wehub-resource-sync
665 lines
27 KiB
Python
665 lines
27 KiB
Python
# Copyright (c) 2020 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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from __future__ import annotations
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import numbers
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import warnings
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from collections import OrderedDict
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from typing import TYPE_CHECKING
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import numpy as np
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from typing_extensions import TypedDict
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import paddle
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from paddle import Tensor, nn
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from paddle.autograd import no_grad
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from paddle.static import InputSpec
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if TYPE_CHECKING:
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from collections.abc import Sequence
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__all__ = []
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class ModelSummary(TypedDict):
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total_params: int
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trainable_params: int
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def summary(
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net: nn.Layer,
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input_size: (
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int
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| tuple[int, ...]
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| InputSpec
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| list[tuple[int, ...] | InputSpec]
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| None
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) = None,
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dtypes: str | Sequence[str] | None = None,
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input: Tensor | Sequence[Tensor] | dict[str, Tensor] | None = None,
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) -> ModelSummary:
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"""Prints a string summary of the network.
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Args:
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net (Layer): The network which must be a subinstance of Layer.
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input_size (tuple|InputSpec|list[tuple|InputSpec]|None, optional): Size of input tensor. if model only
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have one input, input_size can be tuple or InputSpec. if model
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have multiple input, input_size must be a list which contain
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every input's shape. Note that input_size only dim of
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batch_size can be None or -1. Default: None. Note that
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input_size and input cannot be None at the same time.
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dtypes (str|Sequence[str]|None, optional): If dtypes is None, 'float32' will be used, Default: None.
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input (Tensor|Sequence[paddle.Tensor]|dict[str, paddle.Tensor]|None, optional): If input is given, input_size and dtype will be ignored, Default: None.
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Returns:
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dict: A summary of the network including total params and total trainable params.
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Examples:
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.. code-block:: pycon
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:name: code-example-1
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>>> # example 1: Single Input Demo
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>>> import paddle
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>>> import paddle.nn as nn
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>>> # Define Network
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>>> class LeNet(nn.Layer):
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... def __init__(self, num_classes=10):
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... super().__init__()
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... self.num_classes = num_classes
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... self.features = nn.Sequential(
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... nn.Conv2D(1, 6, 3, stride=1, padding=1),
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... nn.ReLU(),
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... nn.MaxPool2D(2, 2),
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... nn.Conv2D(6, 16, 5, stride=1, padding=0),
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... nn.ReLU(),
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... nn.MaxPool2D(2, 2),
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... )
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...
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... if num_classes > 0:
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... self.fc = nn.Sequential(nn.Linear(400, 120), nn.Linear(120, 84), nn.Linear(84, 10))
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...
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... def forward(self, inputs):
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... x = self.features(inputs)
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...
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... if self.num_classes > 0:
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... x = paddle.flatten(x, 1)
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... x = self.fc(x)
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... return x
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>>> lenet = LeNet()
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>>> params_info = paddle.summary(lenet, (1, 1, 28, 28)) # doctest: +NORMALIZE_WHITESPACE
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---------------------------------------------------------------------------
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Layer (type) Input Shape Output Shape Param #
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===========================================================================
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Conv2D-1 [[1, 1, 28, 28]] [1, 6, 28, 28] 60
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ReLU-1 [[1, 6, 28, 28]] [1, 6, 28, 28] 0
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MaxPool2D-1 [[1, 6, 28, 28]] [1, 6, 14, 14] 0
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Conv2D-2 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416
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ReLU-2 [[1, 16, 10, 10]] [1, 16, 10, 10] 0
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MaxPool2D-2 [[1, 16, 10, 10]] [1, 16, 5, 5] 0
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Linear-1 [[1, 400]] [1, 120] 48,120
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Linear-2 [[1, 120]] [1, 84] 10,164
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Linear-3 [[1, 84]] [1, 10] 850
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===========================================================================
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Total params: 61,610
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Trainable params: 61,610
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Non-trainable params: 0
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---------------------------------------------------------------------------
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Input size (MB): 0.00
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Forward/backward pass size (MB): 0.11
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Params size (MB): 0.24
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Estimated Total Size (MB): 0.35
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---------------------------------------------------------------------------
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<BLANKLINE>
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>>> print(params_info)
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{'total_params': 61610, 'trainable_params': 61610}
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.. code-block:: pycon
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:name: code-example-2
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>>> # example 2: multi input demo
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>>> import paddle
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>>> import paddle.nn as nn
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>>> class LeNetMultiInput(nn.Layer):
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... def __init__(self, num_classes=10):
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... super().__init__()
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... self.num_classes = num_classes
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... self.features = nn.Sequential(
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... nn.Conv2D(1, 6, 3, stride=1, padding=1),
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... nn.ReLU(),
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... nn.MaxPool2D(2, 2),
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... nn.Conv2D(6, 16, 5, stride=1, padding=0),
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... nn.ReLU(),
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... nn.MaxPool2D(2, 2),
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... )
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...
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... if num_classes > 0:
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... self.fc = nn.Sequential(nn.Linear(400, 120), nn.Linear(120, 84), nn.Linear(84, 10))
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...
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... def forward(self, inputs, y):
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... x = self.features(inputs)
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...
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... if self.num_classes > 0:
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... x = paddle.flatten(x, 1)
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... x = self.fc(x + y)
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... return x
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>>> lenet_multi_input = LeNetMultiInput()
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>>> params_info = paddle.summary(
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... lenet_multi_input, [(1, 1, 28, 28), (1, 400)], dtypes=['float32', 'float32']
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... ) # doctest: +NORMALIZE_WHITESPACE
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---------------------------------------------------------------------------
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Layer (type) Input Shape Output Shape Param #
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===========================================================================
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Conv2D-1 [[1, 1, 28, 28]] [1, 6, 28, 28] 60
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ReLU-1 [[1, 6, 28, 28]] [1, 6, 28, 28] 0
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MaxPool2D-1 [[1, 6, 28, 28]] [1, 6, 14, 14] 0
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Conv2D-2 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416
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ReLU-2 [[1, 16, 10, 10]] [1, 16, 10, 10] 0
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MaxPool2D-2 [[1, 16, 10, 10]] [1, 16, 5, 5] 0
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Linear-1 [[1, 400]] [1, 120] 48,120
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Linear-2 [[1, 120]] [1, 84] 10,164
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Linear-3 [[1, 84]] [1, 10] 850
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===========================================================================
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Total params: 61,610
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Trainable params: 61,610
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Non-trainable params: 0
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---------------------------------------------------------------------------
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Input size (MB): 0.00
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Forward/backward pass size (MB): 0.11
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Params size (MB): 0.24
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Estimated Total Size (MB): 0.35
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---------------------------------------------------------------------------
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<BLANKLINE>
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>>> print(params_info)
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{'total_params': 61610, 'trainable_params': 61610}
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.. code-block:: pycon
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:name: code-example-3
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>>> # example 3: List Input Demo
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>>> import paddle
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>>> import paddle.nn as nn
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>>> # list input demo
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>>> class LeNetListInput(nn.Layer):
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... def __init__(self, num_classes=10):
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... super().__init__()
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... self.num_classes = num_classes
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... self.features = nn.Sequential(
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... nn.Conv2D(1, 6, 3, stride=1, padding=1),
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... nn.ReLU(),
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... nn.MaxPool2D(2, 2),
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... nn.Conv2D(6, 16, 5, stride=1, padding=0),
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... nn.ReLU(),
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... nn.MaxPool2D(2, 2),
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... )
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...
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... if num_classes > 0:
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... self.fc = nn.Sequential(nn.Linear(400, 120), nn.Linear(120, 84), nn.Linear(84, 10))
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...
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... def forward(self, inputs):
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... x = self.features(inputs[0])
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...
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... if self.num_classes > 0:
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... x = paddle.flatten(x, 1)
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... x = self.fc(x + inputs[1])
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... return x
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>>> lenet_list_input = LeNetListInput()
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>>> input_data = [paddle.rand([1, 1, 28, 28]), paddle.rand([1, 400])]
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>>> params_info = paddle.summary(lenet_list_input, input=input_data) # doctest: +NORMALIZE_WHITESPACE
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---------------------------------------------------------------------------
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Layer (type) Input Shape Output Shape Param #
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===========================================================================
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Conv2D-1 [[1, 1, 28, 28]] [1, 6, 28, 28] 60
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ReLU-1 [[1, 6, 28, 28]] [1, 6, 28, 28] 0
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MaxPool2D-1 [[1, 6, 28, 28]] [1, 6, 14, 14] 0
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Conv2D-2 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416
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ReLU-2 [[1, 16, 10, 10]] [1, 16, 10, 10] 0
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MaxPool2D-2 [[1, 16, 10, 10]] [1, 16, 5, 5] 0
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Linear-1 [[1, 400]] [1, 120] 48,120
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Linear-2 [[1, 120]] [1, 84] 10,164
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Linear-3 [[1, 84]] [1, 10] 850
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===========================================================================
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Total params: 61,610
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Trainable params: 61,610
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Non-trainable params: 0
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---------------------------------------------------------------------------
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Input size (MB): 0.00
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Forward/backward pass size (MB): 0.11
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Params size (MB): 0.24
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Estimated Total Size (MB): 0.35
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---------------------------------------------------------------------------
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<BLANKLINE>
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>>> print(params_info)
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{'total_params': 61610, 'trainable_params': 61610}
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.. code-block:: pycon
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:name: code-example-4
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>>> # example 4: Dict Input Demo
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>>> import paddle
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>>> import paddle.nn as nn
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>>> # Dict input demo
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>>> class LeNetDictInput(nn.Layer):
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... def __init__(self, num_classes=10):
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... super().__init__()
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... self.num_classes = num_classes
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... self.features = nn.Sequential(
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... nn.Conv2D(1, 6, 3, stride=1, padding=1),
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... nn.ReLU(),
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... nn.MaxPool2D(2, 2),
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... nn.Conv2D(6, 16, 5, stride=1, padding=0),
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... nn.ReLU(),
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... nn.MaxPool2D(2, 2),
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... )
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...
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... if num_classes > 0:
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... self.fc = nn.Sequential(
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... nn.Linear(400, 120),
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... nn.Linear(120, 84),
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... nn.Linear(84, 10),
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... )
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...
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... def forward(self, inputs):
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... x = self.features(inputs['x1'])
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...
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... if self.num_classes > 0:
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... x = paddle.flatten(x, 1)
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... x = self.fc(x + inputs['x2'])
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... return x
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>>> lenet_dict_input = LeNetDictInput()
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>>> input_data = {'x1': paddle.rand([1, 1, 28, 28]), 'x2': paddle.rand([1, 400])}
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>>> # The module suffix number indicates its sequence in modules of the same type, used for differentiation identification
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>>> params_info = paddle.summary(lenet_dict_input, input=input_data) # doctest: +NORMALIZE_WHITESPACE
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---------------------------------------------------------------------------
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Layer (type) Input Shape Output Shape Param #
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===========================================================================
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Conv2D-1 [[1, 1, 28, 28]] [1, 6, 28, 28] 60
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ReLU-1 [[1, 6, 28, 28]] [1, 6, 28, 28] 0
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MaxPool2D-1 [[1, 6, 28, 28]] [1, 6, 14, 14] 0
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Conv2D-2 [[1, 6, 14, 14]] [1, 16, 10, 10] 2,416
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ReLU-2 [[1, 16, 10, 10]] [1, 16, 10, 10] 0
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MaxPool2D-2 [[1, 16, 10, 10]] [1, 16, 5, 5] 0
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Linear-1 [[1, 400]] [1, 120] 48,120
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Linear-2 [[1, 120]] [1, 84] 10,164
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Linear-3 [[1, 84]] [1, 10] 850
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===========================================================================
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Total params: 61,610
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Trainable params: 61,610
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Non-trainable params: 0
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---------------------------------------------------------------------------
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Input size (MB): 0.00
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Forward/backward pass size (MB): 0.11
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Params size (MB): 0.24
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Estimated Total Size (MB): 0.35
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---------------------------------------------------------------------------
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<BLANKLINE>
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>>> print(params_info)
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{'total_params': 61610, 'trainable_params': 61610}
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"""
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if input_size is None and input is None:
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raise ValueError("input_size and input cannot be None at the same time")
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if input_size is None and input is not None:
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if paddle.is_tensor(input):
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input_size = tuple(input.shape)
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elif isinstance(input, (list, tuple)):
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input_size = []
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for x in input:
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input_size.append(tuple(x.shape))
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elif isinstance(input, dict):
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input_size = []
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for key in input.keys():
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input_size.append(tuple(input[key].shape))
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elif isinstance(input, paddle.base.framework.Variable):
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input_size = tuple(input.shape)
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else:
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raise ValueError(
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"Input is not tensor, list, tuple and dict, unable to determine input_size, please input input_size."
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)
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if isinstance(input_size, InputSpec):
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_input_size = tuple(input_size.shape)
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elif isinstance(input_size, list):
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_input_size = []
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for item in input_size:
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if isinstance(item, int):
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item = (item,)
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assert isinstance(item, (tuple, InputSpec)), (
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f'When input_size is list, \
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expect item in input_size is a tuple or InputSpec, but got {type(item)}'
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)
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if isinstance(item, InputSpec):
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_input_size.append(tuple(item.shape))
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else:
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_input_size.append(item)
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elif isinstance(input_size, int):
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_input_size = (input_size,)
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else:
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_input_size = input_size
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if not paddle.in_dynamic_mode():
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warnings.warn(
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"Your model was created in static graph mode, this may not get correct summary information!"
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)
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in_train_mode = False
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else:
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in_train_mode = net.training
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if in_train_mode:
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net.eval()
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def _is_shape(shape):
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for item in shape:
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if isinstance(item, (list, tuple)):
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return False
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return True
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def _check_shape(shape):
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num_unknown = 0
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new_shape = []
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for i in range(len(shape)):
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item = shape[i]
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if item is None or item == -1:
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num_unknown += 1
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if num_unknown > 1:
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raise ValueError(
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'Option input_size only the dim of batch_size can be None or -1.'
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)
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item = 1
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elif isinstance(item, numbers.Number):
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if item <= 0:
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raise ValueError(
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f"Expected element in input size greater than zero, but got {item}"
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)
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new_shape.append(item)
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return tuple(new_shape)
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def _check_input(input_size):
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if isinstance(input_size, (list, tuple)) and _is_shape(input_size):
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return _check_shape(input_size)
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else:
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return [_check_input(i) for i in input_size]
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_input_size = _check_input(_input_size)
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result, params_info = summary_string(net, _input_size, dtypes, input)
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print(result)
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if in_train_mode:
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net.train()
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return params_info
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@no_grad()
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def summary_string(model, input_size=None, dtypes=None, input=None):
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def _all_is_number(items):
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for item in items:
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if not isinstance(item, numbers.Number):
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return False
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return True
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def _build_dtypes(input_size, dtype):
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if dtype is None:
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dtype = 'float32'
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if isinstance(input_size, (list, tuple)) and _all_is_number(input_size):
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return [dtype]
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else:
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return [_build_dtypes(i, dtype) for i in input_size]
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if not isinstance(dtypes, (list, tuple)):
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dtypes = _build_dtypes(input_size, dtypes)
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summary_str = ''
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depth = len(list(model.sublayers()))
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def _get_shape_from_tensor(x):
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if isinstance(x, (paddle.base.Variable, paddle.base.core.eager.Tensor)):
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return list(x.shape)
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elif isinstance(x, (list, tuple)):
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return [_get_shape_from_tensor(xx) for xx in x]
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def _get_output_shape(output):
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if isinstance(output, (list, tuple)):
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output_shape = [_get_output_shape(o) for o in output]
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elif hasattr(output, 'shape'):
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output_shape = list(output.shape)
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else:
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output_shape = []
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return output_shape
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def register_hook(layer):
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def hook(layer, input, output):
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class_name = str(layer.__class__).split(".")[-1].split("'")[0]
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try:
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layer_idx = int(layer._full_name.split('_')[-1])
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except:
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layer_idx = len(summary)
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m_key = f"{class_name}-{layer_idx + 1}"
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summary[m_key] = OrderedDict()
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try:
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summary[m_key]["input_shape"] = _get_shape_from_tensor(input)
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except:
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warnings.warn('Get layer {} input shape failed!')
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summary[m_key]["input_shape"] = []
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try:
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summary[m_key]["output_shape"] = _get_output_shape(output)
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except:
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warnings.warn('Get layer {} output shape failed!')
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summary[m_key]["output_shape"]
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params = 0
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if paddle.in_dynamic_mode():
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layer_state_dict = layer._parameters
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else:
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layer_state_dict = layer.state_dict()
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summary[m_key]["trainable_params"] = 0
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trainable_flag = False
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for k, v in layer_state_dict.items():
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params += int(np.prod(v.shape))
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try:
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if (getattr(layer, k).trainable) and (
|
|
not getattr(layer, k).stop_gradient
|
|
):
|
|
summary[m_key]["trainable_params"] += int(
|
|
np.prod(v.shape)
|
|
)
|
|
summary[m_key]["trainable"] = True
|
|
trainable_flag = True
|
|
elif not trainable_flag:
|
|
summary[m_key]["trainable"] = False
|
|
except:
|
|
summary[m_key]["trainable"] = True
|
|
|
|
summary[m_key]["nb_params"] = params
|
|
|
|
if (
|
|
not isinstance(layer, nn.Sequential)
|
|
and not isinstance(layer, nn.LayerList)
|
|
and (not (layer == model) or depth < 1)
|
|
):
|
|
hooks.append(layer.register_forward_post_hook(hook))
|
|
# For rnn, gru and lstm layer
|
|
elif hasattr(layer, 'could_use_cudnn') and layer.could_use_cudnn:
|
|
hooks.append(layer.register_forward_post_hook(hook))
|
|
|
|
if isinstance(input_size, tuple):
|
|
input_size = [input_size]
|
|
|
|
def build_input(input_size, dtypes):
|
|
if isinstance(input_size, (list, tuple)) and _all_is_number(input_size):
|
|
if isinstance(dtypes, (list, tuple)):
|
|
dtype = dtypes[0]
|
|
else:
|
|
dtype = dtypes
|
|
return paddle.cast(paddle.rand(list(input_size)), dtype)
|
|
else:
|
|
return [
|
|
build_input(i, dtype) for i, dtype in zip(input_size, dtypes)
|
|
]
|
|
|
|
# create properties
|
|
summary = OrderedDict()
|
|
hooks = []
|
|
# register hook
|
|
model.apply(register_hook)
|
|
if input is not None:
|
|
x = input
|
|
model(x)
|
|
else:
|
|
x = build_input(input_size, dtypes)
|
|
# make a forward pass
|
|
model(*x)
|
|
|
|
# remove these hooks
|
|
for h in hooks:
|
|
h.remove()
|
|
|
|
def _get_str_length(summary):
|
|
head_length = {
|
|
'layer_width': 15,
|
|
'input_shape_width': 20,
|
|
'output_shape_width': 20,
|
|
'params_width': 15,
|
|
'table_width': 75,
|
|
}
|
|
|
|
for layer in summary:
|
|
if head_length['output_shape_width'] < len(
|
|
str(summary[layer]["output_shape"])
|
|
):
|
|
head_length['output_shape_width'] = len(
|
|
str(summary[layer]["output_shape"])
|
|
)
|
|
if head_length['input_shape_width'] < len(
|
|
str(summary[layer]["input_shape"])
|
|
):
|
|
head_length['input_shape_width'] = len(
|
|
str(summary[layer]["input_shape"])
|
|
)
|
|
if head_length['layer_width'] < len(str(layer)):
|
|
head_length['layer_width'] = len(str(layer))
|
|
if head_length['params_width'] < len(
|
|
str(summary[layer]["nb_params"])
|
|
):
|
|
head_length['params_width'] = len(
|
|
str(summary[layer]["nb_params"])
|
|
)
|
|
|
|
_temp_width = 0
|
|
for k, v in head_length.items():
|
|
if k != 'table_width':
|
|
_temp_width += v
|
|
|
|
if head_length['table_width'] < _temp_width + 5:
|
|
head_length['table_width'] = _temp_width + 5
|
|
|
|
return head_length
|
|
|
|
table_width = _get_str_length(summary)
|
|
|
|
summary_str += "-" * table_width['table_width'] + "\n"
|
|
line_new = "{:^{}} {:^{}} {:^{}} {:^{}}".format(
|
|
"Layer (type)",
|
|
table_width['layer_width'],
|
|
"Input Shape",
|
|
table_width['input_shape_width'],
|
|
"Output Shape",
|
|
table_width['output_shape_width'],
|
|
"Param #",
|
|
table_width['params_width'],
|
|
)
|
|
summary_str += line_new + "\n"
|
|
summary_str += "=" * table_width['table_width'] + "\n"
|
|
total_params = 0
|
|
total_output = 0
|
|
trainable_params = 0
|
|
max_length = 0
|
|
for layer in summary:
|
|
# input_shape, output_shape, trainable, nb_params
|
|
line_new = "{:^{}} {:^{}} {:^{}} {:^{}}".format(
|
|
layer,
|
|
table_width['layer_width'],
|
|
str(summary[layer]["input_shape"]),
|
|
table_width['input_shape_width'],
|
|
str(summary[layer]["output_shape"]),
|
|
table_width['output_shape_width'],
|
|
"{:,}".format(summary[layer]["nb_params"]),
|
|
table_width['params_width'],
|
|
)
|
|
total_params += summary[layer]["nb_params"]
|
|
|
|
try:
|
|
total_output += int(
|
|
np.sum(np.prod(summary[layer]["output_shape"], axis=-1))
|
|
)
|
|
except:
|
|
for output_shape in summary[layer]["output_shape"]:
|
|
total_output += int(np.sum(np.prod(output_shape, axis=-1)))
|
|
|
|
if "trainable" in summary[layer]:
|
|
if summary[layer]["trainable"]:
|
|
trainable_params += summary[layer]["trainable_params"]
|
|
summary_str += line_new + "\n"
|
|
|
|
def _get_input_size(input_size, size):
|
|
if isinstance(input_size, (list, tuple)) and _all_is_number(input_size):
|
|
size = abs(int(np.prod(input_size)) * 4.0 / (1024**2.0))
|
|
else:
|
|
size = sum([_get_input_size(i, size) for i in input_size])
|
|
return size
|
|
|
|
total_input_size = _get_input_size(input_size, 0)
|
|
|
|
total_output_size = abs(
|
|
2.0 * total_output * 4.0 / (1024**2.0)
|
|
) # x2 for gradients
|
|
total_params_size = abs(total_params * 4.0 / (1024**2.0))
|
|
total_size = total_params_size + total_output_size + total_input_size
|
|
|
|
summary_str += "=" * table_width['table_width'] + "\n"
|
|
summary_str += f"Total params: {total_params:,}" + "\n"
|
|
summary_str += f"Trainable params: {trainable_params:,}" + "\n"
|
|
summary_str += (
|
|
f"Non-trainable params: {total_params - trainable_params:,}" + "\n"
|
|
)
|
|
summary_str += "-" * table_width['table_width'] + "\n"
|
|
summary_str += f"Input size (MB): {total_input_size:0.2f}" + "\n"
|
|
summary_str += (
|
|
f"Forward/backward pass size (MB): {total_output_size:0.2f}" + "\n"
|
|
)
|
|
summary_str += f"Params size (MB): {total_params_size:0.2f}" + "\n"
|
|
summary_str += f"Estimated Total Size (MB): {total_size:0.2f}" + "\n"
|
|
summary_str += "-" * table_width['table_width'] + "\n"
|
|
|
|
# return summary
|
|
return summary_str, {
|
|
'total_params': total_params,
|
|
'trainable_params': trainable_params,
|
|
}
|