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zeakey/hed-caffe-netspec.py

Last active August 11, 2017 12:08
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Python script for automatically generating HED(https://github.com/s9xie/hed) network, compatitable with newest caffe(https://github.com/bvlc/caffe)
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hed-caffe-netspec.py
import sys, os
sys.path.insert(0, 'path/to/caffe/python')
import caffe
from caffe import layers as L, params as P
from caffe.coord_map import crop
import numpy as np
def conv_relu(bottom, nout, ks=3, stride=1, pad=1, mult=[1,1,2,0]):
conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
num_output=nout, pad=pad, weight_filler=dict(type='xavier'),
param=[dict(lr_mult=mult[0], decay_mult=mult[1]), dict(lr_mult=mult[2], decay_mult=mult[3])])
return conv, L.ReLU(conv, in_place=True)
def max_pool(bottom, ks=2, stride=2):
return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)
def full_conv(bottom, name, lr):
return L.Convolution(bottom, name=name, kernel_size=1,num_output=1,# weight_filler=dict(type='xavier'),
param=[dict(lr_mult=0.01*lr, decay_mult=1), dict(lr_mult=0.02*lr, decay_mult=0)])
def fcn(split):
n = caffe.NetSpec()
n.data = L.Input(name = 'data', input_param=dict(shape=dict(dim=[1,3,500,500])))
if split=='train':
n.label = L.Input(name='label', input_param=dict(shape=dict(dim=[1,1,500,500])))
n.conv1_1, n.relu1_1 = conv_relu(n.data, 64, pad=100)
n.conv1_2, n.relu1_2 = conv_relu(n.relu1_1, 64)
n.pool1 = max_pool(n.relu1_2)
n.conv2_1, n.relu2_1 = conv_relu(n.pool1, 128)
n.conv2_2, n.relu2_2 = conv_relu(n.relu2_1, 128)
n.pool2 = max_pool(n.relu2_2)
n.conv3_1, n.relu3_1 = conv_relu(n.pool2, 256)
n.conv3_2, n.relu3_2 = conv_relu(n.relu3_1, 256)
n.conv3_3, n.relu3_3 = conv_relu(n.relu3_2, 256)
n.pool3 = max_pool(n.relu3_3)
n.conv4_1, n.relu4_1 = conv_relu(n.pool3, 512)
n.conv4_2, n.relu4_2 = conv_relu(n.relu4_1, 512)
n.conv4_3, n.relu4_3 = conv_relu(n.relu4_2, 512)
n.pool4 = max_pool(n.relu4_3)
n.conv5_1, n.relu5_1 = conv_relu(n.pool4, 512, mult=[100,1,200,0])
n.conv5_2, n.relu5_2 = conv_relu(n.relu5_1, 512, mult=[100,1,200,0])
n.conv5_3, n.relu5_3 = conv_relu(n.relu5_2, 512, mult=[100,1,200,0])
# DSN1
n.score_dsn1=full_conv(n.conv1_2, 'score-dsn1', lr=1)
n.upscore_dsn1 = crop(n.score_dsn1, n.data)
if split=='train':
n.loss1 = L.SigmoidCrossentropyLoss(n.upscore_dsn1, n.label)
if split=='test':
n.sigmoid_dsn1 = L.Sigmoid(n.upscore_dsn1)
# n.sigmoid_dsn1 = L.Sigmoid(n.upscore_dsn1)
# DSN2
n.score_dsn2=full_conv(n.conv2_2, 'score-dsn2', lr=1)
n.score_dsn2_up = L.Deconvolution(n.score_dsn2, name='upsample_2',
convolution_param=dict(num_output=1, kernel_size=4, stride=2),
param=[dict(lr_mult=0, decay_mult=1), dict(lr_mult=0, decay_mult=0)])
n.upscore_dsn2 = crop(n.score_dsn2_up, n.data)
if split=='train':
n.loss2 = L.SigmoidCrossentropyLoss(n.upscore_dsn2, n.label)
if split=='test':
n.sigmoid_dsn2 = L.Sigmoid(n.upscore_dsn2)
# n.sigmoid_dsn2 = L.Sigmoid(n.upscore_dsn2)
# DSN3
n.score_dsn3=full_conv(n.conv3_3, 'score-dsn3', lr=1)
n.score_dsn3_up = L.Deconvolution(n.score_dsn3, name='upsample_4',
convolution_param=dict(num_output=1, kernel_size=8, stride=4),
param=[dict(lr_mult=0, decay_mult=1), dict(lr_mult=0, decay_mult=0)])
n.upscore_dsn3 = crop(n.score_dsn3_up, n.data)
if split=='train':
n.loss3 = L.SigmoidCrossentropyLoss(n.upscore_dsn3, n.label)
if split=='test':
n.sigmoid_dsn3 = L.Sigmoid(n.upscore_dsn3)
# n.sigmoid_dsn3 = L.Sigmoid(n.upscore_dsn3)
# DSN4
n.score_dsn4=full_conv(n.conv4_3, 'score-dsn4', lr=1)
n.score_dsn4_up = L.Deconvolution(n.score_dsn4, name='upsample_8',
convolution_param=dict(num_output=1, kernel_size=16, stride=8),
param=[dict(lr_mult=0, decay_mult=1), dict(lr_mult=0, decay_mult=0)])
n.upscore_dsn4 = crop(n.score_dsn4_up, n.data)
if split=='train':
n.loss4 = L.SigmoidCrossentropyLoss(n.upscore_dsn4, n.label)
if split=='test':
n.sigmoid_dsn4 = L.Sigmoid(n.upscore_dsn4)
# n.sigmoid_dsn4 = L.Sigmoid(n.upscore_dsn4)
# DSN5
n.score_dsn5=full_conv(n.conv5_3, 'score-dsn5', lr=1)
n.score_dsn5_up = L.Deconvolution(n.score_dsn5, name='upsample_16',
convolution_param=dict(num_output=1, kernel_size=32, stride=16),
param=[dict(lr_mult=0, decay_mult=1), dict(lr_mult=0, decay_mult=0)])
n.upscore_dsn5 = crop(n.score_dsn5_up, n.data)
if split=='train':
n.loss5 = L.SigmoidCrossentropyLoss(n.upscore_dsn5, n.label)
if split=='test':
n.sigmoid_dsn5 = L.Sigmoid(n.upscore_dsn5)
# n.sigmoid_dsn5 = L.Sigmoid(n.upscore_dsn5)
# concat and fuse
n.concat_upscore = L.Concat(n.upscore_dsn1,
n.upscore_dsn2,
n.upscore_dsn3,
n.upscore_dsn4,
n.upscore_dsn5,
name='concat', concat_param=dict({'concat_dim':1}))
n.upscore_fuse = L.Convolution(n.concat_upscore, name='new-score-weighting',
num_output=1, kernel_size=1,
param=[dict(lr_mult=0.001, decay_mult=1), dict(lr_mult=0.002, decay_mult=0)],
weight_filler=dict(type='constant', value=0.2))
if split=='test':
n.sigmoid_fuse = L.Sigmoid(n.upscore_fuse)
if split=='train':
n.loss_fuse = L.SigmoidCrossentropyLoss(n.upscore_fuse, n.label)
return n.to_proto()
def make_net():
with open('hed_train.pt', 'w') as f:
f.writelines(os.linesep+'force_backward: true'+os.linesep)
f.write(str(fcn('train')))
with open('hed_test.pt', 'w') as f:
f.write(str(fcn('test')))
def make_solver():
sp = {}
sp['net'] = '"train.pt"'
sp['base_lr'] = '0.001'
sp['lr_policy'] = '"step"'
sp['momentum'] = '0.9'
sp['weight_decay'] = '0.0002'
sp['iter_size'] = '10'
sp['stepsize'] = '1000'
sp['display'] = '20'
sp['snapshot'] = '100000'
sp['snapshot_prefix'] = '"net"'
sp['gamma'] = '0.1'
sp['max_iter'] = '100000'
sp['solver_mode'] = 'CPU'
f = open('solver.pt', 'w')
for k, v in sorted(sp.items()):
if not(type(v) is str):
raise TypeError('All solver parameters must be strings')
f.write('%s: %s\n'%(k, v))
f.close()
if __name__ == '__main__':
make_net()
make_solver()
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