236 lines
8.7 KiB
Lua
236 lines
8.7 KiB
Lua
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-- Modified from https://github.com/oxford-cs-ml-2015/practical6
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-- the modification included support for train/val/test splits
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local CharSplitLMMinibatchLoader = {}
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CharSplitLMMinibatchLoader.__index = CharSplitLMMinibatchLoader
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function CharSplitLMMinibatchLoader.create(data_dir, batch_size, seq_length, split_fractions, min_freq)
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-- split_fractions is e.g. {0.9, 0.05, 0.05}
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local self = {}
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setmetatable(self, CharSplitLMMinibatchLoader)
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local input_file = path.join(data_dir, 'input.txt')
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local vocab_file = path.join(data_dir, 'vocab.t7')
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local tensor_file = path.join(data_dir, 'data.t7')
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-- fetch file attributes to determine if we need to rerun preprocessing
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local run_prepro = false
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if not (path.exists(vocab_file) or path.exists(tensor_file)) then
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-- prepro files do not exist, generate them
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print('vocab.t7 and data.t7 do not exist. Running preprocessing...')
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run_prepro = true
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else
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-- check if the input file was modified since last time we
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-- ran the prepro. if so, we have to rerun the preprocessing
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local input_attr = lfs.attributes(input_file)
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local vocab_attr = lfs.attributes(vocab_file)
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local tensor_attr = lfs.attributes(tensor_file)
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if input_attr.modification > vocab_attr.modification or input_attr.modification > tensor_attr.modification then
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print('vocab.t7 or data.t7 detected as stale. Re-running preprocessing...')
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run_prepro = true
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end
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end
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if run_prepro then
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-- construct a tensor with all the data, and vocab file
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print('one-time setup: preprocessing input text file ' .. input_file .. '...')
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CharSplitLMMinibatchLoader.text_to_tensor(input_file, vocab_file, tensor_file, min_freq)
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end
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print('loading data files...')
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local data = torch.load(tensor_file)
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self.vocab_mapping = torch.load(vocab_file)
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-- cut off the end so that it divides evenly
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local len = data:size(1)
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if len % (batch_size * seq_length) ~= 0 then
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print('cutting off end of data so that the batches/sequences divide evenly')
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data = data:sub(1, batch_size * seq_length
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* math.floor(len / (batch_size * seq_length)))
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end
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-- count vocab
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self.vocab_size = 0
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for _ in pairs(self.vocab_mapping) do
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self.vocab_size = self.vocab_size + 1
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end
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-- self.batches is a table of tensors
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print('reshaping tensor...')
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self.batch_size = batch_size
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self.seq_length = seq_length
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local ydata = data:clone()
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ydata:sub(1,-2):copy(data:sub(2,-1))
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ydata[-1] = data[1]
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self.x_batches = data:view(batch_size, -1):split(seq_length, 2) -- #rows = #batches
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self.nbatches = #self.x_batches
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self.y_batches = ydata:view(batch_size, -1):split(seq_length, 2) -- #rows = #batches
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assert(#self.x_batches == #self.y_batches)
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-- lets try to be helpful here
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if self.nbatches < 50 then
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print('WARNING: less than 50 batches in the data in total? Looks like very small dataset. You probably want to use smaller batch_size and/or seq_length.')
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end
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-- perform safety checks on split_fractions
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assert(split_fractions[1] >= 0 and split_fractions[1] <= 1, 'bad split fraction ' .. split_fractions[1] .. ' for train, not between 0 and 1')
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assert(split_fractions[2] >= 0 and split_fractions[2] <= 1, 'bad split fraction ' .. split_fractions[2] .. ' for val, not between 0 and 1')
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assert(split_fractions[3] >= 0 and split_fractions[3] <= 1, 'bad split fraction ' .. split_fractions[3] .. ' for test, not between 0 and 1')
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if split_fractions[3] == 0 then
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-- catch a common special case where the user might not want a test set
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self.ntrain = math.floor(self.nbatches * split_fractions[1])
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self.nval = self.nbatches - self.ntrain
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self.ntest = 0
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else
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-- divide data to train/val and allocate rest to test
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self.ntrain = math.floor(self.nbatches * split_fractions[1])
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self.nval = math.floor(self.nbatches * split_fractions[2])
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self.ntest = self.nbatches - self.nval - self.ntrain -- the rest goes to test (to ensure this adds up exactly)
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end
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self.split_sizes = {self.ntrain, self.nval, self.ntest}
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self.batch_ix = {0,0,0}
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print(string.format('data load done. Number of data batches in train: %d, val: %d, test: %d', self.ntrain, self.nval, self.ntest))
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collectgarbage()
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return self
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end
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function CharSplitLMMinibatchLoader:reset_batch_pointer(split_index, batch_index)
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batch_index = batch_index or 0
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self.batch_ix[split_index] = batch_index
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end
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function CharSplitLMMinibatchLoader:next_batch(split_index)
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if self.split_sizes[split_index] == 0 then
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-- perform a check here to make sure the user isn't screwing something up
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local split_names = {'train', 'val', 'test'}
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print('ERROR. Code requested a batch for split ' .. split_names[split_index] .. ', but this split has no data.')
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os.exit() -- crash violently
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end
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-- split_index is integer: 1 = train, 2 = val, 3 = test
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self.batch_ix[split_index] = self.batch_ix[split_index] + 1
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if self.batch_ix[split_index] > self.split_sizes[split_index] then
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self.batch_ix[split_index] = 1 -- cycle around to beginning
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end
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-- pull out the correct next batch
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local ix = self.batch_ix[split_index]
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if split_index == 2 then ix = ix + self.ntrain end -- offset by train set size
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if split_index == 3 then ix = ix + self.ntrain + self.nval end -- offset by train + val
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return self.x_batches[ix], self.y_batches[ix]
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end
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-- chinese vocab
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function get_vocab(str, min_freq)
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local len = #str
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local left = 0
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local arr = {0, 0xc0, 0xe0, 0xf0, 0xf8, 0xfc}
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local unordered = {}
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local start = 1
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local wordLen = 0
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g_total_chars = 0
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while len ~= left do
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local tmp = string.byte(str, start)
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local i = #arr
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while arr[i] do
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if tmp >= arr[i] then
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break
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end
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i = i - 1
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end
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wordLen = i + wordLen
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local tmpString = string.sub(str, start, wordLen)
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start = start + i
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left = left + i
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if not unordered[tmpString] then
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unordered[tmpString] = 1
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else
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unordered[tmpString] = unordered[tmpString] + 1 end
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g_total_chars = g_total_chars + 1
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end
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final_res = {}
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for char_val, char_cnt in pairs(unordered) do
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if char_cnt >= min_freq then
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final_res[char_val] = true
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end
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end
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return final_res
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end
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-- change raw data to tokens
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function get_data(str, vocab_mapping)
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-- can not use torch.ByteTensor because it support mo more than 256
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-- local data = torch.ByteTensor(g_total_chars) -- store it into 1D first, then rearrange
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local data = torch.ShortTensor(g_total_chars)
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local len = #str
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local left = 0
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local arr = {0, 0xc0, 0xe0, 0xf0, 0xf8, 0xfc}
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local start = 1
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local wordLen = 0
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local count = 1
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while len ~= left do
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local tmp = string.byte(str, start)
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local i = #arr
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while arr[i] do
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if tmp >= arr[i] then
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break
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end
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i = i - 1
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end
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wordLen = i + wordLen
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local tmpString = string.sub(str, start, wordLen)
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start = start + i
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left = left + i
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if vocab_mapping[tmpString] then
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data[count] = vocab_mapping[tmpString]
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else
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data[count] = vocab_mapping['UNKNOW']
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end
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count = count + 1
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end
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return data
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end
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-- *** STATIC method ***
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function CharSplitLMMinibatchLoader.text_to_tensor(in_textfile, out_vocabfile, out_tensorfile, min_freq)
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local timer = torch.Timer()
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print('loading text file...')
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local f = torch.DiskFile(in_textfile)
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local rawdata = f:readString('*a') -- NOTE: this reads the whole file at once
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f:close()
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-- create vocabulary if it doesn't exist yet
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print('creating vocabulary mapping...')
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-- record all characters to a set
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local unordered = get_vocab(rawdata, min_freq)
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-- sort into a table (i.e. keys become 1..N)
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local ordered = {}
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for char in pairs(unordered) do ordered[#ordered + 1] = char end
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table.sort(ordered)
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-- invert `ordered` to create the char->int mapping
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local vocab_mapping = {}
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count_vocab = 0
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for i, char in ipairs(ordered) do
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vocab_mapping[char] = i
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count_vocab = count_vocab + 1
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end
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vocab_mapping['UNKNOW'] = count_vocab + 1
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-- construct a tensor with all the data
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print('putting data into tensor, it takes a lot of time...')
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local data = get_data(rawdata, vocab_mapping)
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-- save output preprocessed files
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print('saving ' .. out_vocabfile)
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torch.save(out_vocabfile, vocab_mapping)
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print('saving ' .. out_tensorfile)
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torch.save(out_tensorfile, data)
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end
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return CharSplitLMMinibatchLoader
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