Minor modifications and formatting changes to conv1d layers

Author: AnirudhBHarish

c_reference/include/conv1d.h

@@ -6,8 +6,8 @@
 
 /**
  * @brief Model parameters for the 1D Convolution Layer
- * @var   W    pointer to convolutional weights W, size for regular = out_channels*in_channels*kernel_size, size for depth based = out_channels*kernel_size
- * @var   B    pointer to the bias vector for the convolution, shape = [out_channels]
+ * @var   W    pointer to convolution weights W, size for regular = out_channels*in_channels*kernel_size, size for depth based = out_channels*kernel_size
+ * @var   B    pointer to the bias vector for the convolution, size = out_channels
  */
 typedef struct ConvLayers_Params {
   float* W;
@@ -17,14 +17,14 @@ typedef struct ConvLayers_Params {
 /**
  * @brief Model definition for the 1D Convolution Layer
  * @param[out]   output_signal    pointer to the output signal, size = out_time * out_channels
- * @param[in]    out_time            number of time steps in the output
+ * @param[in]    out_time         number of time steps in the output
  * @param[in]    out_channels     number of output channels for the output of the conv layer
  * @param[in]    input_signal     pointer to the input signal. size = in_time * in_channels
- * @param[in]    in_time             number of time steps in the input
+ * @param[in]    in_time          number of time steps in the input
  * @param[in]    in_channels      number of input channels
  * @param[in]    padding          padding applied to the input before the conv is performed.
  *                                Note: padding is applied to both the starting and ending of the input, along the time axis
- *                                E.g : padding = 3, the input is padded with zeros(for 3 time steps), both before the input_signal(time step 0) and after the input_signal(teim step in_time).
+ *                                E.g : padding = 3, the input is padded with zeros(for 3 time steps), both before the input_signal(time step 0) and after the input_signal(time step in_time).
  * 
  * @param[in]    kernel_size      kernel size of the conv filter
  * @param[in]    params           weights, bias and other essential parameters used to describe the layer
@@ -35,19 +35,19 @@ typedef struct ConvLayers_Params {
  *                                3: relu
  */
 int conv1d(float *output_signal, unsigned out_time, unsigned out_channels, const float *input_signal, 
-  unsigned in_time, unsigned in_channels, int padding, unsigned kernel_size, 
+  unsigned in_time, unsigned in_channels, unsigned padding, unsigned kernel_size, 
   const void* params, int activations);
 
 /**
  * @brief Model definition for the 1D Depthwise Convolution Layer
  * @param[out]   output_signal    pointer to the output signal, size = out_time * in_channels
- * @param[in]    out_time            number of time steps in the output
+ * @param[in]    out_time         number of time steps in the output
  * @param[in]    input_signal     pointer to the input signal. size = in_time * in_channels
- * @param[in]    in_time             number of time steps in the input
+ * @param[in]    in_time          number of time steps in the input
  * @param[in]    in_channels      number of input channels. The output will have the same number of channels
  * @param[in]    padding          padding applied to the input before the conv is performed.
  *                                Note: padding is applied to both the starting and ending of the input, along the time axis
- *                                E.g : padding = 3, the input is padded with zeros(for 3 time steps), both before the input_signal(time step 0) and after the input_signal(teim step in_time).
+ *                                E.g : padding = 3, the input is padded with zeros(for 3 time steps), both before the input_signal(time step 0) and after the input_signal(time step in_time).
  * 
  * @param[in]    kernel_size      kernel size of the conv filter
  * @param[in]    params           weights, bias and other essential parameters used to describe the layer
@@ -58,7 +58,7 @@ int conv1d(float *output_signal, unsigned out_time, unsigned out_channels, const
  *                                3: relu
  */
 int conv1d_depth(float *output_signal, unsigned out_time, const float *input_signal, 
-  unsigned in_time, unsigned in_channels, int padding, unsigned kernel_size, 
+  unsigned in_time, unsigned in_channels, unsigned padding, unsigned kernel_size, 
   const void* params, int activations);
 
 
@@ -67,7 +67,7 @@ int conv1d_depth(float *output_signal, unsigned out_time, const float *input_sig
  * @var    W1      pointer to the 1st low-rank component of the weights, size = out_channels * rank
  * @var    W2      pointer to the 2nd low-rank component of the weights, size for regular = rank * in_channels * kernel_size, size for depthwise = rank * kernel_size
  * @var    B       pointer to the bias vector for the convolution, shape = [out_channels]
- * @var    rank    rank of the weight tensor. A low rank decomposition typically used to reduce computation and storage
+ * @var    rank    rank of the weight tensor. A low-rank decomposition typically used to reduce computation and storage
  */
 typedef struct ConvLayers_LR_Params {
   float* W1;
@@ -77,17 +77,17 @@ typedef struct ConvLayers_LR_Params {
 } ConvLayers_LR_Params;
 
 /**
- * @brief Model definition for the 1D Low Rank Convolution Layer
- * @brief Identical to the non-low-rank form. One modification is the mulitplication of the weights handeled within the layer
+ * @brief Model definition for the 1D Low-Rank Convolution Layer
+ * @brief Identical to the non-low-rank form. One modification is the multiplication of the weights handled within the layer
  * @param[out]   output_signal    pointer to the output signal, size = out_time * out_channels
- * @param[in]    out_time            number of time steps in the output
+ * @param[in]    out_time         number of time steps in the output
  * @param[in]    out_channels     number of output channels for the ouput of the conv layer
  * @param[in]    input_signal     pointer to the input signal. size = in_time * in_channels
- * @param[in]    in_time             number of time steps in the input
+ * @param[in]    in_time          number of time steps in the input
  * @param[in]    in_channels      number of input channels
  * @param[in]    padding          padding applied to the input before the conv is performed.
  *                                Note: padding is applied to both the starting and ending of the input, along the time axis
- *                                E.g : padding = 3, the input is padded with zeros(for 3 time steps), both before the input_signal(time step 0) and after the input_signal(teim step in_time).
+ *                                E.g : padding = 3, the input is padded with zeros(for 3 time steps), both before the input_signal(time step 0) and after the input_signal(time step in_time).
  * 
  * @param[in]    kernel_size      kernel size of the conv filter
  * @param[in]    params           weights, bias and other essential parameters used to describe the layer
@@ -98,20 +98,20 @@ typedef struct ConvLayers_LR_Params {
  *                                3: relu
  */
 int conv1d_lr(float *output_signal, unsigned out_time, unsigned out_channels, const float *input_signal, 
-  unsigned in_time, unsigned in_channels, int padding, unsigned kernel_size, 
+  unsigned in_time, unsigned in_channels, unsigned padding, unsigned kernel_size, 
   const void* params, int activations);
 
 /**
- * @brief Model definition for the 1D Depthwise Convolution Layer
- * @brief Identical to the non-low-rank form. One modification is the mulitplication of the weights handeled witin the layer
+ * @brief Model definition for the 1D Low-Rank Depthwise Convolution Layer
+ * @brief Identical to the non-low-rank form. One modification is the multiplication of the weights handled within the layer
  * @param[out]   output_signal    pointer to the output signal, size = out_time * in_channels
- * @param[in]    out_time            number of time steps in the output
+ * @param[in]    out_time         number of time steps in the output
  * @param[in]    input_signal     pointer to the input signal. size = in_time * in_channels
- * @param[in]    in_time             number of time steps in the input
+ * @param[in]    in_time          number of time steps in the input
  * @param[in]    in_channels      number of input channels. The output will have the same number of channels
  * @param[in]    padding          padding applied to the input before the conv is performed.
  *                                Note: padding is applied to both the starting and ending of the input, along the time axis
- *                                E.g : padding = 3, the input is padded with zeros(for 3 time steps), both before the input_signal(time step 0) and after the input_signal(teim step in_time).
+ *                                E.g : padding = 3, the input is padded with zeros(for 3 time steps), both before the input_signal(time step 0) and after the input_signal(time step in_time).
  * 
  * @param[in]    kernel_size      kernel size of the conv filter
  * @param[in]    params           weights, bias and other essential parameters used to describe the layer
@@ -122,20 +122,20 @@ int conv1d_lr(float *output_signal, unsigned out_time, unsigned out_channels, co
  *                                3: relu
  */
 int conv1d_depth_lr(float *output_signal, unsigned out_time, const float *input_signal, 
-  unsigned in_time, unsigned in_channels, int padding, unsigned kernel_size, 
+  unsigned in_time, unsigned in_channels, unsigned padding, unsigned kernel_size, 
   const void* params, int activations);
 
-// Auxillary Layers
+// Auxiliary Layers
 /**
  * @brief Model definition for the 1D Average Pooling Layer
- * @param[out]   output_signal    pointer to the output signal, size = out_time * in_channels. Provide Null/0 incase of in-place computation
- * @param[in]    out_time            number of time steps in the output
+ * @param[out]   output_signal    pointer to the output signal, size = out_time * in_channels. Provide Null/0 in case of in-place computation
+ * @param[in]    out_time         number of time steps in the output
  * @param[in]    input_signal     pointer to the input signal. size = in_time * in_channels
- * @param[in]    in_time             number of time steps in the input
+ * @param[in]    in_time          number of time steps in the input
  * @param[in]    in_channels      number of input channels. The output will have the same number of channels
  * @param[in]    padding          padding applied to the input before the conv is performed.
  *                                Note: padding is applied to both the starting and ending of the input, along the time axis
- *                                E.g : padding = 3, the input is padded with zeros(for 3 time steps), both before the input_signal(time step 0) and after the input_signal(teim step in_time).
+ *                                E.g : padding = 3, the input is padded with zeros(for 3 time steps), both before the input_signal(time step 0) and after the input_signal(time step in_time).
  * 
  * @param[in]    kernel_size      kernel size of the pool filter
  * @param[in]    activations      an integer to choose the type of activation function.
@@ -145,13 +145,13 @@ int conv1d_depth_lr(float *output_signal, unsigned out_time, const float *input_
  *                                3: relu
  */
 int avgpool1d(float *output_signal, unsigned out_time, const float *input_signal, unsigned in_time, unsigned in_channels, 
-  int padding, unsigned kernel_size, int activations);
+  unsigned padding, unsigned kernel_size, int activations);
 
 /**
  * @brief Model definition for the 1D batch Normalization Layer
- * @param[out]   output_signal    pointer to the output signal, size = out_time * in_channels. Provide Null/0 incase of in-place computation
+ * @param[out]   output_signal    pointer to the output signal, size = out_time * in_channels. Provide Null/0 in case of in-place computation
  * @param[in]    input_signal     pointer to the input signal. size = in_time * in_channels
- * @param[in]    in_time             number of time steps in the input
+ * @param[in]    in_time          number of time steps in the input
  * @param[in]    in_channels      number of input channels. The output will have the same number of channels
  * @param[in]    mean             pointer to the mean for the batch normalization, size = in_channels
  * @param[in]    var              pointer to the variance for the batch normalization, size = in_channels
@@ -160,6 +160,8 @@ int avgpool1d(float *output_signal, unsigned out_time, const float *input_signal
  * @param[in]    beta             pointer to the scalar offsets for the post-norm affine operation, size = in_channels
  * @param[in]    in_place         in-place computation of the batchnorm i.e. the output is stored in-place of the input signal. Storage efficient
  * @param[in]    eps              a very small +ve value to avoid division by 0. For the default value, assign = 0.00001
+ * 
+ * NOTE: if affine is equated to False(0), then null-pointers(or 0) can be passed for the gamma and beta arguments
  */
 int batchnorm1d(float* output_signal, float* input_signal, unsigned in_time, unsigned in_channels, 
   float* mean, float* var, unsigned affine, float* gamma , float * beta, unsigned in_place, float eps);