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coco annotations not needed when not in do_eval mode. #9368

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coco annotations not needed when not in do_eval mode. #9368

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0b3deb2
Add explicit assign to avoid inplace write a view tensor cause incons…
SigureMo Mar 17, 2025
b20369f
npu(bugfix): fix the ce problem for detr. (#9341)
Jonathans575 Mar 28, 2025
dbb9f5b
Use right dtype for 0 size `gt_class` and `is_crowd` (#9354)
SigureMo Apr 16, 2025
0778747
Fix infer error when annotation is not needed.
digital-nomad-cheng May 1, 2025
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4 changes: 2 additions & 2 deletions ppdet/data/source/dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -217,14 +217,14 @@ def _load_images(self, do_eval=False):
images = self._parse()
ct = 0
records = []
anno_file = self.get_anno()
coco = COCO(anno_file)
for image in images:
assert image != '' and os.path.isfile(image), \
"Image {} not found".format(image)
if self.sample_num > 0 and ct >= self.sample_num:
break
if do_eval:
anno_file = self.get_anno()
coco = COCO(anno_file)
image_id = self.get_image_id(image, coco)
ct = image_id
rec = {'im_id': np.array([ct]), 'im_file': image}
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4 changes: 2 additions & 2 deletions ppdet/data/transform/operators.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3165,15 +3165,15 @@ def crop(self, sample, region):
[0, 4], dtype=np.float32)
sample['gt_class'] = sample['gt_class'][keep_index] if len(
keep_index) > 0 else np.zeros(
[0, 1], dtype=np.float32)
[0, 1], dtype=np.int32)
if 'gt_score' in sample:
sample['gt_score'] = sample['gt_score'][keep_index] if len(
keep_index) > 0 else np.zeros(
[0, 1], dtype=np.float32)
if 'is_crowd' in sample:
sample['is_crowd'] = sample['is_crowd'][keep_index] if len(
keep_index) > 0 else np.zeros(
[0, 1], dtype=np.float32)
[0, 1], dtype=np.int32)
if 'gt_areas' in sample:
sample['gt_areas'] = np.take(
sample['gt_areas'], keep_index, axis=0)
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14 changes: 10 additions & 4 deletions ppdet/modeling/losses/detr_loss.py
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Original file line number Diff line number Diff line change
Expand Up @@ -281,10 +281,16 @@ def _get_index_updates(self, num_query_objects, target, match_indices):
])
src_idx = paddle.concat([src for (src, _) in match_indices])
src_idx += (batch_idx * num_query_objects)
target_assign = paddle.concat([
paddle.gather(
t, dst, axis=0) for t, (_, dst) in zip(target, match_indices)
])
if 'npu' in paddle.device.get_device():
target_assign = paddle.concat([
paddle.gather(
t.to(paddle.int32), dst.to(paddle.int32), axis=0) for t, (_, dst) in zip(target, match_indices)
])
else:
target_assign = paddle.concat([
paddle.gather(
t, dst, axis=0) for t, (_, dst) in zip(target, match_indices)
])
return src_idx, target_assign

def _get_src_target_assign(self, src, target, match_indices):
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3 changes: 3 additions & 0 deletions ppdet/modeling/transformers/matchers.py
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Expand Up @@ -103,6 +103,9 @@ def forward(self,
out_bbox = boxes.detach().flatten(0, 1)

# Also concat the target labels and boxes
if 'npu' in paddle.device.get_device():
gt_class = [tensor.to(paddle.int32) for tensor in gt_class]

tgt_ids = paddle.concat(gt_class).flatten()
tgt_bbox = paddle.concat(gt_bbox)

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8 changes: 4 additions & 4 deletions ppdet/modeling/transformers/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -248,8 +248,8 @@ def get_denoising_training_group(targets,
num_denoising = int(max_gt_num * num_group)

if label_noise_ratio > 0:
input_query_class = input_query_class.flatten()
pad_gt_mask = pad_gt_mask.flatten()
input_query_class = paddle.assign(input_query_class.flatten())
pad_gt_mask = paddle.assign(pad_gt_mask.flatten())
# half of bbox prob, cast mask from bool to float bacause dtype promotaion
# between bool and float is not supported in static mode.
mask = paddle.cast(
Expand Down Expand Up @@ -356,8 +356,8 @@ def get_contrastive_denoising_training_group(targets,
num_denoising = int(max_gt_num * 2 * num_group)

if label_noise_ratio > 0:
input_query_class = input_query_class.flatten()
pad_gt_mask = pad_gt_mask.flatten()
input_query_class = paddle.assign(input_query_class.flatten())
pad_gt_mask = paddle.assign(pad_gt_mask.flatten())
# half of bbox prob
mask = paddle.rand(input_query_class.shape) < (label_noise_ratio * 0.5)
chosen_idx = paddle.nonzero(mask.cast(pad_gt_mask.dtype) *
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