def forward(self, image, im_info, gt_boxes=None):
image = self.preprocess_image(image)
features = self.backbone(image)
features = [features[f] for f in self.in_features]
box_logits, box_offsets, box_ctrness = self.head(features)
box_logits_list = [
_.transpose(0, 2, 3, 1).reshape(image.shape[0], -1,
self.cfg.num_classes)
for _ in box_logits
]
box_offsets_list = [
_.transpose(0, 2, 3, 1).reshape(image.shape[0], -1, 4)
for _ in box_offsets
]
box_ctrness_list = [
_.transpose(0, 2, 3, 1).reshape(image.shape[0], -1, 1)
for _ in box_ctrness
]
anchors_list = self.anchor_generator(features)
all_level_box_logits = F.concat(box_logits_list, axis=1)
all_level_box_offsets = F.concat(box_offsets_list, axis=1)
all_level_box_ctrness = F.concat(box_ctrness_list, axis=1)
if self.training:
gt_labels, gt_offsets, gt_ctrness = self.get_ground_truth(
anchors_list,
gt_boxes,
im_info[:, 4].astype(np.int32),
)
all_level_box_logits = all_level_box_logits.reshape(
-1, self.cfg.num_classes)
all_level_box_offsets = all_level_box_offsets.reshape(-1, 4)
all_level_box_ctrness = all_level_box_ctrness.flatten()
gt_labels = gt_labels.flatten()
gt_offsets = gt_offsets.reshape(-1, 4)
gt_ctrness = gt_ctrness.flatten()
valid_mask = gt_labels >= 0
fg_mask = gt_labels > 0
num_fg = fg_mask.sum()
sum_ctr = gt_ctrness[fg_mask].sum()
# add detach() to avoid syncing across ranks in backward
num_fg = layers.all_reduce_mean(num_fg).detach()
sum_ctr = layers.all_reduce_mean(sum_ctr).detach()
gt_targets = F.zeros_like(all_level_box_logits)
gt_targets[fg_mask, gt_labels[fg_mask] - 1] = 1
loss_cls = layers.sigmoid_focal_loss(
all_level_box_logits[valid_mask],
gt_targets[valid_mask],
alpha=self.cfg.focal_loss_alpha,
gamma=self.cfg.focal_loss_gamma,
).sum() / F.maximum(num_fg, 1)
loss_bbox = (layers.iou_loss(
all_level_box_offsets[fg_mask],
gt_offsets[fg_mask],
box_mode="ltrb",
loss_type=self.cfg.iou_loss_type,
) * gt_ctrness[fg_mask]).sum() / F.maximum(
sum_ctr, 1e-5) * self.cfg.loss_bbox_weight
loss_ctr = layers.binary_cross_entropy(
all_level_box_ctrness[fg_mask],
gt_ctrness[fg_mask],
).sum() / F.maximum(num_fg, 1)
total = loss_cls + loss_bbox + loss_ctr
loss_dict = {
"total_loss": total,
"loss_cls": loss_cls,
"loss_bbox": loss_bbox,
"loss_ctr": loss_ctr,
}
self.cfg.losses_keys = list(loss_dict.keys())
return loss_dict
else:
# currently not support multi-batch testing
assert image.shape[0] == 1
all_level_anchors = F.concat(anchors_list, axis=0)
pred_boxes = self.point_coder.decode(all_level_anchors,
all_level_box_offsets[0])
pred_boxes = pred_boxes.reshape(-1, 4)
scale_w = im_info[0, 1] / im_info[0, 3]
scale_h = im_info[0, 0] / im_info[0, 2]
pred_boxes = pred_boxes / F.concat(
[scale_w, scale_h, scale_w, scale_h], axis=0)
clipped_boxes = layers.get_clipped_boxes(pred_boxes,
im_info[0, 2:4]).reshape(
-1, 4)
pred_score = F.sqrt(
F.sigmoid(all_level_box_logits) *
F.sigmoid(all_level_box_ctrness))[0]
return pred_score, clipped_boxes
def forward(self, image, im_info, gt_boxes=None):
image = self.preprocess_image(image)
features = self.backbone(image)
features = [features[f] for f in self.in_features]
box_logits, box_offsets = self.head(features)
box_logits_list = [
_.transpose(0, 2, 3, 1).reshape(image.shape[0], -1,
self.cfg.num_classes)
for _ in box_logits
]
box_offsets_list = [
_.transpose(0, 2, 3, 1).reshape(image.shape[0], -1, 4)
for _ in box_offsets
]
anchors_list = self.anchor_generator(features)
all_level_box_logits = F.concat(box_logits_list, axis=1)
all_level_box_offsets = F.concat(box_offsets_list, axis=1)
all_level_anchors = F.concat(anchors_list, axis=0)
if self.training:
gt_labels, gt_offsets = self.get_ground_truth(
all_level_anchors,
gt_boxes,
im_info[:, 4].astype(np.int32),
)
all_level_box_logits = all_level_box_logits.reshape(
-1, self.cfg.num_classes)
all_level_box_offsets = all_level_box_offsets.reshape(-1, 4)
gt_labels = gt_labels.flatten()
gt_offsets = gt_offsets.reshape(-1, 4)
valid_mask = gt_labels >= 0
fg_mask = gt_labels > 0
num_fg = fg_mask.sum()
gt_targets = F.zeros_like(all_level_box_logits)
gt_targets[fg_mask, gt_labels[fg_mask] - 1] = 1
loss_cls = layers.sigmoid_focal_loss(
all_level_box_logits[valid_mask],
gt_targets[valid_mask],
alpha=self.cfg.focal_loss_alpha,
gamma=self.cfg.focal_loss_gamma,
).sum() / F.maximum(num_fg, 1)
loss_bbox = layers.smooth_l1_loss(
all_level_box_offsets[fg_mask],
gt_offsets[fg_mask],
beta=self.cfg.smooth_l1_beta,
).sum() / F.maximum(num_fg, 1) * self.cfg.loss_bbox_weight
total = loss_cls + loss_bbox
loss_dict = {
"total_loss": total,
"loss_cls": loss_cls,
"loss_bbox": loss_bbox,
}
self.cfg.losses_keys = list(loss_dict.keys())
return loss_dict
else:
# currently not support multi-batch testing
assert image.shape[0] == 1
pred_boxes = self.box_coder.decode(all_level_anchors,
all_level_box_offsets[0])
pred_boxes = pred_boxes.reshape(-1, 4)
scale_w = im_info[0, 1] / im_info[0, 3]
scale_h = im_info[0, 0] / im_info[0, 2]
pred_boxes = pred_boxes / F.concat(
[scale_w, scale_h, scale_w, scale_h], axis=0)
clipped_boxes = layers.get_clipped_boxes(pred_boxes,
im_info[0, 2:4]).reshape(
-1, 4)
pred_score = F.sigmoid(all_level_box_logits)[0]
return pred_score, clipped_boxes
