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frame_level_models.py
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# Copyright 2018 Deep Topology 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."""
# noinspection PyUnresolvedReferences
import pathmagic
from tensorflow import flags
import tensorflow.contrib.layers as layers
import tensorflow as tf
import model_utils as utils
import tensorflow.contrib.slim as slim
import video_pooling_modules
import aggregation_modules
import video_level_models
import rnn_modules
import math
import models
###############################################################################
# Necessary FLAGS #############################################################
###############################################################################
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("video_level_classifier_model", "MoeModel",
"Some Frame-Level models can be decomposed into a "
"generalized pooling operation followed by a "
"classifier layer")
###############################################################################
# Triangulation Prototype models ##############################################
###############################################################################
# All flags start with jtmv1_ to differentiate from other flags.
flags.DEFINE_integer("jtmv1_iteration", 30,
"Number of frames per batch.")
flags.DEFINE_bool("jtmv1_add_batch_norm", True,
"Add batch normalization.")
flags.DEFINE_bool("jtmv1_sample_random_frames", True,
"Iff true, tccm samples random frames.")
flags.DEFINE_integer("jtmv1_video_anchor_size", 64,
"Number of anchors for video features.")
flags.DEFINE_integer("jtmv1_audio_anchor_size", 16,
"Number of anchors for audio features.")
flags.DEFINE_integer("jtmv1_video_hidden", 1024,
"Number of anchors for video features.")
flags.DEFINE_integer("jtmv1_video_output_dim", 2048,
"Output dimension for video features.")
flags.DEFINE_integer("jtmv1_audio_hidden", 128,
"Number of anchors for audio features.")
flags.DEFINE_integer("jtmv1_audio_output_dim", 256,
"Output dimension for audio features.")
flags.DEFINE_bool("jtmv1_use_attention", True,
"True -> use attention.")
flags.DEFINE_bool("jtmv1_use_relu", True,
"True -> use relu.")
class JuhanTestModelV1(models.BaseModel):
def create_model(self,
model_input,
vocab_size,
num_frames,
iterations=None,
add_batch_norm=None,
sample_random_frames=None,
hidden_size=None,
is_training=True,
**unused_params):
iterations = iterations or FLAGS.jtmv1_iteration
add_batch_norm = add_batch_norm or FLAGS.jtmv1_add_batch_norm
video_anchor_size = FLAGS.jtmv1_video_anchor_size
audio_anchor_size = FLAGS.jtmv1_audio_anchor_size
video_hidden_size = FLAGS.jtmv1_video_hidden
audio_hidden_size = FLAGS.jtmv1_audio_hidden
video_output_dim = FLAGS.jtmv1_video_output_dim
audio_output_dim = FLAGS.jtmv1_audio_output_dim
use_attention = FLAGS.jtmv1_use_attention
use_relu = FLAGS.jtmv1_use_relu
num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
model_input = utils.SampleRandomFrames(model_input, num_frames, iterations)
# model_input: batch_size x max_frames x feature_size
max_frames = model_input.get_shape().as_list()[1]
feature_size = model_input.get_shape().as_list()[2]
# model_input: (batch_size * max_frames) x feature_size
reshaped_input = tf.reshape(model_input, [-1, feature_size])
video_module = video_pooling_modules.TriangulationCnnIndirectAttentionModule(
feature_size=1024,
max_frames=max_frames,
anchor_size=video_anchor_size,
self_attention=use_attention,
hidden_layer_size=video_hidden_size,
output_dim=video_output_dim,
add_relu=use_relu,
batch_norm=add_batch_norm,
is_training=is_training,
scope_id=None)
audio_module = video_pooling_modules.TriangulationCnnIndirectAttentionModule(
feature_size=128,
max_frames=max_frames,
anchor_size=audio_anchor_size,
self_attention=use_attention,
hidden_layer_size=audio_hidden_size,
output_dim=audio_output_dim,
add_relu=use_relu,
batch_norm=add_batch_norm,
is_training=is_training,
scope_id=None)
with tf.variable_scope("video_triangulation_embedding"):
video_feature = video_module.forward(reshaped_input[:, 0:1024])
# -> (batch_size * max_frames) x video_output_dim
with tf.variable_scope("audio_triangulation_embedding"):
audio_feature = audio_module.forward(reshaped_input[:, 1024:])
# -> (batch_size * max_frames) x audio_output_dim
activation = tf.concat([video_feature, audio_feature], 1)
aggregated_model = getattr(video_level_models,
"ClassLearningFourNnModel")
return aggregated_model().create_model(
model_input=activation,
vocab_size=vocab_size,
is_training=is_training,
**unused_params)
# All flags start with jtmv2_ to differentiate from other flags.
flags.DEFINE_integer("jtmv2_iteration", 200,
"Number of frames per batch.")
flags.DEFINE_bool("jtmv2_add_batch_norm", True,
"Add batch normalization.")
flags.DEFINE_bool("jtmv2_sample_random_frames", True,
"Iff true, tccm samples random frames.")
flags.DEFINE_integer("jtmv2_video_anchor_size", 32,
"Number of anchors for video features.")
flags.DEFINE_integer("jtmv2_audio_anchor_size", 8,
"Number of anchors for audio features.")
flags.DEFINE_integer("jtmv2_video_kernel_size", 64,
"Number of kernels for video features.")
flags.DEFINE_integer("jtmv2_audio_kernel_size", 16,
"Number of kernels for audio features.")
flags.DEFINE_integer("jtmv2_video_hidden", 2048,
"Number of anchors for video features.")
flags.DEFINE_integer("jtmv2_video_output_dim", 2048,
"Output dimension for video features.")
flags.DEFINE_integer("jtmv2_audio_hidden", 256,
"Number of anchors for audio features.")
flags.DEFINE_integer("jtmv2_audio_output_dim", 256,
"Output dimension for audio features.")
flags.DEFINE_bool("jtmv2_use_attention", True,
"True -> use attention.")
flags.DEFINE_bool("jtmv2_use_relu", False,
"True -> use relu.")
class JuhanTestModelV2(models.BaseModel):
def create_model(self,
model_input,
vocab_size,
num_frames,
iterations=None,
add_batch_norm=None,
sample_random_frames=None,
hidden_size=None,
is_training=True,
**unused_params):
iterations = iterations or FLAGS.jtmv2_iteration
add_batch_norm = add_batch_norm or FLAGS.jtmv2_add_batch_norm
video_anchor_size = FLAGS.jtmv2_video_anchor_size
audio_anchor_size = FLAGS.jtmv2_audio_anchor_size
video_hidden_size = FLAGS.jtmv2_video_hidden
audio_hidden_size = FLAGS.jtmv2_audio_hidden
video_kernel_size = FLAGS.jtmv2_video_kernel_size
audio_kernel_size = FLAGS.jtmv2_audio_kernel_size
video_output_dim = FLAGS.jtmv2_video_output_dim
audio_output_dim = FLAGS.jtmv2_audio_output_dim
use_attention = FLAGS.jtmv2_use_attention
use_relu = FLAGS.jtmv2_use_relu
num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
model_input = utils.SampleRandomFrames(model_input, num_frames, iterations)
# model_input: batch_size x max_frames x feature_size
max_frames = model_input.get_shape().as_list()[1]
feature_size = model_input.get_shape().as_list()[2]
# model_input: (batch_size * max_frames) x feature_size
reshaped_input = tf.reshape(model_input, [-1, feature_size])
video_module = video_pooling_modules.TriangulationNsCnnIndirectAttentionModule(
feature_size=1024,
max_frames=max_frames,
anchor_size=video_anchor_size,
self_attention=use_attention,
hidden_layer_size=video_hidden_size,
kernel_size=video_kernel_size,
output_dim=video_output_dim,
add_relu=use_relu,
batch_norm=add_batch_norm,
is_training=is_training,
scope_id=None)
audio_module = video_pooling_modules.TriangulationNsCnnIndirectAttentionModule(
feature_size=128,
max_frames=max_frames,
anchor_size=audio_anchor_size,
self_attention=use_attention,
hidden_layer_size=audio_hidden_size,
kernel_size=audio_kernel_size,
output_dim=audio_output_dim,
add_relu=use_relu,
batch_norm=add_batch_norm,
is_training=is_training,
scope_id=None)
with tf.variable_scope("video_triangulation_embedding"):
video_feature = video_module.forward(reshaped_input[:, 0:1024])
# -> (batch_size * max_frames) x video_output_dim
with tf.variable_scope("audio_triangulation_embedding"):
audio_feature = audio_module.forward(reshaped_input[:, 1024:])
# -> (batch_size * max_frames) x audio_output_dim
activation = tf.concat([video_feature, audio_feature], 1)
if add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=is_training,
scope="final_activation_bn")
aggregated_model = getattr(video_level_models,
"ClassLearningFourNnModel")
return aggregated_model().create_model(
model_input=activation,
vocab_size=vocab_size,
is_training=is_training,
**unused_params)
# All flags start with jtmv3_ to differentiate from other flags.
flags.DEFINE_integer("jtmv3_iteration", 200,
"Number of frames per batch.")
flags.DEFINE_bool("jtmv3_add_batch_norm", True,
"Add batch normalization.")
flags.DEFINE_bool("jtmv3_sample_random_frames", True,
"Iff true, tccm samples random frames.")
flags.DEFINE_integer("jtmv3_video_anchor_size", 32,
"Number of anchors for video features.")
flags.DEFINE_integer("jtmv3_audio_anchor_size", 8,
"Number of anchors for audio features.")
flags.DEFINE_integer("jtmv3_video_kernel_size", 64,
"Number of kernels for video features.")
flags.DEFINE_integer("jtmv3_audio_kernel_size", 16,
"Number of kernels for audio features.")
flags.DEFINE_integer("jtmv3_video_hidden", 2048,
"Number of anchors for video features.")
flags.DEFINE_integer("jtmv3_video_output_dim", 2048,
"Output dimension for video features.")
flags.DEFINE_integer("jtmv3_audio_hidden", 256,
"Number of anchors for audio features.")
flags.DEFINE_integer("jtmv3_audio_output_dim", 256,
"Output dimension for audio features.")
flags.DEFINE_bool("jtmv3_use_attention", True,
"True -> use attention.")
flags.DEFINE_bool("jtmv3_use_relu", False,
"True -> use relu.")
flags.DEFINE_string("jtmv3_video_level_model", "ClassLearningFourNnModel",
"Model for video level.")
class JuhanTestModelV3(models.BaseModel):
def create_model(self,
model_input,
vocab_size,
num_frames,
iterations=None,
add_batch_norm=None,
sample_random_frames=None,
hidden_size=None,
is_training=True,
**unused_params):
iterations = iterations or FLAGS.jtmv3_iteration
add_batch_norm = add_batch_norm or FLAGS.jtmv3_add_batch_norm
video_anchor_size = FLAGS.jtmv3_video_anchor_size
audio_anchor_size = FLAGS.jtmv3_audio_anchor_size
video_hidden_size = FLAGS.jtmv3_video_hidden
audio_hidden_size = FLAGS.jtmv3_audio_hidden
video_kernel_size = FLAGS.jtmv3_video_kernel_size
audio_kernel_size = FLAGS.jtmv3_audio_kernel_size
video_output_dim = FLAGS.jtmv3_video_output_dim
audio_output_dim = FLAGS.jtmv3_audio_output_dim
use_attention = FLAGS.jtmv3_use_attention
use_relu = FLAGS.jtmv3_use_relu
video_level_model = FLAGS.jtmv3_video_level_model
num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
model_input = utils.SampleRandomFrames(model_input, num_frames, iterations)
# model_input: batch_size x max_frames x feature_size
max_frames = model_input.get_shape().as_list()[1]
feature_size = model_input.get_shape().as_list()[2]
# model_input: (batch_size * max_frames) x feature_size
reshaped_input = tf.reshape(model_input, [-1, feature_size])
video_module = video_pooling_modules.TriangulationMagnitudeNsCnnIndirectAttentionModule(
feature_size=1024,
max_frames=max_frames,
anchor_size=video_anchor_size,
self_attention=use_attention,
hidden_layer_size=video_hidden_size,
kernel_size=video_kernel_size,
output_dim=video_output_dim,
add_relu=use_relu,
add_norm=True,
batch_norm=add_batch_norm,
is_training=is_training,
scope_id=None)
audio_module = video_pooling_modules.TriangulationMagnitudeNsCnnIndirectAttentionModule(
feature_size=128,
max_frames=max_frames,
anchor_size=audio_anchor_size,
self_attention=use_attention,
hidden_layer_size=audio_hidden_size,
kernel_size=audio_kernel_size,
output_dim=audio_output_dim,
add_norm=True,
add_relu=use_relu,
batch_norm=add_batch_norm,
is_training=is_training,
scope_id=None)
with tf.variable_scope("video_triangulation_embedding"):
video_feature = video_module.forward(reshaped_input[:, 0:1024])
# -> (batch_size * max_frames) x video_output_dim
with tf.variable_scope("audio_triangulation_embedding"):
audio_feature = audio_module.forward(reshaped_input[:, 1024:])
# -> (batch_size * max_frames) x audio_output_dim
activation = tf.concat([video_feature, audio_feature], 1)
aggregated_model = getattr(video_level_models,
video_level_model)
return aggregated_model().create_model(
model_input=activation,
vocab_size=vocab_size,
is_training=is_training,
**unused_params)
# All flags start with jtmv3_ to differentiate from other flags.
flags.DEFINE_integer("jtmv4_iteration", 200,
"Number of frames per batch.")
flags.DEFINE_bool("jtmv4_add_batch_norm", True,
"Add batch normalization.")
flags.DEFINE_bool("jtmv4_sample_random_frames", True,
"Iff true, tccm samples random frames.")
flags.DEFINE_integer("jtmv4_video_anchor_size", 32,
"Number of anchors for video features.")
flags.DEFINE_integer("jtmv4_audio_anchor_size", 8,
"Number of anchors for audio features.")
flags.DEFINE_integer("jtmv4_video_kernel_size", 64,
"Number of kernels for video features.")
flags.DEFINE_integer("jtmv4_audio_kernel_size", 16,
"Number of kernels for audio features.")
flags.DEFINE_integer("jtmv4_video_hidden", 2048,
"Number of anchors for video features.")
flags.DEFINE_integer("jtmv4_video_output_dim", 2048,
"Output dimension for video features.")
flags.DEFINE_integer("jtmv4_audio_hidden", 256,
"Number of anchors for audio features.")
flags.DEFINE_integer("jtmv4_audio_output_dim", 256,
"Output dimension for audio features.")
flags.DEFINE_bool("jtmv4_use_attention", True,
"True -> use attention.")
flags.DEFINE_bool("jtmv4_use_relu", False,
"True -> use relu.")
flags.DEFINE_string("jtmv4_video_level_model", "ClassLearningFourNnModel",
"Model for video level.")
class JuhanTestModelV4(models.BaseModel):
def create_model(self,
model_input,
vocab_size,
num_frames,
iterations=None,
add_batch_norm=None,
sample_random_frames=None,
hidden_size=None,
is_training=True,
**unused_params):
iterations = iterations or FLAGS.jtmv4_iteration
add_batch_norm = add_batch_norm or FLAGS.jtmv4_add_batch_norm
video_anchor_size = FLAGS.jtmv4_video_anchor_size
audio_anchor_size = FLAGS.jtmv4_audio_anchor_size
video_hidden_size = FLAGS.jtmv4_video_hidden
audio_hidden_size = FLAGS.jtmv4_audio_hidden
video_kernel_size = FLAGS.jtmv4_video_kernel_size
audio_kernel_size = FLAGS.jtmv4_audio_kernel_size
video_output_dim = FLAGS.jtmv4_video_output_dim
audio_output_dim = FLAGS.jtmv4_audio_output_dim
use_attention = FLAGS.jtmv4_use_attention
use_relu = FLAGS.jtmv4_use_relu
video_level_model = FLAGS.jtmv3_video_level_model
num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
model_input = utils.SampleRandomFrames(model_input, num_frames, iterations)
# model_input: batch_size x max_frames x feature_size
max_frames = model_input.get_shape().as_list()[1]
feature_size = model_input.get_shape().as_list()[2]
# model_input: (batch_size * max_frames) x feature_size
reshaped_input = tf.reshape(model_input, [-1, feature_size])
video_module = video_pooling_modules.TriangulationMagnitudeNsCnnNetVladModule(
feature_size=1024,
max_frames=max_frames,
anchor_size=video_anchor_size,
self_attention=use_attention,
hidden_layer_size=video_hidden_size,
kernel_size=video_kernel_size,
output_dim=video_output_dim,
add_relu=use_relu,
add_norm=True,
batch_norm=add_batch_norm,
is_training=is_training,
scope_id=None)
audio_module = video_pooling_modules.TriangulationMagnitudeNsCnnNetVladModule(
feature_size=128,
max_frames=max_frames,
anchor_size=audio_anchor_size,
self_attention=use_attention,
hidden_layer_size=audio_hidden_size,
kernel_size=audio_kernel_size,
output_dim=audio_output_dim,
add_norm=True,
add_relu=use_relu,
batch_norm=add_batch_norm,
is_training=is_training,
scope_id=None)
with tf.variable_scope("video_triangulation_embedding"):
video_feature = video_module.forward(reshaped_input[:, 0:1024])
# -> (batch_size * max_frames) x video_output_dim
with tf.variable_scope("audio_triangulation_embedding"):
audio_feature = audio_module.forward(reshaped_input[:, 1024:])
# -> (batch_size * max_frames) x audio_output_dim
activation = tf.concat([video_feature, audio_feature], 1)
aggregated_model = getattr(video_level_models,
video_level_model)
return aggregated_model().create_model(
model_input=activation,
vocab_size=vocab_size,
is_training=is_training,
**unused_params)
# All flags start with jtmv5_ to differentiate from other flags.
flags.DEFINE_integer("jtmv5_iteration", 30,
"Number of frames per batch.")
flags.DEFINE_bool("jtmv5_add_batch_norm", True,
"Add batch normalization.")
flags.DEFINE_bool("jtmv5_sample_random_frames", True,
"Iff true, tccm samples random frames.")
flags.DEFINE_integer("jtmv5_video_anchor_size", 256,
"Number of anchors for video features.")
flags.DEFINE_integer("jtmv5_audio_anchor_size", 32,
"Number of anchors for audio features.")
flags.DEFINE_integer("jtmv5_video_kernel_size", 512,
"Number of kernels for video features.")
flags.DEFINE_integer("jtmv5_audio_kernel_size", 64,
"Number of kernels for audio features.")
flags.DEFINE_integer("jtmv5_video_hidden", 2048,
"Number of anchors for video features.")
flags.DEFINE_integer("jtmv5_video_output_dim", 4096,
"Output dimension for video features.")
flags.DEFINE_integer("jtmv5_audio_hidden", 256,
"Number of anchors for audio features.")
flags.DEFINE_integer("jtmv5_audio_output_dim", 512,
"Output dimension for audio features.")
class JuhanTestModelV5(models.BaseModel):
def create_model(self,
model_input,
vocab_size,
num_frames,
iterations=None,
add_batch_norm=None,
sample_random_frames=None,
hidden_size=None,
is_training=True,
**unused_params):
iterations = iterations or FLAGS.jtmv5_iteration
add_batch_norm = add_batch_norm or FLAGS.jtmv5_add_batch_norm
video_anchor_size = FLAGS.jtmv5_video_anchor_size
audio_anchor_size = FLAGS.jtmv5_audio_anchor_size
video_kernel_size = FLAGS.jtmv5_video_kernel_size
video_hidden_size = FLAGS.jtmv5_video_hidden
audio_kernel_size = FLAGS.jtmv5_audio_kernel_size
audio_hidden_size = FLAGS.jtmv5_audio_hidden
video_output_dim = FLAGS.jtmv5_video_output_dim
audio_output_dim = FLAGS.jtmv5_audio_output_dim
num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
model_input = utils.SampleRandomFrames(model_input, num_frames, iterations)
# model_input: batch_size x max_frames x feature_size
max_frames = model_input.get_shape().as_list()[1]
feature_size = model_input.get_shape().as_list()[2]
# model_input: (batch_size * max_frames) x feature_size
reshaped_input = tf.reshape(model_input, [-1, feature_size])
# Obtain video & audio features.
video_features = reshaped_input[:, 0:1024]
audio_features = reshaped_input[:, 1024:]
# Batch normalize video & audio inputs for fixing scales.
if add_batch_norm:
video_features = slim.batch_norm(
video_features,
center=True,
scale=True,
is_training=is_training,
scope="video_bn")
audio_features = slim.batch_norm(
audio_features,
center=True,
scale=True,
is_training=is_training,
scope="audio_bn")
video_module = video_pooling_modules.TriangulationV5Module(
feature_size=1024,
max_frames=max_frames,
anchor_size=video_anchor_size,
kernel_size=video_kernel_size,
self_attention=False,
hidden_layer_size=video_hidden_size,
output_dim=video_output_dim,
add_relu=True,
batch_norm=add_batch_norm,
is_training=is_training,
scope_id=None)
audio_module = video_pooling_modules.TriangulationV5Module(
feature_size=128,
max_frames=max_frames,
anchor_size=audio_anchor_size,
kernel_size=audio_kernel_size,
self_attention=False,
hidden_layer_size=audio_hidden_size,
output_dim=audio_output_dim,
add_relu=True,
batch_norm=add_batch_norm,
is_training=is_training,
scope_id=None)
with tf.variable_scope("video_triangulation_embedding"):
video_feature = video_module.forward(video_features)
# -> (batch_size * max_frames) x video_output_dim
with tf.variable_scope("audio_triangulation_embedding"):
audio_feature = audio_module.forward(audio_features)
# -> (batch_size * max_frames) x audio_output_dim
activation = tf.concat([video_feature, audio_feature], 1)
aggregated_model = getattr(video_level_models,
"FourLayerBatchNeuralModel")
return aggregated_model().create_model(
model_input=activation,
vocab_size=vocab_size,
is_training=is_training,
**unused_params)
# All flags start with jtmv5_ to differentiate from other flags.
flags.DEFINE_integer("jtmv6_iteration", 30,
"Number of frames per batch.")
flags.DEFINE_bool("jtmv6_add_batch_norm", True,
"Add batch normalization.")
flags.DEFINE_bool("jtmv6_sample_random_frames", True,
"Iff true, tccm samples random frames.")
flags.DEFINE_integer("jtmv6_video_anchor_size", 256,
"Number of anchors for video features.")
flags.DEFINE_integer("jtmv6_audio_anchor_size", 32,
"Number of anchors for audio features.")
flags.DEFINE_integer("jtmv6_video_kernel_size", 512,
"Number of kernels for video features.")
flags.DEFINE_integer("jtmv6_audio_kernel_size", 64,
"Number of kernels for audio features.")
flags.DEFINE_integer("jtmv6_video_hidden", 2048,
"Number of anchors for video features.")
flags.DEFINE_integer("jtmv6_video_output_dim", 4096,
"Output dimension for video features.")
flags.DEFINE_integer("jtmv6_audio_hidden", 256,
"Number of anchors for audio features.")
flags.DEFINE_integer("jtmv6_audio_output_dim", 512,
"Output dimension for audio features.")
flags.DEFINE_integer("jtmv6_video_cluster_size", 64,
"Output dimension for audio features.")
flags.DEFINE_integer("jtmv6_audio_cluster_size", 8,
"Output dimension for audio features.")
class JuhanTestModelV6(models.BaseModel):
def create_model(self,
model_input,
vocab_size,
num_frames,
iterations=None,
add_batch_norm=None,
sample_random_frames=None,
hidden_size=None,
is_training=True,
**unused_params):
iterations = iterations or FLAGS.jtmv6_iteration
add_batch_norm = add_batch_norm or FLAGS.jtmv6_add_batch_norm
video_anchor_size = FLAGS.jtmv6_video_anchor_size
audio_anchor_size = FLAGS.jtmv6_audio_anchor_size
video_kernel_size = FLAGS.jtmv6_video_kernel_size
video_hidden_size = FLAGS.jtmv6_video_hidden
audio_kernel_size = FLAGS.jtmv6_audio_kernel_size
audio_hidden_size = FLAGS.jtmv6_audio_hidden
video_output_dim = FLAGS.jtmv6_video_output_dim
audio_output_dim = FLAGS.jtmv6_audio_output_dim
video_cluster_size = FLAGS.jtmv6_video_cluster_size
audio_cluster_size = FLAGS.jtmv6_audio_cluster_size
num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
model_input = utils.SampleRandomFrames(model_input, num_frames, iterations)
# model_input: batch_size x max_frames x feature_size
max_frames = model_input.get_shape().as_list()[1]
feature_size = model_input.get_shape().as_list()[2]
# model_input: (batch_size * max_frames) x feature_size
reshaped_input = tf.reshape(model_input, [-1, feature_size])
# Obtain video & audio features.
video_features = reshaped_input[:, 0:1024]
audio_features = reshaped_input[:, 1024:]
# Batch normalize video & audio inputs for fixing scales.
if add_batch_norm:
video_features = slim.batch_norm(
video_features,
center=True,
scale=True,
is_training=is_training,
scope="video_bn")
audio_features = slim.batch_norm(
audio_features,
center=True,
scale=True,
is_training=is_training,
scope="audio_bn")
video_module = video_pooling_modules.TriangulationV6Module(
feature_size=1024,
max_frames=max_frames,
anchor_size=video_anchor_size,
kernel_size=video_kernel_size,
self_attention=False,
cluster_size=video_cluster_size,
hidden_layer_size=video_hidden_size,
output_dim=video_output_dim,
add_relu=True,
batch_norm=add_batch_norm,
is_training=is_training,
scope_id=None)
audio_module = video_pooling_modules.TriangulationV6Module(
feature_size=128,
max_frames=max_frames,
anchor_size=audio_anchor_size,
kernel_size=audio_kernel_size,
self_attention=False,
hidden_layer_size=audio_hidden_size,
cluster_size=audio_cluster_size,
output_dim=audio_output_dim,
add_relu=True,
batch_norm=add_batch_norm,
is_training=is_training,
scope_id=None)
with tf.variable_scope("video_triangulation_embedding"):
video_feature = video_module.forward(video_features)
# -> (batch_size * max_frames) x video_output_dim
with tf.variable_scope("audio_triangulation_embedding"):
audio_feature = audio_module.forward(audio_features)
# -> (batch_size * max_frames) x audio_output_dim
activation = tf.concat([video_feature, audio_feature], 1)
aggregated_model = getattr(video_level_models,
"FourLayerBatchNeuralModel")
return aggregated_model().create_model(
model_input=activation,
vocab_size=vocab_size,
is_training=is_training,
**unused_params)
# All flags start with tccm_ to differentiate from other flags.
flags.DEFINE_integer("tccm_iterations", 200,
"Number of frames per batch.")
flags.DEFINE_bool("tccm_add_batch_norm", True,
"Add batch normalization.")
flags.DEFINE_bool("tccm_sample_random_frames", True,
"Iff true, tccm samples random frames.")
flags.DEFINE_integer("tccm_video_anchor_size", 128,
"Number of anchors for video features.")
flags.DEFINE_integer("tccm_audio_anchor_size", 32,
"Number of anchors for audio features.")
flags.DEFINE_integer("tccm_video_kernel_size", 128,
"Number of anchors for video features.")
flags.DEFINE_integer("tccm_audio_kernel_size", 128,
"Number of anchors for video features.")
flags.DEFINE_integer("tccm_video_hidden", 2048,
"Number of anchors for video features.")
flags.DEFINE_integer("tccm_audio_hidden", 256,
"Number of anchors for video features.")
class TriangulationCnnClusterModel(models.BaseModel):
def create_model(self,
model_input,
vocab_size,
num_frames,
iterations=None,
add_batch_norm=None,
sample_random_frames=None,
hidden_size=None,
is_training=True,
**unused_params):
iterations = iterations or FLAGS.tccm_iterations
add_batch_norm = add_batch_norm or FLAGS.tccm_add_batch_norm
video_anchor_size = FLAGS.tccm_video_anchor_size
audio_anchor_size = FLAGS.tccm_audio_anchor_size
video_kernel_size = FLAGS.tccm_video_kernel_size
audio_kernel_size = FLAGS.tccm_audio_kernel_size
video_hidden_size = FLAGS.tccm_video_hidden
audio_hidden_size = FLAGS.tccm_audio_hidden
num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
model_input = utils.SampleRandomFrames(model_input, num_frames, iterations)
# model_input: batch_size x max_frames x feature_size
max_frames = model_input.get_shape().as_list()[1]
feature_size = model_input.get_shape().as_list()[2]
# model_input: (batch_size * max_frames) x feature_size
reshaped_input = tf.reshape(model_input, [-1, feature_size])
video_features = reshaped_input[:, 0:1024]
audio_features = reshaped_input[:, 1024:]
if add_batch_norm:
video_features = slim.batch_norm(
video_features,
center=True,
scale=True,
is_training=is_training,
scope="video_bn")
audio_features = slim.batch_norm(
audio_features,
center=True,
scale=True,
is_training=is_training,
scope="audio_bn")
video_d_module = video_pooling_modules.TriangulationEmbedding(1024,
max_frames,
video_anchor_size,
add_batch_norm,
is_training)
audio_d_module = video_pooling_modules.TriangulationEmbedding(128,
max_frames,
audio_anchor_size,
add_batch_norm,
is_training)
video_d_cnn_module = video_pooling_modules.TriangulationCnnModule(1024,
max_frames,
video_kernel_size,
video_anchor_size,
add_batch_norm,
is_training,
"video_d")
video_t_cnn_module = video_pooling_modules.TriangulationCnnModule(1024,
max_frames - 1,
video_kernel_size,
video_anchor_size,
add_batch_norm,
is_training,
"video_t")
audio_d_cnn_module = video_pooling_modules.TriangulationCnnModule(128,
max_frames,
audio_kernel_size,
audio_anchor_size,
add_batch_norm,
is_training,
"audio_d")
audio_t_cnn_module = video_pooling_modules.TriangulationCnnModule(128,
max_frames - 1,
audio_kernel_size,
audio_anchor_size,
add_batch_norm,
is_training,
"audio_t")
ic_mean_pool = aggregation_modules.IndirectClusterMeanPoolModule(l2_normalize=False)
mean_std_pool = aggregation_modules.MeanStdPoolModule(l2_normalize=False)
video_t_module = video_pooling_modules.TriangulationTemporalEmbedding(1024,
max_frames,
video_anchor_size,
add_batch_norm,
is_training)
audio_t_module = video_pooling_modules.TriangulationTemporalEmbedding(128,
max_frames,
audio_anchor_size,
add_batch_norm,
is_training)
with tf.variable_scope("video_triangulation_embedding"):
video_d = video_d_module.forward(video_features)
# -> (batch_size * max_frames) x (feature_size * anchor_size)
with tf.variable_scope("video_d"):
video_d_cnn = video_d_cnn_module.forward(video_d)
# -> batch_size x max_frames x (anchor_size * num_filters)
video_d_temp = tf.reshape(video_d, [-1, max_frames, 1024 * video_anchor_size])
agg_video_d = ic_mean_pool.forward(video_d_temp, video_d_cnn)
# -> batch_size x (anchor_size * num_filters)
video_t = video_t_module.forward(video_d)
# -> batch_size x (max_frames - 1) x (feature_size * anchor_size)
video_t = tf.reshape(video_t, [-1, 1024 * video_anchor_size])
with tf.variable_scope("video_t"):
video_t_cnn = video_t_cnn_module.forward(video_t)
# -> batch_size x (max_frames - 1) x (anchor_size * num_filters)
agg_video_t = mean_std_pool.forward(video_t_cnn)
agg_video = tf.concat([agg_video_d, agg_video_t], 1)
if add_batch_norm:
agg_video = slim.batch_norm(
agg_video,
center=True,
scale=True,
is_training=is_training,
scope="agg_video_bn")
with tf.variable_scope("audio_triangulation_embedding"):
audio_d = audio_d_module.forward(audio_features)
# -> (batch_size * max_frames) x (feature_size * anchor_size)
with tf.variable_scope("audio_d"):
audio_d_cnn = audio_d_cnn_module.forward(audio_d)
# -> batch_size x max_frames x (anchor_size * num_filters)
audio_d_temp = tf.reshape(audio_d, [-1, max_frames, 128 * audio_anchor_size])
agg_audio_d = ic_mean_pool.forward(audio_d_temp, audio_d_cnn)
# -> batch_size x (anchor_size * num_filters)
audio_t = audio_t_module.forward(audio_d)
# -> batch_size x (max_frames - 1) x (feature_size * anchor_size)
audio_t = tf.reshape(audio_t, [-1, 128 * audio_anchor_size])
with tf.variable_scope("audio_t"):
audio_t_cnn = audio_t_cnn_module.forward(audio_t)
# -> batch_size x (max_frames - 1) x (anchor_size * num_filters)
agg_audio_t = mean_std_pool.forward(audio_t_cnn)
agg_audio = tf.concat([agg_audio_d, agg_audio_t], 1)
if add_batch_norm:
agg_audio = slim.batch_norm(
agg_audio,
center=True,
scale=True,
is_training=is_training,
scope="agg_audio_bn")
agg_video_dim = agg_video.get_shape().as_list()[1]
video_hidden_weight = tf.get_variable("video_hidden",
[agg_video_dim, video_hidden_size],
initializer=tf.random_normal_initializer(
stddev=1 / math.sqrt(video_hidden_size)))
video_activation = tf.matmul(agg_video, video_hidden_weight)
agg_audio_dim = agg_audio.get_shape().as_list()[1]
audio_hidden_weight = tf.get_variable("audio_hidden",
[agg_audio_dim, audio_hidden_size],
initializer=tf.random_normal_initializer(
stddev=1 / math.sqrt(audio_hidden_size)))
audio_activation = tf.matmul(agg_audio, audio_hidden_weight)
activation = tf.concat([video_activation, audio_activation], 1)
aggregated_model = getattr(video_level_models,
"ClassLearningFourNnModel")
return aggregated_model().create_model(
model_input=activation,
vocab_size=vocab_size,
is_training=is_training,
**unused_params)
# NOTE: These are the best achievable parameters for a P100 GPU (16gb RAM), V100 are to be decided...
#
# All flags start with 'sftm_' to differentiate from original flags.
flags.DEFINE_integer("sftm_iterations", 64,
"Number of frames per batch.")
flags.DEFINE_bool("sftm_add_batch_norm", True,
"Add batch normalization.")
flags.DEFINE_bool("sftm_sample_random_frames", True,
"Iff true, sftm samples random frames.")
flags.DEFINE_integer("sftm_video_anchor_size", 128,
"Number of anchors for video features.")
flags.DEFINE_integer("sftm_audio_anchor_size", 16,
"Number of anchors for audio features.")
flags.DEFINE_integer("sftm_video_bottleneck", 100,
"Size of bottleneck weights for video features.")
flags.DEFINE_integer("sftm_audio_bottleneck", 16,
"Size of bottleneck weights for audio features.")
flags.DEFINE_string("sftm_video_level_classifier_model", "MoeModel",
"Some Frame-Level models can be decomposed into a "
"generalized pooling operation followed by a "
"classifier layer")
class SoftAttentionTriangulationModel(models.BaseModel):
def create_model(self,
model_input,
vocab_size,
num_frames,
iterations=None,
add_batch_norm=None,
sample_random_frames=None,
hidden_size=None,
is_training=True,
**unused_params):
iterations = iterations or FLAGS.sftm_iterations
add_batch_norm = add_batch_norm or FLAGS.sftm_add_batch_norm
video_anchor_size = FLAGS.sftm_video_anchor_size
audio_anchor_size = FLAGS.sftm_audio_anchor_size
video_bottleneck = FLAGS.sftm_video_bottleneck
audio_bottleneck = FLAGS.sftm_audio_bottleneck
num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
model_input = utils.SampleRandomFrames(model_input, num_frames, iterations)
# model_input: batch_size x max_frames x feature_size
max_frames = model_input.get_shape().as_list()[1]
feature_size = model_input.get_shape().as_list()[2]
# model_input: (batch_size * max_frames) x feature_size
reshaped_input = tf.reshape(model_input, [-1, feature_size])
video_features = reshaped_input[:, 0:1024]
audio_features = reshaped_input[:, 1024:]
if add_batch_norm:
video_features = slim.batch_norm(
video_features,
center=True,
scale=True,
is_training=is_training,
scope="video_bn")