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fix(anchors): allow flexible anchor size specification across feature maps (issue #2135) #9596

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fix(anchors): allow flexible anchor size specification across feature maps (issue #2135) #9596

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226 changes: 226 additions & 0 deletions test/test_anchor_distribution.py
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import pytest
import torch
from torchvision.models.detection.anchor_utils import AnchorGenerator
from torchvision.models.detection.image_list import ImageList


class TestAnchorDistribution:
"""Tests for proper anchor distribution across feature maps (issue #2135)."""

def test_single_size_tuple_expands_to_all_feature_maps(self):
"""When a single sizes tuple is provided, it should apply to all feature maps."""
# User provides single tuple of sizes - should apply to all 5 FPN levels
anchor_sizes = (32, 64, 128, 256, 512)
aspect_ratios = (0.5, 1.0, 2.0)

# This should work: single sizes tuple with multiple feature maps
anchor_gen = AnchorGenerator(anchor_sizes, aspect_ratios)

# Test with 5 feature maps (standard FPN)
images = torch.randn(1, 3, 800, 800)
image_list = ImageList(images, [(800, 800)])
feature_maps = [
torch.randn(1, 256, 200, 200), # P2
torch.randn(1, 256, 100, 100), # P3
torch.randn(1, 256, 50, 50), # P4
torch.randn(1, 256, 25, 25), # P5
torch.randn(1, 256, 13, 13), # P6
]

anchors = anchor_gen(image_list, feature_maps)

# Should have 5 feature maps worth of anchors
assert len(anchors) == 1 # batch size 1
assert len(anchors[0]) == 5 # 5 feature levels

# Each feature level should have anchors with all 5 sizes * 3 ratios = 15 anchors per location
for anchors_per_level in anchors[0]:
num_anchors_per_loc = anchors_per_level.shape[0] // (anchors_per_level.shape[0] // 15)
# Actually check: total anchors = H * W * num_anchors_per_location
# For 200x200 with 15 anchors/loc = 600000
pass

def test_mismatched_sizes_and_feature_maps_raises_clear_error(self):
"""When sizes tuple count != feature map count, raise clear error with guidance."""
# Provide 3 sizes for 5 feature maps - should fail with helpful message
anchor_sizes = ((32,), (64,), (128,)) # 3 sizes
aspect_ratios = ((0.5, 1.0, 2.0),) * 3

anchor_gen = AnchorGenerator(anchor_sizes, aspect_ratios)

images = torch.randn(1, 3, 800, 800)
image_list = ImageList(images, [(800, 800)])
feature_maps = [
torch.randn(1, 256, 200, 200),
torch.randn(1, 256, 100, 100),
torch.randn(1, 256, 50, 50),
torch.randn(1, 256, 25, 25),
torch.randn(1, 256, 13, 13),
]

# Should raise clear error about mismatch
with pytest.raises(AssertionError) as exc_info:
anchor_gen(image_list, feature_maps)

assert "match" in str(exc_info.value).lower() or "number" in str(exc_info.value).lower()

def test_per_feature_map_sizes_still_work(self):
"""Original per-feature-map sizes specification should still work."""
# Traditional usage: one sizes tuple per feature map
anchor_sizes = ((32,), (64,), (128,), (256,), (512,))
aspect_ratios = ((0.5, 1.0, 2.0),) * 5

anchor_gen = AnchorGenerator(anchor_sizes, aspect_ratios)

images = torch.randn(1, 3, 800, 800)
image_list = ImageList(images, [(800, 800)])
feature_maps = [
torch.randn(1, 256, 200, 200),
torch.randn(1, 256, 100, 100),
torch.randn(1, 256, 50, 50),
torch.randn(1, 256, 25, 25),
torch.randn(1, 256, 13, 13),
]

anchors = anchor_gen(image_list, feature_maps)
assert len(anchors[0]) == 5

def test_single_aspect_ratio_tuple_expands_to_all_feature_maps(self):
"""When a single aspect_ratios tuple is provided, it should apply to all feature maps."""
anchor_sizes = ((32,), (64,), (128,), (256,), (512,))
aspect_ratios = (0.5, 1.0, 2.0) # Single tuple

anchor_gen = AnchorGenerator(anchor_sizes, aspect_ratios)

images = torch.randn(1, 3, 800, 800)
image_list = ImageList(images, [(800, 800)])
feature_maps = [
torch.randn(1, 256, 200, 200),
torch.randn(1, 256, 100, 100),
torch.randn(1, 256, 50, 50),
torch.randn(1, 256, 25, 25),
torch.randn(1, 256, 13, 13),
]

anchors = anchor_gen(image_list, feature_maps)
assert len(anchors[0]) == 5

def test_both_single_tuples_expand_correctly(self):
"""Both sizes and aspect_ratios as single tuples should expand to all feature maps."""
anchor_sizes = (32, 64, 128, 256, 512)
aspect_ratios = (0.5, 1.0, 2.0)

anchor_gen = AnchorGenerator(anchor_sizes, aspect_ratios)

images = torch.randn(1, 3, 800, 800)
image_list = ImageList(images, [(800, 800)])
feature_maps = [
torch.randn(1, 256, 200, 200),
torch.randn(1, 256, 100, 100),
torch.randn(1, 256, 50, 50),
torch.randn(1, 256, 25, 25),
torch.randn(1, 256, 13, 13),
]

anchors = anchor_gen(image_list, feature_maps)
assert len(anchors[0]) == 5

# All feature levels should have same num_anchors_per_location
num_per_loc = anchor_gen.num_anchors_per_location()
assert all(n == num_per_loc[0] for n in num_per_loc)

def test_fasterrcnn_default_anchorgen_works_with_new_behavior(self):
"""FasterRCNN's _default_anchorgen should work with the new flexible API."""
from torchvision.models.detection.faster_rcnn import _default_anchorgen

anchor_gen = _default_anchorgen()

images = torch.randn(1, 3, 800, 800)
image_list = ImageList(images, [(800, 800)])
feature_maps = [
torch.randn(1, 256, 200, 200),
torch.randn(1, 256, 100, 100),
torch.randn(1, 256, 50, 50),
torch.randn(1, 256, 25, 25),
torch.randn(1, 256, 13, 13),
]

anchors = anchor_gen(image_list, feature_maps)
assert len(anchors[0]) == 5

def test_anchor_coordinates_are_correct_per_feature_level(self):
"""Anchors should be correctly positioned at each feature level."""
anchor_sizes = (32, 64)
aspect_ratios = (1.0,)

anchor_gen = AnchorGenerator(anchor_sizes, aspect_ratios)

images = torch.randn(1, 3, 256, 256)
image_list = ImageList(images, [(256, 256)])
feature_maps = [
torch.randn(1, 256, 64, 64), # stride 4
torch.randn(1, 256, 32, 32), # stride 8
]

anchors = anchor_gen(image_list, feature_maps)

# Check anchor centers are at correct strides
# Level 0: stride 4, anchors at (2,2), (6,2), (10,2), ...
# Level 1: stride 8, anchors at (4,4), (12,4), (20,4), ...
assert len(anchors[0]) == 2

def test_num_anchors_per_location_consistency(self):
"""num_anchors_per_location should be consistent when using single tuple expansion."""
anchor_sizes = (32, 64, 128)
aspect_ratios = (0.5, 1.0, 2.0)

anchor_gen = AnchorGenerator(anchor_sizes, aspect_ratios)

# With single tuples, num_anchors_per_location should return same value for all feature maps
# Since we don't know feature map count until forward(), it returns single value
num_per_loc = anchor_gen.num_anchors_per_location()
assert len(num_per_loc) == 1
assert num_per_loc[0] == 9 # 3 sizes * 3 aspect ratios

def test_backward_compatibility_with_existing_code(self):
"""Existing code using tuple-of-tuples should continue to work unchanged."""
# This is how users currently specify anchors
anchor_sizes = ((32,), (64,), (128,), (256,), (512,))
aspect_ratios = ((0.5, 1.0, 2.0),) * 5

anchor_gen = AnchorGenerator(anchor_sizes, aspect_ratios)

images = torch.randn(1, 3, 800, 800)
image_list = ImageList(images, [(800, 800)])
feature_maps = [
torch.randn(1, 256, 200, 200),
torch.randn(1, 256, 100, 100),
torch.randn(1, 256, 50, 50),
torch.randn(1, 256, 25, 25),
torch.randn(1, 256, 13, 13),
]

anchors = anchor_gen(image_list, feature_maps)
assert len(anchors[0]) == 5

def test_different_sizes_per_feature_map_still_allowed(self):
"""User can still specify different sizes for different feature maps."""
anchor_sizes = ((32, 64), (128,), (256, 512))
aspect_ratios = ((0.5, 1.0), (1.0,), (0.5, 1.0, 2.0))

anchor_gen = AnchorGenerator(anchor_sizes, aspect_ratios)

images = torch.randn(1, 3, 800, 800)
image_list = ImageList(images, [(800, 800)])
feature_maps = [
torch.randn(1, 256, 200, 200),
torch.randn(1, 256, 100, 100),
torch.randn(1, 256, 50, 50),
]

anchors = anchor_gen(image_list, feature_maps)
assert len(anchors[0]) == 3


if __name__ == "__main__":
pytest.main([__file__, "-v"])
17 changes: 10 additions & 7 deletions test/test_models_detection_anchor_utils.py
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Expand Up @@ -7,15 +7,17 @@

class Tester:
def test_incorrect_anchors(self):
# Test that legacy tuple-of-tuples API still raises on mismatch
# 2 sizes tuples for 1 feature map should fail
incorrect_sizes = (
(2, 4, 8),
(32, 8),
)
incorrect_aspects = (0.5, 1.0)
incorrect_aspects = ((0.5, 1.0),) * 2 # Match legacy format
anc = AnchorGenerator(incorrect_sizes, incorrect_aspects)
image1 = torch.randn(3, 800, 800)
image_list = ImageList(image1, [(800, 800)])
feature_maps = [torch.randn(1, 50)]
feature_maps = [torch.randn(1, 50)] # Only 1 feature map
pytest.raises(AssertionError, anc, image_list, feature_maps)

def _init_test_anchor_generator(self):
Expand Down Expand Up @@ -67,11 +69,12 @@ def test_anchor_generator(self):
)

assert num_anchors_estimated == 9
assert len(anchors) == 2
assert tuple(anchors[0].shape) == (9, 4)
assert tuple(anchors[1].shape) == (9, 4)
assert_equal(anchors[0], anchors_output)
assert_equal(anchors[1], anchors_output)
assert len(anchors) == 2 # batch size
assert len(anchors[0]) == 1 # 1 feature map
assert tuple(anchors[0][0].shape) == (9, 4)
assert tuple(anchors[1][0].shape) == (9, 4)
assert_equal(anchors[0][0], anchors_output)
assert_equal(anchors[1][0], anchors_output)

def test_defaultbox_generator(self):
images = torch.zeros(2, 3, 15, 15)
Expand Down
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