トーチサマリは、モデルが cuda に渡されるかどうかに注意を払う必要があり、デバイス パラメータと一致している必要があります。
参考 https://www.csdn.net/tags/OtDaMg0sMjg2OTEtYmxvZwO0O0OO0O0O.html
torchsummary.summary(model, input_size, batch_size=-1, device="cuda")
model：pytorch 模型，必须继承自 nn.Module
input_size：模型输入 size，形状为 C，H ，W
batch_size：batch_size，默认为 -1，在展示模型每层输出的形状时显示的 batch_size
device："cuda"或者"cpu"
使用时需要注意，默认device=‘cuda’，如果是在‘cpu’，那么就需要更改。不匹配就会出现下面的错误：
# RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same
1 モデルもデバイスも cuda に渡されません
from torchsummary import summary
from Unet import Unet
model = Unet()
summary(model=model, input_size=(3,256,256),batch_size=2, device="cpu")
# 结果
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [2, 8, 256, 256] 224
ReLU-2 [2, 8, 256, 256] 0
Conv2d-3 [2, 8, 256, 256] 584
ReLU-4 [2, 8, 256, 256] 0
MaxPool2d-5 [2, 8, 128, 128] 0
Conv2d-6 [2, 16, 128, 128] 1,168
ReLU-7 [2, 16, 128, 128] 0
Conv2d-8 [2, 16, 128, 128] 2,320
ReLU-9 [2, 16, 128, 128] 0
MaxPool2d-10 [2, 16, 64, 64] 0
Conv2d-11 [2, 32, 64, 64] 4,640
ReLU-12 [2, 32, 64, 64] 0
Conv2d-13 [2, 32, 64, 64] 9,248
ReLU-14 [2, 32, 64, 64] 0
MaxPool2d-15 [2, 32, 32, 32] 0
Conv2d-16 [2, 64, 32, 32] 18,496
ReLU-17 [2, 64, 32, 32] 0
Conv2d-18 [2, 64, 32, 32] 36,928
ReLU-19 [2, 64, 32, 32] 0
MaxPool2d-20 [2, 64, 16, 16] 0
Conv2d-21 [2, 128, 16, 16] 73,856
ReLU-22 [2, 128, 16, 16] 0
Conv2d-23 [2, 128, 16, 16] 147,584
ReLU-24 [2, 128, 16, 16] 0
MaxPool2d-25 [2, 128, 8, 8] 0
Conv2d-26 [2, 256, 8, 8] 295,168
ReLU-27 [2, 256, 8, 8] 0
Conv2d-28 [2, 256, 8, 8] 590,080
ReLU-29 [2, 256, 8, 8] 0
MaxPool2d-30 [2, 256, 4, 4] 0
Conv2d-31 [2, 512, 4, 4] 1,180,160
ReLU-32 [2, 512, 4, 4] 0
Conv2d-33 [2, 512, 4, 4] 2,359,808
ReLU-34 [2, 512, 4, 4] 0
MaxPool2d-35 [2, 512, 2, 2] 0
Conv2d-36 [2, 1024, 2, 2] 4,719,616
ReLU-37 [2, 1024, 2, 2] 0
Conv2d-38 [2, 1024, 2, 2] 9,438,208
ReLU-39 [2, 1024, 2, 2] 0
ConvTranspose2d-40 [2, 512, 4, 4] 8,388,608
ReLU-41 [2, 512, 4, 4] 0
Conv2d-42 [2, 512, 4, 4] 4,719,104
ReLU-43 [2, 512, 4, 4] 0
Conv2d-44 [2, 512, 4, 4] 2,359,808
ReLU-45 [2, 512, 4, 4] 0
ConvTranspose2d-46 [2, 256, 8, 8] 2,097,152
ReLU-47 [2, 256, 8, 8] 0
Conv2d-48 [2, 256, 8, 8] 1,179,904
ReLU-49 [2, 256, 8, 8] 0
Conv2d-50 [2, 256, 8, 8] 590,080
ReLU-51 [2, 256, 8, 8] 0
ConvTranspose2d-52 [2, 128, 16, 16] 524,288
ReLU-53 [2, 128, 16, 16] 0
Conv2d-54 [2, 128, 16, 16] 295,040
ReLU-55 [2, 128, 16, 16] 0
Conv2d-56 [2, 128, 16, 16] 147,584
ReLU-57 [2, 128, 16, 16] 0
ConvTranspose2d-58 [2, 64, 32, 32] 131,072
ReLU-59 [2, 64, 32, 32] 0
Conv2d-60 [2, 64, 32, 32] 73,792
ReLU-61 [2, 64, 32, 32] 0
Conv2d-62 [2, 64, 32, 32] 36,928
ReLU-63 [2, 64, 32, 32] 0
ConvTranspose2d-64 [2, 32, 64, 64] 32,768
ReLU-65 [2, 32, 64, 64] 0
Conv2d-66 [2, 32, 64, 64] 18,464
ReLU-67 [2, 32, 64, 64] 0
Conv2d-68 [2, 32, 64, 64] 9,248
ReLU-69 [2, 32, 64, 64] 0
ConvTranspose2d-70 [2, 16, 128, 128] 8,192
ReLU-71 [2, 16, 128, 128] 0
Conv2d-72 [2, 16, 128, 128] 4,624
ReLU-73 [2, 16, 128, 128] 0
Conv2d-74 [2, 16, 128, 128] 2,320
ReLU-75 [2, 16, 128, 128] 0
ConvTranspose2d-76 [2, 8, 256, 256] 2,048
ReLU-77 [2, 8, 256, 256] 0
Conv2d-78 [2, 8, 256, 256] 1,160
ReLU-79 [2, 8, 256, 256] 0
Conv2d-80 [2, 8, 256, 256] 584
ReLU-81 [2, 8, 256, 256] 0
Conv2d-82 [2, 1, 256, 256] 73
Sigmoid-83 [2, 1, 256, 256] 0
================================================================
Total params: 39,500,929
Trainable params: 39,500,929
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 1.50
Forward/backward pass size (MB): 164.97
Params size (MB): 150.68
Estimated Total Size (MB): 317.15
----------------------------------------------------------------
2 モデルとデバイスが cuda に渡される
from torchsummary import summary
from Unet import Unet
model = Unet().cuda()
summary(model=model, input_size=(3,256,256),batch_size=2) # 默认device="cuda"
# 结果
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [2, 8, 256, 256] 224
ReLU-2 [2, 8, 256, 256] 0
Conv2d-3 [2, 8, 256, 256] 584
ReLU-4 [2, 8, 256, 256] 0
MaxPool2d-5 [2, 8, 128, 128] 0
Conv2d-6 [2, 16, 128, 128] 1,168
ReLU-7 [2, 16, 128, 128] 0
Conv2d-8 [2, 16, 128, 128] 2,320
ReLU-9 [2, 16, 128, 128] 0
MaxPool2d-10 [2, 16, 64, 64] 0
Conv2d-11 [2, 32, 64, 64] 4,640
ReLU-12 [2, 32, 64, 64] 0
Conv2d-13 [2, 32, 64, 64] 9,248
ReLU-14 [2, 32, 64, 64] 0
MaxPool2d-15 [2, 32, 32, 32] 0
Conv2d-16 [2, 64, 32, 32] 18,496
ReLU-17 [2, 64, 32, 32] 0
Conv2d-18 [2, 64, 32, 32] 36,928
ReLU-19 [2, 64, 32, 32] 0
MaxPool2d-20 [2, 64, 16, 16] 0
Conv2d-21 [2, 128, 16, 16] 73,856
ReLU-22 [2, 128, 16, 16] 0
Conv2d-23 [2, 128, 16, 16] 147,584
ReLU-24 [2, 128, 16, 16] 0
MaxPool2d-25 [2, 128, 8, 8] 0
Conv2d-26 [2, 256, 8, 8] 295,168
ReLU-27 [2, 256, 8, 8] 0
Conv2d-28 [2, 256, 8, 8] 590,080
ReLU-29 [2, 256, 8, 8] 0
MaxPool2d-30 [2, 256, 4, 4] 0
Conv2d-31 [2, 512, 4, 4] 1,180,160
ReLU-32 [2, 512, 4, 4] 0
Conv2d-33 [2, 512, 4, 4] 2,359,808
ReLU-34 [2, 512, 4, 4] 0
MaxPool2d-35 [2, 512, 2, 2] 0
Conv2d-36 [2, 1024, 2, 2] 4,719,616
ReLU-37 [2, 1024, 2, 2] 0
Conv2d-38 [2, 1024, 2, 2] 9,438,208
ReLU-39 [2, 1024, 2, 2] 0
ConvTranspose2d-40 [2, 512, 4, 4] 8,388,608
ReLU-41 [2, 512, 4, 4] 0
Conv2d-42 [2, 512, 4, 4] 4,719,104
ReLU-43 [2, 512, 4, 4] 0
Conv2d-44 [2, 512, 4, 4] 2,359,808
ReLU-45 [2, 512, 4, 4] 0
ConvTranspose2d-46 [2, 256, 8, 8] 2,097,152
ReLU-47 [2, 256, 8, 8] 0
Conv2d-48 [2, 256, 8, 8] 1,179,904
ReLU-49 [2, 256, 8, 8] 0
Conv2d-50 [2, 256, 8, 8] 590,080
ReLU-51 [2, 256, 8, 8] 0
ConvTranspose2d-52 [2, 128, 16, 16] 524,288
ReLU-53 [2, 128, 16, 16] 0
Conv2d-54 [2, 128, 16, 16] 295,040
ReLU-55 [2, 128, 16, 16] 0
Conv2d-56 [2, 128, 16, 16] 147,584
ReLU-57 [2, 128, 16, 16] 0
ConvTranspose2d-58 [2, 64, 32, 32] 131,072
ReLU-59 [2, 64, 32, 32] 0
Conv2d-60 [2, 64, 32, 32] 73,792
ReLU-61 [2, 64, 32, 32] 0
Conv2d-62 [2, 64, 32, 32] 36,928
ReLU-63 [2, 64, 32, 32] 0
ConvTranspose2d-64 [2, 32, 64, 64] 32,768
ReLU-65 [2, 32, 64, 64] 0
Conv2d-66 [2, 32, 64, 64] 18,464
ReLU-67 [2, 32, 64, 64] 0
Conv2d-68 [2, 32, 64, 64] 9,248
ReLU-69 [2, 32, 64, 64] 0
ConvTranspose2d-70 [2, 16, 128, 128] 8,192
ReLU-71 [2, 16, 128, 128] 0
Conv2d-72 [2, 16, 128, 128] 4,624
ReLU-73 [2, 16, 128, 128] 0
Conv2d-74 [2, 16, 128, 128] 2,320
ReLU-75 [2, 16, 128, 128] 0
ConvTranspose2d-76 [2, 8, 256, 256] 2,048
ReLU-77 [2, 8, 256, 256] 0
Conv2d-78 [2, 8, 256, 256] 1,160
ReLU-79 [2, 8, 256, 256] 0
Conv2d-80 [2, 8, 256, 256] 584
ReLU-81 [2, 8, 256, 256] 0
Conv2d-82 [2, 1, 256, 256] 73
Sigmoid-83 [2, 1, 256, 256] 0
================================================================
Total params: 39,500,929
Trainable params: 39,500,929
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 1.50
Forward/backward pass size (MB): 164.97
Params size (MB): 150.68
Estimated Total Size (MB): 317.15
----------------------------------------------------------------
# 输出说明
# 上述信息分别有模型每层的输出形状，每层的参数数量，总的参数数量，以及模型大小等信息。