In [5]:

    

require 'image';
paths.dofile('./../eval_helper.lua');
net = nil
collectgarbage()


    

In [6]:

    

--local net_dir = '/home/kivan/source/deep-learning/semantic_segmentation/output/nets/16s_SunMar615:58:072016/'
--local net_dir = '/home/kivan/source/deep-learning/semantic_segmentation/output/results/SunMar617:01:082016/' SunMar617:36:082016
--local net_dir = '/home/kivan/source/deep-learning/semantic_segmentation/output/nets/wgt100_SunMar617:36:082016/'
--local net_dir = '/home/kivan/source/deep-learning/semantic_segmentation/output/results/SatMar12_11:53:04/'
local net_dir = '/home/kivan/source/deep-learning/semantic_segmentation/output/nets/pyramid_2s_concat_TueMar15_13:31:21'
--local net_dir = '/home/kivan/source/deep-learning/semantic_segmentation/output/nets/wgt1000_MonMar711:18:232016/'
local model_path = net_dir .. '/model_copy.lua'
_, loss, train_container, validation_container = paths.dofile(model_path)
net = torch.load(net_dir .. "net.bin")
net:evaluate()
print(validation_container:size())


    

    




cannot open /home/kivan/source/deep-learning/semantic_segmentation/output/train_helper.lua: No such file or directory
stack traceback:
	[C]: in function 'dofile'
	.../nets/pyramid_2s_concat_TueMar15_13:31:21/model_copy.lua:18: in main chunk
	[C]: in function 'dofile'
	[string "--local net_dir = '/home/kivan/source/deep-le..."]:8: in main chunk
	[C]: in function 'xpcall'
	...ries/install/torch/install/share/lua/5.1/itorch/main.lua:209: in function <...ries/install/torch/install/share/lua/5.1/itorch/main.lua:173>
	...ries/install/torch/install/share/lua/5.1/lzmq/poller.lua:75: in function 'poll'
	...s/install/torch/install/share/lua/5.1/lzmq/impl/loop.lua:307: in function 'poll'
	...s/install/torch/install/share/lua/5.1/lzmq/impl/loop.lua:325: in function 'sleep_ex'
	...s/install/torch/install/share/lua/5.1/lzmq/impl/loop.lua:370: in function 'start'
	...ries/install/torch/install/share/lua/5.1/itorch/main.lua:381: in main chunk
	[C]: in function 'require'
	(command line):1: in main chunk
	[C]: at 0x00405be0

In [4]:

    

function ComputePrediction(net, data, name)
  --local out_dir = param_data_dir .. '/img/' .. name .. '/softmax_potentials/'
  --os.execute('mkdir -p ' .. out_dir)
  net:evaluate()
  local x, yt, weights, filename = data:GetNextBatch()
  local num_batches = 0
  while x do
    num_batches = num_batches + 1
    local y = net:forward(x)
    local _, pred = y:max(2)
    pred = pred[1][1]:int()
    print(pred:size())
    local rgb_img = image.load(param_data_dir .. '/../img/data/' .. name .. '/' .. filename:sub(1,filename:find('_')-1) .. '/' .. filename)
    local rgb_label = image.load(param_data_dir .. '/../img/labels/' .. name .. '/' .. filename:sub(1,filename:find('_')-1) .. '/' .. filename)
    itorch.image(rgb_label)
    itorch.image(rgb_img)
    local pred_rgb = DrawPrediction(pred)
    itorch.image(pred_rgb)
    yt = yt[1]:int()
    print(pred_rgb[1]:size())
    local mask = yt:eq(pred)
    mask[yt:eq(0)] = 1
    pred_rgb[1][mask] = 0
    pred_rgb[2][mask] = 0
    pred_rgb[3][mask] = 0
    --pred_rgb[{{},mask}] = 0
    itorch.image(pred_rgb)

    x, yt, weights, filename = data:GetNextBatch()
    collectgarbage()
  end
end


    

In [6]:

    

--ComputePrediction(net, validation_container, 'valid')
ComputePrediction(net, validation_container, 'val')


    

    




cannot open </home/kivan/datasets/Cityscapes/1024x464/torch//val/1_val_data.t7> in mode r  at /home/kivan/libraries/install/torch/pkg/torch/lib/TH/THDiskFile.c:640
stack traceback:
	[C]: at 0x7fa230b5b160
	[C]: in function 'DiskFile'
	...aries/install/torch/install/share/lua/5.1/torch/File.lua:388: in function 'load'
	...semantic_segmentation/torch/data_container_multifile.lua:60: in function 'ReadNextFile'
	...semantic_segmentation/torch/data_container_multifile.lua:77: in function 'GetNextBatch'
	[string "function ComputePrediction(net, data, name)..."]:5: in function 'f'
	[string "local f = function() return --ComputePredicti..."]:2: in main chunk
	[C]: in function 'xpcall'
	...ries/install/torch/install/share/lua/5.1/itorch/main.lua:209: in function <...ries/install/torch/install/share/lua/5.1/itorch/main.lua:173>
	...ries/install/torch/install/share/lua/5.1/lzmq/poller.lua:75: in function 'poll'
	...s/install/torch/install/share/lua/5.1/lzmq/impl/loop.lua:307: in function 'poll'
	...s/install/torch/install/share/lua/5.1/lzmq/impl/loop.lua:325: in function 'sleep_ex'
	...s/install/torch/install/share/lua/5.1/lzmq/impl/loop.lua:370: in function 'start'
	...ries/install/torch/install/share/lua/5.1/itorch/main.lua:381: in main chunk
	[C]: in function 'require'
	(command line):1: in main chunk
	[C]: at 0x00405be0

In [3]:

    

rgb = torch.randn(1,3,1632,736):cuda()
net:training()
out = net:forward(rgb)
net:backward(out, out)


    

In [ ]:

    




    

In [ ]:

    

print(net:get(38);


    

In [ ]:

    




    

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