68 lines
2.0 KiB
Python
68 lines
2.0 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
|
|
import copy
|
|
import time
|
|
|
|
# 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 numpy as np
|
|
from torch import nn
|
|
|
|
from . import measure
|
|
|
|
|
|
def get_score(net, x, target, device, split_data):
|
|
result_list = []
|
|
result_t = []
|
|
def forward_hook(module, data_input, data_output):
|
|
s = time.time()
|
|
fea = data_output[0].detach().cpu().numpy()
|
|
fea = fea.reshape(fea.shape[0], -1)
|
|
result = 1 / np.var(np.corrcoef(fea))
|
|
e = time.time()
|
|
t = e - s
|
|
result_list.append(result)
|
|
result_t.append(t)
|
|
for name, modules in net.named_modules():
|
|
modules.register_forward_hook(forward_hook)
|
|
|
|
|
|
|
|
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])
|
|
results = np.array(result_list)
|
|
results = results[np.logical_not(np.isnan(results))]
|
|
v = np.sum(results)
|
|
t = sum(result_t)
|
|
result_list.clear()
|
|
result_t.clear()
|
|
return v, t
|
|
|
|
|
|
|
|
@measure('cova', bn=True)
|
|
def compute_cova(net, inputs, targets, split_data=1, loss_fn=None):
|
|
device = inputs.device
|
|
# Compute gradients (but don't apply them)
|
|
net.zero_grad()
|
|
|
|
try:
|
|
cova, t = get_score(net, inputs, targets, device, split_data=split_data)
|
|
except Exception as e:
|
|
print(e)
|
|
cova, t = np.nan, None
|
|
return cova, t
|