MeCo/zero-cost-nas/foresight/pruners/measures/ntk.py
HamsterMimi 993d55076e update
2023-05-14 10:57:08 +08:00

95 lines
3.4 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 recal_bn(network, inputs, targets, recalbn, device):
for m in network.modules():
if isinstance(m, torch.nn.BatchNorm2d):
m.running_mean.data.fill_(0)
m.running_var.data.fill_(0)
m.num_batches_tracked.data.zero_()
m.momentum = None
network.train()
with torch.no_grad():
for i, (inputs, targets) in enumerate(zip(inputs, targets)):
if i >= recalbn: break
inputs = inputs.cuda(device=device, non_blocking=True)
_, _ = network(inputs)
return network
def get_ntk_n(inputs, targets, network, device, recalbn=0, train_mode=False, num_batch=1):
device = device
# if recalbn > 0:
# network = recal_bn(network, xloader, recalbn, device)
# if network_2 is not None:
# network_2 = recal_bn(network_2, xloader, recalbn, device)
network.eval()
networks = []
networks.append(network)
ntks = []
# if train_mode:
# networks.train()
# else:
# networks.eval()
######
grads = [[] for _ in range(len(networks))]
for i in range(num_batch):
if num_batch > 0 and i >= num_batch: break
inputs = inputs.cuda(device=device, non_blocking=True)
for net_idx, network in enumerate(networks):
network.zero_grad()
# print(inputs.size())
inputs_ = inputs.clone().cuda(device=device, non_blocking=True)
logit = network(inputs_)
if isinstance(logit, tuple):
logit = logit[1] # 201 networks: return features and logits
for _idx in range(len(inputs_)):
logit[_idx:_idx + 1].backward(torch.ones_like(logit[_idx:_idx + 1]), retain_graph=True)
grad = []
for name, W in network.named_parameters():
if 'weight' in name and W.grad is not None:
grad.append(W.grad.view(-1).detach())
grads[net_idx].append(torch.cat(grad, -1))
network.zero_grad()
torch.cuda.empty_cache()
######
grads = [torch.stack(_grads, 0) for _grads in grads]
ntks = [torch.einsum('nc,mc->nm', [_grads, _grads]) for _grads in grads]
for ntk in ntks:
eigenvalues, _ = torch.linalg.eigh(ntk) # ascending
conds = np.nan_to_num((eigenvalues[0] / eigenvalues[-1]).item(), copy=True, nan=100000.0)
return conds
@measure('ntk', bn=True)
def compute_ntk(net, inputs, targets, split_data=1, loss_fn=None):
device = inputs.device
# Compute gradients (but don't apply them)
net.zero_grad()
try:
conds = get_ntk_n(inputs, targets, net, device)
except Exception as e:
print(e)
conds= np.nan
return conds