I am trying to create a .csv file from a lua table. I've read some of the documentation online and on this forum... but can't seem to get it. I think it's because of the format of the lua table - take a look for yourselves.
This script is all from a great open-source software called NeuralTalk2. The main point of the software is to caption images. You can read about it more on that page.
Anyways, let me introduce to you the first piece of code: a function that takes the lua table and writes it to a .json file. This is how it looks like:
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
Once the code compiles, the .json file looks like this:
[{"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"}]
It goes on much longer, but generally, there is a "caption" following by some text, and an "image_id" followed by the image id.
When I print the table onto the terminal, it looks like this:
{
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"
}
}
It has things before it and after it... I am just showing you the general format of the table.
You may wonder how the table is defined... I am not sure there is a very clear definition of it inside the script. I will share it just for you to see, the file where it is defined depends on so many other files, so it's messy.
I am hoping from the terminal output, you can understand generally the structure of the table, and from that know how the table is structured. I want to output it to a .csv file that will look like this
image_id captions
1 xxxx
2 xxxx
3 xxxx
How can I do this..? Not sure, given the format of the lua table...
Here is the script where it is defined. Specifically, it is define at the end, but again, not sure itll be too much help.
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
