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

56 lines
1.6 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 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
import torch
from . import measure
def get_score(net, x, target, device, split_data):
result_list = []
def forward_hook(module, data_input, data_output):
norm = torch.norm(data_input[0])
result_list.append(norm)
net.classifier.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])
n = result_list[0].item()
result_list.clear()
return n
@measure('norm', bn=True)
def compute_norm(net, inputs, targets, split_data=1, loss_fn=None):
device = inputs.device
# Compute gradients (but don't apply them)
net.zero_grad()
# print('var:', feature.shape)
try:
norm, t = get_score(net, inputs, targets, device, split_data=split_data)
except Exception as e:
print(e)
norm, t = np.nan, None
# print(jc)
# print(f'norm time: {t} s')
return norm, t