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import os |
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import torch |
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import torch.nn as nn |
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from torch.nn import init |
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from torch.optim import lr_scheduler |
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class BaseModel(nn.Module): |
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def __init__(self, opt): |
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super(BaseModel, self).__init__() |
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self.opt = opt |
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self.total_steps = 0 |
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self.save_dir = os.path.join(opt.checkpoints_dir, opt.name) |
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self.device = torch.device('cuda:{}'.format(opt.gpu_ids[0])) if opt.gpu_ids else torch.device('cpu') |
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if opt.gpu_ids: |
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self.device= torch.device('cuda:{}'.format(opt.gpu_ids[0])) |
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else: |
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print("gpu is not available! ") |
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self.device = torch.device('cpu') |
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def save_networks(self, save_filename): |
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save_path = os.path.join(self.save_dir, save_filename) |
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state_dict = { |
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'model': self.model.state_dict(), |
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'optimizer' : self.optimizer.state_dict(), |
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'total_steps' : self.total_steps, |
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} |
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torch.save(state_dict, save_path) |
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def eval(self): |
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self.model.eval() |
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def test(self): |
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with torch.no_grad(): |
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self.forward() |