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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,142 @@ | ||
| """ | ||
| Adapted from https://github.com/locuslab/TCN/blob/master/TCN/ | ||
| """ | ||
| import pickle | ||
| from collections import Counter | ||
| import os | ||
| import numpy as np | ||
| import torch | ||
| import pathlib | ||
| from .utils import load_data, save_data | ||
| import observations | ||
|
|
||
|
|
||
| class PennTreeBankChar(torch.utils.data.Dataset): | ||
| def __init__( | ||
| self, | ||
| partition: int, | ||
| seq_length: int, | ||
| valid_seq_len: int, | ||
| batch_size: int, | ||
| **kwargs, | ||
| ): | ||
| self.seq_len = seq_length | ||
| self.valid_seq_len = valid_seq_len | ||
| self.batch_size = batch_size | ||
| self.root = pathlib.Path("./data") | ||
| self.base_loc = self.root / "penn" | ||
| data_loc = self.base_loc / "preprocessed_data_char" | ||
|
|
||
| if os.path.exists(data_loc): | ||
| self.dictionary = pickle.load(open(str(data_loc / 'dictionary_char'), 'rb')) | ||
| else: | ||
| train, valid, test = self._process_data() | ||
|
|
||
| if not os.path.exists(data_loc): | ||
| os.mkdir(data_loc) | ||
| pickle.dump(self.dictionary, open(str(data_loc / 'dictionary_char'), 'wb')) | ||
| save_data( | ||
| data_loc, | ||
| train=train, | ||
| valid=valid, | ||
| test=test, | ||
| ) | ||
|
|
||
| self.X, self.y = self.load_data(data_loc, partition) | ||
| if partition == 'train': | ||
| self.sampler = SequentialBatchSampler(self) | ||
| else: | ||
| self.sampler = SequentialBatchSampler(self, shuffle=False) | ||
| super(PennTreeBankChar, self).__init__() | ||
|
|
||
| def __getitem__(self, ind): | ||
| b = ind // len(self.X[0]) | ||
| i = ind - b * len(self.X[0]) | ||
| return self.X[b][i], self.y[b][i] | ||
|
|
||
| def __len__(self): | ||
| return len(self.X[0]) * len(self.X) | ||
|
|
||
| def create_seq(self, data, batch_size): | ||
| nbatch = data.size(0) // batch_size | ||
| data = data.narrow(0, 0, nbatch * batch_size).view(batch_size, -1) ## crop tail | ||
| x = [] | ||
| y = [] | ||
| L = data.shape[1] | ||
| for i in range(0, L-1, self.valid_seq_len): | ||
| if i + self.seq_len - self.valid_seq_len >= L - 1: | ||
| continue | ||
| end = min(i + self.seq_len, L - 1) | ||
| x.append(data[:, i: end].contiguous()) | ||
| y.append(data[:, i+1: end+1].contiguous()) | ||
| return x, y | ||
|
|
||
| def _process_data(self): | ||
| self.dictionary = Dictionary() | ||
| train, test, valid = getattr(observations, 'ptb')(self.base_loc) | ||
| for c in train + ' ' + test + '' + valid: | ||
| self.dictionary.add_word(c) | ||
| self.dictionary.prep_dict() | ||
|
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||
|
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||
| train = self._char_to_tensor(train) | ||
| valid = self._char_to_tensor(valid) | ||
| test = self._char_to_tensor(test) | ||
| return train, valid, test | ||
|
|
||
| def _char_to_tensor(self, string): | ||
| tensor = torch.zeros(len(string)).long() | ||
| for i in range(len(string)): | ||
| tensor[i] = self.dictionary.char2idx[string[i]] | ||
| return tensor | ||
|
|
||
| def load_data(self, data_loc, partition): | ||
| tensors = load_data(data_loc) | ||
| if partition == "train": | ||
| data = tensors["train"] | ||
| elif partition == "val": | ||
| data = tensors["valid"] | ||
| elif partition == "test": | ||
| data = tensors["test"] | ||
| else: | ||
| raise NotImplementedError("the set {} is not implemented.".format(set)) | ||
| X, y = self.create_seq(data, self.batch_size) | ||
| return X, y | ||
|
|
||
|
|
||
| class Dictionary(object): | ||
| def __init__(self): | ||
| self.char2idx = {} | ||
| self.idx2char = [] | ||
| self.counter = Counter() | ||
|
|
||
| def add_word(self, word): | ||
| self.counter[word] += 1 | ||
|
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||
| def prep_dict(self): | ||
| for char in self.counter: | ||
| if char not in self.char2idx: | ||
| self.idx2char.append(char) | ||
| self.char2idx[char] = len(self.idx2char) - 1 | ||
|
|
||
| def __len__(self): | ||
| return len(self.idx2char) | ||
|
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||
|
|
||
| class SequentialBatchSampler(torch.utils.data.Sampler): | ||
| def __init__(self, data_source, shuffle=True): | ||
| super(SequentialBatchSampler, self).__init__(data_source) | ||
| self.X = data_source.X | ||
| if shuffle: | ||
| self.sampler = torch.utils.data.SubsetRandomSampler(np.arange(len(self.X))) | ||
| else: | ||
| self.sampler = np.arange(len(self.X)) | ||
| self.batch_size = self.X[0].shape[0] | ||
|
|
||
| def __iter__(self): | ||
| for idx in self.sampler: | ||
| batch = [idx * self.batch_size + j for j in range(self.batch_size)] | ||
| yield batch | ||
|
|
||
| def __len__(self): | ||
| return len(self.X) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,3 +1,3 @@ | ||
| from .tcn import AddProblem_TCN, CopyMemory_TCN, MNIST_TCN | ||
| from .ckcnn import CopyMemory_CKCNN, AddProblem_CKCNN, seqImg_CKCNN | ||
| from .tcn import AddProblem_TCN, CopyMemory_TCN, MNIST_TCN, PTB_TCN | ||
| from .ckcnn import CopyMemory_CKCNN, AddProblem_CKCNN, seqImg_CKCNN, seqText_CKCNN | ||
| from .bfcnn import seqImg_BFCNN |
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