neuraltalk2源码分析--eval.lua(完善中)

require 'torch'require 'nn'require 'nngraph'-- exoticsrequire 'loadcaffe'-- local importslocal utils = require 'misc.utils'require 'misc.DataLoader'require 'misc.DataLoaderRaw'require 'misc.LanguageModel'local net_utils = require 'misc.net_utils'--------------------------------------------------------------------------------- Input arguments and options-------------------------------------------------------------------------------cmd = torch.CmdLine()cmd:text()cmd:text('Train an Image Captioning model')cmd:text()cmd:text('Options')-- Input pathscmd:option('-model','','path to model to evaluate')-- Basic optionscmd: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 optionscmd: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.')-- misccmd: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 CPUif 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-indexedend--------------------------------------------------------------------------------- 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 parametersif string.len(opt.input_h5) == 0 then opt.input_h5 = checkpoint.opt.input_h5 endif string.len(opt.input_json) == 0 then opt.input_json = checkpoint.opt.input_json endif opt.batch_size == 0 then opt.batch_size = checkpoint.opt.batch_size endlocal 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 modelendlocal vocab = checkpoint.vocab -- ix -> word mapping--------------------------------------------------------------------------------- Create the Data Loader instance-------------------------------------------------------------------------------local loaderif 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.protosprotos.expander = nn.FeatExpander(opt.seq_per_img)protos.crit = nn.LanguageModelCriterion()protos.lm:createClones() -- reconstruct clones inside the language modelif 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 }    -- 得到结果的地方    local seq = protos.lm:sample(feats, sample_opts)    -- 通过模型中的对应关系，将结果转化成英文结果    local sents = net_utils.decode_sequence(vocab, seq)    for k=1,#sents do      -- 将sents的值封装到entry中      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      -- 将entry的值传给predictions      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_statsendlocal loss, split_predictions, lang_stats = eval_split(opt.split, {num_images = opt.num_images})print('loss: ', loss)if lang_stats then  print(lang_stats)endif opt.dump_json == 1 then  -- dump the json  -- 将结果存入到vis.json   utils.write_json('vis/vis.json', split_predictions)end
最后显示的时候，通过vis.json中的数据得到生成的对应图片的描述加上对应的图片显示在前端
实力有显，不断完善中....