# 파이토치 모델의 개요(summary)를 어떻게 확인할 수 있나요? **URL:** \ **Category:** 자주 묻는 질문&답변\ **Tags:** 구조, deeplearning, 모델, 개요\ **Created:** [5월 25, 2021, 2:12오후 UTC](https://discuss.pytorch.kr/t/summary/32 "2021-05-25T14:12:25Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![9bow](https://discuss.pytorch.kr/user_avatar/discuss.pytorch.kr/9bow/32/16301_2.png) [@9bow](https://discuss.pytorch.kr/u/9bow)\ **Post date:** [5월 25, 2021, 2:12오후 UTC](https://discuss.pytorch.kr/t/summary/32/1 "2021-05-25T14:12:25Z")
공식 홈페이지와 [StackOverflow](https://stackoverflow.com/questions/tagged/pytorch) 등에서 자주 보이는 질문과 답변을 번역하고 있습니다. 다음 링크에서 원문을 함께 찾아보실 수 있습니다. - 원문: [https://stackoverflow.com/questions/42480111/model-summary-in-pytorch](https://stackoverflow.com/questions/42480111/model-summary-in-pytorch) * * * ## 질문 - 케라스(Keras)의 `model.summary()`처럼 모델의 개요(구조)를 보여주는 메소드는 없나요? ```python Model Summary: ____________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ==================================================================================================== input_1 (InputLayer) (None, 1, 15, 27) 0 ____________________________________________________________________________________________________ convolution2d_1 (Convolution2D) (None, 8, 15, 27) 872 input_1[0][0] ____________________________________________________________________________________________________ maxpooling2d_1 (MaxPooling2D) (None, 8, 7, 27) 0 convolution2d_1[0][0] ____________________________________________________________________________________________________ flatten_1 (Flatten) (None, 1512) 0 maxpooling2d_1[0][0] ____________________________________________________________________________________________________ dense_1 (Dense) (None, 1) 1513 flatten_1[0][0] ==================================================================================================== Total params: 2,385 Trainable params: 2,385 Non-trainable params: 0 ``` ---
**Author:** ![9bow](https://discuss.pytorch.kr/user_avatar/discuss.pytorch.kr/9bow/32/16301_2.png) [@9bow](https://discuss.pytorch.kr/u/9bow)\ **Post date:** [4월 23, 2022, 6:27오전 UTC](https://discuss.pytorch.kr/t/summary/32/2 "2022-04-23T06:27:33Z")
## 답변 - `torchinfo` 패키지 이용하면 비슷한 결과를 볼 수도 있습니다. (`torch-summary` 패키지도 있습니다.) ```python # `pip install torchinfo` 명령어로 패키지를 설치한 다음에 사용할 수 있습니다. >>> from torchvision import models >>> from torchinfo import summary >>> model = models.vgg16() >>> summary(model) ================================================================= Layer (type:depth-idx) Param # ================================================================= VGG -- ├─Sequential: 1-1 -- │ └─Conv2d: 2-1 1,792 │ └─ReLU: 2-2 -- │ └─Conv2d: 2-3 36,928 │ └─ReLU: 2-4 -- │ └─MaxPool2d: 2-5 -- │ └─Conv2d: 2-6 73,856 │ └─ReLU: 2-7 -- │ └─Conv2d: 2-8 147,584 │ └─ReLU: 2-9 -- │ └─MaxPool2d: 2-10 -- │ └─Conv2d: 2-11 295,168 │ └─ReLU: 2-12 -- │ └─Conv2d: 2-13 590,080 │ └─ReLU: 2-14 -- │ └─Conv2d: 2-15 590,080 │ └─ReLU: 2-16 -- │ └─MaxPool2d: 2-17 -- │ └─Conv2d: 2-18 1,180,160 │ └─ReLU: 2-19 -- │ └─Conv2d: 2-20 2,359,808 │ └─ReLU: 2-21 -- │ └─Conv2d: 2-22 2,359,808 │ └─ReLU: 2-23 -- │ └─MaxPool2d: 2-24 -- │ └─Conv2d: 2-25 2,359,808 │ └─ReLU: 2-26 -- │ └─Conv2d: 2-27 2,359,808 │ └─ReLU: 2-28 -- │ └─Conv2d: 2-29 2,359,808 │ └─ReLU: 2-30 -- │ └─MaxPool2d: 2-31 -- ├─AdaptiveAvgPool2d: 1-2 -- ├─Sequential: 1-3 -- │ └─Linear: 2-32 102,764,544 │ └─ReLU: 2-33 -- │ └─Dropout: 2-34 -- │ └─Linear: 2-35 16,781,312 │ └─ReLU: 2-36 -- │ └─Dropout: 2-37 -- │ └─Linear: 2-38 4,097,000 ================================================================= Total params: 138,357,544 Trainable params: 138,357,544 Non-trainable params: 0 ================================================================= ``` - 하지만, 아래와 같이 그냥 모델을 출력하기만 해도 어느 정도의 정보는 확인할 수 있습니다. ```python >>> from torchvision import models >>> model = models.vgg16() >>> print(model) VGG( (features): Sequential( (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): ReLU(inplace=True) (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): ReLU(inplace=True) (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (6): ReLU(inplace=True) (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (8): ReLU(inplace=True) (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (11): ReLU(inplace=True) (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (13): ReLU(inplace=True) (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (15): ReLU(inplace=True) (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (18): ReLU(inplace=True) (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (20): ReLU(inplace=True) (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (22): ReLU(inplace=True) (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (25): ReLU(inplace=True) (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (27): ReLU(inplace=True) (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (29): ReLU(inplace=True) (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ) (avgpool): AdaptiveAvgPool2d(output_size=(7, 7)) (classifier): Sequential( (0): Linear(in_features=25088, out_features=4096, bias=True) (1): ReLU(inplace=True) (2): Dropout(p=0.5, inplace=False) (3): Linear(in_features=4096, out_features=4096, bias=True) (4): ReLU(inplace=True) (5): Dropout(p=0.5, inplace=False) (6): Linear(in_features=4096, out_features=1000, bias=True) ) ) ``` ## 더 알아보기 - [GitHub - TylerYep/torchinfo: View model summaries in PyTorch!](https://github.com/TylerYep/torchinfo) - [GitHub - sksq96/pytorch-summary: Model summary in PyTorch similar to `model.summary()` in Keras](https://github.com/sksq96/pytorch-summary) - [Implement similar PyTorch function as model.summary() in keras? · Issue #2001 · pytorch/pytorch · GitHub](https://github.com/pytorch/pytorch/issues/2001)