Hi,
I've trained an encoder and a decoder, and the training configuration as following:

Encoder:

th run.lua
--dataset cv
--datapath data/CamVid/
--model models/encoder.lua
--save ../trained-model/encoder
--cachepath ../trained-model/encoder/cache_dataset/
--devid 1
--nGPU 1
--imHeight 360
--imWidth 480
--labelHeight 45
--labelWidth 60

Decoder:

th run.lua
--dataset cv
--datapath data/CamVid/
--model models/decoder.lua
--save ../trained-model/decoder
--cachepath ../trained-model/decoder/cache_dataset/
--CNNEncoder ../trained-model/encoder/model-best.net
--devid 1
--nGPU 1
--imHeight 360
--imWidth 480
--labelHeight 360
--labelWidth 480

I used following script to try to forward a single image in the CamVid dataset,

require "image"
require "nn"
require "cunn"
require "cudnn"

torch.setdefaulttensortype('torch.FloatTensor')

-- Get arguments
local opts = require "opts"
opt = opts.parse(arg)

-- Path to decoder/model-best.net and a single image in CamVid dataset
local model = opt.model
local img = opt.image

-- Load model from file
model = torch.load(model)
print(model)

-- Load image from file
img = image.load(img)
img = img:cuda()
input = {}
input[0] = img
print(input)

-- Forward the image and print out the result
local output = model:forward(input)
print(output)

, and I got the following error message:

My question is

1. Is there any mistakes when training encoder or decorder?
   Why "print(model)" only show the part of the network?
   (I DO NOT modify any lua files)

2. It seems the image is loaded succesfully,
   and I also create a 4-D tensor for the input.
   What's going wrong?

Many thanks.