Cannot have access to all data when enumerate the dataloader

pyxiea: <p>I defined a custom <code>Dataset</code> and a custom <code>Dataloader</code>, and I want to access all the batches using <code>for i,batch in enumerate(loader)</code>. But this for loop gives me different number of batches in every epoch, and all of them are far smaller then the actual number batches (which equals to <code>number_of_samples/batch_size</code>).</p>
<p>Here is how I define my dataset and dataloader:</p>
<pre><code class="lang-python">

class UsptoDataset(Dataset):
    def __init__(self, csv_file):
        df = pd.read_csv(csv_file)
        self.rea_trees = df['reactants_trees'].to_numpy()
        self.syn_trees = df['synthons_trees'].to_numpy()
        self.syn_smiles = df['synthons'].to_numpy()
        self.product_smiles = df['product'].to_numpy()

    def __len__(self):
        return len(self.rea_trees)

    def __getitem__(self, item):
        rea_tree = self.rea_trees[item]
        syn_tree = self.syn_trees[item]
        syn_smile = self.syn_smiles[item]
        pro_smile = self.product_smiles[item]
        # omit the snippet used to process the data here, which gives us the variables used in the return statement.
        return {'input_words': input_words,
                'input_chars': input_chars,
                'syn_tree_indices': syn_tree_indices,
                'syn_rule_nl_left': syn_rule_nl_left,
                'syn_rule_nl_right': syn_rule_nl_right,
                'rea_tree_indices': rea_tree_indices,
                'rea_rule_nl_left': rea_rule_nl_left,
                'rea_rule_nl_right': rea_rule_nl_right,
                'class_mask': class_mask,
                'query_paths': query_paths,
                'labels': labels,
                'parent_matrix': parent_matrix,
                'syn_parent_matrix': syn_parent_matrix,
                'path_lens': path_lens,
                'syn_path_lens': syn_path_lens}

    @staticmethod
    def collate_fn(batch):
        input_words = torch.tensor(np.stack([_['input_words'] for _ in batch], axis=0), dtype=torch.long)
        input_chars = torch.tensor(np.stack([_['input_chars'] for _ in batch], axis=0), dtype=torch.long)
        syn_tree_indices = torch.tensor(np.stack([_['syn_tree_indices'] for _ in batch], axis=0), dtype=torch.long)
        syn_rule_nl_left = torch.tensor(np.stack([_['syn_rule_nl_left'] for _ in batch], axis=0), dtype=torch.long)
        syn_rule_nl_right = torch.tensor(np.stack([_['syn_rule_nl_right'] for _ in batch], axis=0), dtype=torch.long)
        rea_tree_indices = torch.tensor(np.stack([_['rea_tree_indices'] for _ in batch], axis=0), dtype=torch.long)
        rea_rule_nl_left = torch.tensor(np.stack([_['rea_rule_nl_left'] for _ in batch], axis=0), dtype=torch.long)
        rea_rule_nl_right = torch.tensor(np.stack([_['rea_rule_nl_right'] for _ in batch], axis=0), dtype=torch.long)
        class_mask = torch.tensor(np.stack([_['class_mask'] for _ in batch], axis=0), dtype=torch.float32)
        query_paths = torch.tensor(np.stack([_['query_paths'] for _ in batch], axis=0), dtype=torch.long)
        labels = torch.tensor(np.stack([_['labels'] for _ in batch], axis=0), dtype=torch.long)
        parent_matrix = torch.tensor(np.stack([_['parent_matrix'] for _ in batch], axis=0), dtype=torch.float)
        syn_parent_matrix = torch.tensor(np.stack([_['syn_parent_matrix'] for _ in batch], axis=0), dtype=torch.float)
        path_lens = torch.tensor(np.stack([_['path_lens'] for _ in batch], axis=0), dtype=torch.long)
        syn_path_lens = torch.tensor(np.stack([_['syn_path_lens'] for _ in batch], axis=0), dtype=torch.long)

        return_dict = {'input_words': input_words,
                       'input_chars': input_chars,
                       'syn_tree_indices': syn_tree_indices,
                       'syn_rule_nl_left': syn_rule_nl_left,
                       'syn_rule_nl_right': syn_rule_nl_right,
                       'rea_tree_indices': rea_tree_indices,
                       'rea_rule_nl_left': rea_rule_nl_left,
                       'rea_rule_nl_right': rea_rule_nl_right,
                       'class_mask': class_mask,
                       'query_paths': query_paths,
                       'labels': labels,
                       'parent_matrix': parent_matrix,
                       'syn_parent_matrix': syn_parent_matrix,
                       'path_lens': path_lens,
                       'syn_path_lens': syn_path_lens}
        return return_dict


train_dataset=UsptoDataset("train_trees.csv")

train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True, num_workers=1, collate_fn=UsptoDataset.collate_fn)         

</code></pre>
<p>And when I use the dataloader as follows, it gives me different number of batches every epoch:</p>
<pre><code class="lang-python">epoch_steps = len(train_loader)
for e in range(epochs):
    for j, batch_data in enumerate(train_loader):
        step = e * epoch_steps + j
</code></pre>
<p>The log shows that the first epoch only has 5 batches, the second epoch has 3 batches, and the third epoch has 5 batches, and so on.</p>
<pre><code class="lang-auto"> 1 Config:
  2 Namespace(batch_size_per_gpu=4, epochs=400, eval_every_epoch=1, hidden_size=128, keep=10, log_every_step=1, lr=0.001, new_model=False, save_dir='saved_model/', workers=1)
  3 2021-01-06 15:33:17,909 - __main__ - WARNING - Checkpoints not found in dir saved_model/, creating a new model.
  4 2021-01-06 15:33:18,340 - __main__ - INFO - Step: 0, Loss: 5.4213, Rule acc: 0.1388
  5 2021-01-06 15:33:18,686 - __main__ - INFO - Step: 1, Loss: 4.884, Rule acc: 0.542
  6 2021-01-06 15:33:18,941 - __main__ - INFO - Step: 2, Loss: 4.6205, Rule acc: 0.6122
  7 2021-01-06 15:33:19,174 - __main__ - INFO - Step: 3, Loss: 4.4442, Rule acc: 0.61
  8 2021-01-06 15:33:19,424 - __main__ - INFO - Step: 4, Loss: 4.3033, Rule acc: 0.6211
  9 2021-01-06 15:33:20,684 - __main__ - INFO - Dev Loss: 3.5034, Dev Sample Acc: 0.0, Dev Rule Acc: 0.5970844200679234, in epoch 0
 10 2021-01-06 15:33:22,203 - __main__ - INFO - Test Loss: 3.4878, Test Sample Acc: 0.0, Test Rule Acc: 0.6470248053471247
 11 2021-01-06 15:33:22,394 - __main__ - INFO - Found better dev sample accuracy 0.0 in epoch 0
 12 2021-01-06 15:33:22,803 - __main__ - INFO - Step: 10002, Loss: 3.6232, Rule acc: 0.6555
 13 2021-01-06 15:33:23,046 - __main__ - INFO - Step: 10003, Loss: 3.53, Rule acc: 0.6442
 14 2021-01-06 15:33:23,286 - __main__ - INFO - Step: 10004, Loss: 3.4907, Rule acc: 0.6498
 15 2021-01-06 15:33:24,617 - __main__ - INFO - Dev Loss: 3.3081, Dev Sample Acc: 0.0, Dev Rule Acc: 0.5980878387178693, in epoch 1
 16 2021-01-06 15:33:26,215 - __main__ - INFO - Test Loss: 3.2859, Test Sample Acc: 0.0, Test Rule Acc: 0.6466992994149526
 17 2021-01-06 15:33:26,857 - __main__ - INFO - Step: 20004, Loss: 3.3965, Rule acc: 0.6493
 18 2021-01-06 15:33:27,093 - __main__ - INFO - Step: 20005, Loss: 3.3797, Rule acc: 0.6314
 19 2021-01-06 15:33:27,353 - __main__ - INFO - Step: 20006, Loss: 3.3959, Rule acc: 0.5727
 20 2021-01-06 15:33:27,609 - __main__ - INFO - Step: 20007, Loss: 3.3632, Rule acc: 0.6279
 21 2021-01-06 15:33:27,837 - __main__ - INFO - Step: 20008, Loss: 3.3331, Rule acc: 0.6158
 22 2021-01-06 15:33:29,122 - __main__ - INFO - Dev Loss: 3.0911, Dev Sample Acc: 0.0, Dev Rule Acc: 0.6016287207603455, in epoch 2
 23 2021-01-06 15:33:30,689 - __main__ - INFO - Test Loss: 3.0651, Test Sample Acc: 0.0, Test Rule Acc: 0.6531393428643545
 24 2021-01-06 15:33:32,143 - __main__ - INFO - Dev Loss: 3.0911, Dev Sample Acc: 0.0, Dev Rule Acc: 0.6016287207603455, in epoch 3
 25 2021-01-06 15:33:33,765 - __main__ - INFO - Test Loss: 3.0651, Test Sample Acc: 0.0, Test Rule Acc: 0.6531393428643545
 26 2021-01-06 15:33:34,359 - __main__ - INFO - Step: 40008, Loss: 3.108, Rule acc: 0.6816
 27 2021-01-06 15:33:34,604 - __main__ - INFO - Step: 40009, Loss: 3.0756, Rule acc: 0.6732
 28 2021-01-06 15:33:35,823 - __main__ - INFO - Dev Loss: 3.0419, Dev Sample Acc: 0.0, Dev Rule Acc: 0.613776079245976, in epoch 4

</code></pre>
<p>FYI, the value of <code>len(train_loader.dataset)</code>, <code>batch_size</code> and <code>len(train_loader)</code> are <code>40008</code>, <code>4</code> and <code>10002</code> respectively, which are exactly what I expected. So it is so confusing that using <code>enumerate</code> gives me the only several batches such as <code>3</code> or <code>5</code>.</p>

ptrblck: <p>Are you using any <code>break</code> statements or other conditions inside the <code>DataLoader</code> loop?<br>
If not, could you post an executable code snippet with random values for the dataset inputs, so that we could try to reproduce it?</p>