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import os |
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import numpy as np |
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import torch |
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from torch.autograd import Variable |
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from pdb import set_trace as st |
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from IPython import embed |
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class BaseModel(): |
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def __init__(self): |
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pass; |
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def name(self): |
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return 'BaseModel' |
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def initialize(self, use_gpu=True, gpu_ids=[0]): |
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self.use_gpu = use_gpu |
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self.gpu_ids = gpu_ids |
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def forward(self): |
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pass |
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def get_image_paths(self): |
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pass |
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def optimize_parameters(self): |
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pass |
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def get_current_visuals(self): |
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return self.input |
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def get_current_errors(self): |
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return {} |
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def save(self, label): |
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pass |
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def save_network(self, network, path, network_label, epoch_label): |
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save_filename = '%s_net_%s.pth' % (epoch_label, network_label) |
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save_path = os.path.join(path, save_filename) |
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torch.save(network.state_dict(), save_path) |
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def load_network(self, network, network_label, epoch_label): |
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save_filename = '%s_net_%s.pth' % (epoch_label, network_label) |
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save_path = os.path.join(self.save_dir, save_filename) |
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print('Loading network from %s'%save_path) |
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network.load_state_dict(torch.load(save_path)) |
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def update_learning_rate(): |
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pass |
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def get_image_paths(self): |
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return self.image_paths |
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def save_done(self, flag=False): |
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np.save(os.path.join(self.save_dir, 'done_flag'),flag) |
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np.savetxt(os.path.join(self.save_dir, 'done_flag'),[flag,],fmt='%i') |
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