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resnext.lua
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resnext.lua
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--
-- Copyright (c) 2017, Facebook, Inc.
-- All rights reserved.
--
-- This source code is licensed under the BSD-style license found in the
-- LICENSE file in the root directory of this source tree. An additional grant
-- of patent rights can be found in the PATENTS file in the same directory.
--
-- The ResNeXt model definition
--
local nn = require 'nn'
require 'cunn'
local Convolution = cudnn.SpatialConvolution
local Avg = cudnn.SpatialAveragePooling
local ReLU = cudnn.ReLU
local Max = nn.SpatialMaxPooling
local SBatchNorm = nn.SpatialBatchNormalization
local function createModel(opt)
local depth = opt.depth
local shortcutType = opt.shortcutType or 'B'
local iChannels
-- The shortcut layer is either identity or 1x1 convolution
local function shortcut(nInputPlane, nOutputPlane, stride)
local useConv = shortcutType == 'C' or
(shortcutType == 'B' and nInputPlane ~= nOutputPlane)
if useConv then
-- 1x1 convolution
return nn.Sequential()
:add(Convolution(nInputPlane, nOutputPlane, 1, 1, stride, stride))
:add(SBatchNorm(nOutputPlane))
elseif nInputPlane ~= nOutputPlane then
-- Strided, zero-padded identity shortcut
return nn.Sequential()
:add(nn.SpatialAveragePooling(1, 1, stride, stride))
:add(nn.Concat(2)
:add(nn.Identity())
:add(nn.MulConstant(0)))
else
return nn.Identity()
end
end
-- The original bottleneck residual layer
local function resnet_bottleneck(n, stride)
local nInputPlane = iChannels
iChannels = n * 4
local s = nn.Sequential()
s:add(Convolution(nInputPlane,n,1,1,1,1,0,0))
s:add(SBatchNorm(n))
s:add(ReLU(true))
s:add(Convolution(n,n,3,3,stride,stride,1,1))
s:add(SBatchNorm(n))
s:add(ReLU(true))
s:add(Convolution(n,n*4,1,1,1,1,0,0))
s:add(SBatchNorm(n * 4))
return nn.Sequential()
:add(nn.ConcatTable()
:add(s)
:add(shortcut(nInputPlane, n * 4, stride)))
:add(nn.CAddTable(true))
:add(ReLU(true))
end
-- The aggregated residual transformation bottleneck layer, Form (B)
local function split(nInputPlane, d, c, stride)
local cat = nn.ConcatTable()
for i=1,c do
local s = nn.Sequential()
s:add(Convolution(nInputPlane,d,1,1,1,1,0,0))
s:add(SBatchNorm(d))
s:add(ReLU(true))
s:add(Convolution(d,d,3,3,stride,stride,1,1))
s:add(SBatchNorm(d))
s:add(ReLU(true))
cat:add(s)
end
return cat
end
local function resnext_bottleneck_B(n, stride)
local nInputPlane = iChannels
iChannels = n * 4
local D = math.floor(n * (opt.baseWidth/64))
local C = opt.cardinality
local s = nn.Sequential()
s:add(split(nInputPlane, D, C, stride))
s:add(nn.JoinTable(2))
s:add(Convolution(D*C,n*4,1,1,1,1,0,0))
s:add(SBatchNorm(n*4))
return nn.Sequential()
:add(nn.ConcatTable()
:add(s)
:add(shortcut(nInputPlane, n * 4, stride)))
:add(nn.CAddTable(true))
:add(ReLU(true))
end
-- The aggregated residual transformation bottleneck layer, Form (C)
local function resnext_bottleneck_C(n, stride)
local nInputPlane = iChannels
iChannels = n * 4
local D = math.floor(n * (opt.baseWidth/64))
local C = opt.cardinality
local s = nn.Sequential()
s:add(Convolution(nInputPlane,D*C,1,1,1,1,0,0))
s:add(SBatchNorm(D*C))
s:add(ReLU(true))
s:add(Convolution(D*C,D*C,3,3,stride,stride,1,1,C))
s:add(SBatchNorm(D*C))
s:add(ReLU(true))
s:add(Convolution(D*C,n*4,1,1,1,1,0,0))
s:add(SBatchNorm(n*4))
return nn.Sequential()
:add(nn.ConcatTable()
:add(s)
:add(shortcut(nInputPlane, n * 4, stride)))
:add(nn.CAddTable(true))
:add(ReLU(true))
end
-- Creates count residual blocks with specified number of features
local function layer(block, features, count, stride)
local s = nn.Sequential()
for i=1,count do
s:add(block(features, i == 1 and stride or 1))
end
return s
end
local model = nn.Sequential()
local bottleneck
if opt.bottleneckType == 'resnet' then
bottleneck = resnet_bottleneck
print('Deploying ResNet bottleneck block')
elseif opt.bottleneckType == 'resnext_B' then
bottleneck = resnext_bottleneck_B
print('Deploying ResNeXt bottleneck block form B')
elseif opt.bottleneckType == 'resnext_C' then
bottleneck = resnext_bottleneck_C
print('Deploying ResNeXt bottleneck block form C (group convolution)')
else
error('invalid bottleneck type: ' .. opt.bottleneckType)
end
if opt.dataset == 'imagenet' then
-- Configurations for ResNet:
-- num. residual blocks, num features, residual block function
local cfg = {
[50] = {{3, 4, 6, 3}, 2048, bottleneck},
[101] = {{3, 4, 23, 3}, 2048, bottleneck},
[152] = {{3, 8, 36, 3}, 2048, bottleneck},
}
assert(cfg[depth], 'Invalid depth: ' .. tostring(depth))
local def, nFeatures, block = table.unpack(cfg[depth])
iChannels = 64
print(' | ResNet-' .. depth .. ' ImageNet')
-- The ResNet ImageNet model
model:add(Convolution(3,64,7,7,2,2,3,3))
model:add(SBatchNorm(64))
model:add(ReLU(true))
model:add(Max(3,3,2,2,1,1))
model:add(layer(block, 64, def[1]))
model:add(layer(block, 128, def[2], 2))
model:add(layer(block, 256, def[3], 2))
model:add(layer(block, 512, def[4], 2))
model:add(Avg(7, 7, 1, 1))
model:add(nn.View(nFeatures):setNumInputDims(3))
model:add(nn.Linear(nFeatures, 1000))
elseif opt.dataset == 'cifar10' or opt.dataset == 'cifar100' then
-- Model type specifies number of layers for CIFAR-10 and CIFAR-100 model
assert((depth - 2) % 9 == 0, 'depth should be one of 29, 38, 47, 56, 101')
local n = (depth - 2) / 9
iChannels = 64
print(' | ResNet-' .. depth .. ' ' .. opt.dataset)
model:add(Convolution(3,64,3,3,1,1,1,1))
model:add(SBatchNorm(64))
model:add(ReLU(true))
model:add(layer(bottleneck, 64, n, 1))
model:add(layer(bottleneck, 128, n, 2))
model:add(layer(bottleneck, 256, n, 2))
model:add(Avg(8, 8, 1, 1))
model:add(nn.View(1024):setNumInputDims(3))
local nCategories = opt.dataset == 'cifar10' and 10 or 100
model:add(nn.Linear(1024, nCategories))
else
error('invalid dataset: ' .. opt.dataset)
end
local function ConvInit(name)
for k,v in pairs(model:findModules(name)) do
local n = v.kW*v.kH*v.nOutputPlane
v.weight:normal(0,math.sqrt(2/n))
if cudnn.version >= 4000 then
v.bias = nil
v.gradBias = nil
else
v.bias:zero()
end
end
end
local function BNInit(name)
for k,v in pairs(model:findModules(name)) do
v.weight:fill(1)
v.bias:zero()
end
end
ConvInit('cudnn.SpatialConvolution')
ConvInit('nn.SpatialConvolution')
BNInit('fbnn.SpatialBatchNormalization')
BNInit('cudnn.SpatialBatchNormalization')
BNInit('nn.SpatialBatchNormalization')
for k,v in pairs(model:findModules('nn.Linear')) do
v.bias:zero()
end
model:type(opt.tensorType)
if opt.cudnn == 'deterministic' then
model:apply(function(m)
if m.setMode then m:setMode(1,1,1) end
end)
end
model:get(1).gradInput = nil
return model
end
return createModel