#dragon.operators.ndarray.Flatten
.. _Reshape: operators/ndarray.html#dragon.operators.ndarray.Reshape
.. _Squeeze: operators/ndarray.html#dragon.operators.ndarray.Squeeze
.. _ExpandDims: operators/ndarray.html#dragon.operators.ndarray.ExpandDims
.. _Shape: operators/ndarray.html#dragon.operators.ndarray.Shape
.. _Arange: operators/ndarray.html#dragon.operators.ndarray.Arange
.. _Gather: operators/array.html#dragon.operators.array.Gather
.. _Crop: operators/array.html#dragon.operators.array.Crop
.. _Reduce: operators/array.html#dragon.operators.array.Reduce
.. _Sum: operators/array.html#dragon.operators.array.Sum
.. _Mean: operators/array.html#dragon.operators.array.Mean
.. _Max: operators/array.html#dragon.operators.array.Max
.. _ArgMax: operators/array.html#dragon.operators.array.ArgMax
.. _Min: operators/array.html#dragon.operators.array.Min
.. _ArgMin: operators/array.html#dragon.operators.array.ArgMin
.. _Slice: operators/array.html#dragon.operators.array.Slice
.. _Stack: operators/array.html#dragon.operators.array.Stack
.. _Concat: operators/array.html#dragon.operators.array.Concat
.. _Transpose: operators/array.html#dragon.operators.array.Transpose
.. _Repeat: operators/array.html#dragon.operators.array.Repeat
.. _Tile: operators/array.html#dragon.operators.array.Tile
.. _Pad: operators/array.html#dragon.operators.array.Pad
.. _OneHot: operators/array.html#dragon.operators.array.OneHot
.. _Flatten: operators/array.html#dragon.operators.array.Flatten
.. _Reshape: operators/array.html#dragon.operators.array.Reshape
.. _Squeeze: operators/array.html#dragon.operators.array.Squeeze
.. _ExpandDims: operators/array.html#dragon.operators.array.ExpandDims
.. _Shape: operators/array.html#dragon.operators.array.Shape
.. _Arange: operators/array.html#dragon.operators.array.Arange
.. _Multinomial: operators/array.html#dragon.operators.array.Multinomial
.. _Copy: operators/control_flow.html#dragon.operators.control_flow.Copy
.. _Copy: operators/control_flow.html#dAragon.operators.control_flow.Copy
.. _Equal: operators/control_flow.html#dragon.operators.control_flow.Equal
.. _Less: operators/control_flow.html#dragon.operators.control_flow.Less
.. _LessEqual: operators/control_flow.html#dragon.operators.control_flow.LessEqual
.. _Greater: operators/control_flow.html#dragon.operators.control_flow.Greater
.. _GreaterEqual: operators/control_flow.html#dragon.operators.control_flow.GreaterEqual
.. _Cast: operators/misc.html#dragon.operators.misc.Cast
.. _Run: operators/misc.html#dragon.operators.misc.Run
......
Dragon/include/operators/ndarray/arange_op.h → Dragon/include/operators/array/arange_op.h
......@@ -10,8 +10,8 @@
* ------------------------------------------------------------
*/
#ifndef DRAGON_OPERATORS_NDARRAY_ARGMAX_OP_H_
#define DRAGON_OPERATORS_NDARRAY_ARGMAX_OP_H_
#ifndef DRAGON_OPERATORS_ARRAY_ARGMAX_OP_H_
#define DRAGON_OPERATORS_ARRAY_ARGMAX_OP_H_
#include "core/operator.h"
......@@ -46,4 +46,4 @@ DEFINE_ARGUMENT_WITH_DESC(int64_t, ArangeOp, step);
} // namespace dragon
#endif // DRAGON_OPERATORS_NDARRAY_ARANGE_OP_H_
\ No newline at end of file
#endif // DRAGON_OPERATORS_ARRAY_ARANGE_OP_H_
\ No newline at end of file
Dragon/include/operators/ndarray/argreduce_op.h → Dragon/include/operators/array/argreduce_op.h
......@@ -10,8 +10,8 @@
* ------------------------------------------------------------
*/
#ifndef DRAGON_OPERATORS_NDARRAY_ARGREDUCE_OP_H_
#define DRAGON_OPERATORS_NDARRAY_ARGREDUCE_OP_H_
#ifndef DRAGON_OPERATORS_ARRAY_ARGREDUCE_OP_H_
#define DRAGON_OPERATORS_ARRAY_ARGREDUCE_OP_H_
#include "core/operator.h"
......@@ -39,4 +39,4 @@ class ArgReduceOp final : public Operator<Context> {
} // namespace dragon
#endif // DRAGON_OPERATORS_NDARRAY_ARGREDUCE_OP_H_
\ No newline at end of file
#endif // DRAGON_OPERATORS_ARRAY_ARGREDUCE_OP_H_
\ No newline at end of file
Dragon/include/operators/ndarray/concat_op.h → Dragon/include/operators/array/concat_op.h
......@@ -10,8 +10,8 @@
* ------------------------------------------------------------
*/
#ifndef DRAGON_OPERATORS_NDARRAY_CONCAT_OP_H_
#define DRAGON_OPERATORS_NDARRAY_CONCAT_OP_H_
#ifndef DRAGON_OPERATORS_ARRAY_CONCAT_OP_H_
#define DRAGON_OPERATORS_ARRAY_CONCAT_OP_H_
#include "core/operator.h"
......@@ -55,4 +55,4 @@ class ConcatGradientOp : public Operator<Context> {
} // namespace dragon
#endif // DRAGON_OPERATORS_NDARRAY_CONCAT_OP_H_
\ No newline at end of file
#endif // DRAGON_OPERATORS_ARRAY_CONCAT_OP_H_
\ No newline at end of file
Dragon/include/operators/ndarray/crop_op.h → Dragon/include/operators/array/crop_op.h
......@@ -10,8 +10,8 @@
* ------------------------------------------------------------
*/
#ifndef DRAGON_OPERATORS_NDARRAY_CROP_OP_H_
#define DRAGON_OPERATORS_NDARRAY_CROP_OP_H_
#ifndef DRAGON_OPERATORS_ARRAY_CROP_OP_H_
#define DRAGON_OPERATORS_ARRAY_CROP_OP_H_
#include "core/operator.h"
......@@ -63,4 +63,4 @@ class CropGradientOp final : public Operator<Context> {
} // namespace dragon
#endif // DRAGON_OPERATORS_NDARRAY_CROP_OP_H_
\ No newline at end of file
#endif // DRAGON_OPERATORS_ARRAY_CROP_OP_H_
\ No newline at end of file
Dragon/include/operators/ndarray/dimension_op.h → Dragon/include/operators/array/dimension_op.h
......@@ -10,8 +10,8 @@
* ------------------------------------------------------------
*/
#ifndef DRAGON_OPERATORS_NDARRAY_DIMENSION_OP_H_
#define DRAGON_OPERATORS_NDARRAY_DIMENSION_OP_H_
#ifndef DRAGON_OPERATORS_ARRAY_DIMENSION_OP_H_
#define DRAGON_OPERATORS_ARRAY_DIMENSION_OP_H_
#include "core/operator.h"
......@@ -151,4 +151,4 @@ DEFINE_DIMENSION_GRADIENT_OP(Squeeze);
} // namespace dragon
#endif // DRAGON_OPERATORS_NDARRAY_RESHAPE_OP_H_
\ No newline at end of file
#endif // DRAGON_OPERATORS_ARRAY_RESHAPE_OP_H_
\ No newline at end of file
Dragon/include/operators/ndarray/gather_op.h → Dragon/include/operators/array/gather_op.h
......@@ -10,8 +10,8 @@
* ------------------------------------------------------------
*/
#ifndef DRAGON_OPERATORS_NDARRAY_GATHER_OP_H_
#define DRAGON_OPERATORS_NDARRAY_GATHER_OP_H_
#ifndef DRAGON_OPERATORS_ARRAY_GATHER_OP_H_
#define DRAGON_OPERATORS_ARRAY_GATHER_OP_H_
#include "core/operator.h"
......@@ -52,4 +52,4 @@ class GatherGradientOp final : public Operator<Context> {
} // namespace dragon
#endif // DRAGON_OPERATORS_NDARRAY_GATHER_OP_H_
\ No newline at end of file
#endif // DRAGON_OPERATORS_ARRAY_GATHER_OP_H_
\ No newline at end of file
Dragon/include/operators/array/multinomial_op.h 0 → 100644
/*!
* Copyright (c) 2017-present, SeetaTech, Co.,Ltd.
*
* Licensed under the BSD 2-Clause License.
* You should have received a copy of the BSD 2-Clause License
* along with the software. If not, See,
*
* <https://opensource.org/licenses/BSD-2-Clause>
*
* ------------------------------------------------------------
*/
#ifndef DRAGON_OPERATORS_ARRAY_MULTINOMIAL_OP_H_
#define DRAGON_OPERATORS_ARRAY_MULTINOMIAL_OP_H_
#include "core/operator.h"
namespace dragon {
template <class Context>
class MultinomialOp final : public Operator<Context> {
public:
MultinomialOp(const OperatorDef& def, Workspace* ws)
: Operator<Context>(def, ws),
normalize(OperatorBase::Arg<int64_t>("normalize", 0)),
num_samples(OperatorBase::Arg<int64_t>("num_samples", 1)) {}
USE_OPERATOR_FUNCTIONS;
void SoftmaxRun();
void RunOnDevice() override;
template <typename T> void RunWithType();
protected:
Tensor* prob;
int64_t normalize, num_samples, outer_dim, axis;
unique_ptr<OperatorBase> softmax_op;
};
} // namespace dragon
#endif // DRAGON_OPERATORS_ARRAY_MULTINOMIAL_OP_H_
\ No newline at end of file
Dragon/include/operators/ndarray/one_hot_op.h → Dragon/include/operators/array/one_hot_op.h
......@@ -10,8 +10,8 @@
* ------------------------------------------------------------
*/
#ifndef DRAGON_OPERATORS_NDARRAY_ONE_HOT_OP_H_
#define DRAGON_OPERATORS_NDARRAY_ONE_HOT_OP_H_
#ifndef DRAGON_OPERATORS_ARRAY_ONE_HOT_OP_H_
#define DRAGON_OPERATORS_ARRAY_ONE_HOT_OP_H_
#include "core/operator.h"
......@@ -36,4 +36,4 @@ class OneHotOp final : public Operator < Context > {
} // namespace dragon
#endif // DRAGON_OPERATORS_NDARRAY_ONE_HOT_OP_H_
\ No newline at end of file
#endif // DRAGON_OPERATORS_ARRAY_ONE_HOT_OP_H_
\ No newline at end of file
Dragon/include/operators/ndarray/pad_op.h → Dragon/include/operators/array/pad_op.h
......@@ -10,8 +10,8 @@
* ------------------------------------------------------------
*/
#ifndef DRAGON_OPERATORS_NDARRAY_PAD_OP_H_
#define DRAGON_OPERATORS_NDARRAY_PAD_OP_H_
#ifndef DRAGON_OPERATORS_ARRAY_PAD_OP_H_
#define DRAGON_OPERATORS_ARRAY_PAD_OP_H_
#include "core/operator.h"
......@@ -73,4 +73,4 @@ class PadGradientOp final : public Operator<Context> {
} // namespace dragon
#endif // DRAGON_OPERATORS_NDARRAY_PAD_OP_H_
\ No newline at end of file
#endif // DRAGON_OPERATORS_ARRAY_PAD_OP_H_
\ No newline at end of file
Dragon/include/operators/ndarray/reduce_op.h → Dragon/include/operators/array/reduce_op.h
......@@ -10,8 +10,8 @@
* ------------------------------------------------------------
*/
#ifndef DRAGON_OPERATORS_NDARRAY_REDUCE_OP_H_
#define DRAGON_OPERATORS_NDARRAY_REDUCE_OP_H_
#ifndef DRAGON_OPERATORS_ARRAY_REDUCE_OP_H_
#define DRAGON_OPERATORS_ARRAY_REDUCE_OP_H_
#include "core/operator.h"
......@@ -59,4 +59,4 @@ class ReduceGradientOp final : public Operator<Context> {
} // namespace dragon
#endif // DRAGON_OPERATORS_NDARRAY_REDUCE_OP_H_
\ No newline at end of file
#endif // DRAGON_OPERATORS_ARRAY_REDUCE_OP_H_
\ No newline at end of file
Dragon/include/operators/ndarray/repeat_op.h → Dragon/include/operators/array/repeat_op.h
......@@ -10,8 +10,8 @@
* ------------------------------------------------------------
*/
#ifndef DRAGON_OPERATORS_NDARRAY_REPEAT_OP_H_
#define DRAGON_OPERATORS_NDARRAY_REPEAT_OP_H_
#ifndef DRAGON_OPERATORS_ARRAY_REPEAT_OP_H_
#define DRAGON_OPERATORS_ARRAY_REPEAT_OP_H_
#include "core/operator.h"
......@@ -58,4 +58,4 @@ DEFINE_ARGUMENT_WITH_DESC(int64_t, RepeatGradientOp, repeats);
} // namespace dragon
#endif // DRAGON_OPERATORS_NDARRAY_REPEAT_OP_H_
\ No newline at end of file
#endif // DRAGON_OPERATORS_ARRAY_REPEAT_OP_H_
\ No newline at end of file
Dragon/include/operators/ndarray/shape_op.h → Dragon/include/operators/array/shape_op.h
......@@ -10,8 +10,8 @@
* ------------------------------------------------------------
*/
#ifndef DRAGON_OPERATORS_NDARRAY_SHAPE_OP_H_
#define DRAGON_OPERATORS_NDARRAY_SHAPE_OP_H_
#ifndef DRAGON_OPERATORS_ARRAY_SHAPE_OP_H_
#define DRAGON_OPERATORS_ARRAY_SHAPE_OP_H_
#include "core/operator.h"
......@@ -27,4 +27,4 @@ class ShapeOp final : public Operator<Context> {
} // namespace dragon
#endif //DRAGON_OPERATORS_NDARRAY_SHAPE_OP_H_
\ No newline at end of file
#endif //DRAGON_OPERATORS_ARRAY_SHAPE_OP_H_
\ No newline at end of file
Dragon/include/operators/ndarray/slice_op.h → Dragon/include/operators/array/slice_op.h
......@@ -10,8 +10,8 @@
* ------------------------------------------------------------
*/
#ifndef DRAGON_OPERATORS_NDARRAY_SLICE_OP_H_
#define DRAGON_OPERATORS_NDARRAY_SLICE_OP_H_
#ifndef DRAGON_OPERATORS_ARRAY_SLICE_OP_H_
#define DRAGON_OPERATORS_ARRAY_SLICE_OP_H_
#include "core/operator.h"
......@@ -55,4 +55,4 @@ class SliceGradientOp final : public Operator<Context> {
} // namespace dragon
#endif // DRAGON_OPERATORS_NDARRAY_SLICE_OP_H_
\ No newline at end of file
#endif // DRAGON_OPERATORS_ARRAY_SLICE_OP_H_
\ No newline at end of file
Dragon/include/operators/ndarray/stack_op.h → Dragon/include/operators/array/stack_op.h
......@@ -10,8 +10,8 @@
* ------------------------------------------------------------
*/
#ifndef DRAGON_OPERATORS_NDARRAY_STACK_OP_H_
#define DRAGON_OPERATORS_NDARRAY_STACK_OP_H_
#ifndef DRAGON_OPERATORS_ARRAY_STACK_OP_H_
#define DRAGON_OPERATORS_ARRAY_STACK_OP_H_
#include "core/operator.h"
......@@ -50,4 +50,4 @@ class StackGradientOp final : public Operator<Context> {
} // namespace dragon
#endif // DRAGON_OPERATORS_NDARRAY_STACK_OP_H_
\ No newline at end of file
#endif // DRAGON_OPERATORS_ARRAY_STACK_OP_H_
\ No newline at end of file
Dragon/include/operators/ndarray/tile_op.h → Dragon/include/operators/array/tile_op.h
......@@ -10,8 +10,8 @@
* ------------------------------------------------------------
*/
#ifndef DRAGON_OPERATORS_NDARRAY_TILE_OP_H_
#define DRAGON_OPERATORS_NDARRAY_TILE_OP_H_
#ifndef DRAGON_OPERATORS_ARRAY_TILE_OP_H_
#define DRAGON_OPERATORS_ARRAY_TILE_OP_H_
#include "core/operator.h"
......@@ -60,4 +60,4 @@ DEFINE_ARGUMENTS_WITH_DESC(int64_t, TileGradientOp, multiples);
} // namespace dragon
#endif // DRAGON_OPERATORS_NDARRAY_TILE_OP_H_
\ No newline at end of file
#endif // DRAGON_OPERATORS_ARRAY_TILE_OP_H_
\ No newline at end of file
Dragon/include/operators/ndarray/transpose_op.h → Dragon/include/operators/array/transpose_op.h
......@@ -10,8 +10,8 @@
* ------------------------------------------------------------
*/
#ifndef DRAGON_OPERATORS_NDARRAY_TRANSPOSE_OP_H_
#define DRAGON_OPERATORS_NDARRAY_TRANSPOSE_OP_H_
#ifndef DRAGON_OPERATORS_ARRAY_TRANSPOSE_OP_H_
#define DRAGON_OPERATORS_ARRAY_TRANSPOSE_OP_H_
#include "core/operator.h"
......@@ -56,4 +56,4 @@ DEFINE_ARGUMENTS_WITH_DESC(int64_t, TransposeGradientOp, perm);
} // namespace dragon
#endif // DRAGON_OPERATORS_NDARRAY_TRANSPOSE_OP_H_
\ No newline at end of file
#endif // DRAGON_OPERATORS_ARRAY_TRANSPOSE_OP_H_
\ No newline at end of file
Dragon/include/operators/control_flow/compare_op.h
......@@ -30,7 +30,9 @@ class CompareOp final : public Operator<Context> {
void RunOnDevice() override;
template <typename T> void EqualRunWithType();
template <typename T> void LessRunWithType();
template <typename T> void LessEqualRunWithType();
template <typename T> void GreaterRunWithType();
template <typename T> void GreaterEqualRunWithType();
protected:
string operation;
......
Dragon/include/operators/misc/initialize_op.h
......@@ -192,6 +192,6 @@ DEFINE_ARGUMENTS_WITH_DESC(int64_t, InitializeOp, dims);
DEFINE_ARGUMENTS_WITH_DESC(int64_t, FillOp, dims);
DEFINE_ARGUMENTS_WITH_DESC(int64_t, GivenTensorFillOp, dims);
} // namespace
} // namespace dragon
#endif // DRAGON_OPERATORS_MISC_INITIALIZE_OP_H_
\ No newline at end of file
Dragon/include/utils/op_kernel.h
......@@ -351,6 +351,14 @@ void Less(
Context* ctx);
template <typename T, class Context>
void LessEqual(
const int count,
const T* a,
const T* b,
bool* y,
Context* ctx);
template <typename T, class Context>
void Greater(
const int count,
const T* a,
......@@ -358,6 +366,14 @@ void Greater(
bool* y,
Context* ctx);
template <typename T, class Context>
void GreaterEqual(
const int count,
const T* a,
const T* b,
bool* y,
Context* ctx);
/*! loss.l1_loss */
template <typename T, class Context>
......@@ -574,7 +590,7 @@ void ImageData(
Ty* y,
Context* ctx);
/*! ndarray.arange */
/*! array.arange */
template <typename T, class Context>
void Arange(
......@@ -584,7 +600,7 @@ void Arange(
T* y,
Context* ctx);
/*! ndarray.argreduce */
/*! array.argreduce */
template <typename T, class Context>
void ArgMax(
......@@ -608,7 +624,7 @@ void ArgMin(
T* values,
Context* ctx);
/*! ndarray.gather */
/*! array.gather */
template <typename T, class Context>
void Gather(
......@@ -632,7 +648,7 @@ void GatherGrad(
T* dx,
Context* ctx);
/*! ndarray.concat */
/*! array.concat */
template <typename T, class Context>
void Concat(
......@@ -645,7 +661,7 @@ void Concat(
T* y,
Context* ctx);
/*! ndarray.crop */
/*! array.crop */
template <typename T, class Context>
void Crop(
......@@ -669,7 +685,7 @@ void CropGrad(
T* dx,
Context* ctx);
/*! ndarray.pad */
/*! array.pad */
template <typename T, class Context>
void ConstPad(
......@@ -708,7 +724,7 @@ void EdgePad(
T* y,
Context* ctx);
/*! ndarray.one_hot */
/*! array.one_hot */
template <typename T, class Context>
void OneHot(
......@@ -719,7 +735,7 @@ void OneHot(
T* y,
Context* ctx);
/*! ndarray.reduce */
/*! array.reduce */
template <typename T, class Context>
void ReduceSum(
......@@ -744,7 +760,7 @@ void ReduceSumGrad(
T* dx,
Context* ctx);
/*! ndarray.repeat */
/*! array.repeat */
template <typename T, class Context>
void Repeat(
......@@ -766,7 +782,7 @@ void RepeatGrad(
T* dx,
Context* ctx);
/*! ndarray.slice */
/*! array.slice */
template <typename T, class Context>
void Slice(
......@@ -790,7 +806,7 @@ void SliceGrad(
T* x,
Context* ctx);
/*! ndarray.tile */
/*! array.tile */
template <typename T, class Context>
void Tile(
......@@ -812,7 +828,7 @@ void TileGrad(
T* dx,
Context* ctx);
/*! ndarray.transpose */
/*! array.transpose */
template <typename T, class Context>
void Transpose(
......
Dragon/modules/python/py_module.cc
......@@ -70,12 +70,6 @@ void OnImportModule() {
PYBIND11_MODULE(libdragon, m) {
/* ------------------------------------ *
* *
* Workspace *
* *
* ------------------------------------ */
/*! \brief Switch to the specific workspace */
m.def("SwitchWorkspace", &SwitchWorkspace);
......@@ -133,6 +127,7 @@ PYBIND11_MODULE(libdragon, m) {
g_workspaces[target_workspace]->Clear();
});
/*! \brief Copy the array data to the tensor */
m.def("FeedTensor", [](
const string& name,
pybind11::object value,
......@@ -150,6 +145,7 @@ PYBIND11_MODULE(libdragon, m) {
PyArrayObject*>(value.ptr()), tensor);
});
/*! \brief Copy the tensor data to the array */
m.def("FetchTensor", [](const string& name) {
if (!g_workspace->HasTensor(name))
LOG(FATAL) << "Tensor(" + name + ") "
......@@ -169,7 +165,7 @@ PYBIND11_MODULE(libdragon, m) {
}
});
/*! Misc */
/*! \brief Return a unique dummy name */
m.def("GetDummyName", [](
const string& basename,
const string& suffix,
......
Dragon/modules/python/py_proto.h
......@@ -63,12 +63,6 @@ void AddProtoMethods(pybind11::module& m) {
[](OperatorDef* self, const vector<string>& output) {
*(self->mutable_output()) = { output.begin(), output.end() };
});
m.def("TestOperatorDefs", [](vector<OperatorDef*> defs) {
for (auto* def : defs) {
std::cout << def->DebugString() << std::endl;
}
});
}
} // namespace python
......
Dragon/modules/python/py_tensor.h
......@@ -27,7 +27,7 @@ void AddTensorMethods(pybind11::module& m) {
.def_property_readonly("size", &Tensor::size)
.def_property_readonly("dtype", [](Tensor* self) {
return TypeMetaToString(self->meta());
}).def_property_readonly("ctx", [](Tensor* self) {
}).def_property_readonly("device", [](Tensor* self) {
if (self->has_memory()) {
Map<string, string> mem_info = self->memory()->info();
return std::tuple<string, int>(
......
Dragon/python/dragon/__init__.py
......@@ -41,7 +41,7 @@ from dragon.vm.theano.tensor import grad as grad
from dragon.core.scope import name_scope, get_default_name_scope
from dragon.core.scope import phase_scope, get_default_phase
from dragon.core.scope import device_scope, get_default_device
from dragon.core.scope import WorkspaceScope as workspace_scope
from dragon.core.scope import WorkspaceScope as ws_scope
# Version
from dragon.version import version
......
Dragon/python/dragon/config.py
......@@ -20,8 +20,9 @@ import dragon.core.logging as logging
option = {}
# The current device, 'CPU', 'CUDA' or 'CNML'
option['device'] = 'CPU'
# The current device
# enumeration in ('cpu', 'cuda', 'cnml')
option['device'] = 'cpu'
# The device index
option['device_id'] = 0
......@@ -73,7 +74,7 @@ def EnableCPU():
"""
global option
option['device'] = 'CPU'
option['device'] = 'cpu'
def EnableCUDA(gpu_id=0, use_cudnn=True):
......@@ -92,7 +93,7 @@ def EnableCUDA(gpu_id=0, use_cudnn=True):
"""
global option
option['device'] = 'CUDA'
option['device'] = 'cuda'
option['device_id'] = gpu_id
option['use_cudnn'] = use_cudnn
......@@ -111,7 +112,7 @@ def EnableCNML(mlu_id=0):
"""
global option
option['device'] = 'CNML'
option['device'] = 'cnml'
option['device_id'] = mlu_id
......
Dragon/python/dragon/core/cuda.py
......@@ -15,7 +15,7 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import dragon.import_c_api as C
import dragon.import_c_api as _C
def IsCUDADriverSufficient():
......@@ -27,7 +27,7 @@ def IsCUDADriverSufficient():
``True`` if your device(s) support CUDA otherwise ``False``.
"""
return C.IsCUDADriverSufficient()
return _C.IsCUDADriverSufficient()
def GetDevice():
......@@ -39,7 +39,7 @@ def GetDevice():
The device index.
"""
return C.cudaGetDevice()
return _C.cudaGetDevice()
def SynchronizeStream(device_id=None, stream_id=0):
......@@ -55,5 +55,5 @@ def SynchronizeStream(device_id=None, stream_id=0):
The stream index.
"""
return C.cudaStreamSynchronize(
return _C.cudaStreamSynchronize(
device_id if device_id else -1, stream_id)
\ No newline at end of file
Dragon/python/dragon/core/gradient_maker.py
......@@ -93,7 +93,7 @@ class GraphGradientMaker(object):
"""
if forward_op.type in C.NO_GRADIENT_OPERATORS:
for input in forward_op.input: blacklist.add(input)
return (True, None)
return True, None
# Generate virtual grads for targets if necessary
gen_grads = []
......@@ -107,11 +107,11 @@ class GraphGradientMaker(object):
for output in forward_op.output:
if inputs_to_grads.get(output, None) is None:
# check failed: skip backward
if output in blacklist: return (True, gen_grads)
if len(forward_op.output) == 1: return (True, gen_grads)
if output in blacklist: return True, gen_grads
if len(forward_op.output) == 1: return True, gen_grads
# Pass, even if missing some grads
return (False, gen_grads)
return False, gen_grads
@classmethod
def Make(cls, forward_ops, targets, input_grads=None, auto_names=True):
......
Dragon/python/dragon/core/helper.py
......@@ -16,8 +16,8 @@ from __future__ import division
from __future__ import print_function
import math
import numpy as np
import dragon as dg
import numpy
import dragon
class OperatorHelper(object):
......@@ -39,11 +39,11 @@ class OperatorHelper(object):
@classmethod
def get_index_and_name(cls, prefix='Op'):
name = dg.workspace.GetDummyName(prefix, domain='Operator')
name = dragon.workspace.GetDummyName(prefix, domain='Operator')
try:
_, op_idx = name.split('_')
except:
name = dg.workspace.GetDummyName(prefix, domain='Operator')
name = dragon.workspace.GetDummyName(prefix, domain='Operator')
_, op_idx = name.split('_')
return int(op_idx), name
......@@ -216,7 +216,7 @@ class OperatorHelper(object):
for i in range(3):
try:
if i == 0:
outputs[0].shape[i] = np.prod(inputs[0].shape[:axis])
outputs[0].shape[i] = numpy.prod(inputs[0].shape[:axis])
if i >= 1:
outputs[0].shape[i] = inputs[0].shape[axis]
except: pass
......@@ -581,7 +581,7 @@ class OperatorHelper(object):
if axis is None:
try:
fake_shape = inputs[0].shape[:]
total_count = np.prod(fake_shape)
total_count = numpy.prod(fake_shape)
outputs[0].shape = [total_count * repeats]
except:
outputs[0].shape = [None]
......@@ -643,7 +643,7 @@ class OperatorHelper(object):
outputs[0].shape = [None] * len(shape)
n_elements, n_elements_known = None, None
try:
n_elements = int(np.prod(inputs[0].shape))
n_elements = int(numpy.prod(inputs[0].shape))
except:
pass
for i, s in enumerate(shape):
......@@ -654,7 +654,7 @@ class OperatorHelper(object):
except:
pass
try:
n_elements_known = int(np.prod(outputs[0].shape))
n_elements_known = int(numpy.prod(outputs[0].shape))
except:
pass
for i, s in enumerate(shape):
......@@ -738,6 +738,16 @@ class OperatorHelper(object):
outputs[0].shape = [count]
return outputs
@classmethod
def _apply_Multinomial(cls, arguments, inputs, outputs):
outputs[0].dtype = 'int64'
try:
outputs[0].shape = inputs[0].shape[:]
outputs[0].shape[-1] = arguments['num_samples']
except:
pass
return outputs
###############################################
# #
# Vision #
......
Dragon/python/dragon/core/mapping.py
......@@ -15,18 +15,18 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import numpy
TENSOR_TYPE_TO_NP_TYPE = {
'bool': np.bool,
'int8': np.int8,
'uint8': np.uint8,
'int32': np.int32,
'int64': np.int64,
'float16': np.float16,
'float32': np.float32,
'float64': np.float64,
'bool': numpy.bool,
'int8': numpy.int8,
'uint8': numpy.uint8,
'int32': numpy.int32,
'int64': numpy.int64,
'float16': numpy.float16,
'float32': numpy.float32,
'float64': numpy.float64,
}
......
Dragon/python/dragon/core/mpi.py
......@@ -15,7 +15,7 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import dragon.import_c_api as C
import dragon.import_c_api as _C
_GLOBAL_MPI_IS_INIT = False
......@@ -40,7 +40,7 @@ def Init():
This function can only be called once.
"""
C.MPIInit()
_C.MPIInit()
global _GLOBAL_MPI_IS_INIT
global _GLOBAL_MPI_SNAPSHOT_RANKS
_GLOBAL_MPI_IS_INIT = True
......@@ -68,7 +68,7 @@ def Rank():
"""
_check_init()
return C.MPIRank()
return _C.MPIRank()
def Size():
......@@ -81,7 +81,7 @@ def Size():
"""
_check_init()
return C.MPISize()
return _C.MPISize()
def CreateGroup(root=0, incl=[], excl=[]):
......@@ -103,7 +103,7 @@ def CreateGroup(root=0, incl=[], excl=[]):
"""
_check_init()
return C.MPICreateGroup(root, incl, excl)
return _C.MPICreateGroup(root, incl, excl)
def Snapshot(incl):
......@@ -226,4 +226,4 @@ def Finalize():
"""
_check_init()
C.MPIFinalize()
\ No newline at end of file
_C.MPIFinalize()
\ No newline at end of file
Dragon/python/dragon/core/proto_utils.py
......@@ -21,7 +21,7 @@ import numpy as np
from google.protobuf.message import Message
import dragon.config as cfg
import dragon.import_c_api as C
import dragon.import_c_api as _C
from dragon.proto import dragon_pb2 as pb
from dragon.core.scope import get_default_device
......@@ -97,7 +97,7 @@ def MakeCXXOperatorDef(
op_type, inputs=(), outputs=(),
name='', uid=None, device_option=None,
arg=None, engine=None, **kwargs):
c_def = C.OperatorDef()
c_def = _C.OperatorDef()
py_def = MakeOperatorDef(
op_type, inputs, outputs, name, uid,
device_option, arg, engine, **kwargs)
......@@ -118,7 +118,7 @@ def MakeDeviceOption(
_PREDEFINED_DEVICE_LIMITS = 16
_PREDEFINED_DEVICE_ENGINES = ['', 'CUDNN']
_PREDEFINED_DEVICE_DICT = {'CPU': 0, 'CUDA': 1, 'CNML': 2}
_PREDEFINED_DEVICE_DICT = {'cpu': 0, 'cuda': 1, 'cnml': 2}
_PREDEFINED_DEVICE_OPTION_DICT = {}
......@@ -127,8 +127,8 @@ for i in range(_PREDEFINED_DEVICE_LIMITS):
for engine in _PREDEFINED_DEVICE_ENGINES:
_PREDEFINED_DEVICE_OPTION_DICT[(device, i, engine)] = \
MakeDeviceOption(identify, i, engine)
if device == 'CUDA':
_PREDEFINED_DEVICE_OPTION_DICT[('CUDA', i)] = \
if device == 'cuda':
_PREDEFINED_DEVICE_OPTION_DICT[('cuda', i)] = \
MakeDeviceOption(identify, i, 'CUDNN')
......
Dragon/python/dragon/core/scope.py
......@@ -14,7 +14,7 @@ from __future__ import division
from __future__ import print_function
import threading
import dragon.import_c_api as C
import dragon.import_c_api as _C
from contextlib import contextmanager
......@@ -76,7 +76,7 @@ class WorkspaceScope(object):
--------
>>> import dragon as dg
>>> with WorkspaceScope('session1'): pass
>>> with dg.workspace_scope('session2'): pass
>>> with dg.ws_scope('session2'): pass
"""
def __init__(self, ws_name):
......@@ -88,11 +88,11 @@ class WorkspaceScope(object):
self.prev = 'default'
def __enter__(self):
self.prev = C.CurrentWorkspace()
C.SwitchWorkspace(self.ws, True)
self.prev = _C.CurrentWorkspace()
_C.SwitchWorkspace(self.ws, True)
def __exit__(self, type, value, traceback):
C.SwitchWorkspace(self.prev, True)
_C.SwitchWorkspace(self.prev, True)
_GLOBAL_TENSOR_STACK = _ThreadLocalStack()
......@@ -133,7 +133,7 @@ def device_scope(device_type, device_id=0, engine='AUTO'):
Parameters
----------
device_type : {'CPU', 'GPU', 'CUDA', 'CNML'}, required
device_type : {'cpu', 'gpu', 'cuda', 'cnml'}, required
The type of device.
device_id : int, optional
The index of the device.
......@@ -143,9 +143,9 @@ def device_scope(device_type, device_id=0, engine='AUTO'):
"""
device_type, device_id, device_engine = \
device_type.upper(), device_id, engine.upper()
assert device_type in ['CPU', 'GPU', 'CUDA', 'CNML']
assert device_type in ['cpu', 'gpu', 'cuda', 'cnml']
# Default names
if device_type == 'GPU': device_type = 'CUDA'
if device_type == 'gpu': device_type = 'cuda'
if device_engine == 'AUTO': device_engine = 'CUDNN'
return _GLOBAL_DEVICE_STACK.get_controller({
'device_type': device_type,
......
Dragon/python/dragon/core/tensor.py
......@@ -45,11 +45,11 @@ class Tensor(object):
Parameters
----------
name : None or str
name : str, optional
The name of Tensor.
shape : None or list
shape : list, optional
The shape of Tensor.
dtype : None or str
dtype : str, optional
The type of Tensor.
Returns
......@@ -94,7 +94,7 @@ class Tensor(object):
Parameters
----------
value : number
value : number, optional, default=0
The constant value.
Returns
......@@ -105,14 +105,14 @@ class Tensor(object):
"""
return self.Fill('constant', value=value)
def Uniform(self, low=-1, high=1):
def Uniform(self, low=0, high=1):
"""Register as a variable with uniform initializer.
Parameters
----------
low : number
low : number, optional, default=0
The lower bound of uniform distribution.
high : number
high : number, optional, default=1
The higher bound of uniform distribution.
Returns
......@@ -128,9 +128,9 @@ class Tensor(object):
Parameters
----------
mu : number
mu : number, optional, default=0
The mu of normal distribution.
sigma : number
sigma : number, optional, default=1
The sigma of normal distribution.
Returns
......@@ -146,9 +146,9 @@ class Tensor(object):
Parameters
----------
mu : number
mu : number, optional, default=0
The mu of normal distribution.
sigma : number
sigma : number, optional, default=1
The sigma of normal distribution.
Returns
......@@ -164,9 +164,9 @@ class Tensor(object):
Parameters
----------
mean : number
mean : number, optional, default=0
The mean(mu) of normal distribution.
std : number
std : number, optional, default=1
The std(sigma) of normal distribution.
Returns
......@@ -177,12 +177,12 @@ class Tensor(object):
"""
return self.Normal(mu=mean, sigma=std)
def GlorotUniform(self, scale=3.0):
def GlorotUniform(self, scale=3.):
"""Register as a variable with glorot uniform initializer.
Parameters
----------
scale : number
scale : number, optional, default=3.
The scale factor.
Returns
......@@ -193,12 +193,12 @@ class Tensor(object):
"""
return self.Fill('glorot_uniform', scale=scale)
def GlorotNormal(self, scale=2.0):
def GlorotNormal(self, scale=2.):
"""Register as a variable with glorot normal initializer.
Parameters
----------
scale : number
scale : number, optional, default=2.
The scale factor.
Returns
......@@ -244,7 +244,7 @@ class Tensor(object):
Parameters
----------
value : None or str
value : str
The name to set.
Returns
......@@ -270,7 +270,7 @@ class Tensor(object):
Parameters
----------
str
name : str
The name.
Returns
......@@ -284,6 +284,11 @@ class Tensor(object):
def shape(self):
"""Return or Set the shape.
Parameters
---------
value : sequence of int
The shape to set.
Returns
-------
sequence of int
......@@ -344,7 +349,7 @@ class Tensor(object):
----------
dtype : str
The specific dtype.
inplace : boolean
inplace : boolean, optional, default=False
Whether to modify the inputs.
Returns
......@@ -651,6 +656,99 @@ class Tensor(object):
"""
return self.__mul__(-1.0)
def __gt__(self, other):
"""Compute *self* > *other* element-wise.
Parameters
----------
other : Tensor or number
The other tensor.
Returns
-------
Tensor
The output tensor.
"""
if not isinstance(other, Tensor):
other = self._from_constants(other)
return self.CreateOperator('Compare', [self, other], operation='GT')
def __ge__(self, other):
"""Compute *self* > *other* element-wise.
Parameters
----------
other : Tensor or number
The other tensor.
Returns
-------
Tensor
The output tensor.
"""
if not isinstance(other, Tensor):
other = self._from_constants(other)
return self.CreateOperator('Compare', [self, other], operation='GE')
def __lt__(self, other):
"""Compute *self* < *other* element-wise.
Parameters
----------
other : Tensor or number
The other tensor.
Returns
-------
Tensor
The output tensor.
"""
if not isinstance(other, Tensor):
other = self._from_constants(other)
return self.CreateOperator('Compare', [self, other], operation='LT')
def __le__(self, other):
"""Compute *self* <= *other* element-wise.
Parameters
----------
other : Tensor or number
The other tensor.
Returns
-------
Tensor
The output tensor.
"""
if not isinstance(other, Tensor):
other = self._from_constants(other)
return self.CreateOperator('Compare', [self, other], operation='LE')
def __eq__(self, other):
"""Compute *self* == *other* element-wise.
Parameters
----------
other : Tensor or number
The other tensor.
Returns
-------
Tensor
The output tensor.
"""
if not isinstance(other, Tensor):
other = self._from_constants(other)
return self.CreateOperator('Compare', [self, other], operation='EQ')
def __hash__(self):
return id(self)
def __call__(self, *args, **kwargs):
"""Print the expressions.
......@@ -984,7 +1082,7 @@ class Tensor(object):
----------
value : number or Tensor
The value to convert.
dtype : str, optional
dtype : str, optional, default='float32'
The data type of the tensor.
Returns
......
Dragon/python/dragon/core/tensor_utils.py
......@@ -15,11 +15,8 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import dragon as dg
from google.protobuf.message import Message
import dragon.import_c_api as C
import numpy
import dragon
from dragon.core.tensor import Tensor
from dragon.core.proto_utils import GetDeviceOption
......@@ -50,7 +47,7 @@ def FromShape(shape, dtype='float32', name=None):
tensor.shape = list(shape)
if not isinstance(shape, (tuple, list)):
raise TypeError('The shape should be a tuple or list.')
C.TensorFromShape(
dragon.C.TensorFromShape(
_stringify_tensor(tensor),
list(shape), dtype)
return tensor
......@@ -73,7 +70,7 @@ def SetShape(tensor, shape, dtype='float32'):
None
"""
C.TensorFromShape(_stringify_tensor(tensor), shape, dtype)
dragon.C.TensorFromShape(_stringify_tensor(tensor), shape, dtype)
def FromTensor(src, src_ctx=None, name=None, ctx=None):
......@@ -100,9 +97,9 @@ def FromTensor(src, src_ctx=None, name=None, ctx=None):
"""
tensor = _try_get_tensor(name)
if src_ctx is None: src_ctx = GetDeviceOption('CPU')
if ctx is None: ctx = GetDeviceOption('CPU')
C.TensorFromTensor(
if src_ctx is None: src_ctx = GetDeviceOption('cpu')
if ctx is None: ctx = GetDeviceOption('cpu')
dragon.C.TensorFromTensor(
_stringify_tensor(tensor), _stringify_tensor(src),
_stringify_proto(ctx), _stringify_proto(src_ctx))
return tensor
......@@ -130,9 +127,9 @@ def FromPyArray(array, name=None):
"""
tensor = _try_get_tensor(name)
if not isinstance(array, np.ndarray):
if not isinstance(array, numpy.ndarray):
raise TypeError('The given nd-array should be numpy.ndarray.')
C.TensorFromPyArray(_stringify_tensor(tensor), array)
dragon.C.TensorFromPyArray(_stringify_tensor(tensor), array)
return tensor
......@@ -157,7 +154,7 @@ def SetPyArray(tensor, array):
The wrapper of ``TensorFromPyArrayCC``.
"""
C.TensorFromPyArray(_stringify_tensor(tensor), array)
dragon.C.TensorFromPyArray(_stringify_tensor(tensor), array)
def ToPyArray(tensor, readonly=False):
......@@ -178,7 +175,7 @@ def ToPyArray(tensor, readonly=False):
The array sharing the memory with original tensor.
"""
return C.TensorToPyArray(_stringify_tensor(tensor), readonly)
return dragon.C.TensorToPyArray(_stringify_tensor(tensor), readonly)
def GetStorage(tensor):
......@@ -196,8 +193,8 @@ def GetStorage(tensor):
"""
tensor = _stringify_tensor(tensor)
if not dg.workspace.HasTensor(tensor): return None
return C.GetTensor(tensor)
if not dragon.workspace.HasTensor(tensor): return None
return dragon.C.GetTensor(tensor)
def _stringify_proto(obj):
......@@ -213,9 +210,5 @@ def _stringify_tensor(obj):
def _try_get_tensor(name=None):
"""Try to create or get a tensor"""
if name is None or name == '':
return Tensor()
else:
tensor = Tensor('')
tensor.set_name(name)
return tensor
\ No newline at end of file
if name is None or name == '': return Tensor()
else: return Tensor.Ref(name)
\ No newline at end of file
Dragon/python/dragon/core/workspace.py
......@@ -25,20 +25,16 @@ from __future__ import division
from __future__ import print_function
import os
import numpy as np
import numpy
import threading
import six.moves.cPickle as pickle
from google.protobuf.message import Message
import dragon.import_c_api as C
import dragon.import_c_api as _C
import dragon.core.logging as logging
import dragon.proto.dragon_pb2 as pb
from dragon.config import GetGlobalOptions
import dragon.core.mpi as mpi
import dragon.proto.dragon_pb2 as pb
import dragon.core.proto_utils as pb_utils
import dragon.core.mapping as mapping
from dragon.core import mpi, mapping, proto_utils
def CurrentWorkspace():
......@@ -50,7 +46,7 @@ def CurrentWorkspace():
The workspace name.
"""
return C.CurrentWorkspace()
return _C.CurrentWorkspace()
def SwitchWorkspace(workspace_name, create_if_missing=True):
......@@ -70,7 +66,7 @@ def SwitchWorkspace(workspace_name, create_if_missing=True):
"""
if workspace_name == '':
raise ValueError('The workspace name should not be empty.')
C.SwitchWorkspace(workspace_name, create_if_missing)
_C.SwitchWorkspace(workspace_name, create_if_missing)
def MoveWorkspace(target_ws, source_ws):
......@@ -90,7 +86,7 @@ def MoveWorkspace(target_ws, source_ws):
"""
if target_ws == '' or source_ws == '':
raise ValueError('The target or source name can not be empty.')
C.MoveWorkspace(target_ws, source_ws)
_C.MoveWorkspace(target_ws, source_ws)
def ResetWorkspace(workspace_name=''):
......@@ -110,7 +106,7 @@ def ResetWorkspace(workspace_name=''):
None
"""
C.ResetWorkspace(workspace_name)
_C.ResetWorkspace(workspace_name)
def ClearWorkspace(workspace_name=''):
......@@ -130,7 +126,7 @@ def ClearWorkspace(workspace_name=''):
None
"""
C.ClearWorkspace(workspace_name)
_C.ClearWorkspace(workspace_name)
def CreateGraph(graph_def):
......@@ -150,7 +146,7 @@ def CreateGraph(graph_def):
option = GetGlobalOptions()
LogMetaGraph(graph_def)
ExportMetaGraph(graph_def)
return C.CreateGraph(
return _C.CreateGraph(
_stringify_proto(graph_def),
option['log_optimized_graph'],
)
......@@ -173,7 +169,7 @@ def RunOperator(op_def, verbose=False):
"""
if isinstance(op_def, pb.OperatorDef):
op_def = op_def.SerializeToString()
C.RunOperator(op_def, verbose)
_C.RunOperator(op_def, verbose)
def HasTensor(tensor):
......@@ -190,7 +186,7 @@ def HasTensor(tensor):
The query result.
"""
return C.HasTensor(_stringify_tensor(tensor))
return _C.HasTensor(_stringify_tensor(tensor))
def CreateTensor(tensor):
......@@ -206,7 +202,7 @@ def CreateTensor(tensor):
None
"""
return C.CreateTensor(_stringify_tensor(tensor))
return _C.CreateTensor(_stringify_tensor(tensor))
def CreateFiller(filler_def):
......@@ -229,7 +225,7 @@ def CreateFiller(filler_def):
"""
filler_def = filler_def if isinstance(filler_def, str) \
else filler_def.SerializePartialToString()
C.CreateFiller(filler_def)
_C.CreateFiller(filler_def)
def GetFillerType(tensor):
......@@ -250,7 +246,7 @@ def GetFillerType(tensor):
The filler type.
"""
return C.GetFillerType(_stringify_tensor(tensor))
return _C.GetFillerType(_stringify_tensor(tensor))
def GetTensorName(tensor):
......@@ -271,7 +267,7 @@ def GetTensorName(tensor):
The query result may be different from the one used in the frontend.
"""
return C.GetTensorName(_stringify_tensor(tensor))
return _C.GetTensorName(_stringify_tensor(tensor))
def SetTensorAlias(tensor, alias):
......@@ -289,7 +285,7 @@ def SetTensorAlias(tensor, alias):
None
"""
return C.SetTensorAlias(_stringify_tensor(tensor), alias)
return _C.SetTensorAlias(_stringify_tensor(tensor), alias)
def FetchTensor(tensor):
......@@ -306,7 +302,7 @@ def FetchTensor(tensor):
The values copied from the backend.
"""
return C.FetchTensor(_stringify_tensor(tensor))
return _C.FetchTensor(_stringify_tensor(tensor))
def FeedTensor(tensor, array, force_cpu=False, dtype=None):
......@@ -329,14 +325,14 @@ def FeedTensor(tensor, array, force_cpu=False, dtype=None):
Examples
--------
>>> import dragon as dg
>>> a = dg.Tensor().Variable()
>>> dg.workspace.FeedTensor(a, 1)
>>> a_value = dg.workspace.FetchTensor(a)
>>> import dragon
>>> a = dragon.Tensor().Variable()
>>> dragon.workspace.FeedTensor(a, 1)
>>> a_value = dragon.workspace.FetchTensor(a)
>>> a_value, a_value.dtype
>>> [ 1.], "float32"
>>> dg.workspace.FeedTensor(a, [[1, 2, 3]], dtype='float16')
>>> dragon.workspace.FeedTensor(a, [[1, 2, 3]], dtype='float16')
>>> a_value = a.get_value()
>>> a_value, a_value.dtype
>>> [[ 1. 2. 3.]], "float16"
......@@ -344,13 +340,13 @@ def FeedTensor(tensor, array, force_cpu=False, dtype=None):
"""
name = tensor.name if hasattr(tensor, 'name') else str(tensor)
if force_cpu is True:
dev = pb_utils.GetDeviceOption('CPU')
dev = proto_utils.GetDeviceOption('cpu')
else:
dev = pb_utils.GetDefaultDeviceOption()
if dev is None: dev = pb_utils.GetGlobalDeviceOption()
dev = proto_utils.GetDefaultDeviceOption()
if dev is None: dev = proto_utils.GetGlobalDeviceOption()
if not isinstance(array, np.ndarray):
auto_data_type = np.float32 if dtype is None else dtype
if not isinstance(array, numpy.ndarray):
auto_data_type = numpy.float32 if dtype is None else dtype
else:
auto_data_type = array.dtype if dtype is None else dtype
......@@ -365,8 +361,8 @@ def FeedTensor(tensor, array, force_cpu=False, dtype=None):
format(preset_data_type, dtype))
auto_data_type = preset_data_type
nd_array = np.array(array, dtype=auto_data_type, copy=False)
C.FeedTensor(name, nd_array, _stringify_proto(dev))
nd_array = numpy.array(array, dtype=auto_data_type, copy=False)
_C.FeedTensor(name, nd_array, _stringify_proto(dev))
def ResetTensor(tensor):
......@@ -384,7 +380,7 @@ def ResetTensor(tensor):
None
"""
return C.ResetTensor(_stringify_tensor(tensor))
return _C.ResetTensor(_stringify_tensor(tensor))
def RunGraph(
......@@ -427,7 +423,7 @@ def RunGraph(
# Run the graph according to the specified include/exclude rule
runtime_stage = stage if stage else 'default'
rule = _PREDEFINED_GRAPH_RUNTIME_STAGES[runtime_stage]
C.RunGraph(str(graph_name), str(rule['include']), str(rule['exclude']))
_C.RunGraph(str(graph_name), str(rule['include']), str(rule['exclude']))
# Try to return the outputs
# Force to return may lead to asserts if outputs are not computed
......@@ -462,7 +458,7 @@ def FlowGradients(inputs, targets, input_grads=None, ignored_grads=None):
if (option['log_optimized_graph'] or
option['log_meta_graph']) else False
C.FlowGradients(
_C.FlowGradients(
inputs, targets,
input_grads if input_grads else [],
ignored_grads if ignored_grads else [],
......@@ -520,8 +516,7 @@ def ExportMetaGraph(graph_def):
def Snapshot(
tensors, filename,
prefix='', suffix='.bin',
format='default',
):
format='default'):
"""Snapshot tensors into a binary file.
Parameters
......@@ -566,7 +561,7 @@ def Snapshot(
logging.info('Model Format: Pickle')
elif format is 'caffe':
names = [tensor.name for tensor in tensors]
C.Snapshot(file_path, names, 1)
_C.Snapshot(file_path, names, 1)
else: raise TypeError('Unknown binary format: {}'.format(format))
......@@ -606,7 +601,7 @@ def Restore(binary_file, format='default'):
elif format == 'caffe':
# Caffe models can't save the tensor name
# We simply use "layer_name/param:X"
C.Restore(binary_file, 1)
_C.Restore(binary_file, 1)
else:
raise TypeError('Unknown binary format: {}'.format(format))
......@@ -636,7 +631,7 @@ def GetDummyName(basename, suffix='', domain='', zero_based=True):
The unique dummy name.
"""
return C.GetDummyName(basename, suffix, domain, zero_based)
return _C.GetDummyName(basename, suffix, domain, zero_based)
def _stringify_proto(obj):
......
Dragon/python/dragon/operators/__init__.py
......@@ -69,9 +69,15 @@ class OpSchema(object):
def Impl(*args, **kwargs):
inputs = args[0]
if isinstance(inputs, (list, tuple)):
dtype = None
for idx, input in enumerate(inputs):
if isinstance(input, Tensor) and \
input.dtype is not None:
dtype = input.dtype
break
for idx, input in enumerate(inputs):
if not isinstance(input, Tensor):
inputs[idx] = Tensor.Convert(input, dtype=None)
inputs[idx] = Tensor.Convert(input, dtype=dtype)
return op_func(inputs + list(args[1:]), **kwargs)
else:
if not isinstance(inputs, Tensor):
......
Dragon/python/dragon/operators/ndarray.py → Dragon/python/dragon/operators/array.py
......@@ -752,8 +752,8 @@ def Arange(start, stop=None, step=1, dtype='float32', **kwargs):
Parameters
----------
start : int or Tensor
The start of the range.
inputs : Tensor
The input tensor.
stop : int or Tensor, optional
The stop of range.
step : int or Tensor, optional
......@@ -769,4 +769,34 @@ def Arange(start, stop=None, step=1, dtype='float32', **kwargs):
"""
arguments = ParseArgs(locals())
arguments['dtype'] = arguments['dtype'].lower()
return Tensor.CreateOperator('Arange', [], **arguments)
\ No newline at end of file
return Tensor.CreateOperator('Arange', [], **arguments)
@OpSchema.Inputs(1)
def Multinomial(inputs, num_samples=1, normalize=False, **kwargs):
"""Return a tensor where each row contains ``num_samples``,
sampled from the multinomial distribution.
If ``normalize`` is *True*, negative inputs is accepted,
and will be normalized by a softmax function. (*TensorFlow* Style).
Otherwise, inputs should be non-negative. (*Torch* Style).
**Type Constraints**: (*int8*, *uint8*, *int32*, *int64*, *float32*, *float64*)
Parameters
----------
inputs : Tensor
The input tensor.
num_samples : int, optional, default=1
The number of samples.
normalize : boolean, optional, default=False
Whether to normalize the inputs.
Returns
-------
Tensor
A *int64* tensor contains the indices.
"""
return Tensor.CreateOperator('Multinomial', **ParseArgs(locals()))
\ No newline at end of file
Dragon/python/dragon/operators/control_flow.py
......@@ -39,6 +39,7 @@ def Copy(inputs, **kwargs):
return Tensor.CreateOperator('Copy', **arguments)
@OpSchema.ConvertConstantInputs()
@OpSchema.Inputs(2)
def Equal(inputs, to_uint8=False, **kwargs):
"""``Equal`` comparing between A and B.
......@@ -61,9 +62,10 @@ def Equal(inputs, to_uint8=False, **kwargs):
"""
arguments = ParseArgs(locals())
return Tensor.CreateOperator('Compare', operation='EQUAL', **arguments)
return Tensor.CreateOperator('Compare', operation='EQ', **arguments)
@OpSchema.ConvertConstantInputs()
@OpSchema.Inputs(2)
def Less(inputs, to_uint8=False, **kwargs):
"""``Less`` comparing between A and B.
......@@ -86,12 +88,65 @@ def Less(inputs, to_uint8=False, **kwargs):
"""
arguments = ParseArgs(locals())
return Tensor.CreateOperator('Compare', operation='LESS', **arguments)
return Tensor.CreateOperator('Compare', operation='LT', **arguments)
@OpSchema.ConvertConstantInputs()
@OpSchema.Inputs(2)
def LessEqual(inputs, to_uint8=False, **kwargs):
"""``LessEqual`` comparing between A and B.
Set ``to_uint8`` if you expect the ``uint8`` results instead of ``bool``.
**Type Constraints**: (*bool*, *int8*, *uint8*, *int32*, *int64*, *float16*, *float32*, *float64*)
Parameters
----------
inputs : sequence of Tensor
The inputs, represent A and B respectively.
to_uint8 : bool
``True`` to convert to ``uint8`` results.
Returns
-------
Tensor
The comparing results.
"""
arguments = ParseArgs(locals())
return Tensor.CreateOperator('Compare', operation='LE', **arguments)
@OpSchema.ConvertConstantInputs()
@OpSchema.Inputs(2)
def Greater(inputs, to_uint8=False, **kwargs):
"""``Less`` comparing between A and B.
"""``Greater`` comparing between A and B.
Set ``to_uint8`` if you expect the ``uint8`` results instead of ``bool``.
**Type Constraints**: (*bool*, *int8*, *uint8*, *int32*, *int64*, *float16*, *float32*, *float64*)
Parameters
----------
inputs : sequence of Tensor
The inputs, represent A and B respectively.
to_uint8 : bool
``True`` to convert to ``uint8`` results.
Returns
-------
Tensor
The comparing results.
"""
arguments = ParseArgs(locals())
return Tensor.CreateOperator('Compare', operation='GT', **arguments)
@OpSchema.ConvertConstantInputs()
@OpSchema.Inputs(2)
def GreaterEqual(inputs, to_uint8=False, **kwargs):
"""``GreaterEqual`` comparing between A and B.
Set ``to_uint8`` if you expect the ``uint8`` results instead of ``bool``.
......@@ -111,4 +166,4 @@ def Greater(inputs, to_uint8=False, **kwargs):
"""
arguments = ParseArgs(locals())
return Tensor.CreateOperator('Compare', operation='GREATER', **arguments)
\ No newline at end of file
return Tensor.CreateOperator('Compare', operation='GE', **arguments)
\ No newline at end of file
Dragon/python/dragon/operators/rnn/rnn_param.py
......@@ -20,9 +20,8 @@ from .. import *
def RNNParamSet(
inputs, layer_id, param_id, param_type,
rnn_mode, input_size, hidden_size,
num_layers=1, num_directions=1, **kwargs
):
num_layers=1, num_directions=1, **kwargs):
arguments = ParseArgs(locals())
arguments['inputs'] = inputs[1]
arguments['existing_outputs'] = inputs[0]
return Tensor.CreateOperator(op_type='RNNParamSet', **arguments)
\ No newline at end of file
return Tensor.CreateOperator('RNNParamSet', **arguments)
\ No newline at end of file
Dragon/python/dragon/operators/rnn/rnn_wrapper.py
......@@ -13,24 +13,21 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy
import dragon
import warnings
import dragon as dg
import numpy as np
from dragon.core.tensor import Tensor
from dragon.core.tensor_utils import FromShape
from .rnn_param import RNNParamSet
from dragon.operators.rnn.rnn_param import RNNParamSet
class RNNBase(object):
"""A simple class wrapping general RNN ops.
"""A simple class wrapping general RNN ops."""
"""
def __init__(self,
mode, input_size, hidden_size, num_layers=1,
bidirectional=False, dropout=0, name=None
):
bidirectional=False, dropout=0, name=None):
eligible_rnn_modes = ('rnn_tanh', 'rnn_relu', 'lstm', 'gru')
if mode.lower() not in eligible_rnn_modes:
raise ValueError('Unknown rnn mode: {}.'
......@@ -54,26 +51,23 @@ class RNNBase(object):
elif self.mode == 'gru': gate_size = 3 * self.hidden_size
else: gate_size = self.hidden_size
# 1. Plan weights
self._matrix_weights = []; self._bias_weights = []
self._matrix_shape, self._bias_shape = [], []
for layer in range(self.num_layers):
for direction in range(self.num_directions):
layer_input_size = self.input_size if layer == 0 \
else self.hidden_size * self.num_directions
w_names = ['layer_{}/{}/{}'.format(layer, p, 'L' if direction == 0 else 'R')
for p in ('matrix_ih', 'matrix_hh', 'bias_ih', 'bias_hh')]
w_ih = Tensor(name=w_names[0], shape=[gate_size, layer_input_size])
w_hh = Tensor(name=w_names[1], shape=[gate_size, self.hidden_size])
b_ih = Tensor(name=w_names[2], shape=[gate_size,])
b_hh = Tensor(name=w_names[3], shape=[gate_size,])
w_ih_shape = [gate_size, layer_input_size]
w_hh_shape = [gate_size, self.hidden_size]
b_ih_shape, b_hh_shape = [gate_size], [gate_size]
# W (0 ~ 3), R (4 ~ 7)
self._matrix_weights.extend([w_ih, w_hh])
self._matrix_shape.extend([w_ih_shape, w_hh_shape])
# Bw (0 ~ 3), Br (4 ~ 7)
self._bias_weights.extend([b_ih, b_hh])
self._bias_shape.extend([b_ih_shape, b_hh_shape])
# 2. Compute total number of parameters
self._weights_count = 0
for w in self._matrix_weights + self._bias_weights:
self._weights_count += np.prod(w.shape)
for shape in self._matrix_shape + self._bias_shape:
self._weights_count += numpy.prod(shape)
# 3. Register the packed weights
self.weights = FromShape(shape=[self._weights_count],
......@@ -101,8 +95,8 @@ class RNNBase(object):
##############################################
def _uniform_init(self, shape, dtype='float32'):
stdv = 1.0 / np.sqrt(self.hidden_size)
return np.random.uniform(-stdv, stdv, shape).astype(dtype)
stdv = 1.0 / numpy.sqrt(self.hidden_size)
return numpy.random.uniform(-stdv, stdv, shape).astype(dtype)
def _orthogonal_init(self, shape, gain=1, dtype='float32'):
num_rows = 1
......@@ -110,16 +104,16 @@ class RNNBase(object):
num_cols = shape[-1]
flat_shape = (num_cols, num_rows) if num_rows < num_cols \
else (num_rows, num_cols)
W = np.random.randn(*flat_shape)
q, r = np.linalg.qr(W)
W = numpy.random.randn(*flat_shape)
q, r = numpy.linalg.qr(W)
# Make Q uniform
d = np.diag(r)
q *= np.sign(d)
d = numpy.diag(r)
q *= numpy.sign(d)
if num_rows < num_cols: q = q.T
return gain * q.reshape(shape).astype(dtype)
def _zero_init(self, shape, dtype='float32'):
return np.zeros(shape, dtype=dtype)
return numpy.zeros(shape, dtype=dtype)
##############################################
# #
......@@ -137,20 +131,19 @@ class RNNBase(object):
raise ValueError('Unknown param type: ' + type)
def _set_param(self, layer_id, param_id, param_type, param):
if not isinstance(param, Tensor):
if isinstance(param, np.ndarray):
paramT = Tensor('/tmp/rnn_param').Variable()
paramT.set_value(param)
param = paramT
else: raise ValueError('Excepted a tensor or numpy array.')
if isinstance(param, numpy.ndarray):
param_temp = dragon.Tensor.Ref('/tmp/rnn_param')
param_temp.set_value(param)
param = param_temp
else: raise ValueError('Excepted a numpy array.')
self.weights.expressions = dict() # Clear cached expressions
outputs = RNNParamSet([self.weights, param], layer_id, param_id, param_type,
rnn_mode=self.mode, input_size=self.input_size, hidden_size=self.hidden_size,
num_layers=self.num_layers, num_directions=self.num_directions)
for k, v in outputs.expressions.items(): dg.workspace.RunOperator(v)
for k, v in outputs.expressions.items(): dragon.workspace.RunOperator(v)
def _reset_params(self):
np.random.seed(dg.config.GetRandomSeed())
numpy.random.seed(dragon.config.GetRandomSeed())
if self.mode == 'lstm': num_gates = 4
elif self.mode == 'gru': num_gates = 3
else: num_gates = 1
......@@ -166,8 +159,8 @@ class RNNBase(object):
bias_init = getattr(self, '_{}_init'.format(bias_init))
pseudo_layer_id = layer * self.num_directions + direction
packed_id = pseudo_layer_id * 2 + int(param_id / num_gates)
matrix_shape = self._matrix_weights[packed_id].shape[:]
bias_shape = self._bias_weights[packed_id].shape[:]
matrix_shape = self._matrix_shape[packed_id][:]
bias_shape = self._bias_shape[packed_id][:]
matrix_shape[0] = bias_shape[0] = int(matrix_shape[0] / num_gates)
self._set_param(layer_id=pseudo_layer_id, param_id=param_id,
param_type='matrix', param=matrix_init(matrix_shape))
......@@ -202,6 +195,7 @@ class RNNBase(object):
if not self._init_params: self._reset_params()
arguments = {
'op_type': 'Recurrent',
'inputs': [x, self.weights] +
([hx] if hx else []) +
([cx] if cx else []),
......@@ -213,11 +207,11 @@ class RNNBase(object):
'dropout_ratio': self.dropout,
}
if required_cell: n_out = 3
elif required_hidden: n_out = 2
else: n_out = 1
if required_cell: num_outputs = 3
elif required_hidden: num_outputs = 2
else: num_outputs = 1
return Tensor.CreateOperator(num_outputs=n_out, op_type='Recurrent', **arguments)
return Tensor.CreateOperator(num_outputs=num_outputs, **arguments)
def __call__(self, *args, **kwargs):
return self.create(*args, **kwargs)
\ No newline at end of file
Dragon/python/dragon/ops.py
......@@ -24,7 +24,7 @@ from .operators import arithmetic as math_ops
from .operators import control_flow as control_flow_ops
from .operators import misc as misc_ops
from .operators import mpi as mpi_ops
from .operators import ndarray as array_ops
from .operators import array as array_ops
from .operators import norm as norm_ops
from .operators import recurrent as recurrent_ops
from .operators import contrib as contrib_ops
......@@ -137,12 +137,15 @@ ExpandDims = array_ops.ExpandDims
Squeeze = array_ops.Squeeze
Shape = array_ops.Shape
Arange = array_ops.Arange
Multinomial = array_ops.Multinomial
# Control Flow
Copy = control_flow_ops.Copy
Equal = control_flow_ops.Equal
Less = control_flow_ops.Less
Grater = control_flow_ops.Greater
LessEqual = control_flow_ops.LessEqual
Greater = control_flow_ops.Greater
GreaterEqual = control_flow_ops.GreaterEqual
# Misc
Cast = AsType = misc_ops.Cast
......
Dragon/python/dragon/updaters.py
......@@ -22,7 +22,7 @@ from __future__ import print_function
import pprint
import dragon.core.workspace as ws
from dragon.core import workspace
from dragon.core.tensor import Tensor
......@@ -93,7 +93,7 @@ class BaseUpdater(object):
defaults = self.__dict__.get('_defaults')
if item in defaults:
if self._registered:
return ws.FetchTensor(self._slot + '/' + item)
return workspace.FetchTensor(self._slot + '/' + item)
else: return defaults[item]
return self.__dict__[item]
......@@ -101,7 +101,7 @@ class BaseUpdater(object):
defaults = self.__dict__.get('_defaults')
if defaults is not None and key in defaults:
if self._registered:
ws.FeedTensor(self._slot + '/' + key, value,
workspace.FeedTensor(self._slot + '/' + key, value,
dtype='float32', force_cpu=True)
else:
self._defaults[key] = value
......@@ -111,7 +111,7 @@ class BaseUpdater(object):
def register_in_workspace(self):
if not self._registered:
for k, v in self._defaults.items():
ws.FeedTensor(self._slot + "/" + k, v,
workspace.FeedTensor(self._slot + "/" + k, v,
dtype='float32', force_cpu=True)
self._registered = True
if self._verbose:
......
Dragon/python/dragon/utils/vision/data_transformer.py
......@@ -13,7 +13,6 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import numpy as np
import numpy.random as npr
from multiprocessing import Process
......@@ -105,18 +104,8 @@ class DataTransformer(Process):
self._max_random_scale - self._min_random_scale) \
+ self._min_random_scale
if random_scale != 1.0:
if sys.version_info >= (3, 0):
im = cv2.resize(im, None, interpolation=cv2.INTER_LINEAR,
fx=random_scale, fy=random_scale)
else:
# Fuck Fuck Fuck opencv-python2, it always has a BUG
# that leads to duplicate cuDA handles created at gpu:0
new_shape = (
int(np.ceil(im.shape[1] * random_scale)),
int(np.ceil(im.shape[0] * random_scale)))
im = PIL.Image.fromarray(im)
im = im.resize(new_shape, PIL.Image.BILINEAR)
im = np.array(im)
im = cv2.resize(im, None, fx=random_scale,
fy=random_scale, interpolation=cv2.INTER_LINEAR)
# Padding
if self._padding > 0:
......
Dragon/python/dragon/vm/caffe/timer.py deleted 100644 → 0
# ------------------------------------------------------------
# Copyright (c) 2017-present, SeetaTech, Co.,Ltd.
#
# Licensed under the BSD 2-Clause License.
# You should have received a copy of the BSD 2-Clause License
# along with the software. If not, See,
#
# <https://opensource.org/licenses/BSD-2-Clause>
#
# Codes are based on:
#
# <https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/utils/timer.py>
#
# ------------------------------------------------------------
import time
class Timer(object):
"""A simple timer."""
def __init__(self):
self.total_time = 0.
self.calls = 0
self.start_time = 0.
self.diff = 0.
self.average_time = 0.
def tic(self):
self.start_time = time.time()
def toc(self, average=True):
self.diff = time.time() - self.start_time
self.total_time += self.diff
self.calls += 1
self.average_time = self.total_time / self.calls
if average:
return self.average_time
else:
return self.diff
Dragon/python/dragon/vm/onnx/helper.py
......@@ -89,7 +89,7 @@ def native_run_graph(graph_def, inputs, initializer, init_func=None):
# Create an anonymous workspace
ws = Workspace()
with dg.workspace_scope(ws.name):
with dg.ws_scope(ws.name):
# Register all the initializer before feeding them
for name in initializer:
dg.Tensor(name=name).Variable()
......
Dragon/python/dragon/vm/onnx/workspace.py
......@@ -27,7 +27,7 @@ class Workspace(object):
def __getattr__(self, attr):
def f(*args, **kwargs):
with dg.workspace_scope(self.name, ):
with dg.ws_scope(self.name, ):
return getattr(dg.workspace, attr)(*args, **kwargs)
return f
......
Dragon/python/dragon/vm/tensorflow/framework/ops.py
......@@ -290,7 +290,7 @@ class _DefaultGraphStack(_DefaultStack):
@tf_contextlib.contextmanager
def get_controller(self, default):
with super(_DefaultGraphStack, self).get_controller(default) as g:
with dragon.workspace_scope(g._workspace):
with dragon.ws_scope(g._workspace):
yield g
......
Dragon/python/dragon/vm/tensorflow/training/learning_rate_decay.py
......@@ -121,7 +121,6 @@ class _CosineDecayRestarts(_DecayBase):
def run(self, inputs, outputs):
gs = self.get(inputs[0])
global_step = min(gs - self.last_steps, self.decay_steps)
print(global_step, self.decay_steps)
cosine_decay = 0.5 * (1 + math.cos(math.pi * global_step / self.decay_steps))
decayed = (1. - self.alpha) * cosine_decay + self.alpha
new_lr = self.learning_rate * decayed
......
Dragon/python/dragon/vm/theano/compile/function.py
......@@ -178,12 +178,12 @@ def GraphDef_Device(graph_def):
"""
from dragon.config import option
if option['device'] is not 'None':
supports = {'CPU': 0, 'CUDA': 1, 'CNML': 2}
supports = {'cpu': 0, 'cuda': 1, 'cnml': 2}
device_option = pb.DeviceOption()
device_option.device_type = supports[option['device']]
device_option.device_id = option['device_id']
device_option.random_seed = option['random_seed']
if option['device'] == 'CUDA':
if option['device'] == 'cuda':
if option['use_cudnn']: device_option.engine = 'CUDNN'
graph_def.device_option.CopyFrom(device_option)
......
Dragon/python/dragon/vm/torch/__init__.py
......@@ -14,17 +14,16 @@ from __future__ import division
from __future__ import print_function
# Import Dynamic Methods
import dragon.vm.torch.ops.builtin
import dragon.vm.torch.ops.tensor
# Import Core Methods
from dragon.vm.torch.tensor import *
from dragon.vm.torch.tensor_uitls import from_numpy
from dragon.vm.torch.c_api import Size
from dragon.vm.torch.c_api import Size, from_numpy
from dragon.vm.torch.serialization import save, load
# Import Subpackages
import dragon.vm.torch.cuda
from dragon.vm.torch.ops import *
from dragon.vm.torch.ops.builtin import *
from dragon.vm.torch.autograd import *
import dragon.vm.torch.nn
import dragon.vm.torch.optim
......
Dragon/python/dragon/vm/torch/c_api.py
......@@ -14,6 +14,10 @@ from __future__ import division
from __future__ import print_function
import copy
import numpy
import importlib
from dragon.core import mapping, tensor_utils
class Size(tuple):
......@@ -27,30 +31,68 @@ class Size(tuple):
return 'torch.Size([{}])'.format(', '.join([str(s) for s in self]))
class Context(object):
def __init__(self, device_type='CPU', device_id=0):
self._device_type = device_type
self._device_id = device_id
class device(object):
def __init__(self, type='cpu', index=0):
self.type, self.index = type, index
@property
def device_type(self):
return self._device_type
def copy(self):
return copy.deepcopy(self)
@device_type.setter
def device_type(self, value):
self._device_type = value
def __eq__(self, other):
return self.type == other.type and \
self.index == other.index
@property
def device_id(self):
return self._device_id
def __str__(self):
return '{}:{}'.format(self.type, self.index)
@device_id.setter
def device_id(self, value):
self._device_id = value
def __repr__(self):
return 'device(type={}, index={})'.format(self.type, self.index)
def copy(self):
return copy.deepcopy(self)
def __str__(self):
return '{}:{}'.format(
self._device_type, self._device_id)
\ No newline at end of file
def from_numpy(data):
"""Create a tensor from the given numpy array.
Parameters
----------
data : numpy.ndarray
The array with various data type.
Return
------
dragon.vm.torch.Tensor
The torch tensor.
"""
if not isinstance(data, numpy.ndarray):
raise TypeError('The data should be a numpy.ndarray.')
if str(data.dtype) not in mapping.TENSOR_TYPE_TO_TORCH_TENSOR:
raise ValueError('Unsupported type({}) to torch tensor.'.format(data.dtype))
module = importlib.import_module('dragon.vm.torch.tensor')
return getattr(module, mapping.TENSOR_TYPE_TO_TORCH_TENSOR[str(data.dtype)])(data)
def from_dragon(tensor, own_storage=False):
"""Create a torch tensor from a existing dragon tensor.
Set ``own_storage`` as ``True`` for automatically releasing the storage.
Parameters
----------
tensor : Tensor or str
The dragon tensor.
own_storage : boolean
Whether to release storage during deconstructing.
Returns
-------
dragon.vm.torch.Tensor
The torch tensor.
"""
storage = tensor_utils.GetStorage(tensor)
if storage is None: return None
module = importlib.import_module('dragon.vm.torch.tensor')
T = getattr(module, mapping.TENSOR_TYPE_TO_TORCH_TENSOR[storage.dtype])()
T._storage, T._own_storage, T._tensor = storage, own_storage, tensor
T._device = device(*storage.device)
return T
\ No newline at end of file
Dragon/python/dragon/vm/torch/execution.py
......@@ -32,10 +32,10 @@ import dragon as dg
import dragon.import_c_api as C
from dragon.config import option
from .c_api import Context
from .c_api import device as _Device
from .jit import JITRecorder, is_jit_enforced
from .autograd.grad_mode import is_grad_enabled
from .tensor import RuntimeTensor
from .tensor import _RuntimeTensor
from .pool import TensorPool
......@@ -66,9 +66,9 @@ def RunOperator(
outputs_name.append(output)
else:
# Legacy mode, a torch tensor is excepted
if isinstance(output, Context):
if isinstance(output, _Device):
name = TensorPool.get('${JOIN}' if requires_grad else '${DETACH}')
outputs[ix] = RuntimeTensor(name, ctx=output)
outputs[ix] = _RuntimeTensor(name, device=output)
outputs_name.append(outputs[ix].name)
# Key + Inputs + Outputs => Op
......
Dragon/python/dragon/vm/torch/module.py
......@@ -19,16 +19,15 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy
import dragon
import warnings
from collections import OrderedDict
import numpy as np
import dragon as dg
import dragon.core.proto_utils as pb_utils
import dragon.core.logging as logging
from dragon.core import proto_utils, logging
from dragon.core.scope import get_default_name_scope
from dragon.vm.torch.c_api import Context
from dragon.vm.torch.c_api import device as Device
from dragon.vm.torch.tensor import Tensor, Parameter
from dragon.vm.torch.execution import RunOperator
from dragon.vm.torch.environ import add_submodule, get_module_name
......@@ -39,8 +38,9 @@ class Module(object):
self._modules = OrderedDict()
self._parameters = OrderedDict()
self._buffers = OrderedDict()
self._module_key = self._def = None
self._ctx = Context()
self._device = Device()
self._module_key = None
self._module_def = None
self.training = True
def __getattr__(self, item):
......@@ -106,20 +106,8 @@ class Module(object):
module.state_dict(destination, prefix + name + '.', to_numpy=to_numpy)
return destination
def _load_state_dict_key_mismatch(self, full_name, name, is_missing): pass
def load_state_dict(self, state_dict, strict=True, verbose=True):
if verbose: logging.info('Load the state dict.')
def submodule_key_mismatch(full_name, is_missing):
module = self
names = full_name.split(".")
for module_name in names[:-1]:
if module_name in module._modules:
module = module._modules[module_name]
else:
return
module._load_state_dict_key_mismatch(full_name, names[-1], is_missing)
unexpected = []
own_state = self.state_dict()
for name, param in state_dict.items():
......@@ -133,28 +121,24 @@ class Module(object):
', '.join([str(d) for d in param_shape])))
if isinstance(param, Tensor):
own_state[name].copy_(param)
elif isinstance(param, np.ndarray):
dg.tensor_utils.SetPyArray(own_state[name], param)
elif isinstance(param, numpy.ndarray):
dragon.tensor_utils.SetPyArray(own_state[name], param)
else:
raise ValueError('Excepted the type of source state is either '
'dragon.vm.torch.Tensor or numpy.ndarray, got {}.'.format(type(param)))
if verbose:
logging.info('Tensor({}) loaded, Size: ({})'.format(name,
', '.join([str(d) for d in param_shape])))
else:
unexpected.append(name)
if strict:
missing = set(own_state.keys()) - set(state_dict.keys())
# pass the mismatch info to submodules so that they have a chance to
# raise a custom class-specific error
for name in unexpected:
submodule_key_mismatch(name, False)
for name in missing:
submodule_key_mismatch(name, True)
error_msg = ''
if len(unexpected) > 0:
error_msg += 'Unexpected key(s) in state_dict: {}. '.format(
error_msg += 'Unexpected key(s) in state_dict: {}.\n'.format(
', '.join('"{}"'.format(k) for k in unexpected))
if len(missing) > 0:
error_msg += 'Missing key(s) in state_dict: {}. '.format(
error_msg += 'Missing key(s) in state_dict: {}.'.format(
', '.join('"{}"'.format(k) for k in missing))
if len(error_msg) > 0:
raise KeyError(error_msg)
......@@ -201,7 +185,7 @@ class Module(object):
add_submodule(module, name_v2 if name_v2 else name)
def __call__(self, *args, **kwargs):
with dg.name_scope(get_module_name(self)):
with dragon.name_scope(get_module_name(self)):
return self.forward(*args, **kwargs)
def forward(self, *inputs, **kwargs):
......@@ -209,7 +193,10 @@ class Module(object):
def name_scope(self, remove_separator=True):
scope = get_default_name_scope()
if remove_separator and scope[-1] == '/': scope = scope[:-1]
if remove_separator and \
len(scope) > 0 and \
scope[-1] == '/':
scope = scope[:-1]
return scope
def children(self):
......@@ -281,17 +268,17 @@ class Module(object):
return self
def cpu(self):
self._ctx = Context()
# Remove key and op to re-create a one with new ctx
self._module_key = self._def = None
self._device = Device()
# Remove key and op to re-create a one with new device
self._module_key = self._module_def = None
return self._apply(lambda t: t.cpu(),
lambda m: m.cpu())
def cuda(self, device=None):
if device is None: device = dg.config.GetGPU()
self._ctx = Context('CUDA', device)
# Remove key and op to re-create a one with new ctx
self._module_key = self._def = None
if device is None: device = dragon.config.GetGPU()
self._device = Device('cuda', device)
# Remove key and op to re-create a one with new device
self._module_key = self._module_def = None
return self._apply(lambda t: t.cuda(device),
lambda m: m.cuda(device))
......@@ -312,7 +299,7 @@ class Module(object):
def _gen_module_key(self):
self._module_key = '{}{}'.format(
self.name_scope(False), self._ctx)
self.name_scope(False), self._device)
@property
def module_key(self):
......@@ -320,37 +307,37 @@ class Module(object):
self._gen_module_key()
return self._module_key
def _gen_def(self):
self._def = pb_utils.MakeCXXOperatorDef(
name='runtime',
uid=self.module_key,
op_type=self.op_meta['op_type'],
device_option=pb_utils.GetDeviceOption(
self._ctx.device_type,
self._ctx.device_id,
engine='CUDNN'),
**self.op_meta['arguments']
)
def register_op(self): pass
def _gen_module_def(self):
self._module_def = \
proto_utils.MakeCXXOperatorDef(
name='runtime',
uid=self.module_key,
op_type=self.op_meta['op_type'],
device_option=proto_utils.
GetDeviceOption(
self._device.type,
self._device.index,
engine='CUDNN'),
**self.op_meta['arguments']
)
def register_op(self):
pass
def register_output(self):
return self._ctx.copy()
return self._device.copy()
def unify_devices(self, inputs):
for ix, t in enumerate(inputs):
if t._ctx.device_type != self._ctx.device_type or \
t._ctx.device_id != self._ctx.device_id:
print(self._ctx, self.module_key)
raise ValueError('Module({}) is defined at {}:{}, '
'\nFound Input({}) is at {}:{}.'.format(
self.name_scope(True),
self._ctx.device_type, self._ctx.device_id,
ix, t._ctx.device_type, t._ctx.device_id))
if t._device != self._device:
raise ValueError('Module({}) is defined at {}, '
'\nFound Input({}) is at {}.'.format(
self.name_scope(True),
self._device, ix, t._device))
def run(self, inputs, outputs, auto_grad=True, callback=None):
if self._def is None: self._gen_def()
meta = (self.module_key, self._def)
if self._module_def is None: self._gen_module_def()
meta = (self.module_key, self._module_def)
return RunOperator(
inputs, outputs, meta,
auto_grad=auto_grad,
......@@ -366,7 +353,7 @@ class Module(object):
return self.train(False)
def zero_grad(self):
raise NotImplementedError('Deprecated. '
warnings.warn('Module.zero_grad() is deprecated. '
'Use ``torch.optim.Optimizer.zero_grad()`` instead.')
def extra_repr(self):
......
Dragon/python/dragon/vm/torch/nn/__init__.py
......@@ -21,14 +21,13 @@ from dragon.vm.torch.tensor import Parameter
from .modules.conv import Conv2d, ConvTranspose2d
from .modules.depthwise_conv import DepthwiseConv2d
from .modules.pooling import MaxPool2d, AvgPool2d
from .modules.linear import Linear
from .modules.activation import (
ReLU, LeakyReLU, ELU, SELU,
Tanh, Sigmoid, Softmax,
)
from .modules.linear import Linear
from .modules.loss import (
BCEWithLogitsLoss,
NLLLoss, CrossEntropyLoss,
......@@ -36,11 +35,16 @@ from .modules.loss import (
SigmoidFocalLoss, SoftmaxFocalLoss,
)
from .modules.rnn import (
RNNBase, RNNCellBase,
RNN, LSTM, GRU,
LSTMCell,
)
from .modules.container import Container, Sequential, ModuleList
from .modules.batchnorm import BatchNorm1d, BatchNorm2d, BatchNorm3d
from .modules.groupnorm import GroupNorm1d, GroupNorm2d, GroupNorm3d
from .modules.affine import Affine
from .modules.dropout import Dropout, Dropout2d, Dropout3d
from .modules.dropblock import DropBlock2d
from .modules.rnn import RNNBase, RNN, LSTM, GRU
from . import init
\ No newline at end of file
Dragon/python/dragon/vm/torch/nn/modules/affine.py
......@@ -14,7 +14,7 @@ from __future__ import division
from __future__ import print_function
from dragon.vm.torch.nn import Module, Parameter
from dragon.vm.torch.ops.creation import zeros, ones
from dragon.vm.torch.ops.builtin import zeros, ones
class Affine(Module):
......
Dragon/python/dragon/vm/torch/nn/modules/batchnorm.py
......@@ -15,7 +15,7 @@ from __future__ import print_function
from dragon.vm.torch.tensor import Tensor
from dragon.vm.torch.nn import Module, Parameter
from dragon.vm.torch.ops.creation import zeros, ones
from dragon.vm.torch.ops.builtin import zeros, ones
from dragon.vm.torch.module import RunOperator
......@@ -62,10 +62,10 @@ class _BatchNorm(Module):
'track_running_stats={track_running_stats}'.format(**self.__dict__)
def make_meta_from_phase(self, phase):
"""Make the custom meta by referring the phase and ctx.
"""Make the custom meta by referring the phase and device.
We extend this method as the original module can only
detect the mutation of ctx(i.e. cpu -> cuda),
detect the mutation of device(i.e. cpu -> cuda),
but not the (train -> test).
"""
......@@ -75,8 +75,8 @@ class _BatchNorm(Module):
self._module_key += '/{}'.format(phase)
self.op_meta['arguments']['use_stats'] = 0 \
if phase == 'TRAIN' else 1
self._gen_def()
self.op_metas[phase] = (self._module_key, self._def)
self._gen_module_def()
self.op_metas[phase] = (self._module_key, self._module_def)
if self._module_key is None:
# Init or Context has changed
......
Dragon/python/dragon/vm/torch/nn/modules/groupnorm.py
......@@ -15,7 +15,7 @@ from __future__ import print_function
from dragon.vm.torch.tensor import Tensor
from dragon.vm.torch.nn import Module, Parameter
from dragon.vm.torch.ops.creation import zeros, ones
from dragon.vm.torch.ops.builtin import zeros, ones
class _GroupNorm(Module):
......
Dragon/python/dragon/vm/torch/nn/modules/rnn.py
......@@ -17,16 +17,17 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import warnings
import numbers
import numpy as np
import dragon as dg
import numpy
import dragon
from dragon.vm.torch.tensor import Tensor
from dragon.vm.torch.nn import Module, Parameter
from dragon.operators.rnn.rnn_param import RNNParamSet
from dragon.vm.torch.module import RunOperator
from dragon.vm.torch.autograd.grad_mode import is_grad_enabled
from dragon.vm.torch.ops.builtin import zeros as Zeros, xw_plus_b
class RNNBase(Module):
......@@ -49,8 +50,8 @@ class RNNBase(Module):
if not bias:
raise NotImplementedError('Bias is required.')
if not isinstance(dropout, numbers.Number) or not 0 <= dropout <= 1 or \
isinstance(dropout, bool):
if not isinstance(dropout, numbers.Number) or \
not 0 <= dropout <= 1 or isinstance(dropout, bool):
raise ValueError("dropout should be a number in range [0, 1] "
"representing the probability of an element being "
"zeroed")
......@@ -83,8 +84,8 @@ class RNNBase(Module):
_ = self.module_key
self._module_key += '/{}'.format(phase)
self.op_meta['arguments']['phase'] = phase
self._gen_def()
self.op_metas[phase] = (self._module_key, self._def)
self._gen_module_def()
self.op_metas[phase] = (self._module_key, self._module_def)
if self._module_key is None:
# Init or Context has changed
......@@ -106,45 +107,37 @@ class RNNBase(Module):
self.unify_devices(inputs)
outputs = [self.register_output() for _ in range(2)]
requires_grad = False
for input in inputs:
if input.requires_grad: requires_grad = True
requires_grad = requires_grad and is_grad_enabled()
meta = self.make_meta_from_phase(
'TRAIN' if requires_grad else 'TEST')
meta = self.make_meta_from_phase(
'TRAIN' if self.training else 'TEST')
return RunOperator(inputs, outputs, meta)
def _plan_params(self):
if self.mode == 'lstm': gate_size = 4 * self.hidden_size
elif self.mode == 'gru': gate_size = 3 * self.hidden_size
else: gate_size = self.hidden_size
# 1. plan weights
self._matrix_weights = []; self._bias_weights = []
# 1. Plan weights
self._matrix_shape, self._bias_shape = [], []
for layer in range(self.num_layers):
for direction in range(self.num_directions):
layer_input_size = self.input_size if layer == 0 \
else self.hidden_size * self.num_directions
w_names = ['layer_{}/{}/{}'.format(layer, p, 'L' if direction == 0 else 'R')
for p in ('matrix_ih', 'matrix_hh', 'bias_ih', 'bias_hh')]
w_ih = dg.Tensor(name=w_names[0], shape=[gate_size, layer_input_size])
w_hh = dg.Tensor(name=w_names[1], shape=[gate_size, self.hidden_size])
b_ih = dg.Tensor(name=w_names[2], shape=[gate_size,])
b_hh = dg.Tensor(name=w_names[3], shape=[gate_size,])
w_ih_shape = [gate_size, layer_input_size]
w_hh_shape = [gate_size, self.hidden_size]
b_ih_shape, b_hh_shape = [gate_size], [gate_size]
# W (0 ~ 3), R (4 ~ 7)
self._matrix_weights.extend([w_ih, w_hh])
self._matrix_shape.extend([w_ih_shape, w_hh_shape])
# Bw (0 ~ 3), Br (4 ~ 7)
self._bias_weights.extend([b_ih, b_hh])
self._bias_shape.extend([b_ih_shape, b_hh_shape])
# 2. compute total number of parameters
# 2. Compute total number of parameters
self._weights_count = 0
for w in self._matrix_weights + self._bias_weights:
self._weights_count += np.prod(w.shape)
for shape in self._matrix_shape + self._bias_shape:
self._weights_count += numpy.prod(shape)
# 3. register the packed weights
# 3. Register the packed weights
self.weights = Parameter(Tensor(int(self._weights_count)))
# 4. create the initialization grids
# 4. Create the initialization grids
if self.mode == 'lstm': num_params_per_layer = 8
elif self.mode == 'gru': num_params_per_layer = 6
else: num_params_per_layer = 2
......@@ -159,7 +152,7 @@ class RNNBase(Module):
for _ in range(self.num_layers)
]
# 5. set the init flag
# 5. Set the init flag
self._init_params = False
##############################################
......@@ -169,8 +162,8 @@ class RNNBase(Module):
##############################################
def _uniform_init(self, shape, dtype='float32'):
stdv = 1.0 / np.sqrt(self.hidden_size)
return np.random.uniform(-stdv, stdv, shape).astype(dtype)
stdv = 1.0 / numpy.sqrt(self.hidden_size)
return numpy.random.uniform(-stdv, stdv, shape).astype(dtype)
def _orthogonal_init(self, shape, gain=1, dtype='float32'):
num_rows = 1
......@@ -178,16 +171,16 @@ class RNNBase(Module):
num_cols = shape[-1]
flat_shape = (num_cols, num_rows) if num_rows < num_cols \
else (num_rows, num_cols)
W = np.random.randn(*flat_shape)
q, r = np.linalg.qr(W)
W = numpy.random.randn(*flat_shape)
q, r = numpy.linalg.qr(W)
# Make Q uniform
d = np.diag(r)
q *= np.sign(d)
d = numpy.diag(r)
q *= numpy.sign(d)
if num_rows < num_cols: q = q.T
return gain * q.reshape(shape).astype(dtype)
def _zero_init(self, shape, dtype='float32'):
return np.zeros(shape, dtype=dtype)
return numpy.zeros(shape, dtype=dtype)
##############################################
# #
......@@ -205,20 +198,19 @@ class RNNBase(Module):
raise ValueError('Unknown param type: ' + type)
def _set_param(self, layer_id, param_id, param_type, param):
if not isinstance(param, Tensor):
if isinstance(param, np.ndarray):
paramT = dg.Tensor('/tmp/rnn_param').Variable()
paramT.set_value(param)
param = paramT
else: raise ValueError('Excepted a tensor or numpy array.')
if isinstance(param, numpy.ndarray):
param_temp = dragon.Tensor.Ref('/tmp/rnn_param')
param_temp.set_value(param)
param = param_temp
else: raise ValueError('Excepted a numpy array.')
W = self.weights.dragon()
outputs = RNNParamSet([W, param], layer_id, param_id, param_type,
rnn_mode=self.mode, input_size=self.input_size, hidden_size=self.hidden_size,
num_layers=self.num_layers, num_directions=self.num_directions)
for k, v in outputs.expressions.items(): dg.workspace.RunOperator(v)
for k, v in outputs.expressions.items(): dragon.workspace.RunOperator(v)
def _reset_params(self):
np.random.seed(dg.config.GetRandomSeed())
numpy.random.seed(dragon.config.GetRandomSeed())
if self.mode == 'lstm': num_gates = 4
elif self.mode == 'gru': num_gates = 3
else: num_gates = 1
......@@ -233,8 +225,8 @@ class RNNBase(Module):
bias_init = getattr(self, '_{}_init'.format(bias_init))
pseudo_layer_id = layer * self.num_directions + direction
packed_id = pseudo_layer_id * 2 + int(param_id / num_gates)
matrix_shape = self._matrix_weights[packed_id].shape[:]
bias_shape = self._bias_weights[packed_id].shape[:]
matrix_shape = self._matrix_shape[packed_id][:]
bias_shape = self._bias_shape[packed_id][:]
matrix_shape[0] = bias_shape[0] = int(matrix_shape[0] / num_gates)
self._set_param(layer_id=pseudo_layer_id, param_id=param_id,
param_type='matrix', param=matrix_init(matrix_shape))
......@@ -375,4 +367,57 @@ class GRU(RNNBase):
"""
super(GRU, self).__init__('gru', input_size, hidden_size,
num_layers, bias, batch_first, dropout, bidirectional)
\ No newline at end of file
num_layers, bias, batch_first, dropout, bidirectional)
class RNNCellBase(Module):
def __init__(self, input_size, hidden_size, bias, num_chunks):
super(RNNCellBase, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.bias = bias
self.weight_ih = Parameter(Tensor(num_chunks * hidden_size, input_size))
self.weight_hh = Parameter(Tensor(num_chunks * hidden_size, hidden_size))
if bias:
self.bias_ih = Parameter(Tensor(num_chunks * hidden_size))
self.bias_hh = Parameter(Tensor(num_chunks * hidden_size))
else:
self.register_parameter('bias_ih', None)
self.register_parameter('bias_hh', None)
self.reset_parameters()
def extra_repr(self):
s = '{input_size}, {hidden_size}'
if 'bias' in self.__dict__ and self.bias is not True:
s += ', bias={bias}'
if 'nonlinearity' in self.__dict__ and self.nonlinearity != "tanh":
s += ', nonlinearity={nonlinearity}'
return s.format(**self.__dict__)
def reset_parameters(self):
stdv = 1.0 / math.sqrt(self.hidden_size)
for weight in self.parameters():
weight.data.uniform_(-stdv, stdv)
class LSTMCell(RNNCellBase):
def __init__(self, input_size, hidden_size, bias=True):
super(LSTMCell, self).__init__(
input_size, hidden_size, bias, num_chunks=4)
self.register_op()
def register_op(self):
self.op_meta = {'op_type': 'LSTMCell', 'arguments': {}}
def forward(self, input, hx=None):
if hx is None:
zeros = Zeros(
input.size(0), self.hidden_size,
dtype=input.dtype, device=input.device)
hx = (zeros, zeros)
wx = xw_plus_b(input, self.weight_ih, self.bias_ih)
wh = xw_plus_b(hx[0], self.weight_hh, self.bias_hh)
inputs = [wx + wh, hx[1]]
self.unify_devices(inputs)
outputs = [self.register_output() for _ in range(2)]
return self.run(inputs, outputs)
\ No newline at end of file
Dragon/python/dragon/vm/torch/ops/__init__.py
......@@ -7,31 +7,4 @@
#
# <https://opensource.org/licenses/BSD-2-Clause>
#
# ------------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from .creation import (
zeros, zeros_like,
ones, ones_like,
rand, randn,
)
from .arithmetic import (
add, sub, mul, div,
log, exp, sqrt,
maximum, minimum, clamp,
)
from .array import (
squeeze, unsqueeze,
sum, mean, argmin, argmax, max, min, topk,
cat, gather, narrow, one_hot,
)
from .vision import (
nn_resize, bilinear_resize,
roi_pool, roi_align,
)
\ No newline at end of file
# ------------------------------------------------------------
\ No newline at end of file
Dragon/python/dragon/vm/torch/ops/arithmetic.py deleted 100644 → 0
# ------------------------------------------------------------
# Copyright (c) 2017-present, SeetaTech, Co.,Ltd.
#
# Licensed under the BSD 2-Clause License.
# You should have received a copy of the BSD 2-Clause License
# along with the software. If not, See,
#
# <https://opensource.org/licenses/BSD-2-Clause>
#
# ------------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from dragon.vm.torch.tensor import Tensor
from dragon.vm.torch.ops.primitive import MakeContext, WrapScalar
from dragon.vm.torch.ops.factory import get_module
from dragon.vm.torch.ops.modules.arithmetic import (
Fundamental, Log, Exp, Sqrt,
Maximum, Minimum, Clamp,
)
def _fundamental(input, value, op='Add', out=None):
if not isinstance(value, Tensor):
value = WrapScalar(value, input.dtype, input._ctx)
ctx = MakeContext(inputs=[input, value])
key = '{}/{}'.format(op, ctx)
module = get_module(Fundamental, key, ctx, op_type=op)
return module.forward(input, value, out)
def _rfundamental(input, value, op='RAdd', out=None):
if not isinstance(value, Tensor):
value = WrapScalar(value, input.dtype, input._ctx)
ctx = MakeContext(inputs=[input, value])
key = '{}/{}'.format(op, ctx)
module = get_module(Fundamental, key, ctx, op_type=op)
return module.forward(value, input, out)
def _maximum(input, other, out=None):
if not isinstance(input, Tensor):
input = WrapScalar(input, other.dtype, other._ctx)
elif not isinstance(other, Tensor):
other = WrapScalar(other, input.dtype, input._ctx)
ctx = MakeContext(inputs=[input])
key = 'Maximum/{}'.format(ctx)
module = get_module(Maximum, key, ctx)
return module.forward(input, other, out)
def _minimum(input, other, out=None):
if not isinstance(input, Tensor):
input = WrapScalar(input, other.dtype, other._ctx)
elif not isinstance(other, Tensor):
other = WrapScalar(other, input.dtype, input._ctx)
ctx = MakeContext(inputs=[input])
key = 'Minimum/{}'.format(ctx)
module = get_module(Minimum, key, ctx)
return module.forward(input, other, out)
def _clamp(input, min=None, max=None, out=None):
ctx = MakeContext(inputs=[input])
key = 'Clamp/{}/min:{}/max:{}'.format(ctx, min, max)
module = get_module(Clamp, key, ctx, min=min, max=max)
return module.forward(input, out)
def _exp(input, out=None):
ctx = MakeContext(inputs=[input])
key = 'Exp/{}'.format(ctx)
module = get_module(Exp, key, ctx)
return module.forward(input, out)
def _log(input, out=None):
ctx = MakeContext(inputs=[input])
key = 'Log/{}'.format(ctx)
module = get_module(Log, key, ctx)
return module.forward(input, out)
def _sqrt(input, out=None):
ctx = MakeContext(inputs=[input])
key = 'Sqrt/{}'.format(ctx)
module = get_module(Sqrt, key, ctx)
return module.forward(input, out)
def add(input, value, out=None):
"""Add the ``input`` and ``value`` into the output tensor.
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
value : dragon.vm.torch.Tensor, number
The value tensor.
out : dragon.vm.torch.Tensor, optional
The output tensor.
Returns
-------
dragon.vm.torch.Tensor
The output tensor.
"""
return _fundamental(input, value, out=out, op='Add')
def sub(input, value, out=None):
"""Subtract the ``input`` and ``value`` into the output tensor.
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
value : dragon.vm.torch.Tensor or number
The value tensor.
out : dragon.vm.torch.Tensor, optional
The output tensor.
Returns
-------
torch.Tensor
The output tensor.
"""
return _fundamental(input, value, out=out, op='Sub')
def mul(input, value, out=None):
"""Multiply the ``input`` and ``value`` into the output tensor.
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
value : dragon.vm.torch.Tensor or number
The value tensor.
out : dragon.vm.torch.Tensor, optional
The output tensor.
Returns
-------
dragon.vm.torch.Tensor
The output tensor.
"""
return _fundamental(input, value, out=out, op='Mul')
def div(input, value, out=None):
"""Divide the ``input`` and ``value`` into the output tensor.
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
value : dragon.vm.torch.Tensor or number
The value tensor.
out : dragon.vm.torch.Tensor, optional
The output tensor.
Returns
-------
dragon.vm.torch.Tensor
The output tensor.
"""
return _fundamental(input, value, out=out, op='Div')
def maximum(input, other, out=None):
"""Return the max value of given two tensors.
Parameters
----------
input : dragon.vm.torch.Tensor or number
The input tensor.
other : dragon.vm.torch.Tensor or number
The input tensor.
out : dragon.vm.torch.Tensor, optional
The output tensor.
Returns
-------
dragon.vm.torch.Tensor
The output tensor.
"""
return _maximum(input, other, out)
def minimum(input, other, out=None):
"""Return the min value of given two tensors.
Parameters
----------
input : dragon.vm.torch.Tensor or number
The input tensor.
other : dragon.vm.torch.Tensor or number
The input tensor.
out : dragon.vm.torch.Tensor, optional
The output tensor.
Returns
-------
dragon.vm.torch.Tensor
The output tensor.
"""
return _minimum(input, other, out)
def clamp(input, min=None, max=None, out=None):
"""Clamp all elements into the range [min, max].
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
min : number, optional
The min value.
max : number, optional
The max value.
out : dragon.vm.torch.Tensor, optional
The output tensor.
Returns
-------
dragon.vm.torch.Tensor
The output tensor.
"""
return _clamp(input, min, max, out)
def log(input, out=None):
"""Compute the natural logarithm of input.
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
out : dragon.vm.torch.Tensor, optional
The output tensor.
Returns
-------
dragon.vm.torch.Tensor
The output tensor.
"""
return _log(input, out)
def exp(input, out=None):
"""Compute the exponential of input.
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
out : dragon.vm.torch.Tensor, optional
The output tensor.
Returns
-------
dragon.vm.torch.Tensor
The output tensor.
"""
return _exp(input, out)
def sqrt(input, out=None):
"""Compute the square-root of input.
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
out : dragon.vm.torch.Tensor, optional
The output tensor.
Returns
-------
dragon.vm.torch.Tensor
The output tensor.
"""
return _sqrt(input, out)
\ No newline at end of file
Dragon/python/dragon/vm/torch/ops/array.py deleted 100644 → 0
# ------------------------------------------------------------
# Copyright (c) 2017-present, SeetaTech, Co.,Ltd.
#
# Licensed under the BSD 2-Clause License.
# You should have received a copy of the BSD 2-Clause License
# along with the software. If not, See,
#
# <https://opensource.org/licenses/BSD-2-Clause>
#
# ------------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from dragon.vm.torch.ops.primitive import MakeContext
from dragon.vm.torch.ops.factory import get_module
from dragon.vm.torch.ops.modules.init import (
Fill, RandomUniform, RandomNormal,
)
from dragon.vm.torch.ops.modules.array import (
Reshape, Squeeze, UnSqueeze, Permute,
Indexing, Repeat, Concat, Gather,
Reduce, ArgReduce, OneHot,
)
def reshape(input, shape, shape_like=None):
if shape_like is not None: shape = shape_like.shape
ctx = MakeContext(inputs=[input]); n_dim = len(shape)
key = 'Reshape/{}/n_dim:{}'.format(ctx, n_dim)
module = get_module(Reshape, key, ctx, n_dim=n_dim)
return module.forward(input, shape)
def squeeze(input, dim=None, out=None):
ctx = MakeContext(inputs=[input])
key = 'Squeeze/{}/dim:{}'.format(ctx, dim if dim else 'None')
module = get_module(Squeeze, key, ctx, dim=dim)
return module.forward(input, out=out)
def unsqueeze(input, dim, out=None):
ctx = MakeContext(inputs=[input])
key = 'Unsqueeze/{}/dim:{}'.format(ctx, dim if dim else 'None')
module = get_module(UnSqueeze, key, ctx, dim=dim)
return module.forward(input, out=out)
def _permute(input, perm=None):
ctx = MakeContext(inputs=[input]); n_perm = len(perm) if perm else 0
key = 'Permute/{}/n_perm:{}'.format(ctx, n_perm)
module = get_module(Permute, key, ctx, n_perm=n_perm)
return module.forward(input, perm)
def _repeat(input, times):
ctx = MakeContext(inputs=[input]); n_times = len(times)
key = 'Repeat/{}/n_times:{}'.format(ctx, n_times)
module = get_module(Repeat, key, ctx, n_times=n_times)
return module.forward(input, times)
def _fill(input, shape, value):
ctx = MakeContext(inputs=[input]); n_dim = len(shape)
key = 'Fill/{}/dtype:{}/n_dim:{}/value:{}'.format(
ctx, input.dtype, n_dim, value)
module = get_module(Fill, key, ctx, n_dim=n_dim,
value=value, dtype=input.dtype)
return module.forward(input, shape)
def _uniform(input, shape, low, high):
ctx = MakeContext(inputs=[input]); n_dim = len(shape)
key = 'Uniform/{}/dtype:{}/n_dim:{}/low:{}/high:{}'.format(
ctx, input.dtype, n_dim, float(low), float(high))
module = get_module(RandomUniform, key, ctx, n_dim=n_dim,
low=low, high=high, dtype=input.dtype)
return module.forward(input, shape)
def _normal(input, shape, mean, std):
ctx = MakeContext(inputs=[input]); n_dim = len(shape)
key = 'Normal/{}/dtype:{}/n_dim:{}/mean:{}/std:{}'.format(
ctx, input.dtype, n_dim, float(mean), float(std))
module = get_module(RandomNormal, key, ctx, n_dim=n_dim,
mean=mean, std=std, dtype=input.dtype)
return module.forward(input, shape)
def _reduce(input, operation, dim=None, keepdim=False, out=None):
ctx = MakeContext(inputs=[input])
if dim is None: keepdim = False
key = '{}/{}/dim:{}/keepdim:{}'.format(
operation, ctx, dim, int(keepdim))
module = get_module(Reduce, key, ctx,
operation=operation, dim=dim, keepdim=keepdim)
return module.forward(input, out)
def _arg_reduce(input, operation, dim=None, keepdim=False, top_k=1, out=None):
ctx = MakeContext(inputs=[input])
if dim is None: keepdim = False
key = '{}/{}/dim:{}/keepdim:{}/top_k:{}'.format(
operation, ctx, dim, int(keepdim), top_k)
module = get_module(ArgReduce, key, ctx, operation=operation,
axis=dim, keepdim=keepdim, top_k=top_k)
return module.forward(input, out)
def mean(input, dim=None, keepdim=False, out=None):
"""Return the mean of all elements or elements along the given dim.
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
dim : int, optional
The axis of tensor to compute mean value.
keepdim : bool, optional
Whether the output tensor has dim retained or not.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
Returns
-------
dragon.vm.torch.Tensor
The mean-reduced tensor.
"""
return _reduce(input, 'MEAN', dim, keepdim, out)
def sum(input, dim=None, keepdim=False, out=None):
"""Return the sum of all elements or elements along the given dim.
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
dim : int, optional
The axis of tensor to compute sum value.
keepdim : bool, optional
Whether the output tensor has dim retained or not.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
Returns
-------
torch.Tensor
The sum-reduced tensor.
"""
return _reduce(input, 'SUM', dim, keepdim, out)
def argmax(input, dim=None, keepdim=False, out=None):
"""Return the indices of maximum elements along the given axis.
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
dim : int, optional
The axis of tensor to compute sum value.
keepdim : bool, optional
Whether the output tensor has dim retained or not.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
Returns
-------
torch.Tensor
The maximum indices.
"""
return _arg_reduce(input, 'ARGMAX', dim, keepdim, 1, out)
def max(input, dim=None, keepdim=False, out=None):
"""Return the values and indices of maximum elements along the given axis.
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
dim : int, optional
The axis of tensor to compute sum value.
keepdim : bool, optional
Whether the output tensor has dim retained or not.
out : dragon.torch.Tensor, optional
The optional output tensor.
Returns
-------
tuple
The maximum values and indices.
"""
return _arg_reduce(input, 'MAX', dim, keepdim, 1, out)
def argmin(input, dim=None, keepdim=False, out=None):
"""Return the indices of minimum elements along the given axis.
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
dim : int, optional
The axis of tensor to compute sum value.
keepdim : bool, optional
Whether the output tensor has dim retained or not.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
Returns
-------
torch.Tensor
The minimum indices.
"""
return _arg_reduce(input, 'ARGMIN', dim, keepdim, 1, out)
def min(input, dim=None, keepdim=False, out=None):
"""Return the values and indices of maximum elements along the given axis.
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
dim : int, optional
The axis of tensor to compute sum value.
keepdim : bool, optional
Whether the output tensor has dim retained or not.
out : dragon.torch.Tensor, optional
The optional output tensor.
Returns
-------
sequence
The minimum values and indices.
"""
return _arg_reduce(input, 'MIN', dim, keepdim, 1, out)
def topk(input, k, dim=None, largest=True, sorted=True, out=None):
"""Return the k largest/smallest values and indices along the given axis.
If ``dim`` is not given, the last dimension of the input is chosen.
If ``largest`` is False then the k smallest elements are returned.
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
k : int
The top k.
dim : int, optional
The axis of tensor to compute sum value.
largest : bool, optional
Whether to return largest or smallest elements.
sorted : bool, optional
Whether to return in the sorted order.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
Returns
-------
sequence
The values and indices.
"""
operation = 'MAX' if largest else 'MIN'
if dim is None: dim = input.ndimension() - 1
return _arg_reduce(input, operation, dim, True, k, out)
def cat(seq, dim=0, out=None):
"""Concatenate the inputs along the given axis.
Parameters
----------
seq : sequence of dragon.vm.torch.Tensor
The sequence.
dim : int, optional
The dim to concatenate.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
Returns
-------
dragon.vm.torch.Tensor
The output tensor.
"""
ctx = MakeContext(inputs=seq, outputs=[out] if out else [])
key = 'Concat/{}/dim:{}'.format(ctx, dim)
module = get_module(Concat, key, ctx, axis=dim)
return module.forward(seq, out)
def gather(input, dim, index, out=None):
"""Gather the input values along the given axis.
Note that it is a tensorflow style gather, which takes a vector index,
values of other dimension will be copied automatically.
Parameters
----------
input : dragon.vm.torch.Tensor
The values.
dim : int
The dim to gather.
index : dragon.vm.torch.Tensor
The indices.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
Returns
-------
dragon.vm.torch.Tensor
The output tensor.
"""
ctx = MakeContext(inputs=[input, index], outputs=[out] if out else [])
key = 'Gather/{}/dim:{}'.format(ctx, dim)
module = get_module(Gather, key, ctx, axis=dim)
return module.forward(input, index, out)
def _indexing(input, starts, sizes):
n_starts, n_sizes = len(starts), len(sizes)
ctx = MakeContext(inputs=[input])
key = 'Index/{}/n_starts:{}/n_sizes:{}'.format(ctx, n_starts, n_sizes)
module = get_module(Indexing, key, ctx, n_starts=n_starts, n_sizes=n_sizes)
return module.forward(input, starts, sizes)
def narrow(input, dimension, start, length):
"""Return a new tensor that is a narrowed version of input tensor.
Parameters
----------
input : torch.Tensor
The input tensor.
dimension : int
The dimension to narrow.
start : int
The starting position.
length : int
The distance to the ending postion.
Returns
-------
dragon.vm.torch.Tensor
The output tensor.
"""
sizes = list(input.shape[:]); starts = [0] * len(sizes)
starts[dimension], sizes[dimension] = start, length
return _indexing(input, starts, sizes)
def one_hot(input, depth):
"""Return a ont hot tensor according to given input.
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
depth : int
The depth of channels.
Returns
-------
dragon.vm.torch.FloatTensor
The output tensor.
"""
ctx = MakeContext(inputs=[input])
key = 'OneHot/{}/depth:{}'.format(ctx, depth)
module = get_module(OneHot, key, ctx, depth=depth)
return module.forward(input)
\ No newline at end of file
Dragon/python/dragon/vm/torch/ops/builtin.py
......@@ -13,143 +13,231 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from dragon.core.proto_utils import GetDeviceOption
from dragon.core.tensor_utils import FromTensor
from dragon.vm.torch.tensor import Tensor, Size
from dragon.core import mpi
from dragon.vm.torch.tensor import Tensor, _LeafTensor, _Device
from dragon.vm.torch.ops.primitive import MakeDevice, WrapScalar
from dragon.vm.torch.ops.factory import get_module
from dragon.vm.torch.ops.primitive import MakeContext
from dragon.vm.torch.ops.modules.array import Cast
from dragon.vm.torch.ops.modules.control_flow import Compare
from dragon.vm.torch.ops.modules.arithmetic import (
Fundamental, Log, Exp, Sqrt,
MM, FullyConnected,
Maximum, Minimum, Clamp,
)
from dragon.vm.torch.ops.modules.init import (
Fill, RandomUniform, RandomNormal,
)
from dragon.vm.torch.ops.arithmetic import (
_fundamental, _rfundamental,
_log, _exp, _sqrt, _clamp,
from dragon.vm.torch.ops.modules.array import (
Reshape, Squeeze, UnSqueeze, Permute,
Indexing, Repeat, Concat, Gather,
Reduce, ArgReduce, OneHot, Multinomial,
)
from dragon.vm.torch.ops.array import (
reshape, squeeze, unsqueeze,
_permute, _repeat, _indexing, narrow,
_fill, _uniform, _normal,
_reduce, _arg_reduce,
from dragon.vm.torch.ops.modules.update import (
Accumulate, Collective, Update,
)
from dragon.vm.torch.ops.modules.vision import (
Resize2d, RoIPool, RoIAlign,
)
__all__ = [
'add', 'sub', 'mul', 'div',
'maximum', 'minimum', 'clamp',
'log', 'exp', 'sqrt',
'mm', 'xw_plus_b',
'squeeze', 'unsqueeze',
'mean', 'sum', 'min', 'max', 'topk',
'argmin', 'argmax',
'gt', 'lt', 'eq', 'ge', 'le',
'cat', 'gather', 'narrow',
'one_hot', 'multinomial', 'rand', 'randn',
'zeros', 'zeros_like', 'ones', 'ones_like',
'nn_resize', 'bilinear_resize', 'roi_pool', 'roi_align',
]
##############################################
# #
# BASE #
# Arithmetic #
# #
##############################################
def copy_(self, src, non_blocking=False):
"""Copy the elements from ``src`` into this tensor and return ``self``.
def _fundamental(input, value, op='Add', out=None):
if not isinstance(value, Tensor):
value = WrapScalar(value, input.dtype, input.device)
dev = MakeDevice(inputs=[input, value])
key = '{}/{}'.format(op, dev)
module = get_module(Fundamental, key, dev, op_type=op)
return module.forward(input, value, out)
def _rfundamental(input, value, op='RAdd', out=None):
if not isinstance(value, Tensor):
value = WrapScalar(value, input.dtype, input.device)
dev = MakeDevice(inputs=[input, value])
key = '{}/{}'.format(op, dev)
module = get_module(Fundamental, key, dev, op_type=op)
return module.forward(value, input, out)
def add(input, value, out=None):
"""Add the ``input`` and ``value`` into the output tensor.
Parameters
----------
src : dragon.vm.torch.Tensor
The source tensor.
non_blocking : boolean
Whether to copy asynchronously between CPU and GPU.
input : dragon.vm.torch.Tensor
The input tensor.
value : dragon.vm.torch.Tensor, number
The value tensor.
out : dragon.vm.torch.Tensor, optional
The output tensor.
Returns
-------
dragon.vm.torch.Tensor
The ``self`` tensor.
The output tensor.
"""
# Copy memory
FromTensor(
src, GetDeviceOption(src._ctx.device_type, src._ctx.device_id),
self.name, GetDeviceOption(self._ctx.device_type, self._ctx.device_id))
# Transfer the static shape if necessary
self._static_shape = src.size() \
if self._static_shape else None
return self
return _fundamental(input, value, out=out, op='Add')
Tensor.copy_ = copy_
def sub(input, value, out=None):
"""Subtract the ``input`` and ``value`` into the output tensor.
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
value : dragon.vm.torch.Tensor or number
The value tensor.
out : dragon.vm.torch.Tensor, optional
The output tensor.
##############################################
# #
# INITIALIZER #
# #
##############################################
Returns
-------
torch.Tensor
The output tensor.
"""
return _fundamental(input, value, out=out, op='Sub')
def fill_(self, value):
"""Fill self tensor with the specified value.
def mul(input, value, out=None):
"""Multiply the ``input`` and ``value`` into the output tensor.
Parameters
----------
value : numerical type
input : dragon.vm.torch.Tensor
The input tensor.
value : dragon.vm.torch.Tensor or number
The value tensor.
out : dragon.vm.torch.Tensor, optional
The output tensor.
Returns
-------
dragon.vm.torch.Tensor
The self.
The output tensor.
"""
return _fill(self, self.shape, value)
return _fundamental(input, value, out=out, op='Mul')
def uniform_(self, low=0, high=1):
"""Fill self tensor with the specified uniform distribution.
def div(input, value, out=None):
"""Divide the ``input`` and ``value`` into the output tensor.
Parameters
----------
low : numerical type
The lower bound.
high : numerical type
The higher bound.
input : dragon.vm.torch.Tensor
The input tensor.
value : dragon.vm.torch.Tensor or number
The value tensor.
out : dragon.vm.torch.Tensor, optional
The output tensor.
Returns
-------
dragon.vm.torch.Tensor
The self.
The output tensor.
"""
return _uniform(self, self.shape, low, high)
return _fundamental(input, value, out=out, op='Div')
def normal_(self, mean=0, std=1):
"""Fill self tensor with the specified normal distribution.
def maximum(input, other, out=None):
"""Return the max value of given two tensors.
Parameters
----------
mean : numerical type
The mean(mu) of normal distribution.
std : numerical type
The std(sigma) of normal distribution.
input : dragon.vm.torch.Tensor or number
The input tensor.
other : dragon.vm.torch.Tensor or number
The input tensor.
out : dragon.vm.torch.Tensor, optional
The output tensor.
Returns
-------
dragon.vm.torch.Tensor
The self.
The output tensor.
"""
return _normal(self, self.shape, mean, std)
if not isinstance(input, Tensor):
input = WrapScalar(input, other.dtype, other.device)
elif not isinstance(other, Tensor):
other = WrapScalar(other, input.dtype, input.device)
dev = MakeDevice(inputs=[input])
key = 'Maximum/{}'.format(dev)
module = get_module(Maximum, key, dev)
return module.forward(input, other, out)
Tensor.fill_ = fill_
Tensor.uniform_ = uniform_
Tensor.normal_ = normal_
def minimum(input, other, out=None):
"""Return the min value of given two tensors.
Parameters
----------
input : dragon.vm.torch.Tensor or number
The input tensor.
other : dragon.vm.torch.Tensor or number
The input tensor.
out : dragon.vm.torch.Tensor, optional
The output tensor.
##############################################
# #
# ARITHMETIC #
# #
##############################################
Returns
-------
dragon.vm.torch.Tensor
The output tensor.
"""
if not isinstance(input, Tensor):
input = WrapScalar(input, other.dtype, other.device)
elif not isinstance(other, Tensor):
other = WrapScalar(other, input.dtype, input.device)
dev = MakeDevice(inputs=[input])
key = 'Minimum/{}'.format(dev)
module = get_module(Minimum, key, dev)
return module.forward(input, other, out)
def add(self, value):
"""See ``torch.add()``
def clamp(input, min=None, max=None, out=None):
"""Clamp all elements into the range [min, max].
Parameters
----------
value : dragon.vm.torch.Tensor, int or float
The value tensor.
input : dragon.vm.torch.Tensor
The input tensor.
min : number, optional
The min value.
max : number, optional
The max value.
out : dragon.vm.torch.Tensor, optional
The output tensor.
Returns
-------
......@@ -157,37 +245,43 @@ def add(self, value):
The output tensor.
"""
return _fundamental(self, value, op='Add')
dev = MakeDevice(inputs=[input])
key = 'Clamp/{}/min:{}/max:{}'.format(dev, min, max)
module = get_module(Clamp, key, dev, min=min, max=max)
return module.forward(input, out)
def add_(self, value):
"""Inplace of ``torch.add()``
def log(input, out=None):
"""Compute the natural logarithm of input.
Parameters
----------
value : dragon.vm.torch.Tensor, int or float
The value tensor.
input : dragon.vm.torch.Tensor
The input tensor.
out : dragon.vm.torch.Tensor, optional
The output tensor.
Returns
-------
dragon.vm.torch.Tensor
The self.
The output tensor.
"""
return _fundamental(self, value, out=self, op='Add')
def radd(self, value):
return _rfundamental(self, value, op='RAdd')
dev = MakeDevice(inputs=[input])
key = 'Log/{}'.format(dev)
module = get_module(Log, key, dev)
return module.forward(input, out)
def sub(self, value):
"""Subtract the ``self`` and ``value`` into the output tensor.
def exp(input, out=None):
"""Compute the exponential of input.
Parameters
----------
value : dragon.vm.torch.Tensor, int or float
The value tensor.
input : dragon.vm.torch.Tensor
The input tensor.
out : dragon.vm.torch.Tensor, optional
The output tensor.
Returns
-------
......@@ -195,399 +289,865 @@ def sub(self, value):
The output tensor.
"""
return _fundamental(self, value, op='Sub')
dev = MakeDevice(inputs=[input])
key = 'Exp/{}'.format(dev)
module = get_module(Exp, key, dev)
return module.forward(input, out)
def sub_(self, value):
"""Inplace of ``Tensor.sub()``
def sqrt(input, out=None):
"""Compute the square-root of input.
Parameters
----------
value : torch.Tensor, int or float
The value tensor.
input : dragon.vm.torch.Tensor
The input tensor.
out : dragon.vm.torch.Tensor, optional
The output tensor.
Returns
-------
torch.Tensor
The self.
dragon.vm.torch.Tensor
The output tensor.
"""
return _fundamental(self, value, out=self, op='Sub')
dev = MakeDevice(inputs=[input])
key = 'Sqrt/{}'.format(dev)
module = get_module(Sqrt, key, dev)
return module.forward(input, out)
def rsub(self, value):
return _rfundamental(self, value, op='RSub')
def mul(self, value):
"""Multiply the ``self`` and ``value`` into the output tensor.
def mm(mat1, mat2, transA=False, transB=False, out=None):
"""Performs a matrix multiplication of the matrices ``mat1`` and ``mat2.``
Parameters
----------
value : torch.Tensor, int or float
The value tensor.
mat1 : dragon.vm.torch.Tensor
The matrix A.
mat2 : dragon.vm.torch.Tensor
The matrix B.
transA : boolean
Whether to transpose the ``mat1``.
transB : boolean
Whether to transpose the ``mat2``.
Returns
-------
torch.Tensor
dragon.vm.torch.Tensor
The output tensor.
"""
return _fundamental(self, value, op='Mul')
dev = MakeDevice(inputs=[mat1, mat2])
key = 'Matmul/{}/transA:{}/transB:{}'.format(dev, transA, transB)
module = get_module(MM, key, dev, transA=transA, transB=transB)
return module.forward(mat1, mat2, out)
def mul_(self, value):
"""Inplace of ``Tensor.mul()``
def xw_plus_b(x, w, bias=None, transW=True, out=None):
"""Compute *matmul(x, w) + bias.*``
Parameters
----------
value : torch.Tensor, int or float
The value tensor.
x : dragon.vm.torch.Tensor
The x.
w : dragon.vm.torch.Tensor
The w.
bias : dragon.vm.torch.Tensor, optional
The bias.
transW : boolean
Whether to transpose the ``w``.
Returns
-------
torch.Tensor
The self.
dragon.vm.torch.Tensor
The output tensor.
"""
return _fundamental(self, value, out=self, op='Mul')
dev = MakeDevice(inputs=[x, w] + ([bias] if bias else []))
key = 'FullyConnected/{}/transW:{}'.format(dev, transW)
module = get_module(FullyConnected, key, dev, transW=transW)
return module.forward(x, w, bias, out)
##############################################
# #
# Array #
# #
##############################################
def rmul(self, value):
return _rfundamental(self, value, op='RMul')
def _reshape(input, shape, shape_like=None):
if shape_like is not None: shape = shape_like.shape
dev = MakeDevice(inputs=[input]); n_dim = len(shape)
key = 'Reshape/{}/n_dim:{}'.format(dev, n_dim)
module = get_module(Reshape, key, dev, n_dim=n_dim)
return module.forward(input, shape)
def _permute(input, perm):
dev = MakeDevice(inputs=[input]); n_perm = len(perm)
key = 'Permute/{}/n_perm:{}'.format(dev, n_perm)
module = get_module(Permute, key, dev, n_perm=n_perm)
return module.forward(input, perm)
def _repeat(input, times):
dev = MakeDevice(inputs=[input]); n_times = len(times)
key = 'Repeat/{}/n_times:{}'.format(dev, n_times)
module = get_module(Repeat, key, dev, n_times=n_times)
return module.forward(input, times)
def _fill(input, shape, value):
dev = MakeDevice(inputs=[input]); n_dim = len(shape)
key = 'Fill/{}/dtype:{}/n_dim:{}/value:{}'.format(
dev, input.dtype, n_dim, value)
module = get_module(Fill, key, dev, n_dim=n_dim,
value=value, dtype=input.dtype)
return module.forward(input, shape)
def _uniform(input, shape, low, high):
dev = MakeDevice(inputs=[input]); n_dim = len(shape)
key = 'Uniform/{}/dtype:{}/n_dim:{}/low:{}/high:{}'.format(
dev, input.dtype, n_dim, float(low), float(high))
module = get_module(
RandomUniform, key, dev, n_dim=n_dim,
low=low, high=high, dtype=input.dtype)
return module.forward(input, shape)
def _normal(input, shape, mean, std):
dev = MakeDevice(inputs=[input]); n_dim = len(shape)
key = 'Normal/{}/dtype:{}/n_dim:{}/mean:{}/std:{}'.format(
dev, input.dtype, n_dim, float(mean), float(std))
module = get_module(
RandomNormal, key, dev, n_dim=n_dim,
mean=mean, std=std, dtype=input.dtype)
return module.forward(input, shape)
def _reduce(input, operation, dim=None, keepdim=False, out=None):
if dim is None: keepdim = False
dev = MakeDevice(inputs=[input])
key = '{}/{}/dim:{}/keepdim:{}'.format(
operation, dev, dim, int(keepdim))
module = get_module(
Reduce, key, dev, operation=operation,
dim=dim, keepdim=keepdim)
return module.forward(input, out)
def _arg_reduce(input, operation, dim=None, keepdim=False, top_k=1, out=None):
if dim is None: keepdim = False
dev = MakeDevice(inputs=[input])
key = '{}/{}/dim:{}/keepdim:{}/top_k:{}'.format(
operation, dev, dim, int(keepdim), top_k)
module = get_module(
ArgReduce, key, dev,
operation=operation, axis=dim,
keepdim=keepdim, top_k=top_k)
return module.forward(input, out)
def _indexing(input, starts, sizes):
n_starts, n_sizes = len(starts), len(sizes)
dev = MakeDevice(inputs=[input])
key = 'Index/{}/n_starts:{}/n_sizes:{}'.format(dev, n_starts, n_sizes)
module = get_module(Indexing, key, dev, n_starts=n_starts, n_sizes=n_sizes)
return module.forward(input, starts, sizes)
def _compare(input, other, operation, out=None):
if not isinstance(other, Tensor):
other = WrapScalar(other, input.dtype, input.device)
dev = MakeDevice(inputs=[input, other])
key = 'Compare/{}/{}'.format(operation, dev)
module = get_module(Compare, key, dev, operation=operation)
return module.forward(input, other, out)
def div(self, value):
"""Divide the ``self`` and ``value`` into the output tensor.
def squeeze(input, dim=None, out=None):
"""Return a tensor with all the dimensions of input of size 1 removed.
Parameters
----------
value : torch.Tensor, int or float
The value tensor.
dim : int
The optional dim to remove.
out : dragon.vm.torch.Tensor, optional
The output tensor.
Returns
-------
torch.Tensor
The output tensor.
dragon.vm.torch.Tensor
The new tensor.
"""
return _fundamental(self, value, op='Div')
dev = MakeDevice(inputs=[input])
key = 'Squeeze/{}/dim:{}'.format(dev, dim if dim else 'None')
module = get_module(Squeeze, key, dev, dim=dim)
return module.forward(input, out=out)
def div_(self, value):
"""Inplace of ``Tensor.div()``
def unsqueeze(input, dim, out=None):
"""Returns a tensor with a dimension of size 1 inserted at the specified position.
Parameters
----------
value : torch.Tensor, int or float
The value tensor.
dim : int
The dim to remove.
out : dragon.vm.torch.Tensor, optional
The output tensor.
Returns
-------
torch.Tensor
The self.
dragon.vm.torch.Tensor
The new tensor.
"""
return _fundamental(self, value, out=self, op='Div')
dev = MakeDevice(inputs=[input])
key = 'Unsqueeze/{}/dim:{}'.format(dev, dim if dim else 'None')
module = get_module(UnSqueeze, key, dev, dim=dim)
return module.forward(input, out=out)
def mean(input, dim=None, keepdim=False, out=None):
"""Return the mean of all elements or elements along the given dim.
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
dim : int, optional
The axis of tensor to compute mean value.
keepdim : bool, optional
Whether the output tensor has dim retained or not.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
Returns
-------
dragon.vm.torch.Tensor
The mean-reduced tensor.
def rdiv(self, value):
return _rfundamental(self, value, op='RDiv')
"""
return _reduce(input, 'MEAN', dim, keepdim, out)
def clamp(self, min=None, max=None):
"""Return a tensor that all elements are clamped into the range [min, max].
def sum(input, dim=None, keepdim=False, out=None):
"""Return the sum of all elements or elements along the given dim.
Parameters
----------
min : number, optional
The min value.
max : number, optional
The max value.
input : dragon.vm.torch.Tensor
The input tensor.
dim : int, optional
The axis of tensor to compute sum value.
keepdim : bool, optional
Whether the output tensor has dim retained or not.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
Returns
-------
torch.Tensor
The output tensor.
The sum-reduced tensor.
"""
return _clamp(self, min, max)
return _reduce(input, 'SUM', dim, keepdim, out)
def clamp_(self, min=None, max=None):
"""Clamp all elements are clamped into the range [min, max].
def argmax(input, dim=None, keepdim=False, out=None):
"""Return the indices of maximum elements along the given axis.
Parameters
----------
min : number, optional
The min value.
max : number, optional
The max value.
input : dragon.vm.torch.Tensor
The input tensor.
dim : int, optional
The axis of tensor to compute sum value.
keepdim : bool, optional
Whether the output tensor has dim retained or not.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
Returns
-------
torch.Tensor
The output tensor.
The maximum indices.
"""
return _clamp(self, min, max, self)
return _arg_reduce(input, 'ARGMAX', dim, keepdim, 1, out)
def log(self):
"""Compute the natural logarithm of this tensor.
def max(input, dim=None, keepdim=False, out=None):
"""Return the values and indices of maximum elements along the given axis.
Parameters
----------
None
input : dragon.vm.torch.Tensor
The input tensor.
dim : int, optional
The axis of tensor to compute sum value.
keepdim : bool, optional
Whether the output tensor has dim retained or not.
out : dragon.torch.Tensor, optional
The optional output tensor.
Returns
-------
torch.Tensor
The log tensor.
tuple
The maximum values and indices.
"""
return _log(self)
return _arg_reduce(input, 'MAX', dim, keepdim, 1, out)
def exp(self):
"""Compute the exponential of this tensor.
def argmin(input, dim=None, keepdim=False, out=None):
"""Return the indices of minimum elements along the given axis.
Parameters
----------
None
input : dragon.vm.torch.Tensor
The input tensor.
dim : int, optional
The axis of tensor to compute sum value.
keepdim : bool, optional
Whether the output tensor has dim retained or not.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
Returns
-------
torch.Tensor
The exp tensor.
The minimum indices.
"""
return _exp(self)
return _arg_reduce(input, 'ARGMIN', dim, keepdim, 1, out)
def sqrt(self):
"""Compute the square-root of this tensor.
def min(input, dim=None, keepdim=False, out=None):
"""Return the values and indices of maximum elements along the given axis.
Parameters
----------
None
input : dragon.vm.torch.Tensor
The input tensor.
dim : int, optional
The axis of tensor to compute sum value.
keepdim : bool, optional
Whether the output tensor has dim retained or not.
out : dragon.torch.Tensor, optional
The optional output tensor.
Returns
-------
torch.Tensor
The sqrt tensor.
sequence
The minimum values and indices.
"""
return _sqrt(self)
return _arg_reduce(input, 'MIN', dim, keepdim, 1, out)
Tensor.add = add
Tensor.add_ = add_
Tensor.__radd__ = radd
Tensor.sub = sub
Tensor.sub_ = sub_
Tensor.__rsub__ = rsub
Tensor.mul = mul
Tensor.mul_ = mul_
Tensor.__rmul__ = rmul
Tensor.div = div
Tensor.div_ = div_
Tensor.__rdiv__ = rdiv
Tensor.__rtruediv__ = rdiv
Tensor.clamp = clamp
Tensor.clamp_ = clamp_
Tensor.log = log
Tensor.exp = exp
Tensor.sqrt = sqrt
def topk(input, k, dim=None, largest=True, sorted=True, out=None):
"""Return the k largest/smallest values and indices along the given axis.
If ``dim`` is not given, the last dimension of the input is chosen.
##############################################
# #
# ARRAY #
# #
##############################################
If ``largest`` is False then the k smallest elements are returned.
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
k : int
The top k.
dim : int, optional
The axis of tensor to compute sum value.
largest : bool, optional
Whether to return largest or smallest elements.
sorted : bool, optional
Whether to return in the sorted order.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
Returns
-------
sequence
The values and indices.
"""
operation = 'MAX' if largest else 'MIN'
if dim is None: dim = input.ndimension() - 1
return _arg_reduce(input, operation, dim, True, k, out)
def _squeeze(self, dim=None):
"""Returns a tensor with all the dimensions of input of size 1 removed.
def gt(input, other, out=None):
"""Compute *input* > *other* element-wise.
Parameters
----------
dim : int
The optional dim to remove.
input : dragon.vm.torch.Tensor
The input tensor.
other : dragon.vm.torch.Tensor, number
The other tensor.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
Returns
-------
dragon.vm.torch.Tensor
The output byte tensor.
"""
return _compare(input, other, 'GT', out)
def ge(input, other, out=None):
"""Compute *input* >= *other* element-wise.
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
other : dragon.vm.torch.Tensor, number
The other tensor.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
Returns
-------
torch.Tensor
The new tensor.
dragon.vm.torch.Tensor
The output byte tensor.
"""
return squeeze(self, dim=dim)
return _compare(input, other, 'GE', out)
def _squeeze_(self, dim=None):
"""Inplace of ``Tensor.squeeze()``
def lt(input, other, out=None):
"""Compute *input* < *other* element-wise.
Parameters
----------
dim : int
The optional dim to remove.
input : dragon.vm.torch.Tensor
The input tensor.
other : dragon.vm.torch.Tensor, number
The other tensor.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
Returns
-------
torch.Tensor
The self.
dragon.vm.torch.Tensor
The output byte tensor.
"""
return squeeze(self, dim=dim, out=self)
return _compare(input, other, 'LT', out)
def _unsqueeze(self, dim):
"""Returns a tensor with a dimension of size 1 inserted at the specified position.
def le(input, other, out=None):
"""Compute *input* <= *other* element-wise.
Parameters
----------
dim : int
The dim to insert.
input : dragon.vm.torch.Tensor
The input tensor.
other : dragon.vm.torch.Tensor, number
The other tensor.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
Returns
-------
torch.Tensor
The new tensor.
dragon.vm.torch.Tensor
The output byte tensor.
"""
return unsqueeze(self, dim=dim)
return _compare(input, other, 'LE', out)
def _unsqueeze_(self, dim=None):
"""Inplace of ``Tensor.unsqueeze()``
def eq(input, other, out=None):
"""Compute *input* == *other* element-wise.
Parameters
----------
dim : int
The optional dim to remove.
input : dragon.vm.torch.Tensor
The input tensor.
other : dragon.vm.torch.Tensor, number
The other tensor.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
Returns
-------
torch.Tensor
The self.
dragon.vm.torch.Tensor
The output byte tensor.
"""
return unsqueeze(self, dim=dim, out=self)
return _compare(input, other, 'EQ', out)
def view(self, *args):
return reshape(self, shape=args)
def cat(seq, dim=0, out=None):
"""Concatenate the inputs along the given axis.
Parameters
----------
seq : sequence of dragon.vm.torch.Tensor
The sequence.
dim : int, optional
The dim to concatenate.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
Returns
-------
dragon.vm.torch.Tensor
The output tensor.
"""
dev = MakeDevice(inputs=seq, outputs=[out] if out else [])
key = 'Concat/{}/dim:{}'.format(dev, dim)
module = get_module(Concat, key, dev, axis=dim)
return module.forward(seq, out)
def view_as(self, other):
if not isinstance(other, Tensor):
raise ValueError('The other should be a torch tensor.')
return reshape(self, shape=None, shape_like=other)
def gather(input, dim, index, out=None):
"""Gather the input values along the given axis.
Note that it is a tensorflow style gather, which takes a vector index,
values of other dimension will be copied automatically.
Parameters
----------
input : dragon.vm.torch.Tensor
The values.
dim : int
The dim to gather.
index : dragon.vm.torch.Tensor
The indices.
out : dragon.vm.torch.Tensor, optional
The optional output tensor.
def permute(self, dims=None):
return _permute(self, dims)
Returns
-------
dragon.vm.torch.Tensor
The output tensor.
"""
dev = MakeDevice(
inputs=[input, index],
outputs=[out] if out else [])
key = 'Gather/{}/dim:{}'.format(dev, dim)
module = get_module(Gather, key, dev, axis=dim)
return module.forward(input, index, out)
def repeat(self, *sizes):
if len(sizes) == 1 and \
isinstance(sizes[0], Size):
sizes = sizes[0]
return _repeat(self, sizes)
def narrow(input, dimension, start, length):
"""Return a new tensor that is a narrowed version of input tensor.
def indexing(self, starts, ends):
return _indexing(self, starts, ends)
Parameters
----------
input : torch.Tensor
The input tensor.
dimension : int
The dimension to narrow.
start : int
The starting position.
length : int
The distance to the ending postion.
Returns
-------
dragon.vm.torch.Tensor
The output tensor.
def _narrow(self, dimension, start, length):
return narrow(self, dimension, start, length)
"""
sizes = list(input.shape[:]); starts = [0] * len(sizes)
starts[dimension], sizes[dimension] = start, length
return _indexing(input, starts, sizes)
def mean(self, dim=None, keepdim=False):
return _reduce(self, 'MEAN', dim, keepdim)
def one_hot(input, depth):
"""Return a ont hot tensor according to given input.
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
depth : int
The depth of channels.
def sum(self, dim=None, keepdim=False):
return _reduce(self, 'SUM', dim, keepdim)
Returns
-------
dragon.vm.torch.FloatTensor
The output tensor.
"""
dev = MakeDevice(inputs=[input])
key = 'OneHot/{}/depth:{}'.format(dev, depth)
module = get_module(OneHot, key, dev, depth=depth)
return module.forward(input)
def max(self, dim=None, keepdim=False):
return _arg_reduce(self, 'MAX', dim, keepdim)
def multinomial(input, num_samples, normalize=False, out=None):
"""Return a tensor where each row contains ``num_samples``,
sampled from the multinomial distribution.
def min(self, dim=None, keepdim=False):
return _arg_reduce(self, 'MIN', dim, keepdim)
Parameters
----------
input : dragon.vm.torch.Tensor
The input tensor.
num_samples : int
The number of samples.
normalize : boolean, optional, default=False
Whether to normalize the inputs.
Returns
-------
dragon.vm.torch.Tensor
The output tensor.
Tensor.squeeze = _squeeze
Tensor.squeeze_ = _squeeze_
Tensor.unsqueeze = _unsqueeze
Tensor.unsqueeze_ = _unsqueeze_
Tensor.view = view
Tensor.view_as = view_as
Tensor.permute = permute
Tensor.repeat = repeat
Tensor.mean = mean
Tensor.sum = sum
Tensor.max = max
Tensor.min = min
Tensor.narrow = _narrow
Tensor._indexing = indexing
"""
dev = MakeDevice(inputs=[input])
key = 'Multinomial/{}/num_samples:{}/normalize:{}'.format(
dev, num_samples, normalize)
module = get_module(
Multinomial, key, dev,
num_samples=num_samples,
normalize=normalize)
return module.forward(input, out)
##############################################
# #
# TYPE #
# Creation #
# #
##############################################
def _type_to(input, dtype='float32', inplace=False):
if dtype == input.dtype: return input
ctx = MakeContext(inputs=[input])
key = 'Cast/{}/dtype:{}/inplace:{}'.format(
ctx, dtype, 'true' if inplace else 'false')
module = get_module(Cast, key, ctx, dtype=dtype, inplace=inplace)
return module.forward(input)
def _get_leaf_tensor(sizes, kwargs):
return _LeafTensor(sizes,
requires_grad=kwargs['requires_grad'] \
if 'requires_grad' in kwargs else False,
dtype=kwargs.get('dtype', 'float32'),
device=kwargs.get('device', _Device()))
def _type(self, dtype=None):
"""Return the data type of this tensor.
def zeros(*sizes, **kwargs):
"""Return a float tensor with values of ``0``.
Parameters
----------
sizes : tuple, list or int
The sizes indicating the shape of the output tensor.
out : dragon.vm.torch.Tensor
The optional output tensor.
If ``dtype`` is not ``None``, cast ``self`` to the new tensor.
Returns
-------
vm.torch.FloatTensor
The output tensor.
"""
out = kwargs['out'] if 'out' in kwargs else None
if out is None: out = _get_leaf_tensor(sizes, kwargs)
return _fill(out, shape=sizes, value=0)
def zeros_like(input, out=None, **kwargs):
"""Return a float tensor with values of ``0``, shape as the input.
Parameters
----------
input : dragon.vm.torch.Tensor
The tensor for indicating shape.
out : dragon.vm.torch.Tensor
The optional output tensor.
Returns
-------
vm.torch.FloatTensor
The output tensor.
"""
if not hasattr(input, 'shape'):
raise ValueError('Input does not have the shape attribute.')
if out is None: out = _get_leaf_tensor(input.shape, kwargs)
return _fill(out, shape=input.shape, value=0)
def ones(*sizes, **kwargs):
"""Return a float tensor with values of ``1``.
Parameters
----------
dtype : str
The specified type.
sizes : tuple, list or int
The sizes indicating the shape of the output tensor.
out : dragon.vm.torch.Tensor
The optional output tensor.
Returns
-------
str or torch.Tensor
The data type or the new tensor.
vm.torch.FloatTensor
The output tensor.
"""
if dtype is None:
return 'torch.' + self._type2str()
out = kwargs['out'] if 'out' in kwargs else None
if out is None: out = _get_leaf_tensor(sizes, kwargs)
return _fill(out, shape=sizes, value=1)
def ones_like(input, out=None, **kwargs):
"""Return a float tensor with values of ``1``, shape as the input.
Parameters
----------
input : dragon.vm.torch.Tensor
The tensor for indicating shape.
out : dragon.vm.torch.Tensor
The optional output tensor.
Returns
-------
vm.torch.FloatTensor
The output tensor.
"""
if not hasattr(input, 'shape'):
raise ValueError('Input does not have the shape attribute.')
if out is None: out = _get_leaf_tensor(input.shape, kwargs)
return _fill(out, shape=input.shape, value=1)
def rand(*sizes, **kwargs):
"""Return a float tensor with a uniform distribution of U(0, 1).
Parameters
----------
sizes : tuple, list or int
The sizes indicating the shape of the output tensor.
out : dragon.vm.torch.Tensor
The optional output tensor.
Returns
-------
vm.torch.FloatTensor
The output tensor.
"""
out = kwargs['out'] if 'out' in kwargs else None
if out is None: out = _get_leaf_tensor(sizes, kwargs)
return _uniform(out, sizes, low=0, high=1)
def randn(*sizes, **kwargs):
"""Return a float tensor with a normal distribution of N(0, 1).
Parameters
----------
sizes : tuple, list or int
The sizes indicating the shape of the output tensor.
out : dragon.vm.torch.Tensor
The optional output tensor.
Returns
-------
vm.torch.FloatTensor
The output tensor.
"""
out = kwargs['out'] if 'out' in kwargs else None
if out is None: out = _get_leaf_tensor(sizes, kwargs)
return _normal(out, sizes, mean=0, std=1)
##############################################
# #
# Update #
# #
##############################################
def _accumulate(grads):
if len(grads) == 0: return
if not isinstance(grads, (list, tuple)): grads = [grads]
dev = MakeDevice(inputs=grads)
key = 'Accumulate/{}/alpha:1./beta:1.'.format(dev)
module = get_module(Accumulate, key, dev)
return module.forward(grads)
def _allreduce(grads):
if not isinstance(grads, (list, tuple)): grads = [grads]
dev = MakeDevice(inputs=grads)
mode = mpi.GetParallelMode() + '_ALLREDUCE'
key = 'Collective/{}/{}'.format(dev, mode.lower())
module = get_module(Collective, key, dev, mode=mode)
return module.forward(grads)
def _update(param, grad, op_type, slot,
lr_mult=1.0, decay_mult=1.0):
dev = MakeDevice(inputs=[param])
key = '{}/{}/{}/{}'.format(op_type, dev, slot, param.name)
module = get_module(Update, key, dev, op_type=op_type,
lr_mult=lr_mult, decay_mult=decay_mult, slot=slot)
return module.forward(param, grad)
##############################################
# #
# Vision #
# #
##############################################
def _resize_2d(input, op_type, dsize, fx, fy):
if dsize is None:
if fx < 0 or fy < 0:
raise ValueError('Set fx and fy if dsize is None.')
else:
return _type_to(self, dtype=dtype)
Tensor.type = _type
Tensor.half = lambda self: _type_to(self, dtype='float16', inplace=False)
Tensor.half_ = lambda self: _type_to(self, dtype='float16', inplace=True)
Tensor.float = lambda self: _type_to(self, dtype='float32', inplace=False)
Tensor.float_ = lambda self: _type_to(self, dtype='float32', inplace=True)
Tensor.double = lambda self: _type_to(self, dtype='float64', inplace=False)
Tensor.double_ = lambda self: _type_to(self, dtype='float64', inplace=True)
Tensor.byte = lambda self: _type_to(self, dtype='uint8', inplace=False)
Tensor.byte_ = lambda self: _type_to(self, dtype='uint8', inplace=True)
Tensor.char = lambda self: _type_to(self, dtype='int8', inplace=False)
Tensor.char_ = lambda self: _type_to(self, dtype='int8', inplace=True)
Tensor.int = lambda self: _type_to(self, dtype='int32', inplace=False)
Tensor.int_ = lambda self: _type_to(self, dtype='int32', inplace=True)
Tensor.long = lambda self: _type_to(self, dtype='int64', inplace=False)
Tensor.long_ = lambda self: _type_to(self, dtype='int64', inplace=True)
\ No newline at end of file
if len(dsize) != 2:
raise ValueError('The dsize should be a list with 2 elements.')
if dsize is None and (fy == -1.0 or fx == -1.0):
raise RuntimeError('The dsize, fx/fy should be specified either.')
dev = MakeDevice(inputs=[input])
key = '{}/{}/dsize:{}/fx:{}/fy:{}'.format(
op_type, dev, '2' if dsize else 'none', fx, fy)
module = get_module(Resize2d, key, dev,
op_type=op_type, dsize=dsize, fx=fx, fy=fy)
return module.forward(input, dsize)
def nn_resize(input, dsize, fx=-1.0, fy=-1.0):
return _resize_2d(input, 'NNResize', dsize, fx, fy)
def bilinear_resize(input, dsize, fx=-1.0, fy=-1.0):
return _resize_2d(input, 'BilinearResize', dsize, fx, fy)
def roi_pool(feature, rois, pooled_h, pooled_w, spatial_scale):
dev = MakeDevice(inputs=[feature])
key = 'RoIPool/{}/pool_h:{}/pool_w:{}/spatial_scale:{}'.format(
dev, pooled_h, pooled_w, spatial_scale)
module = get_module(
RoIPool, key, dev,
pooled_h=pooled_h, pooled_w=pooled_w,
spatial_scale=spatial_scale)
return module.forward(feature, rois)
def roi_align(feature, rois, pooled_h, pooled_w,
spatial_scale, sampling_ratio=2):
dev = MakeDevice(inputs=[feature])
key = 'RoIAlign/{}/pool_h:{}/pool_w:{}/' \
'spatial_scale:{}/sampling_ratio:{}'.format(
dev, pooled_h, pooled_w, spatial_scale, sampling_ratio)
module = get_module(
RoIAlign, key, dev,
pooled_h=pooled_h, pooled_w=pooled_w,
spatial_scale=spatial_scale,
sampling_ratio=sampling_ratio)
return module.forward(feature, rois)
\ No newline at end of file
Dragon/python/dragon/vm/torch/ops/creation.py deleted 100644 → 0
# ------------------------------------------------------------
# Copyright (c) 2017-present, SeetaTech, Co.,Ltd.
#
# Licensed under the BSD 2-Clause License.
# You should have received a copy of the BSD 2-Clause License
# along with the software. If not, See,
#
# <https://opensource.org/licenses/BSD-2-Clause>
#
# ------------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from dragon.vm.torch.tensor import LeafTensor
from dragon.vm.torch.ops.array import (
_fill, _uniform, _normal,
)
def zeros(*sizes, **kwargs):
"""Return a float tensor with values of ``0``.
Parameters
----------
sizes : tuple, list or int
The sizes indicating the shape of the output tensor.
out : dragon.vm.torch.Tensor
The optional output tensor.
Returns
-------
vm.torch.FloatTensor
The output tensor.
"""
out = kwargs['out'] if 'out' in kwargs else None
if out is None:
out = LeafTensor(sizes, requires_grad=kwargs['requires_grad'] \
if 'requires_grad' in kwargs else False)
return _fill(out, shape=sizes, value=0)
def zeros_like(input, out=None, **kwargs):
"""Return a float tensor with values of ``0``, shape as the input.
Parameters
----------
input : dragon.vm.torch.Tensor
The tensor for indicating shape.
out : dragon.vm.torch.Tensor
The optional output tensor.
Returns
-------
vm.torch.FloatTensor
The output tensor.
"""
if not hasattr(input, 'shape'):
raise ValueError('Input does not have the shape attribute.')
if out is None:
out = LeafTensor(input.shape, requires_grad=kwargs['requires_grad'] \
if 'requires_grad' in kwargs else False)
return _fill(out, shape=input.shape, value=0)
def ones(*sizes, **kwargs):
"""Return a float tensor with values of ``1``.
Parameters
----------
sizes : tuple, list or int
The sizes indicating the shape of the output tensor.
out : dragon.vm.torch.Tensor
The optional output tensor.
Returns
-------
vm.torch.FloatTensor
The output tensor.
"""
out = kwargs['out'] if 'out' in kwargs else None
if out is None:
out = LeafTensor(sizes, requires_grad=kwargs['requires_grad'] \
if 'requires_grad' in kwargs else False)
return _fill(out, shape=sizes, value=1)
def ones_like(input, out=None, **kwargs):
"""Return a float tensor with values of ``1``, shape as the input.
Parameters
----------
input : dragon.vm.torch.Tensor
The tensor for indicating shape.
out : dragon.vm.torch.Tensor
The optional output tensor.
Returns
-------
vm.torch.FloatTensor
The output tensor.
"""
if not hasattr(input, 'shape'):
raise ValueError('Input does not have the shape attribute.')
if out is None:
out = LeafTensor(input.shape, requires_grad=kwargs['requires_grad'] \
if 'requires_grad' in kwargs else False)
return _fill(out, shape=input.shape, value=1)
def rand(*sizes, **kwargs):
"""Return a float tensor with a uniform distribution of U(0, 1).
Parameters
----------
sizes : tuple, list or int
The sizes indicating the shape of the output tensor.
out : dragon.vm.torch.Tensor
The optional output tensor.
Returns
-------
vm.torch.FloatTensor
The output tensor.
"""
out = kwargs['out'] if 'out' in kwargs else None
if out is None:
out = LeafTensor(sizes, requires_grad=kwargs['requires_grad'] \
if 'requires_grad' in kwargs else False)
return _uniform(out, sizes, low=0, high=1)
def randn(*sizes, **kwargs):
"""Return a float tensor with a normal distribution of N(0, 1).
Parameters
----------
sizes : tuple, list or int
The sizes indicating the shape of the output tensor.
out : dragon.vm.torch.Tensor
The optional output tensor.
Returns
-------
vm.torch.FloatTensor
The output tensor.
"""
out = kwargs['out'] if 'out' in kwargs else None
if out is None:
out = LeafTensor(sizes, requires_grad=kwargs['requires_grad'] \
if 'requires_grad' in kwargs else False)
return _normal(out, sizes, mean=0, std=1)
\ No newline at end of file
Dragon/python/dragon/vm/torch/ops/factory.py
......@@ -21,13 +21,13 @@ def has_module(key):
return key in _GLOBAL_TORCH_BUILTIN_MODULES
def register_module(cls, key, ctx, **kwargs):
def register_module(cls, key, dev, **kwargs):
global _GLOBAL_TORCH_BUILTIN_MODULES
module = cls(key, ctx, **kwargs)
module = cls(key, dev, **kwargs)
_GLOBAL_TORCH_BUILTIN_MODULES[key] = module
return module
def get_module(cls, key, ctx, **kwargs):
def get_module(cls, key, dev, **kwargs):
if has_module(key): return _GLOBAL_TORCH_BUILTIN_MODULES[key]
return register_module(cls, key, ctx, **kwargs)
\ No newline at end of file
return register_module(cls, key, dev, **kwargs)
\ No newline at end of file
Dragon/python/dragon/vm/torch/ops/modules/arithmetic.py
......@@ -17,8 +17,8 @@ from dragon.vm.torch.ops.modules.base import BaseModule
class Fundamental(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(Fundamental, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(Fundamental, self).__init__(key, dev, **kwargs)
self.op_type = kwargs.get('op_type', 'Add')
self.register_op()
......@@ -32,8 +32,8 @@ class Fundamental(BaseModule):
class Maximum(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(Maximum, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(Maximum, self).__init__(key, dev, **kwargs)
self.register_op()
def register_op(self):
......@@ -46,8 +46,8 @@ class Maximum(BaseModule):
class Minimum(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(Minimum, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(Minimum, self).__init__(key, dev, **kwargs)
self.register_op()
def register_op(self):
......@@ -60,8 +60,8 @@ class Minimum(BaseModule):
class Clamp(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(Clamp, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(Clamp, self).__init__(key, dev, **kwargs)
self.min = kwargs.get('min', None)
self.max = kwargs.get('max', None)
if self.min is not None: self.min = float(self.min)
......@@ -84,8 +84,8 @@ class Clamp(BaseModule):
class Log(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(Log, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(Log, self).__init__(key, dev, **kwargs)
self.register_op()
def register_op(self):
......@@ -98,8 +98,8 @@ class Log(BaseModule):
class Exp(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(Exp, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(Exp, self).__init__(key, dev, **kwargs)
self.register_op()
def register_op(self):
......@@ -112,8 +112,8 @@ class Exp(BaseModule):
class Sqrt(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(Sqrt, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(Sqrt, self).__init__(key, dev, **kwargs)
self.register_op()
def register_op(self):
......@@ -122,4 +122,44 @@ class Sqrt(BaseModule):
def forward(self, x, y):
inputs = [x]; self.unify_devices(inputs)
outputs = [y] if y else [self.register_output()]
return self.run(inputs, outputs)
class MM(BaseModule):
def __init__(self, key, dev, **kwargs):
super(MM, self).__init__(key, dev, **kwargs)
self.transA = kwargs.get('transA', False)
self.transB = kwargs.get('transB', False)
self.register_op()
def register_op(self):
self.op_meta = {
'op_type': 'Matmul',
'arguments': {
'transA': self.transA,
'transB': self.transB,
}}
def forward(self, x1, x2, y):
inputs = [x1, x2]; self.unify_devices(inputs)
outputs = [y] if y else [self.register_output()]
return self.run(inputs, outputs)
class FullyConnected(BaseModule):
def __init__(self, key, dev, **kwargs):
super(FullyConnected, self).__init__(key, dev, **kwargs)
self.transW = kwargs.get('transW', True)
self.register_op()
def register_op(self):
self.op_meta = {
'op_type': 'FullyConnected',
'arguments': {'transW': self.transW},
}
def forward(self, x, w, b=None, y=None):
inputs = [x, w] + ([b] if b else [])
self.unify_devices(inputs)
outputs = [y] if y else [self.register_output()]
return self.run(inputs, outputs)
\ No newline at end of file
Dragon/python/dragon/vm/torch/ops/modules/array.py
......@@ -14,7 +14,7 @@ from __future__ import division
from __future__ import print_function
from dragon.vm.torch.autograd import no_grad
from dragon.vm.torch.tensor import ReferenceTensor
from dragon.vm.torch.tensor import _ReferenceTensor
from dragon.vm.torch.ops.modules.base import BaseModule
......@@ -25,8 +25,8 @@ class Indexing(BaseModule):
and the resulting memory is deep copied.
"""
def __init__(self, key, ctx, **kwargs):
super(Indexing, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(Indexing, self).__init__(key, dev, **kwargs)
self.n_starts = kwargs.get('n_starts', 0)
self.n_sizes = kwargs.get('n_sizes', 0)
self.register_op()
......@@ -62,8 +62,8 @@ class Concat(BaseModule):
Concatenate the inputs along the given axis.
"""
def __init__(self, key, ctx, **kwargs):
super(Concat, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(Concat, self).__init__(key, dev, **kwargs)
self.axis = kwargs.get('axis', 0)
self.register_op()
......@@ -90,8 +90,8 @@ class Gather(BaseModule):
input.shape[:axis] + indices.shape + input.shape[axis + 1:]
"""
def __init__(self, key, ctx, **kwargs):
super(Gather, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(Gather, self).__init__(key, dev, **kwargs)
self.axis = kwargs.get('axis', 0)
self.register_op()
......@@ -111,8 +111,8 @@ class Gather(BaseModule):
class Reduce(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(Reduce, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(Reduce, self).__init__(key, dev, **kwargs)
self.operation = kwargs.get('operation', 'SUM')
self.dim = kwargs.get('dim', None)
self.keepdim = kwargs.get('keepdim', True)
......@@ -135,8 +135,8 @@ class Reduce(BaseModule):
class ArgReduce(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(ArgReduce, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(ArgReduce, self).__init__(key, dev, **kwargs)
self.operation = kwargs.get('operation', 'ARGMAX')
self.axis = kwargs.get('axis', None)
self.keepdim = kwargs.get('keepdim', True)
......@@ -179,8 +179,8 @@ class ArgReduce(BaseModule):
class Reshape(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(Reshape, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(Reshape, self).__init__(key, dev, **kwargs)
self.n_dim = kwargs.get('n_dim', 0)
self.register_op()
......@@ -201,14 +201,14 @@ class Reshape(BaseModule):
def forward(self, x, shape):
inputs = [x]; self.unify_devices(inputs)
outputs = [ReferenceTensor(x)]
outputs = [_ReferenceTensor(x)]
callback = lambda A: self.update_arguments(A, shape)
return self.run(inputs, outputs, callback=callback)
class Squeeze(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(Squeeze, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(Squeeze, self).__init__(key, dev, **kwargs)
self.dim = kwargs.get('dim', None)
self.register_op()
......@@ -220,13 +220,13 @@ class Squeeze(BaseModule):
def forward(self, x, out=None):
inputs = [x]; self.unify_devices(inputs)
outputs = [out] if out else [ReferenceTensor(x)]
outputs = [out] if out else [_ReferenceTensor(x)]
return self.run(inputs, outputs)
class UnSqueeze(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(UnSqueeze, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(UnSqueeze, self).__init__(key, dev, **kwargs)
self.dim = kwargs.get('dim', None)
self.register_op()
......@@ -238,13 +238,13 @@ class UnSqueeze(BaseModule):
def forward(self, x, out=None):
inputs = [x]; self.unify_devices(inputs)
outputs = [out] if out else [ReferenceTensor(x)]
outputs = [out] if out else [_ReferenceTensor(x)]
return self.run(inputs, outputs)
class Permute(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(Permute, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(Permute, self).__init__(key, dev, **kwargs)
self.n_perm = kwargs.get('n_perm', 0)
self.register_op()
......@@ -270,8 +270,8 @@ class Permute(BaseModule):
class Repeat(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(Repeat, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(Repeat, self).__init__(key, dev, **kwargs)
self.n_times = kwargs.get('n_times', 0)
self.register_op()
......@@ -298,8 +298,8 @@ class Repeat(BaseModule):
class OneHot(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(OneHot, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(OneHot, self).__init__(key, dev, **kwargs)
self.depth = kwargs.get('depth', 1)
self.register_op()
......@@ -318,8 +318,8 @@ class OneHot(BaseModule):
class Cast(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(Cast, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(Cast, self).__init__(key, dev, **kwargs)
self.dtype = kwargs.get('dtype', 'float32')
self.inplace = kwargs.get('inplace', False)
self.register_op()
......@@ -343,4 +343,26 @@ class Cast(BaseModule):
self.unify_devices([x])
with no_grad():
y = self.run([], [x])
return y
\ No newline at end of file
return y
class Multinomial(BaseModule):
def __init__(self, key, dev, **kwargs):
super(Multinomial, self).__init__(key, dev, **kwargs)
self.num_samples = kwargs.get('num_samples', 1)
self.normalize = kwargs.get('normalize', False)
self.register_op()
def register_op(self):
self.op_meta = {
'op_type': 'Multinomial',
'arguments': {
'num_samples': self.num_samples,
'normalize': self.normalize,
},
}
def forward(self, x, y):
inputs = [x]; self.unify_devices(inputs)
outputs = [y] if y else [self.register_output()]
return self.run(inputs, outputs)
\ No newline at end of file
Dragon/python/dragon/vm/torch/ops/modules/base.py
......@@ -16,17 +16,17 @@ from __future__ import print_function
import numpy as np
import dragon as dg
from dragon.core import proto_utils as pb_utils
from dragon.core import proto_utils
from dragon.vm.torch.module import Module
class BaseModule(Module):
def __init__(self, key, ctx, **kwargs):
def __init__(self, key, dev, **kwargs):
super(BaseModule, self).__init__()
self._module_key = key
self._ctx = ctx
self._args_dev = pb_utils.GetDeviceOption(
'CPU').SerializeToString()
self._device = dev
self._args_dev = proto_utils.\
GetDeviceOption('cpu').SerializeToString()
def set_argument_i64(self, name, value):
dg.C.FeedTensor(name, np.array(
......
Dragon/python/dragon/vm/torch/ops/modules/control_flow.py
......@@ -17,8 +17,8 @@ from dragon.vm.torch.ops.modules.base import BaseModule
class Copy(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(Copy, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(Copy, self).__init__(key, dev, **kwargs)
self.register_op()
def register_op(self):
......@@ -26,4 +26,24 @@ class Copy(BaseModule):
def forward(self, dst, src):
outputs = [dst]; self.unify_devices(outputs)
return self.run([src], outputs)
\ No newline at end of file
return self.run([src], outputs)
class Compare(BaseModule):
def __init__(self, key, dev, **kwargs):
super(Compare, self).__init__(key, dev, **kwargs)
self.operation = kwargs.get('operation', 'NONE')
self.register_op()
def register_op(self):
self.op_meta = {
'op_type': 'Compare',
'arguments': {
'operation': self.operation,
'to_uint8': True,
}}
def forward(self, x1, x2, y):
inputs = [x1, x2]; self.unify_devices(inputs)
outputs = [y] if y else [self.register_output()]
return self.run(inputs, outputs)
\ No newline at end of file
Dragon/python/dragon/vm/torch/ops/modules/init.py
......@@ -17,8 +17,8 @@ from dragon.vm.torch.ops.modules.base import BaseModule
class _InitModule(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(_InitModule, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(_InitModule, self).__init__(key, dev, **kwargs)
self.n_dim = kwargs.get('n_dim', 0)
self.dtype = kwargs.get('dtype', 'float32')
......@@ -33,8 +33,8 @@ class _InitModule(BaseModule):
class Fill(_InitModule):
def __init__(self, key, ctx, **kwargs):
super(Fill, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(Fill, self).__init__(key, dev, **kwargs)
self.value = kwargs.get('value', 0.0)
self.register_op()
......@@ -53,8 +53,8 @@ class Fill(_InitModule):
class RandomNormal(_InitModule):
def __init__(self, key, ctx, **kwargs):
super(RandomNormal, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(RandomNormal, self).__init__(key, dev, **kwargs)
self.mean = kwargs.get('mean', 0.0)
self.std = kwargs.get('std', 1.0)
self.register_op()
......@@ -75,8 +75,8 @@ class RandomNormal(_InitModule):
class RandomUniform(_InitModule):
def __init__(self, key, ctx, **kwargs):
super(RandomUniform, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(RandomUniform, self).__init__(key, dev, **kwargs)
self.low = kwargs.get('low', 0.0)
self.high = kwargs.get('high', 1.0)
self.register_op()
......
Dragon/python/dragon/vm/torch/ops/modules/update.py
......@@ -18,8 +18,8 @@ from dragon.vm.torch.ops.modules.base import BaseModule
class Update(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(Update, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(Update, self).__init__(key, dev, **kwargs)
self.op_type = kwargs.get('op_type', 'Update')
self.lr_mult = kwargs.get('lr_mult', 1.0)
self.decay_mult = kwargs.get('decay_mult', 1.0)
......@@ -42,8 +42,8 @@ class Update(BaseModule):
class Collective(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(Collective, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(Collective, self).__init__(key, dev, **kwargs)
self.mode = kwargs.get('mode', None)
if self.mode is None:
raise ValueError('Got invalid collective mode: {}'.format(self.mode))
......@@ -71,8 +71,8 @@ class Collective(BaseModule):
class Accumulate(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(Accumulate, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(Accumulate, self).__init__(key, dev, **kwargs)
self.register_op()
def register_op(self):
......
Dragon/python/dragon/vm/torch/ops/modules/vision.py
......@@ -17,8 +17,8 @@ from dragon.vm.torch.ops.modules.base import BaseModule
class Resize2d(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(Resize2d, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(Resize2d, self).__init__(key, dev, **kwargs)
self.op_type = kwargs.get('op_type', 'NNResize')
self.dsize = kwargs.get('dsize', None)
self.fx = kwargs.get('fx', None)
......@@ -51,8 +51,8 @@ class Resize2d(BaseModule):
class RoIPool(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(RoIPool, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(RoIPool, self).__init__(key, dev, **kwargs)
self.pool_h = kwargs.get('pooled_h', 0)
self.pool_w = kwargs.get('pooled_w', 0)
self.spatial_scale = kwargs.get('spatial_scale', 1.0)
......@@ -74,8 +74,8 @@ class RoIPool(BaseModule):
class RoIAlign(BaseModule):
def __init__(self, key, ctx, **kwargs):
super(RoIAlign, self).__init__(key, ctx, **kwargs)
def __init__(self, key, dev, **kwargs):
super(RoIAlign, self).__init__(key, dev, **kwargs)
self.pool_h = kwargs.get('pooled_h', 0)
self.pool_w = kwargs.get('pooled_w', 0)
self.spatial_scale = kwargs.get('spatial_scale', 1.0)
......
Dragon/python/dragon/vm/torch/ops/primitive.py
......@@ -17,34 +17,33 @@ import numpy as np
import dragon as dg
from dragon.vm.torch.tensor import *
from dragon.vm.torch.c_api import Context
from dragon.vm.torch.c_api import device as _Device
def UnifyDevices(tensors, key='Inputs'):
device_types = [t._ctx.device_type for t in tensors]
device_ids = [0]
if len(set(device_types)) != 1:
types, indices = [t.device.type for t in tensors], [0]
if len(set(types)) != 1:
raise ValueError('{} from different device type: [{}].'
.format(key, ', '.join(device_types)))
if device_types[0] == 'CUDA':
device_ids = [t._ctx.device_id for t in tensors]
if len(set(device_ids)) != 1:
.format(key, ', '.join(types)))
if types[0] == 'cuda':
indices = [t.device.index for t in tensors]
if len(set(indices)) != 1:
raise ValueError('{} from different cuda device: [{}].'
.format(key, ', '.join([str(d) for d in device_ids])))
return Context(device_types[0], device_ids[0])
.format(key, ', '.join([str(d) for d in indices])))
return _Device(types[0], indices[0])
def MakeContext(inputs=(), outputs=()):
def MakeDevice(inputs=(), outputs=()):
# Case #1: [], [] -> CPU
# Case #2: [...], [] -> Refer Inputs
# Case #3: [], [...] -> Refer Outputs
# Case #4: [...], [...] -> Refer Outputs
if len(outputs) > 0: return UnifyDevices(outputs, 'Outputs')
if len(inputs) > 0: return UnifyDevices(inputs, 'Inputs')
return Context()
return _Device()
def WrapScalar(scalar, dtype, ctx):
def WrapScalar(scalar, dtype, device):
# We use (DType + Value) to hash different scalars
# Setting a Tensor with same DType and shape will not deconstruct it
if 'float' in dtype: scalar = float(scalar)
......@@ -52,6 +51,6 @@ def WrapScalar(scalar, dtype, ctx):
name = '/share/scalar/{}/{}'.format(dtype, str(scalar))
if not dg.workspace.HasTensor(name):
dg.workspace.FeedTensor(name, np.array(scalar, dtype=dtype))
t = Tensor(name=name, dtype=dtype, ctx=ctx, own_storage=False)
t = Tensor(name=name, dtype=dtype, device=device, own_storage=False)
t.requires_grad = False
return t
Dragon/python/dragon/vm/torch/ops/tensor.py 0 → 100644
# ------------------------------------------------------------
# Copyright (c) 2017-present, SeetaTech, Co.,Ltd.
#
# Licensed under the BSD 2-Clause License.
# You should have received a copy of the BSD 2-Clause License
# along with the software. If not, See,
#
# <https://opensource.org/licenses/BSD-2-Clause>
#
# ------------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from dragon.vm.torch.tensor import Tensor
from dragon.vm.torch.ops.factory import get_module
from dragon.vm.torch.ops.primitive import MakeDevice
from dragon.vm.torch.ops.modules.array import Cast
from dragon.vm.torch.ops.builtin import (
_fill, _uniform, _normal, multinomial,
_fundamental, _rfundamental,
log, exp, sqrt, clamp,
_reshape, squeeze, unsqueeze,
_permute, _repeat, _indexing, narrow,
mean, sum, max, min,
gt, lt, eq, ge, le,
)
def _type_to(input, dtype='float32', inplace=False):
if dtype == input.dtype: return input
dev = MakeDevice(inputs=[input])
key = 'Cast/{}/dtype:{}/inplace:{}'.format(
dev, dtype, 'true' if inplace else 'false')
module = get_module(Cast, key, dev, dtype=dtype, inplace=inplace)
return module.forward(input)
Tensor.fill_ = lambda self, value: _fill(self, self.shape, value)
Tensor.uniform_ = lambda self, low=0, high=1: _uniform(self, self.shape, low, high)
Tensor.normal_ = lambda self, mean=0, std=1: _normal(self, self.shape, mean, std)
Tensor.multinomial = lambda *args, **kwargs: multinomial(*args, **kwargs)
Tensor.add = lambda self, value: _fundamental(self, value, 'Add')
Tensor.add_ = lambda self, value: _fundamental(self, value, 'Add', self)
Tensor.__radd__ = lambda self, value: _rfundamental(self, value, 'RAdd')
Tensor.sub = lambda self, value: _fundamental(self, value, 'Sub')
Tensor.sub_ = lambda self, value: _fundamental(self, value, 'Sub', self)
Tensor.__rsub__ = lambda self, value: _rfundamental(self, value, 'RSub')
Tensor.mul = lambda self, value: _fundamental(self, value, 'Mul')
Tensor.mul_ = lambda self, value: _fundamental(self, value, 'Mul', self)
Tensor.__rmul__ = lambda self, value: _rfundamental(self, value, 'RMul')
Tensor.div = lambda self, value: _fundamental(self, value, 'Div')
Tensor.div_ = lambda self, value: _fundamental(self, value, 'Div', self)
Tensor.__rdiv__ = lambda self, value: _rfundamental(self, value, 'RDiv')
Tensor.__rtruediv__ = lambda self, value: _rfundamental(self, value, 'RDiv')
Tensor.clamp = lambda *args, **kwargs: clamp(*args, **kwargs)
Tensor.clamp_ = lambda self, min=None, max=None: clamp(self, min, max, self)
Tensor.log = lambda *args, **kwargs: log(*args, **kwargs)
Tensor.exp = lambda *args, **kwargs: exp(*args, **kwargs)
Tensor.sqrt = lambda *args, **kwargs: sqrt(*args, **kwargs)
Tensor.squeeze = lambda *args, **kwargs: squeeze(*args, **kwargs)
Tensor.squeeze_ = lambda self, dim: squeeze(self, dim, self)
Tensor.unsqueeze = lambda *args, **kwargs: unsqueeze(*args, **kwargs)
Tensor.unsqueeze_ = lambda self, dim: unsqueeze(self, dim, self)
Tensor.view = lambda self, *shape: _reshape(self, shape)
Tensor.view_as = lambda *args, **kwargs: _reshape(*args, **kwargs)
Tensor.permute = lambda self, *dims: _permute(self, dims)
Tensor.repeat = lambda self, *args: _repeat(self, args)
Tensor.mean = lambda *args, **kwargs: mean(*args, **kwargs)
Tensor.sum = lambda *args, **kwargs: sum(*args, **kwargs)
Tensor.max = lambda *args, **kwargs: max(*args, **kwargs)
Tensor.min = lambda *args, **kwargs: min(*args, **kwargs)
Tensor.gt = lambda *args, **kwargs: gt(*args, **kwargs)
Tensor.ge = lambda *args, **kwargs: ge(*args, **kwargs)
Tensor.lt = lambda *args, **kwargs: lt(*args, **kwargs)
Tensor.le = lambda *args, **kwargs: le(*args, **kwargs)
Tensor.eq = lambda *args, **kwargs: eq(*args, **kwargs)
Tensor.narrow = lambda *args, **kwargs: narrow(*args, **kwargs)
Tensor._indexing = lambda *args, **kwargs: _indexing(*args, **kwargs)
Tensor.half = lambda self: _type_to(self, dtype='float16', inplace=False)
Tensor.half_ = lambda self: _type_to(self, dtype='float16', inplace=True)
Tensor.float = lambda self: _type_to(self, dtype='float32', inplace=False)
Tensor.float_ = lambda self: _type_to(self, dtype='float32', inplace=True)
Tensor.double = lambda self: _type_to(self, dtype='float64', inplace=False)
Tensor.double_ = lambda self: _type_to(self, dtype='float64', inplace=True)
Tensor.byte = lambda self: _type_to(self, dtype='uint8', inplace=False)
Tensor.byte_ = lambda self: _type_to(self, dtype='uint8', inplace=True)
Tensor.char = lambda self: _type_to(self, dtype='int8', inplace=False)
Tensor.char_ = lambda self: _type_to(self, dtype='int8', inplace=True)
Tensor.int = lambda self: _type_to(self, dtype='int32', inplace=False)
Tensor.int_ = lambda self: _type_to(self, dtype='int32', inplace=True)
Tensor.long = lambda self: _type_to(self, dtype='int64', inplace=False)
Tensor.long_ = lambda self: _type_to(self, dtype='int64', inplace=True)
Tensor.type = lambda self, dtype=None: _type_to(self, dtype=dtype) \
if dtype is not None else 'torch.' + self._type2str()
\ No newline at end of file
Dragon/python/dragon/vm/torch/ops/update.py deleted 100644 → 0
# ------------------------------------------------------------
# Copyright (c) 2017-present, SeetaTech, Co.,Ltd.
#
# Licensed under the BSD 2-Clause License.
# You should have received a copy of the BSD 2-Clause License
# along with the software. If not, See,
#
# <https://opensource.org/licenses/BSD-2-Clause>
#
# ------------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import dragon.core.mpi as mpi
from dragon.vm.torch.ops.primitive import MakeContext
from dragon.vm.torch.ops.factory import get_module
from dragon.vm.torch.ops.modules.update import Accumulate
from dragon.vm.torch.ops.modules.update import Collective
from dragon.vm.torch.ops.modules.update import Update
def _accumulate(grads):
if len(grads) == 0: return
if not isinstance(grads, (list, tuple)): grads = [grads]
ctx = MakeContext(inputs=grads)
key = 'Accumulate/{}/alpha:1./beta:1.'.format(ctx)
module = get_module(Accumulate, key, ctx)
return module.forward(grads)
def _allreduce(grads):
if not mpi.Is_Init(): return
if not isinstance(grads, (list, tuple)): grads = [grads]
ctx = MakeContext(inputs=grads)
mode = mpi.GetParallelMode() + '_ALLREDUCE'
key = 'Collective/{}/{}'.format(ctx, mode.lower())
module = get_module(Collective, key, ctx, mode=mode)
return module.forward(grads)
def _update(param, grad, op_type, slot,
lr_mult=1.0, decay_mult=1.0):
ctx = MakeContext(inputs=[param])
key = '{}/{}/{}/{}'.format(op_type, ctx, slot, param.name)
module = get_module(Update, key, ctx, op_type=op_type,
lr_mult=lr_mult, decay_mult=decay_mult, slot=slot)
return module.forward(param, grad)
\ No newline at end of file
Dragon/python/dragon/vm/torch/ops/vision.py deleted 100644 → 0
# ------------------------------------------------------------
# Copyright (c) 2017-present, SeetaTech, Co.,Ltd.
#
# Licensed under the BSD 2-Clause License.
# You should have received a copy of the BSD 2-Clause License
# along with the software. If not, See,
#
# <https://opensource.org/licenses/BSD-2-Clause>
#
# ------------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from dragon.vm.torch.ops.primitive import MakeContext
from dragon.vm.torch.ops.factory import get_module
from dragon.vm.torch.ops.modules.vision import Resize2d
from dragon.vm.torch.ops.modules.vision import RoIPool, RoIAlign
def _resize_2d(input, op_type, dsize, fx, fy):
if dsize is None:
if fx < 0 or fy < 0:
raise ValueError('Set fx and fy if dsize is None.')
else:
if len(dsize) != 2:
raise ValueError('The dsize should be a list with 2 elements.')
if dsize is None and (fy == -1.0 or fx == -1.0):
raise RuntimeError('The dsize, fx/fy should be specified either.')
ctx = MakeContext(inputs=[input])
key = '{}/{}/dsize:{}/fx:{}/fy:{}'.format(
op_type, ctx, '2' if dsize else 'none', fx, fy)
module = get_module(Resize2d, key, ctx,
op_type=op_type, dsize=dsize, fx=fx, fy=fy)
return module.forward(input, dsize)
def nn_resize(input, dsize, fx=-1.0, fy=-1.0):
return _resize_2d(input, 'NNResize', dsize, fx, fy)
def bilinear_resize(input, dsize, fx=-1.0, fy=-1.0):
return _resize_2d(input, 'BilinearResize', dsize, fx, fy)
def roi_pool(feature, rois, pooled_h, pooled_w, spatial_scale):
ctx = MakeContext(inputs=[feature])
key = 'RoIPool/{}/pool_h:{}/pool_w:{}/spatial_scale:{}'.format(
ctx, pooled_h, pooled_w, spatial_scale)
module = get_module(
RoIPool, key, ctx,
pooled_h=pooled_h,
pooled_w=pooled_w,
spatial_scale=spatial_scale,
)
return module.forward(feature, rois)
def roi_align(feature, rois, pooled_h, pooled_w,
spatial_scale, sampling_ratio=2):
ctx = MakeContext(inputs=[feature])
key = 'RoIAlign/{}/pool_h:{}/pool_w:{}/' \
'spatial_scale:{}/sampling_ratio:{}'.format(
ctx, pooled_h, pooled_w, spatial_scale, sampling_ratio)
module = get_module(
RoIAlign, key, ctx,
pooled_h=pooled_h,
pooled_w=pooled_w,
spatial_scale=spatial_scale,
sampling_ratio=sampling_ratio,
)
return module.forward(feature, rois)
\ No newline at end of file
Dragon/python/dragon/vm/torch/optim/optimizer.py
......@@ -22,7 +22,7 @@ from collections import defaultdict
from dragon.vm.torch.tensor import Tensor
from dragon.vm.torch.ops.update import (
from dragon.vm.torch.ops.builtin import (
_accumulate, _allreduce, _update,
)
......@@ -51,6 +51,10 @@ class Optimizer(object):
for param_group in param_groups:
self.add_param_group(param_group)
self._update_type = None
self._allow_parallel = False
if dragon.mpi.Is_Init():
local_rank, _ = dragon.mpi.AllowParallel()
if local_rank != -1: self._allow_parallel = True
self._mutable_parameters = {}
def __repr__(self):
......@@ -80,7 +84,7 @@ class Optimizer(object):
return Tensor(
name=grad_name,
own_storage=False,
ctx=param._ctx)
device=param.device)
return None
def _run_update_ops(self, group):
......@@ -109,7 +113,7 @@ class Optimizer(object):
self.feed_parameters(group)
# Run a all-reduce op to accumulate grads if necessary
_allreduce(grads)
if self._allow_parallel: _allreduce(grads)
# Run regular update ops
for p, g in zip(params, grads):
......
Dragon/python/dragon/vm/torch/tensor.py
......@@ -14,35 +14,19 @@ from __future__ import division
from __future__ import print_function
import six
import numpy as np
import dragon as dg
import numpy
import dragon
import dragon.core.mapping as mapping
import dragon.core.tensor_utils as tensor_utils
from dragon.vm.torch.c_api import Size, Context
from dragon.vm.torch.tensor_uitls import from_dragon
from dragon.core import mapping, tensor_utils, proto_utils
from dragon.vm.torch.pool import TensorPool
__all__ = [
'Tensor', 'Parameter',
'CharTensor', 'ByteTensor', 'IntTensor', 'LongTensor',
'HalfTensor', 'FloatTensor', 'DoubleTensor',
]
##############################################
# #
# Tensor-Core #
# #
##############################################
from dragon.vm.torch.c_api import Size, from_dragon
from dragon.vm.torch.c_api import device as _Device
class Tensor(object):
def __init__(self, *args, **kwargs):
# Internal properties
self._ctx = kwargs.get('ctx', Context())
self._device = kwargs.get('device', _Device())
self._requires_grad = kwargs.get('requires_grad', False)
self._tensor = kwargs.get('name', None)
self._own_storage = kwargs.get('own_storage', True)
......@@ -60,13 +44,13 @@ class Tensor(object):
if len(args) == 0:
# + empty tensor, not leaf
if self._tensor is not None:
dg.C.CreateTensor(self._tensor)
dragon.C.CreateTensor(self._tensor)
elif len(args) == 1:
if isinstance(args[0], (list, tuple)):
# + torch.Tensor(sequence)
self._init_from_numpy(np.array(
self._init_from_numpy(numpy.array(
args[0], dtype=kwargs.get('dtype', 'float32')))
elif isinstance(args[0], np.ndarray):
elif isinstance(args[0], numpy.ndarray):
# + torch.Tensor(array)
self._init_from_numpy(args[0])
else:
......@@ -81,7 +65,7 @@ class Tensor(object):
self._init_from_shape(args, kwargs.get('dtype', 'float32'))
# Store the reference of backend
self._storage = dg.C.GetTensor(self.name) \
self._storage = dragon.C.GetTensor(self.name) \
if self.name is not None else None
def _init_from_numpy(self, array):
......@@ -114,6 +98,18 @@ class Tensor(object):
return self._tensor.name if hasattr(
self._tensor, 'name') else self._tensor
@property
def device(self):
"""Return the device of this tensor.
Returns
-------
dragon.vm.torch.device
The device.
"""
return self._device
def cpu(self):
"""Switch the internal storage on cpu memory.
......@@ -123,22 +119,27 @@ class Tensor(object):
The self.
"""
self._ctx.device_type = 'CPU'
self._device.type = 'cpu'
self._storage.ToCPU()
return self
def cuda(self, device=None):
"""Switch the internal storage on cuda memory.
Parameters
----------
device : int, optional
The device index.
Returns
-------
dragon.vm.torch.Tensor
The self.
"""
if device is None: device = dg.config.GetGPU()
if device is None: device = dragon.config.GetGPU()
self._storage.ToCUDA(device)
self._ctx.device_type, self._ctx.device_id = ('CUDA', device)
self._device.type, self._device.index = 'cuda', device
return self
def numpy(self, readonly=False):
......@@ -146,7 +147,7 @@ class Tensor(object):
Parameters
----------
readonly : boolean
readonly : boolean, optional, default=False
Whether to sync the contents with device.
Returns
......@@ -167,7 +168,7 @@ class Tensor(object):
"""
if isinstance(self._tensor, str):
return dg.Tensor.Ref(self._tensor,
return dragon.Tensor.Ref(self._tensor,
shape=self.shape, dtype=self.dtype)
else: return self._tensor
......@@ -342,6 +343,86 @@ class Tensor(object):
"""
return self.mul(-1.0)
def __gt__(self, other):
"""Compute *self* > *other* element-wise.
Parameters
----------
other : dragon.vm.torch.Tensor, number
The other tensor.
Returns
-------
dragon.vm.torch.Tensor
The output byte tensor.
"""
return self.gt(other)
def __ge__(self, other):
"""Compute *self* >= *other* element-wise.
Parameters
----------
other : dragon.vm.torch.Tensor, number
The other tensor.
Returns
-------
dragon.vm.torch.Tensor
The output byte tensor.
"""
return self.ge(other)
def __lt__(self, other):
"""Compute *self* < *other* element-wise.
Parameters
----------
other : dragon.vm.torch.Tensor, number
The other tensor.
Returns
-------
dragon.vm.torch.Tensor
The output byte tensor.
"""
return self.lt(other)
def __le__(self, other):
"""Compute *self* <= *other* element-wise.
Parameters
----------
other : dragon.vm.torch.Tensor, number
The other tensor.
Returns
-------
dragon.vm.torch.Tensor
The output byte tensor.
"""
return self.le(other)
def __eq__(self, other):
"""Compute *self* == *other* element-wise.
Parameters
----------
other : dragon.vm.torch.Tensor, number
The other tensor.
Returns
-------
dragon.vm.torch.Tensor
The output byte tensor.
"""
return self.eq(other)
def __repr__(self):
"""Return a format str representing the internal storage.
......@@ -354,11 +435,12 @@ class Tensor(object):
np_data = self.numpy(readonly=True)
if len(np_data.shape) == 0: return str(np_data)
format_str = str(np_data)
format_shape = 'x'.join([str(dim) for dim in np_data.shape])
meta_info = '\n[torch.{} of size {}]'.\
format(self._type2str(), 'x'.join([str(dim) for dim in np_data.shape]))
if self._ctx.device_type == 'CUDA':
meta_info = '\n[torch.cuda.{} of size {} (GPU {})]'. \
format(self._type2str(), 'x'.join([str(dim) for dim in np_data.shape]), self._ctx.device_id)
format(self._type2str(), format_shape)
if self.device.type == 'cuda':
meta_info = '\n[torch.cuda.{} of size {} (GPU {})]'.format(
self._type2str(), format_shape, self.device.index)
del np_data # DECREF
return format_str + meta_info
......@@ -432,7 +514,7 @@ class Tensor(object):
'The cropping starts and ends of axis {} '
'can not be equal, got {}:{}.'
.format(ix, starts[-1], it.stop))
# handle step
# Handle step
if it.step is not None:
raise NotImplementedError('Indexing with step has not been implemented yet. ')
elif isinstance(it, int):
......@@ -442,8 +524,8 @@ class Tensor(object):
raise TypeError('Unsupported type of indices: {}'.format(type(type(it))))
return self._indexing(starts, sizes)
def device(self):
return self._ctx.device_id
def __hash__(self):
return id(self)
##############################################
# #
......@@ -532,7 +614,7 @@ class Tensor(object):
Parameters
----------
dtype : str
dtype : str, optional
The specified type.
Returns
......@@ -541,7 +623,7 @@ class Tensor(object):
The data type or the new tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.type')
raise NotImplementedError('Refer torch.ops.tensor.type')
def is_floating_point(self):
"""Whether the data type is floating.
......@@ -563,11 +645,11 @@ class Tensor(object):
##############################################
def squeeze(self, dim=None):
"""Returns a tensor with all the dimensions of input of size 1 removed.
"""Return a tensor with all the dimensions of input of size 1 removed.
Parameters
----------
dim : int
dim : int, optional
The optional dim to remove.
......@@ -577,14 +659,14 @@ class Tensor(object):
The new tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin._squeeze')
raise NotImplementedError('Refer torch.ops.tensor.squeeze')
def squeeze_(self, dim=None):
"""Inplace of ``Tensor.squeeze()``
Parameters
----------
dim : int
dim : int, optional
The optional dim to remove.
Returns
......@@ -593,7 +675,7 @@ class Tensor(object):
The self.
"""
raise NotImplementedError('Refer torch.ops.builtin._squeeze_')
raise NotImplementedError('Refer torch.ops.tensor.squeeze_')
def unsqueeze(self, dim):
"""Returns a tensor with a dimension of size 1 inserted at the specified position.
......@@ -609,7 +691,7 @@ class Tensor(object):
The new tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin._unsqueeze')
raise NotImplementedError('Refer torch.ops.tensor.unsqueeze')
def unsqueeze_(self, dim):
"""Inplace of ``Tensor.unsqueeze()``
......@@ -625,14 +707,14 @@ class Tensor(object):
The self.
"""
raise NotImplementedError('Refer torch.ops.builtin._unsqueeze_')
raise NotImplementedError('Refer torch.ops.tensor.unsqueeze_')
def view(self, *args):
def view(self, *shape):
"""Return a new tensor with the same data but a different size.
Parameters
----------
args : tuple or int
shape : int...
The new size.
Returns
......@@ -641,16 +723,16 @@ class Tensor(object):
The output tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.view')
raise NotImplementedError('Refer torch.ops.tensor.view')
def reshape(self, *args):
def reshape(self, *shape):
"""Return a new tensor with the same data but a different size.
See also: *torch.view(*args)*
See also: *torch.view(*shape)*
Parameters
----------
args : tuple or int
shape : int...
The new size.
Returns
......@@ -659,7 +741,7 @@ class Tensor(object):
The output tensor.
"""
return self.view(*args)
return self.view(*shape)
def view_as(self, other):
"""Return a new tensor with the same data but a different size as the given tensor.
......@@ -675,14 +757,14 @@ class Tensor(object):
The output tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.view_as')
raise NotImplementedError('Refer torch.ops.tensor.view_as')
def permute(self, dims=None):
def permute(self, *dims):
"""Return a new tensor with the specific order of dimensions.
Parameters
----------
dims : sequence of int
dims : int...
The new order of dimensions.
Returns
......@@ -691,7 +773,7 @@ class Tensor(object):
The output tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.permute')
raise NotImplementedError('Refer torch.ops.tensor.permute')
def narrow(self, dimension, start, length):
"""Return a new tensor that is a narrowed version of input tensor.
......@@ -703,7 +785,7 @@ class Tensor(object):
start : int
The starting position.
length : int
The distance to the ending postion.
The distance to the ending position.
Returns
-------
......@@ -711,14 +793,14 @@ class Tensor(object):
The output tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.narrow')
raise NotImplementedError('Refer torch.ops.tensor.narrow')
def repeat(self, *sizes):
"""Repeat this tensor along the specified dimensions.
Parameters
----------
sizes : vm.torch.Size or int...
sizes : int...
The number of times to repeat.
Returns
......@@ -727,7 +809,7 @@ class Tensor(object):
The output tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.repeat')
raise NotImplementedError('Refer torch.ops.tensor.repeat')
def copy_(self, src, non_blocking=False):
"""Copy the elements from ``src`` into this tensor and return ``self``.
......@@ -736,7 +818,7 @@ class Tensor(object):
----------
src : dragon.vm.torch.Tensor
The source tensor.
non_blocking : boolean
non_blocking : boolean, optional, default=False
Whether to copy asynchronously between CPU and GPU.
Returns
......@@ -745,14 +827,24 @@ class Tensor(object):
The ``self`` tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.copy_')
# Copy memory
tensor_utils.FromTensor(
src, proto_utils.GetDeviceOption(
src.device.type, src.device.index),
self.name, proto_utils.GetDeviceOption(
self.device.type, self.device.index))
# Transfer the static shape if necessary
self._static_shape = src.size() \
if self._static_shape else None
return self
def fill_(self, value):
"""Fills self tensor with the specified value.
Parameters
----------
value : numerical type
value : number
The value to fill.
Returns
-------
......@@ -760,15 +852,11 @@ class Tensor(object):
The self.
"""
raise NotImplementedError('Refer torch.ops.builtin.fill_')
raise NotImplementedError('Refer torch.ops.tensor.fill_')
def zero_(self):
"""Fills self tensor with zeros.
Parameters
----------
value : numerical type
Returns
-------
dragon.vm.torch.Tensor
......@@ -780,10 +868,6 @@ class Tensor(object):
def one_(self):
"""Fills self tensor with ones.
Parameters
----------
value : numerical type
Returns
-------
dragon.vm.torch.Tensor
......@@ -797,9 +881,9 @@ class Tensor(object):
Parameters
----------
low : numerical type
low : number, optional, default=0
The lower bound.
high : numerical type
high : number, optional, default=1
The higher bound.
Returns
......@@ -808,16 +892,16 @@ class Tensor(object):
The self.
"""
raise NotImplementedError('Refer torch.ops.builtin.uniform_')
raise NotImplementedError('Refer torch.ops.tensor.uniform_')
def normal_(self, mean=0, std=1):
"""Fill self tensor with the specified normal distribution.
Parameters
----------
mean : numerical type
mean : number, optional, default=0
The mean(mu) of normal distribution.
std : numerical type
std : number, optional, default=1
The std(sigma) of normal distribution.
Returns
......@@ -826,7 +910,26 @@ class Tensor(object):
The self.
"""
raise NotImplementedError('Refer torch.ops.builtin.normal_')
raise NotImplementedError('Refer torch.ops.tensor.normal_')
def multinomial(self, num_samples, normalize=False):
"""Return a tensor where each row contains ``num_samples``,
sampled from the multinomial distribution.
Parameters
----------
num_samples : int
The number of samples.
normalize : boolean, optional, default=False
Whether to normalize the inputs.
Returns
-------
dragon.vm.torch.Tensor
The output tensor.
"""
raise NotImplementedError('Refer torch.ops.tensor.multinomial')
def add(self, value):
"""See ``torch.add()``
......@@ -842,7 +945,7 @@ class Tensor(object):
The output tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.add')
raise NotImplementedError('Refer torch.ops.tensor.add')
def add_(self, value):
"""Inplace of ``torch.add()``
......@@ -858,7 +961,7 @@ class Tensor(object):
The self.
"""
raise NotImplementedError('Refer torch.ops.builtin.add_')
raise NotImplementedError('Refer torch.ops.tensor.add_')
def sub(self, value):
"""Subtract the ``self`` and ``value`` into the output tensor.
......@@ -874,7 +977,7 @@ class Tensor(object):
The output tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.sub')
raise NotImplementedError('Refer torch.ops.tensor.sub')
def sub_(self, value):
"""Inplace of ``Tensor.sub()``
......@@ -890,7 +993,7 @@ class Tensor(object):
The self.
"""
raise NotImplementedError('Refer torch.ops.builtin.sub_')
raise NotImplementedError('Refer torch.ops.tensor.sub_')
def mul(self, value):
"""Multiply the ``self`` and ``value`` into the output tensor.
......@@ -906,7 +1009,7 @@ class Tensor(object):
The output tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.mul')
raise NotImplementedError('Refer torch.ops.tensor.mul')
def mul_(self, value):
"""Inplace of ``Tensor.mul()``
......@@ -922,7 +1025,7 @@ class Tensor(object):
The self.
"""
raise NotImplementedError('Refer torch.ops.builtin.mul_')
raise NotImplementedError('Refer torch.ops.tensor.mul_')
def div(self, value):
"""Divide the ``self`` and ``value`` into the output tensor.
......@@ -938,7 +1041,7 @@ class Tensor(object):
The output tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.div')
raise NotImplementedError('Refer torch.ops.tensor.div')
def div_(self, value):
"""Inplace of ``Tensor.div()``
......@@ -954,7 +1057,7 @@ class Tensor(object):
The self.
"""
raise NotImplementedError('Refer torch.ops.builtin.div_')
raise NotImplementedError('Refer torch.ops.tensor.div_')
def clamp(self, min=None, max=None):
"""Return a tensor that all elements are clamped into the range [min, max].
......@@ -972,7 +1075,7 @@ class Tensor(object):
The output tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.clamp')
raise NotImplementedError('Refer torch.ops.tensor.clamp')
def clamp_(self, min=None, max=None):
"""Clamp all elements are clamped into the range [min, max].
......@@ -990,52 +1093,40 @@ class Tensor(object):
The output tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.clamp_')
raise NotImplementedError('Refer torch.ops.tensor.clamp_')
def log(self):
"""Compute the natural logarithm of this tensor.
Parameters
----------
None
Returns
-------
dragon.vm.torch.Tensor
The log tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.log')
raise NotImplementedError('Refer torch.ops.tensor.log')
def exp(self):
"""Compute the exponential of this tensor.
Parameters
----------
None
Returns
-------
dragon.vm.torch.Tensor
The exp tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.exp')
raise NotImplementedError('Refer torch.ops.tensor.exp')
def sqrt(self):
"""Compute the square-root of this tensor.
Parameters
----------
None
Returns
-------
torch.Tensor
The sqrt tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.sqrt')
raise NotImplementedError('Refer torch.ops.tensor.sqrt')
def mean(self, dim=None, keepdim=False):
"""Returns the mean of all elements or elements along the given dim.
......@@ -1044,7 +1135,7 @@ class Tensor(object):
----------
dim : int, optional
The axis of tensor to compute mean value.
keepdim : bool, optional
keepdim : bool, optional, default=False
Whether the output tensor has dim retained or not.
Returns
......@@ -1053,7 +1144,7 @@ class Tensor(object):
The mean-reduced tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.mean')
raise NotImplementedError('Refer torch.ops.tensor.mean')
def sum(self, dim=None, keepdim=False):
"""Returns the sum of all elements or elements along the given dim.
......@@ -1062,7 +1153,7 @@ class Tensor(object):
----------
dim : int, optional
The axis of tensor to compute sum value.
keepdim : bool, optional
keepdim : bool, optional, default=False
Whether the output tensor has dim retained or not.
Returns
......@@ -1071,7 +1162,7 @@ class Tensor(object):
The sum-reduced tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.sum')
raise NotImplementedError('Refer torch.ops.tensor.sum')
def max(self, dim=None, keepdim=False):
"""Return the values and indices of maximum elements along the given axis.
......@@ -1080,7 +1171,7 @@ class Tensor(object):
----------
dim : int, optional
The axis of tensor to compute sum value.
keepdim : bool, optional
keepdim : bool, optional, default=False
Whether the output tensor has dim retained or not.
Returns
......@@ -1089,16 +1180,16 @@ class Tensor(object):
The maximum values and indices.
"""
raise NotImplementedError('Refer torch.ops.builtin.max')
raise NotImplementedError('Refer torch.ops.tensor.max')
def min(input, dim=None, keepdim=False):
def min(self, dim=None, keepdim=False):
"""Return the values and indices of minimum elements along the given axis.
Parameters
----------
dim : int, optional
The axis of tensor to compute sum value.
keepdim : bool, optional
keepdim : bool, optional, default=False
Whether the output tensor has dim retained or not.
Returns
......@@ -1107,7 +1198,87 @@ class Tensor(object):
The minimum values and indices.
"""
raise NotImplementedError('Refer torch.ops.builtin.min')
raise NotImplementedError('Refer torch.ops.tensor.min')
def gt(self, other):
"""Compute *self* > *other* element-wise.
Parameters
----------
other : dragon.vm.torch.Tensor, number
The other tensor.
Returns
-------
dragon.vm.torch.Tensor
The output byte tensor.
"""
raise NotImplementedError('Refer torch.ops.tensor.gt')
def ge(self, other):
"""Compute *self* >= *other* element-wise.
Parameters
----------
other : dragon.vm.torch.Tensor, number
The other tensor.
Returns
-------
dragon.vm.torch.Tensor
The output byte tensor.
"""
raise NotImplementedError('Refer torch.ops.tensor.ge')
def lt(self, other):
"""Compute *self* < *other* element-wise.
Parameters
----------
other : dragon.vm.torch.Tensor, number
The other tensor.
Returns
-------
dragon.vm.torch.Tensor
The output byte tensor.
"""
raise NotImplementedError('Refer torch.ops.tensor.lt')
def le(self, other):
"""Compute *self* <= *other* element-wise.
Parameters
----------
other : dragon.vm.torch.Tensor, number
The other tensor.
Returns
-------
dragon.vm.torch.Tensor
The output byte tensor.
"""
raise NotImplementedError('Refer torch.ops.tensor.le')
def eq(self, other):
"""Compute *self* == *other* element-wise.
Parameters
----------
other : dragon.vm.torch.Tensor, number
The other tensor.
Returns
-------
dragon.vm.torch.Tensor
The output byte tensor.
"""
raise NotImplementedError('Refer torch.ops.tensor.eq')
def half(self):
"""Return a ``float16`` tensor with elements of ``self``.
......@@ -1118,7 +1289,7 @@ class Tensor(object):
The half tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.half')
raise NotImplementedError('Refer torch.ops.tensor.half')
def half_(self):
"""Inplace of ``Tensor.half()``.
......@@ -1129,7 +1300,7 @@ class Tensor(object):
The half tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.half_')
raise NotImplementedError('Refer torch.ops.tensor.half_')
def float(self):
"""Return a ``float32`` tensor with elements of ``self``.
......@@ -1140,7 +1311,7 @@ class Tensor(object):
The float tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.float')
raise NotImplementedError('Refer torch.ops.tensor.float')
def float_(self):
"""Inplace of ``Tensor.float()``.
......@@ -1151,7 +1322,7 @@ class Tensor(object):
The float tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.float_')
raise NotImplementedError('Refer torch.ops.tensor.float_')
def double(self):
"""Return a ``float64`` tensor with elements of ``self``.
......@@ -1162,7 +1333,7 @@ class Tensor(object):
The double tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.double')
raise NotImplementedError('Refer torch.ops.tensor.double')
def double_(self):
"""Inplace of ``Tensor.double()``.
......@@ -1173,7 +1344,7 @@ class Tensor(object):
The double tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.double_')
raise NotImplementedError('Refer torch.ops.tensor.double_')
def int(self):
"""Return a ``int32`` tensor with elements of ``self``.
......@@ -1184,7 +1355,7 @@ class Tensor(object):
The int tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.int')
raise NotImplementedError('Refer torch.ops.tensor.int')
def int_(self):
"""Inplace of ``Tensor.int()``.
......@@ -1195,7 +1366,7 @@ class Tensor(object):
The int tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.int_')
raise NotImplementedError('Refer torch.ops.tensor.int_')
def long(self):
"""Return a ``int64`` tensor with elements of ``self``.
......@@ -1206,7 +1377,7 @@ class Tensor(object):
The long tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.long')
raise NotImplementedError('Refer torch.ops.tensor.long')
def long_(self):
"""Inplace of ``Tensor.long()``.
......@@ -1217,7 +1388,7 @@ class Tensor(object):
The long tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.long_')
raise NotImplementedError('Refer torch.ops.tensor.long_')
def byte(self):
"""Return a ``uint8`` tensor with elements of ``self``.
......@@ -1228,7 +1399,7 @@ class Tensor(object):
The byte tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.byte')
raise NotImplementedError('Refer torch.ops.tensor.byte')
def byte_(self):
"""Inplace of ``Tensor.byte()``.
......@@ -1239,7 +1410,7 @@ class Tensor(object):
The byte tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.byte_')
raise NotImplementedError('Refer torch.ops.tensor.byte_')
def char(self):
"""Return a ``int8`` tensor with elements of ``self``.
......@@ -1250,7 +1421,7 @@ class Tensor(object):
The byte tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.char')
raise NotImplementedError('Refer torch.ops.tensor.char')
def char_(self):
"""Inplace of ``Tensor.char()``.
......@@ -1261,7 +1432,7 @@ class Tensor(object):
The byte tensor.
"""
raise NotImplementedError('Refer torch.ops.builtin.char_')
raise NotImplementedError('Refer torch.ops.tensor.char_')
##############################################
# #
......@@ -1283,7 +1454,7 @@ class Tensor(object):
@property
def data(self):
return Tensor(ctx=self._ctx, name=self.name, own_storage=False)
return Tensor(device=self.device, name=self.name, own_storage=False)
def detach(self):
return self.data
......@@ -1356,28 +1527,28 @@ def DoubleTensor(*args, **kwargs):
return Tensor(*args, **kwargs)
def LeafTensor(shape, dtype='float32', ctx=Context(), requires_grad=False):
"""Create a torch tensor according to shape, dtype and ctx.
def _LeafTensor(shape, dtype='float32', device=_Device(), requires_grad=False):
"""Create a torch tensor according to shape, dtype and device.
Commonly used to create leaf variables, i.e., the parameters or placeholders.
"""
constructor = globals()[mapping.TENSOR_TYPE_TO_TORCH_TENSOR[dtype]]
return constructor(*shape, ctx=ctx, requires_grad=requires_grad)
return constructor(*shape, device=device, requires_grad=requires_grad)
def RuntimeTensor(name, dtype='float32', ctx=Context()):
"""Create a torch tensor according to dtype and ctx.
def _RuntimeTensor(name, dtype='float32', device=_Device()):
"""Create a torch tensor according to dtype and device.
Commonly used to represent the outputs that are hard to compute shape,
i.e., the shape is computed by the backend automatically.
"""
constructor = globals()[mapping.TENSOR_TYPE_TO_TORCH_TENSOR[dtype]]
return constructor(name=name, ctx=ctx)
return constructor(name=name, device=device)
def ReferenceTensor(src):
def _ReferenceTensor(src):
"""Create a reference from source tensor.
Commonly used to hold the same storage but takes different sizes,
......@@ -1385,7 +1556,7 @@ def ReferenceTensor(src):
"""
constructor = globals()[mapping.TENSOR_TYPE_TO_TORCH_TENSOR[src.dtype]]
T = constructor(name=TensorPool.get('${REFERENCE}'), ctx=src._ctx)
T = constructor(name=TensorPool.get('${REFERENCE}'), device=src.device)
T._ref_objects.append(src)
return T
......
Dragon/python/dragon/vm/torch/tensor_uitls.py deleted 100644 → 0
# ------------------------------------------------------------
# Copyright (c) 2017-present, SeetaTech, Co.,Ltd.
#
# Licensed under the BSD 2-Clause License.
# You should have received a copy of the BSD 2-Clause License
# along with the software. If not, See,
#
# <https://opensource.org/licenses/BSD-2-Clause>
#
# ------------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import importlib
import numpy as np
import dragon.core.mapping as mapping
from dragon.core.tensor_utils import GetStorage
from dragon.vm.torch.c_api import Context
def from_numpy(data):
"""Create a tensor from the given numpy array.
Parameters
----------
data : ndarray
The array with various data type.
Return
------
dragon.vm.torch.Tensor
The torch tensor.
"""
if not isinstance(data, np.ndarray):
raise TypeError('The data should be a numpy.ndarray.')
if str(data.dtype) not in mapping.TENSOR_TYPE_TO_TORCH_TENSOR:
raise ValueError('Unsupported type({}) to torch tensor.'.format(data.dtype))
module = importlib.import_module('dragon.vm.torch.tensor')
return getattr(module, mapping.TENSOR_TYPE_TO_TORCH_TENSOR[str(data.dtype)])(data)
def to_numpy(tensor):
"""Create a numpy nd-array from the given tensor.
Parameters
----------
tensor : dragon.vm.torch.Tensor
The tensor with various data type.
Returns
-------
numpy.ndarray
The numpy array.
"""
return tensor.numpy()
def from_dragon(tensor, own_storage=False):
"""Create a torch tensor from a existing dragon tensor.
Set ``own_storage`` as ``True`` for automatically releasing the storage.
Parameters
----------
tensor : Tensor or str
The dragon tensor.
own_storage : boolean
Whether to release storage during deconstructing.
Returns
-------
dragon.vm.torch.Tensor
The torch tensor.
"""
storage = GetStorage(tensor)
if storage is None: return None
module = importlib.import_module('dragon.vm.torch.tensor')
T = getattr(module, mapping.TENSOR_TYPE_TO_TORCH_TENSOR[storage.dtype])()
T._storage, T._own_storage, T._tensor = storage, own_storage, tensor
T._ctx = Context(*storage.ctx)
return T
def to_str(tensor):
"""Return a format str representing the storage of a tensor.
Parameters
----------
tensor : dragon.vm.torch.Tensor
The tensor with various data type.
Returns
-------
str
The format str.
"""
return str(tensor)
\ No newline at end of file
Dragon/python/dragon/vm/torch/timer.py deleted 100644 → 0
# ------------------------------------------------------------
# Copyright (c) 2017-present, SeetaTech, Co.,Ltd.
#
# Licensed under the BSD 2-Clause License.
# You should have received a copy of the BSD 2-Clause License
# along with the software. If not, See,
#
# <https://opensource.org/licenses/BSD-2-Clause>
#
# ------------------------------------------------------------
import time
class Timer(object):
def __init__(self):
self.total_time = 0.
self.calls = 0
self.start_time = 0.
self.diff = 0.
def tic(self):
self.start_time = time.time()
def toc(self, average=False, every_n=-1, name=''):
self.diff = time.time() - self.start_time
self.total_time += self.diff
self.calls += 1
self.average_time = self.total_time / self.calls
if every_n > 0 and self.calls % every_n == 0:
print('[{}]: total = {:.5f}s, average = {:.5f}s'.format(
name, self.total_time, self.total_time / self.calls * every_n))
if average:
return self.average_time
else:
return self.diff
\ No newline at end of file
Dragon/python/dragon/vm/torch/utils/data/data_transformer.py
......@@ -77,7 +77,7 @@ class DataTransformer(Process):
im = im.reshape((datum.height, datum.width, datum.channels))
if datum.channels == 3 and \
self.color_space == 'RGB':
self.color_space == 'RGB':
im = im[:, :, ::-1]
# Labels
......
Dragon/src/contrib/rcnn/proposal_op.cc
......@@ -154,7 +154,7 @@ void ProposalOp<Context>::RunWithType() {
template <class Context>
void ProposalOp<Context>::RunOnDevice() {
ctx()->set_stream_id(0); // Enforce SyncStream
ctx()->set_stream_id(0); // Enforce DefaultStream
num_images = Input(0).dim(0);
CHECK_EQ(Input(-1).dim(0), num_images)
......
Dragon/src/core/mixedmem.cc
......@@ -150,17 +150,17 @@ void MixedMemory::SwitchToCUDADevice(int device_id) {
const Map<string, string> MixedMemory::info() const {
static map<State, string> STATE_TO_STRING {
{ UNINITIALIZED, "UNINITIALIZED" },
{ STATE_AT_CPU, "CPU" },
{ STATE_AT_CUDA, "CUDA" },
{ STATE_AT_CNML, "CNML" },
{ SYNCED, "DEVICE" },
{ UNINITIALIZED, "uninitialized" },
{ STATE_AT_CPU, "cpu" },
{ STATE_AT_CUDA, "cuda" },
{ STATE_AT_CNML, "cnml" },
{ SYNCED, "device" },
};
Map<string, string> s2s;
string _state_ = STATE_TO_STRING[state_];
if (_state_ == "DEVICE") {
if (cuda_ptr_) _state_ = "CUDA";
else if (cnml_ptr_) _state_ = "CNML";
if (_state_ == "device") {
if (cuda_ptr_) _state_ = "cuda";
else if (cnml_ptr_) _state_ = "cnml";
else LOG(FATAL) << "Device activated, "
<< "but got invalid mem pointer.";
}
......
Dragon/src/core/operator.cc
......@@ -126,7 +126,7 @@ OperatorBase* NewOperator(
<< "\nOperator failed to pass the schema checking.";
}
OperatorDef mutable_def(def);
// Heuristically makes each random seed slightly differnet
// Heuristically make each random seed slightly different
static unsigned int op_seed_uuid = 0;
mutable_def.mutable_device_option()->set_random_seed(
op_seed_uuid + def.device_option().random_seed());
......
Dragon/src/kernels/control_flow/compare_op_kernel.cc
......@@ -51,6 +51,22 @@ void _Less(
}
}
/*! LessEqual <T = ?, Device = CPU> */
template <typename T>
void _LessEqual(
const int count,
const T* a,
const T* b,
bool* y) {
#ifdef WITH_OMP
#pragma omp parallel for num_threads(GET_OMP_THREADS(count))
#endif
for (int i = 0; i < count; ++i) {
y[i] = a[i] <= b[i] ? true : false;
}
}
/*! Greater <T = ?, Device = CPU> */
template <typename T>
......@@ -67,6 +83,22 @@ void _Greater(
}
}
/*! GreaterEqual <T = ?, Device = CPU> */
template <typename T>
void _GreaterEqual(
const int count,
const T* a,
const T* b,
bool* y) {
#ifdef WITH_OMP
#pragma omp parallel for num_threads(GET_OMP_THREADS(count))
#endif
for (int i = 0; i < count; ++i) {
y[i] = a[i] >= b[i] ? true : false;
}
}
#define DEFINE_COMPARE_WARPPER(T, OP, IMPL) \
template <> void OP<T, CPUContext>( \
const int count, \
......@@ -93,6 +125,14 @@ DEFINE_COMPARE_WARPPER(int64_t, Less, _Less);
DEFINE_COMPARE_WARPPER(float, Less, _Less);
DEFINE_COMPARE_WARPPER(double, Less, _Less);
DEFINE_COMPARE_WARPPER(bool, LessEqual, _LessEqual);
DEFINE_COMPARE_WARPPER(int8_t, LessEqual, _LessEqual);
DEFINE_COMPARE_WARPPER(uint8_t, LessEqual, _LessEqual);
DEFINE_COMPARE_WARPPER(int, LessEqual, _LessEqual);
DEFINE_COMPARE_WARPPER(int64_t, LessEqual, _LessEqual);
DEFINE_COMPARE_WARPPER(float, LessEqual, _LessEqual);
DEFINE_COMPARE_WARPPER(double, LessEqual, _LessEqual);
DEFINE_COMPARE_WARPPER(bool, Greater, _Greater);
DEFINE_COMPARE_WARPPER(int8_t, Greater, _Greater);
DEFINE_COMPARE_WARPPER(uint8_t, Greater, _Greater);
......@@ -101,6 +141,14 @@ DEFINE_COMPARE_WARPPER(int64_t, Greater, _Greater);
DEFINE_COMPARE_WARPPER(float, Greater, _Greater);
DEFINE_COMPARE_WARPPER(double, Greater, _Greater);
DEFINE_COMPARE_WARPPER(bool, GreaterEqual, _GreaterEqual);
DEFINE_COMPARE_WARPPER(int8_t, GreaterEqual, _GreaterEqual);
DEFINE_COMPARE_WARPPER(uint8_t, GreaterEqual, _GreaterEqual);
DEFINE_COMPARE_WARPPER(int, GreaterEqual, _GreaterEqual);
DEFINE_COMPARE_WARPPER(int64_t, GreaterEqual, _GreaterEqual);
DEFINE_COMPARE_WARPPER(float, GreaterEqual, _GreaterEqual);
DEFINE_COMPARE_WARPPER(double, GreaterEqual, _GreaterEqual);
template <> void Equal<float16, CPUContext>(
const int count,
const float16* a,
......@@ -119,6 +167,15 @@ template <> void Less<float16, CPUContext>(
CPU_FP16_NOT_SUPPORTED;
}
template <> void LessEqual<float16, CPUContext>(
const int count,
const float16* a,
const float16* b,
bool* y,
CPUContext* ctx) {
CPU_FP16_NOT_SUPPORTED;
}
template <> void Greater<float16, CPUContext>(
const int count,
const float16* a,
......@@ -128,6 +185,15 @@ template <> void Greater<float16, CPUContext>(
CPU_FP16_NOT_SUPPORTED;
}
template <> void GreaterEqual<float16, CPUContext>(
const int count,
const float16* a,
const float16* b,
bool* y,
CPUContext* ctx) {
CPU_FP16_NOT_SUPPORTED;
}
#undef DEFINE_COMPARE_WARPPER
} // namespace kernel
......
Dragon/src/kernels/control_flow/compare_op_kernel.cu
......@@ -68,6 +68,31 @@ __global__ void _LessHalf(
}
}
/*! LessEqual <T = ?, Device = CUDA> */
template <typename T>
__global__ void _LessEqual(
const int count,
const T* a,
const T* b,
bool* y) {
CUDA_1D_KERNEL_LOOP(idx, count) {
y[idx] = a[idx] <= b[idx] ? true : false;
}
}
__global__ void _LessEqualHalf(
const int count,
const half* a,
const half* b,
bool* y) {
CUDA_1D_KERNEL_LOOP(idx, count) {
#if __CUDA_ARCH__ >= 530
y[idx] = __hle(a[idx], b[idx]) ? true : false;
#endif
}
}
/*! Greater <T = ?, Device = CUDA> */
template <typename T>
......@@ -93,6 +118,31 @@ __global__ void _GreaterHalf(
}
}
/*! GreaterEqual <T = ?, Device = CUDA> */
template <typename T>
__global__ void _GreaterEqual(
const int count,
const T* a,
const T* b,
bool* y) {
CUDA_1D_KERNEL_LOOP(idx, count) {
y[idx] = a[idx] >= b[idx] ? true : false;
}
}
__global__ void _GreaterEqualHalf(
const int count,
const half* a,
const half* b,
bool* y) {
CUDA_1D_KERNEL_LOOP(idx, count) {
#if __CUDA_ARCH__ >= 530
y[idx] = __hge(a[idx], b[idx]) ? true : false;
#endif
}
}
#define DEFINE_COMPARE_WARPPER(T, OP, IMPL) \
template <> void OP<T, CUDAContext>( \
const int count, \
......@@ -138,6 +188,15 @@ DEFINE_COMPARE_WARPPER(float, Less, _Less);
DEFINE_COMPARE_WARPPER(double, Less, _Less);
DEFINE_COMPARE_FP16_WARPPER(Less);
DEFINE_COMPARE_WARPPER(bool, LessEqual, _LessEqual);
DEFINE_COMPARE_WARPPER(int8_t, LessEqual, _LessEqual);
DEFINE_COMPARE_WARPPER(uint8_t, LessEqual, _LessEqual);
DEFINE_COMPARE_WARPPER(int, LessEqual, _LessEqual);
DEFINE_COMPARE_WARPPER(int64_t, LessEqual, _LessEqual);
DEFINE_COMPARE_WARPPER(float, LessEqual, _LessEqual);
DEFINE_COMPARE_WARPPER(double, LessEqual, _LessEqual);
DEFINE_COMPARE_FP16_WARPPER(LessEqual);
DEFINE_COMPARE_WARPPER(bool, Greater, _Greater);
DEFINE_COMPARE_WARPPER(int8_t, Greater, _Greater);
DEFINE_COMPARE_WARPPER(uint8_t, Greater, _Greater);
......@@ -147,6 +206,15 @@ DEFINE_COMPARE_WARPPER(float, Greater, _Greater);
DEFINE_COMPARE_WARPPER(double, Greater, _Greater);
DEFINE_COMPARE_FP16_WARPPER(Greater);
DEFINE_COMPARE_WARPPER(bool, GreaterEqual, _GreaterEqual);
DEFINE_COMPARE_WARPPER(int8_t, GreaterEqual, _GreaterEqual);
DEFINE_COMPARE_WARPPER(uint8_t, GreaterEqual, _GreaterEqual);
DEFINE_COMPARE_WARPPER(int, GreaterEqual, _GreaterEqual);
DEFINE_COMPARE_WARPPER(int64_t, GreaterEqual, _GreaterEqual);
DEFINE_COMPARE_WARPPER(float, GreaterEqual, _GreaterEqual);
DEFINE_COMPARE_WARPPER(double, GreaterEqual, _GreaterEqual);
DEFINE_COMPARE_FP16_WARPPER(GreaterEqual);
#undef DEFINE_COMPARE_WARPPER
#undef DEFINE_COMPARE_FP16_WARPPER
......
Dragon/src/kernels/loss/softmax_focal_loss_op_kernel.cu
......@@ -129,7 +129,7 @@ __global__ void _SoftmaxFocalLossGrad(
} else {
const int t = (oix * axis_dim + label) * inner_dim + iix;
Tx onemp = 1 - prob[t];
// unstable if gamma is 0
// Unstable if gamma is 0
Tx grad = -gamma * pow(onemp, gamma - 1)
* log(max(prob[t], FLT_MIN))
* prob[t] + pow(onemp, gamma);
......
Dragon/src/operators/ndarray/arange_op.cc → Dragon/src/operators/array/arange_op.cc
#include "core/workspace.h"
#include "utils/op_kernel.h"
#include "operators/ndarray/arange_op.h"
#include "operators/array/arange_op.h"
namespace dragon {
......
Dragon/src/operators/ndarray/argreduce_op.cc → Dragon/src/operators/array/argreduce_op.cc
#include "utils/op_kernel.h"
#include "utils/math_functions.h"
#include "operators/ndarray/argreduce_op.h"
#include "operators/array/argreduce_op.h"
namespace dragon {
......
Dragon/src/operators/ndarray/concat_op.cc → Dragon/src/operators/array/concat_op.cc
#include "core/workspace.h"
#include "utils/op_kernel.h"
#include "operators/ndarray/concat_op.h"
#include "operators/array/concat_op.h"
namespace dragon {
......
Dragon/src/operators/ndarray/crop_op.cc → Dragon/src/operators/array/crop_op.cc
#include "core/workspace.h"
#include "utils/op_kernel.h"
#include "utils/math_functions.h"
#include "operators/ndarray/crop_op.h"
#include "operators/array/crop_op.h"
namespace dragon {
......
Dragon/src/operators/ndarray/expand_dims_op.cc → Dragon/src/operators/array/expand_dims_op.cc
#include "core/workspace.h"
#include "operators/ndarray/dimension_op.h"
#include "operators/array/dimension_op.h"
namespace dragon {
......
Dragon/src/operators/ndarray/flatten_op.cc → Dragon/src/operators/array/flatten_op.cc
#include "core/workspace.h"
#include "operators/ndarray/dimension_op.h"
#include "operators/array/dimension_op.h"
namespace dragon {
......
Dragon/src/operators/ndarray/gather_op.cc → Dragon/src/operators/array/gather_op.cc
#include "core/workspace.h"
#include "utils/op_kernel.h"
#include "utils/math_functions.h"
#include "operators/ndarray/gather_op.h"
#include "operators/array/gather_op.h"
namespace dragon {
......
Dragon/src/operators/array/multinomial_op.cc 0 → 100644
#include "core/workspace.h"
#include "utils/cast.h"
#include "utils/op_kernel.h"
#include "operators/array/multinomial_op.h"
namespace dragon {
template <class Context>
void MultinomialOp<Context>::SoftmaxRun() {
auto softmax_def = MakeOperatorDef("Softmax", "",
vector<string>({ Input(0).name() }),
vector<string>({ mount_name("softmax/prob") }));
Argument arg; arg.set_name("axis"); arg.set_i(axis);
softmax_def.add_arg()->CopyFrom(arg);
if (def().has_device_option())
softmax_def.mutable_device_option()->CopyFrom(
def().device_option());
if (softmax_op) { softmax_op->UpdateFrom(softmax_def); }
else { softmax_op.reset(NewOperator(softmax_def, ws())); }
softmax_op->Run(ctx()->stream_id());
prob = ws()->GetTensor(mount_name("softmax/prob"));
}
template <class Context> template <typename T>
void MultinomialOp<Context>::RunWithType() {
auto* Xdata = normalize ?
prob->template data<T, CPUContext>() :
Input(0).template data<T, CPUContext>();
vector<double> cumsum(Input(0).dim(axis));
auto* Sdata = static_cast<double*>(cumsum.data());
auto* Ydata = Output(0)->template mutable_data<int64_t, CPUContext>();
double running_total, r;
int idx = 0, num_classes = Input(0).dim(axis);
auto* rng = ctx()->rand_generator();
for (int i = 0; i < outer_dim; ++i) {
running_total = 0.;
for (int j = 0; j < num_classes; ++j) {
running_total += (double)Xdata[j];
Sdata[j] = running_total;
}
std::uniform_real_distribution<double> dist(
0.f, running_total);
for (int j = 0; j < (int)num_samples; ++j) {
r = dist(*rng);
auto found_iter = std::upper_bound(
Sdata, Sdata + num_classes, r);
Ydata[idx++] = std::distance(Sdata, found_iter);
}
Xdata += num_classes;
}
Output(0)->template data<int64_t, Context>();
}
template <class Context>
void MultinomialOp<Context>::RunOnDevice() {
ctx()->set_stream_id(0); // Enforce DefaultStream
axis = Input(0).ndim() - 1;
auto output_dims = Input(0).dims();
output_dims[axis] = num_samples;
outer_dim = Input(0).count(0, axis);
Output(0)->Reshape(output_dims);
// Normalize the logits if necessary
if (normalize) SoftmaxRun();
if (XIsType(Input(0), int8_t)) RunWithType<int8_t>();
else if (XIsType(Input(0), uint8_t)) RunWithType<uint8_t>();
else if (XIsType(Input(0), int)) RunWithType<int>();
else if (XIsType(Input(0), int64_t)) RunWithType<int64_t>();
else if (XIsType(Input(0), float)) RunWithType<float>();
else if (XIsType(Input(0), double)) RunWithType<double>();
else LOG(FATAL) << DTypeHelper(Input(0), {
"bool", "int8", "uint8", "int32", "int64",
"float32", "float64",
});
}
DEPLOY_CPU(Multinomial);
#ifdef WITH_CUDA
DEPLOY_CUDA(Multinomial);
#endif
OPERATOR_SCHEMA(Multinomial).NumInputs(1).NumOutputs(1);
NO_GRADIENT(Multinomial);
} // namespace dragon
\ No newline at end of file
Dragon/src/operators/ndarray/one_hot_op.cc → Dragon/src/operators/array/one_hot_op.cc
#include "utils/op_kernel.h"
#include "utils/math_functions.h"
#include "operators/ndarray/one_hot_op.h"
#include "operators/array/one_hot_op.h"
namespace dragon {
......
Dragon/src/operators/ndarray/pad_op.cc → Dragon/src/operators/array/pad_op.cc
#include "core/workspace.h"
#include "utils/op_kernel.h"
#include "utils/math_functions.h"
#include "operators/ndarray/pad_op.h"
#include "operators/array/pad_op.h"
namespace dragon {
......
Dragon/src/operators/ndarray/reduce_op.cc → Dragon/src/operators/array/reduce_op.cc
#include "core/workspace.h"
#include "utils/op_kernel.h"
#include "utils/math_functions.h"
#include "operators/ndarray/reduce_op.h"
#include "operators/array/reduce_op.h"
namespace dragon {
......
Dragon/src/operators/ndarray/repeat_op.cc → Dragon/src/operators/array/repeat_op.cc
#include "core/workspace.h"
#include "utils/op_kernel.h"
#include "operators/ndarray/repeat_op.h"
#include "operators/array/repeat_op.h"
namespace dragon {
......
Dragon/src/operators/ndarray/reshape_op.cc → Dragon/src/operators/array/reshape_op.cc
#include "core/workspace.h"
#include "operators/ndarray/dimension_op.h"
#include "operators/array/dimension_op.h"
namespace dragon {
......
Dragon/src/operators/ndarray/shape_op.cc → Dragon/src/operators/array/shape_op.cc
#include "operators/ndarray/shape_op.h"
#include "operators/array/shape_op.h"
namespace dragon {
......
Dragon/src/operators/ndarray/slice_op.cc → Dragon/src/operators/array/slice_op.cc
#include "utils/op_kernel.h"
#include "utils/math_functions.h"
#include "operators/ndarray/slice_op.h"
#include "operators/array/slice_op.h"
namespace dragon {
......
Dragon/src/operators/ndarray/squeeze_op.cc → Dragon/src/operators/array/squeeze_op.cc
#include "core/workspace.h"
#include "operators/ndarray/dimension_op.h"
#include "operators/array/dimension_op.h"
namespace dragon {
......
Dragon/src/operators/ndarray/stack_op.cc → Dragon/src/operators/array/stack_op.cc
#include "core/workspace.h"
#include "utils/op_kernel.h"
#include "operators/ndarray/stack_op.h"
#include "operators/array/stack_op.h"
namespace dragon {
......
Dragon/src/operators/ndarray/tile_op.cc → Dragon/src/operators/array/tile_op.cc
#include "core/workspace.h"
#include "utils/op_kernel.h"
#include "operators/ndarray/tile_op.h"
#include "operators/array/tile_op.h"
namespace dragon {
......
Dragon/src/operators/ndarray/transpose_op.cc → Dragon/src/operators/array/transpose_op.cc
#include "core/workspace.h"
#include "utils/op_kernel.h"
#include "operators/ndarray/transpose_op.h"
#include "operators/array/transpose_op.h"
namespace dragon {
......
Dragon/src/operators/control_flow/compare_op.cc
#include "core/workspace.h"
#include "utils/op_kernel.h"
#include "utils/math_functions.h"
#include "operators/control_flow/compare_op.h"
namespace dragon {
......@@ -26,31 +28,44 @@ using kernel::Greater;
template <class Context> template <typename T> \
void CompareOp<Context>::Operation##RunWithType() { \
auto* Adata = Input(0).template data<T, Context>(); \
auto* Bdata = Input(1).template data<T, Context>(); \
const T* Bdata = nullptr; \
auto* Ydata = Output(0)->template mutable_data<bool, Context>(); \
if (Input(1).count() == 1) { \
auto* WSdata = ws()->template caches<T, Context> \
({ Input(0).count() })[0]; \
auto* BCdata = Input(1).template data<T, CPUContext>(); \
math::Set(Input(0).count(), BCdata[0], WSdata, ctx()); \
Bdata = WSdata; \
} else { Bdata = Input(1).template data<T, Context>(); } \
kernel::Operation(Output(0)->count(), Adata, Bdata, Ydata, ctx()); \
}
template <class Context>
void CompareOp<Context>::RunOnDevice() {
CHECK_EQ(Input(0).count(), Input(1).count())
<< "\nBoth A and B should have the same number of elements."
<< "\nDimensions of A and B are " << Input(0).DimString()
<< " and " << Input(1).DimString();
if (Input(0).count() != Input(1).count()) {
CHECK_EQ(Input(1).count(), 1)
<< "\nBoth A and B should have the same number of elements."
<< "\nOr the B should be a Scalar."
<< "\nDimensions of A and B are " << Input(0).DimString()
<< " and " << Input(1).DimString();
}
Output(0)->ReshapeLike(Input(0));
if (operation == "EQUAL") { DEFINE_TYPED_CALLER(Equal); }
else if (operation == "LESS") { DEFINE_TYPED_CALLER(Less); }
else if (operation == "GREATER") { DEFINE_TYPED_CALLER(Greater); }
if (operation == "EQ") { DEFINE_TYPED_CALLER(Equal); }
else if (operation == "LT") { DEFINE_TYPED_CALLER(Less); }
else if (operation == "GT") { DEFINE_TYPED_CALLER(Greater); }
else if (operation == "LE") { DEFINE_TYPED_CALLER(LessEqual); }
else if (operation == "GE") { DEFINE_TYPED_CALLER(GreaterEqual); }
else { LOG(FATAL) << "Unsupport operation: " << operation << "."; }
if (to_uint8) Output(0)->SetMeta(TypeMeta::Make<uint8_t>());
}
DEFINE_OP_CALLER(Equal);
DEFINE_OP_CALLER(Less);
DEFINE_OP_CALLER(LessEqual);
DEFINE_OP_CALLER(Greater);
DEFINE_OP_CALLER(GreaterEqual);
DEPLOY_CPU(Compare);
#ifdef WITH_CUDA
......
Dragon/src/operators/control_flow/scan_op.cc
......@@ -4,8 +4,8 @@
#include "utils/string.h"
#include "utils/math_functions.h"
#include "utils/proto_utils.h"
#include "operators/array/slice_op.h"
#include "operators/control_flow/scan_op.h"
#include "operators/ndarray/slice_op.h"
namespace dragon {
......
Dragon/src/operators/loss/l1_loss_op.cc
......@@ -41,7 +41,7 @@ void L1LossOp<Context>::RunWithType() {
template <class Context>
void L1LossOp<Context>::RunOnDevice() {
ctx()->set_stream_id(0); // Enforce SyncStream
ctx()->set_stream_id(0); // Enforce DefaultStream
for (int i = 1; i < InputSize(); i++) {
CHECK_EQ(Input(0).count(), Input(i).count())
......
Dragon/src/operators/loss/softmax_ce_loss_op.cc
......@@ -25,6 +25,7 @@ void SoftmaxCrossEntropyOp<Context>::SoftmaxRun() {
if (softmax_op) { softmax_op->UpdateFrom(softmax_def); }
else { softmax_op.reset(NewOperator(softmax_def, ws())); }
softmax_op->Run(ctx()->stream_id());
prob = ws()->GetTensor(mount_name("softmax/prob"));
}
template <class Context> template <typename T>
......@@ -69,7 +70,6 @@ void SoftmaxCrossEntropyOp<Context>::RunOnDevice() {
CHECK_EQ(Input(0).count(), Input(1).count())
<< "\nNumber of predictions must match the number of labels.";
losses.ReshapeLike(Input(0));
prob = ws()->CreateTensor(mount_name("softmax/prob"));
SoftmaxRun();
......
Dragon/src/operators/loss/softmax_focal_loss_op.cc
......@@ -60,8 +60,6 @@ void SoftmaxFocalLossOp<Context>::RunOnDevice() {
flags.Reshape({ outer_dim * inner_dim });
losses.Reshape({ outer_dim * inner_dim });
this->prob = ws()->CreateTensor(
mount_name("softmax/prob"));
this->SoftmaxRun();
if (XIsType(Input(0), float)) {
......
Dragon/src/operators/loss/sparse_softmax_ce_loss_op.cc
......@@ -25,6 +25,7 @@ void SparseSoftmaxCrossEntropyOp<Context>::SoftmaxRun() {
if (softmax_op) { softmax_op->UpdateFrom(softmax_def); }
else { softmax_op.reset(NewOperator(softmax_def, ws())); }
softmax_op->Run(ctx()->stream_id());
prob = ws()->GetTensor(mount_name("softmax/prob"));
}
template <class Context> template <typename Tx, typename Ty>
......@@ -75,8 +76,6 @@ void SparseSoftmaxCrossEntropyOp<Context>::RunOnDevice() {
losses.Reshape({ outer_dim * inner_dim });
flags.Reshape({ outer_dim * inner_dim });
prob = ws()->CreateTensor(
mount_name("softmax/prob"));
SoftmaxRun();
if (XIsType(Input(0), float)) {
......
Dragon/src/operators/recurrent/cudnn_recurrent_op.cc
......@@ -17,7 +17,7 @@ void CuDNNRecurrentOpBase<Context>::ResetDesc() {
const auto num_directions = bidirectional ? 2 : 1;
const auto output_dim = hidden_size * num_directions;
// setup dropout
// Setup Dropout
if (dropout_ratio < 1.f) {
#if CUDNN_VERSION_MIN(7, 0, 0)
if (!states_initialized) {
......@@ -45,7 +45,7 @@ void CuDNNRecurrentOpBase<Context>::ResetDesc() {
#endif
}
// setup rnn
// Setup RNN
#if CUDNN_VERSION_MIN(7, 0, 0)
CUDNN_CHECK(cudnnSetRNNDescriptor(
ctx()->cudnn_handle(), rnn_desc,
......@@ -63,7 +63,7 @@ void CuDNNRecurrentOpBase<Context>::ResetDesc() {
CUDNNType<T>::type));
#endif
// setup Xs & Ys & Y
// Setup Xs & Ys & Y
xs_desc.reset(new cudnnTensorDescriptors(seq_length));
xs_desc->Set<T>({ batch_size, input_dim, 1 }, { input_dim, 1, 1 });
ys_desc.reset(new cudnnTensorDescriptors(seq_length));
......@@ -72,14 +72,14 @@ void CuDNNRecurrentOpBase<Context>::ResetDesc() {
rnn_desc, seq_length, xs_desc->descs(), &workspace_size));
output_dims = { seq_length, batch_size, output_dim };
// setup Hx & Cx & Hy & Cy
// Setup Hx & Cx & Hy & Cy
hidden_dims = { num_layers * num_directions, batch_size, hidden_size };
cudnnSetTensorDesc<T>(&hx_desc, hidden_dims);
cudnnSetTensorDesc<T>(&cx_desc, hidden_dims);
cudnnSetTensorDesc<T>(&hy_desc, hidden_dims);
cudnnSetTensorDesc<T>(&cy_desc, hidden_dims);
// setup packed weights
// Setup packed weights
size_t weights_size; int64_t weights_count;
CUDNN_CHECK(cudnnGetRNNParamsSize(
ctx()->cudnn_handle(), rnn_desc, xs_desc->descs()[0],
......@@ -94,7 +94,7 @@ void CuDNNRecurrentOpBase<Context>::ResetDesc() {
CUDNNType<T>::type, CUDNN_TENSOR_NCHW, 3,
vector<int>({ (int)weights_count, 1, 1 }).data()));
// setup rnn workspace
// Determine the RNN workspace
CUDNN_CHECK(cudnnGetRNNWorkspaceSize(
ctx()->cudnn_handle(), rnn_desc, seq_length,
xs_desc->descs(), &workspace_size));
......@@ -181,7 +181,7 @@ void CuDNNRecurrentGradientOp<Context>::RunWithType() {
};
auto* WSdata = ws()->template caches<Context>({ workspace_size })[0];
// check the reserve space
// Check the ReserveSpace
CUDNN_CHECK(cudnnGetRNNTrainingReserveSize(ctx()->cudnn_handle(),
rnn_desc, seq_length, xs_desc->descs(), &reserve_size));
auto* reserveT = ws()->GetTensor(mount_name("rnn/reserve"));
......
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