39 lines
1.3 KiB
Python
39 lines
1.3 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 torch.nn.functional as F
|
|
|
|
import copy
|
|
|
|
from . import measure
|
|
from ..p_utils import get_layer_metric_array
|
|
|
|
@measure('grad_norm', bn=True)
|
|
def get_grad_norm_arr(net, inputs, targets, loss_fn, split_data=1, skip_grad=False):
|
|
net.zero_grad()
|
|
N = inputs.shape[0]
|
|
for sp in range(split_data):
|
|
st=sp*N//split_data
|
|
en=(sp+1)*N//split_data
|
|
|
|
outputs = net.forward(inputs[st:en])
|
|
loss = loss_fn(outputs, targets[st:en])
|
|
loss.backward()
|
|
|
|
grad_norm_arr = get_layer_metric_array(net, lambda l: l.weight.grad.norm() if l.weight.grad is not None else torch.zeros_like(l.weight), mode='param')
|
|
|
|
return grad_norm_arr
|