# Copyright (c) Facebook, Inc. and its affiliates.
import logging
import unittest
from copy import deepcopy
import torch
from torch import nn
from detectron2 import model_zoo
from detectron2.config import get_cfg
from detectron2.export.torchscript_patch import (
freeze_training_mode,
patch_builtin_len,
patch_instances,
)
from detectron2.layers import ShapeSpec
from detectron2.modeling.proposal_generator.build import build_proposal_generator
from detectron2.modeling.roi_heads import (
FastRCNNConvFCHead,
KRCNNConvDeconvUpsampleHead,
MaskRCNNConvUpsampleHead,
StandardROIHeads,
build_roi_heads,
)
from detectron2.projects import point_rend
from detectron2.structures import BitMasks, Boxes, ImageList, Instances, RotatedBoxes
from detectron2.utils.events import EventStorage
from detectron2.utils.testing import assert_instances_allclose, random_boxes
logger = logging.getLogger(__name__)
"""
Make sure the losses of ROIHeads/RPN do not change, to avoid
breaking the forward logic by mistake.
This relies on assumption that pytorch's RNG is stable.
"""
class ROIHeadsTest(unittest.TestCase):
def test_roi_heads(self):
torch.manual_seed(121)
cfg = get_cfg()
cfg.MODEL.ROI_BOX_HEAD.NAME = "FastRCNNConvFCHead"
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)
cfg.MODEL.MASK_ON = True
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)}
feature_shape = {"res4": ShapeSpec(channels=num_channels, stride=16)}
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_instance0.gt_masks = BitMasks(torch.rand((2,) + image_shape) > 0.5)
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_instance1.gt_masks = BitMasks(torch.rand((2,) + image_shape) > 0.5)
gt_instances = [gt_instance0, gt_instance1]
proposal_generator = build_proposal_generator(cfg, feature_shape)
roi_heads = StandardROIHeads(cfg, feature_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)
detector_losses.update(proposal_losses)
expected_losses = {
"loss_cls": 4.5253729820251465,
"loss_box_reg": 0.009785720147192478,
"loss_mask": 0.693184494972229,
"loss_rpn_cls": 0.08186662942171097,
"loss_rpn_loc": 0.1104838103055954,
}
succ = all(
torch.allclose(detector_losses[name], torch.tensor(expected_losses.get(name, 0.0)))
for name in detector_losses.keys()
)
self.assertTrue(
succ,
"Losses has changed! New losses: {}".format(
{k: v.item() for k, v in detector_losses.items()}
),
)
def test_rroi_heads(self):
torch.manual_seed(121)
cfg = get_cfg()
cfg.MODEL.PROPOSAL_GENERATOR.NAME = "RRPN"
cfg.MODEL.ANCHOR_GENERATOR.NAME = "RotatedAnchorGenerator"
cfg.MODEL.ROI_HEADS.NAME = "RROIHeads"
cfg.MODEL.ROI_BOX_HEAD.NAME = "FastRCNNConvFCHead"
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)
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)}
feature_shape = {"res4": ShapeSpec(channels=num_channels, stride=16)}
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]
proposal_generator = build_proposal_generator(cfg, feature_shape)
roi_heads = build_roi_heads(cfg, feature_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)
detector_losses.update(proposal_losses)
expected_losses = {
"loss_cls": 4.365657806396484,
"loss_box_reg": 0.0015851043863222003,
"loss_rpn_cls": 0.2427729219198227,
"loss_rpn_loc": 0.3646621108055115,
}
succ = all(
torch.allclose(detector_losses[name], torch.tensor(expected_losses.get(name, 0.0)))
for name in detector_losses.keys()
)
self.assertTrue(
succ,
"Losses has changed! New losses: {}".format(
{k: v.item() for k, v in detector_losses.items()}
),
)
def test_box_head_scriptability(self):
input_shape = ShapeSpec(channels=1024, height=14, width=14)
box_features = torch.randn(4, 1024, 14, 14)
box_head = FastRCNNConvFCHead(
input_shape, conv_dims=[512, 512], fc_dims=[1024, 1024]
).eval()
script_box_head = torch.jit.script(box_head)
origin_output = box_head(box_features)
script_output = script_box_head(box_features)
self.assertTrue(torch.equal(origin_output, script_output))
def test_mask_head_scriptability(self):
input_shape = ShapeSpec(channels=1024)
mask_features = torch.randn(4, 1024, 14, 14)
image_shapes = [(10, 10), (15, 15)]
pred_instance0 = Instances(image_shapes[0])
pred_classes0 = torch.tensor([1, 2, 3], dtype=torch.int64)
pred_instance0.pred_classes = pred_classes0
pred_instance1 = Instances(image_shapes[1])
pred_classes1 = torch.tensor([4], dtype=torch.int64)
pred_instance1.pred_classes = pred_classes1
mask_head = MaskRCNNConvUpsampleHead(
input_shape, num_classes=80, conv_dims=[256, 256]
).eval()
# pred_instance will be in-place changed during the inference
# process of `MaskRCNNConvUpsampleHead`
origin_outputs = mask_head(mask_features, deepcopy([pred_instance0, pred_instance1]))
fields = {"pred_masks": torch.Tensor, "pred_classes": torch.Tensor}
with freeze_training_mode(mask_head), patch_instances(fields) as NewInstances:
sciript_mask_head = torch.jit.script(mask_head)
pred_instance0 = NewInstances.from_instances(pred_instance0)
pred_instance1 = NewInstances.from_instances(pred_instance1)
script_outputs = sciript_mask_head(mask_features, [pred_instance0, pred_instance1])
for origin_ins, script_ins in zip(origin_outputs, script_outputs):
assert_instances_allclose(origin_ins, script_ins, rtol=0)
def test_keypoint_head_scriptability(self):
input_shape = ShapeSpec(channels=1024, height=14, width=14)
keypoint_features = torch.randn(4, 1024, 14, 14)
image_shapes = [(10, 10), (15, 15)]
pred_boxes0 = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6], [1, 5, 2, 8]], dtype=torch.float32)
pred_instance0 = Instances(image_shapes[0])
pred_instance0.pred_boxes = Boxes(pred_boxes0)
pred_boxes1 = torch.tensor([[7, 3, 10, 5]], dtype=torch.float32)
pred_instance1 = Instances(image_shapes[1])
pred_instance1.pred_boxes = Boxes(pred_boxes1)
keypoint_head = KRCNNConvDeconvUpsampleHead(
input_shape, num_keypoints=17, conv_dims=[512, 512]
).eval()
origin_outputs = keypoint_head(
keypoint_features, deepcopy([pred_instance0, pred_instance1])
)
fields = {
"pred_boxes": Boxes,
"pred_keypoints": torch.Tensor,
"pred_keypoint_heatmaps": torch.Tensor,
}
with freeze_training_mode(keypoint_head), patch_instances(fields) as NewInstances:
sciript_keypoint_head = torch.jit.script(keypoint_head)
pred_instance0 = NewInstances.from_instances(pred_instance0)
pred_instance1 = NewInstances.from_instances(pred_instance1)
script_outputs = sciript_keypoint_head(
keypoint_features, [pred_instance0, pred_instance1]
)
for origin_ins, script_ins in zip(origin_outputs, script_outputs):
assert_instances_allclose(origin_ins, script_ins, rtol=0)
def test_StandardROIHeads_scriptability(self):
cfg = get_cfg()
cfg.MODEL.ROI_BOX_HEAD.NAME = "FastRCNNConvFCHead"
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)
cfg.MODEL.MASK_ON = True
cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST = 0.01
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.01
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)}
feature_shape = {"res4": ShapeSpec(channels=num_channels, stride=16)}
roi_heads = StandardROIHeads(cfg, feature_shape).eval()
proposal0 = Instances(image_sizes[0])
proposal_boxes0 = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6]], dtype=torch.float32)
proposal0.proposal_boxes = Boxes(proposal_boxes0)
proposal0.objectness_logits = torch.tensor([0.5, 0.7], dtype=torch.float32)
proposal1 = Instances(image_sizes[1])
proposal_boxes1 = torch.tensor([[1, 5, 2, 8], [7, 3, 10, 5]], dtype=torch.float32)
proposal1.proposal_boxes = Boxes(proposal_boxes1)
proposal1.objectness_logits = torch.tensor([0.1, 0.9], dtype=torch.float32)
proposals = [proposal0, proposal1]
pred_instances, _ = roi_heads(images, features, proposals)
fields = {
"objectness_logits": torch.Tensor,
"proposal_boxes": Boxes,
"pred_classes": torch.Tensor,
"scores": torch.Tensor,
"pred_masks": torch.Tensor,
"pred_boxes": Boxes,
"pred_keypoints": torch.Tensor,
"pred_keypoint_heatmaps": torch.Tensor,
}
with freeze_training_mode(roi_heads), patch_instances(fields) as new_instances:
proposal0 = new_instances.from_instances(proposal0)
proposal1 = new_instances.from_instances(proposal1)
proposals = [proposal0, proposal1]
scripted_rot_heads = torch.jit.script(roi_heads)
scripted_pred_instances, _ = scripted_rot_heads(images, features, proposals)
for instance, scripted_instance in zip(pred_instances, scripted_pred_instances):
assert_instances_allclose(instance, scripted_instance, rtol=0)
def test_PointRend_mask_head_tracing(self):
cfg = model_zoo.get_config("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml")
point_rend.add_pointrend_config(cfg)
cfg.MODEL.ROI_HEADS.IN_FEATURES = ["p2", "p3"]
cfg.MODEL.ROI_MASK_HEAD.NAME = "PointRendMaskHead"
cfg.MODEL.ROI_MASK_HEAD.POOLER_TYPE = ""
cfg.MODEL.ROI_MASK_HEAD.POINT_HEAD_ON = True
chan = 256
head = point_rend.PointRendMaskHead(
cfg,
{
"p2": ShapeSpec(channels=chan, stride=4),
"p3": ShapeSpec(channels=chan, stride=8),
},
)
def gen_inputs(h, w, N):
p2 = torch.rand(1, chan, h, w)
p3 = torch.rand(1, chan, h // 2, w // 2)
boxes = random_boxes(N, max_coord=h)
return p2, p3, boxes
class Wrap(nn.ModuleDict):
def forward(self, p2, p3, boxes):
features = {
"p2": p2,
"p3": p3,
}
inst = Instances((p2.shape[2] * 4, p2.shape[3] * 4))
inst.pred_boxes = Boxes(boxes)
inst.pred_classes = torch.zeros(inst.__len__(), dtype=torch.long)
out = self.head(features, [inst])[0]
return out.pred_masks
model = Wrap({"head": head})
model.eval()
with torch.no_grad(), patch_builtin_len():
traced = torch.jit.trace(model, gen_inputs(302, 208, 20))
inputs = gen_inputs(100, 120, 30)
out_eager = model(*inputs)
out_trace = traced(*inputs)
self.assertTrue(torch.allclose(out_eager, out_trace))
if __name__ == "__main__":
unittest.main()