MeCo/zero-cost-nas/foresight/pruners/measures/jacob_cov.py
HamsterMimi 189df25fd3 upload
2023-05-04 13:09:03 +08:00

58 lines
1.7 KiB
Python

# 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