def prepare_inputs(self, inputs):
"""Prepares inputs on device to be fed to model in eval mode."""
params = self.params.eval.input
video_shape = processing.get_video_shape(params)
audio_shape = processing.get_audio_shape(params, REF_FPS, REF_SR)
if FeatureNames.VISION in inputs:
images = inputs[FeatureNames.VISION]
else:
images = tf.zeros([1] + video_shape, dtype=tf.float32)
if FeatureNames.AUDIO_MEL in inputs or FeatureNames.AUDIO in inputs:
if params.raw_audio:
audio = inputs[FeatureNames.AUDIO]
else:
audio = inputs[FeatureNames.AUDIO_MEL]
else:
audio = tf.zeros([1] + audio_shape, dtype=tf.float32)
if FeatureNames.TEXT_INDEX in inputs:
words = inputs[FeatureNames.TEXT_INDEX]
else:
words = tf.zeros([1, params.max_num_words], dtype=tf.int32)
audio = tf.reshape(audio, [-1] + audio_shape)
words = tf.reshape(words, [-1, words.shape.as_list()[-1]])
labels_onehot = inputs.get(FeatureNames.LABEL_INDEX, None)
labels = {'one_hot': labels_onehot}
inputs = {'video': images, 'audio': audio, 'text': words}
return inputs, labels
def prepare_eval_inputs(self, inputs):
"""Prepares inputs on device to be fed to model in eval mode."""
params = self.params.eval.input
images = inputs[FeatureNames.VISION]
labels_onehot = inputs[FeatureNames.LABEL_INDEX]
if params.linearize_vision:
img_shape = [params.frame_size, params.frame_size, 3]
if params.name in dataloaders.VID_CLS_DS:
space_to_depth = params.space_to_depth
img_shape = processing.get_video_shape(
params, is_space_to_depth=space_to_depth)
else:
img_shape = [1] + img_shape
img_shape = [-1] + img_shape
images = tf.reshape(images, img_shape)
if params.name in dataloaders.IMG_CLS_DS:
num_replica = self.params.model_config.temporal_patch_size
images = tf.tile(images, [1, num_replica, 1, 1, 1])
labels = {'one_hot': labels_onehot}
inputs = {'images': images}
return inputs, labels
def prepare_inputs(self, inputs):
"""Prepares inputs on device to be fed to model in train mode."""
params = self.params.train.input
images = inputs[FeatureNames.VISION]
space_to_depth = params.space_to_depth
if params.linearize_vision:
vid_shape = processing.get_video_shape(
params, is_space_to_depth=space_to_depth)
images = tf.reshape(images, [-1] + vid_shape)
if params.raw_audio:
audio = inputs[FeatureNames.AUDIO]
else:
audio = inputs[FeatureNames.AUDIO_MEL]
words = inputs[FeatureNames.TEXT_INDEX]
words = tf.reshape(words, [-1, words.shape.as_list()[-1]])
audio_mask = inputs[FeatureNames.AUDIO_MASK]
text_mask = inputs[FeatureNames.TEXT_MASK]
labels = {
FeatureNames.AUDIO_MASK: audio_mask,
FeatureNames.TEXT_MASK: text_mask
}
inputs = {'video': images, 'audio': audio, 'text': words}
return inputs, labels
def construct_model(self, params):
"""Build models for train/eval."""
if params.mode == 'train':
input_params = params.train.input
space_to_depth = input_params.space_to_depth
else:
input_params = params.eval.input
space_to_depth = input_params.space_to_depth
video_shape = processing.get_video_shape(input_params, space_to_depth)
audio_shape = processing.get_audio_shape(input_params, REF_FPS, REF_SR)
text_shape = (input_params.max_num_words, )
inputs = {
'video': tf.keras.Input(shape=video_shape),
'audio': tf.keras.Input(shape=audio_shape),
'text': tf.keras.Input(shape=text_shape),
}
model = model_factory.build_model(params.model_config)
outputs = model(inputs, None)
keras_model = tf.keras.Model(inputs=inputs, outputs=outputs)
keras_model.loss_fn = model.loss_fn
# Restoring word embeddings
self.restore_text_embeddings(keras_model, params)
logging.info('Number of parameters in model: %f M.',
keras_model.count_params() / 10.**6)
learning_rate = schedules.get_learning_rate(
params.train.optimizer.learning_rate)
keras_model.optimizer = optimizers.get_optimizer(
learning_rate, params.train.optimizer)
return keras_model
def construct_model(self, params):
"""Build models for train/eval."""
num_test_samples = 1
if params.mode == 'train':
input_params = params.train.input
ds_name = input_params.name
is_vid_cls = ds_name in dataloaders.VID_CLS_DS
is_img_cls = ds_name in dataloaders.IMG_CLS_DS
is_aud_cls = ds_name in dataloaders.AUD_CLS_DS
elif params.mode == 'eval':
input_params = params.eval.input
ds_name = input_params.name
is_vid_cls = ds_name in dataloaders.VID_CLS_DS
is_img_cls = ds_name in dataloaders.IMG_CLS_DS
is_aud_cls = ds_name in dataloaders.AUD_CLS_DS
if not is_img_cls:
num_test_samples = params.eval.input.num_windows_test
if params.eval.input.multi_crop and not is_aud_cls:
num_test_samples *= 3
else:
raise ValueError('Invalid mode!')
if is_aud_cls:
input_shape = processing.get_audio_shape(input_params, REF_FPS,
REF_SR)
elif is_vid_cls:
space_to_depth = input_params.space_to_depth
input_shape = processing.get_video_shape(
input_params, is_space_to_depth=space_to_depth)
elif is_img_cls:
input_shape = processing.get_video_shape(input_params)
if is_img_cls:
input_shape[0] = params.model_config.temporal_patch_size
num_classes = dataloaders.CLS_DS[ds_name]['num_classes']
model_kwargs = {
'num_classes': num_classes,
'num_test_samples': num_test_samples
}
if is_aud_cls:
inputs = {'audio': tf.keras.Input(shape=input_shape)}
model_factory = aud_factory
else:
inputs = {'images': tf.keras.Input(shape=input_shape)}
model_factory = vid_factory
model = model_factory.build_model(params=params.model_config,
override_params=model_kwargs,
mode='predict')
outputs = model(inputs, None)
keras_model = tf.keras.Model(inputs=inputs, outputs=outputs)
keras_model.loss_fn = model.loss_fn
if params.mode == 'train':
self.partial_restore(params, keras_model)
logging.info('Number of parameters in model: %f M.',
keras_model.count_params() / 10.**6)
learning_rate = schedules.get_learning_rate(
params.train.optimizer.learning_rate)
keras_model.optimizer = optimizers.get_optimizer(
learning_rate, params.train.optimizer)
return keras_model