add some code

managed_components/78__esp-opus/dnn/training_tf2/plc_loader.py

@@ -0,0 +1,73 @@
+#!/usr/bin/python3
+'''Copyright (c) 2021-2022 Amazon
+
+   Redistribution and use in source and binary forms, with or without
+   modification, are permitted provided that the following conditions
+   are met:
+
+   - Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+
+   - Redistributions in binary form must reproduce the above copyright
+   notice, this list of conditions and the following disclaimer in the
+   documentation and/or other materials provided with the distribution.
+
+   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+   ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+   LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+   A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE FOUNDATION OR
+   CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+   EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+   PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+   PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+   LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+   NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+   SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+'''
+
+import numpy as np
+from tensorflow.keras.utils import Sequence
+
+class PLCLoader(Sequence):
+    def __init__(self, features, lost, nb_burg_features, batch_size):
+        self.batch_size = batch_size
+        self.nb_batches = features.shape[0]//self.batch_size
+        self.features = features[:self.nb_batches*self.batch_size, :, :]
+        self.lost = lost.astype('float')
+        self.lost = self.lost[:(len(self.lost)//features.shape[1]-1)*features.shape[1]]
+        self.nb_burg_features = nb_burg_features
+        self.on_epoch_end()
+
+    def on_epoch_end(self):
+        self.indices = np.arange(self.nb_batches*self.batch_size)
+        np.random.shuffle(self.indices)
+        offset = np.random.randint(0, high=self.features.shape[1])
+        self.lost_offset = np.reshape(self.lost[offset:-self.features.shape[1]+offset], (-1, self.features.shape[1]))
+        self.lost_indices = np.random.randint(0, high=self.lost_offset.shape[0], size=self.nb_batches*self.batch_size)
+
+    def __getitem__(self, index):
+        features = self.features[self.indices[index*self.batch_size:(index+1)*self.batch_size], :, :]
+        burg_lost = (np.random.rand(features.shape[0], features.shape[1]) > .1).astype('float')
+        burg_lost = np.reshape(burg_lost, (features.shape[0], features.shape[1], 1))
+        burg_mask = np.tile(burg_lost, (1,1,self.nb_burg_features))
+
+        lost = self.lost_offset[self.lost_indices[index*self.batch_size:(index+1)*self.batch_size], :]
+        lost = np.reshape(lost, (features.shape[0], features.shape[1], 1))
+        lost_mask = np.tile(lost, (1,1,features.shape[2]))
+        in_features = features*lost_mask
+        in_features[:,:,:self.nb_burg_features] = in_features[:,:,:self.nb_burg_features]*burg_mask
+
+        #For the first frame after a loss, we don't have valid features, but the Burg estimate is valid.
+        #in_features[:,1:,self.nb_burg_features:] = in_features[:,1:,self.nb_burg_features:]*lost_mask[:,:-1,self.nb_burg_features:]
+        out_lost = np.copy(lost)
+        #out_lost[:,1:,:] = out_lost[:,1:,:]*out_lost[:,:-1,:]
+
+        out_features = np.concatenate([features[:,:,self.nb_burg_features:], 1.-out_lost], axis=-1)
+        burg_sign = 2*burg_lost - 1
+        # last dim is 1 for received packet, 0 for lost packet, and -1 when just the Burg info is missing
+        inputs = [in_features*lost_mask, lost*burg_sign]
+        outputs = [out_features]
+        return (inputs, outputs)
+
+    def __len__(self):
+        return self.nb_batches