def forward(self, features, im_info, boxes=None):
assert len(features) == config.num_anchor_scales
# get the predict results
pred_cls_score_list = []
pred_bbox_offsets_list = []
all_anchors_list = []
for x in features:
t = F.relu(self.rpn_conv(x))
pred_cls_score_list.append(self.rpn_cls_score(t))
pred_bbox_offsets_list.append(self.rpn_bbox_offsets(t))
# get anchors
max_fm_stride = 2**(len(features) + 1)
fm_stride = max_fm_stride
anchor_scales = config.anchor_scales[::-1]
for i in range(config.num_anchor_scales):
anchor_scale = [anchor_scales[i]]
layer_anchors = generate_anchors_opr(features[i], fm_stride,
anchor_scale,
config.anchor_aspect_ratios,
config.anchor_base_size)
fm_stride = fm_stride // 2
all_anchors_list.append(layer_anchors)
# sample from the predictions
with torch.no_grad():
rpn_rois, rpn_rois_inds = find_top_rpn_proposals(
self.training, pred_bbox_offsets_list, pred_cls_score_list,
all_anchors_list, im_info)
if self.training:
with torch.no_grad():
rpn_labels, rpn_bbox_targets = fpn_anchor_target(
boxes, im_info, all_anchors_list)
pred_cls_score, pred_bbox_offsets = fpn_rpn_reshape(
pred_cls_score_list, pred_bbox_offsets_list)
# rpn loss
valid_masks = rpn_labels >= 0
objectness_loss = F.binary_cross_entropy_with_logits(
pred_cls_score[valid_masks],
rpn_labels[valid_masks].to(torch.float32),
reduction="sum")
pos_masks = rpn_labels == 1
localization_loss = smooth_l1_loss(pred_bbox_offsets[pos_masks],
rpn_bbox_targets[pos_masks],
config.rpn_smooth_l1_beta,
reduction="sum")
normalizer = 1.0 / (config.train_batch_per_gpu *
config.num_sample_anchors)
loss_rpn_cls = objectness_loss * normalizer # cls: classification loss
loss_rpn_loc = localization_loss * normalizer
loss_dict = {}
loss_dict['loss_rpn_cls'] = loss_rpn_cls
loss_dict['loss_rpn_loc'] = loss_rpn_loc
return rpn_rois, rpn_rois_inds, loss_dict
else:
return rpn_rois, rpn_rois_inds
def forward(self, features, im_info, boxes=None):
# prediction
pred_cls_score_list = []
pred_bbox_offsets_list = []
for x in features:
t = F.relu(self.rpn_conv(x))
pred_cls_score_list.append(self.rpn_cls_score(t))
pred_bbox_offsets_list.append(self.rpn_bbox_offsets(t))
# get anchors
all_anchors_list = []
fm_stride = 2**(len(features) + 1)
for fm in features:
layer_anchors = self.anchors_generator(fm, fm_stride)
fm_stride = fm_stride // 2
all_anchors_list.append(layer_anchors)
# sample from the predictions
rpn_rois, rpn_probs = find_top_rpn_proposals(self.training,
pred_bbox_offsets_list,
pred_cls_score_list,
all_anchors_list, im_info)
if self.training:
rpn_labels, rpn_bbox_targets = fpn_anchor_target(
boxes, im_info, all_anchors_list)
#rpn_labels = rpn_labels.astype(np.int32)
pred_cls_score, pred_bbox_offsets = fpn_rpn_reshape(
pred_cls_score_list, pred_bbox_offsets_list)
# rpn loss
valid_masks = rpn_labels >= 0
valid_inds = mask_to_inds(valid_masks)
objectness_loss = softmax_loss(pred_cls_score.ai[valid_inds],
rpn_labels.ai[valid_inds])
#objectness_loss = objectness_loss * valid_masks
pos_masks = rpn_labels > 0
localization_loss = smooth_l1_loss(pred_bbox_offsets,
rpn_bbox_targets,
config.rpn_smooth_l1_beta)
localization_loss = localization_loss * pos_masks
normalizer = 1.0 / (valid_masks.sum())
loss_rpn_cls = objectness_loss.sum() * normalizer
loss_rpn_loc = localization_loss.sum() * normalizer
loss_dict = {}
loss_dict['loss_rpn_cls'] = loss_rpn_cls
loss_dict['loss_rpn_loc'] = loss_rpn_loc
return rpn_rois, loss_dict
else:
return rpn_rois