SpatialMaxPooling
Scala:
val mp = SpatialMaxPooling(2, 2, dW=2, dH=2, padW=0, padH=0)
Python:
mp = SpatialMaxPooling(2, 2, dw=2, dh=2, pad_w=0, pad_h=0, to_ceil=false)
Applies 2D max-pooling operation in kWxkH regions by step size dWxdH steps. The number of output features is equal to the number of input planes. If the input image is a 3D tensor nInputPlane x height x width, the output image size will be nOutputPlane x oheight x owidth where
- owidth = op((width + 2*padW - kW) / dW + 1)
- oheight = op((height + 2*padH - kH) / dH + 1)
op is a rounding operator. By default, it is floor. It can be changed by calling ceil() or floor() methods.
Scala example:
import com.intel.analytics.bigdl.nn.SpatialMaxPooling
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat
val mp = SpatialMaxPooling(2, 2, 2, 2)
val input = Tensor(1, 3, 3)
input(Array(1, 1, 1)) = 0.5336726f
input(Array(1, 1, 2)) = 0.7963769f
input(Array(1, 1, 3)) = 0.5674766f
input(Array(1, 2, 1)) = 0.1803996f
input(Array(1, 2, 2)) = 0.2460861f
input(Array(1, 2, 3)) = 0.2295625f
input(Array(1, 3, 1)) = 0.3073633f
input(Array(1, 3, 2)) = 0.5973460f
input(Array(1, 3, 3)) = 0.4298954f
val gradOutput = Tensor(1, 1, 1)
gradOutput(Array(1, 1, 1)) = 0.023921491578221f
val output = mp.forward(input)
val gradInput = mp.backward(input, gradOutput)
println(output)
println(gradInput)
The output is,
(1,.,.) =
0.7963769
[com.intel.analytics.bigdl.tensor.DenseTensor of size 1x1x1]
The gradInput is,
(1,.,.) =
0.0 0.023921492 0.0
0.0 0.0 0.0
0.0 0.0 0.0
[com.intel.analytics.bigdl.tensor.DenseTensor of size 1x3x3]
Python example:
from bigdl.nn.layer import *
from bigdl.nn.criterion import *
from bigdl.optim.optimizer import *
from bigdl.util.common import *
mp = SpatialMaxPooling(2, 2, 2, 2)
input = np.array([0.5336726, 0.7963769, 0.5674766, 0.1803996, 0.2460861, 0.2295625, 0.3073633, 0.5973460, 0.4298954]).astype("float32")
input = input.reshape(1, 3, 3)
output = mp.forward(input)
print output
gradOutput = np.array([0.023921491578221]).astype("float32")
gradOutput = gradOutput.reshape(1, 1, 1)
gradInput = mp.backward(input, gradOutput)
print gradInput
The output is,
[array([[[ 0.79637688]]], dtype=float32)]
The gradInput is,
[array([[[ 0. , 0.02392149, 0. ],
[ 0. , 0. , 0. ],
[ 0. , 0. , 0. ]]], dtype=float32)]
SpatialAveragePooling
Scala:
val m = SpatialAveragePooling(kW, kH, dW=1, dH=1, padW=0, padH=0, ceilMode=false, countIncludePad=true, divide=true)
Python:
m = SpatialAveragePooling(kw, kh, dw=1, dh=1, pad_w=0, pad_h=0,ceil_mode=False, count_include_pad=True, divide=True)
SpatialAveragePooling is a module that applies 2D average-pooling operation in kWxkH regions by step size dWxdH.
The number of output features is equal to the number of input planes.
Scala example:
scala>
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat
import com.intel.analytics.bigdl.nn._
import com.intel.analytics.bigdl.tensor._
val input = Tensor(1, 3, 3).randn()
val m = SpatialAveragePooling(3, 2, 2, 1)
val output = m.forward(input)
val gradOut = Tensor(1, 2, 1).randn()
val gradIn = m.backward(input,gradOut)
scala> print(input)
(1,.,.) =
0.9916249 1.0299556 0.5608558
-0.1664829 1.5031902 0.48598626
0.37362042 -0.0966136 -1.4257964
[com.intel.analytics.bigdl.tensor.DenseTensor$mcF$sp of size 1x3x3]
scala> print(output)
(1,.,.) =
0.7341883
0.1123173
[com.intel.analytics.bigdl.tensor.DenseTensor of size 1x2x1]
scala> print(gradOut)
(1,.,.) =
-0.42837557
-1.5104272
[com.intel.analytics.bigdl.tensor.DenseTensor$mcF$sp of size 1x2x1]
scala> print(gradIn)
(1,.,.) =
-0.071395926 -0.071395926 -0.071395926
-0.3231338 -0.3231338 -0.3231338
-0.25173786 -0.25173786 -0.25173786
[com.intel.analytics.bigdl.tensor.DenseTensor of size 1x3x3]
Python example:
from bigdl.nn.layer import *
import numpy as np
input = np.random.randn(1,3,3)
print "input is :",input
m = SpatialAveragePooling(3,2,2,1)
out = m.forward(input)
print "output of m is :",out
grad_out = np.random.rand(1,3,1)
grad_in = m.backward(input,grad_out)
print "grad input of m is :",grad_in
produces output:
input is : [[[ 1.50602425 -0.92869054 -1.9393117 ]
[ 0.31447547 0.63450578 -0.92485516]
[-2.07858315 -0.05688643 0.73648798]]]
creating: createSpatialAveragePooling
output of m is : [array([[[-0.22297533],
[-0.22914261]]], dtype=float32)]
grad input of m is : [array([[[ 0.06282618, 0.06282618, 0.06282618],
[ 0.09333335, 0.09333335, 0.09333335],
[ 0.03050717, 0.03050717, 0.03050717]]], dtype=float32)]
VolumetricMaxPooling
Scala:
val layer = VolumetricMaxPooling(
kernelT, kernelW, kernelH,
strideT, strideW, strideH,
paddingT, paddingW, paddingH
)
Python:
layer = VolumetricMaxPooling(
kernelT, kernelW, kernelH,
strideT, strideW, strideH,
paddingT, paddingW, paddingH
)
Applies 3D max-pooling operation in kT x kW x kH regions by step size dT x dW x dH. The number of output features is equal to the number of input planes / dT. The input can optionally be padded with zeros. Padding should be smaller than half of kernel size. That is, padT < kT/2, padW < kW/2 and padH < kH/2
The input layout should be [batch, plane, time, height, width] or [plane, time, height, width]
Scala example:
import com.intel.analytics.bigdl.nn._
import com.intel.analytics.bigdl.utils.T
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat
val layer = VolumetricMaxPooling(
2, 2, 2,
1, 1, 1,
0, 0, 0
)
val input = Tensor(T(T(
T(
T(1.0f, 2.0f, 3.0f),
T(4.0f, 5.0f, 6.0f),
T(7.0f, 8.0f, 9.0f)
),
T(
T(4.0f, 5.0f, 6.0f),
T(1.0f, 3.0f, 9.0f),
T(2.0f, 3.0f, 7.0f)
)
)))
layer.forward(input)
layer.backward(input, Tensor(T(T(T(
T(0.1f, 0.2f),
T(0.3f, 0.4f)
)))))
Its output should be
(1,1,.,.) =
5.0 9.0
8.0 9.0
[com.intel.analytics.bigdl.tensor.DenseTensor of size 1x1x2x2]
(1,1,.,.) =
0.0 0.0 0.0
0.0 0.1 0.0
0.0 0.3 0.4
(1,2,.,.) =
0.0 0.0 0.0
0.0 0.0 0.2
0.0 0.0 0.0
[com.intel.analytics.bigdl.tensor.DenseTensor of size 1x2x3x3]
Python example:
from bigdl.nn.layer import VolumetricMaxPooling
import numpy as np
layer = VolumetricMaxPooling(
2, 2, 2,
1, 1, 1,
0, 0, 0
)
input = np.array([[
[
[1.0, 2.0, 3.0],
[4.0, 5.0, 6.0],
[7.0, 8.0, 9.0]
],
[
[4.0, 5.0, 6.0],
[1.0, 3.0, 9.0],
[2.0, 3.0, 7.0]
]
]])
layer.forward(input)
layer.backward(input, np.array([[[
[0.1, 0.2],
[0.3, 0.4]
]]]))
Its output should be
array([[[[ 5., 9.],
[ 8., 9.]]]], dtype=float32)
array([[[[ 0. , 0. , 0. ],
[ 0. , 0.1 , 0. ],
[ 0. , 0.30000001, 0.40000001]],
[[ 0. , 0. , 0. ],
[ 0. , 0. , 0.2 ],
[ 0. , 0. , 0. ]]]], dtype=float32)
RoiPooling
Scala:
val m = RoiPooling(pooled_w, pooled_h, spatial_scale)
Python:
m = RoiPooling(pooled_w, pooled_h, spatial_scale)
RoiPooling is a module that performs Region of Interest pooling.
It uses max pooling to convert the features inside any valid region of interest into a small feature map with a fixed spatial extent of pooledH × pooledW (e.g., 7 × 7).
An RoI is a rectangular window into a conv feature map. Each RoI is defined by a four-tuple (x1, y1, x2, y2) that specifies its top-left corner (x1, y1) and its bottom-right corner (x2, y2).
RoI max pooling works by dividing the h × w RoI window into an pooledH × pooledW grid of sub-windows of approximate size h/H × w/W and then max-pooling the values in each sub-window into the corresponding output grid cell. Pooling is applied independently to each feature map channel
forward accepts a table containing 2 tensors as input, the first tensor is the input image, the second tensor is the ROI regions. The dimension of the second tensor should be (*,5) (5 are batch_num, x1, y1, x2, y2).
Scala example:
scala>
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric.NumericFloat
import com.intel.analytics.bigdl.nn._
import com.intel.analytics.bigdl.tensor._
import com.intel.analytics.bigdl.tensor.Storage
val input_data = Tensor(2,2,6,8).randn()
val rois = Array(0, 0, 0, 7, 5, 1, 6, 2, 7, 5, 1, 3, 1, 6, 4, 0, 3, 3, 3, 3)
val input_rois = Tensor(Storage(rois.map(x => x.toFloat))).resize(4, 5)
val input = T(input_data,input_rois)
val m = RoiPooling(3, 2, 1)
val output = m.forward(input)
scala> print(input)
{
2: 0.0 0.0 0.0 7.0 5.0
1.0 6.0 2.0 7.0 5.0
1.0 3.0 1.0 6.0 4.0
0.0 3.0 3.0 3.0 3.0
[com.intel.analytics.bigdl.tensor.DenseTensor$mcF$sp of size 4x5]
1: (1,1,.,.) =
0.48066297 1.0994664 0.32474303 2.3391871 -0.79605865 0.836963950.36107457 1.2622415
0.657079 0.12720469 0.39894578 -0.41185552 -0.53111094 -0.36016005 -0.9726423 -2.5785272
0.3091435 -0.03613516 0.2375721 -1.1920663 -0.6757661 1.10612681.5409279 -0.17411499
0.23274016 -0.7149633 0.5473105 -0.40570387 -1.7966263 0.2071798-1.1530842 -0.010083453
-1.5769979 0.17043112 -0.28578365 -0.90779626 0.61457515 -0.1553582-0.3912479 -0.15326484
-0.24283029 1.3215472 1.3795123 -0.36933053 0.7077386 -0.56398267 0.6159163 0.5802894
(1,2,.,.) =
-1.1817129 -0.20470592 -1.3201113 0.36523122 -0.18260211 1.30210171.214403 1.1019816
0.7186407 0.78731173 1.5452348 0.0396181 0.5927014 1.17697431.0501136 -0.58295316
-0.96753055 0.6427254 -1.1396345 0.8701054 -0.22860864 -1.18719451.3372624 0.8616691
0.796831 -0.16609778 0.2950535 0.4595303 0.192339 0.6086106-0.76351887 -0.65964174
-0.12746814 -0.036058053 0.8858275 0.9677718 -1.1074747 -1.36859390.8783633 -0.11723315
-0.6947403 -0.23226547 -1.8510057 -1.3695518 -0.22317407 -0.36249024 -0.24097045 1.5691053
(2,1,.,.) =
0.84056973 1.144949 -1.0660682 0.4416162 -0.94440234 -0.24461010.91145027 -0.88650835
-0.81542057 0.14578317 -0.6531974 0.60776395 -0.32058007 -1.80771481.7660322 1.0680646
1.1328241 0.43677545 -0.9402618 -1.3002211 0.26012567 1.69481340.37849447 0.39286092
1.9443163 0.5415504 1.0793099 1.3312546 0.48346 1.2019655 0.3718734 0.21091922
0.5499047 1.6418253 0.8064177 0.37626198 0.8736181 -0.40816033 -0.5806787 1.286581
-0.5904657 -0.21188398 -0.040509004 1.2989452 1.6827602 1.3229258-0.68433124 0.87974
(2,2,.,.) =
-0.09759476 -0.32767114 0.16223079 2.3114302 -0.48496276 1.19290720.8572289 0.43429425
-1.0245247 0.19002944 1.5659521 -1.3689835 -1.4437296 -0.38216656 0.6333655 -0.57124794
-0.31111157 1.5184602 -1.3835855 -0.9295573 2.244521 -1.11849820.5451996 -0.4441631
-1.534093 -0.5599659 1.1980947 -1.0140935 1.3288999 0.19487387-0.1261734 -1.2222558
-0.070535585 0.9047848 -0.6719811 -1.6532638 -0.5290511 -0.18300447 0.69385433 0.018756092
0.24767837 0.620484 -0.5346291 1.0685066 -0.36903372 -0.26955062 1.1042496 0.5944603
[com.intel.analytics.bigdl.tensor.DenseTensor$mcF$sp of size 2x2x6x8]
}
scala> print(output)
(1,1,.,.) =
1.0994664 2.3391871 1.5409279
1.3795123 1.3795123 0.6159163
(1,2,.,.) =
1.5452348 1.5452348 1.3372624
0.8858275 0.9677718 1.5691053
(2,1,.,.) =
0.37849447 0.39286092 0.39286092
-0.5806787 1.286581 1.286581
(2,2,.,.) =
0.5451996 0.5451996 -0.4441631
1.1042496 1.1042496 0.5944603
(3,1,.,.) =
0.60776395 1.6948134 1.7660322
1.3312546 1.2019655 1.2019655
(3,2,.,.) =
2.244521 2.244521 0.6333655
1.3288999 1.3288999 0.69385433
(4,1,.,.) =
-0.40570387 -0.40570387 -0.40570387
-0.40570387 -0.40570387 -0.40570387
(4,2,.,.) =
0.4595303 0.4595303 0.4595303
0.4595303 0.4595303 0.4595303
[com.intel.analytics.bigdl.tensor.DenseTensor of size 4x2x2x3]
Python example:
from bigdl.nn.layer import *
import numpy as np
input_data = np.random.randn(2,2,6,8)
input_rois = np.array([0, 0, 0, 7, 5, 1, 6, 2, 7, 5, 1, 3, 1, 6, 4, 0, 3, 3, 3, 3],dtype='float64').reshape(4,5)
print "input is :",[input_data, input_rois]
m = RoiPooling(3,2,1.0)
out = m.forward([input_data,input_rois])
print "output of m is :",out
produces output:
input is : [array([[[[ 0.08500103, 0.33421796, 0.29084699, 1.60344635, -0.24289341,
-0.4793888 , 0.09452426, 0.16842477],
[-1.18575497, -0.53337542, 0.11661001, 0.9647904 , -0.25187936,
0.36516823, -0.16647209, -0.08095158],
[ 1.1982232 , -0.33549174, 0.11721347, -0.29319686, -0.01290122,
0.12344296, 0.30074829, -2.34951463],
[-0.60470899, -0.84657051, 0.1269276 , -0.06152321, -1.68838416,
-0.69808296, -2.06112892, -1.44790449],
[ 1.03944288, 0.13871728, 0.91478479, 0.47517105, 1.24638374,
0.98666841, 0.49403488, 1.26101127],
[-1.03949343, -0.39291108, 1.39107512, 1.73779253, 0.91656129,
0.103381 , 0.956243 , 0.44743548]],
[[ 0.79028054, 0.64244228, -0.37997334, -0.09130215, -2.3903429 ,
0.71919208, -0.14079786, 0.98304272],
[ 1.14678457, 1.58825227, 0.17137367, -0.62121819, -0.36103113,
-0.04452576, -0.0886136 , -1.32884721],
[ 0.06728957, -0.29701304, -0.52754207, -1.5785875 , 1.47354834,
-0.28545156, 0.49874194, 0.10277613],
[-0.10117571, -1.34902427, -1.40789327, 0.09853599, 0.60420022,
0.54869115, -0.49067696, 0.26696793],
[ 1.11780279, -0.77929016, 1.13772094, 0.14374057, 0.33199688,
-0.54057374, -0.45718861, 1.1577623 ],
[-1.4005645 , 1.15870496, 0.39292003, 0.88379515, 0.06440974,
0.65013063, 0.03759244, 0.18730126]]],
[[[-2.28272906, 0.06056305, 0.73632597, 0.10063274, -1.27497525,
-0.95597581, -0.22745785, 0.40146498],
[-1.37783475, 1.66000653, -1.80071745, -0.11805539, -0.27160583,
0.30691418, 2.62243232, -1.95274516],
[ 1.61364148, 1.91470546, -1.51984424, 2.13598224, -0.23156685,
-0.74203698, 0.65316888, 0.08018846],
[-1.8912854 , -0.50106158, 0.94937966, -0.10930541, 0.82136627,
-1.33209063, 1.43371302, -1.36370916],
[-0.52737928, -0.0681305 , -0.63472587, 0.41979229, -0.57093624,
-0.15968764, -1.005951 , -2.06873572],
[-2.34089346, 1.02593977, 0.90183415, 0.09504819, 0.53185448,
1.11305345, 1.290016 , -1.76216646]],
[[-0.10885459, -0.57089742, -0.55340708, -1.94445884, 1.30130049,
0.6333372 , -1.03100083, 0.0111167 ],
[ 0.59678149, -0.67601521, -1.25288718, -0.10922251, 3.06808996,
-1.46701513, -0.42140765, 1.12485412],
[ 1.21301567, -1.43304957, -0.56047239, 0.20716087, 1.40737646,
-0.08386437, -0.21916043, 0.85692906],
[ 1.59992399, -1.37044315, -0.71884386, 2.61830979, -0.74305496,
-0.32021174, 1.43275058, -0.3891857 ],
[-0.41355145, 0.22589689, 0.33154415, 0.86146815, -1.66326091,
0.37581697, -3.2250516 , -0.48807863],
[-2.52968957, 0.95801598, -1.20118154, 0.01141421, -0.11871498,
0.04555184, 1.3950473 , 0.37887998]]]]), array([[ 0., 0., 0., 7., 5.],
[ 1., 6., 2., 7., 5.],
[ 1., 3., 1., 6., 4.],
[ 0., 3., 3., 3., 3.]])]
creating: createRoiPooling
output of m is : [[[[ 1.19822323 1.60344636 0.36516821]
[ 1.39107513 1.73779249 1.26101124]]
[[ 1.58825231 1.47354829 0.98304272]
[ 1.158705 1.13772094 1.15776229]]]
[[[ 1.43371308 1.43371308 0.08018846]
[ 1.29001606 1.29001606 -1.7621665 ]]
[[ 1.43275058 1.43275058 0.85692906]
[ 1.39504731 1.39504731 0.37887999]]]
[[[ 2.13598228 0.30691418 2.62243223]
[ 0.82136625 0.82136625 1.43371308]]
[[ 3.06808996 3.06808996 -0.08386437]
[ 2.61830974 0.37581697 1.43275058]]]
[[[-0.06152321 -0.06152321 -0.06152321]
[-0.06152321 -0.06152321 -0.06152321]]
[[ 0.09853599 0.09853599 0.09853599]
[ 0.09853599 0.09853599 0.09853599]]]]