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import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
import tensorflow as tf
import numpy as np
def pad_sequences(sequences, max_seq_len=0):
"""Pad sequences to max length in sequence."""
max_seq_len = max(max_seq_len, max(len(sequence) for sequence in sequences))
padded_sequences = np.zeros((len(sequences), max_seq_len))
for i, sequence in enumerate(sequences):
padded_sequences[i][:len(sequence)] = sequence
return padded_sequences
class SkimlitDataset(Dataset):
def __init__(self, text_seq, line_num, total_line):
self.text_seq = text_seq
self.line_num_one_hot = line_num
self.total_line_one_hot = total_line
def __len__(self):
return len(self.text_seq)
def __str__(self):
return f"<Dataset(N={len(self)})>"
def __getitem__(self, index):
X = self.text_seq[index]
line_num = self.line_num_one_hot[index]
total_line = self.total_line_one_hot[index]
return [X, len(X), line_num, total_line]
def collate_fn(self, batch):
"""Processing on a batch"""
# Getting Input
batch = np.array(batch)
text_seq = batch[:,0]
seq_lens = batch[:, 1]
line_nums = batch[:, 2]
total_lines = batch[:, 3]
# padding inputs
pad_text_seq = pad_sequences(sequences=text_seq) # max_seq_len=max_length
# converting line nums into one-hot encoding
line_nums = tf.one_hot(line_nums, depth=20)
# converting total lines into one-hot encoding
total_lines = tf.one_hot(total_lines, depth=24)
# converting inputs to tensors
pad_text_seq = torch.LongTensor(pad_text_seq.astype(np.int32))
seq_lens = torch.LongTensor(seq_lens.astype(np.int32))
line_nums = torch.tensor(line_nums.numpy())
total_lines = torch.tensor(total_lines.numpy())
return pad_text_seq, seq_lens, line_nums, total_lines
def create_dataloader(self, batch_size, shuffle=False, drop_last=False):
dataloader = DataLoader(dataset=self, batch_size=batch_size, collate_fn=self.collate_fn, shuffle=shuffle, drop_last=drop_last, pin_memory=True)
return dataloader
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