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add operator SpaceToBatch and BatchToSpace for ge

This commit is contained in:
zhaozhenlong 2020-03-31 15:15:08 +08:00
parent d84bf8d357
commit cf40305bf0
8 changed files with 228 additions and 2 deletions
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6
mindspore/ccsrc/transform/convert.cc
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@ -180,6 +180,8 @@ const char kNamePrint[] = "Print";
const char kNameApplyFtrl[] = "ApplyFtrl";
const char kNameDiag[] = "Diag";
const char kNameDiagPart[] = "DiagPart";
const char kNameSpaceToBatch[] = "SpaceToBatch";
const char kNameBatchToSpace[] = "BatchToSpace";
// -----------------OpAdapter initialization--------------
std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_map() {
@ -361,7 +363,9 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
{string(kNameRound), ADPT_DESC(Round)},
{string(kNameApplyFtrl), ADPT_DESC(ApplyFtrl)},
{string(kNameDiag), ADPT_DESC(Diag)},
{string(kNameDiagPart), ADPT_DESC(DiagPart)}};
{string(kNameDiagPart), ADPT_DESC(DiagPart)},
{string(kNameSpaceToBatch), ADPT_DESC(SpaceToBatchD)},
{string(kNameBatchToSpace), ADPT_DESC(BatchToSpaceD)}};
#ifdef ENABLE_GE
adpt_map[string(kNamePrint)] = ADPT_DESC(Print);
#endif

22
mindspore/ccsrc/transform/op_adapter.h
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@ -744,6 +744,28 @@ class OpAdapter : public BaseOpAdapter {
return list;
}
static std::vector<int64_t> ConvertAny(const ValuePtr& value, const AnyTraits<std::vector<std::vector<int64_t>>>,
const AnyTraits<std::vector<int64_t>>) {
MS_EXCEPTION_IF_NULL(value);
MS_LOG(DEBUG) << "Value: " << value->type_name();
if (!value->isa<ValueList>()) {
MS_LOG(EXCEPTION) << "Value should be ValueList, but got " << value->type_name();
}
auto vec = value->cast<ValueListPtr>();
std::vector<int64_t> list;
for (auto& it : vec->value()) {
MS_EXCEPTION_IF_NULL(it);
if (!it->isa<ValueList>()) {
MS_LOG(EXCEPTION) << "It should be ValueList, but got " << it->type_name();
}
auto sub_vector = it->cast<ValueListPtr>();
for (auto& item : sub_vector->value()) {
list.push_back(static_cast<int64_t>(GetValue<int>(item)));
}
}
return list;
}
static std::vector<int64_t> ConvertAny(const ValuePtr& value, const AnyTraits<std::vector<int64_t>>,
const AnyTraits<std::vector<int64_t>>) {
MS_EXCEPTION_IF_NULL(value);

13
mindspore/ccsrc/transform/op_declare.cc
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@ -1183,6 +1183,19 @@ INPUT_MAP(DiagPart) = {{1, INPUT_DESC(x)}};
ATTR_MAP(DiagPart) = EMPTY_ATTR_MAP;
OUTPUT_MAP(DiagPart) = {{0, OUTPUT_DESC(y)}};
// SpaceToBatchD
INPUT_MAP(SpaceToBatchD) = {{1, INPUT_DESC(x)}};
ATTR_MAP(SpaceToBatchD) = {
{"block_size", ATTR_DESC(block_size, AnyTraits<int64_t>())},
{"paddings", ATTR_DESC(paddings, AnyTraits<std::vector<std::vector<int64_t>>>(), AnyTraits<std::vector<int64_t>>())}};
OUTPUT_MAP(SpaceToBatchD) = {{0, OUTPUT_DESC(y)}};
// BatchToSpaceD
INPUT_MAP(BatchToSpaceD) = {{1, INPUT_DESC(x)}};
ATTR_MAP(BatchToSpaceD) = {
{"block_size", ATTR_DESC(block_size, AnyTraits<int64_t>())},
{"crops", ATTR_DESC(crops, AnyTraits<std::vector<std::vector<int64_t>>>(), AnyTraits<std::vector<int64_t>>())}};
OUTPUT_MAP(BatchToSpaceD) = {{0, OUTPUT_DESC(y)}};
#ifdef ENABLE_GE
// Print
INPUT_MAP(Print) = EMPTY_INPUT_MAP;

4
mindspore/ccsrc/transform/op_declare.h
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@ -439,6 +439,10 @@ DECLARE_OP_ADAPTER(Diag)
DECLARE_OP_USE_OUTPUT(Diag)
DECLARE_OP_ADAPTER(DiagPart)
DECLARE_OP_USE_OUTPUT(DiagPart)
DECLARE_OP_ADAPTER(SpaceToBatchD)
DECLARE_OP_USE_OUTPUT(SpaceToBatchD)
DECLARE_OP_ADAPTER(BatchToSpaceD)
DECLARE_OP_USE_OUTPUT(BatchToSpaceD)
#ifdef ENABLE_GE
DECLARE_OP_ADAPTER(Print)
DECLARE_OP_USE_DYN_INPUT(Print)

20
mindspore/ops/_grad/grad_array_ops.py
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@ -430,3 +430,23 @@ def get_bprop_diag_part(self):
return (op(dout),)
return bprop
@bprop_getters.register(P.SpaceToBatch)
def get_bprop_space_to_batch(self):
"""Generate bprop for SpaceToBatch"""
space_to_batch_grad = P.BatchToSpace(self.block_size, self.paddings)
def bprop(x, out, dout):
dx = space_to_batch_grad(dout)
return (dx,)
return bprop
@bprop_getters.register(P.BatchToSpace)
def get_bprop_batch_to_space(self):
"""Generate bprop for BatchToSpace"""
batch_to_space_grad = P.SpaceToBatch(self.block_size, self.crops)
def bprop(x, out, dout):
dx = batch_to_space_grad(dout)
return (dx,)
return bprop

4
mindspore/ops/operations/__init__.py
View File

@ -29,7 +29,7 @@ from .array_ops import (Argmax, Argmin, Cast, ConcatOffset, Concat,
Shape, Size, Slice, Split,
Squeeze, StridedSlice, Tile,
Transpose, TruncatedNormal, TupleToArray,
UnsortedSegmentSum, SpaceToDepth, DepthToSpace)
UnsortedSegmentSum, SpaceToDepth, DepthToSpace, SpaceToBatch, BatchToSpace)
from .comm_ops import (AllGather, AllReduce, _AlltoAll, ReduceScatter, Broadcast,
_MirrorOperator, ReduceOp, _VirtualDataset,
_VirtualDiv, _GetTensorSlice)
@ -225,6 +225,8 @@ __all__ = [
"LARSUpdate",
"Round",
"ApplyFtrl",
"SpaceToBatch",
"BatchToSpace"
]
__all__.sort()

141
mindspore/ops/operations/array_ops.py
View File

@ -20,6 +20,7 @@
import copy
import functools
import itertools
import numbers
import numpy as np
@ -2020,3 +2021,143 @@ class DepthToSpace(PrimitiveWithInfer):
def infer_dtype(self, x_dtype):
validator.check_subclass("x_dtype", x_dtype, mstype.tensor)
return x_dtype
class SpaceToBatch(PrimitiveWithInfer):
r"""
Divide spatial dimensions into blocks and combine the block size with the original batch.
This operation will divide spatial dimensions (H, W) into blocks with block_size, the output tensor's H and W
dimension is the corresponding number of blocks after division. The output tensor's batch dimension is the
product of the original batch and the square of block_size. Prior to division into blocks, the spatial dimensions
of the input are zero padded according to paddings if necessary.
Args:
block_size (int): The block size of dividing block with value >= 1.
paddings (list): The padding value for H and W dimension, containing 2 sub list, each containing 2 int value.
All values must be >= 0. paddings[i] specifies the paddings for spatial dimension i, which corresponds to
input dimension i+2. It is required that input_shape[i+2]+paddings[i][0]+paddings[i][1] is divisible
by block_size.
Inputs:
- **input_x** (Tensor) - The input tensor.
Outputs:
Tensor, the output tensor with the same type as input. Assume input shape is :math:`(n, c, h, w)` with
:math:`block\_size` and :math:`padddings`. The output tensor shape will be :math:`(n', c', h', w')`, where
:math:`n' = n*(block\_size*block\_size)`
:math:`c' = c`
:math:`h' = (h+paddings[0][0]+paddings[0][1])//block\_size`
:math:`w' = (w+paddings[1][0]+paddings[1][1])//block\_size`
Examples:
>>> block_size = 2
>>> paddings = [[0, 0], [0, 0]]
>>> space_to_batch = P.SpaceToBatch(block_size, paddings)
>>> x = Tensor(np.array([[[[1, 2], [3, 4]]]]), mstype.float32)
>>> space_to_batch(x)
[[[[1.]]], [[[2.]]], [[[3.]]], [[[4.]]]]
"""
@prim_attr_register
def __init__(self, block_size, paddings):
"""Init SpaceToBatch"""
validator.check_type('block_size', block_size, [int])
validator.check('block_size', block_size, '', 1, Rel.GT)
self.block_size = block_size
validator.check('paddings shape', np.array(paddings).shape, '', (2, 2))
for elem in itertools.chain(*paddings):
validator.check_type('paddings element', elem, [int])
self.paddings = paddings
def infer_dtype(self, x_dtype):
validator.check_subclass("input_x", x_dtype, mstype.tensor)
validator.check_typename('input_x', x_dtype, mstype.number_type)
return x_dtype
def infer_shape(self, x_shape):
validator.check('rank of input_x', len(x_shape), '', 4)
out_shape = copy.deepcopy(x_shape)
for i in range(2):
padded = out_shape[i+2] + self.paddings[i][0] + \
self.paddings[i][1]
if padded % self.block_size != 0:
raise ValueError(f'padded[{i}] {padded} should be divisible by '
f'block_size {self.block_size}')
out_shape[i+2] = padded // self.block_size
out_shape[0] *= self.block_size * self.block_size
return out_shape
class BatchToSpace(PrimitiveWithInfer):
r"""
Divide batch dimension with blocks and interleaves these blocks back into spatial dimensions.
This operation will divide batch dimension N into blocks with block_size, the output tensor's N dimension
is the corresponding number of blocks after division. The output tensor's H, W dimension is product of original H, W
dimension and block_size with given amount to crop from dimension, respectively.
Args:
block_size (int): The block size of dividing block with value >= 1.
crops (list): The crop value for H and W dimension, containing 2 sub list, each containing 2 int value.
All values must be >= 0. crops[i] specifies the crop values for spatial dimension i, which corresponds to
input dimension i+2. It is required that input_shape[i+2]*block_size >= crops[i][0]+crops[i][1].
Inputs:
- **input_x** (Tensor) - The input tensor.
Outputs:
Tensor, the output tensor with the same type as input. Assume input shape is (n, c, h, w) with block_size
and crops. The output shape will be (n', c', h', w'), where
:math:`n' = n//(block\_size*block\_size)`
:math:`c' = c`
:math:`h' = h*block\_size-crops[0][0]-crops[0][1]`
:math:`w' = w*block\_size-crops[1][0]-crops[1][1]`
Examples:
>>> block_size = 2
>>> crops = [[0, 0], [0, 0]]
>>> op = P.BatchToSpace(block_size, crops)
>>> x = Tensor(np.array([[[[1]]], [[[2]]], [[[3]]], [[[4]]]]), mstype.float32)
>>> output = op(x)
[[[[1., 2.], [3., 4.]]]]
"""
@prim_attr_register
def __init__(self, block_size, crops):
"""Init BatchToSpace"""
validator.check_type('block_size', block_size, [int])
validator.check('block_size', block_size, '', 1, Rel.GT)
self.block_size = block_size
validator.check('crops shape', np.array(crops).shape, '', (2, 2))
for elem in itertools.chain(*crops):
validator.check_type('crops element', elem, [int])
self.crops = crops
def infer_dtype(self, x_dtype):
validator.check_subclass("input_x", x_dtype, mstype.tensor)
validator.check_typename('input_x', x_dtype, mstype.number_type)
return x_dtype
def infer_shape(self, x_shape):
validator.check('rank of input_x', len(x_shape), '', 4)
out_shape = copy.deepcopy(x_shape)
for i in range(2):
x_block_prod = out_shape[i+2] * self.block_size
crops_sum = self.crops[i][0] + self.crops[i][1]
validator.check("x block shape prod", x_block_prod, 'crops sum', crops_sum, Rel.GT)
out_shape[i+2] = x_block_prod - crops_sum
block_size_prod = self.block_size * self.block_size
if out_shape[0] % block_size_prod != 0:
raise ValueError(f'input_x dimension 0 {out_shape[0]} should be divisible by '
f'block_size_prod {block_size_prod}')
out_shape[0] = out_shape[0] // block_size_prod
return out_shape

20
tests/ut/python/ops/test_ops.py
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@ -952,6 +952,26 @@ test_case_array_ops = [
'desc_inputs': [[4, 4]],
'desc_bprop': [[4]],
}),
('SpaceToBatch_1', {
'block': P.SpaceToBatch(2, [[0, 0], [0, 0]]),
'desc_inputs': [[1, 3, 2, 2]],
'desc_bprop': [[4, 3, 1, 1]],
}),
('SpaceToBatch_2', {
'block': P.SpaceToBatch(2, [[1, 1], [0, 4]]),
'desc_inputs': [[1, 3, 2, 2]],
'desc_bprop': [[4, 3, 2, 4]],
}),
('BatchToSpace_1', {
'block': P.BatchToSpace(2, [[0, 0], [0, 0]]),
'desc_inputs': [[4, 3, 1, 1]],
'desc_bprop': [[1, 3, 2, 2]],
}),
('BatchToSpace_2', {
'block': P.BatchToSpace(2, [[0, 0], [0, 1]]),
'desc_inputs': [[4, 3, 1, 1]],
'desc_bprop': [[1, 3, 2, 1]],
}),
]
test_case_other_ops = [