Evision v0.1.17

View Source Evision.DNN.Layer (Evision v0.1.17)

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Types

t()

Type that represents an Evision.DNN.Layer struct.

Functions

finalize(self, inputs)

Computes and sets internal parameters according to inputs, outputs and blobs.

finalize(self, inputs, opts)

Computes and sets internal parameters according to inputs, outputs and blobs.

get_blobs(self)
get_name(self)
get_preferableTarget(self)
get_type(self)
outputNameToIndex(self, outputName)

Returns index of output blob in output array.

run(self, inputs, internals)

Allocates layer and computes output.

run(self, inputs, internals, opts)

Allocates layer and computes output.

set_blobs(self, prop)

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t()

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@type t() :: %Evision.DNN.Layer{ref: reference()}

Type that represents an Evision.DNN.Layer struct.

  • ref. reference()

    The underlying erlang resource variable.

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finalize(self, inputs)

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@spec finalize(t(), [Evision.Mat.maybe_mat_in()]) ::
  [Evision.Mat.t()] | {:error, String.t()}

Computes and sets internal parameters according to inputs, outputs and blobs.

Positional Arguments
  • self: Evision.DNN.Layer.t()
  • inputs: [Evision.Mat]
Return

  • outputs: [Evision.Mat].

    vector of already allocated output blobs

If this method is called after network has allocated all memory for input and output blobs and before inferencing.

Python prototype (for reference only):

finalize(inputs[, outputs]) -> outputs
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finalize(self, inputs, opts)

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@spec finalize(t(), [Evision.Mat.maybe_mat_in()], [{atom(), term()}, ...] | nil) ::
  [Evision.Mat.t()] | {:error, String.t()}

Computes and sets internal parameters according to inputs, outputs and blobs.

Positional Arguments
  • self: Evision.DNN.Layer.t()
  • inputs: [Evision.Mat]
Return

  • outputs: [Evision.Mat].

    vector of already allocated output blobs

If this method is called after network has allocated all memory for input and output blobs and before inferencing.

Python prototype (for reference only):

finalize(inputs[, outputs]) -> outputs
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get_blobs(self)

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@spec get_blobs(t()) :: [Evision.Mat.t()]
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get_name(self)

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@spec get_name(t()) :: binary()
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get_preferableTarget(self)

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@spec get_preferableTarget(t()) :: integer()
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get_type(self)

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@spec get_type(t()) :: binary()
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outputNameToIndex(self, outputName)

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@spec outputNameToIndex(t(), binary()) :: integer() | {:error, String.t()}

Returns index of output blob in output array.

Positional Arguments
  • self: Evision.DNN.Layer.t()
  • outputName: String
Return
  • retval: int

@see inputNameToIndex()

Python prototype (for reference only):

outputNameToIndex(outputName) -> retval
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run(self, inputs, internals)

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@spec run(t(), [Evision.Mat.maybe_mat_in()], [Evision.Mat.maybe_mat_in()]) ::
  {[Evision.Mat.t()], [Evision.Mat.t()]} | {:error, String.t()}

Allocates layer and computes output.

Positional Arguments
  • self: Evision.DNN.Layer.t()
  • inputs: [Evision.Mat]
Return
  • outputs: [Evision.Mat].
  • internals: [Evision.Mat]

@deprecated This method will be removed in the future release.

Python prototype (for reference only):

run(inputs, internals[, outputs]) -> outputs, internals
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run(self, inputs, internals, opts)

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@spec run(
  t(),
  [Evision.Mat.maybe_mat_in()],
  [Evision.Mat.maybe_mat_in()],
  [{atom(), term()}, ...] | nil
) :: {[Evision.Mat.t()], [Evision.Mat.t()]} | {:error, String.t()}

Allocates layer and computes output.

Positional Arguments
  • self: Evision.DNN.Layer.t()
  • inputs: [Evision.Mat]
Return
  • outputs: [Evision.Mat].
  • internals: [Evision.Mat]

@deprecated This method will be removed in the future release.

Python prototype (for reference only):

run(inputs, internals[, outputs]) -> outputs, internals
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set_blobs(self, prop)

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@spec set_blobs(t(), [Evision.Mat.maybe_mat_in()]) :: t()