mirrors/mmdeploy
1
0
Fork 0
mirror of https://github.com/open-mmlab/mmdeploy.git synced 2025-01-14 08:09:43 +08:00
Code Issues Projects Releases Wiki Activity

Fix SDK backend (#844)

Browse Source
This commit is contained in:
Chen Xin 2022-08-02 11:49:25 +08:00 committed by GitHub
parent a15365e93f
commit e12dfddc26
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
8 changed files with 11 additions and 13 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
mmdeploy/codebase/mmcls/deploy/classification_model.py
View File

@ -139,8 +139,7 @@ class SDKEnd2EndModel(End2EndModel):
list: A list contains predictions. list: A list contains predictions.
""" """
pred = self.wrapper.invoke( pred = self.wrapper.invoke(img[0].contiguous().detach().cpu().numpy())
[img[0].contiguous().detach().cpu().numpy()])[0]
pred = np.array(pred, dtype=np.float32) pred = np.array(pred, dtype=np.float32)
return pred[np.argsort(pred[:, 0])][np.newaxis, :, 1] return pred[np.argsort(pred[:, 0])][np.newaxis, :, 1]

2
mmdeploy/codebase/mmdet/deploy/object_detection_model.py
View File

@ -637,7 +637,7 @@ class SDKEnd2EndModel(End2EndModel):
list: A list contains predictions. list: A list contains predictions.
""" """
dets, labels, masks = self.wrapper.invoke( dets, labels, masks = self.wrapper.invoke(
[img[0].contiguous().detach().cpu().numpy()])[0] img[0].contiguous().detach().cpu().numpy())
det_results = bbox2result(dets[np.newaxis, ...], labels[np.newaxis, det_results = bbox2result(dets[np.newaxis, ...], labels[np.newaxis,
...], ...],
len(self.CLASSES)) len(self.CLASSES))

2
mmdeploy/codebase/mmedit/deploy/super_resolution_model.py
View File

@ -223,7 +223,7 @@ class SDKEnd2EndModel(End2EndModel):
list | dict: High resolution image or a evaluation results. list | dict: High resolution image or a evaluation results.
""" """
img = tensor2img(lq) img = tensor2img(lq)
output = self.wrapper.invoke([img])[0] output = self.wrapper.invoke(img)
if test_mode: if test_mode:
output = torch.from_numpy(output) output = torch.from_numpy(output)
output = output.permute(2, 0, 1) output = output.permute(2, 0, 1)

2
mmdeploy/codebase/mmocr/deploy/text_detection_model.py
View File

@ -169,7 +169,7 @@ class SDKEnd2EndModel(End2EndModel):
list: A list contains predictions. list: A list contains predictions.
""" """
boundaries = self.wrapper.invoke( boundaries = self.wrapper.invoke(
[img[0].contiguous().detach().cpu().numpy()])[0] img[0].contiguous().detach().cpu().numpy())
boundaries = [list(x) for x in boundaries] boundaries = [list(x) for x in boundaries]
return [ return [
dict( dict(

6
mmdeploy/codebase/mmocr/deploy/text_recognition_model.py
View File

@ -181,9 +181,9 @@ class SDKEnd2EndModel(End2EndModel):
Returns: Returns:
list[str]: Text label result of each image. list[str]: Text label result of each image.
""" """
results = self.wrapper.invoke( text, score = self.wrapper.invoke(
[img[0].contiguous().detach().cpu().numpy()]) img[0].contiguous().detach().cpu().numpy())
results = [dict(text=text, score=score) for text, score in results] results = [dict(text=text, score=score)]
return results return results

4
mmdeploy/codebase/mmpose/deploy/pose_detection_model.py
View File

@ -218,8 +218,8 @@ class SDKEnd2EndModel(End2EndModel):
image_paths.append(img_meta['image_file']) image_paths.append(img_meta['image_file'])
bbox_ids.append(img_meta['bbox_id']) bbox_ids.append(img_meta['bbox_id'])
pred = self.wrapper.handle( pred = self.wrapper.handle(img[0].contiguous().detach().cpu().numpy(),
[img[0].contiguous().detach().cpu().numpy()], [sdk_boxes])[0] sdk_boxes)
result = dict( result = dict(
preds=pred, preds=pred,

2
mmdeploy/codebase/mmrotate/deploy/rotated_detection_model.py
View File

@ -212,7 +212,7 @@ class SDKEnd2EndModel(End2EndModel):
""" """
results = [] results = []
dets, labels = self.wrapper.invoke( dets, labels = self.wrapper.invoke(
[img[0].contiguous().detach().cpu().numpy()])[0] img[0].contiguous().detach().cpu().numpy())
dets_results = [dets[labels == i, :] for i in range(len(self.CLASSES))] dets_results = [dets[labels == i, :] for i in range(len(self.CLASSES))]
results.append(dets_results) results.append(dets_results)

3
mmdeploy/codebase/mmseg/deploy/segmentation_model.py
View File

@ -166,8 +166,7 @@ class SDKEnd2EndModel(End2EndModel):
Returns: Returns:
list: A list contains predictions. list: A list contains predictions.
""" """
masks = self.wrapper.invoke( masks = self.wrapper.invoke(img[0].contiguous().detach().cpu().numpy())
[img[0].contiguous().detach().cpu().numpy()])[0]
return masks return masks