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Functions234 in github.com/NVIDIA-AI-IOT/nanosam

⨍ Functions 234◇ Types & classes 40
Methodfuse
(self)
nanosam/mobile_sam/modeling/tiny_vit_sam.py:33
Methodgenerate
Generates masks for the given image. Arguments: image (np.ndarray): The image to generate masks for, in HWC uint8 format.
nanosam/mobile_sam/automatic_mask_generator.py:137
Functionget_face_points
(pose)
examples/segment_from_pose.py:28
Methodget_image_embedding
Returns the image embeddings for the currently set image, with shape 1xCxHxW, where C is the embedding dimension and (H,W) are
nanosam/mobile_sam/predictor.py:247
Functionget_left_hand_points
(pose)
examples/segment_from_pose.py:49
Functionget_left_leg_points
(pose)
examples/segment_from_pose.py:56
Functionget_right_hand_points
(pose)
examples/segment_from_pose.py:42
Functionget_right_leg_points
(pose)
examples/segment_from_pose.py:63
Functionget_torso_points
(pose)
examples/segment_from_pose.py:21
Methodinit
(self, image, point=None, box=None)
nanosam/utils/tracker_online_learning.py:146
Functioninit_track
(event,x,y,flags,param)
examples/demo_click_segment_track.py:45
Methodmask_postprocessing
(self, masks: torch.Tensor, orig_im_size: torch.Tensor)
nanosam/mobile_sam/utils/onnx.py:76
Functionmask_to_box
(mask)
nanosam/utils/tracker.py:38
Functionmask_to_centroid
(mask)
nanosam/utils/tracker_online_learning.py:48
Functionmask_to_centroid_soft
(mask)
nanosam/utils/tracker_online_learning.py:57
Functionmask_to_sample_points
(mask)
nanosam/utils/tracker_online_learning.py:73
Functionmask_to_sample_points
(mask)
nanosam/utils/tracker.py:57
Methodno_weight_decay_keywords
(self)
nanosam/mobile_sam/modeling/tiny_vit_sam.py:597
Methodpredict
(self, points, point_labels, mask_input=None)
nanosam/utils/predictor.py:157
Methodpredict
(self, image)
nanosam/utils/trt_pose.py:97
Methodpredict
Predict masks for the given input prompts, using the currently set image. Arguments: point_coords (np.ndarray or None): A
nanosam/mobile_sam/predictor.py:93
Functionregister_tiny_vit_model
Register a TinyViT model It is a wrapper of `register_model` with loading the pretrained checkpoint.
nanosam/mobile_sam/modeling/tiny_vit_sam.py:634
Methodreset
(self)
nanosam/utils/tracker_online_learning.py:199
Functionresnet18
()
nanosam/models/timm_image_encoder.py:78
Functionresnet34
()
nanosam/models/timm_image_encoder.py:83
Functionresnet50
()
nanosam/models/timm_image_encoder.py:88
Methodset_image
(self, image)
nanosam/utils/predictor.py:152
Functiontiny_vit_11m_224
(pretrained=False, num_classes=1000, drop_path_rate=0.1)
nanosam/mobile_sam/modeling/tiny_vit_sam.py:672
Functiontiny_vit_21m_224
(pretrained=False, num_classes=1000, drop_path_rate=0.2)
nanosam/mobile_sam/modeling/tiny_vit_sam.py:684
Functiontiny_vit_21m_384
(pretrained=False, num_classes=1000, drop_path_rate=0.1)
nanosam/mobile_sam/modeling/tiny_vit_sam.py:696
Functiontiny_vit_21m_512
(pretrained=False, num_classes=1000, drop_path_rate=0.1)
nanosam/mobile_sam/modeling/tiny_vit_sam.py:709
Functiontiny_vit_5m_224
(pretrained=False, num_classes=1000, drop_path_rate=0.0)
nanosam/mobile_sam/modeling/tiny_vit_sam.py:660
Methodtrain
(self, mode=True)
nanosam/mobile_sam/modeling/tiny_vit_sam.py:251
Methodupdate
(self, image)
nanosam/utils/tracker_online_learning.py:204
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