text stringlengths 1 93.6k |
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'Accuracy {accs.val:.3f} ({accs.avg:.3f})'.format(i_batch, len(val_loader),
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batch_time=batch_time,
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loss=losses,
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accs=accs))
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return accs.avg, losses.avg
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def main(args):
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superclass = args['superclass']
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if superclass is None:
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superclass = 'Animals'
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train_loader = DataLoader(dataset=ZslDataset(superclass, 'train'), batch_size=batch_size, shuffle=True,
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pin_memory=True, drop_last=True)
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val_loader = DataLoader(dataset=ZslDataset(superclass, 'valid'), batch_size=batch_size, pin_memory=True,
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drop_last=True)
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embedding_size = get_embedding_size_by_superclass(superclass)
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print('embedding_size: ' + str(embedding_size))
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W = torch.randn(feature_size, embedding_size, requires_grad=True, device=device)
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torch.nn.init.xavier_uniform_(W)
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attributes_per_class = get_attributes_per_class_by_superclass(superclass)
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# Initialize encoder
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model = Encoder(embedding_size=embedding_size)
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# Use appropriate device
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model = model.to(device)
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# Initialize optimizers
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optimizer = optim.Adam([{'params': model.parameters()}, {'params': W}], lr=learning_rate)
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best_acc = 0
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epochs_since_improvement = 0
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# Epochs
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for epoch in range(start_epoch, epochs):
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# Decay learning rate if there is no improvement for 8 consecutive epochs, and terminate training after 20
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if epochs_since_improvement == 20:
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break
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if epochs_since_improvement > 0 and epochs_since_improvement % 8 == 0:
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adjust_learning_rate(optimizer, 0.8)
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# One epoch's training
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train(epoch, train_loader, model, W, optimizer, attributes_per_class)
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# One epoch's validation
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val_acc, val_loss = valid(val_loader, model, W, attributes_per_class)
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print('\n * ACCURACY - {acc:.3f}, LOSS - {loss:.3f}\n'.format(acc=val_acc, loss=val_loss))
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# Check if there was an improvement
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is_best = val_acc > best_acc
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best_acc = max(best_acc, val_acc)
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if not is_best:
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epochs_since_improvement += 1
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print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement,))
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else:
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epochs_since_improvement = 0
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# Save checkpoint
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save_checkpoint(epoch, model, W, optimizer, val_acc, is_best, superclass)
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if __name__ == '__main__':
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# Parse arguments
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ap = argparse.ArgumentParser()
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ap.add_argument("-s", "--superclass",
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help="superclass ('Animals', 'Fruits', 'Vehicles', 'Electronics', 'Hairstyles')")
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args = vars(ap.parse_args())
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main(args)
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# <FILESEP>
|
#!/usr/bin/env python
|
#_MIT License
|
#_
|
#_Copyright (c) 2017 Dan Persons (dpersonsdev@gmail.com)
|
#_
|
#_Permission is hereby granted, free of charge, to any person obtaining a copy
|
#_of this software and associated documentation files (the "Software"), to deal
|
#_in the Software without restriction, including without limitation the rights
|
#_to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
#_copies of the Software, and to permit persons to whom the Software is
|
#_furnished to do so, subject to the following conditions:
|
#_
|
#_The above copyright notice and this permission notice shall be included in all
|
#_copies or substantial portions of the Software.
|
#_
|
#_THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
#_IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
#_FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
#_AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
#_LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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#_OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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#_SOFTWARE.
|
from siemstress.triggercore import SiemTriggerCore
|
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