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import torch | |
def get_batch_jacobian(net, x, to): | |
# noutputs: total output dim (e.g. net(x).shape(b,1,4,4) noutputs=1*4*4 | |
# b: batch | |
# i: in_dim | |
# o: out_dim | |
# ti: total input dim | |
# to: total output dim | |
x_batch = x.shape[0] | |
x_shape = x.shape[1:] | |
x = x.unsqueeze(1) # b, 1 ,i | |
x = x.repeat(1, to, *(1,)*len(x.shape[2:])) # b * to,i copy to o dim | |
x.requires_grad_(True) | |
tmp_shape = x.shape | |
y = net(x.reshape(-1, *tmp_shape[2:])) # x.shape = b*to,i y.shape = b*to,to | |
y_shape = y.shape[1:] # y.shape = b*to,to | |
y = y.reshape(x_batch, to, to) # y.shape = b,to,to | |
input_val = torch.eye(to).reshape(1, to, to).repeat(x_batch, 1, 1) # input_val.shape = b,to,to value is (eye) | |
y.backward(input_val) # y.shape = b,to,to | |
return x.grad.reshape(x_batch, *y_shape, *x_shape).data # x.shape = b,o,i | |
class CNNNet(torch.nn.Module): | |
def __init__(self): | |
super(CNNNet, self).__init__() | |
self.cnn = torch.nn.Conv2d(1, 3, 5) | |
self.fc1 = torch.nn.Linear(3, 4) | |
def forward(self, x): | |
print('x: {}'.format(x.shape)) | |
x = torch.nn.functional.relu(self.cnn(x)) | |
print('co: {}'.format(x.shape)) | |
#x = x.reshape(x.shape[0], -1) | |
#x = torch.nn.functional.relu(self.fc1(x)) | |
#print('li: {}'.format(x.shape)) | |
return x | |
cnet = CNNNet() | |
batch = 10 | |
x = torch.randn(batch,1,5,5) | |
y = cnet(x) | |
ret = get_batch_jacobian(cnet, x, 3) # y.shape=10,3,1.1 | |
print(ret.shape) | |
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@RylanSchaeffer
Thank you!
Could you show me your complete code for reproducing your error?