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| 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': | |
| # adaptive_avg_pool2d can fail on MPS, workaround with CPU | |
| 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 | |