csv - 从 Lua 表创建 .CSV 文件

Question

我正在尝试从 lua 表创建一个 .csv 文件。我已经在网上和这个论坛上阅读了一些文档......但似乎无法理解。我认为这是因为 lua 表的格式 - 自己看看。

这个脚本全部来自一个很棒的开源软件NeuralTalk2 .该软件的要点是为图像添加标题。您可以在该页面上阅读更多信息。

无论如何，让我向您介绍第一段代码:一个获取lua表并将其写入.json文件的函数。这是它的样子:

function utils.write_json(path, j)
  -- API reference http://www.kyne.com.au/~mark/software/lua-cjson-manual.html#encode
  cjson.encode_sparse_array(true, 2, 10)
  local text = cjson.encode(j)
  local file = io.open(path, 'w')
  file:write(text)
  file:close()
end

代码编译后，.json 文件如下所示:

[{"caption":"a view of a UNK UNK in a cloudy sky","image_id":"0001"},{"caption":"a view of a UNK UNK in a cloudy sky","image_id":"0002"}]

它会持续更长的时间，但一般来说，有一个“标题”跟在一些文本之后，还有一个“image_id”跟在图像 ID 之后。

当我将表格打印到终端上时，它看起来像这样:

{
  1681 : 
    {
      caption : "a person holding a cell phone in their hand"
      image_id : "1681"
    }
  1682 : 
    {
      caption : "a person is taking a picture of a mirror"
      image_id : "1682"
    }
}

它之前和之后都有内容...我只是向您展示表格的一般格式。

您可能想知道表是如何定义的……我不确定脚本中是否有非常明确的定义。分享给大家看看，定义它的文件依赖了很多其他文件，所以很乱。

我希望从终端输出中，您可以大致了解表的结构，并从中了解表的结构。我想将它输出到一个如下所示的 .csv 文件

image_id     captions
1            xxxx
2            xxxx
3            xxxx

我该怎么做...？不确定，鉴于 lua 表的格式...

这是定义它的脚本。具体来说，它在最后定义，但同样，不确定它会有多大帮助。

require 'torch'
require 'nn'
require 'nngraph'
-- exotics
require 'loadcaffe'
-- local imports
local utils = require 'misc.utils'
require 'misc.DataLoader'
require 'misc.DataLoaderRaw'
require 'misc.LanguageModel'
local net_utils = require 'misc.net_utils'
local csv_utils = require 'misc.csv_utils'

-------------------------------------------------------------------------------
-- Input arguments and options
-------------------------------------------------------------------------------
cmd = torch.CmdLine()
cmd:text()
cmd:text('Train an Image Captioning model')
cmd:text()
cmd:text('Options')

-- Input paths
cmd:option('-model','','path to model to evaluate')
-- Basic options
cmd:option('-batch_size', 1, 'if > 0 then overrule, otherwise load from checkpoint.')
cmd:option('-num_images', 100, 'how many images to use when periodically evaluating the loss? (-1 = all)')
cmd:option('-language_eval', 0, 'Evaluate language as well (1 = yes, 0 = no)? BLEU/CIDEr/METEOR/ROUGE_L? requires coco-caption code from Github.')
cmd:option('-dump_images', 1, 'Dump images into vis/imgs folder for vis? (1=yes,0=no)')
cmd:option('-dump_json', 1, 'Dump json with predictions into vis folder? (1=yes,0=no)')
cmd:option('-dump_path', 0, 'Write image paths along with predictions into vis json? (1=yes,0=no)')
-- Sampling options
cmd:option('-sample_max', 1, '1 = sample argmax words. 0 = sample from distributions.')
cmd:option('-beam_size', 2, 'used when sample_max = 1, indicates number of beams in beam search. Usually 2 or 3 works well. More is not better. Set this to 1 for faster runtime but a bit worse performance.')
cmd:option('-temperature', 1.0, 'temperature when sampling from distributions (i.e. when sample_max = 0). Lower = "safer" predictions.')
-- For evaluation on a folder of images:
cmd:option('-image_folder', '', 'If this is nonempty then will predict on the images in this folder path')
cmd:option('-image_root', '', 'In case the image paths have to be preprended with a root path to an image folder')
-- For evaluation on MSCOCO images from some split:
cmd:option('-input_h5','','path to the h5file containing the preprocessed dataset. empty = fetch from model checkpoint.')
cmd:option('-input_json','','path to the json file containing additional info and vocab. empty = fetch from model checkpoint.')
cmd:option('-split', 'test', 'if running on MSCOCO images, which split to use: val|test|train')
cmd:option('-coco_json', '', 'if nonempty then use this file in DataLoaderRaw (see docs there). Used only in MSCOCO test evaluation, where we have a specific json file of only test set images.')
-- misc
cmd:option('-backend', 'cudnn', 'nn|cudnn')
cmd:option('-id', 'evalscript', 'an id identifying this run/job. used only if language_eval = 1 for appending to intermediate files')
cmd:option('-seed', 123, 'random number generator seed to use')
cmd:option('-gpuid', 0, 'which gpu to use. -1 = use CPU')
cmd:text()

-------------------------------------------------------------------------------
-- Basic Torch initializations
-------------------------------------------------------------------------------
local opt = cmd:parse(arg)
torch.manualSeed(opt.seed)
torch.setdefaulttensortype('torch.FloatTensor') -- for CPU

if opt.gpuid >= 0 then
  require 'cutorch'
  require 'cunn'
  if opt.backend == 'cudnn' then require 'cudnn' end
  cutorch.manualSeed(opt.seed)
  cutorch.setDevice(opt.gpuid + 1) -- note +1 because lua is 1-indexed
end

-------------------------------------------------------------------------------
-- Load the model checkpoint to evaluate
-------------------------------------------------------------------------------
assert(string.len(opt.model) > 0, 'must provide a model')
local checkpoint = torch.load(opt.model)
-- override and collect parameters
if string.len(opt.input_h5) == 0 then opt.input_h5 = checkpoint.opt.input_h5 end
if string.len(opt.input_json) == 0 then opt.input_json = checkpoint.opt.input_json end
if opt.batch_size == 0 then opt.batch_size = checkpoint.opt.batch_size end
local fetch = {'rnn_size', 'input_encoding_size', 'drop_prob_lm', 'cnn_proto', 'cnn_model', 'seq_per_img'}
for k,v in pairs(fetch) do 
  opt[v] = checkpoint.opt[v] -- copy over options from model
end
local vocab = checkpoint.vocab -- ix -> word mapping

-------------------------------------------------------------------------------
-- Create the Data Loader instance
-------------------------------------------------------------------------------
local loader
if string.len(opt.image_folder) == 0 then
  loader = DataLoader{h5_file = opt.input_h5, json_file = opt.input_json}
else
  loader = DataLoaderRaw{folder_path = opt.image_folder, coco_json = opt.coco_json}
end

-------------------------------------------------------------------------------
-- Load the networks from model checkpoint
-------------------------------------------------------------------------------
local protos = checkpoint.protos
protos.expander = nn.FeatExpander(opt.seq_per_img)
protos.crit = nn.LanguageModelCriterion()
protos.lm:createClones() -- reconstruct clones inside the language model
if opt.gpuid >= 0 then for k,v in pairs(protos) do v:cuda() end end

-------------------------------------------------------------------------------
-- Evaluation fun(ction)
-------------------------------------------------------------------------------
local function eval_split(split, evalopt)
  local verbose = utils.getopt(evalopt, 'verbose', true)
  local num_images = utils.getopt(evalopt, 'num_images', true)

  protos.cnn:evaluate()
  protos.lm:evaluate()
  loader:resetIterator(split) -- rewind iteator back to first datapoint in the split
  local n = 0
  local loss_sum = 0
  local loss_evals = 0
  local predictions = {}
  while true do

    -- fetch a batch of data
    local data = loader:getBatch{batch_size = opt.batch_size, split = split, seq_per_img = opt.seq_per_img}
    data.images = net_utils.prepro(data.images, false, opt.gpuid >= 0) -- preprocess in place, and don't augment
    n = n + data.images:size(1)

    -- forward the model to get loss
    local feats = protos.cnn:forward(data.images)

    -- evaluate loss if we have the labels
    local loss = 0
    if data.labels then
      local expanded_feats = protos.expander:forward(feats)
      local logprobs = protos.lm:forward{expanded_feats, data.labels}
      loss = protos.crit:forward(logprobs, data.labels)
      loss_sum = loss_sum + loss
      loss_evals = loss_evals + 1
    end

    -- forward the model to also get generated samples for each image
    local sample_opts = { sample_max = opt.sample_max, beam_size = opt.beam_size, temperature = opt.temperature }
    local seq = protos.lm:sample(feats, sample_opts)
    local sents = net_utils.decode_sequence(vocab, seq)
    for k=1,#sents do
      local entry = {image_id = data.infos[k].id, caption = sents[k]}
      if opt.dump_path == 1 then
        entry.file_name = data.infos[k].file_path
      end
      table.insert(predictions, entry)
      if opt.dump_images == 1 then
        -- dump the raw image to vis/ folder
        local cmd = 'cp "' .. path.join(opt.image_root, data.infos[k].file_path) .. '" vis/imgs/img' .. #predictions .. '.jpg' -- bit gross
        print(cmd)
        os.execute(cmd) -- dont think there is cleaner way in Lua
      end
      if verbose then
        print(string.format('image %s: %s', entry.image_id, entry.caption))
      end
    end

    -- if we wrapped around the split or used up val imgs budget then bail
    local ix0 = data.bounds.it_pos_now
    local ix1 = math.min(data.bounds.it_max, num_images)
    if verbose then
      print(string.format('evaluating performance... %d/%d (%f)', ix0-1, ix1, loss))
    end

    if data.bounds.wrapped then break end -- the split ran out of data, lets break out
    if num_images >= 0 and n >= num_images then break end -- we've used enough images
  end

  local lang_stats
  if opt.language_eval == 1 then
    lang_stats = net_utils.language_eval(predictions, opt.id)
  end

  return loss_sum/loss_evals, predictions, lang_stats
end

local loss, split_predictions, lang_stats = eval_split(opt.split, {num_images = opt.num_images})
print('loss: ', loss)
if lang_stats then
  print(lang_stats)
end

if opt.dump_json == 1 then
  -- dump the json
  print(split_predictions)
  utils.write_json('vis/vis.json', split_predictions)
  csv_utils.write('vis/vis.csv', split_predictions, ";")
end

Answer 1

如果有人想知道，我很久以前就想出了解决方案。

function nt2_write(path, data, sep)
    sep = sep or ','
    local file = assert(io.open(path, "w"))
    file:write('Image ID' .. "," .. 'Caption')
    file:write('\n')
    for k, v in pairs(data) do
      file:write(v["image_id"] .. "," .. v["caption"])
      file:write('\n')
    end
    file:close()
end

当然，您可能需要更改字符串值，但是是的。快乐的编程。