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| <h1>Source code for mxnet.ndarray</h1><div class="highlight"><pre> |
| <span></span><span class="c1"># Licensed to the Apache Software Foundation (ASF) under one</span> |
| <span class="c1"># or more contributor license agreements. See the NOTICE file</span> |
| <span class="c1"># distributed with this work for additional information</span> |
| <span class="c1"># regarding copyright ownership. The ASF licenses this file</span> |
| <span class="c1"># to you under the Apache License, Version 2.0 (the</span> |
| <span class="c1"># "License"); you may not use this file except in compliance</span> |
| <span class="c1"># with the License. You may obtain a copy of the License at</span> |
| <span class="c1">#</span> |
| <span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span> |
| <span class="c1">#</span> |
| <span class="c1"># Unless required by applicable law or agreed to in writing,</span> |
| <span class="c1"># software distributed under the License is distributed on an</span> |
| <span class="c1"># "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY</span> |
| <span class="c1"># KIND, either express or implied. See the License for the</span> |
| <span class="c1"># specific language governing permissions and limitations</span> |
| <span class="c1"># under the License.</span> |
| |
| <span class="c1"># coding: utf-8</span> |
| <span class="c1"># pylint: disable= too-many-lines, redefined-builtin, protected-access</span> |
| <span class="c1"># pylint: disable=import-error, no-name-in-module, undefined-variable</span> |
| <span class="sd">"""NDArray API of MXNet."""</span> |
| <span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">absolute_import</span> |
| <span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">division</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="kn">from</span> <span class="nn">__builtin__</span> <span class="k">import</span> <span class="nb">slice</span> <span class="k">as</span> <span class="n">py_slice</span> |
| <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span> |
| <span class="kn">from</span> <span class="nn">builtins</span> <span class="k">import</span> <span class="nb">slice</span> <span class="k">as</span> <span class="n">py_slice</span> |
| |
| <span class="kn">import</span> <span class="nn">ctypes</span> |
| <span class="kn">import</span> <span class="nn">warnings</span> |
| |
| <span class="kn">import</span> <span class="nn">os</span> <span class="k">as</span> <span class="nn">_os</span> |
| <span class="kn">import</span> <span class="nn">sys</span> <span class="k">as</span> <span class="nn">_sys</span> |
| |
| <span class="kn">import</span> <span class="nn">operator</span> |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> |
| <span class="kn">from</span> <span class="nn">.base</span> <span class="k">import</span> <span class="n">_LIB</span><span class="p">,</span> <span class="n">string_types</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">,</span> <span class="n">integer_types</span> |
| <span class="kn">from</span> <span class="nn">.base</span> <span class="k">import</span> <span class="n">c_array</span><span class="p">,</span> <span class="n">py_str</span><span class="p">,</span> <span class="n">c_str</span><span class="p">,</span> <span class="n">mx_real_t</span><span class="p">,</span> <span class="n">_Null</span> <span class="c1"># pylint: disable=unused-import</span> |
| <span class="kn">from</span> <span class="nn">.base</span> <span class="k">import</span> <span class="n">mx_uint</span><span class="p">,</span> <span class="n">NDArrayHandle</span><span class="p">,</span> <span class="n">check_call</span><span class="p">,</span> <span class="n">OpHandle</span> |
| <span class="kn">from</span> <span class="nn">.base</span> <span class="k">import</span> <span class="n">ctypes2buffer</span> |
| <span class="kn">from</span> <span class="nn">.context</span> <span class="k">import</span> <span class="n">Context</span> |
| <span class="kn">from</span> <span class="nn">.</span> <span class="k">import</span> <span class="n">_ndarray_internal</span> <span class="k">as</span> <span class="n">_internal</span> |
| <span class="kn">from</span> <span class="nn">.ndarray_doc</span> <span class="k">import</span> <span class="n">_build_doc</span> |
| |
| |
| <span class="c1"># Use different version of SymbolBase</span> |
| <span class="c1"># When possible, use cython to speedup part of computation.</span> |
| <span class="c1"># pylint: disable=unused-import</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="k">if</span> <span class="nb">int</span><span class="p">(</span><span class="n">_os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"MXNET_ENABLE_CYTHON"</span><span class="p">,</span> <span class="kc">True</span><span class="p">))</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="kn">from</span> <span class="nn">._ctypes.ndarray</span> <span class="k">import</span> <span class="n">NDArrayBase</span><span class="p">,</span> <span class="n">_set_ndarray_class</span> |
| <span class="kn">from</span> <span class="nn">._ctypes.ndarray</span> <span class="k">import</span> <span class="n">CachedOp</span><span class="p">,</span> <span class="n">_imperative_invoke</span> |
| <span class="k">elif</span> <span class="n">_sys</span><span class="o">.</span><span class="n">version_info</span> <span class="o">>=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">):</span> |
| <span class="kn">from</span> <span class="nn">._cy3.ndarray</span> <span class="k">import</span> <span class="n">NDArrayBase</span><span class="p">,</span> <span class="n">_set_ndarray_class</span><span class="p">,</span> <span class="n">_imperative_invoke</span> |
| <span class="kn">from</span> <span class="nn">._cy3.ndarray</span> <span class="k">import</span> <span class="n">CachedOp</span><span class="p">,</span> <span class="n">_imperative_invoke</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="kn">from</span> <span class="nn">._cy2.ndarray</span> <span class="k">import</span> <span class="n">NDArrayBase</span><span class="p">,</span> <span class="n">_set_ndarray_class</span><span class="p">,</span> <span class="n">_imperative_invoke</span> |
| <span class="kn">from</span> <span class="nn">._cy2.ndarray</span> <span class="k">import</span> <span class="n">CachedOp</span><span class="p">,</span> <span class="n">_imperative_invoke</span> |
| <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span> |
| <span class="k">if</span> <span class="nb">int</span><span class="p">(</span><span class="n">_os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"MXNET_ENFORCE_CYTHON"</span><span class="p">,</span> <span class="kc">False</span><span class="p">))</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="s2">"Cython Module cannot be loaded but MXNET_ENFORCE_CYTHON=1"</span><span class="p">)</span> |
| <span class="kn">from</span> <span class="nn">._ctypes.ndarray</span> <span class="k">import</span> <span class="n">NDArrayBase</span><span class="p">,</span> <span class="n">_set_ndarray_class</span><span class="p">,</span> <span class="n">_imperative_invoke</span> |
| <span class="kn">from</span> <span class="nn">._ctypes.ndarray</span> <span class="k">import</span> <span class="n">CachedOp</span><span class="p">,</span> <span class="n">_imperative_invoke</span> |
| <span class="c1"># pylint: enable=unused-import</span> |
| |
| <span class="c1"># pylint: disable= no-member</span> |
| <span class="n">_DTYPE_NP_TO_MX</span> <span class="o">=</span> <span class="p">{</span> |
| <span class="kc">None</span> <span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">float32</span> <span class="p">:</span> <span class="mi">0</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">float64</span> <span class="p">:</span> <span class="mi">1</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">float16</span> <span class="p">:</span> <span class="mi">2</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">uint8</span> <span class="p">:</span> <span class="mi">3</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">int32</span> <span class="p">:</span> <span class="mi">4</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">int8</span> <span class="p">:</span> <span class="mi">5</span><span class="p">,</span> |
| <span class="n">np</span><span class="o">.</span><span class="n">int64</span> <span class="p">:</span> <span class="mi">6</span><span class="p">,</span> |
| <span class="p">}</span> |
| |
| <span class="n">_DTYPE_MX_TO_NP</span> <span class="o">=</span> <span class="p">{</span> |
| <span class="o">-</span><span class="mi">1</span> <span class="p">:</span> <span class="kc">None</span><span class="p">,</span> |
| <span class="mi">0</span> <span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> |
| <span class="mi">1</span> <span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> |
| <span class="mi">2</span> <span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">float16</span><span class="p">,</span> |
| <span class="mi">3</span> <span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">,</span> |
| <span class="mi">4</span> <span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> |
| <span class="mi">5</span> <span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">int8</span><span class="p">,</span> |
| <span class="mi">6</span> <span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">,</span> |
| <span class="p">}</span> |
| |
| <span class="n">_GRAD_REQ_MAP</span> <span class="o">=</span> <span class="p">{</span> |
| <span class="s1">'null'</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> |
| <span class="s1">'write'</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> |
| <span class="s1">'add'</span><span class="p">:</span> <span class="mi">3</span> |
| <span class="p">}</span> |
| <span class="c1"># pylint: enable= no-member</span> |
| |
| <span class="k">def</span> <span class="nf">_new_empty_handle</span><span class="p">():</span> |
| <span class="sd">"""Returns a new empty handle.</span> |
| |
| <span class="sd"> Empty handle can be used to hold a result.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> handle</span> |
| <span class="sd"> A new empty `NDArray` handle.</span> |
| <span class="sd"> """</span> |
| <span class="n">hdl</span> <span class="o">=</span> <span class="n">NDArrayHandle</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArrayCreateNone</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">hdl</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">hdl</span> |
| |
| <span class="k">def</span> <span class="nf">_new_alloc_handle</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">delay_alloc</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">mx_real_t</span><span class="p">):</span> |
| <span class="sd">"""Return a new handle with specified shape and context.</span> |
| |
| <span class="sd"> Empty handle is only used to hold results.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> handle</span> |
| <span class="sd"> A new empty `NDArray` handle.</span> |
| <span class="sd"> """</span> |
| <span class="n">hdl</span> <span class="o">=</span> <span class="n">NDArrayHandle</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArrayCreateEx</span><span class="p">(</span> |
| <span class="n">c_array</span><span class="p">(</span><span class="n">mx_uint</span><span class="p">,</span> <span class="n">shape</span><span class="p">),</span> |
| <span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">ctx</span><span class="o">.</span><span class="n">device_typeid</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">ctx</span><span class="o">.</span><span class="n">device_id</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">delay_alloc</span><span class="p">)),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">_DTYPE_NP_TO_MX</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span><span class="o">.</span><span class="n">type</span><span class="p">])),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">hdl</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">hdl</span> |
| |
| <div class="viewcode-block" id="waitall"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.waitall">[docs]</a><span class="k">def</span> <span class="nf">waitall</span><span class="p">():</span> |
| <span class="sd">"""Wait for all async operations to finish in MXNet.</span> |
| |
| <span class="sd"> This function is used for benchmarking only.</span> |
| <span class="sd"> """</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArrayWaitAll</span><span class="p">())</span></div> |
| |
| <div class="viewcode-block" id="NDArray"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.NDArray">[docs]</a><span class="k">class</span> <span class="nc">NDArray</span><span class="p">(</span><span class="n">NDArrayBase</span><span class="p">):</span> |
| <span class="sd">"""An array object representing a multidimensional, homogeneous array of</span> |
| <span class="sd">fixed-size items.</span> |
| |
| <span class="sd"> """</span> |
| <span class="vm">__slots__</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="c1"># make numpy functions return NDArray instead of numpy object array</span> |
| <span class="n">__array_priority__</span> <span class="o">=</span> <span class="mf">1000.0</span> |
| <span class="c1"># pylint: disable= no-member, undefined-variable</span> |
| <span class="k">def</span> <span class="nf">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Returns a string representation of the array."""</span> |
| <span class="n">shape_info</span> <span class="o">=</span> <span class="s1">'x'</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">'</span><span class="si">%d</span><span class="s1">'</span> <span class="o">%</span> <span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">])</span> |
| <span class="k">return</span> <span class="s1">'</span><span class="se">\n</span><span class="si">%s</span><span class="se">\n</span><span class="s1"><</span><span class="si">%s</span><span class="s1"> </span><span class="si">%s</span><span class="s1"> @</span><span class="si">%s</span><span class="s1">>'</span> <span class="o">%</span> <span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()),</span> |
| <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> |
| <span class="n">shape_info</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__add__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="sd">"""x.__add__(y) <=> x+y <=> mx.nd.add(x, y) """</span> |
| <span class="k">return</span> <span class="n">add</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__iadd__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="sd">"""x.__iadd__(y) <=> x+=y """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">writable</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'trying to add to a readonly NDArray'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">broadcast_add</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_plus_scalar</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">other</span><span class="p">),</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'type </span><span class="si">%s</span><span class="s1"> not supported'</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">other</span><span class="p">)))</span> |
| |
| <span class="k">def</span> <span class="nf">__radd__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="fm">__add__</span><span class="p">(</span><span class="n">other</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__sub__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="sd">"""x.__sub__(y) <=> x-y <=> mx.nd.subtract(x, y) """</span> |
| <span class="k">return</span> <span class="n">subtract</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__isub__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="sd">"""x.__isub__(y) <=> x-=y """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">writable</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'trying to subtract from a readonly NDArray'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">broadcast_sub</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_minus_scalar</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">other</span><span class="p">),</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'type </span><span class="si">%s</span><span class="s1"> not supported'</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">other</span><span class="p">)))</span> |
| |
| <span class="k">def</span> <span class="nf">__rsub__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="sd">"""x.__rsub__(y) <=> y-x <=> mx.nd.subtract(y, x) """</span> |
| <span class="k">return</span> <span class="n">subtract</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__mul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="sd">"""x.__mul__(y) <=> x*y <=> mx.nd.multiply(x, y) """</span> |
| <span class="k">return</span> <span class="n">multiply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__neg__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""x.__neg__(y) <=> -x """</span> |
| <span class="k">return</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_mul_scalar</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.0</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__imul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="sd">"""x.__imul__(y) <=> x*=y """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">writable</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'trying to multiply to a readonly NDArray'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">broadcast_mul</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_mul_scalar</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">other</span><span class="p">),</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'type </span><span class="si">%s</span><span class="s1"> not supported'</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">other</span><span class="p">)))</span> |
| |
| <span class="k">def</span> <span class="nf">__rmul__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="fm">__mul__</span><span class="p">(</span><span class="n">other</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__div__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="sd">"""x.__div__(y) <=> x/y <=> mx.nd.divide(x, y) """</span> |
| <span class="k">return</span> <span class="n">divide</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__rdiv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="sd">"""x.__rdiv__(y) <=> y/x <=> mx.nd.divide(y, x) """</span> |
| <span class="k">return</span> <span class="n">divide</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__idiv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="sd">"""x.__rdiv__(y) <=> x/=y """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">writable</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'trying to divide from a readonly NDArray'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">broadcast_div</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_div_scalar</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">other</span><span class="p">),</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'type </span><span class="si">%s</span><span class="s1"> not supported'</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">other</span><span class="p">)))</span> |
| |
| <span class="k">def</span> <span class="nf">__truediv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">divide</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__rtruediv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">divide</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__itruediv__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">__idiv__</span><span class="p">(</span><span class="n">other</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__mod__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="sd">"""x.__mod__(y) <=> x%y <=> mx.nd.modulo(x, y) """</span> |
| <span class="k">return</span> <span class="n">modulo</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__rmod__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="sd">"""x.__rmod__(y) <=> y%x <=> mx.nd.modulo(y, x) """</span> |
| <span class="k">return</span> <span class="n">modulo</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__imod__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="sd">"""x.__rmod__(y) <=> x%=y """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">writable</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'trying to take modulo from a readonly NDArray'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">broadcast_mod</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_mod_scalar</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">other</span><span class="p">),</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'type </span><span class="si">%s</span><span class="s1"> not supported'</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">other</span><span class="p">)))</span> |
| |
| <span class="k">def</span> <span class="nf">__pow__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="sd">"""x.__pow__(y) <=> x**y <=> mx.nd.power(x,y) """</span> |
| <span class="k">return</span> <span class="n">power</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__rpow__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="sd">"""x.__pow__(y) <=> y**x <=> mx.nd.power(y,x) """</span> |
| <span class="k">return</span> <span class="n">power</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="sd">"""x.__eq__(y) <=> x==y <=> mx.nd.equal(x, y) """</span> |
| <span class="k">return</span> <span class="n">equal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__ne__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="sd">"""x.__ne__(y) <=> x!=y <=> mx.nd.not_equal(x, y) """</span> |
| <span class="k">return</span> <span class="n">not_equal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__gt__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="sd">"""x.__gt__(y) <=> x>y <=> mx.nd.greater(x, y) """</span> |
| <span class="k">return</span> <span class="n">greater</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__ge__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="sd">"""x.__ge__(y) <=> x>=y <=> mx.nd.greater_equal(x, y) """</span> |
| <span class="k">return</span> <span class="n">greater_equal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__lt__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="sd">"""x.__lt__(y) <=> x<y <=> mx.nd.lesser(x, y) """</span> |
| <span class="k">return</span> <span class="n">lesser</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__le__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="sd">"""x.__le__(y) <=> x<=y <=> mx.nd.less_equal(x, y) """</span> |
| <span class="k">return</span> <span class="n">lesser_equal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__bool__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"The truth value of an NDArray is ambiguous. "</span> \ |
| <span class="s2">"Please convert to number with asscalar() first."</span><span class="p">)</span> |
| |
| <span class="n">__nonzero__</span> <span class="o">=</span> <span class="fm">__bool__</span> |
| |
| <span class="k">def</span> <span class="nf">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Number of element along the first axis."""</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| |
| <span class="k">def</span> <span class="nf">__getstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="n">handle</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">handle</span> |
| <span class="n">this</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'handle'</span> <span class="p">:</span> <span class="kc">None</span><span class="p">}</span> |
| <span class="k">if</span> <span class="n">handle</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">length</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_size_t</span><span class="p">()</span> |
| <span class="n">cptr</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_char</span><span class="p">)()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArraySaveRawBytes</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">length</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">cptr</span><span class="p">)))</span> |
| <span class="n">this</span><span class="p">[</span><span class="s1">'handle'</span><span class="p">]</span> <span class="o">=</span> <span class="n">ctypes2buffer</span><span class="p">(</span><span class="n">cptr</span><span class="p">,</span> <span class="n">length</span><span class="o">.</span><span class="n">value</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">this</span> |
| |
| <span class="k">def</span> <span class="nf">__setstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="c1"># pylint: disable=assigning-non-slot</span> |
| <span class="n">handle</span> <span class="o">=</span> <span class="n">state</span><span class="p">[</span><span class="s1">'handle'</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">handle</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">buf</span> <span class="o">=</span> <span class="n">handle</span> |
| <span class="n">handle</span> <span class="o">=</span> <span class="n">NDArrayHandle</span><span class="p">()</span> |
| <span class="n">ptr</span> <span class="o">=</span> <span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_char</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">buf</span><span class="p">))</span><span class="o">.</span><span class="n">from_buffer</span><span class="p">(</span><span class="n">buf</span><span class="p">)</span> |
| <span class="n">length</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_size_t</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">buf</span><span class="p">))</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArrayLoadFromRawBytes</span><span class="p">(</span><span class="n">ptr</span><span class="p">,</span> <span class="n">length</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">handle</span><span class="p">)))</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span> <span class="o">=</span> <span class="n">handle</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span> <span class="o">=</span> <span class="kc">None</span> |
| |
| <span class="k">def</span> <span class="nf">__setitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span> |
| <span class="sd">"""x.__setitem__(i, y) <=> x[i]=y</span> |
| |
| <span class="sd"> Set self[key] to value.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> key : int, slice or tuple</span> |
| <span class="sd"> The indexing key.</span> |
| <span class="sd"> value : scalar, NDArray or numpy.ndarray</span> |
| <span class="sd"> The value to set.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.zeros((2,3))</span> |
| <span class="sd"> >>> x[:] = 1</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> x[:,1:2] = 2</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 1., 2., 1.],</span> |
| <span class="sd"> [ 1., 2., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> x[1:2,1:] = 3</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 1., 2., 1.],</span> |
| <span class="sd"> [ 1., 3., 3.]], dtype=float32)</span> |
| <span class="sd"> >>> x[1:,0:2] = mx.nd.zeros((1,2))</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 1., 2., 1.],</span> |
| <span class="sd"> [ 0., 0., 3.]], dtype=float32)</span> |
| <span class="sd"> >>> x[1,2] = 4</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 1., 2., 1.],</span> |
| <span class="sd"> [ 0., 0., 4.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable=too-many-branches</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">writable</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Cannot assign to readonly NDArray'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">integer_types</span><span class="p">):</span> |
| <span class="n">sliced_arr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_at</span><span class="p">(</span><span class="n">key</span><span class="p">)</span> |
| <span class="n">sliced_arr</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">value</span> |
| <span class="k">return</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">py_slice</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">key</span><span class="o">.</span><span class="n">step</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'NDArray only supports slicing with step size 1'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">key</span><span class="o">.</span><span class="n">start</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">key</span><span class="o">.</span><span class="n">stop</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">sliced_arr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_slice</span><span class="p">(</span><span class="n">key</span><span class="o">.</span><span class="n">start</span><span class="p">,</span> <span class="n">key</span><span class="o">.</span><span class="n">stop</span><span class="p">)</span> |
| <span class="n">sliced_arr</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">value</span> |
| <span class="k">return</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">value</span><span class="o">.</span><span class="n">handle</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">:</span> |
| <span class="n">value</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_set_value</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">value</span><span class="p">),</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">generic</span><span class="p">)):</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_sync_copyfrom</span><span class="p">(</span><span class="n">value</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span> |
| <span class="s1">'NDArray does not support assignment with </span><span class="si">%s</span><span class="s1"> of type </span><span class="si">%s</span><span class="s1">'</span><span class="o">%</span><span class="p">(</span> |
| <span class="nb">str</span><span class="p">(</span><span class="n">value</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">value</span><span class="p">))))</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span> |
| <span class="c1"># multi-dimension indexing</span> |
| <span class="n">my_shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">key</span><span class="p">)</span> <span class="o"><=</span> <span class="nb">len</span><span class="p">(</span><span class="n">my_shape</span><span class="p">),</span> \ |
| <span class="s2">"Indexing dimensions exceed array dimensions, </span><span class="si">%d</span><span class="s2"> vs </span><span class="si">%d</span><span class="s2">"</span><span class="o">%</span><span class="p">(</span> |
| <span class="nb">len</span><span class="p">(</span><span class="n">key</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">my_shape</span><span class="p">))</span> |
| <span class="n">begin</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">my_shape</span><span class="p">]</span> |
| <span class="n">end</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">my_shape</span><span class="p">]</span> |
| <span class="n">expand</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">slice_i</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">key</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">slice_i</span><span class="p">,</span> <span class="n">integer_types</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="n">slice_i</span> <span class="o"><</span> <span class="n">my_shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> |
| <span class="n">begin</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">slice_i</span> |
| <span class="n">end</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">slice_i</span> <span class="o">+</span> <span class="mi">1</span> |
| <span class="n">expand</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">slice_i</span><span class="p">,</span> <span class="n">py_slice</span><span class="p">):</span> |
| <span class="c1"># only support continuous slicing</span> |
| <span class="k">assert</span> <span class="n">slice_i</span><span class="o">.</span><span class="n">step</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">,</span> \ |
| <span class="s2">"NDArray only supports slicing with step size 1."</span> |
| <span class="n">begin</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">slice_i</span><span class="o">.</span><span class="n">start</span> <span class="ow">or</span> <span class="mi">0</span> |
| <span class="n">end</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">slice_i</span><span class="o">.</span><span class="n">stop</span> <span class="ow">or</span> <span class="n">my_shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> |
| <span class="k">assert</span> <span class="n">begin</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o"><</span> <span class="n">end</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> |
| <span class="k">assert</span> <span class="n">end</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o"><=</span> <span class="n">my_shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> |
| <span class="s2">"NDArray does not support slicing with key </span><span class="si">%s</span><span class="s2"> of type </span><span class="si">%s</span><span class="s2">."</span><span class="o">%</span><span class="p">(</span> |
| <span class="nb">str</span><span class="p">(</span><span class="n">slice_i</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">slice_i</span><span class="p">))))</span> |
| |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">value</span> <span class="o">=</span> <span class="n">value</span><span class="o">.</span><span class="n">as_in_context</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_slice_assign</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">begin</span><span class="p">,</span> <span class="n">end</span><span class="p">,</span> <span class="n">expand</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_crop_assign_scalar</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="p">,</span> |
| <span class="n">begin</span><span class="o">=</span><span class="n">begin</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="n">end</span><span class="p">,</span> |
| <span class="n">scalar</span><span class="o">=</span><span class="n">value</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">generic</span><span class="p">)):</span> |
| <span class="n">value</span> <span class="o">=</span> <span class="n">array</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">_slice_assign</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">begin</span><span class="p">,</span> <span class="n">end</span><span class="p">,</span> <span class="n">expand</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span> |
| <span class="s1">'NDArray does not support assignment with </span><span class="si">%s</span><span class="s1"> of type </span><span class="si">%s</span><span class="s1">'</span><span class="o">%</span><span class="p">(</span> |
| <span class="nb">str</span><span class="p">(</span><span class="n">value</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">value</span><span class="p">))))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> |
| <span class="s2">"NDArray does not support slicing with key </span><span class="si">%s</span><span class="s2"> of type </span><span class="si">%s</span><span class="s2">."</span><span class="o">%</span><span class="p">(</span> |
| <span class="nb">str</span><span class="p">(</span><span class="n">key</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">key</span><span class="p">))))</span> |
| <span class="c1"># pylint: enable=too-many-branches</span> |
| |
| <span class="k">def</span> <span class="nf">_slice_assign</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">begin</span><span class="p">,</span> <span class="n">end</span><span class="p">,</span> <span class="n">expand</span><span class="p">):</span> |
| <span class="n">vshape</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">value</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">expand</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">vshape</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">begin</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">expand</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">vshape</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">begin</span><span class="p">):</span> |
| <span class="n">sshape</span> <span class="o">=</span> <span class="p">[</span><span class="n">e</span> <span class="o">-</span> <span class="n">b</span> <span class="k">for</span> <span class="n">e</span><span class="p">,</span> <span class="n">b</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">end</span><span class="p">,</span> <span class="n">begin</span><span class="p">)]</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">reversed</span><span class="p">(</span><span class="n">expand</span><span class="p">):</span> |
| <span class="n">sshape</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> |
| <span class="s2">"Cannot assign NDArray with shape </span><span class="si">%s</span><span class="s2"> to NDArray slice with "</span> \ |
| <span class="s2">"shape </span><span class="si">%s</span><span class="s2">"</span><span class="o">%</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">vshape</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">sshape</span><span class="p">)))</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">expand</span><span class="p">:</span> |
| <span class="n">vshape</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> |
| <span class="n">value</span> <span class="o">=</span> <span class="n">value</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">vshape</span><span class="p">)</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_crop_assign</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="bp">self</span><span class="p">,</span> |
| <span class="n">begin</span><span class="o">=</span><span class="n">begin</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="n">end</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span> |
| <span class="sd">"""x.__getitem__(i) <=> x[i]</span> |
| |
| <span class="sd"> Returns a sliced view of this array.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> key : int or slice</span> |
| <span class="sd"> Indexing key.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.arange(0,6).reshape((2,3))</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 0., 1., 2.],</span> |
| <span class="sd"> [ 3., 4., 5.]], dtype=float32)</span> |
| <span class="sd"> >>> x[1].asnumpy()</span> |
| <span class="sd"> array([ 3., 4., 5.], dtype=float32)</span> |
| <span class="sd"> >>> y = x[0:1]</span> |
| <span class="sd"> >>> y[:] = 2</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 2., 2., 2.],</span> |
| <span class="sd"> [ 3., 4., 5.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># multi-dimensional slicing is not supported yet</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">integer_types</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">key</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">IndexError</span><span class="p">(</span> |
| <span class="s1">'index </span><span class="si">{}</span><span class="s1"> is out of bounds for axis 0 with size </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> |
| <span class="n">key</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_at</span><span class="p">(</span><span class="n">key</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">py_slice</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">key</span><span class="o">.</span><span class="n">step</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"NDArray only supports slicing with step size 1."</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">key</span><span class="o">.</span><span class="n">start</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">key</span><span class="o">.</span><span class="n">stop</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_slice</span><span class="p">(</span><span class="n">key</span><span class="o">.</span><span class="n">start</span><span class="p">,</span> <span class="n">key</span><span class="o">.</span><span class="n">stop</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span> |
| <span class="n">shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> |
| <span class="n">oshape</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">begin</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">end</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> <span class="o">>=</span> <span class="nb">len</span><span class="p">(</span><span class="n">key</span><span class="p">),</span> \ |
| <span class="s2">"Slicing dimensions exceeds array dimensions, </span><span class="si">%d</span><span class="s2"> vs </span><span class="si">%d</span><span class="s2">"</span><span class="o">%</span><span class="p">(</span> |
| <span class="nb">len</span><span class="p">(</span><span class="n">key</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">))</span> |
| <span class="n">i</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> |
| <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">slice_i</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">key</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">slice_i</span><span class="p">,</span> <span class="n">integer_types</span><span class="p">):</span> |
| <span class="n">begin</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">slice_i</span><span class="p">)</span> |
| <span class="n">end</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">slice_i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">slice_i</span><span class="p">,</span> <span class="n">py_slice</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">slice_i</span><span class="o">.</span><span class="n">step</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"NDArray only supports slicing with step size 1."</span><span class="p">)</span> |
| <span class="n">begin</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">0</span> <span class="k">if</span> <span class="n">slice_i</span><span class="o">.</span><span class="n">start</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">slice_i</span><span class="o">.</span><span class="n">start</span><span class="p">)</span> |
| <span class="n">end</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">if</span> <span class="n">slice_i</span><span class="o">.</span><span class="n">stop</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">slice_i</span><span class="o">.</span><span class="n">stop</span><span class="p">)</span> |
| <span class="n">oshape</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">end</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="n">begin</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> |
| <span class="s2">"NDArray does not support slicing with key </span><span class="si">%s</span><span class="s2"> of type </span><span class="si">%s</span><span class="s2">."</span><span class="o">%</span><span class="p">(</span> |
| <span class="nb">str</span><span class="p">(</span><span class="n">slice_i</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">slice_i</span><span class="p">))))</span> |
| <span class="n">oshape</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">:])</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">oshape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">oshape</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> |
| <span class="k">return</span> <span class="nb">slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">begin</span><span class="p">,</span> <span class="n">end</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">oshape</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> |
| <span class="s2">"NDArray does not support slicing with key </span><span class="si">%s</span><span class="s2"> of type </span><span class="si">%s</span><span class="s2">."</span><span class="o">%</span><span class="p">(</span> |
| <span class="nb">str</span><span class="p">(</span><span class="n">key</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">key</span><span class="p">))))</span> |
| |
| <span class="k">def</span> <span class="nf">_sync_copyfrom</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">source_array</span><span class="p">):</span> |
| <span class="sd">"""Performs a synchronized copy from the `source_array` to the current array.</span> |
| <span class="sd"> This is called through ``x[:] = source_array``, where the `source_array`</span> |
| <span class="sd"> is a `numpy.ndarray` or array-like object.</span> |
| <span class="sd"> This function blocks until all the pending read/write operations with respect</span> |
| <span class="sd"> to the current `NDArray` are finished and carry out the copy operation to the</span> |
| <span class="sd"> current NDArray.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> source_array : array_like</span> |
| <span class="sd"> The data source we would like to copy from.</span> |
| |
| <span class="sd"> Example</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> >>> a = mx.nd.array([1, 2])</span> |
| <span class="sd"> >>> a.asnumpy()</span> |
| <span class="sd"> array([ 1., 2.], dtype=float32)</span> |
| <span class="sd"> >>> a[:] = np.array([3, 4])</span> |
| <span class="sd"> >> a.asnumpy()</span> |
| <span class="sd"> array([ 3., 4.], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">source_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">except</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'array must consist of array-like data,'</span> <span class="o">+</span> |
| <span class="s1">'type </span><span class="si">%s</span><span class="s1"> is not supported'</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">array</span><span class="p">)))</span> |
| <span class="n">source_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ascontiguousarray</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">source_array</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Shape inconsistent: expected </span><span class="si">%s</span><span class="s1"> vs got </span><span class="si">%s</span><span class="s1">'</span><span class="o">%</span><span class="p">(</span> |
| <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">source_array</span><span class="o">.</span><span class="n">shape</span><span class="p">)))</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArraySyncCopyFromCPU</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> |
| <span class="n">source_array</span><span class="o">.</span><span class="n">ctypes</span><span class="o">.</span><span class="n">data_as</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_void_p</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_size_t</span><span class="p">(</span><span class="n">source_array</span><span class="o">.</span><span class="n">size</span><span class="p">)))</span> |
| |
| <span class="k">def</span> <span class="nf">_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">):</span> |
| <span class="sd">"""Returns a sliced NDArray that shares memory with the current one.</span> |
| <span class="sd"> This is called through ``x[start:stop]``.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> start : int</span> |
| <span class="sd"> Starting inclusive index of slice in the first dim.</span> |
| <span class="sd"> stop : int</span> |
| <span class="sd"> Finishing exclusive index of slice in the first dim.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> `NDArray` sharing the memory with the current one sliced from</span> |
| <span class="sd"> start to stop in the first dim.</span> |
| |
| <span class="sd"> Examples:</span> |
| <span class="sd"> >>> a = mx.nd.array([[1,2], [3, 4], [5, 6], [7, 8]])</span> |
| <span class="sd"> >>> a[1:2].asnumpy()</span> |
| <span class="sd"> array([[ 3., 4.]], dtype=float32)</span> |
| <span class="sd"> >>> a[1:1].asnumpy()</span> |
| <span class="sd"> array([], shape=(0, 2), dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="n">handle</span> <span class="o">=</span> <span class="n">NDArrayHandle</span><span class="p">()</span> |
| <span class="n">start</span> <span class="o">=</span> <span class="n">mx_uint</span><span class="p">(</span><span class="n">start</span><span class="p">)</span> <span class="k">if</span> <span class="n">start</span> <span class="k">else</span> <span class="n">mx_uint</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
| <span class="n">stop</span> <span class="o">=</span> <span class="n">mx_uint</span><span class="p">(</span><span class="n">stop</span><span class="p">)</span> <span class="k">if</span> <span class="n">stop</span> <span class="k">else</span> <span class="n">mx_uint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArraySlice</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">handle</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">NDArray</span><span class="p">(</span><span class="n">handle</span><span class="o">=</span><span class="n">handle</span><span class="p">,</span> <span class="n">writable</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">writable</span><span class="p">)</span> |
| |
| <span class="k">def</span> <span class="nf">_at</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">idx</span><span class="p">):</span> |
| <span class="sd">"""Returns a view of the array sliced at `idx` in the first dim.</span> |
| <span class="sd"> This is called through ``x[idx]``.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> idx : int</span> |
| <span class="sd"> index for slicing the `NDArray` in the first dim.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> `NDArray` sharing the memory with the current one sliced at `idx` in the first dim.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> a = mx.nd.array([[1,2], [3, 4]])</span> |
| <span class="sd"> >>> a[1].asnumpy()</span> |
| <span class="sd"> array([ 3., 4.], dtype=float32)</span> |
| <span class="sd"> >>> b = mx.nd.array([1, 2, 3, 4])</span> |
| <span class="sd"> >>> b[0].asnumpy()</span> |
| <span class="sd"> array([ 1.], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="n">handle</span> <span class="o">=</span> <span class="n">NDArrayHandle</span><span class="p">()</span> |
| <span class="n">idx</span> <span class="o">=</span> <span class="n">mx_uint</span><span class="p">(</span><span class="n">idx</span><span class="p">)</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArrayAt</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">idx</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">handle</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">NDArray</span><span class="p">(</span><span class="n">handle</span><span class="o">=</span><span class="n">handle</span><span class="p">,</span> <span class="n">writable</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">writable</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="NDArray.reshape"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.NDArray.reshape">[docs]</a> <span class="k">def</span> <span class="nf">reshape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">shape</span><span class="p">):</span> |
| <span class="sd">"""Returns a **view** of this array with a new shape without altering any data.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> shape : tuple of int</span> |
| <span class="sd"> The new shape should not change the array size, namely</span> |
| <span class="sd"> ``np.prod(new_shape)`` should be equal to ``np.prod(self.shape)``.</span> |
| |
| <span class="sd"> One dimension can be -1. In this case, the value is inferred</span> |
| <span class="sd"> from the length of the array and remaining dimensions.</span> |
| |
| <span class="sd"> 0 Dimensions in shape will be copied from original shape, i.e.</span> |
| <span class="sd"> if x.shape == (3, 4, 5), x.reshape((0, 20)).shape will be (3, 20).</span> |
| |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> An array with desired shape that shares data with this array.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.arange(0,6).reshape((2,3))</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 0., 1., 2.],</span> |
| <span class="sd"> [ 3., 4., 5.]], dtype=float32)</span> |
| <span class="sd"> >>> y = x.reshape((3,2))</span> |
| <span class="sd"> >>> y.asnumpy()</span> |
| <span class="sd"> array([[ 0., 1.],</span> |
| <span class="sd"> [ 2., 3.],</span> |
| <span class="sd"> [ 4., 5.]], dtype=float32)</span> |
| <span class="sd"> >>> y = x.reshape((3,-1))</span> |
| <span class="sd"> >>> y.asnumpy()</span> |
| <span class="sd"> array([[ 0., 1.],</span> |
| <span class="sd"> [ 2., 3.],</span> |
| <span class="sd"> [ 4., 5.]], dtype=float32)</span> |
| <span class="sd"> >>> y[:] = -1</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[-1., -1., -1.],</span> |
| <span class="sd"> [-1., -1., -1.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="n">handle</span> <span class="o">=</span> <span class="n">NDArrayHandle</span><span class="p">()</span> |
| |
| <span class="c1"># Actual reshape</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArrayReshape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> |
| <span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">),</span> |
| <span class="n">c_array</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">,</span> <span class="n">shape</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">handle</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">NDArray</span><span class="p">(</span><span class="n">handle</span><span class="o">=</span><span class="n">handle</span><span class="p">,</span> <span class="n">writable</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">writable</span><span class="p">)</span></div> |
| |
| <span class="c1"># pylint: disable= undefined-variable</span> |
| <div class="viewcode-block" id="NDArray.broadcast_to"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.NDArray.broadcast_to">[docs]</a> <span class="k">def</span> <span class="nf">broadcast_to</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">shape</span><span class="p">):</span> |
| <span class="sd">"""Broadcasts the input array to a new shape.</span> |
| |
| <span class="sd"> Broadcasting is only allowed on axes with size 1. The new shape cannot change</span> |
| <span class="sd"> the number of dimensions.</span> |
| <span class="sd"> For example, you could broadcast from shape (2, 1) to (2, 3), but not from</span> |
| <span class="sd"> shape (2, 3) to (2, 3, 3).</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> shape : tuple of int</span> |
| <span class="sd"> The shape of the desired array.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> A NDArray with the desired shape that is not sharing data with this</span> |
| <span class="sd"> array, even if the new shape is the same as ``self.shape``.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.arange(0,3).reshape((1,3,1))</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[[ 0.],</span> |
| <span class="sd"> [ 1.],</span> |
| <span class="sd"> [ 2.]]], dtype=float32)</span> |
| <span class="sd"> >>> y = x.broadcast_to((2,3,3))</span> |
| <span class="sd"> >>> y.asnumpy()</span> |
| <span class="sd"> array([[[ 0., 0., 0.],</span> |
| <span class="sd"> [ 1., 1., 1.],</span> |
| <span class="sd"> [ 2., 2., 2.]],</span> |
| <span class="sd"> <BLANKLINE></span> |
| <span class="sd"> [[ 0., 0., 0.],</span> |
| <span class="sd"> [ 1., 1., 1.],</span> |
| <span class="sd"> [ 2., 2., 2.]]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="n">cur_shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> |
| <span class="n">err_str</span> <span class="o">=</span> <span class="s1">'operands could not be broadcast together with remapped shapes'</span> \ |
| <span class="s1">'[original->remapped]: </span><span class="si">{}</span><span class="s1"> and requested shape </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">cur_shape</span><span class="p">,</span> <span class="n">shape</span><span class="p">)</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> <span class="o"><</span> <span class="nb">len</span><span class="p">(</span><span class="n">cur_shape</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">err_str</span><span class="p">)</span> |
| <span class="n">cur_shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span> <span class="o">*</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="n">cur_shape</span><span class="p">))</span> <span class="o">+</span> <span class="n">cur_shape</span> |
| <span class="n">cur_shape_arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">cur_shape</span><span class="p">)</span> |
| <span class="n">broadcasting_axes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nonzero</span><span class="p">(</span><span class="n">cur_shape_arr</span> <span class="o">!=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">shape</span><span class="p">))</span> |
| <span class="k">if</span> <span class="p">(</span><span class="n">cur_shape_arr</span><span class="p">[</span><span class="n">broadcasting_axes</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">():</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">err_str</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">cur_shape</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">broadcast_to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">cur_shape</span><span class="p">),</span> <span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">broadcast_to</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="nb">tuple</span><span class="p">(</span><span class="n">shape</span><span class="p">))</span></div> |
| <span class="c1"># pylint: enable= undefined-variable</span> |
| |
| <div class="viewcode-block" id="NDArray.wait_to_read"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.NDArray.wait_to_read">[docs]</a> <span class="k">def</span> <span class="nf">wait_to_read</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Waits until all previous write operations on the current array are finished.</span> |
| |
| <span class="sd"> This method guarantees that all previous write operations that pushed</span> |
| <span class="sd"> into the backend engine for execution are actually finished.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> import time</span> |
| <span class="sd"> >>> tic = time.time()</span> |
| <span class="sd"> >>> a = mx.nd.ones((1000,1000))</span> |
| <span class="sd"> >>> b = mx.nd.dot(a, a)</span> |
| <span class="sd"> >>> print(time.time() - tic) # doctest: +SKIP</span> |
| <span class="sd"> 0.003854036331176758</span> |
| <span class="sd"> >>> b.wait_to_read()</span> |
| <span class="sd"> >>> print(time.time() - tic) # doctest: +SKIP</span> |
| <span class="sd"> 0.0893700122833252</span> |
| <span class="sd"> """</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArrayWaitToRead</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">))</span></div> |
| |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">ndim</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Returns the number of dimensions of this array</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.array([1, 2, 3, 4])</span> |
| <span class="sd"> >>> x.ndim</span> |
| <span class="sd"> 1</span> |
| <span class="sd"> >>> x = mx.nd.array([[1, 2], [3, 4]])</span> |
| <span class="sd"> >>> x.ndim</span> |
| <span class="sd"> 2</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">shape</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Tuple of array dimensions.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.array([1, 2, 3, 4])</span> |
| <span class="sd"> >>> x.shape</span> |
| <span class="sd"> (4L,)</span> |
| <span class="sd"> >>> y = mx.nd.zeros((2, 3, 4))</span> |
| <span class="sd"> >>> y.shape</span> |
| <span class="sd"> (2L, 3L, 4L)</span> |
| <span class="sd"> """</span> |
| <span class="n">ndim</span> <span class="o">=</span> <span class="n">mx_uint</span><span class="p">()</span> |
| <span class="n">pdata</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">mx_uint</span><span class="p">)()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArrayGetShape</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">ndim</span><span class="p">),</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">pdata</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">pdata</span><span class="p">[:</span><span class="n">ndim</span><span class="o">.</span><span class="n">value</span><span class="p">])</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Number of elements in the array.</span> |
| |
| <span class="sd"> Equivalent to the product of the array's dimensions.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> import numpy as np</span> |
| <span class="sd"> >>> x = mx.nd.zeros((3, 5, 2))</span> |
| <span class="sd"> >>> x.size</span> |
| <span class="sd"> 30</span> |
| <span class="sd"> >>> np.prod(x.shape)</span> |
| <span class="sd"> 30</span> |
| <span class="sd"> """</span> |
| <span class="n">size</span> <span class="o">=</span> <span class="mi">1</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span> |
| <span class="n">size</span> <span class="o">*=</span> <span class="n">i</span> |
| <span class="k">return</span> <span class="n">size</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">context</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Device context of the array.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.array([1, 2, 3, 4])</span> |
| <span class="sd"> >>> x.context</span> |
| <span class="sd"> cpu(0)</span> |
| <span class="sd"> >>> type(x.context)</span> |
| <span class="sd"> <class 'mxnet.context.Context'></span> |
| <span class="sd"> >>> y = mx.nd.zeros((2,3), mx.gpu(0))</span> |
| <span class="sd"> >>> y.context</span> |
| <span class="sd"> gpu(0)</span> |
| <span class="sd"> """</span> |
| <span class="n">dev_typeid</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">()</span> |
| <span class="n">dev_id</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArrayGetContext</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">dev_typeid</span><span class="p">),</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">dev_id</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">Context</span><span class="p">(</span><span class="n">Context</span><span class="o">.</span><span class="n">devtype2str</span><span class="p">[</span><span class="n">dev_typeid</span><span class="o">.</span><span class="n">value</span><span class="p">],</span> <span class="n">dev_id</span><span class="o">.</span><span class="n">value</span><span class="p">)</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">dtype</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Data-type of the array's elements.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> numpy.dtype</span> |
| <span class="sd"> This NDArray's data type.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.zeros((2,3))</span> |
| <span class="sd"> >>> x.dtype</span> |
| <span class="sd"> <type 'numpy.float32'></span> |
| <span class="sd"> >>> y = mx.nd.zeros((2,3), dtype='int32')</span> |
| <span class="sd"> >>> y.dtype</span> |
| <span class="sd"> <type 'numpy.int32'></span> |
| <span class="sd"> """</span> |
| <span class="n">mx_dtype</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArrayGetDType</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">mx_dtype</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">_DTYPE_MX_TO_NP</span><span class="p">[</span><span class="n">mx_dtype</span><span class="o">.</span><span class="n">value</span><span class="p">]</span> |
| |
| <span class="nd">@property</span> |
| <span class="c1"># pylint: disable= invalid-name, undefined-variable</span> |
| <span class="k">def</span> <span class="nf">T</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Returns a copy of the array with axes transposed.</span> |
| |
| <span class="sd"> Equivalent to ``mx.nd.transpose(self)`` except that</span> |
| <span class="sd"> self is returned if ``self.ndim < 2``.</span> |
| |
| <span class="sd"> Unlike ``numpy.ndarray.T``, this function returns a copy</span> |
| <span class="sd"> rather than a view of the array unless ``self.ndim < 2``.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.arange(0,6).reshape((2,3))</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 0., 1., 2.],</span> |
| <span class="sd"> [ 3., 4., 5.]], dtype=float32)</span> |
| <span class="sd"> >>> x.T.asnumpy()</span> |
| <span class="sd"> array([[ 0., 3.],</span> |
| <span class="sd"> [ 1., 4.],</span> |
| <span class="sd"> [ 2., 5.]], dtype=float32)</span> |
| |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o"><</span> <span class="mi">2</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span> |
| <span class="k">return</span> <span class="n">transpose</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> |
| <span class="c1"># pylint: enable= invalid-name, undefined-variable</span> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">_fresh_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Whether this array's corresponding gradient array</span> |
| <span class="sd"> (registered via `autograd.mark_variables`) has been</span> |
| <span class="sd"> updated by `autograd.backward` since last reset.</span> |
| |
| <span class="sd"> `_fresh_grad` need to be manually set to False</span> |
| <span class="sd"> after consuming gradient (usually after updating this</span> |
| <span class="sd"> array).</span> |
| <span class="sd"> """</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArrayGetGradState</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">out</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">out</span><span class="o">.</span><span class="n">value</span> |
| |
| <span class="nd">@_fresh_grad</span><span class="o">.</span><span class="n">setter</span> |
| <span class="k">def</span> <span class="nf">_fresh_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArraySetGradState</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">state</span><span class="p">)))</span> |
| |
| <div class="viewcode-block" id="NDArray.asnumpy"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.NDArray.asnumpy">[docs]</a> <span class="k">def</span> <span class="nf">asnumpy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Returns a ``numpy.ndarray`` object with value copied from this array.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.ones((2,3))</span> |
| <span class="sd"> >>> y = x.asnumpy()</span> |
| <span class="sd"> >>> type(y)</span> |
| <span class="sd"> <type 'numpy.ndarray'></span> |
| <span class="sd"> >>> y</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> z = mx.nd.ones((2,3), dtype='int32')</span> |
| <span class="sd"> >>> z.asnumpy()</span> |
| <span class="sd"> array([[1, 1, 1],</span> |
| <span class="sd"> [1, 1, 1]], dtype=int32)</span> |
| <span class="sd"> """</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArraySyncCopyToCPU</span><span class="p">(</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> |
| <span class="n">data</span><span class="o">.</span><span class="n">ctypes</span><span class="o">.</span><span class="n">data_as</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_void_p</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_size_t</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">size</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">data</span></div> |
| |
| <div class="viewcode-block" id="NDArray.asscalar"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.NDArray.asscalar">[docs]</a> <span class="k">def</span> <span class="nf">asscalar</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Returns a scalar whose value is copied from this array.</span> |
| |
| <span class="sd"> This function is equivalent to ``self.asnumpy()[0]``. This NDArray must have shape (1,).</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.ones((1,), dtype='int32')</span> |
| <span class="sd"> >>> x.asscalar()</span> |
| <span class="sd"> 1</span> |
| <span class="sd"> >>> type(x.asscalar())</span> |
| <span class="sd"> <type 'numpy.int32'></span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,):</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"The current array is not a scalar"</span><span class="p">)</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span></div> |
| |
| <div class="viewcode-block" id="NDArray.astype"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.NDArray.astype">[docs]</a> <span class="k">def</span> <span class="nf">astype</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dtype</span><span class="p">):</span> |
| <span class="sd">"""Returns a copy of the array after casting to a specified type.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> dtype : numpy.dtype or str</span> |
| <span class="sd"> The type of the returned array.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.zeros((2,3), dtype='float32')</span> |
| <span class="sd"> >>> y = x.astype('int32')</span> |
| <span class="sd"> >>> y.dtype</span> |
| <span class="sd"> <type 'numpy.int32'></span> |
| <span class="sd"> """</span> |
| <span class="n">res</span> <span class="o">=</span> <span class="n">empty</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="bp">self</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">res</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">res</span></div> |
| |
| <div class="viewcode-block" id="NDArray.copyto"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.NDArray.copyto">[docs]</a> <span class="k">def</span> <span class="nf">copyto</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> |
| <span class="sd">"""Copies the value of this array to another array.</span> |
| |
| <span class="sd"> If ``other`` is a ``NDArray`` object, then ``other.shape`` and</span> |
| <span class="sd"> ``self.shape`` should be the same. This function copies the value from</span> |
| <span class="sd"> ``self`` to ``other``.</span> |
| |
| <span class="sd"> If ``other`` is a context, a new ``NDArray`` will be first created on</span> |
| <span class="sd"> the target context, and the value of ``self`` is copied.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> other : NDArray or Context</span> |
| <span class="sd"> The destination array or context.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> The copied array. If ``other`` is an ``NDArray``, then the return value</span> |
| <span class="sd"> and ``other`` will point to the same ``NDArray``.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.ones((2,3))</span> |
| <span class="sd"> >>> y = mx.nd.zeros((2,3), mx.gpu(0))</span> |
| <span class="sd"> >>> z = x.copyto(y)</span> |
| <span class="sd"> >>> z is y</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> y.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> y.copyto(mx.gpu(0))</span> |
| <span class="sd"> <NDArray 2x3 @gpu(0)></span> |
| |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="k">if</span> <span class="n">other</span><span class="o">.</span><span class="n">handle</span> <span class="ow">is</span> <span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">:</span> |
| <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">'You are attempting to copy an array to itself'</span><span class="p">,</span> <span class="ne">RuntimeWarning</span><span class="p">)</span> |
| <span class="k">return</span> |
| <span class="k">return</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_copyto</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">other</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">Context</span><span class="p">):</span> |
| <span class="n">hret</span> <span class="o">=</span> <span class="n">NDArray</span><span class="p">(</span><span class="n">_new_alloc_handle</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_copyto</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">hret</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'copyto does not support type '</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">other</span><span class="p">)))</span></div> |
| |
| <div class="viewcode-block" id="NDArray.copy"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.NDArray.copy">[docs]</a> <span class="k">def</span> <span class="nf">copy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Makes a copy of this ``NDArray``, keeping the same context.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> The copied array</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.ones((2,3))</span> |
| <span class="sd"> >>> y = x.copy()</span> |
| <span class="sd"> >>> y.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="NDArray.as_in_context"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.NDArray.as_in_context">[docs]</a> <span class="k">def</span> <span class="nf">as_in_context</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">context</span><span class="p">):</span> |
| <span class="sd">"""Returns an array on the target device with the same value as this array.</span> |
| |
| <span class="sd"> If the target context is the same as ``self.context``, then ``self`` is</span> |
| <span class="sd"> returned. Otherwise, a copy is made.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> context : Context</span> |
| <span class="sd"> The target context.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> The target array.</span> |
| |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.ones((2,3))</span> |
| <span class="sd"> >>> y = x.as_in_context(mx.cpu())</span> |
| <span class="sd"> >>> y is x</span> |
| <span class="sd"> True</span> |
| <span class="sd"> >>> z = x.as_in_context(mx.gpu(0))</span> |
| <span class="sd"> >>> z is x</span> |
| <span class="sd"> False</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">context</span> <span class="o">==</span> <span class="n">context</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">self</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">context</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="NDArray.attach_grad"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.NDArray.attach_grad">[docs]</a> <span class="k">def</span> <span class="nf">attach_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">grad_req</span><span class="o">=</span><span class="s1">'write'</span><span class="p">):</span> |
| <span class="sd">"""Attach a gradient buffer to this NDArray, so that `backward`</span> |
| <span class="sd"> can compute gradient with respect to it.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> grad_req : {'write', 'add', 'null'}</span> |
| <span class="sd"> How gradient will be accumulated.</span> |
| <span class="sd"> - 'write': gradient will be overwritten on every backward.</span> |
| <span class="sd"> - 'add': gradient will be added to existing value on every backward.</span> |
| <span class="sd"> - 'null': do not compute gradient for this NDArray.</span> |
| <span class="sd"> """</span> |
| <span class="n">grad</span> <span class="o">=</span> <span class="n">zeros_like</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="c1"># pylint: disable=undefined-variable</span> |
| <span class="n">grad_req</span> <span class="o">=</span> <span class="n">_GRAD_REQ_MAP</span><span class="p">[</span><span class="n">grad_req</span><span class="p">]</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXAutogradMarkVariables</span><span class="p">(</span> |
| <span class="mi">1</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">pointer</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">pointer</span><span class="p">(</span><span class="n">mx_uint</span><span class="p">(</span><span class="n">grad_req</span><span class="p">)),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">pointer</span><span class="p">(</span><span class="n">grad</span><span class="o">.</span><span class="n">handle</span><span class="p">)))</span></div> |
| |
| <span class="nd">@property</span> |
| <span class="k">def</span> <span class="nf">grad</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Returns gradient buffer attached to this NDArray."""</span> |
| <span class="n">hdl</span> <span class="o">=</span> <span class="n">NDArrayHandle</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArrayGetGrad</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">hdl</span><span class="p">)))</span> |
| <span class="k">if</span> <span class="n">hdl</span><span class="o">.</span><span class="n">value</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">return</span> <span class="kc">None</span> |
| <span class="k">return</span> <span class="n">NDArray</span><span class="p">(</span><span class="n">hdl</span><span class="p">)</span> |
| |
| <div class="viewcode-block" id="NDArray.detach"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.NDArray.detach">[docs]</a> <span class="k">def</span> <span class="nf">detach</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
| <span class="sd">"""Returns a new NDArray, detached from the current graph."""</span> |
| <span class="n">hdl</span> <span class="o">=</span> <span class="n">NDArrayHandle</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArrayDetach</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">hdl</span><span class="p">)))</span> |
| <span class="k">return</span> <span class="n">NDArray</span><span class="p">(</span><span class="n">hdl</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="NDArray.backward"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.NDArray.backward">[docs]</a> <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">out_grad</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">retain_graph</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">train_mode</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="sd">"""Compute the gradients of this NDArray w.r.t variables.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> out_grad : NDArray, optional</span> |
| <span class="sd"> Gradient with respect to head.</span> |
| <span class="sd"> retain_graph : bool, optional</span> |
| <span class="sd"> Whether to retain the computaion graph for another backward</span> |
| <span class="sd"> pass on the same graph. By default the computaion history</span> |
| <span class="sd"> is cleared.</span> |
| <span class="sd"> train_mode : bool, optional</span> |
| <span class="sd"> Whether to compute gradient for training or inference.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">out_grad</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">ograd_handles</span> <span class="o">=</span> <span class="p">[</span><span class="n">NDArrayHandle</span><span class="p">(</span><span class="mi">0</span><span class="p">)]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">ograd_handles</span> <span class="o">=</span> <span class="p">[</span><span class="n">out_grad</span><span class="o">.</span><span class="n">handle</span><span class="p">]</span> |
| |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXAutogradBackwardEx</span><span class="p">(</span> |
| <span class="mi">1</span><span class="p">,</span> <span class="n">c_array</span><span class="p">(</span><span class="n">NDArrayHandle</span><span class="p">,</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">handle</span><span class="p">]),</span> |
| <span class="n">c_array</span><span class="p">(</span><span class="n">NDArrayHandle</span><span class="p">,</span> <span class="n">ograd_handles</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">retain_graph</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">c_int</span><span class="p">(</span><span class="n">train_mode</span><span class="p">)))</span></div></div> |
| |
| |
| <div class="viewcode-block" id="onehot_encode"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.onehot_encode">[docs]</a><span class="k">def</span> <span class="nf">onehot_encode</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">out</span><span class="p">):</span> |
| <span class="sd">"""One-hot encoding indices into matrix out.</span> |
| |
| <span class="sd"> .. note:: `onehot_encode` is deprecated. Use `one_hot` instead.</span> |
| |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">return</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_onehot_encode</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">out</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| |
| <div class="viewcode-block" id="empty"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.empty">[docs]</a><span class="k">def</span> <span class="nf">empty</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">mx_real_t</span><span class="p">):</span> |
| <span class="sd">"""Returns a new array of given shape and type, without initializing entries.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> shape : int or tuple of int</span> |
| <span class="sd"> The shape of the empty array.</span> |
| <span class="sd"> ctx : Context, optional</span> |
| <span class="sd"> An optional device context (default is the current default context).</span> |
| <span class="sd"> dtype : str or numpy.dtype, optional</span> |
| <span class="sd"> An optional value type (default is `float32`).</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> A created array.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> mx.nd.empty(1)</span> |
| <span class="sd"> <NDArray 1 @cpu(0)></span> |
| <span class="sd"> >>> mx.nd.empty((1,2), mx.gpu(0))</span> |
| <span class="sd"> <NDArray 1x2 @gpu(0)></span> |
| <span class="sd"> >>> mx.nd.empty((1,2), mx.gpu(0), 'float16')</span> |
| <span class="sd"> <NDArray 1x2 @gpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">integer_types</span><span class="p">):</span> |
| <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="p">)</span> |
| <span class="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">Context</span><span class="o">.</span><span class="n">default_ctx</span> |
| <span class="k">return</span> <span class="n">NDArray</span><span class="p">(</span><span class="n">handle</span><span class="o">=</span><span class="n">_new_alloc_handle</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <span class="n">dtype</span><span class="p">))</span></div> |
| |
| <div class="viewcode-block" id="zeros"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.zeros">[docs]</a><span class="k">def</span> <span class="nf">zeros</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">mx_real_t</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="sd">"""Returns a new array filled with all zeros, with the given shape and type.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> shape : int or tuple of int</span> |
| <span class="sd"> The shape of the empty array.</span> |
| <span class="sd"> ctx : Context, optional</span> |
| <span class="sd"> An optional device context (default is the current default context).</span> |
| <span class="sd"> dtype : str or numpy.dtype, optional</span> |
| <span class="sd"> An optional value type (default is `float32`).</span> |
| <span class="sd"> out : NDArray, optional</span> |
| <span class="sd"> The output NDArray (default is `None`).</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> A created array</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> mx.nd.zeros(1).asnumpy()</span> |
| <span class="sd"> array([ 0.], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.zeros((1,2), mx.gpu(0))</span> |
| <span class="sd"> <NDArray 1x2 @gpu(0)></span> |
| <span class="sd"> >>> mx.nd.zeros((1,2), mx.gpu(0), 'float16').asnumpy()</span> |
| <span class="sd"> array([[ 0., 0.]], dtype=float16)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= unused-argument</span> |
| <span class="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">Context</span><span class="o">.</span><span class="n">default_ctx</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">return</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| <div class="viewcode-block" id="ones"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.ones">[docs]</a><span class="k">def</span> <span class="nf">ones</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">mx_real_t</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> |
| <span class="sd">"""Returns a new array filled with all ones, with the given shape and type.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> shape : int or tuple of int or list of int</span> |
| <span class="sd"> The shape of the empty array.</span> |
| <span class="sd"> ctx : Context, optional</span> |
| <span class="sd"> An optional device context.</span> |
| <span class="sd"> Defaults to the current default context (``mxnet.Context.default_ctx``).</span> |
| <span class="sd"> dtype : str or numpy.dtype, optional</span> |
| <span class="sd"> An optional value type (default is `float32`).</span> |
| <span class="sd"> out : NDArray, optional</span> |
| <span class="sd"> The output NDArray (default is `None`).</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> A new array of the specified shape filled with all ones.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> mx.nd.ones(1).asnumpy()</span> |
| <span class="sd"> array([ 1.], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.ones((1,2), mx.gpu(0))</span> |
| <span class="sd"> <NDArray 1x2 @gpu(0)></span> |
| <span class="sd"> >>> mx.nd.ones((1,2), dtype='float16').asnumpy()</span> |
| <span class="sd"> array([[ 1., 1.]], dtype=float16)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= unused-argument</span> |
| <span class="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">Context</span><span class="o">.</span><span class="n">default_ctx</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">return</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_ones</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| <div class="viewcode-block" id="full"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.full">[docs]</a><span class="k">def</span> <span class="nf">full</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">mx_real_t</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="sd">"""Returns a new array of given shape and type, filled with the given value `val`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> shape : int or tuple of int</span> |
| <span class="sd"> The shape of the new array.</span> |
| <span class="sd"> val : scalar</span> |
| <span class="sd"> Fill value.</span> |
| <span class="sd"> ctx : Context, optional</span> |
| <span class="sd"> Device context (default is the current default context).</span> |
| <span class="sd"> dtype : `str` or `numpy.dtype`, optional</span> |
| <span class="sd"> The data type of the returned `NDArray`. The default datatype is `float32`.</span> |
| <span class="sd"> out : NDArray, optional</span> |
| <span class="sd"> The output NDArray (default is `None`).</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> `NDArray` filled with `val`, with the given shape, ctx, and dtype.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> mx.nd.full(1, 2.0).asnumpy()</span> |
| <span class="sd"> array([ 2.], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.full((1, 2), 2.0, mx.gpu(0))</span> |
| <span class="sd"> <NDArray 1x2 @gpu(0)></span> |
| <span class="sd"> >>> mx.nd.full((1, 2), 2.0, dtype='float16').asnumpy()</span> |
| <span class="sd"> array([[ 2., 2.]], dtype=float16)</span> |
| <span class="sd"> """</span> |
| <span class="n">out</span> <span class="o">=</span> <span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span> <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">out</span> |
| <span class="n">out</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">val</span> |
| <span class="k">return</span> <span class="n">out</span></div> |
| |
| |
| <div class="viewcode-block" id="array"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.array">[docs]</a><span class="k">def</span> <span class="nf">array</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="sd">"""Creates an array from any object exposing the array interface.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> source_array : array_like</span> |
| <span class="sd"> An object exposing the array interface, an object whose `__array__`</span> |
| <span class="sd"> method returns an array, or any (nested) sequence.</span> |
| <span class="sd"> ctx : Context, optional</span> |
| <span class="sd"> Device context (default is the current default context).</span> |
| <span class="sd"> dtype : str or numpy.dtype, optional</span> |
| <span class="sd"> The data type of the output array. The default dtype is ``source_array.dtype``</span> |
| <span class="sd"> if `source_array` is an `NDArray`, `float32` otherwise.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> An `NDArray` with the same contents as the `source_array`.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> import numpy as np</span> |
| <span class="sd"> >>> mx.nd.array([1, 2, 3])</span> |
| <span class="sd"> <NDArray 3 @cpu(0)></span> |
| <span class="sd"> >>> mx.nd.array([[1, 2], [3, 4]])</span> |
| <span class="sd"> <NDArray 2x2 @cpu(0)></span> |
| <span class="sd"> >>> mx.nd.array(np.zeros((3, 2)))</span> |
| <span class="sd"> <NDArray 3x2 @cpu(0)></span> |
| <span class="sd"> >>> mx.nd.array(np.zeros((3, 2)), mx.gpu(0))</span> |
| <span class="sd"> <NDArray 3x2 @gpu(0)></span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">dtype</span> <span class="o">=</span> <span class="n">source_array</span><span class="o">.</span><span class="n">dtype</span> <span class="k">if</span> <span class="n">dtype</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">dtype</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">dtype</span> <span class="o">=</span> <span class="n">mx_real_t</span> <span class="k">if</span> <span class="n">dtype</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">dtype</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">source_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">source_array</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| <span class="k">except</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'source_array must be array like object'</span><span class="p">)</span> |
| <span class="n">arr</span> <span class="o">=</span> <span class="n">empty</span><span class="p">(</span><span class="n">source_array</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">ctx</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span> |
| <span class="n">arr</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">source_array</span> |
| <span class="k">return</span> <span class="n">arr</span></div> |
| |
| |
| <div class="viewcode-block" id="moveaxis"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.moveaxis">[docs]</a><span class="k">def</span> <span class="nf">moveaxis</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">source</span><span class="p">,</span> <span class="n">destination</span><span class="p">):</span> |
| <span class="sd">"""Moves the `source` axis into the `destination` position</span> |
| <span class="sd"> while leaving the other axes in their original order</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> tensor : mx.nd.array</span> |
| <span class="sd"> The array which axes should be reordered</span> |
| <span class="sd"> source : int</span> |
| <span class="sd"> Original position of the axes to move.</span> |
| <span class="sd"> destination : int</span> |
| <span class="sd"> Destination position for each of the original axes.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> result : mx.nd.array</span> |
| <span class="sd"> Array with moved axes.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> X = mx.nd.array([[1, 2, 3], [4, 5, 6]])</span> |
| <span class="sd"> >>> mx.nd.moveaxis(X, 0, 1).shape</span> |
| <span class="sd"> (3L, 2L)</span> |
| <span class="sd"> """</span> |
| <span class="n">axes</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">tensor</span><span class="o">.</span><span class="n">ndim</span><span class="p">))</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">axes</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">source</span><span class="p">)</span> |
| <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Source should verify 0 <= source < tensor.ndim'</span> |
| <span class="s1">'Got </span><span class="si">%d</span><span class="s1">'</span> <span class="o">%</span> <span class="n">source</span><span class="p">)</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">axes</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">destination</span><span class="p">,</span> <span class="n">source</span><span class="p">)</span> |
| <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Destination should verify 0 <= destination < tensor.ndim'</span> |
| <span class="s1">'Got </span><span class="si">%d</span><span class="s1">'</span> <span class="o">%</span> <span class="n">destination</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">transpose</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">axes</span><span class="p">)</span></div> |
| |
| |
| <span class="c1"># pylint: disable= no-member, protected-access, too-many-arguments</span> |
| <div class="viewcode-block" id="arange"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.arange">[docs]</a><span class="k">def</span> <span class="nf">arange</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">repeat</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">mx_real_t</span><span class="p">):</span> |
| <span class="sd">"""Returns evenly spaced values within a given interval.</span> |
| |
| <span class="sd"> Values are generated within the half-open interval [`start`, `stop`). In other</span> |
| <span class="sd"> words, the interval includes `start` but excludes `stop`. The function is</span> |
| <span class="sd"> similar to the built-in Python function `range` and to `numpy.arange`,</span> |
| <span class="sd"> but returns an `NDArray`.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> start : float, optional</span> |
| <span class="sd"> Start of interval. The default start value is 0.</span> |
| <span class="sd"> stop : float</span> |
| <span class="sd"> End of interval.</span> |
| <span class="sd"> step : float, optional</span> |
| <span class="sd"> Spacing between values. The default step size is 1.</span> |
| <span class="sd"> repeat : int, optional</span> |
| <span class="sd"> Number of times to repeat each element. The default repeat count is 1.</span> |
| <span class="sd"> ctx : Context, optional</span> |
| <span class="sd"> Device context. Default context is the current default context.</span> |
| <span class="sd"> dtype : str or numpy.dtype, optional</span> |
| <span class="sd"> The data type of the `NDArray`. The default datatype is `np.float32`.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> `NDArray` of evenly spaced values in the specified range.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> mx.nd.arange(3).asnumpy()</span> |
| <span class="sd"> array([ 0., 1., 2.], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.arange(2, 6).asnumpy()</span> |
| <span class="sd"> array([ 2., 3., 4., 5.], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.arange(2, 6, step=2).asnumpy()</span> |
| <span class="sd"> array([ 2., 4.], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.arange(2, 6, step=1.5, repeat=2).asnumpy()</span> |
| <span class="sd"> array([ 2. , 2. , 3.5, 3.5, 5. , 5. ], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.arange(2, 6, step=2, repeat=3, dtype='int32').asnumpy()</span> |
| <span class="sd"> array([2, 2, 2, 4, 4, 4], dtype=int32)</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="n">ctx</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">ctx</span> <span class="o">=</span> <span class="n">Context</span><span class="o">.</span><span class="n">default_ctx</span> |
| <span class="k">return</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_arange</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="o">=</span><span class="n">stop</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="n">step</span><span class="p">,</span> <span class="n">repeat</span><span class="o">=</span><span class="n">repeat</span><span class="p">,</span> |
| <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="n">ctx</span><span class="p">))</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access, too-many-arguments</span> |
| |
| <span class="c1">#pylint: disable= too-many-arguments, no-member, protected-access</span> |
| <span class="k">def</span> <span class="nf">_ufunc_helper</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">,</span> <span class="n">fn_array</span><span class="p">,</span> <span class="n">fn_scalar</span><span class="p">,</span> <span class="n">lfn_scalar</span><span class="p">,</span> <span class="n">rfn_scalar</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="sd">""" Helper function for element-wise operation.</span> |
| <span class="sd"> The function will perform numpy-like broadcasting if needed and call different functions.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> lhs : NDArray or numeric value</span> |
| <span class="sd"> Left-hand side operand.</span> |
| |
| <span class="sd"> rhs : NDArray or numeric value</span> |
| <span class="sd"> Right-hand operand,</span> |
| |
| <span class="sd"> fn_array : function</span> |
| <span class="sd"> Function to be called if both lhs and rhs are of ``NDArray`` type.</span> |
| |
| <span class="sd"> fn_scalar : function</span> |
| <span class="sd"> Function to be called if both lhs and rhs are numeric values.</span> |
| |
| <span class="sd"> lfn_scalar : function</span> |
| <span class="sd"> Function to be called if lhs is ``NDArray`` while rhs is numeric value</span> |
| |
| <span class="sd"> rfn_scalar : function</span> |
| <span class="sd"> Function to be called if lhs is numeric value while rhs is ``NDArray``;</span> |
| <span class="sd"> if none is provided, then the function is commutative, so rfn_scalar is equal to lfn_scalar</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> result array</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">rhs</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">fn_scalar</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">rfn_scalar</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="c1"># commutative function</span> |
| <span class="k">return</span> <span class="n">lfn_scalar</span><span class="p">(</span><span class="n">rhs</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">lhs</span><span class="p">))</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">rfn_scalar</span><span class="p">(</span><span class="n">rhs</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">lhs</span><span class="p">))</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">rhs</span><span class="p">,</span> <span class="n">numeric_types</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">lfn_scalar</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">rhs</span><span class="p">))</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">rhs</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="k">return</span> <span class="n">fn_array</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'type </span><span class="si">%s</span><span class="s1"> not supported'</span> <span class="o">%</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">rhs</span><span class="p">)))</span> |
| <span class="c1">#pylint: enable= too-many-arguments, no-member, protected-access</span> |
| |
| <div class="viewcode-block" id="add"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.add">[docs]</a><span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">):</span> |
| <span class="sd">"""Returns element-wise sum of the input arrays with broadcasting.</span> |
| |
| <span class="sd"> Equivalent to ``lhs + rhs``, ``mx.nd.broadcast_add(lhs, rhs)`` and</span> |
| <span class="sd"> ``mx.nd.broadcast_plus(lhs, rhs)``.</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> If the corresponding dimensions of two arrays have the same size or one of them has size 1,</span> |
| <span class="sd"> then the arrays are broadcastable to a common shape.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> lhs : scalar or array</span> |
| <span class="sd"> First array to be added.</span> |
| <span class="sd"> rhs : scalar or array</span> |
| <span class="sd"> Second array to be added.</span> |
| <span class="sd"> If ``lhs.shape != rhs.shape``, they must be</span> |
| <span class="sd"> broadcastable to a common shape.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> The element-wise sum of the input arrays.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.ones((2,3))</span> |
| <span class="sd"> >>> y = mx.nd.arange(2).reshape((2,1))</span> |
| <span class="sd"> >>> z = mx.nd.arange(2).reshape((1,2))</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> y.asnumpy()</span> |
| <span class="sd"> array([[ 0.],</span> |
| <span class="sd"> [ 1.]], dtype=float32)</span> |
| <span class="sd"> >>> z.asnumpy()</span> |
| <span class="sd"> array([[ 0., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> (x+2).asnumpy()</span> |
| <span class="sd"> array([[ 3., 3., 3.],</span> |
| <span class="sd"> [ 3., 3., 3.]], dtype=float32)</span> |
| <span class="sd"> >>> (x+y).asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 2., 2., 2.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.add(x,y).asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 2., 2., 2.]], dtype=float32)</span> |
| <span class="sd"> >>> (z + y).asnumpy()</span> |
| <span class="sd"> array([[ 0., 1.],</span> |
| <span class="sd"> [ 1., 2.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">return</span> <span class="n">_ufunc_helper</span><span class="p">(</span> |
| <span class="n">lhs</span><span class="p">,</span> |
| <span class="n">rhs</span><span class="p">,</span> |
| <span class="n">broadcast_add</span><span class="p">,</span> |
| <span class="n">operator</span><span class="o">.</span><span class="n">add</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_plus_scalar</span><span class="p">,</span> |
| <span class="kc">None</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| <div class="viewcode-block" id="subtract"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.subtract">[docs]</a><span class="k">def</span> <span class="nf">subtract</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">):</span> |
| <span class="sd">"""Returns element-wise difference of the input arrays with broadcasting.</span> |
| |
| <span class="sd"> Equivalent to ``lhs - rhs``, ``mx.nd.broadcast_sub(lhs, rhs)`` and</span> |
| <span class="sd"> ``mx.nd.broadcast_minus(lhs, rhs)``.</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> If the corresponding dimensions of two arrays have the same size or one of them has size 1,</span> |
| <span class="sd"> then the arrays are broadcastable to a common shape.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> lhs : scalar or array</span> |
| <span class="sd"> First array to be subtracted.</span> |
| <span class="sd"> rhs : scalar or array</span> |
| <span class="sd"> Second array to be subtracted.</span> |
| <span class="sd"> If ``lhs.shape != rhs.shape``, they must be</span> |
| <span class="sd"> broadcastable to a common shape.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> The element-wise difference of the input arrays.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.ones((2,3))</span> |
| <span class="sd"> >>> y = mx.nd.arange(2).reshape((2,1))</span> |
| <span class="sd"> >>> z = mx.nd.arange(2).reshape((1,2))</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> y.asnumpy()</span> |
| <span class="sd"> array([[ 0.],</span> |
| <span class="sd"> [ 1.]], dtype=float32)</span> |
| <span class="sd"> >>> z.asnumpy()</span> |
| <span class="sd"> array([[ 0., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> (x-2).asnumpy()</span> |
| <span class="sd"> array([[-1., -1., -1.],</span> |
| <span class="sd"> [-1., -1., -1.]], dtype=float32)</span> |
| <span class="sd"> >>> (x-y).asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 0., 0., 0.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.subtract(x,y).asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 0., 0., 0.]], dtype=float32)</span> |
| <span class="sd"> >>> (z-y).asnumpy()</span> |
| <span class="sd"> array([[ 0., 1.],</span> |
| <span class="sd"> [-1., 0.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">return</span> <span class="n">_ufunc_helper</span><span class="p">(</span> |
| <span class="n">lhs</span><span class="p">,</span> |
| <span class="n">rhs</span><span class="p">,</span> |
| <span class="n">broadcast_sub</span><span class="p">,</span> |
| <span class="n">operator</span><span class="o">.</span><span class="n">sub</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_minus_scalar</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_rminus_scalar</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| <div class="viewcode-block" id="multiply"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.multiply">[docs]</a><span class="k">def</span> <span class="nf">multiply</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">):</span> |
| <span class="sd">"""Returns element-wise product of the input arrays with broadcasting.</span> |
| |
| <span class="sd"> Equivalent to ``lhs * rhs`` and ``mx.nd.broadcast_mul(lhs, rhs)``.</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> If the corresponding dimensions of two arrays have the same size or one of them has size 1,</span> |
| <span class="sd"> then the arrays are broadcastable to a common shape.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> lhs : scalar or array</span> |
| <span class="sd"> First array to be multiplied.</span> |
| <span class="sd"> rhs : scalar or array</span> |
| <span class="sd"> Second array to be multiplied.</span> |
| <span class="sd"> If ``lhs.shape != rhs.shape``, they must be</span> |
| <span class="sd"> broadcastable to a common shape.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> The element-wise multiplication of the input arrays.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.ones((2,3))</span> |
| <span class="sd"> >>> y = mx.nd.arange(2).reshape((2,1))</span> |
| <span class="sd"> >>> z = mx.nd.arange(2).reshape((1,2))</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> y.asnumpy()</span> |
| <span class="sd"> array([[ 0.],</span> |
| <span class="sd"> [ 1.]], dtype=float32)</span> |
| <span class="sd"> >>> z.asnumpy()</span> |
| <span class="sd"> array([[ 0., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> (x*2).asnumpy()</span> |
| <span class="sd"> array([[ 2., 2., 2.],</span> |
| <span class="sd"> [ 2., 2., 2.]], dtype=float32)</span> |
| <span class="sd"> >>> (x*y).asnumpy()</span> |
| <span class="sd"> array([[ 0., 0., 0.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.multiply(x, y).asnumpy()</span> |
| <span class="sd"> array([[ 0., 0., 0.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> (z*y).asnumpy()</span> |
| <span class="sd"> array([[ 0., 0.],</span> |
| <span class="sd"> [ 0., 1.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">return</span> <span class="n">_ufunc_helper</span><span class="p">(</span> |
| <span class="n">lhs</span><span class="p">,</span> |
| <span class="n">rhs</span><span class="p">,</span> |
| <span class="n">broadcast_mul</span><span class="p">,</span> |
| <span class="n">operator</span><span class="o">.</span><span class="n">mul</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_mul_scalar</span><span class="p">,</span> |
| <span class="kc">None</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| <div class="viewcode-block" id="divide"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.divide">[docs]</a><span class="k">def</span> <span class="nf">divide</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">):</span> |
| <span class="sd">"""Returns element-wise division of the input arrays with broadcasting.</span> |
| |
| <span class="sd"> Equivalent to ``lhs / rhs`` and ``mx.nd.broadcast_div(lhs, rhs)``.</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> If the corresponding dimensions of two arrays have the same size or one of them has size 1,</span> |
| <span class="sd"> then the arrays are broadcastable to a common shape.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> lhs : scalar or array</span> |
| <span class="sd"> First array in division.</span> |
| <span class="sd"> rhs : scalar or array</span> |
| <span class="sd"> Second array in division.</span> |
| <span class="sd"> The arrays to be divided. If ``lhs.shape != rhs.shape``, they must be</span> |
| <span class="sd"> broadcastable to a common shape.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> The element-wise division of the input arrays.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.ones((2,3))*6</span> |
| <span class="sd"> >>> y = mx.nd.ones((2,1))*2</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 6., 6., 6.],</span> |
| <span class="sd"> [ 6., 6., 6.]], dtype=float32)</span> |
| <span class="sd"> >>> y.asnumpy()</span> |
| <span class="sd"> array([[ 2.],</span> |
| <span class="sd"> [ 2.]], dtype=float32)</span> |
| <span class="sd"> >>> x/2</span> |
| <span class="sd"> <NDArray 2x3 @cpu(0)></span> |
| <span class="sd"> >>> (x/3).asnumpy()</span> |
| <span class="sd"> array([[ 2., 2., 2.],</span> |
| <span class="sd"> [ 2., 2., 2.]], dtype=float32)</span> |
| <span class="sd"> >>> (x/y).asnumpy()</span> |
| <span class="sd"> array([[ 3., 3., 3.],</span> |
| <span class="sd"> [ 3., 3., 3.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.divide(x,y).asnumpy()</span> |
| <span class="sd"> array([[ 3., 3., 3.],</span> |
| <span class="sd"> [ 3., 3., 3.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">return</span> <span class="n">_ufunc_helper</span><span class="p">(</span> |
| <span class="n">lhs</span><span class="p">,</span> |
| <span class="n">rhs</span><span class="p">,</span> |
| <span class="n">broadcast_div</span><span class="p">,</span> |
| <span class="n">operator</span><span class="o">.</span><span class="n">truediv</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_div_scalar</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_rdiv_scalar</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| <div class="viewcode-block" id="modulo"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.modulo">[docs]</a><span class="k">def</span> <span class="nf">modulo</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">):</span> |
| <span class="sd">"""Returns element-wise modulo of the input arrays with broadcasting.</span> |
| |
| <span class="sd"> Equivalent to ``lhs % rhs`` and ``mx.nd.broadcast_mod(lhs, rhs)``.</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> If the corresponding dimensions of two arrays have the same size or one of them has size 1,</span> |
| <span class="sd"> then the arrays are broadcastable to a common shape.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> lhs : scalar or array</span> |
| <span class="sd"> First array in modulo.</span> |
| <span class="sd"> rhs : scalar or array</span> |
| <span class="sd"> Second array in modulo.</span> |
| <span class="sd"> The arrays to be taken modulo. If ``lhs.shape != rhs.shape``, they must be</span> |
| <span class="sd"> broadcastable to a common shape.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> The element-wise modulo of the input arrays.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.ones((2,3))*6</span> |
| <span class="sd"> >>> y = mx.nd.ones((2,1))*4</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 6., 6., 6.],</span> |
| <span class="sd"> [ 6., 6., 6.]], dtype=float32)</span> |
| <span class="sd"> >>> y.asnumpy()</span> |
| <span class="sd"> array([[ 4.],</span> |
| <span class="sd"> [ 4.]], dtype=float32)</span> |
| <span class="sd"> >>> x%5</span> |
| <span class="sd"> <NDArray 2x3 @cpu(0)></span> |
| <span class="sd"> >>> (x%5).asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> (x%y).asnumpy()</span> |
| <span class="sd"> array([[ 2., 2., 2.],</span> |
| <span class="sd"> [ 2., 2., 2.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.modulo(x,y).asnumpy()</span> |
| <span class="sd"> array([[ 2., 2., 2.],</span> |
| <span class="sd"> [ 2., 2., 2.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">return</span> <span class="n">_ufunc_helper</span><span class="p">(</span> |
| <span class="n">lhs</span><span class="p">,</span> |
| <span class="n">rhs</span><span class="p">,</span> |
| <span class="n">broadcast_mod</span><span class="p">,</span> |
| <span class="n">operator</span><span class="o">.</span><span class="n">mod</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_mod_scalar</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_rmod_scalar</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| <div class="viewcode-block" id="power"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.power">[docs]</a><span class="k">def</span> <span class="nf">power</span><span class="p">(</span><span class="n">base</span><span class="p">,</span> <span class="n">exp</span><span class="p">):</span> |
| <span class="sd">"""Returns result of first array elements raised to powers from second array, element-wise</span> |
| <span class="sd"> with broadcasting.</span> |
| |
| <span class="sd"> Equivalent to ``base ** exp`` and ``mx.nd.broadcast_power(lhs, rhs)``.</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> If the corresponding dimensions of two arrays have the same size or one of them has size 1,</span> |
| <span class="sd"> then the arrays are broadcastable to a common shape.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> base : scalar or NDArray</span> |
| <span class="sd"> The base array</span> |
| <span class="sd"> exp : scalar or NDArray</span> |
| <span class="sd"> The exponent array. If ``base.shape != exp.shape``, they must be</span> |
| <span class="sd"> broadcastable to a common shape.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> The bases in x raised to the exponents in y.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.ones((2,3))*2</span> |
| <span class="sd"> >>> y = mx.nd.arange(1,3).reshape((2,1))</span> |
| <span class="sd"> >>> z = mx.nd.arange(1,3).reshape((2,1))</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 2., 2., 2.],</span> |
| <span class="sd"> [ 2., 2., 2.]], dtype=float32)</span> |
| <span class="sd"> >>> y.asnumpy()</span> |
| <span class="sd"> array([[ 1.],</span> |
| <span class="sd"> [ 2.]], dtype=float32)</span> |
| <span class="sd"> >>> z.asnumpy()</span> |
| <span class="sd"> array([[ 1.],</span> |
| <span class="sd"> [ 2.]], dtype=float32)</span> |
| <span class="sd"> >>> (x**2).asnumpy()</span> |
| <span class="sd"> array([[ 4., 4., 4.],</span> |
| <span class="sd"> [ 4., 4., 4.]], dtype=float32)</span> |
| <span class="sd"> >>> (x**y).asnumpy()</span> |
| <span class="sd"> array([[ 2., 2., 2.],</span> |
| <span class="sd"> [ 4., 4., 4.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.power(x,y).asnumpy()</span> |
| <span class="sd"> array([[ 2., 2., 2.],</span> |
| <span class="sd"> [ 4., 4., 4.]], dtype=float32)</span> |
| <span class="sd"> >>> (z**y).asnumpy()</span> |
| <span class="sd"> array([[ 1.],</span> |
| <span class="sd"> [ 4.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">return</span> <span class="n">_ufunc_helper</span><span class="p">(</span> |
| <span class="n">base</span><span class="p">,</span> |
| <span class="n">exp</span><span class="p">,</span> |
| <span class="n">broadcast_power</span><span class="p">,</span> |
| <span class="n">operator</span><span class="o">.</span><span class="n">pow</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_power_scalar</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_rpower_scalar</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| <div class="viewcode-block" id="maximum"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.maximum">[docs]</a><span class="k">def</span> <span class="nf">maximum</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">):</span> |
| <span class="sd">"""Returns element-wise maximum of the input arrays with broadcasting.</span> |
| |
| <span class="sd"> Equivalent to ``mx.nd.broadcast_maximum(lhs, rhs)``.</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> If the corresponding dimensions of two arrays have the same size or one of them has size 1,</span> |
| <span class="sd"> then the arrays are broadcastable to a common shape.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> lhs : scalar or array</span> |
| <span class="sd"> First array to be compared.</span> |
| <span class="sd"> rhs : scalar or array</span> |
| <span class="sd"> Second array to be compared. If ``lhs.shape != rhs.shape``, they must be</span> |
| <span class="sd"> broadcastable to a common shape.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> The element-wise maximum of the input arrays.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.ones((2,3))</span> |
| <span class="sd"> >>> y = mx.nd.arange(2).reshape((2,1))</span> |
| <span class="sd"> >>> z = mx.nd.arange(2).reshape((1,2))</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> y.asnumpy()</span> |
| <span class="sd"> array([[ 0.],</span> |
| <span class="sd"> [ 1.]], dtype=float32)</span> |
| <span class="sd"> >>> z.asnumpy()</span> |
| <span class="sd"> array([[ 0., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.maximum(x, 2).asnumpy()</span> |
| <span class="sd"> array([[ 2., 2., 2.],</span> |
| <span class="sd"> [ 2., 2., 2.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.maximum(x, y).asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.maximum(y, z).asnumpy()</span> |
| <span class="sd"> array([[ 0., 1.],</span> |
| <span class="sd"> [ 1., 1.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">return</span> <span class="n">_ufunc_helper</span><span class="p">(</span> |
| <span class="n">lhs</span><span class="p">,</span> |
| <span class="n">rhs</span><span class="p">,</span> |
| <span class="n">broadcast_maximum</span><span class="p">,</span> |
| <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">x</span> <span class="k">if</span> <span class="n">x</span> <span class="o">></span> <span class="n">y</span> <span class="k">else</span> <span class="n">y</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_maximum_scalar</span><span class="p">,</span> |
| <span class="kc">None</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| <div class="viewcode-block" id="minimum"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.minimum">[docs]</a><span class="k">def</span> <span class="nf">minimum</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">):</span> |
| <span class="sd">"""Returns element-wise minimum of the input arrays with broadcasting.</span> |
| |
| <span class="sd"> Equivalent to ``mx.nd.broadcast_minimum(lhs, rhs)``.</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> If the corresponding dimensions of two arrays have the same size or one of them has size 1,</span> |
| <span class="sd"> then the arrays are broadcastable to a common shape.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> lhs : scalar or array</span> |
| <span class="sd"> First array to be compared.</span> |
| <span class="sd"> rhs : scalar or array</span> |
| <span class="sd"> Second array to be compared. If ``lhs.shape != rhs.shape``, they must be</span> |
| <span class="sd"> broadcastable to a common shape.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> The element-wise minimum of the input arrays.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.ones((2,3))</span> |
| <span class="sd"> >>> y = mx.nd.arange(2).reshape((2,1))</span> |
| <span class="sd"> >>> z = mx.nd.arange(2).reshape((1,2))</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> y.asnumpy()</span> |
| <span class="sd"> array([[ 0.],</span> |
| <span class="sd"> [ 1.]], dtype=float32)</span> |
| <span class="sd"> >>> z.asnumpy()</span> |
| <span class="sd"> array([[ 0., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.minimum(x, 2).asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.minimum(x, y).asnumpy()</span> |
| <span class="sd"> array([[ 0., 0., 0.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.minimum(z, y).asnumpy()</span> |
| <span class="sd"> array([[ 0., 0.],</span> |
| <span class="sd"> [ 0., 1.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">return</span> <span class="n">_ufunc_helper</span><span class="p">(</span> |
| <span class="n">lhs</span><span class="p">,</span> |
| <span class="n">rhs</span><span class="p">,</span> |
| <span class="n">broadcast_minimum</span><span class="p">,</span> |
| <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">x</span> <span class="k">if</span> <span class="n">x</span> <span class="o"><</span> <span class="n">y</span> <span class="k">else</span> <span class="n">y</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_minimum_scalar</span><span class="p">,</span> |
| <span class="kc">None</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| <div class="viewcode-block" id="equal"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.equal">[docs]</a><span class="k">def</span> <span class="nf">equal</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">):</span> |
| <span class="sd">"""Returns the result of element-wise **equal to** (==) comparison operation with</span> |
| <span class="sd"> broadcasting.</span> |
| |
| <span class="sd"> For each element in input arrays, return 1(true) if corresponding elements are same,</span> |
| <span class="sd"> otherwise return 0(false).</span> |
| |
| <span class="sd"> Equivalent to ``lhs == rhs`` and ``mx.nd.broadcast_equal(lhs, rhs)``.</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> If the corresponding dimensions of two arrays have the same size or one of them has size 1,</span> |
| <span class="sd"> then the arrays are broadcastable to a common shape.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> lhs : scalar or array</span> |
| <span class="sd"> First array to be compared.</span> |
| <span class="sd"> rhs : scalar or array</span> |
| <span class="sd"> Second array to be compared. If ``lhs.shape != rhs.shape``, they must be</span> |
| <span class="sd"> broadcastable to a common shape.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> Output array of boolean values.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.ones((2,3))</span> |
| <span class="sd"> >>> y = mx.nd.arange(2).reshape((2,1))</span> |
| <span class="sd"> >>> z = mx.nd.arange(2).reshape((1,2))</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> y.asnumpy()</span> |
| <span class="sd"> array([[ 0.],</span> |
| <span class="sd"> [ 1.]], dtype=float32)</span> |
| <span class="sd"> >>> z.asnumpy()</span> |
| <span class="sd"> array([[ 0., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> (x == 1).asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> (x == y).asnumpy()</span> |
| <span class="sd"> array([[ 0., 0., 0.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.equal(x,y).asnumpy()</span> |
| <span class="sd"> array([[ 0., 0., 0.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> (z == y).asnumpy()</span> |
| <span class="sd"> array([[ 1., 0.],</span> |
| <span class="sd"> [ 0., 1.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">return</span> <span class="n">_ufunc_helper</span><span class="p">(</span> |
| <span class="n">lhs</span><span class="p">,</span> |
| <span class="n">rhs</span><span class="p">,</span> |
| <span class="n">broadcast_equal</span><span class="p">,</span> |
| <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">x</span> <span class="o">==</span> <span class="n">y</span> <span class="k">else</span> <span class="mi">0</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_equal_scalar</span><span class="p">,</span> |
| <span class="kc">None</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| <div class="viewcode-block" id="not_equal"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.not_equal">[docs]</a><span class="k">def</span> <span class="nf">not_equal</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">):</span> |
| <span class="sd">"""Returns the result of element-wise **not equal to** (!=) comparison operation</span> |
| <span class="sd"> with broadcasting.</span> |
| |
| <span class="sd"> For each element in input arrays, return 1(true) if corresponding elements are different,</span> |
| <span class="sd"> otherwise return 0(false).</span> |
| |
| <span class="sd"> Equivalent to ``lhs != rhs`` and ``mx.nd.broadcast_not_equal(lhs, rhs)``.</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> If the corresponding dimensions of two arrays have the same size or one of them has size 1,</span> |
| <span class="sd"> then the arrays are broadcastable to a common shape.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> lhs : scalar or array</span> |
| <span class="sd"> First array to be compared.</span> |
| <span class="sd"> rhs : scalar or array</span> |
| <span class="sd"> Second array to be compared. If ``lhs.shape != rhs.shape``, they must be</span> |
| <span class="sd"> broadcastable to a common shape.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> Output array of boolean values.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.ones((2,3))</span> |
| <span class="sd"> >>> y = mx.nd.arange(2).reshape((2,1))</span> |
| <span class="sd"> >>> z = mx.nd.arange(2).reshape((1,2))</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> y.asnumpy()</span> |
| <span class="sd"> array([[ 0.],</span> |
| <span class="sd"> [ 1.]], dtype=float32)</span> |
| <span class="sd"> >>> z.asnumpy()</span> |
| <span class="sd"> array([[ 0., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> (z == y).asnumpy()</span> |
| <span class="sd"> array([[ 1., 0.],</span> |
| <span class="sd"> [ 0., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> (x != 1).asnumpy()</span> |
| <span class="sd"> array([[ 0., 0., 0.],</span> |
| <span class="sd"> [ 0., 0., 0.]], dtype=float32)</span> |
| <span class="sd"> >>> (x != y).asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 0., 0., 0.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.not_equal(x, y).asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 0., 0., 0.]], dtype=float32)</span> |
| <span class="sd"> >>> (z != y).asnumpy()</span> |
| <span class="sd"> array([[ 0., 1.],</span> |
| <span class="sd"> [ 1., 0.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">return</span> <span class="n">_ufunc_helper</span><span class="p">(</span> |
| <span class="n">lhs</span><span class="p">,</span> |
| <span class="n">rhs</span><span class="p">,</span> |
| <span class="n">broadcast_not_equal</span><span class="p">,</span> |
| <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">x</span> <span class="o">!=</span> <span class="n">y</span> <span class="k">else</span> <span class="mi">0</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_not_equal_scalar</span><span class="p">,</span> |
| <span class="kc">None</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| <div class="viewcode-block" id="greater"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.greater">[docs]</a><span class="k">def</span> <span class="nf">greater</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">):</span> |
| <span class="sd">"""Returns the result of element-wise **greater than** (>) comparison operation</span> |
| <span class="sd"> with broadcasting.</span> |
| |
| <span class="sd"> For each element in input arrays, return 1(true) if lhs elements are greater than rhs,</span> |
| <span class="sd"> otherwise return 0(false).</span> |
| |
| <span class="sd"> Equivalent to ``lhs > rhs`` and ``mx.nd.broadcast_greater(lhs, rhs)``.</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> If the corresponding dimensions of two arrays have the same size or one of them has size 1,</span> |
| <span class="sd"> then the arrays are broadcastable to a common shape.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> lhs : scalar or array</span> |
| <span class="sd"> First array to be compared.</span> |
| <span class="sd"> rhs : scalar or array</span> |
| <span class="sd"> Second array to be compared. If ``lhs.shape != rhs.shape``, they must be</span> |
| <span class="sd"> broadcastable to a common shape.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> Output array of boolean values.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.ones((2,3))</span> |
| <span class="sd"> >>> y = mx.nd.arange(2).reshape((2,1))</span> |
| <span class="sd"> >>> z = mx.nd.arange(2).reshape((1,2))</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> y.asnumpy()</span> |
| <span class="sd"> array([[ 0.],</span> |
| <span class="sd"> [ 1.]], dtype=float32)</span> |
| <span class="sd"> >>> z.asnumpy()</span> |
| <span class="sd"> array([[ 0., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> (x > 1).asnumpy()</span> |
| <span class="sd"> array([[ 0., 0., 0.],</span> |
| <span class="sd"> [ 0., 0., 0.]], dtype=float32)</span> |
| <span class="sd"> >>> (x > y).asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 0., 0., 0.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.greater(x, y).asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 0., 0., 0.]], dtype=float32)</span> |
| <span class="sd"> >>> (z > y).asnumpy()</span> |
| <span class="sd"> array([[ 0., 1.],</span> |
| <span class="sd"> [ 0., 0.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">return</span> <span class="n">_ufunc_helper</span><span class="p">(</span> |
| <span class="n">lhs</span><span class="p">,</span> |
| <span class="n">rhs</span><span class="p">,</span> |
| <span class="n">broadcast_greater</span><span class="p">,</span> |
| <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">x</span> <span class="o">></span> <span class="n">y</span> <span class="k">else</span> <span class="mi">0</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_greater_scalar</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_lesser_scalar</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| <div class="viewcode-block" id="greater_equal"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.greater_equal">[docs]</a><span class="k">def</span> <span class="nf">greater_equal</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">):</span> |
| <span class="sd">"""Returns the result of element-wise **greater than or equal to** (>=) comparison</span> |
| <span class="sd"> operation with broadcasting.</span> |
| |
| <span class="sd"> For each element in input arrays, return 1(true) if lhs elements are greater than equal to rhs,</span> |
| <span class="sd"> otherwise return 0(false).</span> |
| |
| <span class="sd"> Equivalent to ``lhs >= rhs`` and ``mx.nd.broadcast_greater_equal(lhs, rhs)``.</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> If the corresponding dimensions of two arrays have the same size or one of them has size 1,</span> |
| <span class="sd"> then the arrays are broadcastable to a common shape.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> lhs : scalar or array</span> |
| <span class="sd"> First array to be compared.</span> |
| <span class="sd"> rhs : scalar or array</span> |
| <span class="sd"> Second array to be compared. If ``lhs.shape != rhs.shape``, they must be</span> |
| <span class="sd"> broadcastable to a common shape.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> Output array of boolean values.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.ones((2,3))</span> |
| <span class="sd"> >>> y = mx.nd.arange(2).reshape((2,1))</span> |
| <span class="sd"> >>> z = mx.nd.arange(2).reshape((1,2))</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> y.asnumpy()</span> |
| <span class="sd"> array([[ 0.],</span> |
| <span class="sd"> [ 1.]], dtype=float32)</span> |
| <span class="sd"> >>> z.asnumpy()</span> |
| <span class="sd"> array([[ 0., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> (x >= 1).asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> (x >= y).asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.greater_equal(x, y).asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> (z >= y).asnumpy()</span> |
| <span class="sd"> array([[ 1., 1.],</span> |
| <span class="sd"> [ 0., 1.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">return</span> <span class="n">_ufunc_helper</span><span class="p">(</span> |
| <span class="n">lhs</span><span class="p">,</span> |
| <span class="n">rhs</span><span class="p">,</span> |
| <span class="n">broadcast_greater_equal</span><span class="p">,</span> |
| <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">x</span> <span class="o">>=</span> <span class="n">y</span> <span class="k">else</span> <span class="mi">0</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_greater_equal_scalar</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_lesser_equal_scalar</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| <div class="viewcode-block" id="lesser"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.lesser">[docs]</a><span class="k">def</span> <span class="nf">lesser</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">):</span> |
| <span class="sd">"""Returns the result of element-wise **lesser than** (<) comparison operation</span> |
| <span class="sd"> with broadcasting.</span> |
| |
| <span class="sd"> For each element in input arrays, return 1(true) if lhs elements are less than rhs,</span> |
| <span class="sd"> otherwise return 0(false).</span> |
| |
| <span class="sd"> Equivalent to ``lhs < rhs`` and ``mx.nd.broadcast_lesser(lhs, rhs)``.</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> If the corresponding dimensions of two arrays have the same size or one of them has size 1,</span> |
| <span class="sd"> then the arrays are broadcastable to a common shape.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> lhs : scalar or array</span> |
| <span class="sd"> First array to be compared.</span> |
| <span class="sd"> rhs : scalar or array</span> |
| <span class="sd"> Second array to be compared. If ``lhs.shape != rhs.shape``, they must be</span> |
| <span class="sd"> broadcastable to a common shape.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> Output array of boolean values.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.ones((2,3))</span> |
| <span class="sd"> >>> y = mx.nd.arange(2).reshape((2,1))</span> |
| <span class="sd"> >>> z = mx.nd.arange(2).reshape((1,2))</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> y.asnumpy()</span> |
| <span class="sd"> array([[ 0.],</span> |
| <span class="sd"> [ 1.]], dtype=float32)</span> |
| <span class="sd"> >>> z.asnumpy()</span> |
| <span class="sd"> array([[ 0., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> (x < 1).asnumpy()</span> |
| <span class="sd"> array([[ 0., 0., 0.],</span> |
| <span class="sd"> [ 0., 0., 0.]], dtype=float32)</span> |
| <span class="sd"> >>> (x < y).asnumpy()</span> |
| <span class="sd"> array([[ 0., 0., 0.],</span> |
| <span class="sd"> [ 0., 0., 0.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.lesser(x, y).asnumpy()</span> |
| <span class="sd"> array([[ 0., 0., 0.],</span> |
| <span class="sd"> [ 0., 0., 0.]], dtype=float32)</span> |
| <span class="sd"> >>> (z < y).asnumpy()</span> |
| <span class="sd"> array([[ 0., 0.],</span> |
| <span class="sd"> [ 1., 0.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">return</span> <span class="n">_ufunc_helper</span><span class="p">(</span> |
| <span class="n">lhs</span><span class="p">,</span> |
| <span class="n">rhs</span><span class="p">,</span> |
| <span class="n">broadcast_lesser</span><span class="p">,</span> |
| <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">x</span> <span class="o"><</span> <span class="n">y</span> <span class="k">else</span> <span class="mi">0</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_lesser_scalar</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_greater_scalar</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| |
| <div class="viewcode-block" id="lesser_equal"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.lesser_equal">[docs]</a><span class="k">def</span> <span class="nf">lesser_equal</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">):</span> |
| <span class="sd">"""Returns the result of element-wise **lesser than or equal to** (<=) comparison</span> |
| <span class="sd"> operation with broadcasting.</span> |
| |
| <span class="sd"> For each element in input arrays, return 1(true) if lhs elements are</span> |
| <span class="sd"> lesser than equal to rhs, otherwise return 0(false).</span> |
| |
| <span class="sd"> Equivalent to ``lhs <= rhs`` and ``mx.nd.broadcast_lesser_equal(lhs, rhs)``.</span> |
| |
| <span class="sd"> .. note::</span> |
| |
| <span class="sd"> If the corresponding dimensions of two arrays have the same size or one of them has size 1,</span> |
| <span class="sd"> then the arrays are broadcastable to a common shape.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> lhs : scalar or array</span> |
| <span class="sd"> First array to be compared.</span> |
| <span class="sd"> rhs : scalar or array</span> |
| <span class="sd"> Second array to be compared. If ``lhs.shape != rhs.shape``, they must be</span> |
| <span class="sd"> broadcastable to a common shape.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> Output array of boolean values.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.ones((2,3))</span> |
| <span class="sd"> >>> y = mx.nd.arange(2).reshape((2,1))</span> |
| <span class="sd"> >>> z = mx.nd.arange(2).reshape((1,2))</span> |
| <span class="sd"> >>> x.asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> y.asnumpy()</span> |
| <span class="sd"> array([[ 0.],</span> |
| <span class="sd"> [ 1.]], dtype=float32)</span> |
| <span class="sd"> >>> z.asnumpy()</span> |
| <span class="sd"> array([[ 0., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> (x <= 1).asnumpy()</span> |
| <span class="sd"> array([[ 1., 1., 1.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> (x <= y).asnumpy()</span> |
| <span class="sd"> array([[ 0., 0., 0.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> mx.nd.lesser_equal(x, y).asnumpy()</span> |
| <span class="sd"> array([[ 0., 0., 0.],</span> |
| <span class="sd"> [ 1., 1., 1.]], dtype=float32)</span> |
| <span class="sd"> >>> (z <= y).asnumpy()</span> |
| <span class="sd"> array([[ 1., 0.],</span> |
| <span class="sd"> [ 1., 1.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access</span> |
| <span class="k">return</span> <span class="n">_ufunc_helper</span><span class="p">(</span> |
| <span class="n">lhs</span><span class="p">,</span> |
| <span class="n">rhs</span><span class="p">,</span> |
| <span class="n">broadcast_lesser_equal</span><span class="p">,</span> |
| <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">x</span> <span class="o"><=</span> <span class="n">y</span> <span class="k">else</span> <span class="mi">0</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_lesser_equal_scalar</span><span class="p">,</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_greater_equal_scalar</span><span class="p">)</span></div> |
| <span class="c1"># pylint: enable= no-member, protected-access</span> |
| |
| <div class="viewcode-block" id="true_divide"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.true_divide">[docs]</a><span class="k">def</span> <span class="nf">true_divide</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">):</span> |
| |
| <span class="sd">"""This function is similar to :meth:`divide`.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">divide</span><span class="p">(</span><span class="n">lhs</span><span class="p">,</span> <span class="n">rhs</span><span class="p">)</span></div> |
| |
| <div class="viewcode-block" id="negative"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.negative">[docs]</a><span class="k">def</span> <span class="nf">negative</span><span class="p">(</span><span class="n">arr</span><span class="p">):</span> |
| <span class="sd">"""Numerical negative, element-wise.</span> |
| |
| <span class="sd"> Equals ``-arr``</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> arr : NDArray</span> |
| <span class="sd"> The input array</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> ``-arr``</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.ones((2,3))</span> |
| <span class="sd"> >>> (-x).asnumpy()</span> |
| <span class="sd"> array([[-1., -1., -1.],</span> |
| <span class="sd"> [-1., -1., -1.]], dtype=float32)</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="n">multiply</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.0</span><span class="p">)</span></div> |
| |
| |
| <div class="viewcode-block" id="load"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.load">[docs]</a><span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="n">fname</span><span class="p">):</span> |
| <span class="sd">"""Loads an array from file.</span> |
| |
| <span class="sd"> See more details in ``save``.</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> fname : str</span> |
| <span class="sd"> The filename.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> list of NDArray or dict of str to NDArray</span> |
| <span class="sd"> Loaded data.</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">fname</span><span class="p">,</span> <span class="n">string_types</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'fname required to be a string'</span><span class="p">)</span> |
| <span class="n">out_size</span> <span class="o">=</span> <span class="n">mx_uint</span><span class="p">()</span> |
| <span class="n">out_name_size</span> <span class="o">=</span> <span class="n">mx_uint</span><span class="p">()</span> |
| <span class="n">handles</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">NDArrayHandle</span><span class="p">)()</span> |
| <span class="n">names</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_char_p</span><span class="p">)()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArrayLoad</span><span class="p">(</span><span class="n">c_str</span><span class="p">(</span><span class="n">fname</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">out_size</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">handles</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">out_name_size</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">names</span><span class="p">)))</span> |
| <span class="k">if</span> <span class="n">out_name_size</span><span class="o">.</span><span class="n">value</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="k">return</span> <span class="p">[</span><span class="n">NDArray</span><span class="p">(</span><span class="n">NDArrayHandle</span><span class="p">(</span><span class="n">handles</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">out_size</span><span class="o">.</span><span class="n">value</span><span class="p">)]</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">assert</span> <span class="n">out_name_size</span><span class="o">.</span><span class="n">value</span> <span class="o">==</span> <span class="n">out_size</span><span class="o">.</span><span class="n">value</span> |
| <span class="k">return</span> <span class="nb">dict</span><span class="p">(</span> |
| <span class="p">(</span><span class="n">py_str</span><span class="p">(</span><span class="n">names</span><span class="p">[</span><span class="n">i</span><span class="p">]),</span> <span class="n">NDArray</span><span class="p">(</span><span class="n">NDArrayHandle</span><span class="p">(</span><span class="n">handles</span><span class="p">[</span><span class="n">i</span><span class="p">])))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">out_size</span><span class="o">.</span><span class="n">value</span><span class="p">))</span></div> |
| |
| |
| <div class="viewcode-block" id="save"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.save">[docs]</a><span class="k">def</span> <span class="nf">save</span><span class="p">(</span><span class="n">fname</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span> |
| <span class="sd">"""Saves a list of arrays or a dict of str->array to file.</span> |
| |
| <span class="sd"> Examples of filenames:</span> |
| |
| <span class="sd"> - ``/path/to/file``</span> |
| <span class="sd"> - ``s3://my-bucket/path/to/file`` (if compiled with AWS S3 supports)</span> |
| <span class="sd"> - ``hdfs://path/to/file`` (if compiled with HDFS supports)</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> fname : str</span> |
| <span class="sd"> The filename.</span> |
| <span class="sd"> data : ``NDArray``, list of ``NDArray` or dict of str to ``NDArray``</span> |
| <span class="sd"> The data to save.</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> x = mx.nd.zeros((2,3))</span> |
| <span class="sd"> >>> y = mx.nd.ones((1,4))</span> |
| <span class="sd"> >>> mx.nd.save('my_list', [x,y])</span> |
| <span class="sd"> >>> mx.nd.save('my_dict', {'x':x, 'y':y})</span> |
| <span class="sd"> >>> mx.nd.load('my_list')</span> |
| <span class="sd"> [<NDArray 2x3 @cpu(0)>, <NDArray 1x4 @cpu(0)>]</span> |
| <span class="sd"> >>> mx.nd.load('my_dict')</span> |
| <span class="sd"> {'y': <NDArray 1x4 @cpu(0)>, 'x': <NDArray 2x3 @cpu(0)>}</span> |
| <span class="sd"> """</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="p">]</span> |
| <span class="n">handles</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> |
| <span class="n">keys</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">string_types</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'save only accept dict str->NDArray or list of NDArray'</span><span class="p">)</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'save only accept dict str->NDArray or list of NDArray'</span><span class="p">)</span> |
| <span class="n">keys</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">c_str</span><span class="p">(</span><span class="n">key</span><span class="p">))</span> |
| <span class="n">handles</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">val</span><span class="o">.</span><span class="n">handle</span><span class="p">)</span> |
| <span class="n">keys</span> <span class="o">=</span> <span class="n">c_array</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_char_p</span><span class="p">,</span> <span class="n">keys</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span> |
| <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span> |
| <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">NDArray</span><span class="p">):</span> |
| <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'save only accept dict str->NDArray or list of NDArray'</span><span class="p">)</span> |
| <span class="n">handles</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">val</span><span class="o">.</span><span class="n">handle</span><span class="p">)</span> |
| <span class="n">keys</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"data needs to either be a NDArray, dict of str, NDArray pairs "</span> |
| <span class="s2">"or a list of NDarrays."</span><span class="p">)</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXNDArraySave</span><span class="p">(</span><span class="n">c_str</span><span class="p">(</span><span class="n">fname</span><span class="p">),</span> |
| <span class="n">mx_uint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">handles</span><span class="p">)),</span> |
| <span class="n">c_array</span><span class="p">(</span><span class="n">NDArrayHandle</span><span class="p">,</span> <span class="n">handles</span><span class="p">),</span> |
| <span class="n">keys</span><span class="p">))</span></div> |
| |
| |
| <div class="viewcode-block" id="concatenate"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.concatenate">[docs]</a><span class="k">def</span> <span class="nf">concatenate</span><span class="p">(</span><span class="n">arrays</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">always_copy</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> |
| <span class="sd">"""DEPRECATED, use ``concat`` instead</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> arrays : list of `NDArray`</span> |
| <span class="sd"> Arrays to be concatenate. They must have identical shape except</span> |
| <span class="sd"> the first dimension. They also must have the same data type.</span> |
| <span class="sd"> axis : int</span> |
| <span class="sd"> The axis along which to concatenate.</span> |
| <span class="sd"> always_copy : bool</span> |
| <span class="sd"> Default `True`. When not `True`, if the arrays only contain one</span> |
| <span class="sd"> `NDArray`, that element will be returned directly, avoid copying.</span> |
| |
| <span class="sd"> Returns</span> |
| <span class="sd"> -------</span> |
| <span class="sd"> NDArray</span> |
| <span class="sd"> An `NDArray` that lives on the same context as `arrays[0].context`.</span> |
| <span class="sd"> """</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arrays</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">arrays</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span> |
| <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arrays</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">NDArray</span><span class="p">)</span> |
| |
| <span class="k">if</span> <span class="ow">not</span> <span class="n">always_copy</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">arrays</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">arrays</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> |
| |
| <span class="n">shape_axis</span> <span class="o">=</span> <span class="n">arrays</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">axis</span><span class="p">]</span> |
| <span class="n">shape_rest1</span> <span class="o">=</span> <span class="n">arrays</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">axis</span><span class="p">]</span> |
| <span class="n">shape_rest2</span> <span class="o">=</span> <span class="n">arrays</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">axis</span><span class="o">+</span><span class="mi">1</span><span class="p">:]</span> |
| <span class="n">dtype</span> <span class="o">=</span> <span class="n">arrays</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span> |
| <span class="k">for</span> <span class="n">arr</span> <span class="ow">in</span> <span class="n">arrays</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span> |
| <span class="n">shape_axis</span> <span class="o">+=</span> <span class="n">arr</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">axis</span><span class="p">]</span> |
| <span class="k">assert</span> <span class="n">shape_rest1</span> <span class="o">==</span> <span class="n">arr</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">axis</span><span class="p">]</span> |
| <span class="k">assert</span> <span class="n">shape_rest2</span> <span class="o">==</span> <span class="n">arr</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">axis</span><span class="o">+</span><span class="mi">1</span><span class="p">:]</span> |
| <span class="k">assert</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">arr</span><span class="o">.</span><span class="n">dtype</span> |
| <span class="n">ret_shape</span> <span class="o">=</span> <span class="n">shape_rest1</span> <span class="o">+</span> <span class="p">(</span><span class="n">shape_axis</span><span class="p">,)</span> <span class="o">+</span> <span class="n">shape_rest2</span> |
| <span class="n">ret</span> <span class="o">=</span> <span class="n">empty</span><span class="p">(</span><span class="n">ret_shape</span><span class="p">,</span> <span class="n">ctx</span><span class="o">=</span><span class="n">arrays</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">context</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> |
| |
| <span class="n">idx</span> <span class="o">=</span> <span class="mi">0</span> |
| <span class="n">begin</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">ret_shape</span><span class="p">]</span> |
| <span class="n">end</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">ret_shape</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">arr</span> <span class="ow">in</span> <span class="n">arrays</span><span class="p">:</span> |
| <span class="k">if</span> <span class="n">axis</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> |
| <span class="n">ret</span><span class="p">[</span><span class="n">idx</span><span class="p">:</span><span class="n">idx</span><span class="o">+</span><span class="n">arr</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">=</span> <span class="n">arr</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">begin</span><span class="p">[</span><span class="n">axis</span><span class="p">]</span> <span class="o">=</span> <span class="n">idx</span> |
| <span class="n">end</span><span class="p">[</span><span class="n">axis</span><span class="p">]</span> <span class="o">=</span> <span class="n">idx</span><span class="o">+</span><span class="n">arr</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">axis</span><span class="p">]</span> |
| <span class="c1"># pylint: disable=no-member,protected-access</span> |
| <span class="n">_internal</span><span class="o">.</span><span class="n">_crop_assign</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">arr</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">ret</span><span class="p">,</span> |
| <span class="n">begin</span><span class="o">=</span><span class="nb">tuple</span><span class="p">(</span><span class="n">begin</span><span class="p">),</span> |
| <span class="n">end</span><span class="o">=</span><span class="nb">tuple</span><span class="p">(</span><span class="n">end</span><span class="p">))</span> |
| <span class="c1"># pylint: enable=no-member,protected-access</span> |
| <span class="n">idx</span> <span class="o">+=</span> <span class="n">arr</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">axis</span><span class="p">]</span> |
| |
| <span class="k">return</span> <span class="n">ret</span></div> |
| |
| <div class="viewcode-block" id="imdecode"><a class="viewcode-back" href="../../api/python/ndarray.html#mxnet.ndarray.imdecode">[docs]</a><span class="k">def</span> <span class="nf">imdecode</span><span class="p">(</span><span class="n">str_img</span><span class="p">,</span> <span class="n">clip_rect</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">channels</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> |
| <span class="sd">"""DEPRECATED, use mx.img instead</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> str_img : str</span> |
| <span class="sd"> Binary image data</span> |
| <span class="sd"> clip_rect : iterable of 4 int</span> |
| <span class="sd"> Clip decoded image to rectangle (x0, y0, x1, y1).</span> |
| <span class="sd"> out : NDArray</span> |
| <span class="sd"> Output buffer. Can be 3 dimensional (c, h, w) or 4 dimensional (n, c, h, w).</span> |
| <span class="sd"> index : int</span> |
| <span class="sd"> Output decoded image to i-th slice of 4 dimensional buffer.</span> |
| <span class="sd"> channels : int</span> |
| <span class="sd"> Number of channels to output. Decode to grey scale when channels = 1.</span> |
| <span class="sd"> mean : NDArray</span> |
| <span class="sd"> Subtract mean from decode image before outputing.</span> |
| <span class="sd"> """</span> |
| <span class="c1"># pylint: disable= no-member, protected-access, too-many-arguments</span> |
| <span class="k">if</span> <span class="n">mean</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">mean</span> <span class="o">=</span> <span class="n">NDArray</span><span class="p">(</span><span class="n">_new_empty_handle</span><span class="p">())</span> |
| <span class="k">if</span> <span class="n">out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_imdecode</span><span class="p">(</span><span class="n">mean</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> |
| <span class="n">clip_rect</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> |
| <span class="n">clip_rect</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> |
| <span class="n">clip_rect</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> |
| <span class="n">clip_rect</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> |
| <span class="n">channels</span><span class="p">,</span> |
| <span class="nb">len</span><span class="p">(</span><span class="n">str_img</span><span class="p">),</span> |
| <span class="n">str_img</span><span class="o">=</span><span class="n">str_img</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="k">return</span> <span class="n">_internal</span><span class="o">.</span><span class="n">_imdecode</span><span class="p">(</span><span class="n">mean</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> |
| <span class="n">clip_rect</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> |
| <span class="n">clip_rect</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> |
| <span class="n">clip_rect</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> |
| <span class="n">clip_rect</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> |
| <span class="n">channels</span><span class="p">,</span> |
| <span class="nb">len</span><span class="p">(</span><span class="n">str_img</span><span class="p">),</span> |
| <span class="n">str_img</span><span class="o">=</span><span class="n">str_img</span><span class="p">,</span> |
| <span class="n">out</span><span class="o">=</span><span class="n">out</span><span class="p">)</span></div> |
| |
| |
| <span class="c1"># pylint: disable=too-many-locals, invalid-name</span> |
| <span class="k">def</span> <span class="nf">_make_ndarray_function</span><span class="p">(</span><span class="n">handle</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span> |
| <span class="sd">"""Create a NDArray function from the FunctionHandle."""</span> |
| <span class="n">real_name</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_char_p</span><span class="p">()</span> |
| <span class="n">desc</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_char_p</span><span class="p">()</span> |
| <span class="n">num_args</span> <span class="o">=</span> <span class="n">mx_uint</span><span class="p">()</span> |
| <span class="n">arg_names</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_char_p</span><span class="p">)()</span> |
| <span class="n">arg_types</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_char_p</span><span class="p">)()</span> |
| <span class="n">arg_descs</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_char_p</span><span class="p">)()</span> |
| <span class="n">key_var_num_args</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_char_p</span><span class="p">()</span> |
| <span class="n">ret_type</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_char_p</span><span class="p">()</span> |
| |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXSymbolGetAtomicSymbolInfo</span><span class="p">(</span> |
| <span class="n">handle</span><span class="p">,</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">real_name</span><span class="p">),</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">desc</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">num_args</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">arg_names</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">arg_types</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">arg_descs</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">key_var_num_args</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">ret_type</span><span class="p">)))</span> |
| <span class="n">narg</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">num_args</span><span class="o">.</span><span class="n">value</span><span class="p">)</span> |
| <span class="n">arg_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">py_str</span><span class="p">(</span><span class="n">arg_names</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">narg</span><span class="p">)]</span> |
| <span class="n">arg_types</span> <span class="o">=</span> <span class="p">[</span><span class="n">py_str</span><span class="p">(</span><span class="n">arg_types</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">narg</span><span class="p">)]</span> |
| <span class="n">func_name</span> <span class="o">=</span> <span class="n">name</span> |
| <span class="n">key_var_num_args</span> <span class="o">=</span> <span class="n">py_str</span><span class="p">(</span><span class="n">key_var_num_args</span><span class="o">.</span><span class="n">value</span><span class="p">)</span> |
| <span class="n">ret_type</span> <span class="o">=</span> <span class="n">py_str</span><span class="p">(</span><span class="n">ret_type</span><span class="o">.</span><span class="n">value</span><span class="p">)</span> <span class="k">if</span> <span class="n">ret_type</span><span class="o">.</span><span class="n">value</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="s1">''</span> |
| <span class="n">doc_str</span> <span class="o">=</span> <span class="n">_build_doc</span><span class="p">(</span><span class="n">func_name</span><span class="p">,</span> |
| <span class="n">py_str</span><span class="p">(</span><span class="n">desc</span><span class="o">.</span><span class="n">value</span><span class="p">),</span> |
| <span class="n">arg_names</span><span class="p">,</span> |
| <span class="n">arg_types</span><span class="p">,</span> |
| <span class="p">[</span><span class="n">py_str</span><span class="p">(</span><span class="n">arg_descs</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">narg</span><span class="p">)],</span> |
| <span class="n">key_var_num_args</span><span class="p">,</span> |
| <span class="n">ret_type</span><span class="p">)</span> |
| |
| <span class="n">dtype_name</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="n">arr_name</span> <span class="o">=</span> <span class="kc">None</span> |
| <span class="n">ndsignature</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">signature</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">ndarg_names</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="n">kwarg_names</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">narg</span><span class="p">):</span> |
| <span class="n">name</span><span class="p">,</span> <span class="n">atype</span> <span class="o">=</span> <span class="n">arg_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">arg_types</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> |
| <span class="k">if</span> <span class="n">name</span> <span class="o">==</span> <span class="s1">'dtype'</span><span class="p">:</span> |
| <span class="n">dtype_name</span> <span class="o">=</span> <span class="n">name</span> |
| <span class="n">signature</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">'</span><span class="si">%s</span><span class="s1">=_Null'</span><span class="o">%</span><span class="n">name</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">atype</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'NDArray'</span><span class="p">)</span> <span class="ow">or</span> <span class="n">atype</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'Symbol'</span><span class="p">):</span> |
| <span class="k">assert</span> <span class="ow">not</span> <span class="n">arr_name</span><span class="p">,</span> \ |
| <span class="s2">"Op can only have one argument with variable "</span> \ |
| <span class="s2">"size and it must be the last argument."</span> |
| <span class="k">if</span> <span class="n">atype</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">'[]'</span><span class="p">):</span> |
| <span class="n">ndsignature</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">'*</span><span class="si">%s</span><span class="s1">'</span><span class="o">%</span><span class="n">name</span><span class="p">)</span> |
| <span class="n">arr_name</span> <span class="o">=</span> <span class="n">name</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">ndsignature</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">'</span><span class="si">%s</span><span class="s1">=None'</span><span class="o">%</span><span class="n">name</span><span class="p">)</span> |
| <span class="n">ndarg_names</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">name</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">signature</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">'</span><span class="si">%s</span><span class="s1">=_Null'</span><span class="o">%</span><span class="n">name</span><span class="p">)</span> |
| <span class="n">kwarg_names</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">name</span><span class="p">)</span> |
| <span class="n">signature</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">'out=None'</span><span class="p">)</span> |
| <span class="n">signature</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">'name=None'</span><span class="p">)</span> |
| <span class="n">signature</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">'**kwargs'</span><span class="p">)</span> |
| <span class="n">signature</span> <span class="o">=</span> <span class="n">ndsignature</span> <span class="o">+</span> <span class="n">signature</span> |
| |
| <span class="n">code</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">if</span> <span class="n">arr_name</span><span class="p">:</span> |
| <span class="n">code</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"""</span> |
| <span class="s2">def </span><span class="si">%s</span><span class="s2">(*</span><span class="si">%s</span><span class="s2">, **kwargs):"""</span><span class="o">%</span><span class="p">(</span><span class="n">func_name</span><span class="p">,</span> <span class="n">arr_name</span><span class="p">))</span> |
| <span class="n">code</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"""</span> |
| <span class="s2"> ndargs = []</span> |
| <span class="s2"> for i in </span><span class="si">{}</span><span class="s2">:</span> |
| <span class="s2"> assert isinstance(i, NDArrayBase), </span><span class="se">\\</span><span class="s2"></span> |
| <span class="s2"> "Positional arguments must have NDArray type, " </span><span class="se">\\</span><span class="s2"></span> |
| <span class="s2"> "but got </span><span class="si">%s</span><span class="s2">"</span><span class="si">%s</span><span class="s2">tr(i)</span> |
| <span class="s2"> ndargs.append(i)"""</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">arr_name</span><span class="p">))</span> |
| <span class="k">if</span> <span class="n">dtype_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">code</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"""</span> |
| <span class="s2"> if '</span><span class="si">%s</span><span class="s2">' in kwargs:</span> |
| <span class="s2"> kwargs['</span><span class="si">%s</span><span class="s2">'] = np.dtype(kwargs['</span><span class="si">%s</span><span class="s2">']).name"""</span><span class="o">%</span><span class="p">(</span> |
| <span class="n">dtype_name</span><span class="p">,</span> <span class="n">dtype_name</span><span class="p">,</span> <span class="n">dtype_name</span><span class="p">))</span> |
| <span class="n">code</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"""</span> |
| <span class="s2"> _ = kwargs.pop('name', None)</span> |
| <span class="s2"> out = kwargs.pop('out', None)</span> |
| <span class="s2"> keys = list(kwargs.keys())</span> |
| <span class="s2"> vals = list(kwargs.values())"""</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="n">code</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"""</span> |
| <span class="s2">def </span><span class="si">%s</span><span class="s2">(</span><span class="si">%s</span><span class="s2">):</span> |
| <span class="s2"> ndargs = []</span> |
| <span class="s2"> keys = list(kwargs.keys())</span> |
| <span class="s2"> vals = list(kwargs.values())"""</span><span class="o">%</span><span class="p">(</span><span class="n">func_name</span><span class="p">,</span> <span class="s1">', '</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">signature</span><span class="p">)))</span> |
| <span class="c1"># NDArray args</span> |
| <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">ndarg_names</span><span class="p">:</span> <span class="c1"># pylint: disable=redefined-argument-from-local</span> |
| <span class="n">code</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"""</span> |
| <span class="s2"> if </span><span class="si">{name}</span><span class="s2"> is not None:</span> |
| <span class="s2"> assert isinstance(</span><span class="si">{name}</span><span class="s2">, NDArrayBase), </span><span class="se">\\</span><span class="s2"></span> |
| <span class="s2"> "Argument </span><span class="si">{name}</span><span class="s2"> must have NDArray type, but got </span><span class="si">%s</span><span class="s2">"</span><span class="si">%s</span><span class="s2">tr(</span><span class="si">{name}</span><span class="s2">)</span> |
| <span class="s2"> ndargs.append(</span><span class="si">{name}</span><span class="s2">)"""</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">))</span> |
| <span class="c1"># kwargs</span> |
| <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">kwarg_names</span><span class="p">:</span> <span class="c1"># pylint: disable=redefined-argument-from-local</span> |
| <span class="n">code</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"""</span> |
| <span class="s2"> if </span><span class="si">%s</span><span class="s2"> is not _Null:</span> |
| <span class="s2"> keys.append('</span><span class="si">%s</span><span class="s2">')</span> |
| <span class="s2"> vals.append(</span><span class="si">%s</span><span class="s2">)"""</span><span class="o">%</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">name</span><span class="p">))</span> |
| <span class="c1"># dtype</span> |
| <span class="k">if</span> <span class="n">dtype_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> |
| <span class="n">code</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"""</span> |
| <span class="s2"> if </span><span class="si">%s</span><span class="s2"> is not _Null:</span> |
| <span class="s2"> keys.append('</span><span class="si">%s</span><span class="s2">')</span> |
| <span class="s2"> vals.append(np.dtype(</span><span class="si">%s</span><span class="s2">).name)"""</span><span class="o">%</span><span class="p">(</span><span class="n">dtype_name</span><span class="p">,</span> <span class="n">dtype_name</span><span class="p">,</span> <span class="n">dtype_name</span><span class="p">))</span> |
| |
| <span class="n">code</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"""</span> |
| <span class="s2"> return _imperative_invoke(</span><span class="si">%d</span><span class="s2">, ndargs, keys, vals, out)"""</span><span class="o">%</span><span class="p">(</span> |
| <span class="n">handle</span><span class="o">.</span><span class="n">value</span><span class="p">))</span> |
| |
| <span class="n">local</span> <span class="o">=</span> <span class="p">{}</span> |
| <span class="n">exec</span><span class="p">(</span><span class="s1">''</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">code</span><span class="p">),</span> <span class="kc">None</span><span class="p">,</span> <span class="n">local</span><span class="p">)</span> <span class="c1"># pylint: disable=exec-used</span> |
| <span class="n">ndarray_function</span> <span class="o">=</span> <span class="n">local</span><span class="p">[</span><span class="n">func_name</span><span class="p">]</span> |
| <span class="n">ndarray_function</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">=</span> <span class="n">func_name</span> |
| <span class="n">ndarray_function</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="n">doc_str</span> |
| <span class="n">ndarray_function</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">=</span> <span class="s1">'mxnet.ndarray'</span> |
| <span class="k">return</span> <span class="n">ndarray_function</span> |
| |
| |
| <span class="c1"># pylint: enable=too-many-locals, invalid-name</span> |
| <span class="k">def</span> <span class="nf">_init_ndarray_module</span><span class="p">(</span><span class="n">ndarray_class</span><span class="p">,</span> <span class="n">root_namespace</span><span class="p">):</span> |
| <span class="sd">"""List and add all the ndarray functions to current module."""</span> |
| <span class="n">_set_ndarray_class</span><span class="p">(</span><span class="n">ndarray_class</span><span class="p">)</span> |
| <span class="n">plist</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">POINTER</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">c_char_p</span><span class="p">)()</span> |
| <span class="n">size</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_uint</span><span class="p">()</span> |
| |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">MXListAllOpNames</span><span class="p">(</span><span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">size</span><span class="p">),</span> |
| <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">plist</span><span class="p">)))</span> |
| <span class="n">op_names</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">size</span><span class="o">.</span><span class="n">value</span><span class="p">):</span> |
| <span class="n">op_names</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">py_str</span><span class="p">(</span><span class="n">plist</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span> |
| |
| <span class="n">module_obj</span> <span class="o">=</span> <span class="n">_sys</span><span class="o">.</span><span class="n">modules</span><span class="p">[</span><span class="s2">"</span><span class="si">%s</span><span class="s2">.ndarray"</span> <span class="o">%</span> <span class="n">root_namespace</span><span class="p">]</span> |
| <span class="n">module_internal</span> <span class="o">=</span> <span class="n">_sys</span><span class="o">.</span><span class="n">modules</span><span class="p">[</span><span class="s2">"</span><span class="si">%s</span><span class="s2">._ndarray_internal"</span> <span class="o">%</span> <span class="n">root_namespace</span><span class="p">]</span> |
| <span class="n">module_contrib</span> <span class="o">=</span> <span class="n">_sys</span><span class="o">.</span><span class="n">modules</span><span class="p">[</span><span class="s2">"</span><span class="si">%s</span><span class="s2">.contrib.ndarray"</span> <span class="o">%</span> <span class="n">root_namespace</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">op_names</span><span class="p">:</span> |
| <span class="n">hdl</span> <span class="o">=</span> <span class="n">OpHandle</span><span class="p">()</span> |
| <span class="n">check_call</span><span class="p">(</span><span class="n">_LIB</span><span class="o">.</span><span class="n">NNGetOpHandle</span><span class="p">(</span><span class="n">c_str</span><span class="p">(</span><span class="n">name</span><span class="p">),</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">byref</span><span class="p">(</span><span class="n">hdl</span><span class="p">)))</span> |
| <span class="n">function</span> <span class="o">=</span> <span class="n">_make_ndarray_function</span><span class="p">(</span><span class="n">hdl</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">function</span><span class="o">.</span><span class="vm">__name__</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'_contrib_'</span><span class="p">):</span> |
| <span class="n">function</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">=</span> <span class="n">function</span><span class="o">.</span><span class="vm">__name__</span><span class="p">[</span><span class="mi">9</span><span class="p">:]</span> |
| <span class="n">function</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">=</span> <span class="s1">'mxnet.contrib.ndarray'</span> |
| <span class="nb">setattr</span><span class="p">(</span><span class="n">module_contrib</span><span class="p">,</span> <span class="n">function</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="n">function</span><span class="p">)</span> |
| <span class="k">elif</span> <span class="n">function</span><span class="o">.</span><span class="vm">__name__</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'_'</span><span class="p">):</span> |
| <span class="nb">setattr</span><span class="p">(</span><span class="n">module_internal</span><span class="p">,</span> <span class="n">function</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="n">function</span><span class="p">)</span> |
| <span class="k">else</span><span class="p">:</span> |
| <span class="nb">setattr</span><span class="p">(</span><span class="n">module_obj</span><span class="p">,</span> <span class="n">function</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="n">function</span><span class="p">)</span> |
| |
| <span class="n">_init_ndarray_module</span><span class="p">(</span><span class="n">NDArray</span><span class="p">,</span> <span class="s2">"mxnet"</span><span class="p">)</span> |
| |
| <span class="c1"># from .base import add_fileline_to_docstring</span> |
| <span class="c1"># add_fileline_to_docstring(__name__)</span> |
| </pre></div> |
| </div> |
| </div> |
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| <p> |
| Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), <strong>sponsored by the <i>Apache Incubator</i></strong>. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF. |
| </p> |
| <p> |
| "Copyright © 2017-2018, The Apache Software Foundation |
| Apache MXNet, MXNet, Apache, the Apache feather, and the Apache MXNet project logo are either registered trademarks or trademarks of the Apache Software Foundation." |
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