なにこれ
- torchsummaryとtorch-summaryの話
- 結論：torchsummaryを使っていた人はtorchinfoに変えよう。
追記(2021.2.5)
- 名前がtorch-summaryからtorchinfoに変わりました。
- タイトル、結論、記事末尾のリンクだけ修正しました。
環境
tensorflow: 2.3.0
pytorch: 1.6.0
python: 3.7.2
torchsummary: 1.5.1
torch-summary: 1.4.1
summaryがほしいよね
書いたモデルをデバグする際に、さっと可視化できると非常に便利ですが、PyTorchにはtf.kerasのmodel.summary()がなく、print(model)することになります。
keras_summary.py
# https://keras.io/api/models/model/
import tensorflow as tf
inputs = tf.keras.Input(shape=(3,))
x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)
outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
if __name__ == "__main__":
model.summary()
output
$ python keras_summary.py
Model: "functional_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense (Dense) (None, 4) 16
_________________________________________________________________
dense_1 (Dense) (None, 5) 25
=================================================================
Total params: 41
Trainable params: 41
Non-trainable params: 0
_________________________________________________________________
pytorch_print.py
import torch.nn as nn
model = nn.Sequential(
nn.Linear(3, 4),
nn.ReLU(),
nn.Linear(4, 5),
nn.Softmax(dim=1)
)
if __name__ == "__main__":
print(model)
output
$ python pytorch_print.py
Model(
(dense1): Linear(in_features=3, out_features=4, bias=True)
(relu): ReLU()
(dense2): Linear(in_features=4, out_features=5, bias=True)
(softmax): Softmax(dim=1)
)
そこで現れたtorchsummary
issueの流れで颯爽と現れ、この味気ないモデル表示をkerasっぽくして情報をリッチにできるのがtorchsummaryです。
installation
$ pip install torchsummary
pytorch_summary.py
import torch.nn as nn
from torchsummary import summary
model = nn.Sequential(
nn.Linear(3, 4),
nn.ReLU(),
nn.Linear(4, 5),
nn.Softmax(dim=1)
)
if __name__ == "__main__":
model = model.to("cuda")
summary(model, (1,3))
output
$ python pytorch_summary.py
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Linear-1 [-1, 1, 4] 16
ReLU-2 [-1, 1, 4] 0
Linear-3 [-1, 1, 5] 25
Softmax-4 [-1, 1, 5] 0
================================================================
Total params: 41
Trainable params: 41
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.00
Params size (MB): 0.00
Estimated Total Size (MB): 0.00
----------------------------------------------------------------
しかしこのtorchsummary、開発が止まっている模様。
pypiからインストールするとコードが古く、これをしないとmultiple inputsに対応できませんでした。
torch-summaryが更に情報をリッチに
- torchsummaryがmodelをユーザーがto("cuda")しなければならなかった点を解消
- 実際のコードを書き換える必要がない
- 親子関係が見やすくなる
installation
# torchsummaryが機能しなくなります。
$ pip install torch-summary
pytorch_torch-summry
# pipでインストールするだけで出力が変わる
$ python pytorch_summary.py
==========================================================================================
Layer (type:depth-idx) Output Shape Param #
==========================================================================================
├─Sequential: 1-1 [-1, 1, 5] --
| └─Linear: 2-1 [-1, 1, 4] 16
| └─ReLU: 2-2 [-1, 1, 4] --
| └─Linear: 2-3 [-1, 1, 5] 25
| └─Softmax: 2-4 [-1, 1, 5] --
==========================================================================================
Total params: 41
Trainable params: 41
Non-trainable params: 0
Total mult-adds (M): 0.00
==========================================================================================
Input size (MB): 0.00
Forward/backward pass size (MB): 0.00
Params size (MB): 0.00
Estimated Total Size (MB): 0.00
==========================================================================================
この他にも、複数インプットがあるときに構造が見やすくなります。
torchsummary_multiple.py
import torch.nn as nn
from torchsummary import summary
class Model(nn.Module):
def __init__(self):
super().__init__()
self.model = nn.Sequential(
nn.Linear(3, 4),
nn.ReLU(),
nn.Linear(4, 5),
nn.Softmax(dim=1)
)
def forward(self, x, y):
x = self.model(x)
y = self.model(y)
return x, y
if __name__ == "__main__":
model = Model().cuda()
summary(model, [(1, 3), (1, 3)])
output
$ python torchsummary_multiple.py
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Linear-1 [-1, 1, 4] 16
ReLU-2 [-1, 1, 4] 0
Linear-3 [-1, 1, 5] 25
Softmax-4 [-1, 1, 5] 0
Linear-5 [-1, 1, 4] 16
ReLU-6 [-1, 1, 4] 0
Linear-7 [-1, 1, 5] 25
Softmax-8 [-1, 1, 5] 0
================================================================
torch-summary_multiple.py
import torch.nn as nn
from torchsummary import summary
class Model(nn.Module):
def __init__(self):
super().__init__()
self.model = nn.Sequential(
nn.Linear(3, 4),
nn.ReLU(),
nn.Linear(4, 5),
nn.Softmax(dim=1)
)
def forward(self, x, y):
x = self.model(x)
y = self.model(y)
return x, y
if __name__ == "__main__":
model = Model()
summary(model, [(1, 3), (1, 3)])
output
$ python torch-summary_multiple.py
==========================================================================================
Layer (type:depth-idx) Output Shape Param #
==========================================================================================
├─Sequential: 1-1 [-1, 1, 5] --
| └─Linear: 2-1 [-1, 1, 4] 16
| └─ReLU: 2-2 [-1, 1, 4] --
| └─Linear: 2-3 [-1, 1, 5] 25
| └─Softmax: 2-4 [-1, 1, 5] --
├─Sequential: 1-2 [-1, 1, 5] (recursive)
| └─Linear: 2-5 [-1, 1, 4] (recursive)
| └─ReLU: 2-6 [-1, 1, 4] --
| └─Linear: 2-7 [-1, 1, 5] (recursive)
| └─Softmax: 2-8 [-1, 1, 5] --
==========================================================================================
実はtorchsummaryXもある
torch-summaryは、torchsummaryとtorchsummaryXの後継と自称しています。
torchsummaryXから乗り換える際は下の2つの変更が必要かと思います。
-
from torchsummaryX=>from torchsummary - summary()の引数
inputは、input.shapeに変更