Hi, I am newbie to captum and I have a problem that I can’t solve by myself.
I trained ACGAN model and tried to apply gradcam on Discriminator part only.
Input size is (1, 110, 408)
This is my Discriminator / Critic part.
class Discriminator(nn.Module):
def __init__(self, num_class):
super(Discriminator, self).__init__()
self.main = nn.Sequential(
# input is 1 x 110 x 408
nn.Conv2d(1, ndf, (4,6), (2,3), bias=False),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf) x 54 x 135
nn.Conv2d(ndf, ndf * 2, (8,9), (2,3), bias=False),
nn.InstanceNorm2d(ndf * 2, affine= True),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf*2) x 24 x 43
nn.Conv2d(ndf * 2, ndf * 4, (6,3), (2,2), bias=False),
nn.InstanceNorm2d(ndf * 4, affine= True),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf*4) x 10 x 21
nn.Conv2d(ndf * 4, ndf * 8, (4,6), (2,3), bias=False),
nn.InstanceNorm2d(ndf * 8, affine= True),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf*8) x 4 x 6
)
self.critic_layer = nn.Sequential(nn.Conv2d(ndf * 8, 1, (4,6), 1, bias=False))
self.aux_layer = nn.Sequential(nn.Flatten(), nn.Linear(ndf * 8 * 4 * 6, num_class))
def forward(self, input):
input1 = self.main(input)
critic = self.critic_layer(input1)
pred_label = self.aux_layer(input1)
return critic, pred_label
Discriminator(
(main): Sequential(
(0): Conv2d(1, 64, kernel_size=(4, 6), stride=(2, 3), bias=False)
(1): LeakyReLU(negative_slope=0.2, inplace=True)
(2): Conv2d(64, 128, kernel_size=(8, 9), stride=(2, 3), bias=False)
(3): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(4): LeakyReLU(negative_slope=0.2, inplace=True)
(5): Conv2d(128, 256, kernel_size=(6, 3), stride=(2, 2), bias=False)
(6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(7): LeakyReLU(negative_slope=0.2, inplace=True)
(8): Conv2d(256, 512, kernel_size=(4, 6), stride=(2, 3), bias=False)
(9): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(10): LeakyReLU(negative_slope=0.2, inplace=True)
)
(critic_layer): Sequential(
(0): Conv2d(512, 1, kernel_size=(4, 6), stride=(1, 1), bias=False)
)
(aux_layer): Sequential(
(0): Flatten(start_dim=1, end_dim=-1)
(1): Linear(in_features=12288, out_features=129, bias=True)
)
)
And I followed the guided gradcam example as this doc
guided_gc = GuidedGradCam(netD_i, netD_i.main[8])
input3 = input_tensor[0]
input3.requires_grad = True
print(input3)
tensor([[[-0.9815, -0.9815, -0.9898, ..., -0.9708, -0.9451, -0.9735],
[-0.9648, -0.9803, -0.9869, ..., -0.9776, -0.9829, -0.9773],
[-0.9951, -0.9984, -0.9986, ..., -0.9944, -0.9847, -0.9956],
...,
[-0.9856, -0.9924, -0.9898, ..., -0.9958, -0.9944, -0.9928],
[-0.9368, -0.9677, -0.9770, ..., -0.9674, -0.9666, -0.9691],
[-0.9358, -0.9468, -0.9367, ..., -0.9777, -0.9739, -0.9577]]],
device='cuda:0', requires_grad=True)
traget is like this
label_y[0]
tensor(29., device='cuda:0')
When I run this code, I get the error.
guided_gc.attribute(input3, label_y[0])
RuntimeError: mat1 and mat2 shapes cannot be multiplied (512x24 and 12288x129)
FYI, 512 x 24 = 12288.
I assume that the cause of this error is for the Flatten layer in aux_layer.
But it didn’t cause any issue when I trained the model.
How can I fix this problem??
Thx! 
