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PyCharm Code and Docs Inspect fixes v1 (#18461)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan Munawar 2025-01-03 01:16:18 +05:00 • committed by GitHub
parent 126867e355
commit 7f1a50e893
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26 changed files with 90 additions and 91 deletions
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59
ultralytics/models/sam/predict.py
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@ -1096,7 +1096,7 @@ class SAM2VideoPredictor(SAM2Predictor):
# to `propagate_in_video_preflight`).
consolidated_frame_inds = self.inference_state["consolidated_frame_inds"]
for is_cond in {False, True}:
# Separately consolidate conditioning and non-conditioning temp outptus
# Separately consolidate conditioning and non-conditioning temp outputs
storage_key = "cond_frame_outputs" if is_cond else "non_cond_frame_outputs"
# Find all the frames that contain temporary outputs for any objects
# (these should be the frames that have just received clicks for mask inputs
@ -1161,36 +1161,35 @@ class SAM2VideoPredictor(SAM2Predictor):
assert predictor.dataset is not None
assert predictor.dataset.mode == "video"
inference_state = {}
inference_state["num_frames"] = predictor.dataset.frames
# inputs on each frame
inference_state["point_inputs_per_obj"] = {}
inference_state["mask_inputs_per_obj"] = {}
# values that don't change across frames (so we only need to hold one copy of them)
inference_state["constants"] = {}
# mapping between client-side object id and model-side object index
inference_state["obj_id_to_idx"] = OrderedDict()
inference_state["obj_idx_to_id"] = OrderedDict()
inference_state["obj_ids"] = []
# A storage to hold the model's tracking results and states on each frame
inference_state["output_dict"] = {
"cond_frame_outputs": {}, # dict containing {frame_idx: <out>}
"non_cond_frame_outputs": {}, # dict containing {frame_idx: <out>}
inference_state = {
"num_frames": predictor.dataset.frames,
"point_inputs_per_obj": {}, # inputs points on each frame
"mask_inputs_per_obj": {}, # inputs mask on each frame
"constants": {}, # values that don't change across frames (so we only need to hold one copy of them)
# mapping between client-side object id and model-side object index
"obj_id_to_idx": OrderedDict(),
"obj_idx_to_id": OrderedDict(),
"obj_ids": [],
# A storage to hold the model's tracking results and states on each frame
"output_dict": {
"cond_frame_outputs": {}, # dict containing {frame_idx: <out>}
"non_cond_frame_outputs": {}, # dict containing {frame_idx: <out>}
},
# Slice (view) of each object tracking results, sharing the same memory with "output_dict"
"output_dict_per_obj": {},
# A temporary storage to hold new outputs when user interact with a frame
# to add clicks or mask (it's merged into "output_dict" before propagation starts)
"temp_output_dict_per_obj": {},
# Frames that already holds consolidated outputs from click or mask inputs
# (we directly use their consolidated outputs during tracking)
"consolidated_frame_inds": {
"cond_frame_outputs": set(), # set containing frame indices
"non_cond_frame_outputs": set(), # set containing frame indices
},
# metadata for each tracking frame (e.g. which direction it's tracked)
"tracking_has_started": False,
"frames_already_tracked": [],
}
# Slice (view) of each object tracking results, sharing the same memory with "output_dict"
inference_state["output_dict_per_obj"] = {}
# A temporary storage to hold new outputs when user interact with a frame
# to add clicks or mask (it's merged into "output_dict" before propagation starts)
inference_state["temp_output_dict_per_obj"] = {}
# Frames that already holds consolidated outputs from click or mask inputs
# (we directly use their consolidated outputs during tracking)
inference_state["consolidated_frame_inds"] = {
"cond_frame_outputs": set(), # set containing frame indices
"non_cond_frame_outputs": set(), # set containing frame indices
}
# metadata for each tracking frame (e.g. which direction it's tracked)
inference_state["tracking_has_started"] = False
inference_state["frames_already_tracked"] = []
predictor.inference_state = inference_state
def get_im_features(self, im, batch=1):