fangchangma / sparse-to-dense

ICRA 2018 "Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image" (Torch Implementation)
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question about test: #14

Closed angelbeibei closed 5 years ago

angelbeibei commented 5 years ago

Hi,when i run th main.lua -testOnly true -dataset nyudepthv2 -inputType rgbd -nSample 200 -criterion l1 -encoderType conv -decoderType upproj -pretrain true and then i met this error, can you help me?

/home/zhangwei/torch/install/bin/luajit: ...zhangwei/torch/install/share/lua/5.1/threads/threads.lua:183: [thread 2 callback] bad argument #2 to '?' (dimension 1 out of range of 0D tensor at /home/zhangwei/torch/pkg/torch/generic/Tensor.c:19) stack traceback: [C]: at 0x7f7b3ebbf1e0 [C]: in function 'xpcall' ...zhangwei/torch/install/share/lua/5.1/threads/threads.lua:234: in function 'callback' ...e/zhangwei/torch/install/share/lua/5.1/threads/queue.lua:65: in function <...e/zhangwei/torch/install/share/lua/5.1/threads/queue.lua:41> [C]: in function 'pcall' ...e/zhangwei/torch/install/share/lua/5.1/threads/queue.lua:40: in function 'dojob' [string " local Queue = require 'threads.queue'..."]:13: in main chunk stack traceback: [C]: in function 'error' ...zhangwei/torch/install/share/lua/5.1/threads/threads.lua:183: in function 'dojob' ...zhangwei/torch/install/share/lua/5.1/threads/threads.lua:264: in function 'synchronize' ...zhangwei/torch/install/share/lua/5.1/threads/threads.lua:142: in function 'specific' ...zhangwei/torch/install/share/lua/5.1/threads/threads.lua:125: in function 'Threads' ./dataloader.lua:47: in function '__init' /home/zhangwei/torch/install/share/lua/5.1/torch/init.lua:91: in function </home/zhangwei/torch/install/share/lua/5.1/torch/init.lua:87> [C]: in function 'DataLoader' ./dataloader.lua:26: in function 'create' main.lua:30: in main chunk [C]: in function 'dofile' ...gwei/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:150: in main chunk [C]: at 0x00405d50

fangchangma commented 5 years ago

I'm not sure what the problem was. Did you download both the data and the trained models?