{"action":"created","issue":{"url":"https://api.github.com/repos/dmlc/cxxnet/issues/125","labels_url":"https://api.github.com/repos/dmlc/cxxnet/issues/125/labels{/name}","comments_url":"https://api.github.com/repos/dmlc/cxxnet/issues/125/comments","events_url":"https://api.github.com/repos/dmlc/cxxnet/issues/125/events","html_url":"https://github.com/dmlc/cxxnet/issues/125","id":72712737,"number":125,"title":"libpng error: Too many IDATs found!","user":{"login":"Adesun","id":5615543,"avatar_url":"https://avatars.githubusercontent.com/u/5615543?v=3","gravatar_id":"","url":"https://api.github.com/users/Adesun","html_url":"https://github.com/Adesun","followers_url":"https://api.github.com/users/Adesun/followers","following_url":"https://api.github.com/users/Adesun/following{/other_user}","gists_url":"https://api.github.com/users/Adesun/gists{/gist_id}","starred_url":"https://api.github.com/users/Adesun/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Adesun/subscriptions","organizations_url":"https://api.github.com/users/Adesun/orgs","repos_url":"https://api.github.com/users/Adesun/repos","events_url":"https://api.github.com/users/Adesun/events{/privacy}","received_events_url":"https://api.github.com/users/Adesun/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":4,"created_at":"2015-05-02T22:57:01Z","updated_at":"2015-05-03T00:45:35Z","closed_at":null,"body":"it displayed error as follows  when training cifar images.\r\n\r\ninitializing end, start working\r\nround        0:[     100] 56 sec elapsedlibpng error: Too many IDATs found\r\nOpenCV Error: Assertion failed (src.cols > 0 && src.rows > 0) in cv::warpAffine,\r\n file C:\\builds\\2_4_PackSlave-win64-vc12-shared\\opencv\\modules\\imgproc\\src\\imgwa\r\nrp.cpp, line 3591\r\n\r\nit's werid that the format of cifar image is jpg ! \r\nwhat should I do next?\r\nthanks!\r\n"},"comment":{"url":"https://api.github.com/repos/dmlc/cxxnet/issues/comments/98422407","html_url":"https://github.com/dmlc/cxxnet/issues/125#issuecomment-98422407","issue_url":"https://api.github.com/repos/dmlc/cxxnet/issues/125","id":98422407,"user":{"login":"Adesun","id":5615543,"avatar_url":"https://avatars.githubusercontent.com/u/5615543?v=3","gravatar_id":"","url":"https://api.github.com/users/Adesun","html_url":"https://github.com/Adesun","followers_url":"https://api.github.com/users/Adesun/followers","following_url":"https://api.github.com/users/Adesun/following{/other_user}","gists_url":"https://api.github.com/users/Adesun/gists{/gist_id}","starred_url":"https://api.github.com/users/Adesun/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Adesun/subscriptions","organizations_url":"https://api.github.com/users/Adesun/orgs","repos_url":"https://api.github.com/users/Adesun/repos","events_url":"https://api.github.com/users/Adesun/events{/privacy}","received_events_url":"https://api.github.com/users/Adesun/received_events","type":"User","site_admin":false},"created_at":"2015-05-03T00:45:35Z","updated_at":"2015-05-03T00:45:35Z","body":"I used the python scripts from project DIGITS of NVIDIA to extract images from cifar binary file.\r\nhttps://github.com/NVIDIA/DIGITS/tree/master/tools/download_data\r\nthen pack images extracted into binary file by im2bin.exe.\r\n\r\nthe config modified as follows:\r\n```\r\ndata = train\r\niter = imgbin\r\n  image_list = \"./cifar/train.txt\"\r\n  image_bin  = \"./cifar/TRAIN.BIN\"\r\n  rand_crop=1\r\n  rand_mirror=1\r\niter = threadbuffer\r\niter = end\r\n\r\neval = test\r\niter = imgbin\r\n  image_list = \"./cifar/test.txt\"\r\n  image_bin = \"./cifar/TEST.BIN\"\r\n# no random crop and mirror in test\r\niter = end\r\n\r\nnetconfig=start\r\nlayer[0->1] = conv\r\n  kernel_size = 11\r\n  stride = 4\r\n  nchannel = 96\r\nlayer[1->2] = relu\r\nlayer[2->3] = max_pooling\r\n  kernel_size = 3\r\n  stride = 2\r\nlayer[3->4] = lrn\r\n  local_size = 5\r\n  alpha = 0.001\r\n  beta = 0.75\r\n  knorm = 1\r\n###############\r\nlayer[4->5] = conv\r\n  ngroup = 2\r\n  nchannel = 256\r\n  kernel_size = 5\r\n  pad = 2\r\nlayer[5->6] = relu\r\nlayer[6->7] = max_pooling\r\n  kernel_size = 3\r\n  stride = 2\r\nlayer[7->8] = lrn\r\n  local_size = 5\r\n  alpha = 0.001\r\n  beta = 0.75\r\n  knorm = 1\r\n#############\r\nlayer[8->9] = conv\r\n  nchannel = 384\r\n  kernel_size = 3\r\n  pad = 1\r\nlayer[9->10]= relu\r\nlayer[10->11] = conv\r\n  nchannel = 384\r\n  ngroup = 2\r\n  kernel_size = 3\r\n  pad = 1\r\nlayer[11->12] = relu\r\nlayer[12->13] = conv\r\n  nchannel = 256\r\n  ngroup = 2\r\n  kernel_size = 3\r\n  pad = 1\r\n  init_bias = 1.0\r\nlayer[13->14] = relu\r\nlayer[14->15] = max_pooling\r\n  kernel_size = 3\r\n  stride = 2\r\nlayer[15->16] = flatten\r\nlayer[16->17] = fullc\r\n  nhidden = 4096\r\n  init_sigma = 0.005\r\n  init_bias = 1.0\r\nlayer[17->18] = relu\r\nlayer[18->18] = dropout\r\n  threshold = 0.5\r\nlayer[18->19] = fullc\r\n  nhidden = 4096\r\n  init_sigma = 0.005\r\n  init_bias = 1.0\r\nlayer[19->20] = relu\r\nlayer[20->20] = dropout\r\n  threshold = 0.5\r\nlayer[20->21] = fullc\r\n  nhidden = 1000\r\nlayer[21->21] = softmax\r\nnetconfig=end\r\n\r\n# evaluation metric\r\nmetric = error\r\nmetric = rec@1\r\nmetric = rec@5\r\n\r\nmax_round = 45\r\nnum_round = 45\r\n\r\n# input shape not including batch\r\ninput_shape = 3,227,227\r\n\r\n#update_period=2\r\nbatch_size = 64\r\n\r\n# global parameters in any sectiion outside netconfig, and iter\r\nmomentum = 0.9\r\nwmat:lr  = 0.01\r\nwmat:wd  = 0.0005\r\n\r\nbias:wd  = 0.000\r\nbias:lr  = 0.02\r\n\r\n# all the learning rate schedule starts with lr\r\nlr:schedule = expdecay\r\nlr:gamma = 0.1\r\nlr:step = 100000\r\n\r\nsave_model=1\r\nmodel_dir=models\r\n\r\n# random config\r\nrandom_type = xavier\r\n\r\n\r\n# new line\r\n```"}}