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adapter new type promotion rule for Paddle 2.6 #8856

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adapter new type promotion rule for Paddle 2.6 #8856

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2 changes: 1 addition & 1 deletion ppdet/modeling/assigners/atss_assigner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -192,7 +192,7 @@ def forward(self,
# assigned target
batch_ind = paddle.arange(
end=batch_size, dtype=gt_labels.dtype).unsqueeze(-1)
assigned_gt_index = assigned_gt_index + batch_ind * num_max_boxes
assigned_gt_index = assigned_gt_index + (batch_ind * num_max_boxes).astype(assigned_gt_index.dtype)
assigned_labels = paddle.gather(
gt_labels.flatten(), assigned_gt_index.flatten(), axis=0)
assigned_labels = assigned_labels.reshape([batch_size, num_anchors])
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4 changes: 2 additions & 2 deletions ppdet/modeling/assigners/fcosr_assigner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -193,7 +193,7 @@ def forward(self,
# assigned target
batch_ind = paddle.arange(
end=batch_size, dtype=gt_labels.dtype).unsqueeze(-1)
assigned_gt_index = assigned_gt_index + batch_ind * num_max_boxes
assigned_gt_index = assigned_gt_index + (batch_ind * num_max_boxes).astype(assigned_gt_index.dtype)
assigned_labels = paddle.gather(
gt_labels.flatten(), assigned_gt_index.flatten(), axis=0)
assigned_labels = assigned_labels.reshape([batch_size, num_anchors])
Expand Down Expand Up @@ -224,4 +224,4 @@ def forward(self,
(-1, 5))).reshape((b, l, 1))
assigned_scores = assigned_scores * iou_score

return assigned_labels, assigned_rboxes, assigned_scores
return assigned_labels, assigned_rboxes, assigned_scores
2 changes: 1 addition & 1 deletion ppdet/modeling/assigners/rotated_task_aligned_assigner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -113,7 +113,7 @@ def forward(self,
# select topk largest alignment metrics pred bbox as candidates
# for each gt, [B, n, L]
is_in_topk = gather_topk_anchors(
alignment_metrics * is_in_gts, self.topk, topk_mask=pad_gt_mask)
alignment_metrics * is_in_gts.astype(alignment_metrics.dtype), self.topk, topk_mask=pad_gt_mask)

# select positive sample, [B, n, L]
mask_positive = is_in_topk * is_in_gts * pad_gt_mask
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2 changes: 1 addition & 1 deletion ppdet/modeling/assigners/task_aligned_assigner.py
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Expand Up @@ -163,7 +163,7 @@ def forward(self,
assigned_gt_index = mask_positive.argmax(axis=-2)

# assigned target
assigned_gt_index = assigned_gt_index + batch_ind * num_max_boxes
assigned_gt_index = assigned_gt_index + (batch_ind * num_max_boxes).astype(assigned_gt_index.dtype)
assigned_labels = paddle.gather(
gt_labels.flatten(), assigned_gt_index.flatten(), axis=0)
assigned_labels = assigned_labels.reshape([batch_size, num_anchors])
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2 changes: 1 addition & 1 deletion ppdet/modeling/assigners/task_aligned_assigner_cr.py
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Expand Up @@ -151,7 +151,7 @@ def forward(self,
assigned_gt_index = mask_positive.argmax(axis=-2)

# assigned target
assigned_gt_index = assigned_gt_index + batch_ind * num_max_boxes
assigned_gt_index = assigned_gt_index + (batch_ind * num_max_boxes).astype(assigned_gt_index.dtype)
assigned_labels = paddle.gather(
gt_labels.flatten(), assigned_gt_index.flatten(), axis=0)
assigned_labels = assigned_labels.reshape([batch_size, num_anchors])
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2 changes: 1 addition & 1 deletion ppdet/modeling/heads/ttf_head.py
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Expand Up @@ -281,7 +281,7 @@ def get_loss(self, pred_hm, pred_wh, target_hm, box_target, target_weight):
base_loc.stop_gradient = True

pred_boxes = paddle.concat(
[0 - pred_wh[:, 0:2, :, :] + base_loc, pred_wh[:, 2:4] + base_loc],
[0 - pred_wh[:, 0:2, :, :] + base_loc.astype(pred_wh.dtype), pred_wh[:, 2:4] + base_loc.astype(pred_wh.dtype)],
axis=1)
pred_boxes = paddle.transpose(pred_boxes, [0, 2, 3, 1])
boxes = paddle.transpose(box_target, [0, 2, 3, 1])
Expand Down
2 changes: 1 addition & 1 deletion ppdet/modeling/losses/gfocal_loss.py
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Expand Up @@ -75,7 +75,7 @@ def quality_focal_loss(pred, target, beta=2.0, use_sigmoid=True):

loss_pos = func(
pred, score, reduction='none') * scale_factor_new.abs().pow(beta)
loss = loss * paddle.logical_not(pos_mask) + loss_pos * pos_mask
loss = loss * paddle.logical_not(pos_mask).astype(loss.dtype) + loss_pos * pos_mask
loss = loss.sum(axis=1)
return loss

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2 changes: 1 addition & 1 deletion ppdet/modeling/losses/iou_loss.py
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Expand Up @@ -184,7 +184,7 @@ def __call__(self, pbox, gbox, iou_weight=1.):

intsctk = (xkis2 - xkis1) * (ykis2 - ykis1)
intsctk = intsctk * paddle.greater_than(
xkis2, xkis1) * paddle.greater_than(ykis2, ykis1)
xkis2, xkis1).astype(intsctk.dtype) * paddle.greater_than(ykis2, ykis1).astype(intsctk.dtype)
unionk = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g
) - intsctk + self.eps
iouk = intsctk / unionk
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2 changes: 1 addition & 1 deletion ppdet/modeling/losses/ssd_loss.py
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Expand Up @@ -124,7 +124,7 @@ def _mine_hard_example(self,
[pos.shape[1] * mine_neg_ratio])
num_negs.append(num_neg)
num_negs = paddle.stack(num_negs).expand_as(idx_rank)
neg_mask = (idx_rank < num_negs).astype(conf_loss.dtype)
neg_mask = (idx_rank.astype(num_negs.dtype) < num_negs).astype(conf_loss.dtype)

return (neg_mask + pos).astype('bool')

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8 changes: 4 additions & 4 deletions ppdet/modeling/proposal_generator/anchor_generator.py
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Expand Up @@ -187,11 +187,11 @@ def gen_base_anchors(self):
h_ratios = paddle.sqrt(self.ratios)
w_ratios = 1 / h_ratios
if self.scale_major:
ws = (w * w_ratios[:] * self.scales[:]).reshape([-1])
hs = (h * h_ratios[:] * self.scales[:]).reshape([-1])
ws = (w * w_ratios[:] * self.scales[:].astype(w_ratios.dtype)).reshape([-1])
hs = (h * h_ratios[:] * self.scales[:].astype(h_ratios.dtype)).reshape([-1])
else:
ws = (w * self.scales[:] * w_ratios[:]).reshape([-1])
hs = (h * self.scales[:] * h_ratios[:]).reshape([-1])
ws = (w * self.scales[:].astype(w_ratios.dtype) * w_ratios[:]).reshape([-1])
hs = (h * self.scales[:].astype(h_ratios.dtype) * h_ratios[:]).reshape([-1])

base_anchors = paddle.stack(
[
Expand Down