# Copyright (c) 2021 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", "print_grads",
"print_params"
]
[docs]def summary(layer: nn.Layer, print_func=print):
if print_func is None:
return
num_params = num_elements = 0
for name, param in layer.state_dict().items():
if print_func:
print_func(
"{} | {} | {}".format(name, param.shape, np.prod(param.shape)))
num_elements += np.prod(param.shape)
num_params += 1
if print_func:
num_elements = num_elements / 1024**2
print_func(
f"Total parameters: {num_params}, {num_elements:.2f}M elements.")
[docs]def print_grads(model, print_func=print):
if print_func is None:
return
for n, p in model.named_parameters():
msg = f"param grad: {n}: shape: {p.shape} grad: {p.grad}"
print_func(msg)
[docs]def print_params(model, print_func=print):
if print_func is None:
return
total = 0.0
num_params = 0.0
for n, p in model.named_parameters():
msg = f"{n} | {p.shape} | {np.prod(p.shape)} | {not p.stop_gradient}"
total += np.prod(p.shape)
num_params += 1
if print_func:
print_func(msg)
if print_func:
total = total / 1024**2
print_func(f"Total parameters: {num_params}, {total:.2f}M 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() # return numpy.ndarray
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 ValueError 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