def __init__(self, cfg):
super().__init__()
self.in_features = cfg.MODEL.FCOS.IN_FEATURES
# Loss parameters:
# defined by method<get_ground_truth>
self.num_points_per_level = None
self.fpn_strides = cfg.MODEL.FCOS.FPN_STRIDES
self.center_sampling_radius = cfg.MODEL.FCOS.CENTER_SAMPLING_RADIUS
self.norm_reg_targets = cfg.MODEL.FCOS.NORM_REG_TARGETS
self.focal_loss_alpha = cfg.MODEL.FCOS.FOCAL_LOSS_ALPHA
self.focal_loss_gamma = cfg.MODEL.FCOS.FOCAL_LOSS_GAMMA
self.iou_loss_type = cfg.MODEL.FCOS.IOU_LOSS_TYPE
# Inference parameters:
self.score_thresh = 0.3
self.pre_nms_thresh = cfg.MODEL.FCOS.INFERENCE_TH
self.pre_nms_top_n = cfg.MODEL.FCOS.PRE_NMS_TOP_N
self.nms_thresh = cfg.MODEL.FCOS.NMS_TH
self.max_detections_per_image = cfg.TEST.DETECTIONS_PER_IMAGE
self.min_size = 0
self.num_classes = cfg.MODEL.FCOS.NUM_CLASSES
self.backbone = build_backbone(cfg)
backbone_shape = self.backbone.output_shape()
feature_shapes = [backbone_shape[f] for f in self.in_features]
self.head = FCOSHead(cfg, feature_shapes)
self.register_buffer("pixel_mean",
torch.Tensor(cfg.MODEL.PIXEL_MEAN).view(-1, 1, 1))
self.register_buffer("pixel_std",
torch.Tensor(cfg.MODEL.PIXEL_STD).view(-1, 1, 1))
def test_backbone():
backbone = build_backbone(cfg).to(device)
images = torch.empty((2, 3, 512, 512)).to(device)
assert isinstance(backbone, nn.Module)
outputs = backbone(images)
ipdb.set_trace()
def test_backbone():
backbone = build_backbone(cfg).to(device)
images = torch.empty((2, 3, 512, 512)).to(device)
assert isinstance(backbone, nn.Module)
num = 0
for module in backbone.modules():
if isinstance(module, nn.Conv2d):
num += 1
print(num)
outputs = backbone(images)
import pdb
pdb.set_trace()