torch_jacobian

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)    
    

I have a question. What would the consequence be of not repeating input x before the forward pass, but repeating the output y after the forward pass? What would the resulting .grad field contain?

@RylanSchaeffer

I believe that in the comment, y.shape should be b*to, to. Just for anyone else that comes along after me!

Thank you!
Could you show me your complete code for reproducing your error?