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test_models_detection_negative_samples.py
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import pytest
import torch
import torchvision.models
from common_utils import assert_equal
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor, TwoMLPHead
from torchvision.models.detection.roi_heads import RoIHeads
from torchvision.models.detection.rpn import AnchorGenerator, RPNHead, RegionProposalNetwork
from torchvision.ops import MultiScaleRoIAlign
class TestModelsDetectionNegativeSamples:
def _make_empty_sample(self, add_masks=False, add_keypoints=False):
images = [torch.rand((3, 100, 100), dtype=torch.float32)]
boxes = torch.zeros((0, 4), dtype=torch.float32)
negative_target = {
"boxes": boxes,
"labels": torch.zeros(0, dtype=torch.int64),
"image_id": 4,
"area": (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0]),
"iscrowd": torch.zeros((0,), dtype=torch.int64),
}
if add_masks:
negative_target["masks"] = torch.zeros(0, 100, 100, dtype=torch.uint8)
if add_keypoints:
negative_target["keypoints"] = torch.zeros(17, 0, 3, dtype=torch.float32)
targets = [negative_target]
return images, targets
def test_targets_to_anchors(self):
_, targets = self._make_empty_sample()
anchors = [torch.randint(-50, 50, (3, 4), dtype=torch.float32)]
anchor_sizes = ((32,), (64,), (128,), (256,), (512,))
aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes)
rpn_anchor_generator = AnchorGenerator(anchor_sizes, aspect_ratios)
rpn_head = RPNHead(4, rpn_anchor_generator.num_anchors_per_location()[0])
head = RegionProposalNetwork(rpn_anchor_generator, rpn_head, 0.5, 0.3, 256, 0.5, 2000, 2000, 0.7, 0.05)
labels, matched_gt_boxes = head.assign_targets_to_anchors(anchors, targets)
assert labels[0].sum() == 0
assert labels[0].shape == torch.Size([anchors[0].shape[0]])
assert labels[0].dtype == torch.float32
assert matched_gt_boxes[0].sum() == 0
assert matched_gt_boxes[0].shape == anchors[0].shape
assert matched_gt_boxes[0].dtype == torch.float32
def test_assign_targets_to_proposals(self):
proposals = [torch.randint(-50, 50, (20, 4), dtype=torch.float32)]
gt_boxes = [torch.zeros((0, 4), dtype=torch.float32)]
gt_labels = [torch.tensor([[0]], dtype=torch.int64)]
box_roi_pool = MultiScaleRoIAlign(featmap_names=["0", "1", "2", "3"], output_size=7, sampling_ratio=2)
resolution = box_roi_pool.output_size[0]
representation_size = 1024
box_head = TwoMLPHead(4 * resolution ** 2, representation_size)
representation_size = 1024
box_predictor = FastRCNNPredictor(representation_size, 2)
roi_heads = RoIHeads(
# Box
box_roi_pool,
box_head,
box_predictor,
0.5,
0.5,
512,
0.25,
None,
0.05,
0.5,
100,
)
matched_idxs, labels = roi_heads.assign_targets_to_proposals(proposals, gt_boxes, gt_labels)
assert matched_idxs[0].sum() == 0
assert matched_idxs[0].shape == torch.Size([proposals[0].shape[0]])
assert matched_idxs[0].dtype == torch.int64
assert labels[0].sum() == 0
assert labels[0].shape == torch.Size([proposals[0].shape[0]])
assert labels[0].dtype == torch.int64
@pytest.mark.parametrize(
"name",
[
"fasterrcnn_resnet50_fpn",
"fasterrcnn_mobilenet_v3_large_fpn",
"fasterrcnn_mobilenet_v3_large_320_fpn",
],
)
def test_forward_negative_sample_frcnn(self, name):
model = torchvision.models.detection.__dict__[name](num_classes=2, min_size=100, max_size=100)
images, targets = self._make_empty_sample()
loss_dict = model(images, targets)
assert_equal(loss_dict["loss_box_reg"], torch.tensor(0.0))
assert_equal(loss_dict["loss_rpn_box_reg"], torch.tensor(0.0))
def test_forward_negative_sample_mrcnn(self):
model = torchvision.models.detection.maskrcnn_resnet50_fpn(num_classes=2, min_size=100, max_size=100)
images, targets = self._make_empty_sample(add_masks=True)
loss_dict = model(images, targets)
assert_equal(loss_dict["loss_box_reg"], torch.tensor(0.0))
assert_equal(loss_dict["loss_rpn_box_reg"], torch.tensor(0.0))
assert_equal(loss_dict["loss_mask"], torch.tensor(0.0))
def test_forward_negative_sample_krcnn(self):
model = torchvision.models.detection.keypointrcnn_resnet50_fpn(num_classes=2, min_size=100, max_size=100)
images, targets = self._make_empty_sample(add_keypoints=True)
loss_dict = model(images, targets)
assert_equal(loss_dict["loss_box_reg"], torch.tensor(0.0))
assert_equal(loss_dict["loss_rpn_box_reg"], torch.tensor(0.0))
assert_equal(loss_dict["loss_keypoint"], torch.tensor(0.0))
def test_forward_negative_sample_retinanet(self):
model = torchvision.models.detection.retinanet_resnet50_fpn(
num_classes=2, min_size=100, max_size=100, pretrained_backbone=False
)
images, targets = self._make_empty_sample()
loss_dict = model(images, targets)
assert_equal(loss_dict["bbox_regression"], torch.tensor(0.0))
def test_forward_negative_sample_ssd(self):
model = torchvision.models.detection.ssd300_vgg16(num_classes=2, pretrained_backbone=False)
images, targets = self._make_empty_sample()
loss_dict = model(images, targets)
assert_equal(loss_dict["bbox_regression"], torch.tensor(0.0))
if __name__ == "__main__":
pytest.main([__file__])