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import logging
from typing import Optional, Sequence
import numpy as np
import torch
from PIL import Image
from transformers import Sam3Model, Sam3Processor
from .base import Segmenter, SegmentationResult
class SAM3Segmenter(Segmenter):
"""
SAM3 (Segment Anything Model 3) segmenter.
Performs automatic instance segmentation on images without prompts.
Uses facebook/sam3 model from HuggingFace.
"""
name = "sam3"
def __init__(
self,
model_id: str = "facebook/sam3",
device: Optional[str] = None,
threshold: float = 0.5,
mask_threshold: float = 0.5,
):
"""
Initialize SAM3 segmenter.
Args:
model_id: HuggingFace model ID
device: Device to run on (cuda/cpu), auto-detected if None
threshold: Confidence threshold for filtering instances
mask_threshold: Threshold for binarizing masks
"""
self.device = device or (
"cuda" if torch.cuda.is_available() else "cpu"
)
self.threshold = threshold
self.mask_threshold = mask_threshold
logging.info(
"Loading SAM3 model %s on device %s", model_id, self.device
)
try:
self.model = Sam3Model.from_pretrained(model_id).to(self.device)
self.processor = Sam3Processor.from_pretrained(model_id)
self.model.eval()
except Exception:
logging.exception("Failed to load SAM3 model")
raise
logging.info("SAM3 model loaded successfully")
supports_batch = True
max_batch_size = 8
def _parse_single_result(self, results, frame_shape) -> SegmentationResult:
# Extract results
masks = results.get("masks", [])
scores = results.get("scores", None)
boxes = results.get("boxes", None)
# Convert to numpy arrays
if len(masks) > 0:
# Stack masks: list of (H, W) -> (N, H, W)
masks_array = np.stack([m.cpu().numpy() for m in masks])
else:
# No objects detected
masks_array = np.zeros(
(0, frame_shape[0], frame_shape[1]), dtype=bool
)
scores_array = (
scores.cpu().numpy() if scores is not None else None
)
boxes_array = (
boxes.cpu().numpy() if boxes is not None else None
)
return SegmentationResult(
masks=masks_array,
scores=scores_array,
boxes=boxes_array,
)
def _expand_inputs_if_needed(self, inputs):
"""
Helper to expand vision inputs (pixel_values or vision_embeds) to match text prompts.
Handles:
1. 1 image, N texts (Expand 1 -> N)
2. N images, N*M texts (Expand N -> N*M)
"""
pixel_values = inputs.get("pixel_values")
input_ids = inputs.get("input_ids")
if (
pixel_values is not None
and input_ids is not None
):
img_batch = pixel_values.shape[0]
text_batch = input_ids.shape[0]
should_expand = False
expansion_factor = 1
if img_batch == 1 and text_batch > 1:
should_expand = True
expansion_factor = text_batch
elif img_batch > 1 and text_batch > img_batch and text_batch % img_batch == 0:
should_expand = True
expansion_factor = text_batch // img_batch
if should_expand:
logging.debug(f"Expanding SAM3 vision inputs from {img_batch} to {text_batch} (factor {expansion_factor}) using embeddings reuse.")
# 1. Compute vision embeddings once for original images
with torch.no_grad():
vision_outputs = self.model.get_vision_features(
pixel_values=pixel_values
)
# Iterate over keys to expand
keys_to_expand = list(vision_outputs.keys())
for key in keys_to_expand:
value = getattr(vision_outputs, key, None)
if value is None:
# Try getItem
try:
value = vision_outputs[key]
except:
continue
new_value = None
if isinstance(value, torch.Tensor):
# Ensure we only expand the batch dimension (dim 0)
if value.shape[0] == img_batch:
new_value = value.repeat_interleave(expansion_factor, dim=0)
elif isinstance(value, (list, tuple)):
new_list = []
valid_expansion = False
for i, v in enumerate(value):
if isinstance(v, torch.Tensor) and v.shape[0] == img_batch:
new_list.append(v.repeat_interleave(expansion_factor, dim=0))
valid_expansion = True
else:
new_list.append(v)
if valid_expansion:
# Preserve type
new_value = type(value)(new_list)
if new_value is not None:
# Update dict item if possible
try:
vision_outputs[key] = new_value
except:
pass
# Update attribute explicitly if it exists
if hasattr(vision_outputs, key):
setattr(vision_outputs, key, new_value)
# 3. Update inputs for model call
inputs["vision_embeds"] = vision_outputs
del inputs["pixel_values"] # Mutually exclusive with vision_embeds
# 4. Expand other metadata
if "original_sizes" in inputs and inputs["original_sizes"].shape[0] == img_batch:
inputs["original_sizes"] = inputs["original_sizes"].repeat_interleave(expansion_factor, dim=0)
if "reshape_input_sizes" in inputs and inputs["reshape_input_sizes"].shape[0] == img_batch:
inputs["reshape_input_sizes"] = inputs["reshape_input_sizes"].repeat_interleave(expansion_factor, dim=0)
def predict(self, frame: np.ndarray, text_prompts: Optional[list] = None) -> SegmentationResult:
"""
Run SAM3 segmentation on a frame.
Args:
frame: Input image (HxWx3 numpy array in RGB)
text_prompts: List of text prompts for segmentation
Returns:
SegmentationResult with instance masks
"""
# Convert numpy array to PIL Image
if frame.dtype == np.uint8:
pil_image = Image.fromarray(frame)
else:
# Normalize to 0-255 if needed
frame_uint8 = (frame * 255).astype(np.uint8)
pil_image = Image.fromarray(frame_uint8)
# Use default prompts if none provided
if not text_prompts:
text_prompts = ["object"]
# Process image with text prompts
inputs = self.processor(
images=pil_image, text=text_prompts, return_tensors="pt"
).to(self.device)
# Handle batch expansion
self._expand_inputs_if_needed(inputs)
# Run inference
try:
if "pixel_values" in inputs:
logging.debug(f"SAM3 Input pixel_values shape: {inputs['pixel_values'].shape}")
with torch.no_grad():
outputs = self.model(**inputs)
except RuntimeError as e:
logging.error(f"RuntimeError during SAM3 inference: {e}")
logging.error(f"Input keys: {inputs.keys()}")
if 'pixel_values' in inputs:
logging.error(f"Pixel values shape: {inputs['pixel_values'].shape}")
# Re-raise to let user know
raise
# Post-process to get instance masks
try:
results = self.processor.post_process_instance_segmentation(
outputs,
threshold=self.threshold,
mask_threshold=self.mask_threshold,
target_sizes=inputs.get("original_sizes").tolist(),
)[0]
return self._parse_single_result(results, frame.shape)
except Exception:
logging.exception("SAM3 post-processing failed")
# Return empty result
return SegmentationResult(
masks=np.zeros((0, frame.shape[0], frame.shape[1]), dtype=bool),
scores=None,
boxes=None,
)
def predict_batch(self, frames: Sequence[np.ndarray], text_prompts: Optional[list] = None) -> Sequence[SegmentationResult]:
pil_images = []
for f in frames:
if f.dtype == np.uint8:
pil_images.append(Image.fromarray(f))
else:
f_uint8 = (f * 255).astype(np.uint8)
pil_images.append(Image.fromarray(f_uint8))
prompts = text_prompts or ["object"]
# Flatten prompts for all images: [img1_p1, img1_p2, img2_p1, img2_p2, ...]
flattened_prompts = []
for _ in frames:
flattened_prompts.extend(prompts)
inputs = self.processor(images=pil_images, text=flattened_prompts, return_tensors="pt").to(self.device)
# Handle batch expansion
self._expand_inputs_if_needed(inputs)
with torch.no_grad():
outputs = self.model(**inputs)
try:
results_list = self.processor.post_process_instance_segmentation(
outputs,
threshold=self.threshold,
mask_threshold=self.mask_threshold,
target_sizes=inputs.get("original_sizes").tolist(),
)
return [self._parse_single_result(r, f.shape) for r, f in zip(results_list, frames)]
except Exception:
logging.exception("SAM3 batch post-processing failed")
return [
SegmentationResult(
masks=np.zeros((0, f.shape[0], f.shape[1]), dtype=bool),
scores=None,
boxes=None
) for f in frames
]
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