You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
57 lines
1.8 KiB
57 lines
1.8 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# 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 numpy as np
|
|
from paddle import nn
|
|
|
|
__all__ = ["summary", "gradient_norm", "freeze", "unfreeze"]
|
|
|
|
|
|
def summary(layer: nn.Layer):
|
|
num_params = num_elements = 0
|
|
print("layer summary:")
|
|
for name, param in layer.state_dict().items():
|
|
print("{}|{}|{}".format(name, param.shape, np.prod(param.shape)))
|
|
num_elements += np.prod(param.shape)
|
|
num_params += 1
|
|
print("layer has {} parameters, {} elements.".format(num_params,
|
|
num_elements))
|
|
|
|
|
|
def gradient_norm(layer: nn.Layer):
|
|
grad_norm_dict = {}
|
|
for name, param in layer.state_dict().items():
|
|
if param.trainable:
|
|
grad = param.gradient()
|
|
grad_norm_dict[name] = np.linalg.norm(grad) / grad.size
|
|
return grad_norm_dict
|
|
|
|
|
|
def recursively_remove_weight_norm(layer: nn.Layer):
|
|
for layer in layer.sublayers():
|
|
try:
|
|
nn.utils.remove_weight_norm(layer)
|
|
except Exception as e:
|
|
# ther is not weight norm hoom in this layer
|
|
pass
|
|
|
|
|
|
def freeze(layer: nn.Layer):
|
|
for param in layer.parameters():
|
|
param.trainable = False
|
|
|
|
|
|
def unfreeze(layer: nn.Layer):
|
|
for param in layer.parameters():
|
|
param.trainable = True
|