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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/06/30 01:05:40 UTC
[GitHub] eric-haibin-lin closed pull request #10889: [MXNET-382] Shape and
Size Operator
eric-haibin-lin closed pull request #10889: [MXNET-382] Shape and Size Operator
URL: https://github.com/apache/incubator-mxnet/pull/10889
This is a PR merged from a forked repository.
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diff --git a/docs/api/python/ndarray/ndarray.md b/docs/api/python/ndarray/ndarray.md
index 323344d69c0..dda534151a1 100644
--- a/docs/api/python/ndarray/ndarray.md
+++ b/docs/api/python/ndarray/ndarray.md
@@ -124,6 +124,8 @@ The `ndarray` package provides several classes:
:nosignatures:
NDArray.T
+ NDArray.shape_array
+ NDArray.size_array
NDArray.reshape
NDArray.reshape_like
NDArray.flatten
@@ -375,6 +377,8 @@ The `ndarray` package provides several classes:
:nosignatures:
cast
+ shape_array
+ size_array
reshape
reshape_like
flatten
diff --git a/docs/api/python/symbol/symbol.md b/docs/api/python/symbol/symbol.md
index cc63e13e6ec..304b17803ed 100644
--- a/docs/api/python/symbol/symbol.md
+++ b/docs/api/python/symbol/symbol.md
@@ -191,6 +191,8 @@ Composite multiple symbols into a new one by an operator.
:nosignatures:
Symbol.astype
+ Symbol.shape_array
+ Symbol.size_array
Symbol.reshape
Symbol.reshape_like
Symbol.flatten
@@ -373,6 +375,8 @@ Composite multiple symbols into a new one by an operator.
:nosignatures:
cast
+ shape_array
+ size_array
reshape
reshape_like
flatten
diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index 002ce3ebbc2..09395e2ec82 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -1254,6 +1254,22 @@ def flatten(self, *args, **kwargs):
"""
return op.flatten(self, *args, **kwargs)
+ def shape_array(self, *args, **kwargs):
+ """Convenience fluent method for :py:func:`shape_array`.
+
+ The arguments are the same as for :py:func:`shape_array`, with
+ this array as data.
+ """
+ return op.shape_array(self, *args, **kwargs)
+
+ def size_array(self, *args, **kwargs):
+ """Convenience fluent method for :py:func:`size_array`.
+
+ The arguments are the same as for :py:func:`size_array`, with
+ this array as data.
+ """
+ return op.size_array(self, *args, **kwargs)
+
def expand_dims(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`expand_dims`.
diff --git a/python/mxnet/symbol/symbol.py b/python/mxnet/symbol/symbol.py
index c5e2f5cb77d..b041f4ef646 100644
--- a/python/mxnet/symbol/symbol.py
+++ b/python/mxnet/symbol/symbol.py
@@ -1982,6 +1982,22 @@ def flatten(self, *args, **kwargs):
"""
return op.flatten(self, *args, **kwargs)
+ def shape_array(self, *args, **kwargs):
+ """Convenience fluent method for :py:func:`shape_array`.
+
+ The arguments are the same as for :py:func:`shape_op`, with
+ this array as data.
+ """
+ return op.shape_array(self, *args, **kwargs)
+
+ def size_array(self, *args, **kwargs):
+ """Convenience fluent method for :py:func:`size_array`.
+
+ The arguments are the same as for :py:func:`size_array`, with
+ this array as data.
+ """
+ return op.size_array(self, *args, **kwargs)
+
def expand_dims(self, *args, **kwargs):
"""Convenience fluent method for :py:func:`expand_dims`.
diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py
index 19fe0749598..ae5a473d228 100644
--- a/python/mxnet/test_utils.py
+++ b/python/mxnet/test_utils.py
@@ -1251,13 +1251,15 @@ def check_consistency(sym, ctx_list, scale=1.0, grad_req='write',
np.dtype(np.float32): 1e-3,
np.dtype(np.float64): 1e-5,
np.dtype(np.uint8): 0,
- np.dtype(np.int32): 0}
+ np.dtype(np.int32): 0,
+ np.dtype(np.int64): 0}
elif isinstance(tol, numbers.Number):
tol = {np.dtype(np.float16): tol,
np.dtype(np.float32): tol,
np.dtype(np.float64): tol,
np.dtype(np.uint8): tol,
- np.dtype(np.int32): tol}
+ np.dtype(np.int32): tol,
+ np.dtype(np.int64): tol}
assert len(ctx_list) > 1
if isinstance(sym, Symbol):
diff --git a/src/operator/tensor/elemwise_unary_op_basic.cc b/src/operator/tensor/elemwise_unary_op_basic.cc
index 46f62651c75..5b89d49f430 100644
--- a/src/operator/tensor/elemwise_unary_op_basic.cc
+++ b/src/operator/tensor/elemwise_unary_op_basic.cc
@@ -398,6 +398,98 @@ NNVM_REGISTER_OP(reshape_like)
.add_argument("lhs", "NDArray-or-Symbol", "First input.")
.add_argument("rhs", "NDArray-or-Symbol", "Second input.");
+void ShapeComputeCPU(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<TBlob>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<TBlob>& outputs) {
+ CHECK_EQ(inputs.size(), 1U);
+ CHECK_EQ(outputs.size(), 1U);
+ CHECK_EQ(req.size(), 1U);
+ const TBlob& in_data = inputs[0];
+ const TBlob& out_data = outputs[0];
+ memcpy(out_data.dptr_, in_data.shape_.data(), in_data.ndim() * sizeof(int64_t));
+}
+
+NNVM_REGISTER_OP(shape_array)
+.describe(R"code(Returns a 1D int64 array containing the shape of data.
+
+Example::
+
+ shape_array([[1,2,3,4], [5,6,7,8]]) = [2,4]
+
+)code" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<FCompute>("FCompute<cpu>", ShapeComputeCPU)
+.set_attr<nnvm::FInferShape>("FInferShape",
+ [](const nnvm::NodeAttrs& attrs,
+ std::vector<TShape> *in_attrs,
+ std::vector<TShape> *out_attrs) {
+ CHECK_EQ(in_attrs->size(), 1U);
+ CHECK_EQ(out_attrs->size(), 1U);
+ TShape target_shape(1);
+ target_shape[0] = in_attrs->at(0).ndim();
+ SHAPE_ASSIGN_CHECK(*out_attrs, 0, target_shape);
+ return !shape_is_none(out_attrs->at(0));
+ })
+.set_attr<nnvm::FInferType>("FInferType",
+ [](const nnvm::NodeAttrs& attrs,
+ std::vector<int>* in_attrs,
+ std::vector<int>* out_attrs) {
+ CHECK_EQ(in_attrs->size(), 1U);
+ CHECK_EQ(out_attrs->size(), 1U);
+ TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kInt64);
+ return out_attrs->at(0) != -1;
+ })
+.add_argument("data", "NDArray-or-Symbol", "Input Array.");
+
+void SizeComputeCPU(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<TBlob>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<TBlob>& outputs) {
+ using namespace mshadow;
+ using namespace mxnet_op;
+ CHECK_EQ(inputs.size(), 1U);
+ CHECK_EQ(outputs.size(), 1U);
+ CHECK_EQ(req.size(), 1U);
+ const TBlob& in_data = inputs[0];
+ const TBlob& out_data = outputs[0];
+ const index_t size_var = in_data.Size();
+ memcpy(out_data.dptr_, &size_var, 1U * sizeof(int64_t));
+}
+
+NNVM_REGISTER_OP(size_array)
+.describe(R"code(Returns a 1D int64 array containing the size of data.
+
+Example::
+
+ size_array([[1,2,3,4], [5,6,7,8]]) = [8]
+
+)code" ADD_FILELINE)
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<FCompute>("FCompute<cpu>", SizeComputeCPU)
+.set_attr<nnvm::FInferShape>("FInferShape",
+ [](const nnvm::NodeAttrs& attrs,
+ std::vector<TShape> *in_attrs,
+ std::vector<TShape> *out_attrs) {
+ CHECK_EQ(in_attrs->size(), 1U);
+ CHECK_EQ(out_attrs->size(), 1U);
+ SHAPE_ASSIGN_CHECK(*out_attrs, 0, 1U);
+ return !shape_is_none(out_attrs->at(0));
+ })
+.set_attr<nnvm::FInferType>("FInferType",
+ [](const nnvm::NodeAttrs& attrs,
+ std::vector<int>* in_attrs,
+ std::vector<int>* out_attrs) {
+ CHECK_EQ(in_attrs->size(), 1U);
+ CHECK_EQ(out_attrs->size(), 1U);
+ TYPE_ASSIGN_CHECK(*out_attrs, 0, mshadow::kInt64);
+ return out_attrs->at(0) != -1;
+ })
+.add_argument("data", "NDArray-or-Symbol", "Input Array.");
DMLC_REGISTER_PARAMETER(CastParam);
NNVM_REGISTER_OP(Cast)
diff --git a/src/operator/tensor/elemwise_unary_op_basic.cu b/src/operator/tensor/elemwise_unary_op_basic.cu
index 3c8b49ac0a2..8e2a0991b01 100644
--- a/src/operator/tensor/elemwise_unary_op_basic.cu
+++ b/src/operator/tensor/elemwise_unary_op_basic.cu
@@ -77,6 +77,52 @@ NNVM_REGISTER_OP(_identity_with_attr_like_rhs)
NNVM_REGISTER_OP(reshape_like)
.set_attr<FCompute>("FCompute<gpu>", UnaryOp::IdentityCompute<gpu>);
+void ShapeComputeGPU(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<TBlob>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<TBlob>& outputs) {
+ using namespace mshadow;
+ CHECK_EQ(inputs.size(), 1U);
+ CHECK_EQ(outputs.size(), 1U);
+ CHECK_EQ(req.size(), 1U);
+ const TBlob& in_data = inputs[0];
+ const TBlob& out_data = outputs[0];
+ mshadow::Stream<gpu> *s = ctx.get_stream<gpu>();
+ cudaMemcpyAsync(out_data.dptr_,
+ in_data.shape_.data(),
+ in_data.ndim() * sizeof(int64_t),
+ cudaMemcpyHostToDevice,
+ mshadow::Stream<gpu>::GetStream(s));
+}
+
+NNVM_REGISTER_OP(shape_array)
+.set_attr<FCompute>("FCompute<gpu>", ShapeComputeGPU);
+
+void SizeComputeGPU(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<TBlob>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<TBlob>& outputs) {
+ using namespace mshadow;
+ using namespace mxnet_op;
+ CHECK_EQ(inputs.size(), 1U);
+ CHECK_EQ(outputs.size(), 1U);
+ CHECK_EQ(req.size(), 1U);
+ const TBlob& in_data = inputs[0];
+ const TBlob& out_data = outputs[0];
+ mshadow::Stream<gpu> *s = ctx.get_stream<gpu>();
+ const index_t size_var = in_data.Size();
+ cudaMemcpyAsync(out_data.dptr_,
+ &size_var,
+ 1U * sizeof(int64_t),
+ cudaMemcpyHostToDevice,
+ mshadow::Stream<gpu>::GetStream(s));
+}
+
+NNVM_REGISTER_OP(size_array)
+.set_attr<FCompute>("FCompute<gpu>", SizeComputeGPU);
+
NNVM_REGISTER_OP(Cast)
.set_attr<FCompute>("FCompute<gpu>", CastCompute<gpu>);
diff --git a/tests/python/unittest/test_ndarray.py b/tests/python/unittest/test_ndarray.py
index aeaa0b72679..4f0662de2c0 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -931,8 +931,8 @@ def check_fluent_regular(func, kwargs, shape=(5, 17, 1), equal_nan=False):
assert almost_equal(regular.asnumpy(), fluent.asnumpy(), equal_nan=equal_nan)
for func in ['flatten', 'norm', 'round', 'rint', 'fix', 'floor', 'ceil', 'trunc', 'zeros_like',
- 'ones_like', 'abs', 'sign', 'sin', 'cos', 'degrees', 'radians',
- 'exp', 'expm1', 'square', 'reciprocal', 'argmax_channel']:
+ 'ones_like', 'abs', 'sign', 'sin', 'cos', 'degrees', 'radians', 'exp', 'expm1',
+ 'square', 'reciprocal', 'argmax_channel', 'shape_array', 'size_array']:
check_fluent_regular(func, {})
for func in ['arccosh', 'arcsin', 'arccos', 'arctan', 'tan', 'sinh', 'cosh', 'tanh',
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index e07a602b8c1..0b429520f9a 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -820,6 +820,24 @@ def fsigmoid(a):
check_symbolic_forward(y, [xa], [ya])
check_symbolic_backward(y, [xa], [np.ones(shape)], [ya * (1 - ya)])
+@with_seed()
+def test_shape_array():
+ for i in range(1,6):
+ shape = rand_shape_nd(i)
+ x = np.random.ranf(shape)
+ y = mx.nd.shape_array(mx.nd.array(x))
+ expected_y = np.shape(x)
+ same(y.asnumpy(), expected_y)
+
+@with_seed()
+def test_size_array():
+ for i in range(1,6):
+ shape = rand_shape_nd(i)
+ x = np.random.ranf(shape)
+ y = mx.nd.size_array(mx.nd.array(x))
+ expected_y = np.size(x)
+ same(y.asnumpy(), expected_y)
+
@with_seed()
def test_hard_sigmoid():
def fhardsigmoid(a, alpha=0.2, beta=0.5):
diff --git a/tests/python/unittest/test_symbol.py b/tests/python/unittest/test_symbol.py
index 387428ab296..aece9a37812 100644
--- a/tests/python/unittest/test_symbol.py
+++ b/tests/python/unittest/test_symbol.py
@@ -190,8 +190,8 @@ def check_fluent_regular(func, kwargs, shape=(5, 17, 1), equal_nan=False):
equal_nan=equal_nan)
for func in ['flatten', 'norm', 'round', 'rint', 'fix', 'floor', 'ceil', 'trunc', 'zeros_like',
- 'ones_like', 'abs', 'sign', 'sin', 'cos', 'degrees', 'radians',
- 'exp', 'expm1', 'square', 'reciprocal', 'argmax_channel']:
+ 'ones_like', 'abs', 'sign', 'sin', 'cos', 'degrees', 'radians', 'exp', 'expm1',
+ 'square', 'reciprocal', 'argmax_channel', 'shape_array', 'size_array']:
check_fluent_regular(func, {})
for func in ['arccosh', 'arcsin', 'arccos', 'arctan', 'tan', 'sinh', 'cosh', 'tanh',
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