model_type = "vit_b" device = "cuda" # or "cuda" sam = sam_model_registry[model_type](checkpoint=sam_checkpoint) sam.to(device=device) """ for epoch in range(epoches): # 加载符合模型数据集标准的数据:每一次输入是一个列表,每一项是一个字典 # 字典包括:"image",("point_coords","poi...
sam_model = sam_model_registry['vit_b'](checkpoint='models/sam_vit_b_01ec64.pth').to(device='cuda') mask_generator = SamAutomaticMaskGenerator(sam_model) masks = mask_generator.generate(image) plt.imshow(image) plt.axis('off') matplotlib_plot_sam__single_mask(masks) plt.show() 3....
复制代码 from mobile_sam import SamAutomaticMaskGenerator, sam_model_registrydef load_mask():model_type = "vit_t"sam_checkpoint = "./weights/mobile_sam.pt"mobile_sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)mobile_sam.to(device="cuda")mobile_sam.eval()mask_generator = Sa...
sys.path.append("..")fromsegment_anything import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor sam_checkpoint="sam_vit_b_01ec64.pth"model_type="vit_b"device="cuda"sam= sam_model_registry[model_type](checkpoint=sam_checkpoint) sam.to(device=device) #自动生成采样点对图像进行分割...
model_type="vit_b"# model_type="vit_h"# model_type="vit_l"device="cuda"sam=sam_model_registry[model_type](checkpoint=sam_checkpoint)sam.to(device=device)predictor=SamPredictor(sam)predictor.set_image(image)#---加载模型---#鼠标标定(x,y)位置 # 因为可以有多个标定,所以有多个...
cd path/to/playground/label_anything label-studio-ml start sam --port 8003 --with \ sam_config=vit_b \ sam_checkpoint_file=./sam_vit_b_01ec64.pth \ out_mask=True \ out_bbox=True \ device=cuda:0 \ #device=cuda:0 为使用 ...
file=./sam_vit_b_01ec64.pth \out_mask=True \out_bbox=True \device=cuda:0 \# device=cuda:0 为使用 GPU 推理，如果使用 cpu 推理，将 cuda:0 替换为 cpu# out_poly=True 返回外接多边形的标注PS: 在 Windows 环境中，在 Anaconda Powershell Prompt 输入以下内容等价于上方的输入:cd path/to/...
cd path/to/playground/label_anything label-studio-ml start sam--port8003--with\ sam_config=vit_b \ sam_checkpoint_file=./sam_vit_b_01ec64.pth \ out_mask=True \ out_bbox=True \ device=cuda:0\ # device=cuda:0为使用GPU推理,如果使用 cpu 推理,将 cuda:0替换为 cpu ...
device = "cuda" if __name__ == '__main__': net = SamOut(235, 256, 16, 4) net.to(device) net(torch.randint(0, 200, [2, 8 * 13]).to(device)) # # epoch___0___loss___8.586270___steps___65760: 0%| | 0/1 [01:21<?, ?it/s] cummax # ...
device = "cuda" if __name__ == '__main__': net = SamOut(235, 256, 16, 4) net.to(device) net(torch.randint(0, 200, [2, 8 * 13]).to(device)) # 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.