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preprocess.lua
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preprocess.lua
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require 'torch'
require 'cunn'
require 'nn'
require 'cudnn'
require 'nngraph'
require 'loadcaffe'
local vgg_mean = {103.939, 116.779, 123.68}
function preprocess_image(img)
nb = img:size(1)
local mean = img.new(vgg_mean):view(1, 3, 1, 1):expandAs(img)
return img:add(1):mul(127.5):add(-1, mean)
end
function createVggmodel()
input = nn.Identity()()
conv1_1 = cudnn.SpatialConvolution(3, 64, 3, 3, 1, 1, 1, 1)(input) -- 2
relu1_1 = cudnn.ReLU(true)(conv1_1)
conv1_2 = cudnn.SpatialConvolution(64, 64, 3, 3, 1, 1, 1, 1)(relu1_1)
relu1_2 = cudnn.ReLU(true)(conv1_2)
pool1 = cudnn.SpatialMaxPooling(2, 2, 2, 2)(relu1_2)
conv2_1 = cudnn.SpatialConvolution(64, 128, 3, 3, 1, 1, 1, 1)(pool1) -- 7
relu2_1 = cudnn.ReLU(true)(conv2_1)
conv2_2 = cudnn.SpatialConvolution(128, 128, 3, 3, 1, 1, 1, 1)(relu2_1)
relu2_2 = cudnn.ReLU(true)(conv2_2)
pool2 = cudnn.SpatialMaxPooling(2, 2, 2, 2)(relu2_2)
conv3_1 = cudnn.SpatialConvolution(128, 256, 3, 3, 1, 1, 1, 1)(pool2) -- 12
relu3_1 = cudnn.ReLU(true)(conv3_1)
conv3_2 = cudnn.SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1)(relu3_1)
relu3_2 = cudnn.ReLU(true)(conv3_2)
-- conv3_3 = cudnn.SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1)(relu3_2)
-- relu3_3 = cudnn.ReLU(true)(conv3_3)
-- conv3_4 = cudnn.SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1)(relu3_3)
-- relu3_4 = cudnn.ReLU(true)(conv3_4)
-- pool3 = cudnn.SpatialMaxPooling(2, 2, 2, 2)(relu3_4)
-- conv4_1 = cudnn.SpatialConvolution(256, 512, 3, 3, 1, 1, 1, 1)(pool3) -- 21
-- relu4_1 = cudnn.ReLU(true)(conv4_1)
-- conv4_2 = cudnn.SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1)(relu4_1)
-- relu4_2 = cudnn.ReLU(true)(conv4_2)
-- conv4_3 = cudnn.SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1)(relu4_2)
-- relu4_3 = cudnn.ReLU(true)(conv4_3)
-- conv4_4 = cudnn.SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1)(relu4_3)
-- relu4_4 = cudnn.ReLU(true)(conv4_4)
-- pool4 = cudnn.SpatialMaxPooling(2, 2, 2, 2)(relu4_4)
-- conv5_1 = cudnn.SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1)(pool4) -- 30
-- relu5_1 = cudnn.ReLU(true)(conv5_1)
-- conv5_2 = cudnn.SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1)(relu5_1)
-- relu5_2 = cudnn.ReLU(true)(conv5_2)
-- conv5_3 = cudnn.SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1)(relu5_2)
-- relu5_3 = cudnn.ReLU(true)(conv5_3)
-- conv5_4 = cudnn.SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1)(relu5_3)
-- relu5_4 = cudnn.ReLU(true)(conv5_4)
-- pool5 = cudnn.SpatialMaxPooling(2, 2, 2, 2)(relu5_4)
Vgg_model = nn.gModule({input},{relu3_2})
-- Vgg_model = nn.gModule({input},{relu4_1})
-- print(Vgg_model.modules[2].weight)
-- print(torch.type(Vgg_model.modules[2]))
-- for i, node in pairs(Vgg_model.modules[2]) do
-- print(i)
-- end
Vgg_model:cuda()
proto_file = 'dataset/VGG_ILSVRC_19_layers_deploy.prototxt'
model_file = 'dataset/VGG_ILSVRC_19_layers.caffemodel'
loadcaffe_backend = 'cudnn'
local cnn = loadcaffe.load(proto_file, model_file, loadcaffe_backend):cuda()
--print(cnn) -- org cnn
while #cnn > 21 do
cnn:remove(#cnn)
end
--print(cnn) -- crop cnn
local num_layers = #Vgg_model.modules
print(num_layers)
for i = 2,num_layers do
if torch.type(cnn:get(i-1)) == 'cudnn.SpatialConvolution' then
local w = cnn:get(i-1).weight:clone()
local b = cnn:get(i-1).bias:clone()
Vgg_model.modules[i].weight = w:cuda()
Vgg_model.modules[i].bias = b:cuda()
--print(torch.all(torch.eq(cnn:get(i-1).weight, Vgg_model.modules[i].weight)))
end
end
--[[
Vgg_model:zeroGradParameters()
cnn:zeroGradParameters()
Vgg_model:evaluate()
cnn:evaluate()
a = torch.CudaTensor(1,3,32,32):fill(0)
t = torch.CudaTensor(1,512,4,4):fill(1)
-- a = preprocess_image(a)
cri1 = nn.MSECriterion():cuda()
b1 = Vgg_model:forward(a)
c1 = cri1:forward(b1,t)
d1 = cri1:backward(b1,t)
e1 = Vgg_model:backward(a,d1)
cri2 = nn.MSECriterion():cuda()
b2 = cnn:forward(a)
c2 = cri2:forward(b2,t)
d2 = cri2:backward(b2,t)
e2 = cnn:backward(a,d2)
print(b1,b2)
print(#e1)
if torch.all(torch.eq(e1, e2)) then
print('they are the same')
else
print(torch.sum(e1-e2))
--print(e1[1][2][2],e2[1][2][2])
end
--]]
return Vgg_model
end
--Vgg_model = createVggmodel()
--a = torch.CudaTensor(1,3,32,32):fill(0)
--b1 = Vgg_model:forward(a)
--print(b1[4][{{1},{512}}])
function createVggmodel_trim()
input = nn.Identity()()
conv1_1 = cudnn.SpatialConvolution(3, 64, 3, 3, 1, 1)(input) -- 2
relu1_1 = cudnn.ReLU(true)(conv1_1)
conv1_2 = cudnn.SpatialConvolution(64, 64, 3, 3, 1, 1)(relu1_1)
relu1_2 = cudnn.ReLU(true)(conv1_2)
pool1 = cudnn.SpatialMaxPooling(2, 2, 2, 2)(relu1_2)
conv2_1 = cudnn.SpatialConvolution(64, 128, 3, 3, 1, 1)(pool1) -- 7
relu2_1 = cudnn.ReLU(true)(conv2_1)
conv2_2 = cudnn.SpatialConvolution(128, 128, 3, 3, 1, 1)(relu2_1)
relu2_2 = cudnn.ReLU(true)(conv2_2)
pool2 = cudnn.SpatialMaxPooling(2, 2, 2, 2)(relu2_2)
conv3_1 = cudnn.SpatialConvolution(128, 256, 3, 3, 1, 1)(pool2) -- 12
relu3_1 = cudnn.ReLU(true)(conv3_1)
conv3_2 = cudnn.SpatialConvolution(256, 256, 3, 3, 1, 1)(relu3_1)
relu3_2 = cudnn.ReLU(true)(conv3_2)
-- conv3_3 = cudnn.SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1, 1)(relu3_2)
-- relu3_3 = cudnn.ReLU(true)(conv3_3)
-- conv3_4 = cudnn.SpatialConvolution(256, 256, 3, 3, 1, 1, 1, 1, 1)(relu3_3)
-- relu3_4 = cudnn.ReLU(true)(conv3_4)
-- pool3 = cudnn.SpatialMaxPooling(2, 2, 2, 2)(relu3_4)
-- conv4_1 = cudnn.SpatialConvolution(256, 512, 3, 3, 1, 1, 1, 1, 1)(pool3) -- 21
-- relu4_1 = cudnn.ReLU(true)(conv4_1)
-- conv4_2 = cudnn.SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1, 1)(relu4_1)
-- relu4_2 = cudnn.ReLU(true)(conv4_2)
-- conv4_3 = cudnn.SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1, 1)(relu4_2)
-- relu4_3 = cudnn.ReLU(true)(conv4_3)
-- conv4_4 = cudnn.SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1, 1)(relu4_3)
-- relu4_4 = cudnn.ReLU(true)(conv4_4)
-- pool4 = cudnn.SpatialMaxPooling(2, 2, 2, 2)(relu4_4)
-- conv5_1 = cudnn.SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1, 1)(pool4) -- 30
-- relu5_1 = cudnn.ReLU(true)(conv5_1)
-- conv5_2 = cudnn.SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1, 1)(relu5_1)
-- relu5_2 = cudnn.ReLU(true)(conv5_2)
-- conv5_3 = cudnn.SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1, 1)(relu5_2)
-- relu5_3 = cudnn.ReLU(true)(conv5_3)
-- conv5_4 = cudnn.SpatialConvolution(512, 512, 3, 3, 1, 1, 1, 1, 1)(relu5_3)
-- relu5_4 = cudnn.ReLU(true)(conv5_4)
-- pool5 = cudnn.SpatialMaxPooling(2, 2, 2, 2)(relu5_4)
Vgg_model = nn.gModule({input},{relu3_2})
-- Vgg_model = nn.gModule({input},{relu4_1})
-- print(Vgg_model.modules[2].weight)
-- print(torch.type(Vgg_model.modules[2]))
-- for i, node in pairs(Vgg_model.modules[2]) do
-- print(i)
-- end
Vgg_model:cuda()
proto_file = 'dataset/VGG_ILSVRC_19_layers_deploy.prototxt'
model_file = 'dataset/VGG_ILSVRC_19_layers.caffemodel'
loadcaffe_backend = 'cudnn'
local cnn = loadcaffe.load(proto_file, model_file, loadcaffe_backend):cuda()
--print(cnn) -- org cnn
while #cnn > 21 do
cnn:remove(#cnn)
end
--print(cnn) -- crop cnn
local num_layers = #Vgg_model.modules
print(num_layers)
for i = 2,num_layers do
if torch.type(cnn:get(i-1)) == 'cudnn.SpatialConvolution' then
local w = cnn:get(i-1).weight:clone()
local b = cnn:get(i-1).bias:clone()
Vgg_model.modules[i].weight = w:cuda()
Vgg_model.modules[i].bias = b:cuda()
--print(torch.all(torch.eq(cnn:get(i-1).weight, Vgg_model.modules[i].weight)))
end
end
return Vgg_model
end