Merge pull request #1095 from lrzpellegrini/sampler_base_dataloading

Implement sampler-based dataloading logic

Author: AntonioCarta

avalanche/benchmarks/utils/collate_functions.py

@@ -0,0 +1,90 @@
+################################################################################
+# Copyright (c) 2021 ContinualAI.                                              #
+# Copyrights licensed under the MIT License.                                   #
+# See the accompanying LICENSE file for terms.                                 #
+#                                                                              #
+# Date: 21-04-2022                                                             #
+# Author(s): Antonio Carta, Lorenzo Pellegrini                                 #
+# E-mail: contact@continualai.org                                              #
+# Website: avalanche.continualai.org                                           #
+################################################################################
+
+import itertools
+from collections import defaultdict
+
+import torch
+
+
+def classification_collate_mbatches_fn(mbatches):
+    """Combines multiple mini-batches together.
+
+        Concatenates each tensor in the mini-batches along dimension 0 (usually
+        this is the batch size).
+
+        :param mbatches: sequence of mini-batches.
+        :return: a single mini-batch
+        """
+    batch = []
+    for i in range(len(mbatches[0])):
+        t = classification_single_values_collate_fn(
+            [el[i] for el in mbatches], i)
+        batch.append(t)
+    return batch
+
+
+def classification_single_values_collate_fn(values_list, index):
+    """
+    Collate function used to merge the single elements (x or y or t,
+    etcetera) of a minibatch of data from a classification dataset.
+
+    This function assumes that all values are tensors of the same shape
+    (excluding the first dimension).
+
+    :param values_list: The list of values to merge.
+    :param index: The index of the element. 0 for x values, 1 for y values,
+        etcetera. In this implementation, this parameter is ignored.
+    :return: The merged values.
+    """
+    return torch.cat(values_list, dim=0)
+
+
+def detection_collate_fn(batch):
+    """
+    Collate function used when loading detection datasets using a DataLoader.
+
+    This will merge the single samples of a batch to create a minibatch.
+    This collate function follows the torchvision format for detection tasks.
+    """
+    return tuple(zip(*batch))
+
+
+def detection_collate_mbatches_fn(mbatches):
+    """
+    Collate function used when loading detection datasets using a DataLoader.
+
+    This will merge multiple batches to create a concatenated batch.
+
+    Beware that merging multiple batches is different from creating a batch
+    from single dataset elements: Batches can be created from a
+    list of single dataset elements by using :func:`detection_collate_fn`.
+    """
+    lists_dict = defaultdict(list)
+    for mb in mbatches:
+        for mb_elem_idx, mb_elem in enumerate(mb):
+            lists_dict[mb_elem_idx].append(mb_elem)
+
+    lists = []
+    for mb_elem_idx in range(max(lists_dict.keys()) + 1):
+        lists.append(list(itertools.chain.from_iterable(
+            lists_dict[mb_elem_idx]
+        )))
+
+    return lists
+
+
+__all__ = [
+    'classification_collate_mbatches_fn',
+    'classification_single_values_collate_fn',
+    'detection_collate_fn',
+    'detection_collate_mbatches_fn'
+]