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79 lines
2.4 KiB
79 lines
2.4 KiB
4 years ago
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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from paddle import nn
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__all__ = [
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"summary", "gradient_norm", "freeze", "unfreeze", "print_grads",
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"print_params"
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]
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def summary(layer: nn.Layer, print_func=print):
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num_params = num_elements = 0
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print_func("layer summary:")
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for name, param in layer.state_dict().items():
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print_func("{}|{}|{}".format(name, param.shape, np.prod(param.shape)))
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num_elements += np.prod(param.shape)
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num_params += 1
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print_func("layer has {} parameters, {} elements.".format(num_params,
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num_elements))
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def gradient_norm(layer: nn.Layer):
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grad_norm_dict = {}
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for name, param in layer.state_dict().items():
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if param.trainable:
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grad = param.gradient()
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grad_norm_dict[name] = np.linalg.norm(grad) / grad.size
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return grad_norm_dict
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def recursively_remove_weight_norm(layer: nn.Layer):
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for layer in layer.sublayers():
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try:
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nn.utils.remove_weight_norm(layer)
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except:
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# ther is not weight norm hoom in this layer
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pass
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def freeze(layer: nn.Layer):
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for param in layer.parameters():
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param.trainable = False
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def unfreeze(layer: nn.Layer):
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for param in layer.parameters():
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param.trainable = True
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def print_grads(model, print_func=print):
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for n, p in model.named_parameters():
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msg = f"param grad: {n}: shape: {p.shape} grad: {p.grad}"
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if print_func:
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print_func(msg)
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def print_params(model, print_func=print):
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total = 0.0
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for n, p in model.named_parameters():
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msg = f"param: {n}: shape: {p.shape} stop_grad: {p.stop_gradient}"
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total += np.prod(p.shape)
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if print_func:
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print_func(msg)
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if print_func:
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print_func(f"Total parameters: {total}!")
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