def setLayers(data_source,
batch_size,
layername,
kernel,
stride,
outCH,
label_name,
transform_param_in,
deploy=False):
# it is tricky to produce the deploy prototxt file, as the data input is not from a layer, so we have to creat a workaround
# producing training and testing prototxt files is pretty straight forward
n = caffe.NetSpec()
assert len(layername) == len(kernel)
assert len(layername) == len(stride)
assert len(layername) == len(outCH)
# produce data definition for deploy net
if deploy == False:
n.data, n.tops['label'] = L.CPMData(cpmdata_param=dict(
backend=1, source=data_source, batch_size=batch_size),
transform_param=transform_param_in,
ntop=2)
n.tops[label_name[1]], n.tops[label_name[0]] = L.Slice(
n.label, slice_param=dict(axis=1, slice_point=15), ntop=2)
else:
input = "data"
dim1 = 1
dim2 = 4
dim3 = 368
dim4 = 368
# make an empty "data" layer so the next layer accepting input will be able to take the correct blob name "data",
# we will later have to remove this layer from the serialization string, since this is just a placeholder
n.data = L.Layer()
# something special before everything
n.image, n.center_map = L.Slice(n.data,
slice_param=dict(axis=1, slice_point=3),
ntop=2)
n.pool_center_lower = L.Pooling(n.center_map,
kernel_size=9,
stride=8,
pool=P.Pooling.AVE)
# just follow arrays..CPCPCPCPCCCC....
last_layer = 'image'
stage = 1
conv_counter = 1
pool_counter = 1
drop_counter = 1
state = 'image' # can be image or fuse
share_point = 0
for l in range(0, len(layername)):
if layername[l] == 'C':
if state == 'image':
conv_name = 'conv%d_stage%d' % (conv_counter, stage)
else:
conv_name = 'Mconv%d_stage%d' % (conv_counter, stage)
if stage == 1:
lr_m = 5
else:
lr_m = 1
n.tops[conv_name] = L.Convolution(
n.tops[last_layer],
kernel_size=kernel[l],
num_output=outCH[l],
pad=int(math.floor(kernel[l] / 2)),
param=[
dict(lr_mult=lr_m, decay_mult=1),
dict(lr_mult=lr_m * 2, decay_mult=0)
],
weight_filler=dict(type='gaussian', std=0.01),
bias_filler=dict(type='constant'))
last_layer = conv_name
if layername[l + 1] != 'L':
if (state == 'image'):
ReLUname = 'relu%d_stage%d' % (conv_counter, stage)
n.tops[ReLUname] = L.ReLU(n.tops[last_layer],
in_place=True)
else:
ReLUname = 'Mrelu%d_stage%d' % (conv_counter, stage)
n.tops[ReLUname] = L.ReLU(n.tops[last_layer],
in_place=True)
last_layer = ReLUname
conv_counter += 1
elif layername[l] == 'P': # Pooling
n.tops['pool%d_stage%d' % (pool_counter, stage)] = L.Pooling(
n.tops[last_layer],
kernel_size=kernel[l],
stride=stride[l],
pool=P.Pooling.MAX)
last_layer = 'pool%d_stage%d' % (pool_counter, stage)
pool_counter += 1
elif layername[l] == 'L':
# Loss: n.loss layer is only in training and testing nets, but not in deploy net.
if deploy == False:
if stage == 1:
n.tops['loss_stage%d' % stage] = L.EuclideanLoss(
n.tops[last_layer], n.tops[label_name[0]])
else:
n.tops['loss_stage%d' % stage] = L.EuclideanLoss(
n.tops[last_layer], n.tops[label_name[1]])
stage += 1
last_connect = last_layer
last_layer = 'image'
conv_counter = 1
pool_counter = 1
drop_counter = 1
state = 'image'
elif layername[l] == 'D':
if deploy == False:
n.tops['drop%d_stage%d' % (drop_counter, stage)] = L.Dropout(
n.tops[last_layer],
in_place=True,
dropout_param=dict(dropout_ratio=0.5))
drop_counter += 1
elif layername[l] == '@':
n.tops['concat_stage%d' % stage] = L.Concat(
n.tops[last_layer],
n.tops[last_connect],
n.pool_center_lower,
concat_param=dict(axis=1))
conv_counter = 1
state = 'fuse'
last_layer = 'concat_stage%d' % stage
elif layername[l] == '$':
if not share_point:
share_point = last_layer
else:
last_layer = share_point
# final process
stage -= 1
if stage == 1:
n.silence = L.Silence(n.pool_center_lower, ntop=0)
if deploy == False:
return str(n.to_proto())
# for generating the deploy net
else:
# generate the input information header string
deploy_str = 'input: {}\ninput_dim: {}\ninput_dim: {}\ninput_dim: {}\ninput_dim: {}'.format(
'"' + input + '"', dim1, dim2, dim3, dim4)
# assemble the input header with the net layers string. remove the first placeholder layer from the net string.
return deploy_str + '\n' + 'layer {' + 'layer {'.join(
str(n.to_proto()).split('layer {')[2:])
def setLayers_twoBranches(data_source,
batch_size,
layername,
kernel,
stride,
outCH,
label_name,
transform_param_in,
deploy=False,
batchnorm=0,
lr_mult_distro=[1, 1, 1]):
# it is tricky to produce the deploy prototxt file, as the data input is not from a layer, so we have to creat a workaround
# producing training and testing prototxt files is pretty straight forward
n = caffe.NetSpec()
assert len(layername) == len(kernel)
assert len(layername) == len(stride)
assert len(layername) == len(outCH)
num_parts = transform_param['num_parts']
if deploy == False and "lmdb" not in data_source:
if (len(label_name) == 1):
n.data, n.tops[label_name[0]] = L.HDF5Data(hdf5_data_param=dict(
batch_size=batch_size, source=data_source),
ntop=2)
elif (len(label_name) == 2):
n.data, n.tops[label_name[0]], n.tops[label_name[1]] = L.HDF5Data(
hdf5_data_param=dict(batch_size=batch_size,
source=data_source),
ntop=3)
# produce data definition for deploy net
elif deploy == False:
n.data, n.tops['label'] = L.CPMData(
data_param=dict(backend=1,
source=data_source,
batch_size=batch_size),
cpm_transform_param=transform_param_in,
ntop=2)
n.tops[label_name[2]], n.tops[label_name[3]], n.tops[
label_name[4]], n.tops[label_name[5]] = L.Slice(
n.label,
slice_param=dict(
axis=1, slice_point=[38, num_parts + 1, num_parts + 39]),
ntop=4)
n.tops[label_name[0]] = L.Eltwise(n.tops[label_name[2]],
n.tops[label_name[4]],
operation=P.Eltwise.PROD)
n.tops[label_name[1]] = L.Eltwise(n.tops[label_name[3]],
n.tops[label_name[5]],
operation=P.Eltwise.PROD)
else:
input = "data"
dim1 = 1
dim2 = 4
dim3 = 368
dim4 = 368
# make an empty "data" layer so the next layer accepting input will be able to take the correct blob name "data",
# we will later have to remove this layer from the serialization string, since this is just a placeholder
n.data = L.Layer()
# something special before everything
n.image, n.center_map = L.Slice(n.data,
slice_param=dict(axis=1, slice_point=3),
ntop=2)
n.silence2 = L.Silence(n.center_map, ntop=0)
#n.pool_center_lower = L.Pooling(n.center_map, kernel_size=9, stride=8, pool=P.Pooling.AVE)
# just follow arrays..CPCPCPCPCCCC....
last_layer = ['image', 'image']
stage = 1
conv_counter = 1
pool_counter = 1
drop_counter = 1
local_counter = 1
state = 'image' # can be image or fuse
share_point = 0
for l in range(0, len(layername)):
if layername[l] == 'V': #pretrained VGG layers
conv_name = 'conv%d_%d' % (pool_counter, local_counter)
lr_m = lr_mult_distro[0]
n.tops[conv_name] = L.Convolution(
n.tops[last_layer[0]],
kernel_size=kernel[l],
num_output=outCH[l],
pad=int(math.floor(kernel[l] / 2)),
param=[
dict(lr_mult=lr_m, decay_mult=1),
dict(lr_mult=lr_m * 2, decay_mult=0)
],
weight_filler=dict(type='gaussian', std=0.01),
bias_filler=dict(type='constant'))
last_layer[0] = conv_name
last_layer[1] = conv_name
print '%s\tch=%d\t%.1f' % (last_layer[0], outCH[l], lr_m)
ReLUname = 'relu%d_%d' % (pool_counter, local_counter)
n.tops[ReLUname] = L.ReLU(n.tops[last_layer[0]], in_place=True)
local_counter += 1
print ReLUname
if layername[l] == 'B':
pool_counter += 1
local_counter = 1
if layername[l] == 'C':
if state == 'image':
#conv_name = 'conv%d_stage%d' % (conv_counter, stage)
conv_name = 'conv%d_%d_CPM' % (
pool_counter, local_counter
) # no image state in subsequent stages
if stage == 1:
lr_m = lr_mult_distro[1]
else:
lr_m = lr_mult_distro[1]
else: # fuse
conv_name = 'Mconv%d_stage%d' % (conv_counter, stage)
lr_m = lr_mult_distro[2]
conv_counter += 1
#if stage == 1:
# lr_m = 1
#else:
# lr_m = lr_sub
n.tops[conv_name] = L.Convolution(
n.tops[last_layer[0]],
kernel_size=kernel[l],
num_output=outCH[l],
pad=int(math.floor(kernel[l] / 2)),
param=[
dict(lr_mult=lr_m, decay_mult=1),
dict(lr_mult=lr_m * 2, decay_mult=0)
],
weight_filler=dict(type='gaussian', std=0.01),
bias_filler=dict(type='constant'))
last_layer[0] = conv_name
last_layer[1] = conv_name
print '%s\tch=%d\t%.1f' % (last_layer[0], outCH[l], lr_m)
if layername[l + 1] != 'L':
if (state == 'image'):
if (batchnorm == 1):
batchnorm_name = 'bn%d_stage%d' % (conv_counter, stage)
n.tops[batchnorm_name] = L.BatchNorm(
n.tops[last_layer[0]],
param=[
dict(lr_mult=0),
dict(lr_mult=0),
dict(lr_mult=0)
])
#scale_filler=dict(type='constant', value=1), shift_filler=dict(type='constant', value=0.001))
last_layer[0] = batchnorm_name
#ReLUname = 'relu%d_stage%d' % (conv_counter, stage)
ReLUname = 'relu%d_%d_CPM' % (pool_counter, local_counter)
n.tops[ReLUname] = L.ReLU(n.tops[last_layer[0]],
in_place=True)
else:
if (batchnorm == 1):
batchnorm_name = 'Mbn%d_stage%d' % (conv_counter,
stage)
n.tops[batchnorm_name] = L.BatchNorm(
n.tops[last_layer[0]],
param=[
dict(lr_mult=0),
dict(lr_mult=0),
dict(lr_mult=0)
])
#scale_filler=dict(type='constant', value=1), shift_filler=dict(type='constant', value=0.001))
last_layer[0] = batchnorm_name
ReLUname = 'Mrelu%d_stage%d' % (conv_counter, stage)
n.tops[ReLUname] = L.ReLU(n.tops[last_layer[0]],
in_place=True)
#last_layer = ReLUname
print ReLUname
#conv_counter += 1
local_counter += 1
elif layername[l] == 'C2':
for level in range(0, 2):
if state == 'image':
#conv_name = 'conv%d_stage%d' % (conv_counter, stage)
conv_name = 'conv%d_%d_CPM_L%d' % (
pool_counter, local_counter, level + 1
) # no image state in subsequent stages
if stage == 1:
lr_m = lr_mult_distro[1]
else:
lr_m = lr_mult_distro[1]
else: # fuse
conv_name = 'Mconv%d_stage%d_L%d' % (conv_counter, stage,
level + 1)
lr_m = lr_mult_distro[2]
#conv_counter += 1
#if stage == 1:
# lr_m = 1
#else:
# lr_m = lr_sub
if layername[l + 1] == 'L2' or layername[l + 1] == 'L3':
if level == 0:
outCH[l] = 38
else:
outCH[l] = 19
n.tops[conv_name] = L.Convolution(
n.tops[last_layer[level]],
kernel_size=kernel[l],
num_output=outCH[l],
pad=int(math.floor(kernel[l] / 2)),
param=[
dict(lr_mult=lr_m, decay_mult=1),
dict(lr_mult=lr_m * 2, decay_mult=0)
],
weight_filler=dict(type='gaussian', std=0.01),
bias_filler=dict(type='constant'))
last_layer[level] = conv_name
print '%s\tch=%d\t%.1f' % (last_layer[level], outCH[l], lr_m)
if layername[l + 1] != 'L2' and layername[l + 1] != 'L3':
if (state == 'image'):
if (batchnorm == 1):
batchnorm_name = 'bn%d_stage%d_L%d' % (
conv_counter, stage, level + 1)
n.tops[batchnorm_name] = L.BatchNorm(
n.tops[last_layer[level]],
param=[
dict(lr_mult=0),
dict(lr_mult=0),
dict(lr_mult=0)
])
#scale_filler=dict(type='constant', value=1), shift_filler=dict(type='constant', value=0.001))
last_layer[level] = batchnorm_name
#ReLUname = 'relu%d_stage%d' % (conv_counter, stage)
ReLUname = 'relu%d_%d_CPM_L%d' % (
pool_counter, local_counter, level + 1)
n.tops[ReLUname] = L.ReLU(n.tops[last_layer[level]],
in_place=True)
else:
if (batchnorm == 1):
batchnorm_name = 'Mbn%d_stage%d_L%d' % (
conv_counter, stage, level + 1)
n.tops[batchnorm_name] = L.BatchNorm(
n.tops[last_layer[level]],
param=[
dict(lr_mult=0),
dict(lr_mult=0),
dict(lr_mult=0)
])
#scale_filler=dict(type='constant', value=1), shift_filler=dict(type='constant', value=0.001))
last_layer[level] = batchnorm_name
ReLUname = 'Mrelu%d_stage%d_L%d' % (conv_counter,
stage, level + 1)
n.tops[ReLUname] = L.ReLU(n.tops[last_layer[level]],
in_place=True)
print ReLUname
conv_counter += 1
local_counter += 1
elif layername[l] == 'P': # Pooling
n.tops['pool%d_stage%d' % (pool_counter, stage)] = L.Pooling(
n.tops[last_layer[0]],
kernel_size=kernel[l],
stride=stride[l],
pool=P.Pooling.MAX)
last_layer[0] = 'pool%d_stage%d' % (pool_counter, stage)
pool_counter += 1
local_counter = 1
conv_counter += 1
print last_layer[0]
elif layername[l] == 'L':
# Loss: n.loss layer is only in training and testing nets, but not in deploy net.
if deploy == False and "lmdb" not in data_source:
n.tops['map_vec_stage%d' % stage] = L.Flatten(
n.tops[last_layer[0]])
n.tops['loss_stage%d' % stage] = L.EuclideanLoss(
n.tops['map_vec_stage%d' % stage], n.tops[label_name[1]])
elif deploy == False:
level = 1
name = 'weight_stage%d' % stage
n.tops[name] = L.Eltwise(n.tops[last_layer[level]],
n.tops[label_name[(level + 2)]],
operation=P.Eltwise.PROD)
n.tops['loss_stage%d' % stage] = L.EuclideanLoss(
n.tops[name], n.tops[label_name[level]])
print 'loss %d' % stage
stage += 1
conv_counter = 1
pool_counter = 1
drop_counter = 1
local_counter = 1
state = 'image'
elif layername[l] == 'L2':
# Loss: n.loss layer is only in training and testing nets, but not in deploy net.
weight = [lr_mult_distro[3], 1]
# print lr_mult_distro[3]
for level in range(0, 2):
if deploy == False and "lmdb" not in data_source:
n.tops['map_vec_stage%d_L%d' %
(stage, level + 1)] = L.Flatten(
n.tops[last_layer[level]])
n.tops['loss_stage%d_L%d' %
(stage, level + 1)] = L.EuclideanLoss(
n.tops['map_vec_stage%d' % stage],
n.tops[label_name[level]],
loss_weight=weight[level])
elif deploy == False:
name = 'weight_stage%d_L%d' % (stage, level + 1)
n.tops[name] = L.Eltwise(n.tops[last_layer[level]],
n.tops[label_name[(level + 2)]],
operation=P.Eltwise.PROD)
n.tops['loss_stage%d_L%d' %
(stage, level + 1)] = L.EuclideanLoss(
n.tops[name],
n.tops[label_name[level]],
loss_weight=weight[level])
print 'loss %d level %d' % (stage, level + 1)
stage += 1
#last_connect = last_layer
#last_layer = 'image'
conv_counter = 1
pool_counter = 1
drop_counter = 1
local_counter = 1
state = 'image'
elif layername[l] == 'L3':
# Loss: n.loss layer is only in training and testing nets, but not in deploy net.
weight = [lr_mult_distro[3], 1]
# print lr_mult_distro[3]
if deploy == False:
level = 0
n.tops['loss_stage%d_L%d' %
(stage, level + 1)] = L.Euclidean2Loss(
n.tops[last_layer[level]],
n.tops[label_name[level]],
n.tops[label_name[2]],
loss_weight=weight[level])
print 'loss %d level %d' % (stage, level + 1)
level = 1
n.tops['loss_stage%d_L%d' %
(stage, level + 1)] = L.EuclideanLoss(
n.tops[last_layer[level]],
n.tops[label_name[level]],
loss_weight=weight[level])
print 'loss %d level %d' % (stage, level + 1)
stage += 1
#last_connect = last_layer
#last_layer = 'image'
conv_counter = 1
pool_counter = 1
drop_counter = 1
local_counter = 1
state = 'image'
elif layername[l] == 'D':
if deploy == False:
n.tops['drop%d_stage%d' % (drop_counter, stage)] = L.Dropout(
n.tops[last_layer[0]],
in_place=True,
dropout_param=dict(dropout_ratio=0.5))
drop_counter += 1
elif layername[l] == '@':
#if not share_point:
# share_point = last_layer
n.tops['concat_stage%d' % stage] = L.Concat(
n.tops[last_layer[0]],
n.tops[last_layer[1]],
n.tops[share_point],
concat_param=dict(axis=1))
local_counter = 1
state = 'fuse'
last_layer[0] = 'concat_stage%d' % stage
last_layer[1] = 'concat_stage%d' % stage
print last_layer
elif layername[l] == '$':
share_point = last_layer[0]
pool_counter += 1
local_counter = 1
print 'share'
# final process
stage -= 1
#if stage == 1:
# n.silence = L.Silence(n.pool_center_lower, ntop=0)
if deploy == False:
return str(n.to_proto())
# for generating the deploy net
else:
# generate the input information header string
deploy_str = 'input: {}\ninput_dim: {}\ninput_dim: {}\ninput_dim: {}\ninput_dim: {}'.format(
'"' + input + '"', dim1, dim2, dim3, dim4)
# assemble the input header with the net layers string. remove the first placeholder layer from the net string.
return deploy_str + '\n' + 'layer {' + 'layer {'.join(
str(n.to_proto()).split('layer {')[2:])
def setLayers(data_source,
batch_size,
layername,
kernel,
stride,
outCH,
label_name,
transform_param_in,
deploy=False):
# it is tricky to produce the deploy prototxt file, as the data input is not from a layer, so we have to creat a workaround
# producing training and testing prototxt files is pretty straight forward
n = caffe.NetSpec()
assert len(layername) == len(kernel)
assert len(layername) == len(stride)
assert len(layername) == len(outCH)
# produce data definition for deploy net
if deploy == False:
# here we will return the new structure for loading h36m dataset
n.data, n.tops['label'] = L.CPMData(cpmdata_param=dict(
backend=1, source=data_source, batch_size=batch_size),
transform_param=transform_param_in,
ntop=2)
n.tops[label_name[1]], n.tops[label_name[0]], n.tops[
label_name[2]] = L.Slice(n.label,
slice_param=dict(axis=1,
slice_point=[18, 36]),
ntop=3)
n.image, n.center_map = L.Slice(n.data,
slice_param=dict(axis=1,
slice_point=3),
ntop=2)
else:
input = "data"
dim1 = 18
dim2 = 5
dim3 = 368
dim4 = 368
# make an empty "data" layer so the next layer accepting input will be able to take the correct blob name "data",
# we will later have to remove this layer from the serialization string, since this is just a placeholder
n.data = L.Layer()
# Slice layer slices input layer to multiple output along a given dimension
# axis: 1 define in which dimension to slice
# slice_point: 3 define the index in the selected dimension (the number of
# indices must be equal to the number of top blobs minus one)
# Considering input Nx3x1x1, by slice_point = 2
# top1 : Nx2x1x1
# top2 : Nx1x1x1
n.image, n.center_map, n.tops[label_name[2]] = L.Slice(
n.data, slice_param=dict(axis=1, slice_point=[3, 4]), ntop=3)
n.pool_center_lower = L.Pooling(n.center_map,
kernel_size=9,
stride=8,
pool=P.Pooling.AVE)
# just follow arrays..CPCPCPCPCCCC....
last_layer = 'image'
stage = 1
conv_counter = 1
last_manifold = 'NONE'
last_merg = 'NONE'
pool_counter = 1
drop_counter = 1
state = 'image' # can be image or fuse
share_point = 0
manifold_current_stage = False
merge_init_avg = False
for l in range(0, len(layername)):
decay_mult = 1
if layername[l] == 'C':
if state == 'image':
conv_name = 'conv%d_stage%d' % (conv_counter, stage)
else:
conv_name = 'Mconv%d_stage%d' % (conv_counter, stage)
#if stage == 1:
# lr_m = 5
#else:
# lr_m = 1
lr_m = 1e-3 # 1e-3 (best res so far)
if ((stage == 1 and conv_counter == 7) or
(stage > 1 and state != 'image' and (conv_counter in [1, 5]))):
conv_name = '%s_new' % conv_name
lr_m = 1 #1e-2
decay_mult = 1
# if (stage <= 4):
# lr_m = 0
# decay_mult = 0
# additional for python layer
# if (stage > 1 and state != 'image' and (conv_counter == 1)):
# conv_name = '%s_mf' % conv_name
# lr_m = 1 #1e-1
n.tops[conv_name] = L.Convolution(
n.tops[last_layer],
kernel_size=kernel[l],
num_output=outCH[l],
pad=int(math.floor(kernel[l] / 2)),
param=[
dict(lr_mult=lr_m, decay_mult=decay_mult),
dict(lr_mult=lr_m * 2, decay_mult=0)
],
weight_filler=dict(type='gaussian', std=0.01),
bias_filler=dict(type='constant'))
last_layer = conv_name
if not (layername[l + 1] == 'L' or layername[l + 1] == 'M'):
if (state == 'image'):
ReLUname = 'relu%d_stage%d' % (conv_counter, stage)
n.tops[ReLUname] = L.ReLU(n.tops[last_layer],
in_place=True)
else:
ReLUname = 'Mrelu%d_stage%d' % (conv_counter, stage)
n.tops[ReLUname] = L.ReLU(n.tops[last_layer],
in_place=True)
last_layer = ReLUname
conv_counter += 1
elif layername[l] == 'P': # Pooling
n.tops['pool%d_stage%d' % (pool_counter, stage)] = L.Pooling(
n.tops[last_layer],
kernel_size=kernel[l],
stride=stride[l],
pool=P.Pooling.MAX)
last_layer = 'pool%d_stage%d' % (pool_counter, stage)
pool_counter += 1
elif layername[l] == 'M':
last_manifold = 'manifolds_stage%d' % stage
last_merg = 'merge_hm_stage%d' % stage
debug_mode = 0
# if (stage == 5):
# debug_mode = 1
manifold_current_stage = True
if (stage >= 4):
merge_init_avg = True
# TODO: change it back
# if stage == 4:
# debug_mode = 4
parameters = '{"njoints": 17,"sigma": 1, "debug_mode": %r, "max_area": 100, "percentage_max": 3, "train": %u, "Lambda": %.3f }' % (
debug_mode, not deploy, 0.05)
# DONE: change it back
# if manifold_current_stage:
n.tops[last_manifold] = L.Python(
n.tops[last_layer],
n.tops[label_name[2]],
python_param=dict(module='newheatmaps',
layer='MyCustomLayer',
param_str=parameters)) #,loss_weight=1)
# n.tops[last_manifold] = L.Python(n.tops[label_name[1]],n.tops[label_name[2]],python_param=dict(module='newheatmaps',layer='MyCustomLayer',param_str=parameters))#,loss_weight=1)
init_str = 'zero'
if merge_init_avg:
init_str = 'avg'
merge_lr = 5e-2
parameters = '{"init": %r, "learning_rate": %r}' % (init_str,
merge_lr)
n.tops[last_merg] = L.Python(n.tops[last_layer],
n.tops[last_manifold],
python_param=dict(
module='processheatmaps',
layer='MergeHeatMaps',
param_str=parameters))
elif layername[l] == 'L':
# Loss: n.loss layer is only in training and testing nets, but not in deploy net.
if deploy == False:
if stage == 1:
n.tops['loss_stage%d' % stage] = L.EuclideanLoss(
n.tops[last_layer], n.tops[label_name[0]])
else:
n.tops['loss_stage%d' % stage] = L.EuclideanLoss(
n.tops[last_layer], n.tops[label_name[1]])
stage += 1
last_connect = last_layer
last_layer = 'image'
conv_counter = 1
pool_counter = 1
drop_counter = 1
state = 'image'
elif layername[l] == 'D':
if deploy == False:
n.tops['drop%d_stage%d' % (drop_counter, stage)] = L.Dropout(
n.tops[last_layer],
in_place=True,
dropout_param=dict(dropout_ratio=0.5))
drop_counter += 1
elif layername[l] == '@':
# DONE: change it back
# no this n.tops['concat_stage%d' % stage] = L.Concat(n.tops[last_layer], n.tops[last_connect], n.pool_center_lower, n.tops[label_name[1]], concat_param=dict(axis=1))
# n.tops['concat_stage%d' % stage] = L.Concat(n.tops[last_layer], n.tops[last_connect], n.pool_center_lower, n.tops[last_manifold], concat_param=dict(axis=1))
if manifold_current_stage:
n.tops['concat_stage%d' % stage] = L.Concat(
n.tops[last_layer],
n.tops[last_merg],
n.pool_center_lower,
concat_param=dict(axis=1))
else:
n.tops['concat_stage%d' % stage] = L.Concat(
n.tops[last_layer],
n.tops[last_connect],
n.pool_center_lower,
concat_param=dict(axis=1))
conv_counter = 1
state = 'fuse'
last_layer = 'concat_stage%d' % stage
elif layername[l] == '$':
if not share_point:
share_point = last_layer
else:
last_layer = share_point
# final process
stage -= 1
if stage == 1:
n.silence = L.Silence(n.pool_center_lower, ntop=0)
if deploy == False:
return str(n.to_proto())
# for generating the deploy net
else:
# generate the input information header string
deploy_str = 'input: {}\ninput_dim: {}\ninput_dim: {}\ninput_dim: {}\ninput_dim: {}'.format(
'"' + input + '"', dim1, dim2, dim3, dim4)
# assemble the input header with the net layers string. remove the first placeholder layer from the net string.
return deploy_str + '\n' + 'layer {' + 'layer {'.join(
str(n.to_proto()).split('layer {')[2:])
