Padding Layers

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SpatialZeroPadding

Scala:

val spatialZeroPadding = SpatialZeroPadding(padLeft, padRight, padTop, padBottom)

Python:

spatialZeroPadding = SpatialZeroPadding(pad_left, pad_right, pad_top, pad_bottom)

Each feature map of a given input is padded with specified number of zeros.

If padding values are negative, then input will be cropped.

Scala example:

import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat
import com.intel.analytics.bigdl.nn._
import com.intel.analytics.bigdl.tensor._
val spatialZeroPadding = SpatialZeroPadding(1, 0, -1, 0)
val input = Tensor(3, 3, 3).rand()
> print(input)
(1,.,.) =
0.9494078   0.31556255  0.8432871   
0.0064580487    0.6487367   0.151881    
0.8822722   0.3634125   0.7034494   

(2,.,.) =
0.32691675  0.07487922  0.08813124  
0.4564806   0.37191486  0.05507739  
0.10097649  0.6589037   0.8721945   

(3,.,.) =
0.068939745 0.040364727 0.4893642   
0.39481318  0.17923461  0.15748173  
0.87117475  0.9933199   0.6097995

[com.intel.analytics.bigdl.tensor.DenseTensor$mcF$sp of size 3x3x3]

>  print(spatialZeroPadding.forward(input))
(1,.,.) =
0.0 0.0064580487    0.6487367   0.151881    
0.0 0.8822722   0.3634125   0.7034494   

(2,.,.) =
0.0 0.4564806   0.37191486  0.05507739  
0.0 0.10097649  0.6589037   0.8721945   

(3,.,.) =
0.0 0.39481318  0.17923461  0.15748173  
0.0 0.87117475  0.9933199   0.6097995   

[com.intel.analytics.bigdl.tensor.DenseTensor of size 3x2x4]

Python example:

from bigdl.nn.layer import *
spatialZeroPadding = SpatialZeroPadding(1, 0, -1, 0)
> spatialZeroPadding.forward(np.array([[[1, 2],[3, 4]],[[1, 2],[3, 4]]]))
[array([[[ 0.,  3.,  4.]],
       [[ 0.,  3.,  4.]]], dtype=float32)]

Padding

Scala:

val module = Padding(dim,pad,nInputDim,value=0.0,nIndex=1)

Python:

module = Padding(dim,pad,n_input_dim,value=0.0,n_index=1,bigdl_type="float")

This module adds pad units of padding to dimension dim of the input. If pad is negative, padding is added to the left, otherwise, it is added to the right of the dimension. The input to this layer is expected to be a tensor, or a batch of tensors; when using mini-batch, a batch of sample tensors will be passed to the layer and the user need to specify the number of dimensions of each sample tensor in the batch using nInputDims.

• @param dim the dimension to be applied padding operation
• @param pad num of the pad units
• @param nInputDim specify the number of dimensions that this module will receive If it is more than the dimension of input tensors, the first dimension would be considered as batch size
• @param value padding value, default is 0

Scala example:

val module = Padding(1,-1,3,value=0.0,nIndex=1)
val input = Tensor(3,2,1).rand()
input: com.intel.analytics.bigdl.tensor.Tensor[Float] =
(1,.,.) =
0.673425
0.9350421

(2,.,.) =
0.35407698
0.52607465

(3,.,.) =
0.7226349
0.70645845

[com.intel.analytics.bigdl.tensor.DenseTensor$mcF$sp of size 3x2x1]

module.forward(input)
res14: com.intel.analytics.bigdl.tensor.Tensor[Float] =
(1,.,.) =
0.0
0.0

(2,.,.) =
0.673425
0.9350421

(3,.,.) =
0.35407698
0.52607465

(4,.,.) =
0.7226349
0.70645845

[com.intel.analytics.bigdl.tensor.DenseTensor of size 4x2x1]

Python example:

module = Padding(1, -1, 3, value=0.0,n_index=1,bigdl_type="float")
input = np.random.rand(3, 2, 1)
array([[[ 0.81505274],
        [ 0.55769512]],

       [[ 0.13193961],
        [ 0.32610741]],

       [[ 0.29855582],
        [ 0.47394154]]])

module.forward(input)
array([[[ 0.        ],
        [ 0.        ]],

       [[ 0.81505275],
        [ 0.55769515]],

       [[ 0.1319396 ],
        [ 0.32610741]],

       [[ 0.29855582],
        [ 0.47394153]]], dtype=float32)