def test_rroi_heads(self):
torch.manual_seed(121)
cfg = RCNNConfig()
cfg.MODEL.ANCHOR_GENERATOR.NAME = "RotatedAnchorGenerator"
# PROPOSAL_GENERATOR: "RRPN"
# ROI_HEADS: "RROIHeads"
# ROI_BOX_HEAD.NAME: "FastRCNNConvFCHead"
def build_box_head(cfg, input_shape):
return FastRCNNConvFCHead(cfg, input_shape)
cfg.build_box_head = build_box_head
cfg.MODEL.RESNETS.DEPTH = 50
cfg.MODEL.ROI_BOX_HEAD.NUM_FC = 2
cfg.MODEL.RPN.BBOX_REG_WEIGHTS = (1, 1, 1, 1, 1)
cfg.MODEL.RPN.HEAD_NAME = "StandardRPNHead"
cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignRotated"
cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10, 10, 5, 5, 1)
backbone = build_backbone(cfg)
num_images = 2
images_tensor = torch.rand(num_images, 20, 30)
image_sizes = [(10, 10), (20, 30)]
images = ImageList(images_tensor, image_sizes)
num_channels = 1024
features = {"res4": torch.rand(num_images, num_channels, 1, 2)}
image_shape = (15, 15)
gt_boxes0 = torch.tensor([[2, 2, 2, 2, 30], [4, 4, 4, 4, 0]], dtype=torch.float32)
gt_instance0 = Instances(image_shape)
gt_instance0.gt_boxes = RotatedBoxes(gt_boxes0)
gt_instance0.gt_classes = torch.tensor([2, 1])
gt_boxes1 = torch.tensor([[1.5, 5.5, 1, 3, 0], [8.5, 4, 3, 2, -50]], dtype=torch.float32)
gt_instance1 = Instances(image_shape)
gt_instance1.gt_boxes = RotatedBoxes(gt_boxes1)
gt_instance1.gt_classes = torch.tensor([1, 2])
gt_instances = [gt_instance0, gt_instance1]
# currently using DefaultAnchorGenerator in RRPN
proposal_generator = RRPN(cfg, backbone.output_shape())
roi_heads = RROIHeads(cfg, backbone.output_shape())
with EventStorage(): # capture events in a new storage to discard them
proposals, proposal_losses = proposal_generator(images, features, gt_instances)
_, detector_losses = roi_heads(images, features, proposals, gt_instances)
expected_losses = {
"loss_cls": torch.tensor(4.381618499755859),
"loss_box_reg": torch.tensor(0.0011829272843897343),
}
for name in expected_losses.keys():
err_msg = "detector_losses[{}] = {}, expected losses = {}".format(
name, detector_losses[name], expected_losses[name]
)
self.assertTrue(torch.allclose(detector_losses[name], expected_losses[name]), err_msg)
def annotations_to_instances_rotated(annos, image_size):
"""
Create an :class:`Instances` object used by the models,
from instance annotations in the dataset dict.
Compared to `annotations_to_instances`, this function is for rotated boxes only
Args:
annos (list[dict]): a list of instance annotations in one image, each
element for one instance.
image_size (tuple): height, width
Returns:
Instances:
Containing fields "gt_boxes", "gt_classes",
if they can be obtained from `annos`.
This is the format that builtin models expect.
"""
boxes = [obj["bbox"] for obj in annos]
target = Instances(image_size)
boxes = target.gt_boxes = RotatedBoxes(boxes)
boxes.clip(image_size)
classes = [obj["category_id"] for obj in annos]
classes = torch.tensor(classes, dtype=torch.int64)
target.gt_classes = classes
return target
def test_roi_heads(self):
torch.manual_seed(121)
cfg = RCNNConfig()
# PROPOSAL_GENERATOR: "RPN"
# ROI_HEADS: "StandardROIHeads"
# ROI_BOX_HEAD: "FastRCNNConvFCHead"
cfg.MODEL.RESNETS.DEPTH = 50
cfg.MODEL.ROI_BOX_HEAD.NUM_FC = 2
cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignV2"
cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10, 10, 5, 5)
def build_box_head(cfg, input_shape):
return FastRCNNConvFCHead(cfg, input_shape)
cfg.build_box_head = build_box_head
backbone = build_backbone(cfg)
num_images = 2
images_tensor = torch.rand(num_images, 20, 30)
image_sizes = [(10, 10), (20, 30)]
images = ImageList(images_tensor, image_sizes)
num_channels = 1024
features = {"res4": torch.rand(num_images, num_channels, 1, 2)}
image_shape = (15, 15)
gt_boxes0 = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6]], dtype=torch.float32)
gt_instance0 = Instances(image_shape)
gt_instance0.gt_boxes = Boxes(gt_boxes0)
gt_instance0.gt_classes = torch.tensor([2, 1])
gt_boxes1 = torch.tensor([[1, 5, 2, 8], [7, 3, 10, 5]], dtype=torch.float32)
gt_instance1 = Instances(image_shape)
gt_instance1.gt_boxes = Boxes(gt_boxes1)
gt_instance1.gt_classes = torch.tensor([1, 2])
gt_instances = [gt_instance0, gt_instance1]
proposal_generator = RPN(cfg, backbone.output_shape())
roi_heads = StandardROIHeads(cfg, backbone.output_shape())
with EventStorage(): # capture events in a new storage to discard them
proposals, proposal_losses = proposal_generator(images, features, gt_instances)
_, detector_losses = roi_heads(images, features, proposals, gt_instances)
expected_losses = {
"loss_cls": torch.tensor(4.4236516953),
"loss_box_reg": torch.tensor(0.0091214813),
}
for name in expected_losses.keys():
self.assertTrue(torch.allclose(detector_losses[name], expected_losses[name]))
def test_fast_rcnn_rotated(self):
torch.manual_seed(132)
cfg = RCNNConfig()
cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10, 10, 5, 5, 1)
box2box_transform = Box2BoxTransformRotated(weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS)
box_head_output_size = 8
num_classes = 5
cls_agnostic_bbox_reg = False
box_predictor = FastRCNNOutputLayers(
box_head_output_size, num_classes, cls_agnostic_bbox_reg, box_dim=5
)
feature_pooled = torch.rand(2, box_head_output_size)
pred_class_logits, pred_proposal_deltas = box_predictor(feature_pooled)
image_shape = (10, 10)
proposal_boxes = torch.tensor(
[[2, 1.95, 2.4, 1.7, 0], [4.65, 5.25, 4.7, 5.5, 0]], dtype=torch.float32
)
gt_boxes = torch.tensor([[2, 2, 2, 2, 0], [4, 4, 4, 4, 0]], dtype=torch.float32)
result = Instances(image_shape)
result.proposal_boxes = RotatedBoxes(proposal_boxes)
result.gt_boxes = RotatedBoxes(gt_boxes)
result.gt_classes = torch.tensor([1, 2])
proposals = []
proposals.append(result)
smooth_l1_beta = cfg.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA
outputs = RotatedFastRCNNOutputs(
box2box_transform, pred_class_logits, pred_proposal_deltas, proposals, smooth_l1_beta
)
with EventStorage(): # capture events in a new storage to discard them
losses = outputs.losses()
# Note: the expected losses are slightly different even if
# the boxes are essentially the same as in the FastRCNNOutput test, because
# bbox_pred in FastRCNNOutputLayers have different Linear layers/initialization
# between the two cases.
expected_losses = {
"loss_cls": torch.tensor(1.7920907736),
"loss_box_reg": torch.tensor(4.0410838127),
}
for name in expected_losses.keys():
assert torch.allclose(losses[name], expected_losses[name])
def test_fast_rcnn(self):
torch.manual_seed(132)
cfg = RCNNConfig()
cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10, 10, 5, 5)
box2box_transform = Box2BoxTransform(weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS)
box_head_output_size = 8
num_classes = 5
cls_agnostic_bbox_reg = False
box_predictor = FastRCNNOutputLayers(
box_head_output_size, num_classes, cls_agnostic_bbox_reg, box_dim=4
)
feature_pooled = torch.rand(2, box_head_output_size)
pred_class_logits, pred_proposal_deltas = box_predictor(feature_pooled)
image_shape = (10, 10)
proposal_boxes = torch.tensor([[0.8, 1.1, 3.2, 2.8], [2.3, 2.5, 7, 8]], dtype=torch.float32)
gt_boxes = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6]], dtype=torch.float32)
result = Instances(image_shape)
result.proposal_boxes = Boxes(proposal_boxes)
result.gt_boxes = Boxes(gt_boxes)
result.gt_classes = torch.tensor([1, 2])
proposals = []
proposals.append(result)
smooth_l1_beta = cfg.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA
outputs = FastRCNNOutputs(
box2box_transform, pred_class_logits, pred_proposal_deltas, proposals, smooth_l1_beta
)
with EventStorage(): # capture events in a new storage to discard them
losses = outputs.losses()
expected_losses = {
"loss_cls": torch.tensor(1.7951188087),
"loss_box_reg": torch.tensor(4.0357131958),
}
for name in expected_losses.keys():
assert torch.allclose(losses[name], expected_losses[name])
def annotations_to_instances(annos, image_size, mask_format="polygon"):
"""
Create an :class:`Instances` object used by the models,
from instance annotations in the dataset dict.
Args:
annos (list[dict]): a list of instance annotations in one image, each
element for one instance.
image_size (tuple): height, width
Returns:
Instances:
It will contain fields "gt_boxes", "gt_classes",
"gt_masks", "gt_keypoints", if they can be obtained from `annos`.
This is the format that builtin models expect.
"""
boxes = [
BoxMode.convert(obj["bbox"], obj["bbox_mode"], BoxMode.XYXY_ABS)
for obj in annos
]
target = Instances(image_size)
boxes = target.gt_boxes = Boxes(boxes)
boxes.clip(image_size)
classes = [obj["category_id"] for obj in annos]
classes = torch.tensor(classes, dtype=torch.int64)
target.gt_classes = classes
if len(annos) and "segmentation" in annos[0]:
segms = [obj["segmentation"] for obj in annos]
if mask_format == "polygon":
masks = PolygonMasks(segms)
else:
assert mask_format == "bitmask", mask_format
masks = []
for segm in segms:
if isinstance(segm, list):
# polygon
masks.append(polygons_to_bitmask(segm, *image_size))
elif isinstance(segm, dict):
# COCO RLE
masks.append(mask_util.decode(segm))
elif isinstance(segm, np.ndarray):
assert segm.ndim == 2, "Expect segmentation of 2 dimensions, got {}.".format(
segm.ndim)
# mask array
masks.append(segm)
else:
raise ValueError(
"Cannot convert segmentation of type '{}' to BitMasks!"
"Supported types are: polygons as list[list[float] or ndarray],"
" COCO-style RLE as a dict, or a full-image segmentation mask "
"as a 2D ndarray.".format(type(segm)))
# torch.from_numpy does not support array with negative stride.
masks = BitMasks(
torch.stack([
torch.from_numpy(np.ascontiguousarray(x)) for x in masks
]))
target.gt_masks = masks
if len(annos) and "keypoints" in annos[0]:
kpts = np.array([obj.get("keypoints", [])
for obj in annos]) # (N, K, 3)
# Set all out-of-boundary points to "unlabeled"
kpts_xy = kpts[:, :, :2]
inside = (kpts_xy >= np.array([0, 0])) & (kpts_xy <= np.array(
image_size[::-1]))
inside = inside.all(axis=2)
kpts[:, :, :2] = kpts_xy
kpts[:, :, 2][~inside] = 0
target.gt_keypoints = Keypoints(kpts)
return target
