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67 lines
3.4 KiB
Python
67 lines
3.4 KiB
Python
#!/usr/bin/python3
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'''Copyright (c) 2021-2022 Amazon
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions
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are met:
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- Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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- Redistributions in binary form must reproduce the above copyright
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notice, this list of conditions and the following disclaimer in the
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documentation and/or other materials provided with the distribution.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR
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CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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'''
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import numpy as np
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from tensorflow.keras.utils import Sequence
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class PLCLoader(Sequence):
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def __init__(self, features, lost, nb_burg_features, batch_size):
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self.batch_size = batch_size
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self.nb_batches = features.shape[0]//self.batch_size
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self.features = features[:self.nb_batches*self.batch_size, :, :]
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self.lost = lost.astype('float')
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self.lost = self.lost[:(len(self.lost)//features.shape[1]-1)*features.shape[1]]
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self.nb_burg_features = nb_burg_features
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self.on_epoch_end()
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def on_epoch_end(self):
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self.indices = np.arange(self.nb_batches*self.batch_size)
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np.random.shuffle(self.indices)
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offset = np.random.randint(0, high=self.features.shape[1])
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self.lost_offset = np.reshape(self.lost[offset:-self.features.shape[1]+offset], (-1, self.features.shape[1]))
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self.lost_indices = np.random.randint(0, high=self.lost_offset.shape[0], size=self.nb_batches*self.batch_size)
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def __getitem__(self, index):
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features = self.features[self.indices[index*self.batch_size:(index+1)*self.batch_size], :, :]
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#lost = (np.random.rand(features.shape[0], features.shape[1]) > .2).astype('float')
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lost = self.lost_offset[self.lost_indices[index*self.batch_size:(index+1)*self.batch_size], :]
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lost = np.reshape(lost, (features.shape[0], features.shape[1], 1))
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lost_mask = np.tile(lost, (1,1,features.shape[2]))
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in_features = features*lost_mask
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#For the first frame after a loss, we don't have valid features, but the Burg estimate is valid.
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in_features[:,1:,self.nb_burg_features:] = in_features[:,1:,self.nb_burg_features:]*lost_mask[:,:-1,self.nb_burg_features:]
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out_lost = np.copy(lost)
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out_lost[:,1:,:] = out_lost[:,1:,:]*out_lost[:,:-1,:]
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out_features = np.concatenate([features[:,:,self.nb_burg_features:], 1.-out_lost], axis=-1)
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inputs = [in_features*lost_mask, lost]
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outputs = [out_features]
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return (inputs, outputs)
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def __len__(self):
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return self.nb_batches
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