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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
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