np compatible vstack

Author: Fan

python/mxnet/ndarray/numpy/_op.py

@@ -25,7 +25,7 @@
 from ...context import current_context
 from . import _internal as _npi
 
-__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power']
+__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'vstack']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -293,3 +293,56 @@ def power(x1, x2, out=None):
         This is a scalar if both x1 and x2 are scalars.
     """
     return _ufunc_helper(x1, x2, _npi.power, _np.power, _npi.power_scalar, _npi.rpower_scalar, out)
+
+
+@set_module('mxnet.ndarray.numpy')
+def vstack(arrays):
+    r"""vstack(tup)
+
+    Stack arrays in sequence vertically (row wise).
+
+    This is equivalent to concatenation along the first axis after 1-D arrays
+    of shape `(N,)` have been reshaped to `(1,N)`. Rebuilds arrays divided by
+    `vsplit`.
+
+    This function makes most sense for arrays with up to 3 dimensions. For
+    instance, for pixel-data with a height (first axis), width (second axis),
+    and r/g/b channels (third axis). The functions `concatenate` and `stack`
+    provide more general stacking and concatenation operations.
+
+    Parameters
+    ----------
+    tup : sequence of ndarrays
+        The arrays must have the same shape along all but the first axis.
+        1-D arrays must have the same length.
+
+    Returns
+    -------
+    stacked : ndarray
+        The array formed by stacking the given arrays, will be at least 2-D.
+
+    Examples
+    --------
+    >>> a = np.array([1, 2, 3])
+    >>> b = np.array([2, 3, 4])
+    >>> np.vstack((a, b))
+    array([[1., 2., 3.],
+           [2., 3., 4.]])
+
+    >>> a = np.array([[1], [2], [3]])
+    >>> b = np.array([[2], [3], [4]])
+    >>> np.vstack((a, b))
+    array([[1.],
+           [2.],
+           [3.],
+           [2.],
+           [3.],
+           [4.]])
+    """
+    def get_list(arrays):
+        if not hasattr(arrays, '__getitem__') and hasattr(arrays, '__iter__'):
+            raise ValueError("expected iterable for arrays but got {}".format(type(arrays)))
+        return [arr for arr in arrays]
+
+    arrays = get_list(arrays)
+    return _npi.vstack(*arrays)

python/mxnet/numpy/multiarray.py

@@ -44,7 +44,7 @@
 from ..ndarray.numpy import _internal as _npi
 
 __all__ = ['ndarray', 'empty', 'array', 'zeros', 'ones', 'add', 'subtract', 'multiply', 'divide',
-           'mod', 'power']
+           'mod', 'power', 'vstack']
 
 
 # This function is copied from ndarray.py since pylint
@@ -1549,3 +1549,50 @@ def power(x1, x2, out=None):
         This is a scalar if both x1 and x2 are scalars.
     """
     return _mx_nd_np.power(x1, x2, out=out)
+
+
+@set_module('mxnet.numpy')
+def vstack(arrays):
+    r"""vstack(tup)
+
+    Stack arrays in sequence vertically (row wise).
+
+    This is equivalent to concatenation along the first axis after 1-D arrays
+    of shape `(N,)` have been reshaped to `(1,N)`. Rebuilds arrays divided by
+    `vsplit`.
+
+    This function makes most sense for arrays with up to 3 dimensions. For
+    instance, for pixel-data with a height (first axis), width (second axis),
+    and r/g/b channels (third axis). The functions `concatenate` and `stack`
+    provide more general stacking and concatenation operations.
+
+    Parameters
+    ----------
+    tup : sequence of ndarrays
+        The arrays must have the same shape along all but the first axis.
+        1-D arrays must have the same length.
+
+    Returns
+    -------
+    stacked : ndarray
+        The array formed by stacking the given arrays, will be at least 2-D.
+
+    Examples
+    --------
+    >>> a = np.array([1, 2, 3])
+    >>> b = np.array([2, 3, 4])
+    >>> np.vstack((a, b))
+    array([[1., 2., 3.],
+           [2., 3., 4.]])
+
+    >>> a = np.array([[1], [2], [3]])
+    >>> b = np.array([[2], [3], [4]])
+    >>> np.vstack((a, b))
+    array([[1.],
+           [2.],
+           [3.],
+           [2.],
+           [3.],
+           [4.]])
+    """
+    return _mx_nd_np.vstack(arrays)

python/mxnet/symbol/numpy/_symbol.py

@@ -28,7 +28,7 @@
 from .._internal import _set_np_symbol_class
 from . import _internal as _npi
 
-__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power']
+__all__ = ['zeros', 'ones', 'add', 'subtract', 'multiply', 'divide', 'mod', 'power', 'vstack']
 
 
 def _num_outputs(sym):
@@ -1010,4 +1010,39 @@ def power(x1, x2, out=None):
     return _ufunc_helper(x1, x2, _npi.power, _np.power, _npi.power_scalar, _npi.rpower_scalar, out)
 
 
+@set_module('mxnet.symbol.numpy')
+def vstack(arrays):
+    r"""vstack(tup)
+
+    Stack arrays in sequence vertically (row wise).
+
+    This is equivalent to concatenation along the first axis after 1-D arrays
+    of shape `(N,)` have been reshaped to `(1,N)`. Rebuilds arrays divided by
+    `vsplit`.
+
+    This function makes most sense for arrays with up to 3 dimensions. For
+    instance, for pixel-data with a height (first axis), width (second axis),
+    and r/g/b channels (third axis). The functions `concatenate` and `stack`
+    provide more general stacking and concatenation operations.
+
+    Parameters
+    ----------
+    tup : sequence of _Symbol
+        The arrays must have the same shape along all but the first axis.
+        1-D arrays must have the same length.
+
+    Returns
+    -------
+    stacked : _Symbol
+        The array formed by stacking the given arrays, will be at least 2-D.
+    """
+    def get_list(arrays):
+        if not hasattr(arrays, '__getitem__') and hasattr(arrays, '__iter__'):
+            raise ValueError("expected iterable for arrays but got {}".format(type(arrays)))
+        return [arr for arr in arrays]
+
+    arrays = get_list(arrays)
+    return _npi.vstack(*arrays)
+
+
 _set_np_symbol_class(_Symbol)

src/operator/numpy/np_matrix_op-inl.h

@@ -0,0 +1,118 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file np_matrix_op-inl.h
+ * \brief Function definition of matrix related operators
+ */
+#ifndef MXNET_OPERATOR_NUMPY_NP_MATRIX_OP_INL_H_
+#define MXNET_OPERATOR_NUMPY_NP_MATRIX_OP_INL_H_
+
+#include <vector>
+#include "../tensor/matrix_op-inl.h"
+#include "../nn/concat-inl.h"
+
+namespace mxnet {
+namespace op {
+
+struct NumpyVstackParam : public dmlc::Parameter<NumpyVstackParam> {
+  int num_args;
+  DMLC_DECLARE_PARAMETER(NumpyVstackParam) {
+    DMLC_DECLARE_FIELD(num_args).set_lower_bound(1)
+    .describe("Number of inputs to be vstacked.");
+  }
+};
+
+template<typename xpu>
+void NumpyVstackForward(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 mshadow_op;
+
+  const NumpyVstackParam& param = nnvm::get<NumpyVstackParam>(attrs.parsed);
+  CHECK_EQ(inputs.size(), param.num_args);
+  CHECK_EQ(outputs.size(), 1);
+  CHECK_EQ(req.size(), 1);
+
+  // reshape if necessary
+  std::vector<TBlob> data(param.num_args);
+  for (int i = 0; i < param.num_args; i++) {
+    if (inputs[i].shape_.ndim() == 0 || inputs[i].shape_.ndim() == 1) {
+      TShape shape = Shape2(1, inputs[i].shape_.Size());
+      data[i] = inputs[i].reshape(shape);
+    } else {
+      data[i] = inputs[i];
+    }
+  }
+
+  // initialize ConcatOp
+  ConcatParam cparam;
+  cparam.num_args = param.num_args;
+  cparam.dim = 0;
+  MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    ConcatOp<xpu, DType> op;
+    op.Init(cparam);
+    op.Forward(ctx, data, req, outputs);
+  });
+}
+
+template<typename xpu>
+void NumpyVstackBackward(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 mshadow_op;
+
+  const NumpyVstackParam& param = nnvm::get<NumpyVstackParam>(attrs.parsed);
+  CHECK_EQ(inputs.size(), 1);
+  CHECK_EQ(outputs.size(), param.num_args);
+  CHECK_EQ(req.size(), param.num_args);
+
+  // reshape if necessary
+  std::vector<TBlob> data(param.num_args);
+  for (int i = 0; i < param.num_args; i++) {
+    if (outputs[i].shape_.ndim() == 0 || outputs[i].shape_.ndim() == 1) {
+      TShape shape = Shape2(1, outputs[i].shape_.Size());
+      data[i] = outputs[i].reshape(shape);
+    } else {
+      data[i] = outputs[i];
+    }
+  }
+
+  // initialize ConcatOp
+  ConcatParam cparam;
+  cparam.num_args = param.num_args;
+  cparam.dim = 0;
+  MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+    ConcatOp<xpu, DType> op;
+    op.Init(cparam);
+    op.Backward(ctx, inputs[0], req, data);
+  });
+}
+
+}  // namespace op
+}  // namespace mxnet
+
+#endif  // MXNET_OPERATOR_NUMPY_NP_MATRIX_OP_INL_H_