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