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SAM2.1 and example README #1048

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Oct 24, 2024
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SAM2.1 and example README #1048

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Examples readme
cpuhrsch Oct 10, 2024
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Example image
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curl command
cpuhrsch Oct 10, 2024
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Merge branch 'main' of github.com:pytorch/ao into sam2fast2
cpuhrsch Oct 24, 2024
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checkpoint_path argument
cpuhrsch Oct 24, 2024
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SAM 2.1
cpuhrsch Oct 24, 2024
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3 changes: 3 additions & 0 deletions examples/README.md
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# Examples

Various example scripts and applications of torchao and PyTorch in general.
9 changes: 4 additions & 5 deletions examples/sam2_amg_server/README.md
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To run this example you need to download the vit_h checkpoint and put it into a local folder named checkpoints

You can find the checkpoint for vit_h here: https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth

To read the image you also need to install opencv-python: https://pypi.org/project/opencv-python/
## Example curl command
```
curl -X POST http://127.0.0.1:5000/upload -F 'image=@/path/to/file.jpg' --output path/to/output.png
```
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2 changes: 1 addition & 1 deletion examples/sam2_amg_server/example.html
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!DOCTYPE html>
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
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117 changes: 0 additions & 117 deletions examples/sam2_amg_server/sam2_hiera_l.yaml
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10 changes: 5 additions & 5 deletions examples/sam2_amg_server/server.py
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Expand Up @@ -53,19 +53,19 @@ class GenerateRequest(BaseModel):
num_steps: Optional[int] = 30
seed: Optional[int] = 42

def main():
def main(checkpoint_path):

from sam2.build_sam import build_sam2
from sam2.automatic_mask_generator import SAM2AutomaticMaskGenerator

device = "cuda"
sam2_checkpoint = "checkpoints/sam2_hiera_large.pt"
model_cfg = "sam2_hiera_l.yaml"
from pathlib import Path
sam2_checkpoint = Path(checkpoint_path) / Path("sam2.1_hiera_large.pt")
model_cfg = "configs/sam2.1/sam2.1_hiera_l.yaml"
logging.basicConfig(level=logging.INFO)

logging.info(f"Loading model: {sam2_checkpoint}")
logging.info(f"Loading model {sam2_checkpoint} with config {model_cfg}")
sam2 = build_sam2(model_cfg, sam2_checkpoint, device=device, apply_postprocessing=False)
sam2.to(device=device)

mask_generator = SAM2AutomaticMaskGenerator(sam2) #, points_per_batch=None)

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