| | 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 |
| |
|
