mxfusion.util.customop¶
Members¶
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class
mxfusion.util.customop.
MakeDiagonalOp
(**kwargs)¶ Bases:
mxnet.operator.CustomOp
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forward
(is_train, req, in_data, out_data, aux)¶ Forward interface. Can override when creating new operators.
Parameters: - is_train (bool) – whether this is for training
- req (list of str) – how to assign to out_data. can be ‘null’, ‘write’, or ‘add’. You can optionally use self.assign(dst, req, src) to handle this.
- out_data, aux (in_data,) – input, output, and auxiliary states for forward. See document for corresponding arguments of Operator::Forward
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backward
(req, out_grad, in_data, out_data, in_grad, aux)¶ Backward interface. Can override when creating new operators.
Parameters: - req (list of str) – how to assign to in_grad. can be ‘null’, ‘write’, or ‘add’. You can optionally use self.assign(dst, req, src) to handle this.
- in_data, out_data, in_grad, aux (out_grad,) – input and output for backward. See document for corresponding arguments of Operator::Backward
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class
mxfusion.util.customop.
MakeDiagonalOpProp
¶ Bases:
mxnet.operator.CustomOpProp
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list_arguments
()¶ list_arguments interface. Can override when creating new operators.
Returns: arguments – List of argument blob names. Return type: list
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list_outputs
()¶ list_outputs interface. Can override when creating new operators.
Returns: outputs – List of output blob names. Return type: list
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infer_shape
(in_shape)¶ infer_shape interface. Can override when creating new operators.
Parameters: in_shape (list) – List of argument shapes in the same order as declared in list_arguments. Returns: - in_shape (list) – List of argument shapes. Can be modified from in_shape.
- out_shape (list) – List of output shapes calculated from in_shape, in the same order as declared in list_outputs.
- aux_shape (Optional, list) – List of aux shapes calculated from in_shape, in the same order as declared in list_auxiliary_states.
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create_operator
(ctx, shapes, dtypes, **kwargs)¶ Create an operator that carries out the real computation given the context, input shapes, and input data types.
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mxfusion.util.customop.
make_diagonal
(F, x, name='make_diagonal')¶
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class
mxfusion.util.customop.
BroadcastToWithSamplesOp
(isSamples, shape, **kwargs)¶ Bases:
mxnet.operator.CustomOp
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forward
(is_train, req, in_data, out_data, aux)¶ Forward interface. Can override when creating new operators.
Parameters: - is_train (bool) – whether this is for training
- req (list of str) – how to assign to out_data. can be ‘null’, ‘write’, or ‘add’. You can optionally use self.assign(dst, req, src) to handle this.
- out_data, aux (in_data,) – input, output, and auxiliary states for forward. See document for corresponding arguments of Operator::Forward
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backward
(req, out_grad, in_data, out_data, in_grad, aux)¶ Backward interface. Can override when creating new operators.
Parameters: - req (list of str) – how to assign to in_grad. can be ‘null’, ‘write’, or ‘add’. You can optionally use self.assign(dst, req, src) to handle this.
- in_data, out_data, in_grad, aux (out_grad,) – input and output for backward. See document for corresponding arguments of Operator::Backward
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class
mxfusion.util.customop.
BroadcastToWithSamplesOpProp
(**kwargs)¶ Bases:
mxnet.operator.CustomOpProp
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list_arguments
()¶ list_arguments interface. Can override when creating new operators.
Returns: arguments – List of argument blob names. Return type: list
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list_outputs
()¶ list_outputs interface. Can override when creating new operators.
Returns: outputs – List of output blob names. Return type: list
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infer_shape
(in_shapes)¶ infer_shape interface. Can override when creating new operators.
Parameters: in_shape (list) – List of argument shapes in the same order as declared in list_arguments. Returns: - in_shape (list) – List of argument shapes. Can be modified from in_shape.
- out_shape (list) – List of output shapes calculated from in_shape, in the same order as declared in list_outputs.
- aux_shape (Optional, list) – List of aux shapes calculated from in_shape, in the same order as declared in list_auxiliary_states.
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create_operator
(ctx, in_shapes, in_dtypes, **kwargs)¶ Create an operator that carries out the real computation given the context, input shapes, and input data types.
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mxfusion.util.customop.
broadcast_to_w_samples
(F, data, shape, isSamples=True)¶