Bias not taken into account for FLOPs computation.

[pzobel]

The bias of a Linear layer is not taken into account for the FLOPs computation, as shown below:

Example:

import torch
import torch.nn as nn
from thop import profile

class Simple_FC_Net(nn.Module):
    def __init__(self, bias=True):
        super(Simple_FC_Net, self).__init__()
        self.fc = nn.Linear(in_features=17, out_features=49, bias=bias)

    def forward(self, x):
        x = self.fc(x)
        return x

input_simple_fc = torch.randn(1, 17)

# Model without bias
print("Model without bias:")
model = Simple_FC_Net(bias=False)
flops, params = profile(model, inputs=(input_simple_fc,))
print("Flops: {}, Params: {}".format(flops, params))

# Model with bias
print("Model with bias:")
model = Simple_FC_Net(bias=True)
flops, params = profile(model, inputs=(input_simple_fc,))
print("Flops: {}, Params: {}".format(flops, params))

Output:

Model without bias:
Register FLOP counter for module Linear(in_features=17, out_features=49, bias=False)
Flops: 1617.0, Params: 833.0

Model with bias:
Register FLOP counter for module Linear(in_features=17, out_features=49, bias=True)
Flops: 1617.0, Params: 882.0

[Lyken17]

Thanks for pointing out. Can you submit a PR?

[Lyken17]

Close because the PR is merged. Thanks for @pzobel 's contribution!