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interpolating_decoder.lua~
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interpolating_decoder.lua~
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--- this script is divided in two parts based on an if-else statement
-- first part (encoder ==1) takes a trained model and a class model as input and saves the enocder part in a binary file
-- second part (encoder=0, decoder =1) takes input a mat file of
require 'cunn'
require 'nn'
matio = require 'matio'
---
encoder= 1;
class = 'bed'
model_no = '0051_1'
save_en_dir = ('/BS/deep_3d/work/deep_3d/fcn_3D/view-interp/'..class..'/encoded_desc/'..class..'_'..model_no..'.asc')
--parameters for decoder
mat_str = ('/BS/deep_3d/work/deep_3d/fcn_3D/Data-Mat-40/'..class..'/30/train/'..class..'_'..model_no..'.mat')
print(mat_str)
interp_str = ('/BS/deep_3d/work/deep_3d/fcn_3D/view-interp/'..class..'/encoded_desc/interpolated_desc.mat')
model = torch.load('mul-class/AE_6912_.1_10class_r/'..'model.net') --this loads the network
print(model)
desc_dims = 6912
--model = model:double()
input = {data = {}}
if encoder==1 then
print('removing the decoder part')
model:remove(15) --remove sigmoid as well
model:remove(14)
model:remove(13)
model:remove(12)
model:remove(11)
model:remove(10)
model:evaluate()
--for i =1,2 do
inputs = torch.Tensor(1,1,30,30,30)
inputs = inputs:cuda()
input = matio.load(mat_str, 'instance');
input = input:cuda()
inputs[1] = input
outputs = model:forward(inputs)
outputs = outputs:float()
--end
--outputs = torch.reshape(outputs,30,30,30)
--outputs = torch.squeeze(outputs)
--dims = outputs:nDimension()
--if dims > 1 then
--for i=1,math.floor(dims/2) do
--outputs=outputs:transpose(i, dims-i+1)
--end
--outputs = outputs:contiguous()
-- saving the descriptor (encoder) of an object instance
file = torch.DiskFile(save_en_dir, 'w')
file:writeObject(outputs)
file:close()
else
print('decoding the interpolated encoder')
model:remove(9)
model:remove(8)
model:remove(7)
model:remove(6)
model:remove(5)
model:remove(4)
model:remove(3)
model:remove(2)
model:remove(1)
model:evaluate()
print(model)
interp_desc = matio.load(interp_str , 'interp_desc') -- this is 2 by 6912
--interp_desc = matio.load('/BS/deep_3d/work/deep_3d/fcn_3D/view-interp/chair/encoded_desc/chair_25_3.mat', 'desc_6912')
total_desc = interp_desc:size()[1]
for i = 1,total_desc do
--inputs = torch.Tensor(1,6912)
--inputs = inputs:cuda()
--input = interp_desc:select(2,i) -- select second column
input = interp_desc[i]
--input = interp_desc
print(input:size())
input = input:cuda()
inputs = input
outputs = model:forward(input)
-- fwd the interpolaed ones to the network and save the output
outputs = torch.reshape(outputs,30,30,30)
outputs = torch.squeeze(outputs)
dims = outputs:nDimension()
if dims > 1 then
for i=1,math.floor(dims/2) do
outputs=outputs:transpose(i,dims-i+1)
end
outputs = outputs:contiguous()
end
idx = torch.range(1,total_desc)
save_str = ('/BS/deep_3d/work/deep_3d/fcn_3D/view-interp/'..class..'/interp_desc/'..class..'_int_'..idx[i]..'.asc')
print(save_str)
--file = torch.DiskFile('/BS/deep_3d/work/deep_3d/fcn_3D/view-interp/chair/interp_desc/chair_int_2.asc', 'w')
file = torch.DiskFile(save_str, 'w')
file:writeObject(outputs)
file:close()
end
end