Open HamaguchiKazuki opened 3 years ago
An overflow occurred when I ran the following code. This is why the model estimation, including batch size, is not successful.
import torch from torchvision import models from torchsummary import summary device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') vgg = models.vgg16().to(device) summary(vgg, (3, 600, 600), 20)
The output result is this.
---------------------------------------------------------------- Layer (type) Output Shape Param # ================================================================ Conv2d-1 [20, 64, 600, 600] 1,792 ReLU-2 [20, 64, 600, 600] 0 Conv2d-3 [20, 64, 600, 600] 36,928 ReLU-4 [20, 64, 600, 600] 0 MaxPool2d-5 [20, 64, 300, 300] 0 torchsummary.py:93: RuntimeWarning: overflow encountered in long_scalars total_output += np.prod(summary[layer]["output_shape"]) Conv2d-6 [20, 128, 300, 300] 73,856 ReLU-7 [20, 128, 300, 300] 0 Conv2d-8 [20, 128, 300, 300] 147,584 ReLU-9 [20, 128, 300, 300] 0 MaxPool2d-10 [20, 128, 150, 150] 0 Conv2d-11 [20, 256, 150, 150] 295,168 ReLU-12 [20, 256, 150, 150] 0 Conv2d-13 [20, 256, 150, 150] 590,080 ReLU-14 [20, 256, 150, 150] 0 Conv2d-15 [20, 256, 150, 150] 590,080 ReLU-16 [20, 256, 150, 150] 0 MaxPool2d-17 [20, 256, 75, 75] 0 Conv2d-18 [20, 512, 75, 75] 1,180,160 ReLU-19 [20, 512, 75, 75] 0 Conv2d-20 [20, 512, 75, 75] 2,359,808 ReLU-21 [20, 512, 75, 75] 0 Conv2d-22 [20, 512, 75, 75] 2,359,808 ReLU-23 [20, 512, 75, 75] 0 MaxPool2d-24 [20, 512, 37, 37] 0 Conv2d-25 [20, 512, 37, 37] 2,359,808 ReLU-26 [20, 512, 37, 37] 0 Conv2d-27 [20, 512, 37, 37] 2,359,808 ReLU-28 [20, 512, 37, 37] 0 Conv2d-29 [20, 512, 37, 37] 2,359,808 ReLU-30 [20, 512, 37, 37] 0 MaxPool2d-31 [20, 512, 18, 18] 0 AdaptiveAvgPool2d-32 [20, 512, 7, 7] 0 Linear-33 [20, 4096] 102,764,544 ReLU-34 [20, 4096] 0 Dropout-35 [20, 4096] 0 Linear-36 [20, 4096] 16,781,312 ReLU-37 [20, 4096] 0 Dropout-38 [20, 4096] 0 Linear-39 [20, 1000] 4,097,000 ================================================================ Total params: 138,357,544 Trainable params: 138,357,544 Non-trainable params: 0 ---------------------------------------------------------------- Input size (MB): 82.40 Forward/backward pass size (MB): 1444.29 Params size (MB): 527.79 Estimated Total Size (MB): 2054.48 ----------------------------------------------------------------
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I have fixed this problem in #165.
An overflow occurred when I ran the following code. This is why the model estimation, including batch size, is not successful.
The output result is this.
Development Environment