Update doc

README.md

@@ -81,6 +81,9 @@ Please check [examples](https://github.com/majianjia/nnom/tree/master/examples)
 | SoftMax|Beta | SoftMax()| Softmax only has layer API| 
 | Activation|Beta| Activation()|A layer instance for activation|
 | Input/Output |Beta | Input()/Output()| |
+| Up Sampling | Beta|UpSample()||
+| Zero Padding | Beta |ZeroPadding()||
+| Cropping | Beta |Cropping()||
 
 **RNN Layers**
 
@@ -90,7 +93,6 @@ Please check [examples](https://github.com/majianjia/nnom/tree/master/examples)
 | Simple RNN | Under Dev. | SimpleCell()| Under Developpment |
 | Gated Recurrent Network (GRU)| Under Dev. | GRUCell()| Under Developpment |
 
-
 **Activations**
 
 Activation can be used by itself as layer, or can be attached to the previous layer as ["actail"](docs/A_Temporary_Guide_to_NNoM.md#addictionlly-activation-apis) to reduce memory cost.
@@ -111,7 +113,6 @@ Activation can be used by itself as layer, or can be attached to the previous la
 | Global Max Pooling  | Beta|GlobalMaxPool()||
 | Global Average Pooling | Beta|GlobalAvgPool()||
 | Global Sum Pooling | Beta|GlobalSumPool()|A better alternative to Global average pooling in MCU before Softmax|
-| Up Sampling | Beta|UpSample()||
 
 **Matrix Operations Layers**
 
@@ -122,7 +123,6 @@ Activation can be used by itself as layer, or can be attached to the previous la
 | Addition  | Beta|Add()||
 | Substraction  | Beta|Sub()||
 
-
 ## Dependencies
 
 NNoM now use the local pure C backend implementation by default. Thus, there is no special dependency needed. 

docs/api_layers.md

@@ -104,7 +104,6 @@ This funtion is for 1D or 2D, mutiple channels depthwise convolution.
 
 When it is used for 1D convolution, the H should be set to 1 constantly in kernel and stride.  
 
-
 ---
 
 ## Dense()
@@ -124,7 +123,6 @@ A fully connected layer. It will flatten the data if the last output is mutiple-
 
 - The layer instance
 
-
 ---
 
 ## UpSample()
@@ -137,21 +135,55 @@ A basic up sampling, using nearest interpolation
 
 **Arguments**
 
-- **kernel:** a shape subject return by `kernel()`, the interpolation size.
+- **kernel:** a shape object returned by `kernel()`, the interpolation size.
 
 **Return**
 
 - The layer instance
 
+---
+
+## ZeroPadding()
 
+~~~C
+nnom_layer_t *ZeroPadding(nnom_border_t pad);
+~~~
+
+Pad zeros to the image for each edge (top, bottom, left, right)
+
+**Arguments**
+
+- **pad:** a border object returned by `border()`, contains top, bottom, left and right padding.
+
+**Return**
+
+- The layer instance
+
+---
+
+## Cropping()
+
+~~~C
+nnom_layer_t *Cropping(nnom_border_t pad);
+~~~
+
+It crops along spatial dimensions.
+
+**Arguments**
+
+- **pad:** a border object returned by `border()`, contains top, bottom, left and right size.
+
+**Return**
+
+- The layer instance
 
 ---
 
 ## Lambda()
 
 ~~~C
-// Lambda Layers
-// layer.run()   , required
+
+// layer.run()   , compulsory
 // layer.oshape(), optional, call default_output_shape() if left NULL
 // layer.free()  , optional, called while model is deleting, to free private resources
 // parameters    , private parameters for run method, left NULL if not needed.
@@ -165,9 +197,9 @@ Lambda layer is an anonymous layer (interface), which allows user to do customiz
 
 **Arguments**
 
-- **(*run)(nnom_layer_t *):** or so called run method, is the method to do the customized operation. 
-- **(*oshape)(nnom_layer_t *):** is to calculate the output shape according to the input shape during compiling. If this method is not presented, the input shape will be passed to the output shape.   
-- **(*free)(nnom_layer_t *):** is to free the resources allocated by users. this will be called when deleting models. Leave it NULL if no resources need to be released. 
+- **`(*run)(nnom_layer_t *)`:** or so called run method, is the method to do the customized operation. 
+- **`(*oshape)(nnom_layer_t *)`:** is to calculate the output shape according to the input shape during compiling. If this method is not presented, the input shape will be passed to the output shape.   
+- **`(*free)(nnom_layer_t *)`:** is to free the resources allocated by the users. This method will be called when the model is deleting. Leave it NULL if no resources need to be released. 
 - **parameters:** is the pointer to user configurations. User can access to it in all three methods above.
 
 **Return**
@@ -176,6 +208,7 @@ Lambda layer is an anonymous layer (interface), which allows user to do customiz
 
 **Notes**
 
+- All methods with type `nnom_status_t` must return `NN_SUCCESS` to allow the inference process. Any return other than that will stop the inference of the model. 
 - When `oshape()` is presented, please refer to examples of other similar layers. The shape passing must be handle carefully.
 - This method is called in compiling, thus it can also do works other than calculating output shape only. An exmaple is the `global_pooling_output_shape()` fills in the parameters left by `GlobalXXXPool()`
 

docs/api_properties.md

@@ -5,8 +5,11 @@ Properties include some basic properties such as shape of the data buffer, Q-for
 
 ---
 
-## typesdef
+## Typedef
+
 ~~~C
+#define nnom_shape_data_t uint16_t
+
 typedef struct _nnom_shape
 {
 	nnom_shape_data_t h, w, c;
@@ -29,6 +32,11 @@ typedef struct _nnom_qformat
 	int8_t n, m;
 } nnom_qformat_t;
 
+typedef struct _nnom_border_t
+{
+	nnom_shape_data_t top, bottom, left, right;
+} nnom_border_t;
+
 ~~~
 
 ---
@@ -46,7 +54,7 @@ nnom_shape_t shape(size_t h, size_t w, size_t c);
 **Arguments**
 
 - ** h:** size of H, or number of row, or y axis in image. 
-- ** w:** size of W, or number of row, or x axis in image.
+- ** w:** size of W, or number of column, or x axis in image.
 - ** c:** size of channel. 
 
 **Return**
@@ -66,7 +74,7 @@ Use in pooling or convolutional layer to specified the kernel size.
 **Arguments**
 
 - ** h:** size of kernel in H, or number of row, or y axis in image. 
-- ** w:** size of kernel in W, or number of row, or x axis in image.
+- ** w:** size of kernel in W, or number of column, or x axis in image.
 
 **Return**
 
@@ -85,7 +93,28 @@ Use in pooling or convolutional layer to specified the stride size.
 **Arguments**
 
 - ** h:** size of stride in H, or number of row, or y axis in image. 
-- ** w:** size of stride in  W, or number of row, or x axis in image.
+- ** w:** size of stride in  W, or number of column, or x axis in image.
+
+**Return**
+
+- A shape instance. 
+
+---
+
+## border() 
+
+~~~C
+nnom_border_t border(size_t top, size_t bottom, size_t left, size_t right);
+~~~
+
+It pack the 4 padding/cropping value to a border object. 
+
+**Arguments**
+
+- ** top:** the padding/cropping at the top edge of the image.
+- ** bottom:** the padding/cropping at the bottom edge of the image.
+- ** left:** the padding/cropping at the left edge of the image.
+- ** right:** the padding/cropping at the right edge of the image.
 
 **Return**
 

docs/index.md

@@ -104,12 +104,15 @@ Check [Porting and optimising Guide](Porting_and_Optimisation_Guide.md) for deta
 | Fully-connected | Beta| Dense()| |
 | Lambda |Alpha| Lambda() |single input / single output anonymous operation| 
 | Batch Normalization |Beta | N/A| This layer is merged to the last Conv by the script|
-| Input/Output |Beta | Input()/Output()| |
 | Flatten|Beta | Flatten()| |
 | SoftMax|Beta | SoftMax()| Softmax only has layer API| 
 | Activation|Beta| Activation()|A layer instance for activation|
+| Input/Output |Beta | Input()/Output()| |
+| Up Sampling | Beta|UpSample()||
+| Zero Padding | Beta |ZeroPadding()||
+| Cropping | Beta |Cropping()||
 
-** RNN Layers **
+**RNN Layers**
 
 | Layers | Status |Layer API|Comments|
 | ------ |-- |--|--|
@@ -137,7 +140,6 @@ Activation can be used by itself as layer, or can be attached to the previous la
 | Global Max Pooling  | Beta|GlobalMaxPool()||
 | Global Average Pooling | Beta|GlobalAvgPool()||
 | Global Sum Pooling | Beta|GlobalSumPool()|A better alternative to Global average pooling in MCU before Softmax|
-| Up Sampling | Beta|UpSample()||
 
 **Matrix Operations Layers**
 
@@ -147,6 +149,3 @@ Activation can be used by itself as layer, or can be attached to the previous la
 | Multiple  |Beta |Mult()||
 | Addition  | Beta|Add()||
 | Substraction  | Beta|Sub()||
-
-
-