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"""
SAM3 Static Image Segmentation - Correct Implementation
Uses Sam3Model (not Sam3VideoModel) for text-prompted static image segmentation.
"""
import base64
import io
import asyncio
import torch
import numpy as np
from PIL import Image
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import AutoProcessor, AutoModel
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Load SAM3 model for STATIC IMAGES
processor = AutoProcessor.from_pretrained("./model", trust_remote_code=True)
model = AutoModel.from_pretrained(
"./model",
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
trust_remote_code=True
)
model.eval()
if torch.cuda.is_available():
model.cuda()
logger.info(f"GPU: {torch.cuda.get_device_name()}")
logger.info(f"VRAM: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB")
logger.info(f"✓ Loaded {model.__class__.__name__} for static image segmentation")
# Simple concurrency control
class VRAMManager:
def __init__(self):
self.semaphore = asyncio.Semaphore(2)
self.processing_count = 0
def get_vram_status(self):
if not torch.cuda.is_available():
return {}
return {
"total_gb": torch.cuda.get_device_properties(0).total_memory / 1e9,
"allocated_gb": torch.cuda.memory_allocated() / 1e9,
"free_gb": (torch.cuda.get_device_properties(0).total_memory - torch.cuda.memory_reserved()) / 1e9,
"processing_now": self.processing_count
}
async def acquire(self, rid):
await self.semaphore.acquire()
self.processing_count += 1
def release(self, rid):
self.processing_count -= 1
self.semaphore.release()
if torch.cuda.is_available():
torch.cuda.empty_cache()
vram_manager = VRAMManager()
app = FastAPI(title="SAM3 Static Image API")
class Request(BaseModel):
inputs: str
parameters: dict
def run_inference(image_b64: str, classes: list, request_id: str):
"""
Sam3Model inference for static images with text prompts.
Uses official SAM3 processor post-processing for correct mask generation.
"""
try:
# Decode image
image_bytes = base64.b64decode(image_b64)
pil_image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
logger.info(f"[{request_id}] Image: {pil_image.size}, Classes: {classes}")
# Process with Sam3Processor
# Sam3Model expects: batch of images matching text prompts
# For multiple objects in ONE image, repeat the image for each class
images_batch = [pil_image] * len(classes)
inputs = processor(
images=images_batch, # Repeat image for each text prompt
text=classes, # List of text prompts
return_tensors="pt"
)
# Store original sizes for post-processing
# Format: [[height, width]] for EACH image in batch
# Since we repeat the image for each class, repeat the size too
original_size = [pil_image.size[1], pil_image.size[0]] # [height, width]
original_sizes = torch.tensor([original_size] * len(classes))
inputs["original_sizes"] = original_sizes
logger.info(f"[{request_id}] Processing {len(classes)} classes with batched images")
logger.info(f"[{request_id}] Original size: {pil_image.size} (W x H)")
# Move to GPU and match model dtype
if torch.cuda.is_available():
model_dtype = next(model.parameters()).dtype
inputs = {
k: v.cuda().to(model_dtype) if isinstance(v, torch.Tensor) and v.dtype.is_floating_point else v.cuda() if isinstance(v, torch.Tensor) else v
for k, v in inputs.items()
}
logger.info(f"[{request_id}] Moved inputs to GPU (float tensors to {model_dtype})")
# Sam3Model Inference
with torch.no_grad():
outputs = model(**inputs)
logger.info(f"[{request_id}] Forward pass successful!")
logger.info(f"[{request_id}] Output type: {type(outputs)}")
# Use processor's official post-processing method
# This handles:
# - Logit to probability conversion (sigmoid)
# - Proper thresholding (default 0.5)
# - Resizing to original image dimensions
# - Score extraction
logger.info(f"[{request_id}] Using processor.post_process_instance_segmentation()")
try:
processed = processor.post_process_instance_segmentation(
outputs,
threshold=0.3, # Score threshold for detections (lowered to detect road cracks)
mask_threshold=0.5, # Probability threshold for mask pixels
target_sizes=original_sizes.tolist()
)
# Returns a LIST of results, one per image in batch (one per class in our case)
logger.info(f"[{request_id}] Post-processing successful!")
logger.info(f"[{request_id}] Number of batched results: {len(processed)}")
except Exception as proc_error:
logger.error(f"[{request_id}] Post-processing failed: {proc_error}")
logger.info(f"[{request_id}] Falling back to manual processing")
# Fallback to manual processing with sigmoid fix
results = []
# Extract masks from outputs
if hasattr(outputs, 'pred_masks'):
pred_masks = outputs.pred_masks
elif hasattr(outputs, 'masks'):
pred_masks = outputs.masks
elif isinstance(outputs, dict) and 'pred_masks' in outputs:
pred_masks = outputs['pred_masks']
else:
raise ValueError("Cannot find masks in model output")
logger.info(f"[{request_id}] pred_masks shape: {pred_masks.shape}")
for i, cls in enumerate(classes):
if i < pred_masks.shape[1]:
mask_tensor = pred_masks[0, i]
# Resize to original size
if mask_tensor.shape[-2:] != pil_image.size[::-1]:
mask_tensor = torch.nn.functional.interpolate(
mask_tensor.unsqueeze(0).unsqueeze(0),
size=pil_image.size[::-1],
mode='bilinear',
align_corners=False
).squeeze()
# CRITICAL FIX: Convert logits to probabilities THEN threshold
probs = torch.sigmoid(mask_tensor)
binary_mask = (probs > 0.5).float().cpu().numpy().astype("uint8") * 255
else:
binary_mask = np.zeros(pil_image.size[::-1], dtype="uint8")
# Convert to PNG
pil_mask = Image.fromarray(binary_mask, mode="L")
buf = io.BytesIO()
pil_mask.save(buf, format="PNG")
mask_b64 = base64.b64encode(buf.getvalue()).decode("utf-8")
# Extract score
score = 1.0
if hasattr(outputs, 'pred_logits') and i < outputs.pred_logits.shape[1]:
# Convert logits to probability
score = float(torch.sigmoid(outputs.pred_logits[0, i]).cpu())
results.append({
"label": cls,
"mask": mask_b64,
"score": score
})
logger.info(f"[{request_id}] Completed (fallback): {len(results)} masks generated")
return results
# Extract results from processor output
# CRITICAL: processor returns one result dict per class (batched)
# Each result dict contains MULTIPLE instances of that class
results = []
total_instances = 0
for i, cls in enumerate(classes):
class_result = processed[i] # Results for this specific class
num_instances = len(class_result['masks']) if 'masks' in class_result else 0
total_instances += num_instances
if num_instances > 0:
logger.info(f"[{request_id}] {cls}: {num_instances} instance(s) detected")
# Loop through ALL instances of this class
for j in range(num_instances):
# Get mask (already binary, resized to original size)
mask_np = class_result['masks'][j].cpu().numpy().astype("uint8") * 255
# Convert to PNG
pil_mask = Image.fromarray(mask_np, mode="L")
buf = io.BytesIO()
pil_mask.save(buf, format="PNG")
mask_b64 = base64.b64encode(buf.getvalue()).decode("utf-8")
# Get score (already converted to probability by processor)
score = float(class_result['scores'][j]) if 'scores' in class_result else 1.0
# Calculate coverage for logging
coverage = (mask_np > 0).sum() / mask_np.size * 100
results.append({
"label": cls,
"mask": mask_b64,
"score": score,
"instance_id": j
})
logger.info(f"[{request_id}] └─ Instance {j}: score={score:.3f}, coverage={coverage:.2f}%")
else:
logger.info(f"[{request_id}] {cls}: No instances detected")
logger.info(f"[{request_id}] Completed: {total_instances} instance(s) across {len(classes)} class(es)")
return results
except Exception as e:
logger.error(f"[{request_id}] Failed: {str(e)}")
import traceback
traceback.print_exc()
raise
@app.post("/")
async def predict(req: Request):
request_id = str(id(req))[:8]
try:
await vram_manager.acquire(request_id)
try:
results = await asyncio.to_thread(
run_inference,
req.inputs,
req.parameters.get("classes", []),
request_id
)
return results
finally:
vram_manager.release(request_id)
except Exception as e:
logger.error(f"[{request_id}] Error: {str(e)}")
raise HTTPException(status_code=500, detail=str(e))
@app.get("/health")
async def health():
return {
"status": "healthy",
"model": model.__class__.__name__,
"gpu_available": torch.cuda.is_available(),
"vram": vram_manager.get_vram_status()
}
@app.get("/metrics")
async def metrics():
return vram_manager.get_vram_status()
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860, workers=1)
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