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import os
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import platform
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from functools import partial
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import torch
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from fast_sam import FastSamAutomaticMaskGenerator, fast_sam_model_registry
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from ia_check_versions import ia_check_versions
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from ia_config import IAConfig
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from ia_devices import devices
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from ia_logging import ia_logging
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from mobile_sam import SamAutomaticMaskGenerator as SamAutomaticMaskGeneratorMobile
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from mobile_sam import SamPredictor as SamPredictorMobile
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from mobile_sam import sam_model_registry as sam_model_registry_mobile
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from sam2.automatic_mask_generator import SAM2AutomaticMaskGenerator
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from sam2.build_sam import build_sam2
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from segment_anything_fb import SamAutomaticMaskGenerator, SamPredictor, sam_model_registry
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from segment_anything_hq import SamAutomaticMaskGenerator as SamAutomaticMaskGeneratorHQ
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from segment_anything_hq import SamPredictor as SamPredictorHQ
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from segment_anything_hq import sam_model_registry as sam_model_registry_hq
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def check_bfloat16_support() -> bool:
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if torch.cuda.is_available():
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compute_capability = torch.cuda.get_device_capability(torch.cuda.current_device())
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if compute_capability[0] >= 8:
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ia_logging.debug("The CUDA device supports bfloat16")
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return True
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else:
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ia_logging.debug("The CUDA device does not support bfloat16")
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return False
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else:
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ia_logging.debug("CUDA is not available")
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return False
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def partial_from_end(func, /, *fixed_args, **fixed_kwargs):
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def wrapper(*args, **kwargs):
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updated_kwargs = {**fixed_kwargs, **kwargs}
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return func(*args, *fixed_args, **updated_kwargs)
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return wrapper
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def rename_args(func, arg_map):
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def wrapper(*args, **kwargs):
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new_kwargs = {arg_map.get(k, k): v for k, v in kwargs.items()}
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return func(*args, **new_kwargs)
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return wrapper
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arg_map = {"checkpoint": "ckpt_path"}
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rename_build_sam2 = rename_args(build_sam2, arg_map)
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end_kwargs = dict(device="cpu", mode="eval", hydra_overrides_extra=[], apply_postprocessing=False)
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sam2_model_registry = {
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"sam2_hiera_large": partial(partial_from_end(rename_build_sam2, **end_kwargs), "sam2_hiera_l.yaml"),
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"sam2_hiera_base_plus": partial(partial_from_end(rename_build_sam2, **end_kwargs), "sam2_hiera_b+.yaml"),
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"sam2_hiera_small": partial(partial_from_end(rename_build_sam2, **end_kwargs), "sam2_hiera_s.yaml"),
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"sam2_hiera_tiny": partial(partial_from_end(rename_build_sam2, **end_kwargs), "sam2_hiera_t.yaml"),
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}
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def get_sam_mask_generator(sam_checkpoint, anime_style_chk=False):
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"""Get SAM mask generator.
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Args:
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sam_checkpoint (str): SAM checkpoint path
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Returns:
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SamAutomaticMaskGenerator or None: SAM mask generator
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"""
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points_per_batch = 64
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if "_hq_" in os.path.basename(sam_checkpoint):
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model_type = os.path.basename(sam_checkpoint)[7:12]
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sam_model_registry_local = sam_model_registry_hq
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SamAutomaticMaskGeneratorLocal = SamAutomaticMaskGeneratorHQ
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points_per_batch = 32
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elif "FastSAM" in os.path.basename(sam_checkpoint):
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model_type = os.path.splitext(os.path.basename(sam_checkpoint))[0]
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sam_model_registry_local = fast_sam_model_registry
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SamAutomaticMaskGeneratorLocal = FastSamAutomaticMaskGenerator
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points_per_batch = None
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elif "mobile_sam" in os.path.basename(sam_checkpoint):
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model_type = "vit_t"
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sam_model_registry_local = sam_model_registry_mobile
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SamAutomaticMaskGeneratorLocal = SamAutomaticMaskGeneratorMobile
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points_per_batch = 64
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elif "sam2_" in os.path.basename(sam_checkpoint):
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model_type = os.path.splitext(os.path.basename(sam_checkpoint))[0]
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sam_model_registry_local = sam2_model_registry
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SamAutomaticMaskGeneratorLocal = SAM2AutomaticMaskGenerator
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points_per_batch = 128
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else:
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model_type = os.path.basename(sam_checkpoint)[4:9]
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sam_model_registry_local = sam_model_registry
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SamAutomaticMaskGeneratorLocal = SamAutomaticMaskGenerator
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points_per_batch = 64
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pred_iou_thresh = 0.88 if not anime_style_chk else 0.83
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stability_score_thresh = 0.95 if not anime_style_chk else 0.9
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if "sam2_" in model_type:
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pred_iou_thresh = round(pred_iou_thresh - 0.18, 2)
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stability_score_thresh = round(stability_score_thresh - 0.03, 2)
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sam2_gen_kwargs = dict(
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points_per_side=64,
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points_per_batch=points_per_batch,
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pred_iou_thresh=pred_iou_thresh,
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stability_score_thresh=stability_score_thresh,
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stability_score_offset=0.7,
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crop_n_layers=1,
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box_nms_thresh=0.7,
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crop_n_points_downscale_factor=2)
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if platform.system() == "Darwin":
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sam2_gen_kwargs.update(dict(points_per_side=32, points_per_batch=64, crop_n_points_downscale_factor=1))
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if os.path.isfile(sam_checkpoint):
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sam = sam_model_registry_local[model_type](checkpoint=sam_checkpoint)
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if platform.system() == "Darwin":
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if "FastSAM" in os.path.basename(sam_checkpoint) or not ia_check_versions.torch_mps_is_available:
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sam.to(device=torch.device("cpu"))
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else:
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sam.to(device=torch.device("mps"))
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else:
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if IAConfig.global_args.get("sam_cpu", False):
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ia_logging.info("SAM is running on CPU... (the option has been selected)")
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sam.to(device=devices.cpu)
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else:
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sam.to(device=devices.device)
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sam_gen_kwargs = dict(
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model=sam, points_per_batch=points_per_batch, pred_iou_thresh=pred_iou_thresh, stability_score_thresh=stability_score_thresh)
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if "sam2_" in model_type:
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sam_gen_kwargs.update(sam2_gen_kwargs)
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sam_mask_generator = SamAutomaticMaskGeneratorLocal(**sam_gen_kwargs)
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else:
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sam_mask_generator = None
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return sam_mask_generator
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def get_sam_predictor(sam_checkpoint):
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"""Get SAM predictor.
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Args:
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sam_checkpoint (str): SAM checkpoint path
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Returns:
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SamPredictor or None: SAM predictor
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"""
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if "_hq_" in os.path.basename(sam_checkpoint):
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model_type = os.path.basename(sam_checkpoint)[7:12]
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sam_model_registry_local = sam_model_registry_hq
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SamPredictorLocal = SamPredictorHQ
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elif "FastSAM" in os.path.basename(sam_checkpoint):
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raise NotImplementedError("FastSAM predictor is not implemented yet.")
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elif "mobile_sam" in os.path.basename(sam_checkpoint):
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model_type = "vit_t"
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sam_model_registry_local = sam_model_registry_mobile
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SamPredictorLocal = SamPredictorMobile
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else:
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model_type = os.path.basename(sam_checkpoint)[4:9]
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sam_model_registry_local = sam_model_registry
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SamPredictorLocal = SamPredictor
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if os.path.isfile(sam_checkpoint):
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sam = sam_model_registry_local[model_type](checkpoint=sam_checkpoint)
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if platform.system() == "Darwin":
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if "FastSAM" in os.path.basename(sam_checkpoint) or not ia_check_versions.torch_mps_is_available:
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sam.to(device=torch.device("cpu"))
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else:
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sam.to(device=torch.device("mps"))
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else:
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if IAConfig.global_args.get("sam_cpu", False):
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ia_logging.info("SAM is running on CPU... (the option has been selected)")
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sam.to(device=devices.cpu)
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else:
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sam.to(device=devices.device)
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sam_predictor = SamPredictorLocal(sam)
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else:
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sam_predictor = None
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return sam_predictor
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