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layer_param.py
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from __future__ import absolute_import
from . import caffe_pb2 as pb
import numpy as np
def pair_process(item,strict_one=True):
if hasattr(item,'__iter__'):
for i in item:
if i!=item[0]:
if strict_one:
raise ValueError("number in item {} must be the same".format(item))
else:
print("IMPORTANT WARNING: number in item {} must be the same".format(item))
return item[0]
return item
def pair_reduce(item):
if hasattr(item,'__iter__'):
for i in item:
if i!=item[0]:
return item
return [item[0]]
return [item]
class Layer_param():
def __init__(self,name='',type='',top=(),bottom=()):
self.param=pb.LayerParameter()
self.name=self.param.name=name
self.type=self.param.type=type
self.top=self.param.top
self.top.extend(top)
self.bottom=self.param.bottom
self.bottom.extend(bottom)
def fc_param(self, num_output, weight_filler='xavier', bias_filler='constant',has_bias=True):
if self.type != 'InnerProduct':
raise TypeError('the layer type must be InnerProduct if you want set fc param')
fc_param = pb.InnerProductParameter()
fc_param.num_output = num_output
fc_param.weight_filler.type = weight_filler
fc_param.bias_term = has_bias
if has_bias:
fc_param.bias_filler.type = bias_filler
self.param.inner_product_param.CopyFrom(fc_param)
def conv_param(self, num_output, kernel_size, stride=(1), pad=(0,),
weight_filler_type='xavier', bias_filler_type='constant',
bias_term=True, dilation=None,groups=None):
"""
add a conv_param layer if you spec the layer type "Convolution"
Args:
num_output: a int
kernel_size: int list
stride: a int list
weight_filler_type: the weight filer type
bias_filler_type: the bias filler type
Returns:
"""
if self.type not in ['Convolution','Deconvolution']:
raise TypeError('the layer type must be Convolution or Deconvolution if you want set conv param')
conv_param=pb.ConvolutionParameter()
conv_param.num_output=num_output
conv_param.kernel_size.extend(pair_reduce(kernel_size))
conv_param.stride.extend(pair_reduce(stride))
conv_param.pad.extend(pair_reduce(pad))
conv_param.bias_term=bias_term
conv_param.weight_filler.type=weight_filler_type
if bias_term:
conv_param.bias_filler.type = bias_filler_type
if dilation:
conv_param.dilation.extend(pair_reduce(dilation))
if groups:
conv_param.group=groups
if groups != 1:
conv_param.engine = 1
self.param.convolution_param.CopyFrom(conv_param)
def norm_param(self, eps):
"""
add a conv_param layer if you spec the layer type "Convolution"
Args:
num_output: a int
kernel_size: int list
stride: a int list
weight_filler_type: the weight filer type
bias_filler_type: the bias filler type
Returns:
"""
l2norm_param = pb.NormalizeParameter()
l2norm_param.across_spatial = False
l2norm_param.channel_shared = False
l2norm_param.eps = eps
self.param.norm_param.CopyFrom(l2norm_param)
def permute_param(self, order1, order2, order3, order4):
"""
add a conv_param layer if you spec the layer type "Convolution"
Args:
num_output: a int
kernel_size: int list
stride: a int list
weight_filler_type: the weight filer type
bias_filler_type: the bias filler type
Returns:
"""
permute_param = pb.PermuteParameter()
permute_param.order.extend([order1, order2, order3, order4])
self.param.permute_param.CopyFrom(permute_param)
def pool_param(self,type='MAX',kernel_size=2,stride=2,pad=None, ceil_mode = True):
pool_param=pb.PoolingParameter()
pool_param.pool=pool_param.PoolMethod.Value(type)
pool_param.kernel_size=pair_process(kernel_size)
pool_param.stride=pair_process(stride)
pool_param.ceil_mode=ceil_mode
if pad:
if isinstance(pad,tuple):
pool_param.pad_h = pad[0]
pool_param.pad_w = pad[1]
else:
pool_param.pad=pad
self.param.pooling_param.CopyFrom(pool_param)
def batch_norm_param(self,use_global_stats=0,moving_average_fraction=None,eps=None):
bn_param=pb.BatchNormParameter()
bn_param.use_global_stats=use_global_stats
if moving_average_fraction:
bn_param.moving_average_fraction=moving_average_fraction
if eps:
bn_param.eps = eps
self.param.batch_norm_param.CopyFrom(bn_param)
# layer
# {
# name: "upsample_layer"
# type: "Upsample"
# bottom: "some_input_feature_map"
# bottom: "some_input_pool_index"
# top: "some_output"
# upsample_param {
# upsample_h: 224
# upsample_w: 224
# }
# }
def upsample_param(self,size=None, scale_factor=None):
upsample_param=pb.UpsampleParameter()
if scale_factor:
if isinstance(scale_factor,int):
upsample_param.scale = scale_factor
else:
upsample_param.scale_h = scale_factor[0]
upsample_param.scale_w = scale_factor[1]
if size:
if isinstance(size,int):
upsample_param.upsample_h = size
else:
upsample_param.upsample_h = size[0] * scale_factor
upsample_param.\
upsample_w = size[1] * scale_factor
self.param.upsample_param.CopyFrom(upsample_param)
def add_data(self,*args):
"""Args are data numpy array
"""
del self.param.blobs[:]
for data in args:
new_blob = self.param.blobs.add()
for dim in data.shape:
new_blob.shape.dim.append(dim)
new_blob.data.extend(data.flatten().astype(float))
def set_params_by_dict(self,dic):
pass
def copy_from(self,layer_param):
pass
def set_enum(param,key,value):
setattr(param,key,param.Value(value))