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Model #6

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merged 5 commits into from
Nov 1, 2017
Merged

Model #6

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c3a1b3b
Add preliminary training code (no eval yet)
auroracramer Oct 21, 2017
bcd53d4
Merge branch 'pescador' into model
auroracramer Oct 23, 2017
5eddbd0
Merge branch 'master' into model
auroracramer Nov 1, 2017
0449b1a
Merge branch 'master' into model
auroracramer Nov 1, 2017
c5bbd03
Change image sampling to follow paper
auroracramer Nov 1, 2017
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173 changes: 140 additions & 33 deletions l3embedding/model.py
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Original file line number Diff line number Diff line change
@@ -1,42 +1,149 @@
from keras.models import Model
from keras.layers import Input, Conv2D, BatchNormalization, MaxPooling2D,\
Flatten, Concatenate, Dense
from kapre.time_frequency import Spectrogram

from keras.layers import Input, Convolution2D, BatchNormalization

def construct_cnn_L3_orig():
"""
Constructs a model that replicates that used in Look, Listen and Learn

Relja Arandjelovic and (2017). Look, Listen and Learn. CoRR, abs/1705.08168, .

Returns
-------
model: L3 CNN model
(Type: keras.models.Model)
"""
####
# Image subnetwork
####
# INPUT
x_i = Input(shape=(224, 224, 3), dtype='float32')

# CONV BLOCK 1
n_filter_i_1 = 64
filt_size_i_1 = (3, 3)
pool_size_i_1 = (2,2)
y_i = Conv2D(n_filter_i_1, filt_size_i_1, padding='same',
activation='relu')(x_i)
y_i = BatchNormalization()(y_i)
y_i = Conv2D(n_filter_i_1, filt_size_i_1, padding='same',
activation='relu')(y_i)
y_i = BatchNormalization()(y_i)
y_i = MaxPooling2D(pool_size=pool_size_i_1, strides=2, padding='same')(y_i)

# CONV BLOCK 2
n_filter_i_2 = 128
filt_size_i_2 = (3, 3)
pool_size_i_2 = (2,2)
y_i = Conv2D(n_filter_i_2, filt_size_i_2, padding='same',
activation='relu')(y_i)
y_i = BatchNormalization()(y_i)
y_i = Conv2D(n_filter_i_2, filt_size_i_2, padding='same',
activation='relu')(y_i)
y_i = BatchNormalization()(y_i)
y_i = MaxPooling2D(pool_size=pool_size_i_2, strides=2, padding='same')(y_i)

# CONV BLOCK 3
n_filter_i_3 = 256
filt_size_i_3 = (3, 3)
pool_size_i_3 = (2,2)
y_i = Conv2D(n_filter_i_3, filt_size_i_3, padding='same',
activation='relu')(y_i)
y_i = BatchNormalization()(y_i)
y_i = Conv2D(n_filter_i_3, filt_size_i_3, padding='same',
activation='relu')(y_i)
y_i = BatchNormalization()(y_i)
y_i = MaxPooling2D(pool_size=pool_size_i_3, strides=2, padding='same')(y_i)

# CONV BLOCK 4
n_filter_i_4 = 512
filt_size_i_4 = (3, 3)
pool_size_i_4 = (28, 28)
y_i = Conv2D(n_filter_i_4, filt_size_i_4, padding='same',
activation='relu')(y_i)
y_i = BatchNormalization()(y_i)
y_i = Conv2D(n_filter_i_4, filt_size_i_4, padding='same',
activation='relu')(y_i)
y_i = BatchNormalization()(y_i)
y_i = MaxPooling2D(pool_size=pool_size_i_4, strides=2, padding='same')(y_i)
y_i = Flatten()(y_i)


####
# Audio subnetwork
####
n_dft = 512
n_hop = 16
asr = 48000
audio_window_dur = 1
# INPUT
x = Input(shape=(n_freq_cnn, n_frames_cnn, 1), dtype='float32')

# CONV 1
y = Convolution2D(n_filters_cnn, filter_size_cnn, padding='valid',
activation='relu')(x)
y = MaxPooling2D(pool_size=pool_size_cnn, strides=None, padding='valid')(y)
y = BatchNormalization()(y)

# CONV 2
y = Convolution2D(n_filters_cnn, filter_size_cnn, padding='valid',
activation='relu')(y)
y = MaxPooling2D(pool_size=pool_size_cnn, strides=None, padding='valid')(y)
y = BatchNormalization()(y)

# CONV 3
y = Convolution2D(n_filters_cnn, filter_size_cnn, padding='valid',
activation='relu')(y)
# y = MaxPooling2D(pool_size=pool_size_cnn, strides=None, padding='valid')(y)
y = BatchNormalization()(y)

# Flatten for dense layers
y = Flatten()(y)
y = Dropout(0.5)(y)
y = Dense(n_dense_cnn, activation='relu')(y)
if large_cnn:
y = Dropout(0.5)(y)
y = Dense(n_dense_cnn, activation='relu')(y)
y = Dropout(0.5)(y)
y = Dense(n_classes, activation='sigmoid')(y)

m = Model(inputs=x, outputs=y)
return m
x_a = Input(shape=(1, asr * audio_window_dur), dtype='float32')

# SPECTROGRAM PREPROCESSING
# 257 x 199 x 1
y_a = Spectrogram(n_dft=n_dft, n_hop=n_hop,
return_decibel_spectrogram=True)(x_a)
# CONV BLOCK 1
n_filter_a_1 = 64
filt_size_a_1 = (3, 3)
pool_size_a_1 = (2,2)
y_a= Conv2D(n_filter_a_1, filt_size_a_1, padding='same',
activation='relu')(y_a)
y_a= BatchNormalization()(y_a)
y_a= Conv2D(n_filter_a_1, filt_size_a_1, padding='same',
activation='relu')(y_a)
y_a= BatchNormalization()(y_a)
y_a= MaxPooling2D(pool_size=pool_size_a_1, strides=2, padding='same')(y_a)

# CONV BLOCK 2
n_filter_a_2 = 128
filt_size_a_2 = (3, 3)
pool_size_a_2 = (2,2)
y_a = Conv2D(n_filter_a_2, filt_size_a_2, padding='same',
activation='relu')(y_a)
y_a = BatchNormalization()(y_a)
y_a = Conv2D(n_filter_a_2, filt_size_a_2, padding='same',
activation='relu')(y_a)
y_a = BatchNormalization()(y_a)
y_a = MaxPooling2D(pool_size=pool_size_a_2, strides=2, padding='same')(y_a)

# CONV BLOCK 3
n_filter_a_3 = 256
filt_size_a_3 = (3, 3)
pool_size_a_3 = (2,2)
y_a = Conv2D(n_filter_a_3, filt_size_a_3, padding='same',
activation='relu')(y_a)
y_a = BatchNormalization()(y_a)
y_a = Conv2D(n_filter_a_3, filt_size_a_3, padding='same',
activation='relu')(y_a)
y_a = BatchNormalization()(y_a)
y_a = MaxPooling2D(pool_size=pool_size_a_3, strides=2, padding='same')(y_a)

# CONV BLOCK 4
n_filter_a_4 = 512
filt_size_a_4 = (3, 3)
pool_size_a_4 = (32, 24)
y_a = Conv2D(n_filter_a_4, filt_size_a_4, padding='same',
activation='relu')(y_a)
y_a = BatchNormalization()(y_a)
y_a = Conv2D(n_filter_a_4, filt_size_a_4, padding='same',
activation='relu')(y_a)
y_a = BatchNormalization()(y_a)
y_a = MaxPooling2D(pool_size=pool_size_a_4, strides=2, padding='same')(y_a)

y_a = Flatten()(y_a)



# Merge the subnetworks
y = Concatenate()([y_i, y_a])
y = Dense(128, activation='relu')(y)
y = Dense(2, activation='softmax')(y)

m = Model(inputs=[x_i, x_a], outputs=y)
return m, [x_i, x_a], y


MODELS = {'cnn_L3_orig': construct_cnn_L3_orig}
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