def get_phocnet(self, word_image_lmdb_path, phoc_lmdb_path,
phoc_size=604, generate_deploy=False):
'''
Returns a NetSpec definition of the PHOCNet. The definition can then be transformed
into a protobuffer message by casting it into a str.
'''
n = NetSpec()
# Data
self.set_phocnet_data(n=n, generate_deploy=generate_deploy,
word_image_lmdb_path=word_image_lmdb_path,
phoc_lmdb_path=phoc_lmdb_path)
# Conv Part
self.set_phocnet_conv_body(n=n, relu_in_place=True)
# FC Part
n.spp5 = L.SPP(n.relu4_3, spp_param=dict(pool=P.SPP.MAX, pyramid_height=3, engine=self.spp_engine))
n.fc6, n.relu6, n.drop6 = self.fc_relu(bottom=n.spp5, layer_size=4096,
dropout_ratio=0.5, relu_in_place=True)
n.fc7, n.relu7, n.drop7 = self.fc_relu(bottom=n.drop6, layer_size=4096,
dropout_ratio=0.5, relu_in_place=True)
n.fc8 = L.InnerProduct(n.drop7, num_output=phoc_size,
weight_filler=dict(type=self.initialization),
bias_filler=dict(type='constant'))
n.sigmoid = L.Sigmoid(n.fc8, include=dict(phase=self.phase_test))
# output part
if not generate_deploy:
n.silence = L.Silence(n.sigmoid, ntop=0, include=dict(phase=self.phase_test))
n.loss = L.SigmoidCrossEntropyLoss(n.fc8, n.phocs)
return n.to_proto()
def gen_net(batch_size=512):
n=NetSpec();
n.data = L.DummyData(shape={"dim":[batch_size,3,96,96]})
n.select1 = L.DummyData(shape={"dim":[2]})
n.select2 = L.DummyData(shape={"dim":[2]})
n.label = L.DummyData(shape={"dim":[2]})
caffenet_stack(n.data, n)
n.first = L.BatchReindex(n.fc6, n.select1)
n.second = L.BatchReindex(n.fc6, n.select2)
n.fc6_concat=L.Concat(n.first, n.second);
n.fc7, n.bn7, n.relu7 = fc_relu(n.fc6_concat, 4096, batchnorm=True);
n.fc8, n.relu8 = fc_relu(n.relu7, 4096);
n.fc9 = L.InnerProduct(n.relu8, num_output=8,
weight_filler=dict(type='xavier'));
n.loss = L.SoftmaxWithLoss(n.fc9, n.label, loss_param=dict(normalization=P.Loss.NONE));
prot=n.to_proto()
prot.debug_info=True
return prot;
def get_phocnet(self,
word_image_lmdb_path,
phoc_lmdb_path,
phoc_size=604,
generate_deploy=False):
'''
Returns a NetSpec definition of the PHOCNet. The definition can then be transformed
into a protobuffer message by casting it into a str.
'''
n = NetSpec()
relu_in_place = True
# Data
if generate_deploy:
n.word_images = L.Input(shape=dict(dim=[1, 1, 100, 250]))
relu_in_place = False
else:
n.word_images, n.label = L.Data(batch_size=1,
backend=P.Data.LMDB,
source=word_image_lmdb_path,
prefetch=20,
transform_param=dict(
mean_value=255,
scale=-1. / 255,
),
ntop=2)
n.phocs, n.label_phocs = L.Data(batch_size=1,
backend=P.Data.LMDB,
source=phoc_lmdb_path,
prefetch=20,
ntop=2)
# Conv Part
n.conv1_1, n.relu1_1 = self.conv_relu(n.word_images,
nout=64,
relu_in_place=relu_in_place)
n.conv1_2, n.relu1_2 = self.conv_relu(n.relu1_1,
nout=64,
relu_in_place=relu_in_place)
n.pool1 = L.Pooling(n.relu1_2,
pooling_param=dict(pool=P.Pooling.MAX,
kernel_size=2,
stride=2))
n.conv2_1, n.relu2_1 = self.conv_relu(n.pool1,
nout=128,
relu_in_place=relu_in_place)
n.conv2_2, n.relu2_2 = self.conv_relu(n.relu2_1,
nout=128,
relu_in_place=relu_in_place)
n.pool2 = L.Pooling(n.relu2_2,
pooling_param=dict(pool=P.Pooling.MAX,
kernel_size=2,
stride=2))
n.conv3_1, n.relu3_1 = self.conv_relu(n.pool2,
nout=256,
relu_in_place=relu_in_place)
n.conv3_2, n.relu3_2 = self.conv_relu(n.relu3_1,
nout=256,
relu_in_place=relu_in_place)
n.conv3_3, n.relu3_3 = self.conv_relu(n.relu3_2,
nout=256,
relu_in_place=relu_in_place)
n.conv3_4, n.relu3_4 = self.conv_relu(n.relu3_3,
nout=256,
relu_in_place=relu_in_place)
n.conv3_5, n.relu3_5 = self.conv_relu(n.relu3_4,
nout=256,
relu_in_place=relu_in_place)
n.conv3_6, n.relu3_6 = self.conv_relu(n.relu3_5,
nout=256,
relu_in_place=relu_in_place)
n.conv4_1, n.relu4_1 = self.conv_relu(n.relu3_6,
nout=512,
relu_in_place=relu_in_place)
n.conv4_2, n.relu4_2 = self.conv_relu(n.relu4_1,
nout=512,
relu_in_place=relu_in_place)
n.conv4_3, n.relu4_3 = self.conv_relu(n.relu4_2,
nout=512,
relu_in_place=relu_in_place)
# FC Part
n.spp5 = L.SPP(n.relu4_3,
spp_param=dict(pool=P.SPP.MAX,
pyramid_height=3,
engine=self.spp_engine))
n.fc6, n.relu6, n.drop6 = self.fc_relu(bottom=n.spp5,
layer_size=4096,
dropout_ratio=0.5,
relu_in_place=relu_in_place)
n.fc7, n.relu7, n.drop7 = self.fc_relu(bottom=n.drop6,
layer_size=4096,
dropout_ratio=0.5,
relu_in_place=relu_in_place)
n.fc8 = L.InnerProduct(n.drop7,
num_output=phoc_size,
weight_filler=dict(type=self.initialization),
bias_filler=dict(type='constant'))
n.sigmoid = L.Sigmoid(n.fc8, include=dict(phase=self.phase_test))
# output part
if not generate_deploy:
n.silence = L.Silence(n.sigmoid,
ntop=0,
include=dict(phase=self.phase_test))
n.loss = L.SigmoidCrossEntropyLoss(n.fc8, n.phocs)
return n.to_proto()