A fully connected layer in a network definition. This layer expects an input tensor of three or more non-batch dimensions. The input is automatically reshaped into an MxV tensor X, where V is a product of the last three dimensions and M is a product of the remaining dimensions (where the product over 0 dimensions is defined as 1). For example:
More...
#include <NvInfer.h>
Public Member Functions | |
| virtual void | setNbOutputChannels (int nbOutputs)=0 |
Set the number of output channels K from the fully connected layer. More... | |
| virtual int | getNbOutputChannels () const =0 |
Get the number of output channels K from the fully connected layer. More... | |
| virtual void | setKernelWeights (Weights weights)=0 |
Set the kernel weights, given as a KxC matrix in row-major order. More... | |
| virtual Weights | getKernelWeights () const =0 |
| Get the kernel weights. More... | |
| virtual void | setBiasWeights (Weights weights)=0 |
| Set the bias weights. More... | |
| virtual Weights | getBiasWeights () const =0 |
| Get the bias weights. More... | |
| void | setInput (int index, ITensor &tensor) _TENSORRT_OVERRIDE=0 |
| Append or replace an input of this layer with a specific tensor. More... | |
 Public Member Functions inherited from nvinfer1::ILayer | |
| virtual LayerType | getType () const =0 |
| Return the type of a layer. More... | |
| virtual void | setName (const char *name)=0 |
| Set the name of a layer. More... | |
| virtual const char * | getName () const =0 |
| Return the name of a layer. More... | |
| virtual int | getNbInputs () const =0 |
| Get the number of inputs of a layer. | |
| virtual ITensor * | getInput (int index) const =0 |
| Get the layer input corresponding to the given index. More... | |
| virtual int | getNbOutputs () const =0 |
| Get the number of outputs of a layer. | |
| virtual ITensor * | getOutput (int index) const =0 |
| Get the layer output corresponding to the given index. More... | |
| virtual void | setPrecision (DataType dataType)=0 |
| Set the computational precision of this layer. More... | |
| virtual DataType | getPrecision () const =0 |
| get the computational precision of this layer More... | |
| virtual bool | precisionIsSet () const =0 |
| whether the computational precision has been set for this layer More... | |
| virtual void | resetPrecision ()=0 |
| reset the computational precision for this layer More... | |
| virtual void | setOutputType (int index, DataType dataType)=0 |
| Set the output type of this layer. More... | |
| virtual DataType | getOutputType (int index) const =0 |
| get the output type of this layer More... | |
| virtual bool | outputTypeIsSet (int index) const =0 |
| whether the output type has been set for this layer More... | |
| virtual void | resetOutputType (int index)=0 |
| reset the output type for this layer More... | |
Detailed Description
A fully connected layer in a network definition. This layer expects an input tensor of three or more non-batch dimensions. The input is automatically reshaped into an MxV tensor X, where V is a product of the last three dimensions and M is a product of the remaining dimensions (where the product over 0 dimensions is defined as 1). For example:
- If the input tensor has shape
{C, H, W}, then the tensor is reshaped into{1, C*H*W}. - If the input tensor has shape
{P, C, H, W}, then the tensor is reshaped into{P, C*H*W}.
The layer then performs the following operation:
Where X is the MxV tensor defined above, W is the KxV weight tensor of the layer, and bias is a row vector size K that is broadcasted to MxK. K is the number of output channels, and configurable via setNbOutputChannels(). If bias is not specified, it is implicitly 0.
The MxK result Y is then reshaped such that the last three dimensions are {K, 1, 1} and the remaining dimensions match the dimensions of the input tensor. For example:
- If the input tensor has shape
{C, H, W}, then the output tensor will have shape{K, 1, 1}. - If the input tensor has shape
{P, C, H, W}, then the output tensor will have shape{P, K, 1, 1}.
- Warning
- Do not inherit from this class, as doing so will break forward-compatibility of the API and ABI.
Member Function Documentation
◆ getBiasWeights()
|
pure virtual |
Get the bias weights.
- See also
- setBiasWeightsWeights()
◆ getKernelWeights()
|
pure virtual |
Get the kernel weights.
- See also
- setKernelWeights()
◆ getNbOutputChannels()
|
pure virtual |
Get the number of output channels K from the fully connected layer.
- See also
- setNbOutputChannels()
◆ setBiasWeights()
|
pure virtual |
Set the bias weights.
Bias is optional. To omit bias, set the count value in the weights structure to zero.
- See also
- getBiasWeightsWeights()
◆ setInput()
|
pure virtual |
Append or replace an input of this layer with a specific tensor.
- Parameters
-
index the index of the input to modify. tensor the new input tensor
For a IFullyConnectedLayer, only index 0 is valid unless explicit precision mode is enabled. With explicit precision mode, values 0-1 are valid where value 1 overrides kernel weights. Kernel weights tensor (computed at build-time) must be an output of dequantize scale layer (i.e. a scale layer with int8 input and float output) in explicit precision network. Conversely, this input tensor can be overridden via appropriate set call. The indices are as follows:
Index | Description 0 | The input activation tensor. 1 | The kernel weights tensor (a constant tensor).
If this function is called with a value greater than 0, then the function getNbInputs() changes
Implements nvinfer1::ILayer.
◆ setKernelWeights()
|
pure virtual |
Set the kernel weights, given as a KxC matrix in row-major order.
- See also
- getKernelWeights()
◆ setNbOutputChannels()
|
pure virtual |
Set the number of output channels K from the fully connected layer.
If executing this layer on DLA, number of output channels must in the range [1,8192].
- See also
- getNbOutputChannels()
The documentation for this class was generated from the following file:
