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Update create_enet_prototxt.py #81

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22 changes: 11 additions & 11 deletions scripts/create_enet_prototxt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,7 +80,7 @@ def bottleneck(n, prev_layer, stage, num_bottle, num_output, type, param_add=Non
kernel_size = 2
stride = 2

setattr(n, conv_name, L.Convolution(getattr(n, prev_layer), num_output=num_output/scale_factor, bias_term=0,
setattr(n, conv_name, L.Convolution(getattr(n, prev_layer), num_output=int(num_output/scale_factor), bias_term=0,
kernel_size=kernel_size, stride=stride, weight_filler=dict(type='msra')))
setattr(n, bn_name, L.BN(getattr(n, conv_name), scale_filler=dict(type='constant', value=1), bn_mode=bn_mode,
shift_filler=dict(type='constant', value=0.001), param=[dict(lr_mult=1, decay_mult=1),
Expand All @@ -97,23 +97,23 @@ def bottleneck(n, prev_layer, stage, num_bottle, num_output, type, param_add=Non
prelu_name = 'prelu{}_{}_{}'.format(stage, num_bottle, module+1)

if type == 'dilated':
setattr(n, conv_name, L.Convolution(prev_layer, num_output=num_output/scale_factor, bias_term=1, kernel_size=3,
setattr(n, conv_name, L.Convolution(prev_layer, num_output=int(num_output/scale_factor), bias_term=1, kernel_size=3,
stride=1, pad=param_add, dilation=param_add,
weight_filler=dict(type='msra')))
elif type == 'asymmetric':
conv_name2 = 'conv{}_{}_{}_a'.format(stage, num_bottle, module+1)
setattr(n, conv_name2, L.Convolution(prev_layer, num_output=num_output/scale_factor, bias_term=0,
setattr(n, conv_name2, L.Convolution(prev_layer, num_output=int(num_output/scale_factor), bias_term=0,
kernel_h=param_add, kernel_w=1, stride=1, pad=1,
weight_filler=dict(type='msra')))
setattr(n, conv_name, L.Convolution(getattr(n, conv_name2), num_output=num_output/scale_factor, bias_term=1,
setattr(n, conv_name, L.Convolution(getattr(n, conv_name2), num_output=int(num_output/scale_factor), bias_term=1,
kernel_h=1, kernel_w=param_add, stride=1, pad=1,
weight_filler=dict(type='msra')))
elif type == 'upsampling':
conv_name = 'deconv{}_{}_{}'.format(stage, num_bottle, module+1)
setattr(n, conv_name, L.Deconvolution(prev_layer, convolution_param=dict(num_output=num_output/scale_factor,
setattr(n, conv_name, L.Deconvolution(prev_layer, convolution_param=dict(num_output=int(num_output/scale_factor),
bias_term=1, kernel_size=2, stride=2)))
else:
setattr(n, conv_name, L.Convolution(prev_layer, num_output=num_output/scale_factor, bias_term=1,
setattr(n, conv_name, L.Convolution(prev_layer, num_output=int(num_output/scale_factor), bias_term=1,
kernel_size=3, stride=1, pad=1, weight_filler=dict(type='msra')))

setattr(n, bn_name, L.BN(getattr(n, conv_name), scale_filler=dict(type='constant', value=1), bn_mode=bn_mode,
Expand Down Expand Up @@ -165,7 +165,7 @@ def bottleneck(n, prev_layer, stage, num_bottle, num_output, type, param_add=Non
n.pool2_0_4 = L.Pooling(getattr(n, input_layer), kernel_size=2, stride=2, pool=P.Pooling.MAX)

else:
print 'downsampling is just available for stage 1 and 2'
print ("downsampling is just available for stage 1 and 2")

setattr(n, conv_name,
L.Convolution(getattr(n, pool_name), num_output=num_output, bias_term=0, kernel_size=1,
Expand All @@ -192,7 +192,7 @@ def bottleneck(n, prev_layer, stage, num_bottle, num_output, type, param_add=Non
elif stage == 5:
setattr(n, upsample_name, L.Upsample(getattr(n, bn_name), n.pool1_0_4_mask, scale=2))
else:
print 'upsampling is just available for stage 4 and 5'
print ("upsampling is just available for stage 4 and 5")

prev_layer2 = getattr(n, upsample_name)

Expand Down Expand Up @@ -284,12 +284,12 @@ def make_parser():
network, prev_layer = bottleneck(n, prev_layer, 1, 0, 64, 'downsampling') # stage, number_bottleneck, num_input,
# type,

for i in xrange(1, 5):
for i in range(1, 5):
network, prev_layer = bottleneck(n, prev_layer, 1, i, 64, 'regular')

network, prev_layer = bottleneck(n, prev_layer, 2, 0, 128, 'downsampling')

for j in xrange(2, 4):
for j in range(2, 4):
network, prev_layer = bottleneck(n, prev_layer, j, 1, 128, 'regular')
network, prev_layer = bottleneck(n, prev_layer, j, 2, 128, 'dilated', 2)
network, prev_layer = bottleneck(n, prev_layer, j, 3, 128, 'asymmetric', 5)
Expand Down Expand Up @@ -319,4 +319,4 @@ def make_parser():
f.write('name: "ENet"\n')
f.write(str(network))

print "Done!"
print ("Done!")