Reduce HungarianMatcher's space complexity.

The memory reduction factor of the cost matrix is sum(#target objects) / max(#target objects).

That is achieved by no longer computing and storing matching costs between predictions and targets at different positions inside the batch. More exactly the original matrix of shape [batch_size * queries, sum(#target objects)] is shrinked to a tensor of shape [batch_size, queries, max(#target objects)].

Besides allowing much larger batch sizes, tested on the table structure recognition task using the Table Transformer (TATR) (125 queries, 7 classes) with pubmed data, this change also results a) on CUDA at all batch sizes and on CPU with small batchs in a small but meaningful speedup, b) on CPU with larger batch sizes in much higher speedups.

The processing time decrease computed as (1 - new_time / old_time) is shown below in various configuration:

Batch |   Device
 size | cuda    cpu
------------------
1       8.2%   1.6%
2       1.6%   9.3%
3       1.6%   7.7%
4       0.9%  11.2%
5       0.8%  13.9%
6       0.9%  15.5%
7       0.9%  23.1%
8             47.1%
16            70.6%
32            88.3%
64            95.0%

Author: alcinos

models/matcher.py

@@ -5,6 +5,7 @@
 import torch
 from scipy.optimize import linear_sum_assignment
 from torch import nn
+from torch.nn.utils.rnn import pad_sequence
 
 from util.box_ops import box_cxcywh_to_xyxy, generalized_box_iou
 
@@ -52,34 +53,66 @@ def forward(self, outputs, targets):
             For each batch element, it holds:
                 len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
         """
-        bs, num_queries = outputs["pred_logits"].shape[:2]
-
-        # We flatten to compute the cost matrices in a batch
-        out_prob = outputs["pred_logits"].flatten(0, 1).softmax(-1)  # [batch_size * num_queries, num_classes]
-        out_bbox = outputs["pred_boxes"].flatten(0, 1)  # [batch_size * num_queries, 4]
-
-        # Also concat the target labels and boxes
-        tgt_ids = torch.cat([v["labels"] for v in targets])
-        tgt_bbox = torch.cat([v["boxes"] for v in targets])
-
-        # Compute the classification cost. Contrary to the loss, we don't use the NLL,
-        # but approximate it in 1 - proba[target class].
-        # The 1 is a constant that doesn't change the matching, it can be ommitted.
-        cost_class = -out_prob[:, tgt_ids]
+        # In the comments below:
+        # - `bs` is the batch size, i.e. outputs["pred_logits"].shape[0];
+        # - `mo` is the maximum number of objects over all the targets,
+        # i.e. `max((len(v["labels"]) for v in targets))`;
+        # - `q` is the number of queries, i.e. outputs["pred_logits"].shape[1];
+        # - `cl` is the number of classes including no-object,
+        # i.e. outputs["pred_logits"].shape[2] or self.num_classes + 1.
+        if len(targets) == 1:
+            # This branch is just an optimization, not needed for correctness.
+            tgt_ids = targets[0]["labels"].unsqueeze(dim=0)
+            tgt_bbox = targets[0]["boxes"].unsqueeze(dim=0)
+        else:
+            tgt_ids = pad_sequence(
+                [target["labels"] for target in targets],
+                batch_first=True,
+                padding_value=0
+            )  # (bs, mo)
+            tgt_bbox = pad_sequence(
+                [target["boxes"] for target in targets],
+                batch_first=True,
+                padding_value=0
+            )  # (bs, mo, 4)
+
+        out_bbox = outputs["pred_boxes"]  # (bs, q, 4)
 
         # Compute the L1 cost between boxes
-        cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1)
+        cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1)  # (bs, q, mo)
 
         # Compute the giou cost betwen boxes
-        cost_giou = -generalized_box_iou(box_cxcywh_to_xyxy(out_bbox), box_cxcywh_to_xyxy(tgt_bbox))
+        out_bbox_xyxy = box_cxcywh_to_xyxy(out_bbox)
+        tgt_bbox_xyxy = box_cxcywh_to_xyxy(tgt_bbox)
+        giou = generalized_box_iou(
+            out_bbox_xyxy, tgt_bbox_xyxy)  # (bs, q, mo)
+
+        # Compute the classification cost. Contrary to the loss, we don't use
+        # the Negative Log Likelihood, but approximate it
+        # in `1 - proba[target class]`. The 1 is a constant that does not
+        # change the matching, it can be ommitted.
+        out_prob = outputs["pred_logits"].softmax(-1)  # (bs, q, c)
+        prob_class = torch.gather(
+            out_prob,
+            dim=2,
+            index=tgt_ids.unsqueeze(dim=1).expand(-1, out_prob.shape[1], -1)
+        )  # (bs, q, mo)
 
         # Final cost matrix
-        C = self.cost_bbox * cost_bbox + self.cost_class * cost_class + self.cost_giou * cost_giou
-        C = C.view(bs, num_queries, -1).cpu()
-
-        sizes = [len(v["boxes"]) for v in targets]
-        indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(sizes, -1))]
-        return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices]
+        C = self.cost_bbox * cost_bbox - self.cost_giou * giou - self.cost_class * prob_class
+        c = C.cpu()
+
+        indices = [
+            linear_sum_assignment(c[i, :, :len(v["labels"])])
+            for i, v in enumerate(targets)
+        ]
+        return [
+            (
+                torch.as_tensor(i, dtype=torch.int64),
+                torch.as_tensor(j, dtype=torch.int64),
+            )
+            for i, j in indices
+        ]
 
 
 def build_matcher(args):

test_all.py

@@ -1,9 +1,14 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
 import io
 import unittest
+import functools
+import operator
+
+from itertools import combinations_with_replacement
 
 import torch
 from torch import nn, Tensor
+from torchvision import ops
 from typing import List
 
 from models.matcher import HungarianMatcher
@@ -40,14 +45,21 @@ def test_hungarian(self):
         matcher = HungarianMatcher()
         targets = [{'labels': tgt_labels, 'boxes': tgt_boxes}]
         indices_single = matcher({'pred_logits': logits, 'pred_boxes': boxes}, targets)
-        indices_batched = matcher({'pred_logits': logits.repeat(2, 1, 1),
-                                   'pred_boxes': boxes.repeat(2, 1, 1)}, targets * 2)
+        batch_size = 2
+        indices_batched = matcher(
+            {
+                'pred_logits': logits.repeat(batch_size, 1, 1),
+                'pred_boxes': boxes.repeat(batch_size, 1, 1),
+            },
+            targets * batch_size,
+        )
         self.assertEqual(len(indices_single[0][0]), n_targets)
         self.assertEqual(len(indices_single[0][1]), n_targets)
-        self.assertEqual(self.indices_torch2python(indices_single),
-                         self.indices_torch2python([indices_batched[0]]))
-        self.assertEqual(self.indices_torch2python(indices_single),
-                         self.indices_torch2python([indices_batched[1]]))
+        for i in range(batch_size):
+            self.assertEqual(
+                self.indices_torch2python(indices_single),
+                self.indices_torch2python([indices_batched[i]]),
+            )
 
         # test with empty targets
         tgt_labels_empty = torch.randint(high=n_classes, size=(0,))
@@ -102,6 +114,57 @@ def test_model_detection_different_inputs(self):
         out = model([x])
         self.assertIn('pred_logits', out)
 
+    def test_box_iou_multiple_dimensions(self):
+        for extra_dims in range(3):
+            for extra_lengths in combinations_with_replacement(range(1, 4), extra_dims):
+                p = functools.reduce(operator.mul, extra_lengths, 1)
+                for n in range(3):
+                    a = torch.rand(extra_lengths + (n, 4))
+                    for m in range(3):
+                        b = torch.rand(extra_lengths + (m, 4))
+                        iou, union = box_ops.box_iou(a, b)
+                        self.assertTupleEqual(iou.shape, union.shape)
+                        self.assertTupleEqual(iou.shape, extra_lengths + (n, m))
+                        iou_it = iter(iou.view(p, n, m))
+                        for x, y in zip(a.view(p, n, 4), b.view(p, m, 4), strict=True):
+                            self.assertTrue(
+                                torch.equal(next(iou_it), ops.box_iou(x, y))
+                            )
+
+    def test_generalized_box_iou_multiple_dimensions(self):
+        a = torch.tensor([1, 1, 2, 2])
+        b = torch.tensor([1, 2, 3, 5])
+        ab = -0.1250
+        self.assertTrue(
+            torch.equal(
+                box_ops.generalized_box_iou(a[None, :], b[None, :]),
+                torch.Tensor([[ab]]),
+            )
+        )
+        self.assertTrue(
+            torch.equal(
+                box_ops.generalized_box_iou(a[None, None, :], b[None, None, :]),
+                torch.Tensor([[[ab]]]),
+            )
+        )
+        self.assertTrue(
+            torch.equal(
+                box_ops.generalized_box_iou(
+                    a[None, None, None, :], b[None, None, None, :]
+                ),
+                torch.Tensor([[[[ab]]]]),
+            )
+        )
+        self.assertTrue(
+            torch.equal(
+                box_ops.generalized_box_iou(
+                    torch.stack([a, a, b, b]), torch.stack([a, b])
+                ),
+                torch.Tensor(torch.Tensor([[1, ab], [1, ab], [ab, 1], [ab, 1]])),
+            )
+        )
+
+
     def test_warpped_model_script_detection(self):
         class WrappedDETR(nn.Module):
             def __init__(self, model):

util/box_ops.py

@@ -20,18 +20,18 @@ def box_xyxy_to_cxcywh(x):
     return torch.stack(b, dim=-1)
 
 
-# modified from torchvision to also return the union
+# Modified from torchvision to also return the union and to work only on the last two dimensions, assuming the other ones are identical.
 def box_iou(boxes1, boxes2):
-    area1 = box_area(boxes1)
-    area2 = box_area(boxes2)
+    area1 = box_area(boxes1.view(-1, 4)).view(boxes1.shape[:-1])
+    area2 = box_area(boxes2.view(-1, 4)).view(boxes2.shape[:-1])
 
-    lt = torch.max(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]
-    rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]
+    lt = torch.max(boxes1[..., None, :2], boxes2[..., None, :, :2])  # [..., N,M,2]
+    rb = torch.min(boxes1[..., None, 2:], boxes2[..., None, :, 2:])  # [..., N,M,2]
 
-    wh = (rb - lt).clamp(min=0)  # [N,M,2]
-    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]
+    wh = (rb - lt).clamp(min=0)  # [..., N,M,2]
+    inter = wh[..., 0] * wh[..., 1]  # [..., N,M]
 
-    union = area1[:, None] + area2 - inter
+    union = area1[..., None] + area2[..., None, :] - inter
 
     iou = inter / union
     return iou, union
@@ -48,15 +48,15 @@ def generalized_box_iou(boxes1, boxes2):
     """
     # degenerate boxes gives inf / nan results
     # so do an early check
-    assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
-    assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
+    assert (boxes1[..., 2:] >= boxes1[..., :2]).all()
+    assert (boxes2[..., 2:] >= boxes2[..., :2]).all()
     iou, union = box_iou(boxes1, boxes2)
 
-    lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])
-    rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])
+    lt = torch.min(boxes1[..., None, :2], boxes2[..., None, :, :2])
+    rb = torch.max(boxes1[..., None, 2:], boxes2[..., None, :, 2:])
 
-    wh = (rb - lt).clamp(min=0)  # [N,M,2]
-    area = wh[:, :, 0] * wh[:, :, 1]
+    wh = (rb - lt).clamp(min=0)  # [..., N,M,2]
+    area = wh[..., 0] * wh[..., 1]
 
     return iou - (area - union) / area