# 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"]
[docs]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))
[docs]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
[docs]def freeze(layer: nn.Layer):
for param in layer.parameters():
param.trainable = False
[docs]def unfreeze(layer: nn.Layer):
for param in layer.parameters():
param.trainable = True