The output of a convolution will have the following dimensions: [batch_size, number_of_kernels, w, h].
I think you would like to see the kernels, which have the dimensions: [number_of_kernels, input_channels, kernel_width, kernel_height].
Here is a small example:
conv = nn.Conv2d(in_channels=3, 
                 out_channels=6, 
                 kernel_size=5)
x = Variable(torch.randn(1, 3, 24, 24))

output = conv(x)
print(output.shape)
print(conv.weight.data.shape)

conv_ = conv.weight.data.view(-1, 5, 5)

import matplotlib.pyplot as plt
plt.imshow(conv_[0, ...])

How did you get the tensor with [F, W, H, C]?