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frame_level_models.py
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frame_level_models.py
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# Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Contains a collection of models which operate on variable-length sequences."""
import math
import model_utils as utils
import models
import tensorflow as tf
from tensorflow import flags
import tensorflow.contrib.slim as slim
import video_level_models
FLAGS = flags.FLAGS
flags.DEFINE_integer("iterations", 30, "Number of frames per batch for DBoF.")
flags.DEFINE_bool("dbof_add_batch_norm", True,
"Adds batch normalization to the DBoF model.")
flags.DEFINE_bool(
"sample_random_frames", True,
"If true samples random frames (for frame level models). If false, a random"
"sequence of frames is sampled instead.")
flags.DEFINE_integer("dbof_cluster_size", 8192,
"Number of units in the DBoF cluster layer.")
flags.DEFINE_integer("dbof_hidden_size", 1024,
"Number of units in the DBoF hidden layer.")
flags.DEFINE_string(
"dbof_pooling_method", "max",
"The pooling method used in the DBoF cluster layer. "
"Choices are 'average' and 'max'.")
flags.DEFINE_string(
"dbof_activation", "sigmoid",
"The nonlinear activation method for cluster and hidden dense layer, e.g., "
"sigmoid, relu6, etc.")
flags.DEFINE_string(
"video_level_classifier_model", "MoeModel",
"Some Frame-Level models can be decomposed into a "
"generalized pooling operation followed by a "
"classifier layer")
flags.DEFINE_integer("lstm_cells", 1024, "Number of LSTM cells.")
flags.DEFINE_integer("lstm_layers", 2, "Number of LSTM layers.")
class FrameLevelLogisticModel(models.BaseModel):
"""Creates a logistic classifier over the aggregated frame-level features."""
def create_model(self, model_input, vocab_size, num_frames, **unused_params):
"""See base class.
This class is intended to be an example for implementors of frame level
models. If you want to train a model over averaged features it is more
efficient to average them beforehand rather than on the fly.
Args:
model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
input features.
vocab_size: The number of classes in the dataset.
num_frames: A vector of length 'batch' which indicates the number of
frames for each video (before padding).
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
'batch_size' x 'num_classes'.
"""
num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
feature_size = model_input.get_shape().as_list()[2]
denominators = tf.reshape(tf.tile(num_frames, [1, feature_size]),
[-1, feature_size])
avg_pooled = tf.reduce_sum(model_input, axis=[1]) / denominators
output = slim.fully_connected(avg_pooled,
vocab_size,
activation_fn=tf.nn.sigmoid,
weights_regularizer=slim.l2_regularizer(1e-8))
return {"predictions": output}
class DbofModel(models.BaseModel):
"""Creates a Deep Bag of Frames model.
The model projects the features for each frame into a higher dimensional
'clustering' space, pools across frames in that space, and then
uses a configurable video-level model to classify the now aggregated features.
The model will randomly sample either frames or sequences of frames during
training to speed up convergence.
"""
ACT_FN_MAP = {
"sigmoid": tf.nn.sigmoid,
"relu6": tf.nn.relu6,
}
def create_model(self,
model_input,
vocab_size,
num_frames,
iterations=None,
add_batch_norm=None,
sample_random_frames=None,
cluster_size=None,
hidden_size=None,
is_training=True,
**unused_params):
"""See base class.
Args:
model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
input features.
vocab_size: The number of classes in the dataset.
num_frames: A vector of length 'batch' which indicates the number of
frames for each video (before padding).
iterations: the number of frames to be sampled.
add_batch_norm: whether to add batch norm during training.
sample_random_frames: whether to sample random frames or random sequences.
cluster_size: the output neuron number of the cluster layer.
hidden_size: the output neuron number of the hidden layer.
is_training: whether to build the graph in training mode.
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
'batch_size' x 'num_classes'.
"""
iterations = iterations or FLAGS.iterations
add_batch_norm = add_batch_norm or FLAGS.dbof_add_batch_norm
random_frames = sample_random_frames or FLAGS.sample_random_frames
cluster_size = cluster_size or FLAGS.dbof_cluster_size
hidden1_size = hidden_size or FLAGS.dbof_hidden_size
act_fn = self.ACT_FN_MAP.get(FLAGS.dbof_activation)
assert act_fn is not None, ("dbof_activation is not valid: %s." %
FLAGS.dbof_activation)
num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
if random_frames:
model_input = utils.SampleRandomFrames(model_input, num_frames,
iterations)
else:
model_input = utils.SampleRandomSequence(model_input, num_frames,
iterations)
max_frames = model_input.get_shape().as_list()[1]
feature_size = model_input.get_shape().as_list()[2]
reshaped_input = tf.reshape(model_input, [-1, feature_size])
tf.compat.v1.summary.histogram("input_hist", reshaped_input)
if add_batch_norm:
reshaped_input = slim.batch_norm(reshaped_input,
center=True,
scale=True,
is_training=is_training,
scope="input_bn")
cluster_weights = tf.compat.v1.get_variable(
"cluster_weights", [feature_size, cluster_size],
initializer=tf.random_normal_initializer(stddev=1 /
math.sqrt(feature_size)))
tf.compat.v1.summary.histogram("cluster_weights", cluster_weights)
activation = tf.matmul(reshaped_input, cluster_weights)
if add_batch_norm:
activation = slim.batch_norm(activation,
center=True,
scale=True,
is_training=is_training,
scope="cluster_bn")
else:
cluster_biases = tf.compat.v1.get_variable(
"cluster_biases", [cluster_size],
initializer=tf.random_normal_initializer(stddev=1 /
math.sqrt(feature_size)))
tf.compat.v1.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = act_fn(activation)
tf.compat.v1.summary.histogram("cluster_output", activation)
activation = tf.reshape(activation, [-1, max_frames, cluster_size])
activation = utils.FramePooling(activation, FLAGS.dbof_pooling_method)
hidden1_weights = tf.compat.v1.get_variable(
"hidden1_weights", [cluster_size, hidden1_size],
initializer=tf.random_normal_initializer(stddev=1 /
math.sqrt(cluster_size)))
tf.compat.v1.summary.histogram("hidden1_weights", hidden1_weights)
activation = tf.matmul(activation, hidden1_weights)
if add_batch_norm:
activation = slim.batch_norm(activation,
center=True,
scale=True,
is_training=is_training,
scope="hidden1_bn")
else:
hidden1_biases = tf.compat.v1.get_variable(
"hidden1_biases", [hidden1_size],
initializer=tf.random_normal_initializer(stddev=0.01))
tf.compat.v1.summary.histogram("hidden1_biases", hidden1_biases)
activation += hidden1_biases
activation = act_fn(activation)
tf.compat.v1.summary.histogram("hidden1_output", activation)
aggregated_model = getattr(video_level_models,
FLAGS.video_level_classifier_model)
return aggregated_model().create_model(model_input=activation,
vocab_size=vocab_size,
**unused_params)
class LstmModel(models.BaseModel):
"""Creates a model which uses a stack of LSTMs to represent the video."""
def create_model(self, model_input, vocab_size, num_frames, **unused_params):
"""See base class.
Args:
model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
input features.
vocab_size: The number of classes in the dataset.
num_frames: A vector of length 'batch' which indicates the number of
frames for each video (before padding).
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
'batch_size' x 'num_classes'.
"""
lstm_size = FLAGS.lstm_cells
number_of_layers = FLAGS.lstm_layers
stacked_lstm = tf.contrib.rnn.MultiRNNCell([
tf.contrib.rnn.BasicLSTMCell(lstm_size, forget_bias=1.0)
for _ in range(number_of_layers)
])
_, state = tf.nn.dynamic_rnn(stacked_lstm,
model_input,
sequence_length=num_frames,
dtype=tf.float32)
aggregated_model = getattr(video_level_models,
FLAGS.video_level_classifier_model)
return aggregated_model().create_model(model_input=state[-1].h,
vocab_size=vocab_size,
**unused_params)