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Allow to choose which blocks have SE units #188

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Allow to choose which blocks have SE units #188

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9 changes: 7 additions & 2 deletions tf/tfprocess.py
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Original file line number Diff line number Diff line change
Expand Up @@ -108,6 +108,7 @@ def __init__(self, cfg):
loss_scale = self.cfg['training'].get('loss_scale', 128)
self.virtual_batch_size = self.cfg['model'].get(
'virtual_batch_size', None)
self.SE_blocks = self.cfg['model'].get('se_blocks', None)

if precision == 'single':
self.model_dtype = tf.float32
Expand Down Expand Up @@ -1036,7 +1037,9 @@ def batch_norm(self, input, name, scale=False):
virtual_batch_size=self.virtual_batch_size,
name=name)(input)

def squeeze_excitation(self, inputs, channels, name):
def squeeze_excitation(self, inputs, channels, block_num, name):
if self.SE_blocks is not None and block_num not in self.SE_blocks:
return inputs
assert channels % self.SE_ratio == 0

pooled = tf.keras.layers.GlobalAveragePooling2D(
Expand Down Expand Up @@ -1069,7 +1072,7 @@ def conv_block(self,
return tf.keras.layers.Activation('relu')(self.batch_norm(
conv, name=name + '/bn', scale=bn_scale))

def residual_block(self, inputs, channels, name):
def residual_block(self, inputs, channels, block_num, name):
conv1 = tf.keras.layers.Conv2D(channels,
3,
use_bias=False,
Expand All @@ -1094,6 +1097,7 @@ def residual_block(self, inputs, channels, name):
name + '/2/bn',
scale=True),
channels,
block_num,
name=name + '/se')
return tf.keras.layers.Activation('relu')(tf.keras.layers.add(
[inputs, out2]))
Expand All @@ -1107,6 +1111,7 @@ def construct_net(self, inputs):
for i in range(self.RESIDUAL_BLOCKS):
flow = self.residual_block(flow,
self.RESIDUAL_FILTERS,
i + 1,
name='residual_{}'.format(i + 1))

# Policy head
Expand Down