文章目錄
背景
生產環境多數是使用java或者C++,本文將介紹在C++中加載PyTorch模型,執行生產環境下的推理。因此,本文的重點在於C++中如何加載模型,並進行推理預測操作,而不是模型的設計和訓練。
可以查看官方提供的說明 https://pytorch.org/tutorials/advanced/cpp_export.html#
TorchScript簡介
TorchScript是PyTorch模型的一種中間形式,可以在高性能環境(例如C ++)中運行。
PyTorch中如何創建基本模型
PyTorch中創建一個模塊包含:
(1)構造函數,爲模塊調用做準備
(2)參數和子模塊,由構造函數初始化,可以由模塊在調用期間使用
(3)forward
函數,調用模塊時運行的代碼
一個簡單示例如下:
class MyCell(torch.nn.Module):
def __init__(self):
super(MyCell, self).__init__()
def forward(self, x, h):
new_h = torch.tanh(x + h)
return new_h, new_h
my_cell = MyCell()
x = torch.rand(3, 4)
h = torch.rand(3, 4)
print(my_cell(x, h))
輸出結果:
(tensor([[0.6454, 0.7223, 0.8207, 0.1638],
[0.6929, 0.7719, 0.9481, 0.6845],
[0.7689, 0.8348, 0.8925, 0.3200]]), tensor([[0.6454, 0.7223, 0.8207, 0.1638],
[0.6929, 0.7719, 0.9481, 0.6845],
[0.7689, 0.8348, 0.8925, 0.3200]]))
以上示例,我們基於torch.nn.Module
創建了一個類MyCell
,並定義了構造函數,這裏的構造函數僅調用了super
函數。
super()
函數是用於調用父類(超類)的一個方法。super
是用來解決多重繼承問題的,直接用類名調用父類方法在使用單繼承的時候沒問題,但是如果使用多繼承,會涉及到查找順序、重複調用等種種問題。同時,我們還定義了forward
函數,這裏的forward
函數輸入是2個參數,返回2個結果。該forward函數的實際內容並不是很重要,但是它是一種僞的RNN單元,即該函數真實場景應用於循環。
我們進一步改動上述MyCell
類,在原有基礎上增加一個self.linear
成員屬性(是一個函數),並在forward
函數中調用該成員。torch.nn.Linear
是PyTorch中的一個標準模塊,如此便完成了模塊的嵌套組合。
class MyCell(torch.nn.Module):
def __init__(self):
super(MyCell, self).__init__()
self.linear = torch.nn.Linear(4, 4)
def forward(self, x, h):
new_h = torch.tanh(self.linear(x) + h)
return new_h, new_h
my_cell = MyCell()
x = torch.rand(3, 4)
h = torch.rand(3, 4)
print(my_cell)
print(my_cell(x, h))
輸出結果:
MyCell(
(linear): Linear(in_features=4, out_features=4, bias=True)
)
(tensor([[ 0.6286, -0.1987, 0.2962, 0.6099],
[ 0.8631, -0.2569, 0.1799, 0.6778],
[ 0.8491, 0.5000, 0.3010, 0.1332]], grad_fn=<TanhBackward>), tensor([[ 0.6286, -0.1987, 0.2962, 0.6099],
[ 0.8631, -0.2569, 0.1799, 0.6778],
[ 0.8491, 0.5000, 0.3010, 0.1332]], grad_fn=<TanhBackward>))
當打印模塊的時候,輸出爲模塊的子類層次結構。比如上述打印的mycell
的結果是linear
子類及其參數。
通過這種方式組合模塊,就可以用可複用的組件輕鬆地創建模型。
此外,從輸出結果可以看出還有grad_fn
。這是PyTorch自動微分求導給出的信息,稱爲autograd
。簡而言之,該系統允許我們通過潛在的複雜程序來計算導數。該設計爲模型創建提供了極大的靈活性。
我們用例子進一步說明模型構建的靈活性。在上述基礎上新增MyDecisionGate
,該模塊中用到形如循環或if語句的控制流。
class MyDecisionGate(torch.nn.Module):
def forward(self, x):
if x.sum() > 0:
return x
else:
return -x
class MyCell(torch.nn.Module):
def __init__(self):
super(MyCell, self).__init__()
self.dg = MyDecisionGate()
self.linear = torch.nn.Linear(4, 4)
def forward(self, x, h):
new_h = torch.tanh(self.dg(self.linear(x)) + h)
return new_h, new_h
my_cell = MyCell()
x = torch.rand(3, 4)
h = torch.rand(3, 4)
print(my_cell)
print(my_cell(x, h))
輸出結果:
MyCell(
(dg): MyDecisionGate()
(linear): Linear(in_features=4, out_features=4, bias=True)
)
(tensor([[ 0.6055, 0.5525, 0.8768, 0.6291],
[ 0.6550, 0.7678, 0.7121, -0.0692],
[ 0.1305, 0.2356, 0.7683, 0.4723]], grad_fn=<TanhBackward>), tensor([[ 0.6055, 0.5525, 0.8768, 0.6291],
[ 0.6550, 0.7678, 0.7121, -0.0692],
[ 0.1305, 0.2356, 0.7683, 0.4723]], grad_fn=<TanhBackward>))
TorchScript
以上述運行過的示例爲例,看看如何應用TorchScript。
追蹤(tracing)
簡而言之,鑑於原生PyTorch具有靈活和動態的特性,TorchScript也提供了捕獲模型定義的工具。其中一個核心的概念就是模型追蹤
(tracing)。
class MyCell(torch.nn.Module):
def __init__(self):
super(MyCell, self).__init__()
self.linear = torch.nn.Linear(4, 4)
def forward(self, x, h):
new_h = torch.tanh(self.linear(x) + h)
return new_h, new_h
my_cell = MyCell()
x, h = torch.rand(3, 4), torch.rand(3, 4)
traced_cell = torch.jit.trace(my_cell, (x, h))
print(traced_cell)
traced_cell(x, h)
運行結果:
TracedModule[MyCell](
(linear): TracedModule[Linear]()
)
與此前一樣,實例化MyCell
,但是這次,使用torch.jit.trace
方法調用Module,然後傳入了網絡的示例輸入。這到底是做什麼的?它已調用Module,記錄了Module運行時發生的操作,並創建了torch.jit.ScriptModule
實例(TracedModule的實例)。TorchScript將其定義記錄在中間表示(或IR)中,在深度學習中通常稱爲graph。我們可以通過訪問.graph
屬性來查看graph:
print(traced_cell.graph)
運行結果:
graph(%self : ClassType<MyCell>,
%input : Float(3, 4),
%h : Float(3, 4)):
%1 : ClassType<Linear> = prim::GetAttr[name="linear"](%self)
%weight : Tensor = prim::GetAttr[name="weight"](%1)
%bias : Tensor = prim::GetAttr[name="bias"](%1)
%6 : Float(4!, 4!) = aten::t(%weight), scope: MyCell/Linear[linear] # /home/data1/software/Anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1369:0
%7 : int = prim::Constant[value=1](), scope: MyCell/Linear[linear] # /home/data1/software/Anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1369:0
%8 : int = prim::Constant[value=1](), scope: MyCell/Linear[linear] # /home/data1/software/Anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1369:0
%9 : Float(3, 4) = aten::addmm(%bias, %input, %6, %7, %8), scope: MyCell/Linear[linear] # /home/data1/software/Anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1369:0
%10 : int = prim::Constant[value=1](), scope: MyCell # test_pytorch.py:9:0
%11 : Float(3, 4) = aten::add(%9, %h, %10), scope: MyCell # test_pytorch.py:9:0
%12 : Float(3, 4) = aten::tanh(%11), scope: MyCell # test_pytorch.py:9:0
%13 : (Float(3, 4), Float(3, 4)) = prim::TupleConstruct(%12, %12)
return (%13)
但是,這是一個非常低級的表示形式,圖中包含的大多數信息對最終用戶沒有用。相反,我們可以使用.code
屬性爲代碼提供Python語法的解釋:
print(traced_cell.code)
輸出結果:
def forward(self,
input: Tensor,
h: Tensor) -> Tuple[Tensor, Tensor]:
_0 = self.linear
weight = _0.weight
bias = _0.bias
_1 = torch.addmm(bias, input, torch.t(weight), beta=1, alpha=1)
_2 = torch.tanh(torch.add(_1, h, alpha=1))
return (_2, _2)
那麼爲什麼我們要做所有這些呢?有以下幾個原因:
- TorchScript代碼可以在其自己的解釋器中調用,該解釋器基本上是受限制的Python解釋器。該解釋器不獲取全局解釋器鎖,因此可以在同一實例上同時處理許多請求。
- 這種格式使我們可以將整個模型保存到磁盤上,並可以在另一個環境中加載,例如在以非Python語言編寫的服務中。
- TorchScript爲我們提供了一種表示形式,通過TorchScript我們可以對代碼進行編譯器優化以提供更有效的執行。
- 通過TorchScript可以與許多後端/設備運行時進行接口,這些運行時比單個操作需要更廣泛的程序視圖。
可以看到調用traced_cell
產生的結果與直接執行Python模塊結果是相同的:
運行:
print(my_cell(x, h))
print(traced_cell(x, h))
運行結果:
(tensor([[0.6964, 0.5208, 0.7205, 0.6677],
[0.6465, 0.3342, 0.7431, 0.5376],
[0.5603, 0.1212, 0.9433, 0.8053]], grad_fn=<TanhBackward>), tensor([[0.6964, 0.5208, 0.7205, 0.6677],
[0.6465, 0.3342, 0.7431, 0.5376],
[0.5603, 0.1212, 0.9433, 0.8053]], grad_fn=<TanhBackward>))
(tensor([[0.6964, 0.5208, 0.7205, 0.6677],
[0.6465, 0.3342, 0.7431, 0.5376],
[0.5603, 0.1212, 0.9433, 0.8053]],
grad_fn=<DifferentiableGraphBackward>), tensor([[0.6964, 0.5208, 0.7205, 0.6677],
[0.6465, 0.3342, 0.7431, 0.5376],
[0.5603, 0.1212, 0.9433, 0.8053]],
grad_fn=<DifferentiableGraphBackward>))
使用 Scripting to Convert Modules
我們使用模塊的第二個版本,即traced_cell(x, h)
是有原因的,而不是使用帶有控制流的子模塊的一個版本。讓我們以下述示例來闡述其背後的原因。
class MyDecisionGate(torch.nn.Module):
def forward(self, x):
if x.sum() > 0:
return x
else:
return -x
class MyCell(torch.nn.Module):
def __init__(self, dg):
super(MyCell, self).__init__()
self.dg = dg
self.linear = torch.nn.Linear(4, 4)
def forward(self, x, h):
new_h = torch.tanh(self.dg(self.linear(x)) + h)
return new_h, new_h
my_cell = MyCell(MyDecisionGate())
x, h = torch.rand(3, 4), torch.rand(3, 4)
traced_cell = torch.jit.trace(my_cell, (x, h))
print(traced_cell.code)
輸出結果:
test_pytorch.py:4: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if x.sum() > 0:
def forward(self,
input: Tensor,
h: Tensor) -> Tuple[Tensor, Tensor]:
_0 = self.linear
weight = _0.weight
bias = _0.bias
x = torch.addmm(bias, input, torch.t(weight), beta=1, alpha=1)
_1 = torch.tanh(torch.add(torch.neg(x), h, alpha=1))
return (_1, _1)
根據.code
的輸出,可以發現if-else
的分支已經杳無蹤跡!爲什麼?Tracing
完全按照我們所說的去做:運行代碼,記錄發生的操作,並構造一個可以做到這一點的ScriptModule
。不幸的是,在這個運行過程,諸如控制流之類的信息被抹去了。
那麼如何在TorchScript
中如實地表示此模塊?PyTorch提供了一個腳本編譯器,它可以直接分Python源代碼以將其轉換爲TorchScript
。對上述的MyDecisionGate
使用腳本編譯器進行轉換:
scripted_gate = torch.jit.script(MyDecisionGate()) # 看這裏
my_cell = MyCell(scripted_gate)
traced_cell = torch.jit.script(my_cell) # 看這裏
print(traced_cell.code)
運行結果:
def forward(self,
x: Tensor,
h: Tensor) -> Tuple[Tensor, Tensor]:
_0 = self.linear
_1 = _0.weight
_2 = _0.bias
if torch.eq(torch.dim(x), 2):
_3 = torch.__isnot__(_2, None)
else:
_3 = False
if _3:
bias = ops.prim.unchecked_unwrap_optional(_2)
ret = torch.addmm(bias, x, torch.t(_1), beta=1, alpha=1)
else:
output = torch.matmul(x, torch.t(_1))
if torch.__isnot__(_2, None):
bias0 = ops.prim.unchecked_unwrap_optional(_2)
output0 = torch.add_(output, bias0, alpha=1)
else:
output0 = output
ret = output0
_4 = torch.gt(torch.sum(ret, dtype=None), 0)
if bool(_4):
_5 = ret
else:
_5 = torch.neg(ret)
new_h = torch.tanh(torch.add(_5, h, alpha=1))
return (new_h, new_h)
現在,已經可以如實地捕獲了在TorchScript中程序的行爲。現在嘗試運行該程序:
# New inputs
x, h = torch.rand(3, 4), torch.rand(3, 4)
print(traced_cell(x, h))
運行結果:
(tensor([[ 0.3430, -0.3471, 0.7990, 0.8313],
[-0.4042, -0.3058, 0.7758, 0.8332],
[-0.3002, -0.3926, 0.8468, 0.7715]],
grad_fn=<DifferentiableGraphBackward>), tensor([[ 0.3430, -0.3471, 0.7990, 0.8313],
[-0.4042, -0.3058, 0.7758, 0.8332],
[-0.3002, -0.3926, 0.8468, 0.7715]],
grad_fn=<DifferentiableGraphBackward>))
注意,本文實驗的PyTorch版本是1.2.0+cu92
。
混合腳本(Scripting)和追蹤(Tracing)
在某些情況下,只需追蹤的的結果而不需要腳本,例如,模塊具有許多條件分支,這些分支我們並不希望展現在TorchScript中。在這種情況下,腳本可以與用以下方法追蹤:torch.jit.script
。torch.jit.script
只會追蹤方法內的腳本,不會展示方法外的腳本情況。
基於上述示例修改如下:
class MyDecisionGate(torch.nn.Module):
def forward(self, x):
if x.sum() > 0:
return x
else:
return -x
class MyCell(torch.nn.Module):
def __init__(self, dg):
super(MyCell, self).__init__()
self.dg = dg
self.linear = torch.nn.Linear(4, 4)
def forward(self, x, h):
new_h = torch.tanh(self.dg(self.linear(x)) + h)
return new_h, new_h
scripted_gate = torch.jit.script(MyDecisionGate())
x, h = torch.rand(3, 4), torch.rand(3, 4)
class MyRNNLoop(torch.nn.Module):
def __init__(self):
super(MyRNNLoop, self).__init__()
self.cell = torch.jit.trace(MyCell(scripted_gate), (x, h)) # 看這裏,混合使用
def forward(self, xs):
h, y = torch.zeros(3, 4), torch.zeros(3, 4)
for i in range(xs.size(0)):
y, h = self.cell(xs[i], h)
return y, h
rnn_loop = torch.jit.script(MyRNNLoop())
print(rnn_loop.code)
運行結果:
def forward(self,
xs: Tensor) -> Tuple[Tensor, Tensor]:
h = torch.zeros([3, 4], dtype=None, layout=None, device=None, pin_memory=None)
y = torch.zeros([3, 4], dtype=None, layout=None, device=None, pin_memory=None)
y0, h0 = y, h
for i in range(torch.size(xs, 0)):
_0 = self.cell
_1 = torch.select(xs, 0, i)
_2 = _0.linear
weight = _2.weight
bias = _2.bias
_3 = torch.addmm(bias, _1, torch.t(weight), beta=1, alpha=1)
_4 = torch.gt(torch.sum(_3, dtype=None), 0)
if bool(_4):
_5 = _3
else:
_5 = torch.neg(_3)
_6 = torch.tanh(torch.add(_5, h0, alpha=1))
y0, h0 = _6, _6
return (y0, h0)
在上面的基礎上再包裝一層WrapRNN
類,具體如下:
class MyDecisionGate(torch.nn.Module):
def forward(self, x):
if x.sum() > 0:
return x
else:
return -x
class MyCell(torch.nn.Module):
def __init__(self, dg):
super(MyCell, self).__init__()
self.dg = dg
self.linear = torch.nn.Linear(4, 4)
def forward(self, x, h):
new_h = torch.tanh(self.dg(self.linear(x)) + h)
return new_h, new_h
scripted_gate = torch.jit.script(MyDecisionGate())
x, h = torch.rand(3, 4), torch.rand(3, 4)
class MyRNNLoop(torch.nn.Module):
def __init__(self):
super(MyRNNLoop, self).__init__()
self.cell = torch.jit.trace(MyCell(scripted_gate), (x, h)) # 看這裏,混合使用
def forward(self, xs):
h, y = torch.zeros(3, 4), torch.zeros(3, 4)
for i in range(xs.size(0)):
y, h = self.cell(xs[i], h)
return y, h
class WrapRNN(torch.nn.Module):
def __init__(self):
super(WrapRNN, self).__init__()
self.loop = torch.jit.script(MyRNNLoop())
def forward(self, xs):
y, h = self.loop(xs)
return torch.relu(y)
traced = torch.jit.trace(WrapRNN(), (torch.rand(10, 3, 4)))
print(traced.code)
運行輸出結果:
def forward(self,
argument_1: Tensor) -> Tensor:
_0 = self.loop
h = torch.zeros([3, 4], dtype=None, layout=None, device=None, pin_memory=None)
h0 = h
for i in range(torch.size(argument_1, 0)):
_1 = _0.cell
_2 = torch.select(argument_1, 0, i)
_3 = _1.linear
weight = _3.weight
bias = _3.bias
_4 = torch.addmm(bias, _2, torch.t(weight), beta=1, alpha=1)
_5 = torch.gt(torch.sum(_4, dtype=None), 0)
if bool(_5):
_6 = _4
else:
_6 = torch.neg(_4)
h0 = torch.tanh(torch.add(_6, h0, alpha=1))
return torch.relu(h0)
保存和加載TorchScript模型
PyTorch提供API,以存檔格式將TorchScript模塊保存到磁盤或從磁盤加載TorchScript模塊。這種格式包括代碼,參數,屬性和調試信息,這意味着歸檔文件是模型的獨立表示形式,可以在完全獨立的過程中加載。
對上述示例中的RNN模型進行保存並加載如下:
traced.save('wrapped_rnn.zip')
loaded = torch.jit.load('wrapped_rnn.zip')
print(loaded)
print(loaded.code)
運行結果:
ScriptModule(
(loop): ScriptModule(
(cell): ScriptModule(
(dg): ScriptModule()
(linear): ScriptModule()
)
)
)
def forward(self,
argument_1: Tensor) -> Tensor:
_0 = self.loop
h = torch.zeros([3, 4], dtype=None, layout=None, device=None, pin_memory=None)
h0 = h
for i in range(torch.size(argument_1, 0)):
_1 = _0.cell
_2 = torch.select(argument_1, 0, i)
_3 = _1.linear
weight = _3.weight
bias = _3.bias
_4 = torch.addmm(bias, _2, torch.t(weight), beta=1, alpha=1)
_5 = torch.gt(torch.sum(_4, dtype=None), 0)
if bool(_5):
_6 = _4
else:
_6 = torch.neg(_4)
h0 = torch.tanh(torch.add(_6, h0, alpha=1))
return torch.relu(h0)
從上述結果可以看出,序列化保留了模塊層次結構和代碼。也可以將模型加載到C ++中以實現不依賴Python的執行。下面我們就介紹在C++中如何加載模型並進行推理操作。
在C++中加載TorchScript模型
Step 1:將PyTorch模型轉換爲Torch Script
將PyTorch模型從Python轉到C++需要通過Torch Script
實現。Torch Script 是PyTorch模型的一種表示,它可以被Torch Script 編譯器理解、編譯和序列化。 如果用普通的“eager”API編寫PyTorch模型,則必須首先將模型轉換爲 Torch Script。
前面章節已經介紹過2種將PyTorch模型轉換爲Torch Script 的方法。第一種是追蹤(tracing),通過實例輸入對模型結構做一次評估,並記錄這些輸入通過模型的流動狀態。該方法適用於模型有限使用控制流的情況。第二種方法是在模型中添加明確的註釋,使得Torch Script 編譯器可以直接解析和編譯模型代碼。更詳細資料可以參考Torch Script reference
通過Tracing
要通過追蹤方式將PyTorch模型轉換爲Torch Script,必須將帶有樣例輸入的模型實例輸入到torch.jit.trace
函數。這將產生一個torch.jit.ScriptModule
對象,該對象在forward 方法中嵌入模型評估的追蹤。
具體使用示例如下:
import torch
import torchvision
# An instance of your model.
model = torchvision.models.resnet18()
# An example input you would normally provide to your model's forward() method.
example = torch.rand(1, 3, 224, 224)
# Use torch.jit.trace to generate a torch.jit.ScriptModule via tracing.
traced_script_module = torch.jit.trace(model, example)
被追蹤的ScriptModule
對象,現在可以被視爲常規的PyTorch模塊。
output = traced_script_module(torch.ones(1, 3, 224, 224))
print(output[0, :5])
輸出結果:
tensor([0.7741, 0.0539, 0.6656, 0.7301, 0.2207], grad_fn=<SliceBackward>)
通過Annotation(註釋)
在某些情況下,例如,如果模型採用控制流的特定形式,那麼直接以Torch Script 寫出模型,並相應地標註模型也許是更好的選擇。以下述Pytorch模型爲例展開說明:
import torch
class MyModule(torch.nn.Module):
def __init__(self, N, M):
super(MyModule, self).__init__()
self.weight = torch.nn.Parameter(torch.rand(N, M))
def forward(self, input):
if input.sum() > 0:
output = self.weight.mv(input)
else:
output = self.weight + input
return output
因爲這個模塊中的forward
方法使用依賴於輸入的控制流依,這種模塊不適合於追蹤方法。相反,可以將其轉換爲ScriptModule
。爲了將模塊轉換爲ScriptModule
,需要用torch.jit.script
編譯模塊:
class MyModule(torch.nn.Module):
def __init__(self, N, M):
super(MyModule, self).__init__()
self.weight = torch.nn.Parameter(torch.rand(N, M))
def forward(self, input):
if input.sum() > 0:
output = self.weight.mv(input)
else:
output = self.weight + input
return output
my_module = MyModule(10,20)
sm = torch.jit.script(my_module)
另外,對於nn.Module
中不需要的方法(因爲TorchScript對於有些python特性目前是不支持的),可以用@torch.jit.ignore
將其去除。
Step 2:將Script Module序列化到文件中
對於獲取到的ScriptModule
對象(不管是用tracing方法還是annotation方法得到的),可以將其序列化爲一個文件,以便後續在其他環境(如C++)中使用。具體序列化方式如下:
traced_script_module.save("traced_resnet_model.pt")
如果同時想要序列化模塊my_module
,可以使用my_module.save("my_module_model.pt")
。
Step 3:在C++中加載Torch Script模塊
在C++中加載序列化的PyTorch模型需要用到PyTorch C++ API,即LibTorch
庫。LibTorch
中有共享庫、頭文件和CMake構建配置文件。
最簡化的C++應用
example-app.cpp
的內容如下:
#include <torch/script.h> // One-stop header.
#include <iostream>
#include <memory>
int main(int argc, const char* argv[]) {
if (argc != 2) {
std::cerr << "usage: example-app <path-to-exported-script-module>\n";
return -1;
}
torch::jit::script::Module module;
try {
// Deserialize the ScriptModule from a file using torch::jit::load().
module = torch::jit::load(argv[1]);
}
catch (const c10::Error& e) {
std::cerr << "error loading the model\n";
return -1;
}
std::cout << "ok\n";
}
其中頭文件<torch/script.h>
包括了運行示例所必需的LibTorch庫中所有的相關依賴。上述示例接收序列化的ScriptModule
文件,並通過torch::jit::load()
加載序列化的文件,返回結果是torch::jit::script::Module
對象。
構建依賴和創建
上述代碼對應的CMakeLists.txt內容如下:
cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
project(custom_ops)
find_package(Torch REQUIRED)
add_executable(example-app example-app.cpp)
target_link_libraries(example-app "${TORCH_LIBRARIES}")
set_property(TARGET example-app PROPERTY CXX_STANDARD 11)
從官方下載libtorch,並解壓:
其中lib
目錄包含鏈接時所需的共享庫;include
包含程序中用到的頭文件;share
目錄包含必要的CMake配置,以方便上面find_package(Torch)
命令的使用。
最後還需要構建應用程序。假設目錄佈局如下:
example-app/
CMakeLists.txt
example-app.cpp
可以運行下面的命令來從example-app/
文件夾內構建應用程序:
mkdir build
cd build
cmake -DCMAKE_PREFIX_PATH=/home/data1/devtools/libtorch/ ..
make
這裏DCMAKE_PREFIX_PATH
值爲下載libtorch
後解包的位置。
編譯後,運行方式如下:
./example-app <path_to_model>/traced_resnet_model.pt
Step 4:在C++中執行Script Module
上述的介紹已經能夠實現在C++中加載序列化的ResNet18,現在需要做的是運行模型進行推理。具體如下:
// Create a vector of inputs.
std::vector<torch::jit::IValue> inputs;
inputs.push_back(torch::ones({1, 3, 224, 224}));
// Execute the model and turn its output into a tensor.
at::Tensor output = module.forward(inputs).toTensor();
std::cout << output.slice(/*dim=*/1, /*start=*/0, /*end=*/5) << '\n';
上述代碼的前2行是模型的輸入,再調用script::Module
中的forward
方法,返回結果的類型是IValue
,需要進一步通過toTensor()
轉爲tensor。
注意:如果想把模型以GPU運行,則只需對模型處理如下:model.to(at::kCUDA);
。同時要確保模型的輸入也在CUDA內存中,可以用以下方式實現:tensor.to(at::kCUDA)
,則會返回一個新的位於CUDA內存中的tensor。
圖像分類實例
環境準備
需要預先安裝cmake、opencv、 PyTroch 1.2。 在opencv安裝過程可能會出現一些諸如gcc版本(本文使用的gcc5.2)過低等環境安裝問題,這裏就展開說明了。
C++中加載模型
以使用resnet18模型進行圖像分類爲例。
Step 1:將PyTorch模型轉爲Torch Script
運行如下腳本:
import torch
import torchvision
from torchvision import transforms
from PIL import Image
from time import time
import numpy as np
# An instance of your model.
model = torchvision.models.resnet18(pretrained=True)
model.eval()
# An example input you would normally provide to your model's forward() method.
example = torch.rand(1, 3, 224, 224)
# Use torch.jit.trace to generate a torch.jit.ScriptModule via tracing.
traced_script_module = torch.jit.trace(model, example)
traced_script_module.save("model.pt")
# evalute time
batch = torch.rand(64, 3, 224, 224)
start = time()
output = traced_script_module(batch)
stop = time()
print(str(stop-start) + "s")
# read image
image = Image.open('dog.png').convert('RGB')
default_transform = transforms.Compose([
transforms.Resize([224, 224]),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
image = default_transform(image)
# forward
output = traced_script_module(image.unsqueeze(0))
print(output[0, :10])
# print top-5 predicted labels
labels = np.loadtxt('synset_words.txt', dtype=str, delimiter='\n')
data_out = output[0].data.numpy()
sorted_idxs = np.argsort(-data_out)
for i,idx in enumerate(sorted_idxs[:5]):
print('top-%d label: %s, score: %f' % (i, labels[idx], data_out[idx]))
獲得model.pt
Step 2:在C++中調用Torch Script
(1)需要先下載LibTorch
並解包,在make編譯時候需要指定該lib的路徑。
(2)利用cmake工具對業務代碼,即使用Torch Script的代碼進行編譯
mkdir build
cd build
cmake -DCMAKE_PREFIX_PATH=/home/data1/devtools/libtorch ..
make
(3)運行
./example-app ../model.pt ../dog.png ../synset_words.txt
打印結果:
top-1 label:n02108422 bull mastiff
its score:17.9795
top-2 label:n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier
its score:13.3846
top-3 label:n02109047 Great Dane
its score:12.8465
top-4 label:n02093256 Staffordshire bullterrier, Staffordshire bull terrier
its score:12.1885
top-5 label:n02110958 pug, pug-dog
its score:11.9975
從打印結果可以看出,預測結果爲n02108422 bull mastiff
,即牛頭獒。
先看下輸入圖像:
再網絡搜索bull mastiff
確認:
附上完整代碼:
#include <torch/script.h>
#include <torch/torch.h>
//#include <torch/serialize/Tensor.h>
#include <ATen/Tensor.h>
#include <opencv2/opencv.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/imgproc/types_c.h>
#include <iostream>
#include <memory>
#include <string>
#include <vector>
/* main */
int main(int argc, const char* argv[]) {
if (argc < 4) {
std::cerr << "usage: example-app <path-to-exported-script-module> "
<< "<path-to-image> <path-to-category-text>\n";
return -1;
}
// Deserialize the ScriptModule from a file using torch::jit::load().
//std::shared_ptr<torch::jit::script::Module> module = torch::jit::load(argv[1]);
torch::jit::script::Module module = torch::jit::load(argv[1]);
std::cout << "load model ok\n";
// Create a vector of inputs.
std::vector<torch::jit::IValue> inputs;
inputs.push_back(torch::rand({64, 3, 224, 224}));
// evalute time
double t = (double)cv::getTickCount();
module.forward(inputs).toTensor();
t = (double)cv::getTickCount() - t;
printf("execution time = %gs\n", t / cv::getTickFrequency());
inputs.pop_back();
// load image with opencv and transform
cv::Mat image;
image = cv::imread(argv[2], 1);
cv::cvtColor(image, image, CV_BGR2RGB);
cv::Mat img_float;
image.convertTo(img_float, CV_32F, 1.0/255);
cv::resize(img_float, img_float, cv::Size(224, 224));
//std::cout << img_float.at<cv::Vec3f>(56,34)[1] << std::endl;
//auto img_tensor = torch::CPU(torch::kFloat32).tensorFromBlob(img_float.data, {1, 224, 224, 3});
auto img_tensor = torch::from_blob(img_float.data, {1, 224, 224, 3});//.to(torch::CPU);
img_tensor = img_tensor.permute({0,3,1,2});
img_tensor[0][0] = img_tensor[0][0].sub_(0.485).div_(0.229);
img_tensor[0][1] = img_tensor[0][1].sub_(0.456).div_(0.224);
img_tensor[0][2] = img_tensor[0][2].sub_(0.406).div_(0.225);
inputs.push_back(img_tensor);
// Execute the model and turn its output into a tensor.
torch::Tensor out_tensor = module.forward(inputs).toTensor();
std::cout << out_tensor.slice(/*dim=*/1, /*start=*/0, /*end=*/10) << '\n';
// Load labels
std::string label_file = argv[3];
std::ifstream rf(label_file.c_str());
CHECK(rf) << "Unable to open labels file " << label_file;
std::string line;
std::vector<std::string> labels;
while (std::getline(rf, line))
labels.push_back(line);
std::cout << "Found all " << labels.size() << " labels"<<std::endl;
// print predicted top-5 labels
std::tuple<torch::Tensor,torch::Tensor> result = out_tensor.sort(-1, true);
torch::Tensor top_scores = std::get<0>(result)[0];
torch::Tensor top_idxs = std::get<1>(result)[0].toType(torch::kInt32);
auto top_scores_a = top_scores.accessor<float,1>();
auto top_idxs_a = top_idxs.accessor<int,1>();
for (int i = 0; i < 5; ++i)
{
int idx = top_idxs_a[i];
std::cout<<"top-" << i+1 << " label:"<<labels[idx]<<std::endl;
//printf("top-%s")
std::cout<<"its score:"<<top_scores_a[i]<<std::endl;
}
// cv::imshow("image", image);
// cv::waitKey(0);
return 0;
}
參考資料
https://pytorch.org/blog/model-serving-in-pyorch/
https://medium.com/datadriveninvestor/deploy-your-pytorch-model-to-production-f69460192217
https://github.com/iamhankai/cpp-pytorch
https://pytorch.org/tutorials/advanced/cpp_export.html#step-1-converting-your-pytorch-model-to-torch-script