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Posted to commits@mxnet.apache.org by ha...@apache.org on 2019/08/10 03:57:24 UTC
[incubator-mxnet] branch master updated: [Numpy] Numpy compatible
slicing (#15798)
This is an automated email from the ASF dual-hosted git repository.
haoj pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/master by this push:
new 44a7fca [Numpy] Numpy compatible slicing (#15798)
44a7fca is described below
commit 44a7fca2cc0afe7c30658be96efcbd81c2be0dad
Author: Mike <ma...@connect.hku.hk>
AuthorDate: Sat Aug 10 11:56:34 2019 +0800
[Numpy] Numpy compatible slicing (#15798)
* Modify ndarray slice to have numpy compatbile behaviou
* Minor syntax fix
* Fix slice inconsistency
* Allow empty outputs after slicing ndarrays
* Fix
---
src/operator/tensor/matrix_op-inl.h | 67 +++++++++++++++++-----------------
src/operator/tensor/matrix_op.cc | 1 +
tests/python/unittest/test_numpy_op.py | 57 +++++++++++++++++++++++++++++
tests/python/unittest/test_operator.py | 9 -----
4 files changed, 91 insertions(+), 43 deletions(-)
diff --git a/src/operator/tensor/matrix_op-inl.h b/src/operator/tensor/matrix_op-inl.h
index 597c92a..96c86c4 100644
--- a/src/operator/tensor/matrix_op-inl.h
+++ b/src/operator/tensor/matrix_op-inl.h
@@ -685,13 +685,13 @@ inline void GetIndexRange(const mxnet::TShape& dshape,
<< "Static array size=" << ndim
<< " is not equal to data shape ndim=" << dshape.ndim();
- if (param_step.ndim() != 0) {
+ if (param_step.ndim() > 0) {
CHECK_EQ(param_step.ndim(), param_begin.ndim())
<< "step and begin must have the same length";
}
for (int i = 0; i < param_begin.ndim(); ++i) {
- index_t s = param_step.ndim() != 0U && param_step[i].has_value() ? param_step[i].value() : 1;
+ index_t s = param_step.ndim() > 0 && param_step[i].has_value() ? param_step[i].value() : 1;
CHECK_NE(s, 0) << "slice op step[" << i << "] cannot be 0";
index_t b = 0, e = 0;
@@ -700,30 +700,23 @@ inline void GetIndexRange(const mxnet::TShape& dshape,
b = param_begin[i].has_value() ? param_begin[i].value() : (s < 0 ? len - 1 : 0);
e = param_end[i].has_value() ? param_end[i].value() : (s < 0 ? -1 : len);
- // checking upper and lower bounds for begin
if (b < 0) {
b += len;
- CHECK_GE(b, 0) << "slicing with begin[" << i << "]=" << b - len
- << " exceeds limit of input dimension[" << i << "]=" << len;
}
- CHECK_LT(b, len) << "slicing with begin[" << i << "]=" << b
- << " exceeds limit of input dimension[" << i << "]=" << len;
-
- // checking upper and lower bounds for end
if (e < 0 && param_end[i].has_value()) {
- if (!(s < 0 && e == -1)) {
- // Keep end=-1 as one-beyond-limits index for negative stride
- e += len;
- }
- CHECK_GE(e, 0) << "slicing with end[" << i << "]=" << e - len
- << " exceeds limit of input dimension[" << i << "]=" << len;
+ e += len;
}
- CHECK_LE(e, len) << "slicing with end[" << i << "]=" << e
- << " exceeds limit of input dimension[" << i << "]=" << len;
- // checking begin==end case which is not supported
- CHECK_NE(b, e) << "slicing with begin[" << i << "]=end[" << i << "]="
- << e << " results in an empty tensor and is not supported";
+ // move the begin and end to correct position for calculating dim size
+ b = (b < 0 && s > 0) ? 0 : b;
+ b = (b > len - 1 && s < 0) ? len - 1 : b;
+ // if the start value lead to empty tensor under step s, use -1 for indication
+ b = (b < 0 || b > len - 1) ? -1 : b;
+ e = e > -1 ? e : -1;
+ e = e > len ? len : e;
+ } else if (len == 0) {
+ b = 0;
+ e = 0;
}
(*begin)[i] = b;
@@ -731,27 +724,30 @@ inline void GetIndexRange(const mxnet::TShape& dshape,
(*step)[i] = s;
}
- for (index_t i = param_begin.ndim(); i < dshape.ndim(); ++i) {
+ for (int i = param_begin.ndim(); i < dshape.ndim(); ++i) {
(*begin)[i] = 0;
(*end)[i] = dshape[i];
(*step)[i] = 1;
}
}
-inline void SetSliceOpOutputDimSize(const index_t i, const int b,
+inline void SetSliceOpOutputDimSize(const mxnet::TShape& dshape,
+ const index_t i, const int b,
const int e, const int s,
mxnet::TShape* oshape) {
- if (e != b) {
+ if (!mxnet::dim_size_is_known(dshape, i)) {
+ (*oshape)[i] = -1;
+ return;
+ }
+ if (e != b && b >= 0) {
if (s > 0) {
- CHECK_LT(b, e) << "slicing with begin[" << i << "]=" << b << ", end[" << i << "]="
- << e << ", and step[" << i << "]=" << s << " is invalid";
- (*oshape)[i] = (e - b - 1) / s + 1;
+ (*oshape)[i] = e > b ? (e - b - 1) / s + 1 : 0;
} else {
- CHECK_LT(e, b) << "slicing with begin[" << i << "]=" << b << ", end[" << i << "]="
- << e << ", and step[" << i << "]=" << s << " is invalid";
- (*oshape)[i] = (b - e - 1) / (-s) + 1;
+ (*oshape)[i] = e < b ? (b - e - 1) / (-s) + 1 : 0;
}
- } // else leave oshape[i] as 0 for partial infer
+ } else {
+ (*oshape)[i] = 0;
+ }
}
inline bool SliceOpShape(const nnvm::NodeAttrs& attrs,
@@ -761,6 +757,7 @@ inline bool SliceOpShape(const nnvm::NodeAttrs& attrs,
CHECK_EQ(out_attrs->size(), 1U);
const mxnet::TShape& dshape = (*in_attrs)[0];
if (!mxnet::ndim_is_known(dshape)) return false;
+ CHECK_GT(dshape.ndim(), 0) << "slice only works for ndim > 0";
const SliceParam& param = nnvm::get<SliceParam>(attrs.parsed);
mxnet::TShape oshape = dshape;
@@ -769,12 +766,12 @@ inline bool SliceOpShape(const nnvm::NodeAttrs& attrs,
GetIndexRange(dshape, param.begin, param.end, param.step, &begin, &end, &step);
for (int i = 0; i < param.begin.ndim(); ++i) {
const int b = begin[i], e = end[i], s = step[i];
- SetSliceOpOutputDimSize(i, b, e, s, &oshape);
+ SetSliceOpOutputDimSize(dshape, i, b, e, s, &oshape);
}
})
SHAPE_ASSIGN_CHECK(*out_attrs, 0, oshape);
- return shape_is_known(oshape);
+ return shape_is_known(dshape) && shape_is_known(oshape);
}
template<int ndim, int req, typename xpu>
@@ -852,6 +849,7 @@ void SliceOpForward(const nnvm::NodeAttrs& attrs,
Stream<xpu>* s = ctx.get_stream<xpu>();
const TBlob& data = inputs[0];
const TBlob& out = outputs[0];
+ if (out.Size() == 0) return;
const SliceParam& param = nnvm::get<SliceParam>(attrs.parsed);
MXNET_NDIM_SWITCH(data.ndim(), ndim, {
common::StaticArray<index_t, ndim> begin, end, step;
@@ -951,6 +949,7 @@ void SliceOpBackward(const nnvm::NodeAttrs& attrs,
} else if (req[0] == kWriteInplace) {
LOG(FATAL) << "_slice_backward does not support kWriteInplace";
}
+ if (ograd.Size() == 0) return;
MXNET_NDIM_SWITCH(ograd.ndim(), ndim, {
common::StaticArray<index_t, ndim> begin, end, step;
GetIndexRange(igrad.shape_, param.begin, param.end, param.step, &begin, &end, &step);
@@ -982,7 +981,7 @@ inline bool SliceAssignOpShape(const nnvm::NodeAttrs& attrs,
GetIndexRange(dshape, param.begin, param.end, param.step, &begin, &end, &step);
for (int i = 0; i < param.begin.ndim(); ++i) {
const int b = begin[i], e = end[i], s = step[i];
- SetSliceOpOutputDimSize(i, b, e, s, &vshape);
+ SetSliceOpOutputDimSize(dshape, i, b, e, s, &vshape);
}
})
SHAPE_ASSIGN_CHECK(*in_attrs, 1, vshape);
@@ -1121,7 +1120,7 @@ void SliceAssignScalarOpForward(const nnvm::NodeAttrs& attrs,
GetIndexRange(data.shape_, param.begin, param.end, param.step, &begin, &end, &step);
for (index_t i = 0; i < param.begin.ndim(); ++i) {
const int b = begin[i], e = end[i], s = step[i];
- SetSliceOpOutputDimSize(i, b, e, s, &vshape);
+ SetSliceOpOutputDimSize(data.shape_, i, b, e, s, &vshape);
}
MSHADOW_TYPE_SWITCH(out.type_flag_, DType, {
mxnet_op::Kernel<slice_assign_scalar<ndim>, xpu>::Launch(s, vshape.FlatTo2D()[0],
diff --git a/src/operator/tensor/matrix_op.cc b/src/operator/tensor/matrix_op.cc
index c8e7b60..f02a38a 100644
--- a/src/operator/tensor/matrix_op.cc
+++ b/src/operator/tensor/matrix_op.cc
@@ -506,6 +506,7 @@ Example::
[5., 7.],
[1., 3.]]
)code" ADD_FILELINE)
+.add_alias("_npx_slice")
.set_attr_parser(ParamParser<SliceParam>)
.set_attr<mxnet::FInferShape>("FInferShape", SliceOpShape)
.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
diff --git a/tests/python/unittest/test_numpy_op.py b/tests/python/unittest/test_numpy_op.py
index b179f67..c172336 100644
--- a/tests/python/unittest/test_numpy_op.py
+++ b/tests/python/unittest/test_numpy_op.py
@@ -92,6 +92,63 @@ def test_np_sum():
assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+@with_seed()
+@use_np
+def test_npx_slice():
+ class TestSlice(HybridBlock):
+ def __init__(self, begin, end, step):
+ super(TestSlice, self).__init__()
+ self._begin = begin
+ self._end = end
+ self._step = step
+
+ def hybrid_forward(self, F, a):
+ return F.npx.slice(a, begin=self._begin, end=self._end, step=self._step)
+
+ shape = (8, 16, 9, 9)
+ np_array = _np.arange(_np.prod(shape), dtype='int32').reshape(shape)
+ configs = [
+ ([], [], None),
+ ([], [], []),
+ ([1], [4], None),
+ ([1], [10], [3]),
+ ([10], [0], [-2]),
+ ([None], [None], [None]),
+ ([None], [None], [-1]),
+ ([10], [None], [-1]),
+ ([1, 0, 3], [-2, 10, -4], [None, 2, 3]),
+ ([-2, -3, -5, -6], [1, 3, 4, 5], None),
+ ([-2, -3, -5, -6], [1, 3, 4, 5], [-1, -2, -3, -4]),
+ ([2, -3, -5, -6], [2, 3, 4, 5], None),
+ ([2, -3, -5, 5], [3, 3, 4, 5], None),
+ ]
+
+ for hybridize in [True, False]:
+ for config in configs:
+ start, end, step = config[0], config[1], config[2]
+ test_slice = TestSlice(begin=start, end=end, step=step)
+ if hybridize:
+ test_slice.hybridize()
+
+ a = np.array(np_array, dtype=np_array.dtype)
+ a.attach_grad()
+ basic_index = tuple([
+ slice(start[i], end[i], step[i]) if step is not None else slice(start[i], end[i])
+ for i in range(len(start))
+ ])
+ expected_ret = np_array[basic_index]
+ with mx.autograd.record():
+ y = test_slice(a)
+
+ assert same(y.asnumpy(), expected_ret)
+
+ # test backward
+ mx.autograd.backward(y)
+ expected_grad = _np.zeros(shape)
+ expected_grad[basic_index] = 1
+ assert same(a.grad.asnumpy(), expected_grad)
+
+
if __name__ == '__main__':
import nose
nose.runmodule()
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 8072cb8..1991b16 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -7529,15 +7529,6 @@ def test_slice():
for index in index_list:
test_slice_forward_backward(arr, index)
- def test_begin_equals_end(shape, begin, end, step):
- in_arr = mx.nd.arange(np.prod(shape)).reshape(shape=shape)
- out_arr = mx.nd.slice(in_arr, begin=begin, end=end, step=step)
-
- assertRaises(MXNetError, test_begin_equals_end, (4,), (2,), (2,), (1,))
- assertRaises(MXNetError, test_begin_equals_end, (1, 5), (None, 3), (None, 3), (-1, 1))
- assertRaises(MXNetError, test_begin_equals_end, (3, 4, 5), (1, 3, 1), (3, 3, 1), (1, -3, 2))
- assertRaises(MXNetError, test_begin_equals_end, (2, 4), (None, 2), (None, 2), (1, -1))
-
# check numeric gradient
in_data = np.arange(36).reshape(2, 2, 3, 3)
data = mx.sym.Variable('data')