def get_predictions(self, frames, scope):
frames = self._reshape_to_conv(frames)
cnn = CNN()
if self.operation == 'training':
cnn_output = cnn.create_model(frames,
cnn.conv_filters,
keep_prob=self.keep_prob)
else:
cnn_output = cnn.create_model(frames,
cnn.conv_filters,
keep_prob=1.0)
cnn_output = self._reshape_to_rnn(cnn_output)
rnn = RNN()
rnn_output = rnn.create_model(cnn_output, scope + '_rnn')
if self.is_attention:
attention = Attention(self.batch_size)
attention_output = attention.create_model(rnn_output,
scope + '_attention')
fc = FC(self.num_classes)
outputs = fc.create_model(attention_output, scope + '_fc')
else:
rnn_output = rnn_output[:, -1, :]
fc = FC(self.num_classes)
outputs = fc.create_model(rnn_output, scope + '_fc')
return outputs
def get_multi_predictions(self, frames):
frames = self._reshape_to_conv(frames)
cnn = CNN()
if self.operation == 'training':
cnn_output = cnn.create_model(frames,
cnn.conv_filters,
keep_prob=self.keep_prob)
else:
cnn_output = cnn.create_model(frames,
cnn.conv_filters,
keep_prob=1.0)
cnn_output = self._reshape_to_rnn(cnn_output)
rnn = RNN()
arousal_rnn_output = rnn.create_model(cnn_output, 'arousal_rnn')
valence_rnn_output = rnn.create_model(cnn_output, 'valence_rnn')
dominance_rnn_output = rnn.create_model(cnn_output, 'dominance_rnn')
if self.is_attention:
attention = Attention(self.batch_size)
arousal_attention_output = attention.create_model(
arousal_rnn_output, 'arousal_attention')
valence_attention_output = attention.create_model(
valence_rnn_output, 'valence_attention')
dominance_attention_output = attention.create_model(
dominance_rnn_output, 'dominance_attention')
fc = FC(self.num_classes)
arousal_fc_outputs = fc.create_model(arousal_attention_output,
'arousal_fc')
valence_fc_outputs = fc.create_model(valence_attention_output,
'valence_fc')
dominance_fc_outputs = fc.create_model(dominance_attention_output,
'dominance_fc')
else:
arousal_rnn_output = arousal_rnn_output[:, -1, :]
valence_rnn_output = valence_rnn_output[:, -1, :]
dominance_rnn_output = dominance_rnn_output[:, -1, :]
fc = FC(self.num_classes)
arousal_fc_outputs = fc.create_model(arousal_rnn_output,
'arousal_fc')
valence_fc_outputs = fc.create_model(valence_rnn_output,
'valence_fc')
dominance_fc_outputs = fc.create_model(dominance_rnn_output,
'dominance_fc')
return arousal_fc_outputs, valence_fc_outputs, dominance_fc_outputs
def get_predictions(self, frames):
frames = self._reshape_to_conv(frames)
cnn = CNN()
cnn_output = cnn.create_model(frames, cnn.conv_filters)
cnn_output = self._reshape_to_rnn(cnn_output)
rnn = RNN()
rnn_output = rnn.create_model(cnn_output)
if self.is_attention:
attention = Attention(self.batch_size)
attention_output = attention.create_model(rnn_output)
fc = FC(self.num_classes)
outputs = fc.create_model(attention_output)
else:
rnn_output = rnn_output[:, -1, :]
fc = FC(self.num_classes)
outputs = fc.create_model(rnn_output)
return outputs