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Trying out bottleneck #186

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Trying out bottleneck #186

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4 changes: 4 additions & 0 deletions tf/net.py
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Original file line number Diff line number Diff line change
Expand Up @@ -315,6 +315,10 @@ def moves_left_to_bp(l, w):
pb_name = 'conv1.' + convblock_to_bp(weights_name)
elif layers[1] == '2':
pb_name = 'conv2.' + convblock_to_bp(weights_name)
elif layers[1] == '3':
pb_name = 'conv3.' + convblock_to_bp(weights_name)
elif layers[1] == '4':
pb_name = 'conv4.' + convblock_to_bp(weights_name)
elif layers[1] == 'se':
pb_name = 'se.' + se_to_bp(layers[-2], weights_name)

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36 changes: 30 additions & 6 deletions tf/tfprocess.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1070,8 +1070,8 @@ def conv_block(self,
conv, name=name + '/bn', scale=bn_scale))

def residual_block(self, inputs, channels, name):
conv1 = tf.keras.layers.Conv2D(channels,
3,
conv1 = tf.keras.layers.Conv2D(channels // 2,
1,
use_bias=False,
padding='same',
kernel_initializer='glorot_normal',
Expand All @@ -1082,21 +1082,45 @@ def residual_block(self, inputs, channels, name):
name +
'/1/bn',
scale=False))
conv2 = tf.keras.layers.Conv2D(channels,
conv2 = tf.keras.layers.Conv2D(channels // 2,
3,
use_bias=False,
padding='same',
kernel_initializer='glorot_normal',
kernel_regularizer=self.l2reg,
data_format='channels_first',
name=name + '/2/conv2d')(out1)
out2 = self.squeeze_excitation(self.batch_norm(conv2,
name + '/2/bn',
out2 = tf.keras.layers.Activation('relu')(self.batch_norm(conv2,
name +
'/2/bn',
scale=False))
conv3 = tf.keras.layers.Conv2D(channels // 2,
3,
use_bias=False,
padding='same',
kernel_initializer='glorot_normal',
kernel_regularizer=self.l2reg,
data_format='channels_first',
name=name + '/3/conv2d')(out2)
out3 = tf.keras.layers.Activation('relu')(self.batch_norm(conv3,
name +
'/3/bn',
scale=False))
conv4 = tf.keras.layers.Conv2D(channels,
1,
use_bias=False,
padding='same',
kernel_initializer='glorot_normal',
kernel_regularizer=self.l2reg,
data_format='channels_first',
name=name + '/4/conv2d')(out3)
out4 = self.squeeze_excitation(self.batch_norm(conv4,
name + '/4/bn',
scale=True),
channels,
name=name + '/se')
return tf.keras.layers.Activation('relu')(tf.keras.layers.add(
[inputs, out2]))
[inputs, out4]))

def construct_net(self, inputs):
flow = self.conv_block(inputs,
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