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Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

Cleanup argument handling in Model class (#4614)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Sebastian Stapf <42514241+Wiqzard@users.noreply.github.com>
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Glenn Jocher 2023-08-28 23:12:32 +02:00 • committed by GitHub
parent 53b4f8c713
commit 7e99804263
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6 changed files with 67 additions and 93 deletions
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11
ultralytics/nn/autobackend.py
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@ -18,7 +18,6 @@ from PIL import Image
from ultralytics.utils import ARM64, LINUX, LOGGER, ROOT, yaml_load
from ultralytics.utils.checks import check_requirements, check_suffix, check_version, check_yaml
from ultralytics.utils.downloads import attempt_download_asset, is_url
from ultralytics.utils.ops import xywh2xyxy
def check_class_names(names):
@ -363,9 +362,13 @@ class AutoBackend(nn.Module):
# im = im.resize((192, 320), Image.BILINEAR)
y = self.model.predict({'image': im_pil}) # coordinates are xywh normalized
if 'confidence' in y:
box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels
conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)
y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
raise TypeError('Ultralytics only supports inference of non-pipelined CoreML models exported with '
f"'nms=False', but 'model={w}' has an NMS pipeline created by an 'nms=True' export.")
# TODO: CoreML NMS inference handling
# from ultralytics.utils.ops import xywh2xyxy
# box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels
# conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float32)
# y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
elif len(y) == 1: # classification model
y = list(y.values())
elif len(y) == 2: # segmentation model

6
ultralytics/nn/modules/transformer.py
View file

@ -355,15 +355,15 @@ class DeformableTransformerDecoder(nn.Module):
for i, layer in enumerate(self.layers):
output = layer(output, refer_bbox, feats, shapes, padding_mask, attn_mask, pos_mlp(refer_bbox))
# refine bboxes, (bs, num_queries+num_denoising, 4)
refined_bbox = torch.sigmoid(bbox_head[i](output) + inverse_sigmoid(refer_bbox))
bbox = bbox_head[i](output)
refined_bbox = torch.sigmoid(bbox + inverse_sigmoid(refer_bbox))
if self.training:
dec_cls.append(score_head[i](output))
if i == 0:
dec_bboxes.append(refined_bbox)
else:
dec_bboxes.append(torch.sigmoid(bbox_head[i](output) + inverse_sigmoid(last_refined_bbox)))
dec_bboxes.append(torch.sigmoid(bbox + inverse_sigmoid(last_refined_bbox)))
elif i == self.eval_idx:
dec_cls.append(score_head[i](output))
dec_bboxes.append(refined_bbox)