Wan2.2/wan/modules/animate/preprocess/video_predictor.py

# Copyright (c) 2025. Your modifications here.
# A wrapper for sam2 functions
from collections import OrderedDict
import torch
from tqdm import tqdm

from sam2.modeling.sam2_base import NO_OBJ_SCORE, SAM2Base
from sam2.sam2_video_predictor import SAM2VideoPredictor as _SAM2VideoPredictor
from sam2.utils.misc import concat_points, fill_holes_in_mask_scores

from sam_utils import load_video_frames_v2, load_video_frames


class SAM2VideoPredictor(_SAM2VideoPredictor):
    def __init__(self, *args, **kwargs):

        super().__init__(*args, **kwargs)
        
    @torch.inference_mode()
    def init_state(
        self,
        video_path,
        offload_video_to_cpu=False,
        offload_state_to_cpu=False,
        async_loading_frames=False,
        frame_names=None
    ):
        """Initialize a inference state."""
        images, video_height, video_width = load_video_frames(
            video_path=video_path,
            image_size=self.image_size,
            offload_video_to_cpu=offload_video_to_cpu,
            async_loading_frames=async_loading_frames,
            frame_names=frame_names
        )
        inference_state = {}
        inference_state["images"] = images
        inference_state["num_frames"] = len(images)
        # whether to offload the video frames to CPU memory
        # turning on this option saves the GPU memory with only a very small overhead
        inference_state["offload_video_to_cpu"] = offload_video_to_cpu
        # whether to offload the inference state to CPU memory
        # turning on this option saves the GPU memory at the cost of a lower tracking fps
        # (e.g. in a test case of 768x768 model, fps dropped from 27 to 24 when tracking one object
        # and from 24 to 21 when tracking two objects)
        inference_state["offload_state_to_cpu"] = offload_state_to_cpu
        # the original video height and width, used for resizing final output scores
        inference_state["video_height"] = video_height
        inference_state["video_width"] = video_width
        inference_state["device"] = torch.device("cuda")
        if offload_state_to_cpu:
            inference_state["storage_device"] = torch.device("cpu")
        else:
            inference_state["storage_device"] = torch.device("cuda")
        # inputs on each frame
        inference_state["point_inputs_per_obj"] = {}
        inference_state["mask_inputs_per_obj"] = {}
        # visual features on a small number of recently visited frames for quick interactions
        inference_state["cached_features"] = {}
        # 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>}
        }
        # 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"] = {}
        # Warm up the visual backbone and cache the image feature on frame 0
        self._get_image_feature(inference_state, frame_idx=0, batch_size=1)
        return inference_state

    @torch.inference_mode()
    def init_state_v2(
            self,
            frames,
            offload_video_to_cpu=False,
            offload_state_to_cpu=False,
            async_loading_frames=False,
            frame_names=None
    ):
        """Initialize a inference state."""
        images, video_height, video_width = load_video_frames_v2(
            frames=frames,
            image_size=self.image_size,
            offload_video_to_cpu=offload_video_to_cpu,
            async_loading_frames=async_loading_frames,
            frame_names=frame_names
        )
        inference_state = {}
        inference_state["images"] = images
        inference_state["num_frames"] = len(images)
        # whether to offload the video frames to CPU memory
        # turning on this option saves the GPU memory with only a very small overhead
        inference_state["offload_video_to_cpu"] = offload_video_to_cpu
        # whether to offload the inference state to CPU memory
        # turning on this option saves the GPU memory at the cost of a lower tracking fps
        # (e.g. in a test case of 768x768 model, fps dropped from 27 to 24 when tracking one object
        # and from 24 to 21 when tracking two objects)
        inference_state["offload_state_to_cpu"] = offload_state_to_cpu
        # the original video height and width, used for resizing final output scores
        inference_state["video_height"] = video_height
        inference_state["video_width"] = video_width
        inference_state["device"] = torch.device("cuda")
        if offload_state_to_cpu:
            inference_state["storage_device"] = torch.device("cpu")
        else:
            inference_state["storage_device"] = torch.device("cuda")
        # inputs on each frame
        inference_state["point_inputs_per_obj"] = {}
        inference_state["mask_inputs_per_obj"] = {}
        # visual features on a small number of recently visited frames for quick interactions
        inference_state["cached_features"] = {}
        # 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>}
        }
        # 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"] = {}
        # Warm up the visual backbone and cache the image feature on frame 0
        self._get_image_feature(inference_state, frame_idx=0, batch_size=1)
        return inference_state