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prepro_img_residule.lua

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+require 'nn'
+require 'optim'
+require 'torch'
+require 'math'
+require 'cunn'
+require 'cutorch'
+require 'image'
+require 'hdf5'
+cjson=require('cjson') 
+require 'xlua'
+require 'cudnn'
+local t = require 'transforms'
+
+-------------------------------------------------------------------------------
+-- Input arguments and options
+-------------------------------------------------------------------------------
+cmd = torch.CmdLine()
+cmd:text()
+cmd:text('Options')
+cmd:option('-input_json','data_prepro_0417.json','path to the json file containing vocab and answers')
+cmd:option('-image_root','/home/zwang15/data/visual7w/images/','path to the image root')
+
+cmd:option('-residule_path', 'resnet-200.t7')
+cmd:option('-batch_size', 10, 'batch_size')
+
+cmd:option('-out_name_train', 'data/spatial_data_img_residule_train.h5', 'output name')
+cmd:option('-out_name_test', 'data/spatial_data_img_residule_test.h5', 'output name')
+
+cmd:option('-gpuid', 1, 'which gpu to use. -1 = use CPU')
+cmd:option('-backend', 'cudnn', 'nn|cudnn')
+
+opt = cmd:parse(arg)
+print(opt)
+
+--cutorch.setDevice(opt.gpuid)
+local model = torch.load(opt.residule_path)
+
+
+for i = 14,12,-1 do
+    model:remove(i)
+end
+print(model)
+model:evaluate()
+model=model:cuda()
+
+-- The model was trained with this input normalization
+local meanstd = {
+   mean = { 0.485, 0.456, 0.406 },
+   std = { 0.229, 0.224, 0.225 },
+}
+
+local transform = t.Compose{
+   t.ColorNormalize(meanstd)
+}
+
+imloader={}
+function imloader:load(fname)
+    self.im=image.load(fname)
+end
+function loadim(imname)
+
+    imloader:load(imname,  3, 'float')
+    im=imloader.im
+    im = image.scale(im, 448, 448)
+
+    if im:size(1)==1 then
+        im2=torch.cat(im,im,1)
+        im2=torch.cat(im2,im,1)
+        im=im2
+    elseif im:size(1)==4 then
+        im=im[{{1,3},{},{}}]
+    end
+
+    im = transform(im)
+    return im
+end
+
+local image_root = opt.image_root
+-- open the mdf5 file
+
+local file = io.open(opt.input_json, 'r')
+local text = file:read()
+file:close()
+json_file = cjson.decode(text)
+
+local img_list_train={}
+for i,imname in pairs(json_file['unique_img_train']) do
+    table.insert(img_list_train, image_root .. imname)
+end
+
+--print(json_file['uniuqe_img_test'])
+local img_list_test={}
+for i,imname in pairs(json_file['unique_img_test']) do
+    table.insert(img_list_test, image_root .. imname)
+end
+
+local ndims=4096
+local batch_size = opt.batch_size
+local sz=#img_list_train
+local feat_train=torch.FloatTensor(sz, 14, 14, 2048) --ndims)
+print(string.format('processing %d images...',sz))
+for i=1,sz,batch_size do
+    xlua.progress(i, sz)
+    r=math.min(sz,i+batch_size-1)
+    local ims=torch.CudaTensor(r-i+1,3,448,448)
+    for j=1,r-i+1 do
+        ims[j]=loadim(img_list_train[i+j-1]):cuda()
+    end
+    local output = model:forward(ims)
+    feat_train[{{i,r},{}}]=output:permute(1,3,4,2):contiguous():float()
+    collectgarbage()
+end
+
+local train_h5_file = hdf5.open(opt.out_name_train, 'w')
+train_h5_file:write('/images_train', feat_train)
+train_h5_file:close()
+
+
+local ndims=4096
+local batch_size = opt.batch_size
+local sz=#img_list_test
+local feat_test=torch.FloatTensor(sz,14, 14, 2048)
+print(string.format('processing %d images...',sz))
+for i=1,sz,batch_size do
+    xlua.progress(i, sz)
+    r=math.min(sz,i+batch_size-1)
+    local ims=torch.CudaTensor(r-i+1,3,448,448)
+    for j=1,r-i+1 do
+        ims[j]=loadim(img_list_test[i+j-1]):cuda()
+    end
+    local output = model:forward(ims)
+    feat_test[{{i,r},{}}]=output:permute(1,3,4,2):contiguous():float()
+    collectgarbage()
+end
+
+local test_h5_file = hdf5.open(opt.out_name_test, 'w')
+test_h5_file:write('/images_test', feat_test)
+test_h5_file:close()