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

70 lines
2.1 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
from . import measure
from ..p_utils import get_layer_metric_array
@measure('synflow', bn=False, mode='param')
@measure('synflow_bn', bn=True, mode='param')
def compute_synflow_per_weight(net, inputs, targets, mode, split_data=1, loss_fn=None):
device = inputs.device
#convert params to their abs. Keep sign for converting it back.
@torch.no_grad()
def linearize(net):
signs = {}
for name, param in net.state_dict().items():
signs[name] = torch.sign(param)
param.abs_()
return signs
#convert to orig values
@torch.no_grad()
def nonlinearize(net, signs):
for name, param in net.state_dict().items():
if 'weight_mask' not in name:
param.mul_(signs[name])
# keep signs of all params
signs = linearize(net)
# Compute gradients with input of 1s
net.zero_grad()
net.double()
input_dim = list(inputs[0,:].shape)
inputs = torch.ones([1] + input_dim).double().to(device)
output = net.forward(inputs)
torch.sum(output).backward()
# select the gradients that we want to use for search/prune
def synflow(layer):
if layer.weight.grad is not None:
return torch.abs(layer.weight * layer.weight.grad)
else:
return torch.zeros_like(layer.weight)
grads_abs = get_layer_metric_array(net, synflow, mode)
# apply signs of all params
nonlinearize(net, signs)
return grads_abs