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<div class="sphx-glr-download-link-note admonition note">
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<p class="admonition-title">Note</p>
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<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-common-errors-py"><span class="std std-ref">here</span></a>
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to download the full example code</p>
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<div class="sphx-glr-example-title section" id="common-errors-with-onnxruntime">
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<span id="l-example-common-error"></span><span id="sphx-glr-auto-examples-plot-common-errors-py"></span><h1>Common errors with onnxruntime<a class="headerlink" href="#common-errors-with-onnxruntime" title="Permalink to this headline">¶</a></h1>
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<p>This example looks into several common situations
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in which <em>onnxruntime</em> does not return the model
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prediction but raises an exception instead.
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It starts by loading the model trained in example
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<a class="reference internal" href="../tutorial.html#l-logreg-example"><span class="std std-ref">Step 1: Train a model using your favorite framework</span></a> which produced a logistic regression
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trained on <em>Iris</em> datasets. The model takes
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a vector of dimension 2 and returns a class among three.</p>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">onnxruntime</span> <span class="k">as</span> <span class="nn">rt</span>
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<span class="kn">from</span> <span class="nn">onnxruntime.capi.onnxruntime_pybind11_state</span> <span class="kn">import</span> <span class="n">InvalidArgument</span>
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<span class="kn">import</span> <span class="nn">numpy</span>
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<span class="kn">from</span> <span class="nn">onnxruntime.datasets</span> <span class="kn">import</span> <span class="n">get_example</span>
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<span class="n">example2</span> <span class="o">=</span> <span class="n">get_example</span><span class="p">(</span><span class="s2">"logreg_iris.onnx"</span><span class="p">)</span>
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<span class="n">sess</span> <span class="o">=</span> <span class="n">rt</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">(</span><span class="n">example2</span><span class="p">)</span>
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<span class="n">input_name</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">get_inputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span>
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<span class="n">output_name</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span>
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</pre></div>
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</div>
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<p>The first example fails due to <em>bad types</em>.
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<em>onnxruntime</em> only expects single floats (4 bytes)
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and cannot handle any other kind of floats.</p>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">try</span><span class="p">:</span>
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<span class="n">x</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">5.0</span><span class="p">,</span> <span class="mf">6.0</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
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<span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">([</span><span class="n">output_name</span><span class="p">],</span> <span class="p">{</span><span class="n">input_name</span><span class="p">:</span> <span class="n">x</span><span class="p">})</span>
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<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
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<span class="nb">print</span><span class="p">(</span><span class="s2">"Unexpected type"</span><span class="p">)</span>
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<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="si">{0}</span><span class="s2">: </span><span class="si">{1}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">e</span><span class="p">),</span> <span class="n">e</span><span class="p">))</span>
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</pre></div>
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</div>
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<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Unexpected type
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<class 'onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument'>: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Unexpected input data type. Actual: (tensor(double)) , expected: (tensor(float))
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</pre></div>
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</div>
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<p>The model fails to return an output if the name
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is misspelled.</p>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">try</span><span class="p">:</span>
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<span class="n">x</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">5.0</span><span class="p">,</span> <span class="mf">6.0</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
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<span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">([</span><span class="s2">"misspelled"</span><span class="p">],</span> <span class="p">{</span><span class="n">input_name</span><span class="p">:</span> <span class="n">x</span><span class="p">})</span>
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<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
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<span class="nb">print</span><span class="p">(</span><span class="s2">"Misspelled output name"</span><span class="p">)</span>
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<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="si">{0}</span><span class="s2">: </span><span class="si">{1}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">e</span><span class="p">),</span> <span class="n">e</span><span class="p">))</span>
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</pre></div>
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</div>
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<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Misspelled output name
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<class 'onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument'>: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid Output Name:misspelled
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</pre></div>
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</div>
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<p>The output name is optional, it can be replaced by <em>None</em>
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and <em>onnxruntime</em> will then return all the outputs.</p>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">5.0</span><span class="p">,</span> <span class="mf">6.0</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
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<span class="k">try</span><span class="p">:</span>
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<span class="n">res</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="p">{</span><span class="n">input_name</span><span class="p">:</span> <span class="n">x</span><span class="p">})</span>
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<span class="nb">print</span><span class="p">(</span><span class="s2">"All outputs"</span><span class="p">)</span>
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<span class="nb">print</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
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<span class="k">except</span> <span class="p">(</span><span class="ne">RuntimeError</span><span class="p">,</span> <span class="n">InvalidArgument</span><span class="p">)</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
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<span class="nb">print</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
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</pre></div>
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</div>
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<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>All outputs
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[array([0, 0, 0], dtype=int64), [{0: 0.9505997896194458, 1: 0.027834143489599228, 2: 0.021566055715084076}, {0: 0.9974970817565918, 1: 5.6270167988259345e-05, 2: 0.0024466365575790405}, {0: 0.9997311234474182, 1: 1.787709464906584e-07, 2: 0.0002686927327886224}]]
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</pre></div>
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</div>
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<p>The same goes if the input name is misspelled.</p>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">try</span><span class="p">:</span>
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<span class="n">x</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">5.0</span><span class="p">,</span> <span class="mf">6.0</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
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<span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">([</span><span class="n">output_name</span><span class="p">],</span> <span class="p">{</span><span class="s2">"misspelled"</span><span class="p">:</span> <span class="n">x</span><span class="p">})</span>
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<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
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<span class="nb">print</span><span class="p">(</span><span class="s2">"Misspelled input name"</span><span class="p">)</span>
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<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="si">{0}</span><span class="s2">: </span><span class="si">{1}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">e</span><span class="p">),</span> <span class="n">e</span><span class="p">))</span>
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</pre></div>
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</div>
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<p class="sphx-glr-script-out">Out:</p>
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<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Misspelled input name
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<class 'onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument'>: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid Feed Input Name:misspelled
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</pre></div>
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</div>
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<p><em>onnxruntime</em> does not necessarily fail if the input
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dimension is a multiple of the expected input dimension.</p>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">[</span>
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<span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span>
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<span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span>
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<span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span>
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<span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span>
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<span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span>
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<span class="p">]:</span>
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<span class="k">try</span><span class="p">:</span>
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<span class="n">r</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">([</span><span class="n">output_name</span><span class="p">],</span> <span class="p">{</span><span class="n">input_name</span><span class="p">:</span> <span class="n">x</span><span class="p">})</span>
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<span class="nb">print</span><span class="p">(</span><span class="s2">"Shape=</span><span class="si">{0}</span><span class="s2"> and predicted labels=</span><span class="si">{1}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">r</span><span class="p">))</span>
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<span class="k">except</span> <span class="p">(</span><span class="ne">RuntimeError</span><span class="p">,</span> <span class="n">InvalidArgument</span><span class="p">)</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
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<span class="nb">print</span><span class="p">(</span><span class="s2">"ERROR with Shape=</span><span class="si">{0}</span><span class="s2"> - </span><span class="si">{1}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">e</span><span class="p">))</span>
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<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">[</span>
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<span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span>
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<span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span>
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<span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span>
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<span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span>
|
|
<span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span>
|
|
<span class="p">]:</span>
|
|
<span class="k">try</span><span class="p">:</span>
|
|
<span class="n">r</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="p">{</span><span class="n">input_name</span><span class="p">:</span> <span class="n">x</span><span class="p">})</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s2">"Shape=</span><span class="si">{0}</span><span class="s2"> and predicted probabilities=</span><span class="si">{1}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
|
|
<span class="k">except</span> <span class="p">(</span><span class="ne">RuntimeError</span><span class="p">,</span> <span class="n">InvalidArgument</span><span class="p">)</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s2">"ERROR with Shape=</span><span class="si">{0}</span><span class="s2"> - </span><span class="si">{1}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">e</span><span class="p">))</span>
|
|
</pre></div>
|
|
</div>
|
|
<p class="sphx-glr-script-out">Out:</p>
|
|
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>ERROR with Shape=(4,) - [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid rank for input: float_input Got: 1 Expected: 2 Please fix either the inputs or the model.
|
|
ERROR with Shape=(1, 4) - [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: float_input for the following indices
|
|
index: 0 Got: 1 Expected: 3
|
|
index: 1 Got: 4 Expected: 2
|
|
Please fix either the inputs or the model.
|
|
ERROR with Shape=(2, 2) - [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: float_input for the following indices
|
|
index: 0 Got: 2 Expected: 3
|
|
Please fix either the inputs or the model.
|
|
ERROR with Shape=(3,) - [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid rank for input: float_input Got: 1 Expected: 2 Please fix either the inputs or the model.
|
|
ERROR with Shape=(1, 3) - [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: float_input for the following indices
|
|
index: 0 Got: 1 Expected: 3
|
|
index: 1 Got: 3 Expected: 2
|
|
Please fix either the inputs or the model.
|
|
ERROR with Shape=(4,) - [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid rank for input: float_input Got: 1 Expected: 2 Please fix either the inputs or the model.
|
|
ERROR with Shape=(1, 4) - [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: float_input for the following indices
|
|
index: 0 Got: 1 Expected: 3
|
|
index: 1 Got: 4 Expected: 2
|
|
Please fix either the inputs or the model.
|
|
ERROR with Shape=(2, 2) - [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: float_input for the following indices
|
|
index: 0 Got: 2 Expected: 3
|
|
Please fix either the inputs or the model.
|
|
ERROR with Shape=(3,) - [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid rank for input: float_input Got: 1 Expected: 2 Please fix either the inputs or the model.
|
|
ERROR with Shape=(1, 3) - [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: float_input for the following indices
|
|
index: 0 Got: 1 Expected: 3
|
|
index: 1 Got: 3 Expected: 2
|
|
Please fix either the inputs or the model.
|
|
</pre></div>
|
|
</div>
|
|
<p>It does not fail either if the number of dimension
|
|
is higher than expects but produces a warning.</p>
|
|
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">[</span>
|
|
<span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">]]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span>
|
|
<span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">]]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span>
|
|
<span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([[[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">]],</span> <span class="p">[[</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">]]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span>
|
|
<span class="p">]:</span>
|
|
<span class="k">try</span><span class="p">:</span>
|
|
<span class="n">r</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">([</span><span class="n">output_name</span><span class="p">],</span> <span class="p">{</span><span class="n">input_name</span><span class="p">:</span> <span class="n">x</span><span class="p">})</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s2">"Shape=</span><span class="si">{0}</span><span class="s2"> and predicted labels=</span><span class="si">{1}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">r</span><span class="p">))</span>
|
|
<span class="k">except</span> <span class="p">(</span><span class="ne">RuntimeError</span><span class="p">,</span> <span class="n">InvalidArgument</span><span class="p">)</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s2">"ERROR with Shape=</span><span class="si">{0}</span><span class="s2"> - </span><span class="si">{1}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">e</span><span class="p">))</span>
|
|
</pre></div>
|
|
</div>
|
|
<p class="sphx-glr-script-out">Out:</p>
|
|
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>ERROR with Shape=(1, 2, 2) - [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid rank for input: float_input Got: 3 Expected: 2 Please fix either the inputs or the model.
|
|
ERROR with Shape=(1, 1, 3) - [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid rank for input: float_input Got: 3 Expected: 2 Please fix either the inputs or the model.
|
|
ERROR with Shape=(2, 1, 2) - [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid rank for input: float_input Got: 3 Expected: 2 Please fix either the inputs or the model.
|
|
</pre></div>
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<p><a class="reference download internal" download="" href="../downloads/3a2955e44bf8f95a0eee6e71695ad788/plot_common_errors.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_common_errors.py</span></code></a></p>
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