记录一个好用的pytorch模型转caffe模型的方法，源码链接如下：
https://github.com/xxradon/PytorchToCaffe
把代码clone下来后，进入example目录便可查看示例，
cd example
python resnet_pytorch_2_caffe.pyimport sys
sys.path.insert(0,'.')
import torch
from torch.autograd import Variable
from torchvision.models import resnet
import pytorch_to_caffe
if __name__=='__main__':
## 转换后的模型名称
name='resnet18'
## 待转换的pytorch模型
resnet18=resnet.resnet18()
## pt模型权重
checkpoint = torch.load("/home/shining/Downloads/resnet18-5c106cde.pth")
## 加载模型
resnet18.load_state_dict(checkpoint)
resnet18.eval()
## 输入张量
input=torch.ones([1, 3, 224, 224])
## 模型转换
pytorch_to_caffe.trans_net(resnet18, input,name)
pytorch_to_caffe.save_prototxt('{}.prototxt'.format(name))
pytorch_to_caffe.save_caffemodel('{}.caffemodel'.format(name))如果想实现把pytorch的flatten转换至caffe中，需修改脚本：pytorch_to_caffe.py。修改后的脚本内容如下，
import torch
import torch.nn as nn
import traceback
from Caffe import caffe_net
import torch.nn.functional as F
from torch.autograd import Variable
from Caffe import layer_param
from torch.nn.modules.utils import _pair
import numpy as np
class Blob_LOG():
def __init__(self):
self.data={}
def __setitem__(self, key, value):
self.data[key]=value
def __getitem__(self, key):
return self.data[key]
def __len__(self):
return len(self.data)
NET_INITTED=False
class TransLog(object):
def __init__(self):
"""
doing init() with inputs Variable before using it
"""
self.layers={}
self.detail_layers={}
self.detail_blobs={}
self._blobs=Blob_LOG()
self._blobs_data=[]
self.cnet=caffe_net.Caffemodel('')
self.debug=True
def init(self,inputs):
"""
:param inputs: is a list of input variables
"""
self.add_blobs(inputs)
def add_layer(self,name='layer'):
if name in self.layers:
return self.layers[name]
if name not in self.detail_layers.keys():
self.detail_layers[name] =0
self.detail_layers[name] +=1
name='{}{}'.format(name,self.detail_layers[name])
self.layers[name]=name
if self.debug:
print("{} was added to layers".format(self.layers[name]))
return self.layers[name]
def add_blobs(self, blobs,name='blob',with_num=True):
rst=[]
for blob in blobs:
self._blobs_data.append(blob) # to block the memory address be rewrited
blob_id=int(id(blob))
if name not in self.detail_blobs.keys():
self.detail_blobs[name] =0
self.detail_blobs[name] +=1
if with_num:
rst.append('{}{}'.format(name,self.detail_blobs[name]))
else:
rst.append('{}'.format(name))
if self.debug:
print("{}:{} was added to blobs".format(blob_id,rst[-1]))
# print('Add blob {} : {}'.format(rst[-1].center(21),blob.size()))
self._blobs[blob_id]=rst[-1]
return rst
def blobs(self, var):
var=id(var)
# if self.debug:
# print("{}:{} getting".format(var, self._blobs[var]))
try:
return self._blobs[var]
except:
print("WARNING: CANNOT FOUND blob {}".format(var))
return None
log=TransLog()
layer_names={}
def _conv2d(raw,input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
print('conv: ',log.blobs(input))
x=raw(input,weight,bias,stride,padding,dilation,groups)
name=log.add_layer(name='conv')
log.add_blobs([x],name='conv_blob')
layer=caffe_net.Layer_param(name=name, type='Convolution',
bottom=[log.blobs(input)], top=[log.blobs(x)])
layer.conv_param(x.size()[1],weight.size()[2:],stride=_pair(stride),
pad=_pair(padding),dilation=_pair(dilation),bias_term=bias is not None,groups=groups)
if bias is not None:
layer.add_data(weight.cpu().data.numpy(),bias.cpu().data.numpy())
else:
layer.param.convolution_param.bias_term=False
layer.add_data(weight.cpu().data.numpy())
log.cnet.add_layer(layer)
return x
def _conv_transpose2d(raw,input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1):
x=raw(input, weight, bias, stride, padding, output_padding, groups, dilation)
name=log.add_layer(name='conv_transpose')
log.add_blobs([x],name='conv_transpose_blob')
layer=caffe_net.Layer_param(name=name, type='Deconvolution',
bottom=[log.blobs(input)], top=[log.blobs(x)])
layer.conv_param(x.size()[1],weight.size()[2:],stride=_pair(stride),
pad=_pair(padding),dilation=_pair(dilation),bias_term=bias is not None, groups = groups)
if bias is not None:
layer.add_data(weight.cpu().data.numpy(),bias.cpu().data.numpy())
else:
layer.param.convolution_param.bias_term=False
layer.add_data(weight.cpu().data.numpy())
log.cnet.add_layer(layer)
return x
def _linear(raw,input, weight, bias=None):
x=raw(input,weight,bias)
layer_name=log.add_layer(name='fc')
top_blobs=log.add_blobs([x],name='fc_blob')
layer=caffe_net.Layer_param(name=layer_name,type='InnerProduct',
bottom=[log.blobs(input)],top=top_blobs)
layer.fc_param(x.size()[1],has_bias=bias is not None)
if bias is not None:
layer.add_data(weight.cpu().data.numpy(),bias.cpu().data.numpy())
else:
layer.add_data(weight.cpu().data.numpy())
log.cnet.add_layer(layer)
return x
def _split(raw,tensor, split_size, dim=0):
# split in pytorch is slice in caffe
x=raw(tensor, split_size, dim)
layer_name=log.add_layer('split')
top_blobs=log.add_blobs(x,name='split_blob')
layer=caffe_net.Layer_param(name=layer_name, type='Slice',
bottom=[log.blobs(tensor)], top=top_blobs)
slice_num=int(np.floor(tensor.size()[dim]/split_size))
slice_param=caffe_net.pb.SliceParameter(axis=dim,slice_point=[split_size*i for i in range(1,slice_num)])
layer.param.slice_param.CopyFrom(slice_param)
log.cnet.add_layer(layer)
return x
def _pool(type,raw,input,x,kernel_size,stride,padding,ceil_mode):
# TODO dilation,ceil_mode,return indices
layer_name = log.add_layer(name='{}_pool'.format(type))
top_blobs = log.add_blobs([x], name='{}_pool_blob'.format(type))
layer = caffe_net.Layer_param(name=layer_name, type='Pooling',
bottom=[log.blobs(input)], top=top_blobs)
# TODO w,h different kernel, stride and padding
# processing ceil mode
layer.pool_param(kernel_size=kernel_size, stride=kernel_size if stride is None else stride,
pad=padding, type=type.upper() , ceil_mode = ceil_mode)
log.cnet.add_layer(layer)
if ceil_mode==False and stride is not None:
oheight = (input.size()[2] - _pair(kernel_size)[0] + 2 * _pair(padding)[0]) % (_pair(stride)[0])
owidth = (input.size()[3] - _pair(kernel_size)[1] + 2 * _pair(padding)[1]) % (_pair(stride)[1])
if oheight!=0 or owidth!=0:
caffe_out=raw(input, kernel_size, stride, padding, ceil_mode=True)
print("WARNING: the output shape miss match at {}: "
"input {} output---Pytorch:{}---Caffe:{}\n"
"This is caused by the different implementation that ceil mode in caffe and the floor mode in pytorch.\n"
"You can add the clip layer in caffe prototxt manually if shape mismatch error is caused in caffe. ".format(layer_name,input.size(),x.size(),caffe_out.size()))
def _max_pool2d(raw,input, kernel_size, stride=None, padding=0, dilation=1,
ceil_mode=False, return_indices=False):
x = raw(input, kernel_size, stride, padding, dilation,ceil_mode, return_indices)
_pool('max',raw,input, x, kernel_size, stride, padding,ceil_mode)
return x
def _avg_pool2d(raw, input, kernel_size, stride = None, padding = 0, ceil_mode = False, count_include_pad = True, divisor_override=None):
x = raw(input, kernel_size, stride, padding, ceil_mode, count_include_pad)
_pool('ave',raw,input, x, kernel_size, stride, padding,ceil_mode)
return x
def _adaptive_avg_pool2d(raw, input, output_size):
x = raw(input, output_size)
if isinstance(output_size, int):
out_dim = output_size
else:
out_dim = output_size[0]
tmp = max(input.shape[2], input.shape[3])
stride = tmp //out_dim
kernel_size = tmp - (out_dim - 1) * stride
_pool('ave', raw, input, x, kernel_size, stride, 0, False)
return x
def _max(raw,*args):
x=raw(*args)
if len(args)==1:
# TODO max in one tensor
assert NotImplementedError
else:
bottom_blobs=[]
for arg in args:
bottom_blobs.append(log.blobs(arg))
layer_name=log.add_layer(name='max')
top_blobs=log.add_blobs([x],name='max_blob')
layer=caffe_net.Layer_param(name=layer_name,type='Eltwise',
bottom=bottom_blobs,top=top_blobs)
layer.param.eltwise_param.operation =2
log.cnet.add_layer(layer)
return x
def _cat(raw, inputs, dimension=0):
x=raw(inputs, dimension)
bottom_blobs=[]
for input in inputs:
bottom_blobs.append(log.blobs(input))
layer_name=log.add_layer(name='cat')
top_blobs=log.add_blobs([x],name='cat_blob')
layer=caffe_net.Layer_param(name=layer_name,type='Concat',
bottom=bottom_blobs,top=top_blobs)
layer.param.concat_param.axis =dimension
log.cnet.add_layer(layer)
return x
def _dropout(raw,input,p=0.5, training=False, inplace=False):
x=raw(input,p, training, inplace)
bottom_blobs=[log.blobs(input)]
layer_name=log.add_layer(name='dropout')
top_blobs=log.add_blobs([x],name=bottom_blobs[0],with_num=False)
layer=caffe_net.Layer_param(name=layer_name,type='Dropout',
bottom=bottom_blobs,top=top_blobs)
layer.param.dropout_param.dropout_ratio = p
layer.param.include.extend([caffe_net.pb.NetStateRule(phase=0)]) # 1 for test, 0 for train
log.cnet.add_layer(layer)
return x
def _threshold(raw,input, threshold, value, inplace=False):
# for threshold or relu
if threshold==0 and value==0:
x = raw(input,threshold, value, inplace)
bottom_blobs=[log.blobs(input)]
name = log.add_layer(name='relu')
log.add_blobs([x], name='relu_blob')
layer = caffe_net.Layer_param(name=name, type='ReLU',
bottom=bottom_blobs, top=[log.blobs(x)])
log.cnet.add_layer(layer)
return x
if value!=0:
raise NotImplemented("value !=0 not implemented in caffe")
x=raw(input,input, threshold, value, inplace)
bottom_blobs=[log.blobs(input)]
layer_name=log.add_layer(name='threshold')
top_blobs=log.add_blobs([x],name='threshold_blob')
layer=caffe_net.Layer_param(name=layer_name,type='Threshold',
bottom=bottom_blobs,top=top_blobs)
layer.param.threshold_param.threshold = threshold
log.cnet.add_layer(layer)
return x
def _relu(raw, input, inplace=False):
# for threshold or prelu
x = raw(input, False)
name = log.add_layer(name='relu')
log.add_blobs([x], name='relu_blob')
layer = caffe_net.Layer_param(name=name, type='ReLU',
bottom=[log.blobs(input)], top=[log.blobs(x)])
log.cnet.add_layer(layer)
return x
def _prelu(raw, input, weight):
# for threshold or prelu
x = raw(input, weight)
bottom_blobs=[log.blobs(input)]
name = log.add_layer(name='prelu')
log.add_blobs([x], name='prelu_blob')
layer = caffe_net.Layer_param(name=name, type='PReLU',
bottom=bottom_blobs, top=[log.blobs(x)])
if weight.size()[0]==1:
layer.param.prelu_param.channel_shared=True
layer.add_data(weight.cpu().data.numpy()[0])
else:
layer.add_data(weight.cpu().data.numpy())
log.cnet.add_layer(layer)
return x
def _leaky_relu(raw, input, negative_slope=0.01, inplace=False):
x = raw(input, negative_slope)
name = log.add_layer(name='leaky_relu')
log.add_blobs([x], name='leaky_relu_blob')
layer = caffe_net.Layer_param(name=name, type='ReLU',
bottom=[log.blobs(input)], top=[log.blobs(x)])
layer.param.relu_param.negative_slope=negative_slope
log.cnet.add_layer(layer)
return x
def _tanh(raw, input):
# for tanh activation
x = raw(input)
name = log.add_layer(name='tanh')
log.add_blobs([x], name='tanh_blob')
layer = caffe_net.Layer_param(name=name, type='TanH',
bottom=[log.blobs(input)], top=[log.blobs(x)])
log.cnet.add_layer(layer)
return x
def _softmax(raw, input, dim=None, _stacklevel=3):
# for F.softmax
x=raw(input, dim=dim)
if dim is None:
dim=F._get_softmax_dim('softmax', input.dim(), _stacklevel)
bottom_blobs=[log.blobs(input)]
name = log.add_layer(name='softmax')
log.add_blobs([x], name='softmax_blob')
layer = caffe_net.Layer_param(name=name, type='Softmax',
bottom=bottom_blobs, top=[log.blobs(x)])
layer.param.softmax_param.axis=dim
log.cnet.add_layer(layer)
return x
def _batch_norm(raw,input, running_mean, running_var, weight=None, bias=None,
training=False, momentum=0.1, eps=1e-5):
# because the runing_mean and runing_var will be changed after the _batch_norm operation, we first save the parameters
x = raw(input, running_mean, running_var, weight, bias,
training, momentum, eps)
bottom_blobs = [log.blobs(input)]
layer_name1 = log.add_layer(name='batch_norm')
top_blobs = log.add_blobs([x], name='batch_norm_blob')
layer1 = caffe_net.Layer_param(name=layer_name1, type='BatchNorm',
bottom=bottom_blobs, top=top_blobs)
if running_mean is None or running_var is None:
# not use global_stats, normalization is performed over the current mini-batch
layer1.batch_norm_param(use_global_stats=0,eps=eps)
else:
layer1.batch_norm_param(use_global_stats=1, eps=eps)
running_mean_clone = running_mean.clone()
running_var_clone = running_var.clone()
layer1.add_data(running_mean_clone.cpu().numpy(), running_var_clone.cpu().numpy(), np.array([1.0]))
log.cnet.add_layer(layer1)
if weight is not None and bias is not None:
layer_name2 = log.add_layer(name='bn_scale')
layer2 = caffe_net.Layer_param(name=layer_name2, type='Scale',
bottom=top_blobs, top=top_blobs)
layer2.param.scale_param.bias_term = True
layer2.add_data(weight.cpu().data.numpy(), bias.cpu().data.numpy())
log.cnet.add_layer(layer2)
return x
def _instance_norm(raw, input, running_mean=None, running_var=None, weight=None,
bias=None, use_input_stats=True, momentum=0.1, eps=1e-5):
# TODO: the batch size!=1 view operations
print("WARNING: The Instance Normalization transfers to Caffe using BatchNorm, so the batch size should be 1")
if running_var is not None or weight is not None:
# TODO: the affine=True or track_running_stats=True case
raise NotImplementedError("not implement the affine=True or track_running_stats=True case InstanceNorm")
x= torch.batch_norm(
input, weight, bias, running_mean, running_var,
use_input_stats, momentum, eps,torch.backends.cudnn.enabled)
bottom_blobs = [log.blobs(input)]
layer_name1 = log.add_layer(name='instance_norm')
top_blobs = log.add_blobs([x], name='instance_norm_blob')
layer1 = caffe_net.Layer_param(name=layer_name1, type='BatchNorm',
bottom=bottom_blobs, top=top_blobs)
if running_mean is None or running_var is None:
# not use global_stats, normalization is performed over the current mini-batch
layer1.batch_norm_param(use_global_stats=0,eps=eps)
running_mean=torch.zeros(input.size()[1])
running_var=torch.ones(input.size()[1])
else:
layer1.batch_norm_param(use_global_stats=1, eps=eps)
running_mean_clone = running_mean.clone()
running_var_clone = running_var.clone()
layer1.add_data(running_mean_clone.cpu().numpy(), running_var_clone.cpu().numpy(), np.array([1.0]))
log.cnet.add_layer(layer1)
if weight is not None and bias is not None:
layer_name2 = log.add_layer(name='bn_scale')
layer2 = caffe_net.Layer_param(name=layer_name2, type='Scale',
bottom=top_blobs, top=top_blobs)
layer2.param.scale_param.bias_term = True
layer2.add_data(weight.cpu().data.numpy(), bias.cpu().data.numpy())
log.cnet.add_layer(layer2)
return x
#upsample layer
def _interpolate(raw, input,size=None, scale_factor=None, mode='nearest', align_corners=None):
# 定义的参数包括 scale,即输出与输入的尺寸比例,如 2;scale_h、scale_w,
# 同 scale,分别为 h、w 方向上的尺寸比例;pad_out_h、pad_out_w,仅在 scale 为 2 时
# 有用,对输出进行额外 padding 在 h、w 方向上的数值;upsample_h、upsample_w,输
# 出图像尺寸的数值。在 Upsample 的相关代码中,推荐仅仅使用 upsample_h、
# upsample_w 准确定义 Upsample 层的输出尺寸,其他所有的参数都不推荐继续使用。
# for nearest _interpolate
if mode != "nearest" or align_corners != None:
raise NotImplementedError("not implement F.interpolate totoaly")
x = raw(input,size , scale_factor ,mode)
layer_name = log.add_layer(name='upsample')
top_blobs = log.add_blobs([x], name='upsample_blob'.format(type))
layer = caffe_net.Layer_param(name=layer_name, type='Upsample',
bottom=[log.blobs(input)], top=top_blobs)
layer.upsample_param(size =(input.size(2),input.size(3)), scale_factor= scale_factor)
log.cnet.add_layer(layer)
return x
#sigmid layer
def _sigmoid(raw, input):
# Applies the element-wise function:
#
# Sigmoid(x)= 1/(1+exp(?x)）
#
# ?
x = raw(input)
name = log.add_layer(name='sigmoid')
log.add_blobs([x], name='sigmoid_blob')
layer = caffe_net.Layer_param(name=name, type='Sigmoid',
bottom=[log.blobs(input)], top=[log.blobs(x)])
log.cnet.add_layer(layer)
return x
#tanh layer
def _tanh(raw, input):
# Applies the element-wise function:
#
# torch.nn.Tanh
#
# ?
x = raw(input)
name = log.add_layer(name='tanh')
log.add_blobs([x], name='tanh_blob')
layer = caffe_net.Layer_param(name=name, type='TanH',
bottom=[log.blobs(input)], top=[log.blobs(x)])
log.cnet.add_layer(layer)
return x
def _hardtanh(raw, input, min_val, max_val, inplace):
# Applies the element-wise function:
#
# torch.nn.ReLu6
#
# ?
print('relu6: ', log.blobs(input))
x = raw(input, min_val, max_val)
name = log.add_layer(name='relu6')
log.add_blobs([x], name='relu6_blob')
layer = caffe_net.Layer_param(name=name, type='ReLU6',
bottom=[log.blobs(input)], top=[log.blobs(x)])
log.cnet.add_layer(layer)
return x
#L2Norm layer
def _l2Norm(raw, input, weight, eps):
# Applies the element-wise function:
#
# L2Norm in vgg_ssd
#
# ?
x = raw(input, weight, eps)
name = log.add_layer(name='normalize')
log.add_blobs([x], name='normalize_blob')
layer = caffe_net.Layer_param(name=name, type='Normalize',
bottom=[log.blobs(input)], top=[log.blobs(x)])
layer.norm_param(eps)
layer.add_data(weight.cpu().data.numpy())
log.cnet.add_layer(layer)
return x
def _div(raw,inputs, inputs2):
x=raw(inputs, inputs2)
log.add_blobs([x],name='div_blob')
return x
# ----- for Variable operations --------
def _view(input, *args):
x=raw_view(input, *args)
if not NET_INITTED:
return x
layer_name=log.add_layer(name='view')
top_blobs=log.add_blobs([x],name='view_blob')
layer=caffe_net.Layer_param(name=layer_name,type='Reshape',
bottom=[log.blobs(input)],top=top_blobs)
# TODO: reshpae added to nn_tools layer
dims=list(args)
dims[0]=0 # the first dim should be batch_size
layer.param.reshape_param.shape.CopyFrom(caffe_net.pb.BlobShape(dim=dims))
log.cnet.add_layer(layer)
return x
def _mean(input, *args,**kwargs):
x=raw_mean(input, *args,**kwargs)
if not NET_INITTED:
return x
layer_name=log.add_layer(name='mean')
top_blobs=log.add_blobs([x],name='mean_blob')
layer=caffe_net.Layer_param(name=layer_name,type='Reduction',
bottom=[log.blobs(input)],top=top_blobs)
if len(args)==1:
dim=args[0]
elif 'dim' in kwargs:
dim=kwargs['dim']
else:
raise NotImplementedError('mean operation must specify a dim')
layer.param.reduction_param.operation=4
layer.param.reduction_param.axis=dim
log.cnet.add_layer(layer)
return x
def _add(input, *args):
x = raw__add__(input, *args)
if not NET_INITTED:
return x
layer_name = log.add_layer(name='add')
top_blobs = log.add_blobs([x], name='add_blob')
if log.blobs(args[0]) == None:
log.add_blobs([args[0]], name='extra_blob')
else:
layer = caffe_net.Layer_param(name=layer_name, type='Eltwise',
bottom=[log.blobs(input),log.blobs(args[0])], top=top_blobs)
layer.param.eltwise_param.operation = 1 # sum is 1
log.cnet.add_layer(layer)
return x
def _iadd(input, *args):
x = raw__iadd__(input, *args)
if not NET_INITTED:
return x
x=x.clone()
layer_name = log.add_layer(name='add')
top_blobs = log.add_blobs([x], name='add_blob')
layer = caffe_net.Layer_param(name=layer_name, type='Eltwise',
bottom=[log.blobs(input),log.blobs(args[0])], top=top_blobs)
layer.param.eltwise_param.operation = 1 # sum is 1
log.cnet.add_layer(layer)
return x
def _sub(input, *args):
x = raw__sub__(input, *args)
if not NET_INITTED:
return x
layer_name = log.add_layer(name='sub')
top_blobs = log.add_blobs([x], name='sub_blob')
layer = caffe_net.Layer_param(name=layer_name, type='Eltwise',
bottom=[log.blobs(input),log.blobs(args[0])], top=top_blobs)
layer.param.eltwise_param.operation = 1 # sum is 1
layer.param.eltwise_param.coeff.extend([1.,-1.])
log.cnet.add_layer(layer)
return x
def _isub(input, *args):
x = raw__isub__(input, *args)
if not NET_INITTED:
return x
x=x.clone()
layer_name = log.add_layer(name='sub')
top_blobs = log.add_blobs([x], name='sub_blob')
layer = caffe_net.Layer_param(name=layer_name, type='Eltwise',
bottom=[log.blobs(input),log.blobs(args[0])], top=top_blobs)
layer.param.eltwise_param.operation = 1 # sum is 1
log.cnet.add_layer(layer)
return x
def _mul(input, *args):
x = raw__mul__(input, *args)
if not NET_INITTED:
return x
layer_name = log.add_layer(name='mul')
top_blobs = log.add_blobs([x], name='mul_blob')
layer = caffe_net.Layer_param(name=layer_name, type='Eltwise',
bottom=[log.blobs(input), log.blobs(args[0])], top=top_blobs)
layer.param.eltwise_param.operation = 0 # product is 1
log.cnet.add_layer(layer)
return x
def _imul(input, *args):
x = raw__imul__(input, *args)
if not NET_INITTED:
return x
x = x.clone()
layer_name = log.add_layer(name='mul')
top_blobs = log.add_blobs([x], name='mul_blob')
layer = caffe_net.Layer_param(name=layer_name, type='Eltwise',
bottom=[log.blobs(input), log.blobs(args[0])], top=top_blobs)
layer.param.eltwise_param.operation = 0 # product is 1
layer.param.eltwise_param.coeff.extend([1., -1.])
log.cnet.add_layer(layer)
return x
#Permute layer
def _permute(input, *args):
x = raw__permute__(input, *args)
name = log.add_layer(name='permute')
log.add_blobs([x], name='permute_blob')
layer = caffe_net.Layer_param(name=name, type='Permute',
bottom=[log.blobs(input)], top=[log.blobs(x)])
order1 = args[0]
order2 = args[1]
order3 = args[2]
order4 = args[3]
layer.permute_param(order1, order2, order3, order4)
log.cnet.add_layer(layer)
return x
#contiguous
def _contiguous(input, *args):
x = raw__contiguous__(input, *args)
name = log.add_layer(name='contiguous')
log.add_blobs([x], name='contiguous_blob')
layer = caffe_net.Layer_param(name=name, type='NeedRemove',
bottom=[log.blobs(input)], top=[log.blobs(x)])
log.cnet.add_layer(layer)
return x
#pow
def _pow(input, *args):
x = raw__pow__(input, *args)
log.add_blobs([x], name='pow_blob')
return x
#sum
def _sum(input, *args):
x = raw__sum__(input, *args)
log.add_blobs([x], name='sum_blob')
return x
# sqrt
def _sqrt(input, *args):
x = raw__sqrt__(input, *args)
log.add_blobs([x], name='sqrt_blob')
return x
# unsqueeze
def _unsqueeze(input, *args):
x = raw__unsqueeze__(input, *args)
log.add_blobs([x], name='unsqueeze_blob')
return x
def _expand_as(input, *args):
# only support expand A(1, 1, H, W) to B(1, C, H, W)
x = raw__expand_as__(input, *args)
layer_name = log.add_layer(name="expand_as", with_num=True)
log.add_blobs([x], name='expand_as_blob')
layer = caffe_net.Layer_param(name=layer_name, type='Convolution',
bottom=[log.blobs(input)], top=[log.blobs(x)])
def constant_weight(shape):
weights = np.ones(shape, dtype='float32')
return weights
channels = args[0].size(1)
weight = constant_weight([channels, 1, 1, 1])
layer.conv_param(channels, kernel_size = 1, bias_term=False, weight_filler_type='xavier')
layer.add_data(weight)
log.cnet.add_layer(layer)
return x
## 这里修改
def _flatten(raw , input, *args):
x = raw(input, *args)
if not NET_INITTED:
return x
layer_name=log.add_layer(name='flatten')
top_blobs=log.add_blobs([x],name='flatten_blob')
layer=caffe_net.Layer_param(name=layer_name,type='Reshape',
bottom=[log.blobs(input)],top=top_blobs)
start_dim = args[0]
end_dim = len(x.shape)
if len(args) > 1:
end_dim = args[1]
dims = []
for i in range(start_dim):
dims.append(x.shape[i])
cum = 1
for i in range(start_dim, end_dim):
cum = cum * x.shape[i]
dims.append(cum)
if end_dim != len(x.shape):
cum = 1
for i in range(end_dim, len(x.shape)):
cum = cum * x.shape[i]
dims.append(cum)
layer.param.reshape_param.shape.CopyFrom(caffe_net.pb.BlobShape(dim=dims))
log.cnet.add_layer(layer)
return x
class Rp(object):
def __init__(self,raw,replace,**kwargs):
# replace the raw function to replace function
self.obj=replace
self.raw=raw
def __call__(self,*args,**kwargs):
if not NET_INITTED:
return self.raw(*args,**kwargs)
for stack in traceback.walk_stack(None):
if 'self' in stack[0].f_locals:
layer=stack[0].f_locals['self']
if layer in layer_names:
log.pytorch_layer_name=layer_names[layer]
print(layer_names[layer])
break
out=self.obj(self.raw,*args,**kwargs)
# if isinstance(out,Variable):
# out=[out]
return out
F.conv2d=Rp(F.conv2d,_conv2d)
F.linear=Rp(F.linear,_linear)
F.relu=Rp(F.relu,_relu)
F.leaky_relu=Rp(F.leaky_relu,_leaky_relu)
F.max_pool2d=Rp(F.max_pool2d,_max_pool2d)
F.avg_pool2d=Rp(F.avg_pool2d,_avg_pool2d)
F.adaptive_avg_pool2d = Rp(F.adaptive_avg_pool2d,_adaptive_avg_pool2d)
F.dropout=Rp(F.dropout,_dropout)
F.threshold=Rp(F.threshold,_threshold)
F.prelu=Rp(F.prelu,_prelu)
F.batch_norm=Rp(F.batch_norm,_batch_norm)
F.instance_norm=Rp(F.instance_norm,_instance_norm)
F.softmax=Rp(F.softmax,_softmax)
F.conv_transpose2d=Rp(F.conv_transpose2d,_conv_transpose2d)
F.interpolate = Rp(F.interpolate,_interpolate)
F.sigmoid = Rp(F.sigmoid,_sigmoid)
F.tanh = Rp(F.tanh,_tanh)
F.tanh = Rp(F.tanh,_tanh)
F.hardtanh = Rp(F.hardtanh,_hardtanh)
# F.l2norm = Rp(F.l2norm,_l2Norm)
torch.split=Rp(torch.split,_split)
torch.max=Rp(torch.max,_max)
torch.cat=Rp(torch.cat,_cat)
torch.div=Rp(torch.div,_div)
## 这里也需要修改
torch.flatten = Rp(torch.flatten,_flatten)
# TODO: other types of the view function
try:
raw_view=Variable.view
Variable.view=_view
raw_mean=Variable.mean
Variable.mean=_mean
raw__add__=Variable.__add__
Variable.__add__=_add
raw__iadd__=Variable.__iadd__
Variable.__iadd__=_iadd
raw__sub__=Variable.__sub__
Variable.__sub__=_sub
raw__isub__=Variable.__isub__
Variable.__isub__=_isub
raw__mul__ = Variable.__mul__
Variable.__mul__ = _mul
raw__imul__ = Variable.__imul__
Variable.__imul__ = _imul
except:
# for new version 0.4.0 and later version
for t in [torch.Tensor]:
raw_view = t.view
t.view = _view
raw_mean = t.mean
t.mean = _mean
raw__add__ = t.__add__
t.__add__ = _add
raw__iadd__ = t.__iadd__
t.__iadd__ = _iadd
raw__sub__ = t.__sub__
t.__sub__ = _sub
raw__isub__ = t.__isub__
t.__isub__ = _isub
raw__mul__ = t.__mul__
t.__mul__=_mul
raw__imul__ = t.__imul__
t.__imul__ = _imul
raw__permute__ = t.permute
t.permute = _permute
raw__contiguous__ = t.contiguous
t.contiguous = _contiguous
raw__pow__ = t.pow
t.pow = _pow
raw__sum__ = t.sum
t.sum = _sum
raw__sqrt__ = t.sqrt
t.sqrt = _sqrt
raw__unsqueeze__ = t.unsqueeze
t.unsqueeze = _unsqueeze
raw__expand_as__ = t.expand_as
t.expand_as = _expand_as
def trans_net(net, input_var, name='TransferedPytorchModel'):
print('Starting Transform, This will take a while')
log.init([input_var])
log.cnet.net.name=name
log.cnet.net.input.extend([log.blobs(input_var)])
log.cnet.net.input_dim.extend(input_var.size())
global NET_INITTED
NET_INITTED=True
for name,layer in net.named_modules():
layer_names[layer]=name
print("torch ops name:", layer_names)
out = net.forward(input_var)
print('Transform Completed')
def save_prototxt(save_name):
log.cnet.remove_layer_by_type("NeedRemove")
log.cnet.save_prototxt(save_name)
def save_caffemodel(save_name):
log.cnet.save(save_name)