xsl
443cfa298f
包含： - hair_service_sd: 主服务（换发型/换发色/生发，端口8801） - photo_service: LoRA调度+训练（端口32678） - hair_grow_service: 调试测试页（端口8888，含4个测试页） - 批量训练脚本（batch_train_hairstyles.py） - 发际线mask自动识别（hairline_mask.py，4种方案） - 手绘mask换发型（hair_swap_manual.py） - 文档：README.md + LARGE_FILES.md + docs/ 大文件（模型权重200G、训练数据123G）已排除，见 LARGE_FILES.md OSS/COS密钥已脱敏为环境变量，原文件备份在本地
25 lines
1.3 KiB
Python
25 lines
1.3 KiB
Python
import os
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import torch
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def load_model_by_path(model_save_path, model, gpu_id = None):
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if not os.path.exists(model_save_path): return
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loc = 'cpu' if gpu_id is None else 'cuda:{}'.format(gpu_id)
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pretrained_dict = torch.load(model_save_path, map_location=loc)
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if 'state_dict' in pretrained_dict: pretrained_dict = pretrained_dict['state_dict']
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model_dict = model.state_dict()
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# pretrained_dict.pop('netG.model.1.weight', '404')
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pretrained_dict_new = {k: v for k, v in pretrained_dict.items()
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if (k in model_dict and model_dict[k].data.shape == v.data.shape)}
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# if 'netG.model.1.weight' not in pretrained_dict_new and 'netG.model.1.weight' in model_dict:
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# pretrained_dict_new['netG.model.1.weight'] = model_dict['netG.model.1.weight']
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# pretrained_dict_new['netG.model.1.weight'][:,:12] = pretrained_dict['netG.model.1.weight']
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model_dict.update(pretrained_dict_new)
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model.load_state_dict(model_dict)
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print("load model: ", model_save_path)
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def save_model_by_path(model_save_path, model):
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save_dir, _ = os.path.split(model_save_path)
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if not os.path.isdir(save_dir): os.makedirs(save_dir)
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model_dic={k.replace('.module', ''):v for k,v in model.state_dict().items()}
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torch.save(model_dic, model_save_path) |