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import torch
import numpy as np
from torchvision.transforms import ToTensor
GPU_EFFICIENT_SAM_CHECKPOINT = "efficient_sam_s_gpu.jit"
CPU_EFFICIENT_SAM_CHECKPOINT = "efficient_sam_s_cpu.jit"
def load(device: torch.device) -> torch.jit.ScriptModule:
if device.type == "cuda":
model = torch.jit.load(GPU_EFFICIENT_SAM_CHECKPOINT)
else:
model = torch.jit.load(CPU_EFFICIENT_SAM_CHECKPOINT)
model.eval()
return model
def inference_with_box(
image: np.ndarray,
box: np.ndarray,
model: torch.jit.ScriptModule,
device: torch.device
) -> np.ndarray:
bbox = torch.reshape(torch.tensor(box), [1, 1, 2, 2])
bbox_labels = torch.reshape(torch.tensor([2, 3]), [1, 1, 2])
img_tensor = ToTensor()(image)
predicted_logits, predicted_iou = model(
img_tensor[None, ...].to(device),
bbox.to(device),
bbox_labels.to(device),
)
predicted_logits = predicted_logits.cpu()
all_masks = torch.ge(torch.sigmoid(predicted_logits[0, 0, :, :, :]), 0.5).numpy()
predicted_iou = predicted_iou[0, 0, ...].cpu().detach().numpy()
max_predicted_iou = -1
selected_mask_using_predicted_iou = None
for m in range(all_masks.shape[0]):
curr_predicted_iou = predicted_iou[m]
if (
curr_predicted_iou > max_predicted_iou
or selected_mask_using_predicted_iou is None
):
max_predicted_iou = curr_predicted_iou
selected_mask_using_predicted_iou = all_masks[m]
return selected_mask_using_predicted_iou
def inference_with_boxes(
image: np.ndarray,
xyxy: np.ndarray,
model: torch.jit.ScriptModule,
device: torch.device
) -> np.ndarray:
masks = []
for [x_min, y_min, x_max, y_max] in xyxy:
box = np.array([[x_min, y_min], [x_max, y_max]])
mask = inference_with_box(image, box, model, device)
masks.append(mask)
return np.array(masks)
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