| import os |
| from annotator.annotator_path import models_path |
| from modules import devices |
| from annotator.uniformer.inference import init_segmentor, inference_segmentor, show_result_pyplot |
|
|
| try: |
| from mmseg.core.evaluation import get_palette |
| except ImportError: |
| from annotator.mmpkg.mmseg.core.evaluation import get_palette |
|
|
| modeldir = os.path.join(models_path, "uniformer") |
| checkpoint_file = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/upernet_global_small.pth" |
| config_file = os.path.join(os.path.dirname(os.path.realpath(__file__)), "upernet_global_small.py") |
| old_modeldir = os.path.dirname(os.path.realpath(__file__)) |
| model = None |
|
|
| def unload_uniformer_model(): |
| global model |
| if model is not None: |
| model = model.cpu() |
|
|
| def apply_uniformer(img): |
| global model |
| if model is None: |
| modelpath = os.path.join(modeldir, "upernet_global_small.pth") |
| old_modelpath = os.path.join(old_modeldir, "upernet_global_small.pth") |
| if os.path.exists(old_modelpath): |
| modelpath = old_modelpath |
| elif not os.path.exists(modelpath): |
| from basicsr.utils.download_util import load_file_from_url |
| load_file_from_url(checkpoint_file, model_dir=modeldir) |
| |
| model = init_segmentor(config_file, modelpath, device=devices.get_device_for("controlnet")) |
| model = model.to(devices.get_device_for("controlnet")) |
| |
| if devices.get_device_for("controlnet").type == 'mps': |
| |
| import torch.nn.functional |
| |
| orig_adaptive_avg_pool2d = torch.nn.functional.adaptive_avg_pool2d |
| def cpu_if_exception(input, *args, **kwargs): |
| try: |
| return orig_adaptive_avg_pool2d(input, *args, **kwargs) |
| except: |
| return orig_adaptive_avg_pool2d(input.cpu(), *args, **kwargs).to(input.device) |
| |
| try: |
| torch.nn.functional.adaptive_avg_pool2d = cpu_if_exception |
| result = inference_segmentor(model, img) |
| finally: |
| torch.nn.functional.adaptive_avg_pool2d = orig_adaptive_avg_pool2d |
| else: |
| result = inference_segmentor(model, img) |
| |
| res_img = show_result_pyplot(model, img, result, get_palette('ade'), opacity=1) |
| return res_img |
|
|
