Dataset

Dataset.py

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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