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Summary

Summary ¶

function	class parent	truncated documentation
`_default_inputs`		Guesses default inputs (float ones) if not specified.
`_demangle`		Demangle a C++ identifier using c++filt
`_existing_names`		Makes the list of existing names. Returns a set of unique names including intermediate results.
`_finalize_new_onnx`
`_loss_elastic`		Implements mixture of losses l1 and l2.
`_loss_l1`		Implements loss l1.
`_loss_l2`		Implements loss l2.
`_loss_log`		This only works for a binary classification. The log loss is ‘log(yt, yp) = (1-yt)log(1-yp) - ytlog(yp), this …
`_munge_time`		Take a time from nvprof and convert it into a chrome://tracing time.
`_onnx_axpy`		Returns the ONNX graph for function $Y = f(X1, X2, \alpha) = \alpha X1 + X2$ .
`_onnx_axpyw`		Returns the ONNX graph for function $Y, Z = f(X1, X2, G, \alpha, \beta) = (Y, Z)$ where $Z = \beta G + \alpha X1$ …
`_onnx_axpyw2`		Returns the ONNX graph for function $Y, Z = f(X1, X2, G, \alpha, \beta) = (Y, Z)$ where $Z = \beta G + \alpha X1$ …
`_onnx_copy`		Returns the ONNX graph for function $Y = X$ .
`_onnx_derivative_fw`		Implements a gradient based on class OrtModuleGraphBuilder.
`_onnx_derivative_loss`		Implements a gradient based on class PyGradientGraphBuilder.
`_onnx_grad_loss_absolute_error`		Returns the ONNX graph for function $Y = f(X1, X2) = \lVert X1 - X2 \rVert$ or $Y = f(X1, X2) = \lVert (X1 - X2)w \rVert$ …
`_onnx_grad_loss_elastic_error`		Returns the ONNX graph for function $Y = f(X1, X2) = \beta \lVert X1 - X2 \rVert + \alpha \lVert X1 - X2 \rVert^2$ …
`_onnx_grad_loss_square_error`		Returns the ONNX graph for function $Y = f(X1, X2) = \lVert (X1 - X2) \rVert ^2$ or $Y = f(X1, X2) = \lVert (\sqrt{w}(X1 - X2) \rVert ^2 w$ …
`_onnx_grad_penalty_elastic_error`		Returns the ONNX graph for function $Y = f(W) = \beta \lVert W \rVert + \alpha \lVert W \rVert^2$ l1_weight …
`_onnx_grad_sigmoid_neg_log_loss_error`		The function the raw scores from a classifier, uses the sigmoid function to compute probabilities, then the log function …
`_onnx_grad_square_error`		Returns the ONNX graph for the gradient of function $Y = f(X1, X2) = \lVert X1 - X2 \rVert ^2$ or $Y = f(X1, X2) = \lVert X1 - X2 \rVert ^2 w$ …
`_onnx_linear_regression`		Returns the ONNX graph for function $Y = f(X, A, B) = A X + B$ .
`_onnx_n_penalty_elastic_error`		Returns the ONNX graph for function $Y = f(W) = \beta \lVert W \rVert + \alpha \lVert W \rVert^2$ l1_weight …
`_onnx_rewrite_operator_node`		Replaces a node by a subgraph.
`_onnx_square_error`		Returns the ONNX graph for function $Y = f(X1, X2) = \lVert X1 - X2 \rVert ^2$ or $Y = f(X1, X2) = \lVert X1 - X2 \rVert ^2 w$ …
`_onnx_update_penalty_elastic_error`		Returns the ONNX graph for function $Y = f(W) = W - 2 \beta W - \alpha sign(W)$ l1 is $\beta$ and …
`_onnx_zero`		Returns the ONNX graph for function $Y = X * 0$ .
`_rewrite_op_no_grad`		Rewrites operators with no gradient.
`_sizeof_fmt`		Format size with metric units (like nvvp)
`_unique_name`		Returns a name different from any name in existing_names.
`_unique_name`		Returns a name different from any name in existing_names.
`add_initializer`		Adds an initializer to graph.
`add_loss_output`		Modifies an ONNX graph to add operators to score and allow training.
`check`		Runs a couple of functions to check the module is working.
`convert_trace_to_json`		Converts traces produced by nvprof and saved with format sqlite3 (extension .sql). The output format …
`device_to_providers`		Returns the corresponding providers for a specific device.
`dtype_to_var_type`		Converts a numpy dtype into a var type.
`fix_link_operator_md`		The redering of file Operator.md breaks links. This …
`function_onnx_graph`		Returns the ONNX graph corresponding to a function.
`get_class_names`		Returns the class names for the ImageNet competition as a dictionary.
`get_max_opset`		Returns the highest available onnx opset version.
`get_onnx_opset`		Returns the opset associated to an opset.
`get_ort_device`		Converts device into C_OrtDevice.
`get_ort_device_type`		Converts device into device type.
`get_supported_functions`		Returns the list of supported function by `function_onnx_graph()`.
`get_train_initializer`		Returns the list of initializers to train.
`json_to_dataframe`		Converts a json dump obtained with function `convert_trace_to_json()` to a dataframe.
`json_to_dataframe_streaming`		Converts a big json dump (from `convert_trace_to_json()`) to a dataframe. The function processes the data by streaming …
`main`		Implements `python -m onnxcustom <command> <args>`.
`measure_time`		Measures a statement and returns the results as a dictionary.
`numpy_to_ort_value`		Converts a numpy array to C_OrtValue.
`nvprof2json`		Converts traces produced by nvprof and saved with format sqlite3 (extension .sql).
`onnx_derivative`		Builds the gradient for an onnx graph.
`onnx_rename_weights`		Renames ONNX initializers to make sure their name follows the alphabetical order. The model is modified inplace. …
`onnx_rewrite_operator`		Replaces one operator by an onnx graph.
`ort_device_to_string`		Returns a string representing the device. Opposite of function `get_ort_device()`.
`penalty_loss_onnx`		Returns onnx nodes to compute $\|w\| \alpha + w^2 \beta$ where $\alpha=l1$ and $\beta=l2$ .
`plot_onnxs`		Plots one or several ONNX graph into a matplotlib graph.
`proto_type_to_dtype`		Converts a ONNX TensorProto type into numpy type.
`provider_to_device`		Converts provider into a device.
`replace_initializers_into_onnx`		Replaces initializers by other initializers, usually trained ones.
`str_ortvalue`		Displays the content of an C_OrtValue.
`unreduced_onnx_loss`		Every loss function reduces the results to compute a loss. The score function needs to get the loss for every observation, …

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