def inception_resnet_v2(net):
net['data'] = layers.DummyData(num=1, channels=3, height=299, width=299)
# 149 x 149 x 32
top_layer = 'Conv2d_1a_3x3'
conv_bn_layer(net,
in_layer='data',
out_layer=top_layer,
use_bn=True,
use_relu=True,
num_output=32,
kernel_size=3,
pad=0,
stride=2)
bottom_layer = top_layer
# 147 x 147 x 32
top_layer = 'Conv2d_2a_3x3'
conv_bn_layer(net,
in_layer=bottom_layer,
out_layer=top_layer,
use_bn=True,
use_relu=True,
num_output=32,
kernel_size=3,
pad=0,
stride=1)
bottom_layer = top_layer
# 147 x 147 x 64
top_layer = 'Conv2d_2b_3x3'
conv_bn_layer(net,
in_layer=bottom_layer,
out_layer=top_layer,
use_bn=True,
use_relu=True,
num_output=64,
kernel_size=3,
pad=0,
stride=1)
bottom_layer = top_layer
# 73 x 73 x 64
top_layer = 'maxpool_3a'
net[top_layer] = layers.Pooling(net[bottom_layer],
pool=params.Pooling.MAX,
kernel_size=3,
stride=2,
pad=0)
bottom_layer = top_layer
# 73 x 73 x 80
top_layer = 'Conv2d_3b_1x1'
conv_bn_layer(net,
in_layer=bottom_layer,
out_layer=top_layer,
use_bn=True,
use_relu=True,
num_output=80,
kernel_size=1,
pad=0,
stride=1)
bottom_layer = top_layer
# 71 x 71 x 192
top_layer = 'Conv2d_4a_3x3'
conv_bn_layer(net,
in_layer=bottom_layer,
out_layer=top_layer,
use_bn=True,
use_relu=True,
num_output=192,
kernel_size=3,
pad=0,
stride=1)
bottom_layer = top_layer
# 35 x 35 x 192
top_layer = 'maxpool_5a'
net[top_layer] = layers.Pooling(net[bottom_layer],
pool=params.Pooling.MAX,
kernel_size=3,
stride=2,
pad=0)
bottom_layer = top_layer
bottom_layer = mixed_5b(net, bottom_layer)
# 35 x 35 x 320 (Mixed 5a)
bottom_layer = inception_block_35(net,
bottom_layer,
10,
0.17,
repeat_name='Repeat')
# 17 x 17 x 1088
bottom_layer = mixed_6a(net, bottom_layer)
bottom_layer = inception_block_17(net,
bottom_layer,
20,
0.10,
repeat_name='Repeat_1')
bottom_layer = mixed_7a(net, bottom_layer)
bottom_layer = inception_block_8(net,
bottom_layer,
9,
0.20,
repeat_name='Repeat_2',
apply_last_relu=True)
bottom_layer = inception_block_8(net,
bottom_layer,
1,
0.20,
repeat_name='',
apply_last_relu=False)
top_layer = 'Conv2d_7b_1x1'
conv_bn_layer(net,
in_layer=bottom_layer,
out_layer=top_layer,
use_bn=True,
use_relu=True,
num_output=1536,
kernel_size=1,
pad=0,
stride=1)
with open('gg.prototxt', 'w') as f:
print(net.to_proto(), file=f)
def mixed_7a(net, common_bottom_layer):
# branch 0
top_layer_branch0 = 'Mixed_7a/Branch_0/Conv2d_0a_1x1'
conv_bn_layer(net,
in_layer=common_bottom_layer,
out_layer=top_layer_branch0,
use_bn=True,
use_relu=True,
num_output=256,
kernel_size=1,
pad=0,
stride=1)
bottom_layer_branch0 = top_layer_branch0
top_layer_branch0 = 'Mixed_7a/Branch_0/Conv2d_1a_3x3'
conv_bn_layer(net,
in_layer=bottom_layer_branch0,
out_layer=top_layer_branch0,
use_bn=True,
use_relu=True,
num_output=384,
kernel_size=3,
pad=0,
stride=2)
# branch 1
top_layer_branch1 = 'Mixed_7a/Branch_1/Conv2d_0a_1x1'
conv_bn_layer(net,
in_layer=common_bottom_layer,
out_layer=top_layer_branch1,
use_bn=True,
use_relu=True,
num_output=256,
kernel_size=1,
pad=0,
stride=1)
bottom_layer_branch1 = top_layer_branch1
top_layer_branch1 = 'Mixed_7a/Branch_1/Conv2d_1a_3x3'
conv_bn_layer(net,
in_layer=bottom_layer_branch1,
out_layer=top_layer_branch1,
use_bn=True,
use_relu=True,
num_output=288,
kernel_size=3,
pad=0,
stride=2)
# branch 2
top_layer_branch2 = 'Mixed_7a/Branch_2/Conv2d_0a_1x1'
conv_bn_layer(net,
in_layer=common_bottom_layer,
out_layer=top_layer_branch2,
use_bn=True,
use_relu=True,
num_output=256,
kernel_size=1,
pad=0,
stride=1)
bottom_layer_branch2 = top_layer_branch2
top_layer_branch2 = 'Mixed_7a/Branch_2/Conv2d_0b_3x3'
conv_bn_layer(net,
in_layer=bottom_layer_branch2,
out_layer=top_layer_branch2,
use_bn=True,
use_relu=True,
num_output=288,
kernel_size=3,
pad=1,
stride=1)
bottom_layer_branch2 = top_layer_branch2
top_layer_branch2 = 'Mixed_7a/Branch_2/Conv2d_1a_3x3'
conv_bn_layer(net,
in_layer=bottom_layer_branch2,
out_layer=top_layer_branch2,
use_bn=True,
use_relu=True,
num_output=320,
kernel_size=3,
pad=0,
stride=2)
# branch 3
top_layer_branch3 = 'mixed7a_branch3_maxpool_0'
net[top_layer_branch3] = layers.Pooling(net[common_bottom_layer],
pool=params.Pooling.MAX,
kernel_size=3,
stride=2,
pad=0)
top_layer = 'mixed7a'
net[top_layer] = layers.Concat(*[
net[top_layer_branch0], net[top_layer_branch1], net[top_layer_branch2],
net[top_layer_branch3]
],
axis=1)
return top_layer
def inception_block_8(net,
bottom_layer,
block_num,
scale=1.0,
repeat_name='Repeat',
apply_last_relu=True):
for block_idx in xrange(block_num):
top_layer_branch0 = '{}/block8_{}/Branch_0/Conv2d_1x1'.format(
repeat_name, block_idx + 1)
conv_bn_layer(net,
in_layer=bottom_layer,
out_layer=top_layer_branch0,
use_bn=True,
use_relu=True,
num_output=192,
kernel_size=1,
pad=0,
stride=1)
top_layer_branch1 = '{}/block8_{}/Branch_1/Conv2d_0a_1x1'.format(
repeat_name, block_idx + 1)
conv_bn_layer(net,
in_layer=bottom_layer,
out_layer=top_layer_branch1,
use_bn=True,
use_relu=True,
num_output=192,
kernel_size=1,
pad=0,
stride=1)
bottom_layer_branch1 = top_layer_branch1
top_layer_branch1 = '{}/block8_{}/Branch_1/Conv2d_0b_1x3'.format(
repeat_name, block_idx + 1)
conv_bn_layer(net,
in_layer=bottom_layer_branch1,
out_layer=top_layer_branch1,
use_bn=True,
use_relu=True,
num_output=224,
kernel_size=[1, 3],
pad=[0, 1],
stride=1)
bottom_layer_branch1 = top_layer_branch1
top_layer_branch1 = '{}/block8_{}/Branch_1/Conv2d_0c_3x1'.format(
repeat_name, block_idx + 1)
conv_bn_layer(net,
in_layer=bottom_layer_branch1,
out_layer=top_layer_branch1,
use_bn=True,
use_relu=True,
num_output=256,
kernel_size=[3, 1],
pad=[1, 0],
stride=1)
top_layer_mixed = '{}/block8_{}_mixed'.format(repeat_name,
block_idx + 1)
net[top_layer_mixed] = layers.Concat(
*[net[top_layer_branch0], net[top_layer_branch1]], axis=1)
bottom_layer_residual = top_layer_mixed
top_layer_residual = '{}/block8_{}/Conv2d_1x1'.format(
repeat_name, block_idx + 1)
net[top_layer_residual] = layers.Convolution(
net[bottom_layer_residual],
num_output=2080,
kernel_size=1,
pad=0,
stride=1)
bottom_layer_shortcut = bottom_layer
bottom_layer_residual = top_layer_residual
top_layer = '{}/block8_{}/eltsum'.format(repeat_name, block_idx + 1)
net[top_layer] = layers.Eltwise(
*[net[bottom_layer_shortcut], net[bottom_layer_residual]],
coeff=[1.0, scale])
if block_idx + 1 < block_num or apply_last_relu:
bottom_layer = top_layer
relu_name = '{}_relu'.format(bottom_layer)
net[relu_name] = layers.ReLU(net[bottom_layer], in_place=True)
return top_layer
def mixed_5b(net, common_bottom_layer):
# branch 0
top_layer_branch0 = 'Mixed_5b/Branch_0/Conv2d_1x1'
conv_bn_layer(net,
in_layer=common_bottom_layer,
out_layer=top_layer_branch0,
use_bn=True,
use_relu=True,
num_output=96,
kernel_size=1,
pad=0,
stride=1)
# branch 1
top_layer_branch1 = 'Mixed_5b/Branch_1/Conv2d_0a_1x1'
conv_bn_layer(net,
in_layer=common_bottom_layer,
out_layer=top_layer_branch1,
use_bn=True,
use_relu=True,
num_output=48,
kernel_size=1,
pad=0,
stride=1)
bottom_layer_branch1 = top_layer_branch1
top_layer_branch1 = 'Mixed_5b/Branch_1/Conv2d_0b_5x5'
conv_bn_layer(net,
in_layer=bottom_layer_branch1,
out_layer=top_layer_branch1,
use_bn=True,
use_relu=True,
num_output=64,
kernel_size=5,
pad=2,
stride=1)
# branch 2
top_layer_branch2 = 'Mixed_5b/Branch_2/Conv2d_0a_1x1'
conv_bn_layer(net,
in_layer=common_bottom_layer,
out_layer=top_layer_branch2,
use_bn=True,
use_relu=True,
num_output=64,
kernel_size=1,
pad=0,
stride=1)
bottom_layer_branch2 = top_layer_branch2
top_layer_branch2 = 'Mixed_5b/Branch_2/Conv2d_0b_3x3'
conv_bn_layer(net,
in_layer=bottom_layer_branch2,
out_layer=top_layer_branch2,
use_bn=True,
use_relu=True,
num_output=96,
kernel_size=3,
pad=1,
stride=1)
bottom_layer_branch2 = top_layer_branch2
top_layer_branch2 = 'Mixed_5b/Branch_2/Conv2d_0c_3x3'
conv_bn_layer(net,
in_layer=bottom_layer_branch2,
out_layer=top_layer_branch2,
use_bn=True,
use_relu=True,
num_output=96,
kernel_size=3,
pad=1,
stride=1)
# branch 3
top_layer_branch3 = 'mixed5b_branch3_avepool_0a'
net[top_layer_branch3] = layers.Pooling(net[common_bottom_layer],
pool=params.Pooling.AVE,
kernel_size=3,
stride=1,
pad=1)
bottom_layer_branch3 = top_layer_branch3
top_layer_branch3 = 'Mixed_5b/Branch_3/Conv2d_0b_1x1'
conv_bn_layer(net,
in_layer=bottom_layer_branch3,
out_layer=top_layer_branch3,
use_bn=True,
use_relu=True,
num_output=64,
kernel_size=1,
pad=0,
stride=1)
top_layer = 'mixed5b'
net[top_layer] = layers.Concat(*[
net[top_layer_branch0], net[top_layer_branch1], net[top_layer_branch2],
net[top_layer_branch3]
],
axis=1)
return top_layer
def inception_block_35(net,
bottom_layer,
block_num,
scale=1.0,
repeat_name='Repeat'):
for block_idx in xrange(block_num):
top_layer_branch0 = '{}/block35_{}/Branch_0/Conv2d_1x1'.format(
repeat_name, block_idx + 1)
conv_bn_layer(net,
in_layer=bottom_layer,
out_layer=top_layer_branch0,
use_bn=True,
use_relu=True,
num_output=32,
kernel_size=1,
pad=0,
stride=1)
top_layer_branch1 = '{}/block35_{}/Branch_1/Conv2d_0a_1x1'.format(
repeat_name, block_idx + 1)
conv_bn_layer(net,
in_layer=bottom_layer,
out_layer=top_layer_branch1,
use_bn=True,
use_relu=True,
num_output=32,
kernel_size=1,
pad=0,
stride=1)
bottom_layer_branch1 = top_layer_branch1
top_layer_branch1 = '{}/block35_{}/Branch_1/Conv2d_0b_3x3'.format(
repeat_name, block_idx + 1)
conv_bn_layer(net,
in_layer=bottom_layer_branch1,
out_layer=top_layer_branch1,
use_bn=True,
use_relu=True,
num_output=32,
kernel_size=3,
pad=1,
stride=1)
top_layer_branch2 = '{}/block35_{}/Branch_2/Conv2d_0a_1x1'.format(
repeat_name, block_idx + 1)
conv_bn_layer(net,
in_layer=bottom_layer,
out_layer=top_layer_branch2,
use_bn=True,
use_relu=True,
num_output=32,
kernel_size=1,
pad=0,
stride=1)
bottom_layer_branch2 = top_layer_branch2
top_layer_branch2 = '{}/block35_{}/Branch_2/Conv2d_0b_3x3'.format(
repeat_name, block_idx + 1)
conv_bn_layer(net,
in_layer=bottom_layer_branch2,
out_layer=top_layer_branch2,
use_bn=True,
use_relu=True,
num_output=48,
kernel_size=3,
pad=1,
stride=1)
bottom_layer_branch2 = top_layer_branch2
top_layer_branch2 = '{}/block35_{}/Branch_2/Conv2d_0c_3x3'.format(
repeat_name, block_idx + 1)
conv_bn_layer(net,
in_layer=bottom_layer_branch2,
out_layer=top_layer_branch2,
use_bn=True,
use_relu=True,
num_output=64,
kernel_size=3,
pad=1,
stride=1)
top_layer_mixed = 'block35_{}_mixed'.format(block_idx + 1)
net[top_layer_mixed] = layers.Concat(*[
net[top_layer_branch0], net[top_layer_branch1],
net[top_layer_branch2]
],
axis=1)
bottom_layer_residual = top_layer_mixed
top_layer_residual = '{}/block35_{}/Conv2d_1x1'.format(
repeat_name, block_idx + 1)
net[top_layer_residual] = layers.Convolution(
net[bottom_layer_residual],
num_output=320,
kernel_size=1,
pad=0,
stride=1)
bottom_layer_shortcut = bottom_layer
bottom_layer_residual = top_layer_residual
top_layer = 'block35_{}_eltsum'.format(block_idx + 1)
net[top_layer] = layers.Eltwise(
*[net[bottom_layer_shortcut], net[bottom_layer_residual]],
coeff=[1.0, scale])
bottom_layer = top_layer
relu_name = '{}_relu'.format(bottom_layer)
net[relu_name] = layers.ReLU(net[bottom_layer], in_place=True)
return top_layer