# Copyright 2021 Samsung Electronics Co., Ltd. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================= import torch import numpy as np from . import measure def get_batch_jacobian(net, x, target, device, split_data): x.requires_grad_(True) N = x.shape[0] for sp in range(split_data): st=sp*N//split_data en=(sp+1)*N//split_data y = net(x[st:en]) y.backward(torch.ones_like(y)) jacob = x.grad.detach() x.requires_grad_(False) return jacob, target.detach() def eval_score(jacob, labels=None): corrs = np.corrcoef(jacob) v, _ = np.linalg.eig(corrs) k = 1e-5 return -np.sum(np.log(v + k) + 1./(v + k)) @measure('jacob_cov', bn=True) def compute_jacob_cov(net, inputs, targets, split_data=1, loss_fn=None): device = inputs.device # Compute gradients (but don't apply them) net.zero_grad() jacobs, labels = get_batch_jacobian(net, inputs, targets, device, split_data=split_data) jacobs = jacobs.reshape(jacobs.size(0), -1).cpu().numpy() try: jc = eval_score(jacobs, labels) except Exception as e: print(e) jc = np.nan return jc