def VGGNetBody(net, from_layer, need_fc=True, fully_conv=False, reduced=False,
dilated=False, nopool=False, dropout=True, freeze_layers=[], dilate_pool4=False):
kwargs = {
'param': [dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
'weight_filler': dict(type='xavier'),
'bias_filler': dict(type='constant', value=0)}
assert from_layer in net.keys()
net.conv1_1 = L.Convolution(net[from_layer], num_output=64, pad=1, kernel_size=3, **kwargs)
net.relu1_1 = L.ReLU(net.conv1_1, in_place=True)
net.conv1_2 = L.Convolution(net.relu1_1, num_output=64, pad=1, kernel_size=3, **kwargs)
net.relu1_2 = L.ReLU(net.conv1_2, in_place=True)
if nopool:
name = 'conv1_3'
net[name] = L.Convolution(net.relu1_2, num_output=64, pad=1, kernel_size=3, stride=2, **kwargs)
else:
name = 'pool1'
net.pool1 = L.Pooling(net.relu1_2, pool=P.Pooling.MAX, kernel_size=2, stride=2)
net.conv2_1 = L.Convolution(net[name], num_output=128, pad=1, kernel_size=3, **kwargs)
net.relu2_1 = L.ReLU(net.conv2_1, in_place=True)
net.conv2_2 = L.Convolution(net.relu2_1, num_output=128, pad=1, kernel_size=3, **kwargs)
net.relu2_2 = L.ReLU(net.conv2_2, in_place=True)
if nopool:
name = 'conv2_3'
net[name] = L.Convolution(net.relu2_2, num_output=128, pad=1, kernel_size=3, stride=2, **kwargs)
else:
name = 'pool2'
net[name] = L.Pooling(net.relu2_2, pool=P.Pooling.MAX, kernel_size=2, stride=2)
net.conv3_1 = L.Convolution(net[name], num_output=256, pad=1, kernel_size=3, **kwargs)
net.relu3_1 = L.ReLU(net.conv3_1, in_place=True)
net.conv3_2 = L.Convolution(net.relu3_1, num_output=256, pad=1, kernel_size=3, **kwargs)
net.relu3_2 = L.ReLU(net.conv3_2, in_place=True)
net.conv3_3 = L.Convolution(net.relu3_2, num_output=256, pad=1, kernel_size=3, **kwargs)
net.relu3_3 = L.ReLU(net.conv3_3, in_place=True)
if nopool:
name = 'conv3_4'
net[name] = L.Convolution(net.relu3_3, num_output=256, pad=1, kernel_size=3, stride=2, **kwargs)
else:
name = 'pool3'
net[name] = L.Pooling(net.relu3_3, pool=P.Pooling.MAX, kernel_size=2, stride=2)
net.conv4_1 = L.Convolution(net[name], num_output=512, pad=1, kernel_size=3, **kwargs)
net.relu4_1 = L.ReLU(net.conv4_1, in_place=True)
net.conv4_2 = L.Convolution(net.relu4_1, num_output=512, pad=1, kernel_size=3, **kwargs)
net.relu4_2 = L.ReLU(net.conv4_2, in_place=True)
net.conv4_3 = L.Convolution(net.relu4_2, num_output=512, pad=1, kernel_size=3, **kwargs)
net.relu4_3 = L.ReLU(net.conv4_3, in_place=True)
if nopool:
name = 'conv4_4'
net[name] = L.Convolution(net.relu4_3, num_output=512, pad=1, kernel_size=3, stride=2, **kwargs)
else:
name = 'pool4'
if dilate_pool4:
net[name] = L.Pooling(net.relu4_3, pool=P.Pooling.MAX, kernel_size=3, stride=1, pad=1)
dilation = 2
else:
net[name] = L.Pooling(net.relu4_3, pool=P.Pooling.MAX, kernel_size=2, stride=2)
dilation = 1
kernel_size = 3
pad = int(int((kernel_size + (dilation - 1) * (kernel_size - 1)) - 1) / 2)
net.conv5_1 = L.Convolution(net[name], num_output=512, pad=pad, kernel_size=kernel_size, dilation=dilation, **kwargs)
net.relu5_1 = L.ReLU(net.conv5_1, in_place=True)
net.conv5_2 = L.Convolution(net.relu5_1, num_output=512, pad=pad, kernel_size=kernel_size, dilation=dilation, **kwargs)
net.relu5_2 = L.ReLU(net.conv5_2, in_place=True)
net.conv5_3 = L.Convolution(net.relu5_2, num_output=512, pad=pad, kernel_size=kernel_size, dilation=dilation, **kwargs)
net.relu5_3 = L.ReLU(net.conv5_3, in_place=True)
if need_fc:
if dilated:
if nopool:
name = 'conv5_4'
net[name] = L.Convolution(net.relu5_3, num_output=512, pad=1, kernel_size=3, stride=1, **kwargs)
else:
name = 'pool5'
net[name] = L.Pooling(net.relu5_3, pool=P.Pooling.MAX, pad=1, kernel_size=3, stride=1)
else:
if nopool:
name = 'conv5_4'
net[name] = L.Convolution(net.relu5_3, num_output=512, pad=1, kernel_size=3, stride=2, **kwargs)
else:
name = 'pool5'
net[name] = L.Pooling(net.relu5_3, pool=P.Pooling.MAX, kernel_size=2, stride=2)
if fully_conv:
if dilated:
if reduced:
dilation = dilation * 6
kernel_size = 3
num_output = 1024
else:
dilation = dilation * 2
kernel_size = 7
num_output = 4096
else:
if reduced:
dilation = dilation * 3
kernel_size = 3
num_output = 1024
else:
kernel_size = 7
num_output = 4096
pad = int(int((kernel_size + (dilation - 1) * (kernel_size - 1)) - 1) / 2)
net.fc6 = L.Convolution(net[name], num_output=num_output, pad=pad, kernel_size=kernel_size, dilation=dilation, **kwargs)
net.relu6 = L.ReLU(net.fc6, in_place=True)
if dropout:
net.drop6 = L.Dropout(net.relu6, dropout_ratio=0.5, in_place=True)
if reduced:
net.fc7 = L.Convolution(net.relu6, num_output=1024, kernel_size=1, **kwargs)
else:
net.fc7 = L.Convolution(net.relu6, num_output=4096, kernel_size=1, **kwargs)
net.relu7 = L.ReLU(net.fc7, in_place=True)
if dropout:
net.drop7 = L.Dropout(net.relu7, dropout_ratio=0.5, in_place=True)
else:
net.fc6 = L.InnerProduct(net.pool5, num_output=4096)
net.relu6 = L.ReLU(net.fc6, in_place=True)
if dropout:
net.drop6 = L.Dropout(net.relu6, dropout_ratio=0.5, in_place=True)
net.fc7 = L.InnerProduct(net.relu6, num_output=4096)
net.relu7 = L.ReLU(net.fc7, in_place=True)
if dropout:
net.drop7 = L.Dropout(net.relu7, dropout_ratio=0.5, in_place=True)
# Update freeze layers.
kwargs['param'] = [dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0)]
layers = net.keys()
for freeze_layer in freeze_layers:
if freeze_layer in layers:
net.update(freeze_layer, kwargs)
return net
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.upscore = L.Deconvolution(n.score_fr,
convolution_param=dict(num_output=21,
kernel_size=64,
stride=32,
bias_term=False),
param=[dict(lr_mult=0)])
n.score = crop(n.upscore, n.data)
n.loss = L.SoftmaxWithLoss(n.score,
n.label,
loss_param=dict(normalize=False,
ignore_label=255))
return n.to_proto()