def call(self, data, steps, seq_lens, training):
"""One pass through the model.
Args:
data: dict, batches of tensors from many videos. Available keys: 'audio',
'frames', 'labels'.
steps: Tensor, batch of indices of chosen frames in videos.
seq_lens: Tensor, batch of sequence length of the full videos.
training: Boolean, if True model is run in training mode.
Returns:
embeddings: Tensor, Float tensor containing embeddings
Raises:
ValueError: In case invalid configs are passed.
"""
cnn = self.model['cnn']
emb = self.model['emb']
if training:
num_steps = CONFIG.TRAIN.NUM_FRAMES
else:
num_steps = CONFIG.EVAL.NUM_FRAMES
cnn_feats = get_cnn_feats(cnn, data, training)
embs = emb(cnn_feats, num_steps)
channels = embs.shape[-1]
embs = tf.reshape(embs, [-1, num_steps, channels])
return embs
def call(self, data, steps, seq_lens, training):
"""One pass through the model."""
cnn = self.model['cnn']
emb = self.model['emb']
if training:
num_steps = CONFIG.TRAIN.NUM_FRAMES * CONFIG.DATA.NUM_STEPS
else:
num_steps = CONFIG.EVAL.NUM_FRAMES * CONFIG.DATA.NUM_STEPS
# Number of steps is doubled due to sampling of positives and anchors.
cnn_feats = get_cnn_feats(cnn, data, training, 2 * num_steps)
if training:
num_steps = CONFIG.TRAIN.NUM_FRAMES
else:
num_steps = CONFIG.EVAL.NUM_FRAMES
embs = emb(cnn_feats, 2 * num_steps)
embs = tf.stack(tf.split(embs, 2 * num_steps, axis=0), axis=1)
return embs
