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net.py
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net.py
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import caffe
from caffe import layers as L, params as P
from caffe.coord_map import crop
def conv_relu(bottom, nout, ks=3, stride=1, pad=1):
conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
num_output=nout, pad=pad,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
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 fcn(split):
n = caffe.NetSpec()
pydata_params = dict(split=split, mean=(104.00699, 116.66877, 122.67892),
seed=1337)
if split == 'train':
pydata_params['sbdd_dir'] = '../../data/sbdd/dataset'
pylayer = 'SBDDSegDataLayer'
else:
pydata_params['voc_dir'] = '../../data/pascal/VOC2011'
pylayer = 'VOCSegDataLayer'
n.data, n.label = L.Python(module='voc_layers', layer=pylayer,
ntop=2, param_str=str(pydata_params))
# the base net
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)
n.conv5_2, n.relu5_2 = conv_relu(n.relu5_1, 512)
n.conv5_3, n.relu5_3 = conv_relu(n.relu5_2, 512)
n.pool5 = max_pool(n.relu5_3)
# fully conv
n.fc6, n.relu6 = conv_relu(n.pool5, 4096, ks=7, pad=0)
n.drop6 = L.Dropout(n.relu6, dropout_ratio=0.5, in_place=True)
n.fc7, n.relu7 = conv_relu(n.drop6, 4096, ks=1, pad=0)
n.drop7 = L.Dropout(n.relu7, dropout_ratio=0.5, in_place=True)
n.score_fr = L.Convolution(n.drop7, num_output=21, kernel_size=1, pad=0,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
n.upscore2 = L.Deconvolution(n.score_fr,
convolution_param=dict(num_output=21, kernel_size=4, stride=2,
bias_term=False),
param=[dict(lr_mult=0)])
n.score_pool4 = L.Convolution(n.pool4, num_output=21, kernel_size=1, pad=0,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
n.score_pool4c = crop(n.score_pool4, n.upscore2)
n.fuse_pool4 = L.Eltwise(n.upscore2, n.score_pool4c,
operation=P.Eltwise.SUM)
n.upscore16 = L.Deconvolution(n.fuse_pool4,
convolution_param=dict(num_output=21, kernel_size=32, stride=16,
bias_term=False),
param=[dict(lr_mult=0)])
n.score = crop(n.upscore16, n.data)
n.loss = L.SoftmaxWithLoss(n.score, n.label,
loss_param=dict(normalize=False, ignore_label=255))
return n.to_proto()
def make_net():
with open('train.prototxt', 'w') as f:
f.write(str(fcn('train')))
with open('val.prototxt', 'w') as f:
f.write(str(fcn('seg11valid')))
if __name__ == '__main__':
make_net()