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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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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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