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Modified network to accept multi channel images #14

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Modified network to accept multi channel images #14

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0f9a8c6
modified network to accept multi channel images
anandcu3 Nov 16, 2017
2f90b75
Update enet.py
anandcu3 Dec 6, 2017
cd58a61
Update enet.py
anandcu3 Dec 6, 2017
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14 changes: 8 additions & 6 deletions enet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -111,11 +111,11 @@ def unpool(updates, mask, k_size=[1, 2, 2, 1], output_shape=None, scope=''):
return ret

@slim.add_arg_scope
def initial_block(inputs, is_training=True, scope='initial_block'):
def initial_block(inputs, num_channels = 3, is_training=True, scope='initial_block'):
'''
The initial block for Enet has 2 branches: The convolution branch and Maxpool branch.

The conv branch has 13 layers, while the maxpool branch gives 3 layers corresponding to the RGB channels.
The conv branch has (16 - num_channels) layers, while the maxpool branch gives layers equal to the number of channels.
Both output layers are then concatenated to give an output of 16 layers.

NOTE: Does not need to store pooling indices since it won't be used later for the final upsampling.
Expand All @@ -127,7 +127,7 @@ def initial_block(inputs, is_training=True, scope='initial_block'):
- net_concatenated(Tensor): a 4D Tensor that contains the
'''
#Convolutional branch
net_conv = slim.conv2d(inputs, 13, [3,3], stride=2, activation_fn=None, scope=scope+'_conv')
net_conv = slim.conv2d(inputs, 16 - num_channels, [3,3], stride=2, activation_fn=None, scope=scope+'_conv')
net_conv = slim.batch_norm(net_conv, is_training=is_training, fused=True, scope=scope+'_batchnorm')
net_conv = prelu(net_conv, scope=scope+'_prelu')

Expand Down Expand Up @@ -387,6 +387,7 @@ def bottleneck(inputs,
def ENet(inputs,
num_classes,
batch_size,
num_channels=3,
num_initial_blocks=1,
stage_two_repeat=2,
skip_connections=True,
Expand Down Expand Up @@ -421,8 +422,9 @@ def ENet(inputs,
slim.arg_scope([slim.batch_norm], fused=True), \
slim.arg_scope([slim.conv2d, slim.conv2d_transpose], activation_fn=None):
#=================INITIAL BLOCK=================
for i in xrange(1, max(num_initial_blocks, 1) + 1):
net = initial_block(inputs, scope='initial_block_' + str(i))
net = initial_block(inputs, scope='initial_block_1')
for i in xrange(2, max(num_initial_blocks, 1) + 1):
net = initial_block(net, num_channels = num_channels, scope='initial_block_' + str(i))

#Save for skip connection later
if skip_connections:
Expand Down Expand Up @@ -512,4 +514,4 @@ def ENet_arg_scope(weight_decay=2e-4,
with slim.arg_scope([slim.batch_norm],
decay=batch_norm_decay,
epsilon=batch_norm_epsilon) as scope:
return scope
return scope