Pytorch轉(zhuǎn)Msnhnet模型思路分享
?GiantPandaCV導(dǎo)語(yǔ)：「這篇文章要為大家介紹一下MsnhNet的模型轉(zhuǎn)換思路，大多數(shù)搞CV的小伙伴都知道：珍愛(ài)生命，遠(yuǎn)離模型轉(zhuǎn)換。但是啊，當(dāng)你想部署訓(xùn)練出來(lái)的模型時(shí)（如Pytorch訓(xùn)練的），模型轉(zhuǎn)換又是必不可少的步驟，這個(gè)時(shí)候就真的很愁。為什么調(diào)用xxx腳本轉(zhuǎn)換出來(lái)的模型報(bào)錯(cuò)了？為什么轉(zhuǎn)換出來(lái)的模型推理結(jié)果不正確呢？所以，我認(rèn)為在做模型轉(zhuǎn)換時(shí)擁有一個(gè)清晰的分析思路是不可少的。這篇文章就為大家分享了一下最近開(kāi)源的前向推理框架MsnhNet是如何將原始的Pytorch模型較為優(yōu)雅的轉(zhuǎn)換過(guò)來(lái)，希望我們介紹的思路可以對(duì)有模型轉(zhuǎn)換需求的同學(xué)帶來(lái)一定啟發(fā)」
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1.網(wǎng)絡(luò)結(jié)構(gòu)的轉(zhuǎn)換
網(wǎng)絡(luò)結(jié)構(gòu)轉(zhuǎn)換比較復(fù)雜,其原因在于涉及到不同的op以及相關(guān)的基礎(chǔ)操作.
「思路一」: 利用print的結(jié)果進(jìn)行構(gòu)建 「優(yōu)點(diǎn)」: 簡(jiǎn)單易用 「缺點(diǎn)」: 大部分網(wǎng)絡(luò),print并不能完全展現(xiàn)出其結(jié)構(gòu).簡(jiǎn)單網(wǎng)絡(luò)可用. 代碼實(shí)現(xiàn):
import?torch
import?torch.nn?as?nn
class?Model(nn.Module):
????def?__init__(self):
????????super(Model,?self).__init__()
????????self.conv1?=?nn.Conv2d(1,?6,?5)
????????self.bn1???=?nn.BatchNorm2d(6,eps=1e-5,momentum=0.1)
????????self.relu1?=?nn.ReLU()
????????self.pool1?=?nn.MaxPool2d(2)
????????self.conv2?=?nn.Conv2d(6,?16,?5)
????????self.bn2???=?nn.BatchNorm2d(16,eps=1e-5,momentum=0.1)
????????self.relu2?=?nn.ReLU()
????????self.pool2?=?nn.MaxPool2d(2)
????def?forward(self,?x):
????????y?=?self.conv1(x)
????????y?=?self.bn1(y)
????????y?=?self.relu1(y)?
????????y?=?self.pool1(y)?
????????y?=?self.conv2(y)
????????y?=?self.bn2(y)?
????????y?=?self.relu2(y)?
????????y?=?self.pool2(y)
????????return?y
nn?=?Model()
print(nn)
結(jié)果: 很顯然對(duì)于用純nn.Module搭建的網(wǎng)絡(luò)是可行的
Model
(
??(conv1):?Conv2d(1,?6,?kernel_size=(5,?5),?stride=(1,?1))
??(bn1):?BatchNorm2d(6,?eps=1e-05,?momentum=0.1,?affine=True,?track_running_stats=True)
??(relu1):?ReLU()
??(pool1):?MaxPool2d(kernel_size=2,?stride=2,?padding=0,?dilation=1,?ceil_mode=False)
??(conv2):?Conv2d(6,?16,?kernel_size=(5,?5),?stride=(1,?1))
??(bn2):?BatchNorm2d(16,?eps=1e-05,?momentum=0.1,?affine=True,?track_running_stats=True)
??(relu2):?ReLU()
??(pool2):?MaxPool2d(kernel_size=2,?stride=2,?padding=0,?dilation=1,?ceil_mode=False)
)
如果在forward內(nèi)添加相關(guān)操作,則此方案將無(wú)效. 代碼實(shí)現(xiàn):
import?torch
import?torch.nn?as?nn
class?Model(nn.Module):
????def?__init__(self):
????????super(Model,?self).__init__()
????????self.conv1?=?nn.Conv2d(1,?6,?5)
????????self.bn1???=?nn.BatchNorm2d(6,eps=1e-5,momentum=0.1)
????????self.relu1?=?nn.ReLU()
????????self.pool1?=?nn.MaxPool2d(2)
????????self.conv2?=?nn.Conv2d(6,?16,?5)
????????self.bn2???=?nn.BatchNorm2d(16,eps=1e-5,momentum=0.1)
????????self.relu2?=?nn.ReLU()
????????self.pool2?=?nn.MaxPool2d(2)
????def?forward(self,?x):
????????y?=?self.conv1(x)
????????y?=?self.bn1(y)
????????y?=?self.relu1(y)?
????????y?=?self.pool1(y)?
????????y?=?self.conv2(y)
????????y?=?self.bn2(y)?
????????y?=?self.relu2(y)?
????????y?=?self.pool2(y)
????????y?=?torch.flatten(y)
????????return?y
nn?=?Model()
print(nn)
結(jié)果: 很顯然forward內(nèi)的flatten操作并沒(méi)有被導(dǎo)出.
Model(
??(conv1):?Conv2d(1,?6,?kernel_size=(5,?5),?stride=(1,?1))
??(bn1):?BatchNorm2d(6,?eps=1e-05,?momentum=0.1,?affine=True,?track_running_stats=True)
??(relu1):?ReLU()
??(pool1):?MaxPool2d(kernel_size=2,?stride=2,?padding=0,?dilation=1,?ceil_mode=False)
??(conv2):?Conv2d(6,?16,?kernel_size=(5,?5),?stride=(1,?1))
??(bn2):?BatchNorm2d(16,?eps=1e-05,?momentum=0.1,?affine=True,?track_running_stats=True)
??(relu2):?ReLU()
??(pool2):?MaxPool2d(kernel_size=2,?stride=2,?padding=0,?dilation=1,?ceil_mode=False)
)
「思路二」: 通過(guò)類似Windows Hook技術(shù).(思路來(lái)源pytorch_to_caffe) 在pytorch的Op在執(zhí)行之前,對(duì)此Op進(jìn)行截取,以獲取相關(guān)信息,從而實(shí)現(xiàn)網(wǎng)絡(luò)構(gòu)建. 優(yōu)點(diǎn): 幾乎可以完成所有pytorch的op導(dǎo)出. 缺點(diǎn): 實(shí)現(xiàn)復(fù)雜,容易誤操作,可能影響pytorch本身結(jié)果錯(cuò)誤. 代碼實(shí)現(xiàn): 通過(guò)構(gòu)建Hook類, 重寫op, 并替換原op操作,獲取op的參數(shù). 層的上下關(guān)系,通過(guò)tensor的 _cdata作為唯一識(shí)別的ID.
import?torch
import?torch.nn?as?nn
from?torchsummary?import?summary
import?torch.nn.functional?as?F
logMsg?=?True
ccc?=?[]
#?Hook截取類
class?Hook(object):
????hookInited?=?False
????def?__init__(self,raw,replace,**kwargs):
????????self.obj=replace?#?被截取之后的op
????????self.raw=raw?#?原op
????def?__call__(self,*args,**kwargs):
????????if?not?Hook.hookInited:?#在Hook類未初始化之前,該信號(hào)原路返回
????????????return?self.raw(*args,**kwargs)
????????else:???????????????????#否則,則按截取之后,實(shí)現(xiàn)的函數(shù)執(zhí)行
????????????out=self.obj(self.raw,*args,**kwargs)
????????????return?out
def?log(*args):
????if?logMsg:
????????print(*args)
#?替換原cov2d函數(shù)的實(shí)現(xiàn)
def?_conv2d(raw,inData,?weight,?bias=None,?stride=1,?padding=0,?dilation=1,?groups=1):
????#?對(duì)于上下層網(wǎng)絡(luò)關(guān)系,可以使用tensor的_cdata,該參數(shù)類似唯一ID
????#?輸入tensor的唯一ID
????log(?"conv2d-i"?,?inData._cdata)?
????x=raw(inData,weight,bias,stride,padding,dilation,groups)
????ccc.append(x)????????????????????#?此處將輸出保存,防止被inplace操作,導(dǎo)致所有tensor的_cdata喪失唯一性
????#?此處就可以根據(jù)conv2d參數(shù)就行網(wǎng)絡(luò)構(gòu)建
????#?msnhnet.buildConv2d(...)?
????#?輸出tensor的唯一ID
????log(?"conv2d-o"?,?x._cdata)
????return?x
#?被替換OP??????????????????原OP?????自定義OP
F.conv2d????????=???Hook(F.conv2d,_conv2d)
完整Demo:
import?torch
import?torch.nn?as?nn
from?torchsummary?import?summary
import?torch.nn.functional?as?F
logMsg?=?True
ccc?=?[]
#?Hook截取類
class?Hook(object):
????hookInited?=?False
????def?__init__(self,raw,replace,**kwargs):
????????self.obj=replace?#?被截取之后的op
????????self.raw=raw?#?原op
????def?__call__(self,*args,**kwargs):
????????if?not?Hook.hookInited:?#在Hook類未初始化之前,該信號(hào)原路返回
????????????return?self.raw(*args,**kwargs)
????????else:???????????????????#否則,則按截取之后,實(shí)現(xiàn)的函數(shù)執(zhí)行
????????????out=self.obj(self.raw,*args,**kwargs)
????????????return?out
def?log(*args):
????if?logMsg:
????????print(*args)
#?替換原cov2d函數(shù)的實(shí)現(xiàn)
def?_conv2d(raw,inData,?weight,?bias=None,?stride=1,?padding=0,?dilation=1,?groups=1):
????#?對(duì)于上下層網(wǎng)絡(luò)關(guān)系,可以使用tensor的_cdata,該參數(shù)類似唯一ID
????#?輸入tensor的唯一ID
????log(?"conv2d-i"?,?inData._cdata)?
????x=raw(inData,weight,bias,stride,padding,dilation,groups)
????ccc.append(x)????????????????????#?此處將輸出保存,防止被inplace操作,導(dǎo)致所有tensor的_cdata喪失唯一性
????#?此處就可以根據(jù)conv2d參數(shù)就行網(wǎng)絡(luò)構(gòu)建
????#?msnhnet.buildConv2d(...)?
????#?輸出tensor的唯一ID
????log(?"conv2d-o"?,?x._cdata)
????return?x
def?_relu(raw,?inData,?inplace=False):
????log(?"relu-i"?,?inData._cdata)
????x?=?raw(inData,False)
????ccc.append(x)
????log(?"relu-o"?,?x._cdata)
????return?x
def?_batch_norm(raw,inData,?running_mean,?running_var,?weight=None,?bias=None,training=False,?momentum=0.1,?eps=1e-5):
????log(?"bn-i"?,?inData._cdata)
????x?=?raw(inData,?running_mean,?running_var,?weight,?bias,?training,?momentum,?eps)
????ccc.append(x)
????log(?"bn-o"?,?x._cdata)
????return?x
def?_flatten(raw,*args):
????log(?"flatten-i"?,?args[0]._cdata)
????x=raw(*args)
????ccc.append(x)
????log(?"flatten-o"?,?x._cdata)
????return?x
#?被替換OP???????????????????原OP???????自定義OP
F.conv2d????????=???Hook(F.conv2d,_conv2d)
F.batch_norm????=???Hook(F.batch_norm,_batch_norm)
F.relu??????????=???Hook(F.relu,_relu)
torch.flatten???=???Hook(torch.flatten,_flatten)
class?Model(nn.Module):
????def?__init__(self):
????????super(Model,?self).__init__()
????????self.conv1?=?nn.Conv2d(1,?6,?5)
????????self.bn1???=?nn.BatchNorm2d(6,eps=1e-5,momentum=0.1)
????????self.relu1?=?nn.ReLU()
????def?forward(self,?x):
????????y?=?self.conv1(x)
????????y?=?self.bn1(y)
????????y?=?self.relu1(y)?
????????y?=?torch.flatten(y)
????????return?y
input_var?=?torch.autograd.Variable(torch.rand(1,?1,?28,?28))
nn?=?Model()
nn.eval()
Hook.hookInited?=?True
res?=?nn(input_var)
結(jié)果: flatten操作也完成了導(dǎo)出, 且每個(gè)op的input的ID都能在前面找到對(duì)應(yīng)op的output的ID.即可知曉上下層之間的關(guān)系,由此,即可構(gòu)建msnhnet.
conv2d-i?2748363239504
conv2d-o?2748363238224
bn-i?2748363238224
bn-o?2748363242832
relu-i?2748363242832
relu-o?2748363235152
flatten-i?2748363235152
flatten-o?2748363242064
2.參數(shù)的轉(zhuǎn)換
「思路一」: 利用pytorch的state_dict字典,可直接進(jìn)行導(dǎo)出. 由于msnhnet和pytorch的內(nèi)存排布是一致的,都為NCHW模式,且對(duì)于BN層的參數(shù)順序也相同,都為scale, bias, mean和var.只需將參數(shù)進(jìn)行逐個(gè)提取,然后按二進(jìn)制存儲(chǔ)即可。 優(yōu)點(diǎn): 可以在不知道網(wǎng)絡(luò)運(yùn)行結(jié)構(gòu)的時(shí)候?qū)?shù)進(jìn)行導(dǎo)出, 簡(jiǎn)單易用. 缺點(diǎn): 當(dāng)網(wǎng)絡(luò)使用參數(shù)的順序和保存的順序不一致時(shí)，會(huì)出現(xiàn)錯(cuò)誤.
import?torchvision.models?as?models
import?torch
from?struct?import?pack
md?=?models.resnet18(pretrained?=?True)
md.to("cpu")
md.eval()
val?=?[]
dd?=?0
for?name?in?md.state_dict():
????????if?"num_batches_tracked"?not?in?name:
????????????????c?=?md.state_dict()[name].data.flatten().numpy().tolist()
????????????????dd?=?dd?+?len(c)
????????????????print(name,?":",?len(c))
????????????????val.extend(c)
with?open("alexnet.msnhbin","wb")?as?f:
????for?i?in?val?:
????????f.write(pack('f',i))
注意上面出現(xiàn)了一行if "num_batches_tracked" not in name:，這一行是Pytorch的一個(gè)坑點(diǎn)，在pytorch 0.4.1及后面的版本里，BatchNorm層新增了num_batches_tracked參數(shù)，用來(lái)統(tǒng)計(jì)訓(xùn)練時(shí)的forward過(guò)的batch數(shù)目，源碼如下（pytorch0.4.1）：
??if?self.training?and?self.track_running_stats:
????????self.num_batches_tracked?+=?1
????????if?self.momentum?is?None:??#?use?cumulative?moving?average
????????????exponential_average_factor?=?1.0?/?self.num_batches_tracked.item()
????????else:??#?use?exponential?moving?average
????????????exponential_average_factor?=?self.momentum
在調(diào)用預(yù)訓(xùn)練參數(shù)模型時(shí)，官方給定的預(yù)訓(xùn)練模型是在pytorch0.4之前。因此，調(diào)用預(yù)訓(xùn)練參數(shù)時(shí)，需要過(guò)濾掉“num_batches_tracked”。
「思路二」: 利用之前的Hook,在算子運(yùn)行時(shí),對(duì)參數(shù)進(jìn)行提取,暫存,最后統(tǒng)一保存. 優(yōu)點(diǎn): 網(wǎng)絡(luò)參數(shù)和網(wǎng)絡(luò)結(jié)構(gòu)同時(shí)導(dǎo)出,保證參數(shù)與網(wǎng)絡(luò)運(yùn)行結(jié)構(gòu)一致性. 缺點(diǎn): 需要獲取網(wǎng)絡(luò)的運(yùn)行順序才能完成轉(zhuǎn)換. 代碼實(shí)現(xiàn):
...
m_weights?=?[]
def?_conv2d(raw,inData,?weight,?bias=None,?stride=1,?padding=0,?dilation=1,?groups=1):
????
????x=raw(inData,weight,bias,stride,padding,dilation,groups)
????if?Hook.hookInited?:
????????log(?"conv2d-i"?,?inData._cdata)
????????ccc.append(x)
????????log(?"conv2d-o"?,?x._cdata)
????????useBias?=?True
????????if?bias?is?None:
????????????useBias?=?False
????????
????????m_weights.extend(weight.numpy().flatten().tolist())?#暫存
????????if?useBias?:
????????????m_weights.extend(bias.numpy().flatten().tolist())?#暫存
????????msnhnet.checkInput(inData,sys._getframe().f_code.co_name)
????????msnhnet.buildConv2d(str(x._cdata),?x.size()[1],?weight.size()[2],?weight.size()[3],?
????????????????????????????padding[0],?padding[1],?stride[0],?stride[1],?dilation[0],?dilation[1],?groups,?useBias)
????return?x
...
def?trans(net,?inputVar,?msnhnet_path,?msnhbin_path):
????Hook.hookInited?=?True
????msnhnet.buildConfig(str(id(inputVar)),?inputVar.size())
????net.forward(inputVar)
????with?open(msnhnet_path,"w")?as?f1:
????????f1.write(msnhnet.net)
????with?open(msnhbin_path,"wb")?as?f:?#?參數(shù)保存
????????for?i?in?m_weights?:
????????????f.write(pack('f',i))?
????Hook.hookInited?=?False
3.詳細(xì)轉(zhuǎn)換過(guò)程代碼編寫
這里先截取一下構(gòu)建MsnhNet的部分代碼，完整代碼見(jiàn)https://github.com/msnh2012/Msnhnet/blob/master/tools/pytorch2Msnhnet/PytorchToMsnhnet.py，如下：
from?collections?import?OrderedDict
import?sys
class?Msnhnet:
????def?__init__(self):
????????self.inAddr?=?""
????????self.net?=?""
????????self.index?=?0
????????self.names?=?[]
????????self.indexes?=?[]
????def?setNameAndIdx(self,?name,?ids):
????????self.names.append(name)
????????self.indexes.append(ids)
????def?getIndexFromName(self,name):
????????ids?=?self.indexes[self.names.index(name)]
????????return?ids
????def?getLastVal(self):
????????return?self.indexes[-1]
????def?getLastKey(self):
????????return?self.names[-1]
????def?checkInput(self,?inAddr,fun):
????????if?self.index?==?0:
????????????return
????????if?str(inAddr._cdata)?!=?self.getLastKey():
????????????try:
????????????????ID?=?self.getIndexFromName(str(inAddr._cdata))
????????????????self.buildRoute(str(inAddr._cdata),str(ID),False)
????????????except:
?????????????????raise?NotImplementedError("last?op?is?not?supported?"?+?fun?+?str(inAddr._cdata))
????????????
????def?buildConfig(self,?inAddr,?shape):
????????self.inAddr?=?inAddr
????????self.net?=?self.net?+?"config:\n"
????????self.net?=?self.net?+?"??batch:?"?+?str(int(shape[0]))?+?"\n"
????????self.net?=?self.net?+?"??channels:?"?+?str(int(shape[1]))?+?"\n"
????????self.net?=?self.net?+?"??height:?"?+?str(int(shape[2]))?+?"\n"
????????self.net?=?self.net?+?"??width:?"?+?str(int(shape[3]))?+?"\n"
?
????def?buildConv2d(self,?name,?filters,?kSizeX,?kSizeY,?paddingX,?paddingY,?strideX,?strideY,?dilationX,?dilationY,?groups,?useBias):
????????self.setNameAndIdx(name,self.index)
????????self.net?=?self.net?+?"#"?+?str(self.index)?+??"\n"
????????self.index?=?self.index?+?1
????????self.net?=?self.net?+?"conv:\n"
????????self.net?=?self.net?+?"??filters:?"?+?str(int(filters))?+?"\n"
????????self.net?=?self.net?+?"??kSizeX:?"?+?str(int(kSizeX))?+?"\n"
????????self.net?=?self.net?+?"??kSizeY:?"?+?str(int(kSizeY))?+?"\n"
????????self.net?=?self.net?+?"??paddingX:?"?+?str(int(paddingX))?+?"\n"
????????self.net?=?self.net?+?"??paddingY:?"?+?str(int(paddingY))?+?"\n"
????????self.net?=?self.net?+?"??strideX:?"?+?str(int(strideX))?+?"\n"
????????self.net?=?self.net?+?"??strideY:?"?+?str(int(strideY))?+?"\n"
????????self.net?=?self.net?+?"??dilationX:?"?+?str(int(dilationX))?+?"\n"
????????self.net?=?self.net?+?"??dilationY:?"?+?str(int(dilationY))?+?"\n"
????????self.net?=?self.net?+?"??groups:?"?+?str(int(groups))?+?"\n"
????????self.net?=?self.net?+?"??useBias:?"?+?str(int(useBias))?+?"\n"
然后Pytorch2MsnhNet就在前向傳播的過(guò)程中按照我們介紹的Hook技術(shù)完成構(gòu)建Pytorch模型對(duì)應(yīng)的MsnhNet模型結(jié)構(gòu)。
至此，我們就獲得了MsnhNet的模型參數(shù)文件和權(quán)重文件，可以利用MsnhNet加載模型進(jìn)行推理了。
4. 已經(jīng)支持的OP以及轉(zhuǎn)換實(shí)例
Pytorch2MsnhNet已經(jīng)支持轉(zhuǎn)換的OP如下：
-??conv2d
-??max_pool2d
-??avg_pool2d
-??adaptive_avg_pool2d
-??linear
-??flatten
-??dropout
-??batch_norm
-??interpolate(nearest,?bilinear)
-??cat???
-??elu
-??selu
-??relu
-??relu6
-??leaky_relu
-??tanh
-??softmax
-??sigmoid
-??softplus
-??abs????
-??acos???
-??asin???
-??atan???
-??cos????
-??cosh???
-??sin????
-??sinh???
-??tan????
-??exp????
-??log????
-??log10??
-??mean
-??permute
-??view
-??contiguous
-??sqrt
-??pow
-??sum
-??pad
-??+|-|x|/|+=|-=|x=|/=|
ResNet18的轉(zhuǎn)換示例：
import?torch
import?torch.nn?as?nn
from?torchvision.models?import?resnet18
from?PytorchToMsnhnet?import?*
resnet18=resnet18(pretrained=True)
resnet18.eval()
input=torch.ones([1,3,224,224])
trans(resnet18,?input,"resnet18.msnhnet","resnet18.msnhbin")
DeepLabV3的轉(zhuǎn)換示例：
import?torch
import?torch.nn?as?nn
from?torchvision.models.segmentation?import?deeplabv3_resnet101
from?PytorchToMsnhnet?import?*
deeplabv3=deeplabv3_resnet101(pretrained=False)
ccc?=?torch.load("C:/Users/msnh/.cache/torch/checkpoints/deeplabv3_resnet101_coco-586e9e4e.pth")
del?ccc["aux_classifier.0.weight"]
del?ccc["aux_classifier.1.weight"]
del?ccc["aux_classifier.1.bias"]
del?ccc["aux_classifier.1.running_mean"]
del?ccc["aux_classifier.1.running_var"]
del?ccc["aux_classifier.1.num_batches_tracked"]
del?ccc["aux_classifier.4.weight"]
del?ccc["aux_classifier.4.bias"]
deeplabv3.load_state_dict(ccc)
deeplabv3.requires_grad_(False)
deeplabv3.eval()
input=torch.ones([1,3,224,224])
#?trans?msnhnet?and?msnhbin?file
trans(deeplabv3,?input,"deeplabv3.msnhnet","deeplabv3.msnhbin")
5. MsnhNet介紹
MsnhNet是一款基于純c++的輕量級(jí)推理框架，此框架受到darknet啟發(fā)，由穆士凝魂主導(dǎo)，并由本公眾號(hào)作者團(tuán)隊(duì)業(yè)余協(xié)助開(kāi)發(fā)。
項(xiàng)目地址：https://github.com/msnh2012/Msnhnet ，歡迎一鍵三連。
本框架目前已經(jīng)支持了X86、Cuda、Arm端的推理（支持的OP有限，正努力開(kāi)發(fā)中），并且可以直接將Pytorch模型（后面也會(huì)嘗試接入更多框架）轉(zhuǎn)為本框架的模型進(jìn)行部署，歡迎對(duì)前向推理框架感興趣的同學(xué)試用或者加入我們一起維護(hù)這個(gè)輪子。
最后，歡迎加入Msnhnet開(kāi)發(fā)QQ交流群，有對(duì)項(xiàng)目的建議或者個(gè)人的需求都可以在群里或者github issue提出。
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