wehub-resource-sync
372 lines
11 KiB
Python
372 lines
11 KiB
Python
# 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.
|
|
|
|
from __future__ import annotations
|
|
|
|
from typing import (
|
|
TYPE_CHECKING,
|
|
Literal,
|
|
TypedDict,
|
|
)
|
|
|
|
from typing_extensions import NotRequired, Unpack
|
|
|
|
import paddle
|
|
from paddle import nn
|
|
from paddle.utils.download import get_weights_path_from_url
|
|
|
|
if TYPE_CHECKING:
|
|
from paddle import Tensor
|
|
from paddle.nn import Layer, Sequential
|
|
|
|
class _VGGOptions(TypedDict):
|
|
num_classes: NotRequired[int]
|
|
with_pool: NotRequired[bool]
|
|
|
|
|
|
__all__ = []
|
|
|
|
model_urls = {
|
|
'vgg16': (
|
|
'https://paddle-hapi.bj.bcebos.com/models/vgg16.pdparams',
|
|
'89bbffc0f87d260be9b8cdc169c991c4',
|
|
),
|
|
'vgg19': (
|
|
'https://paddle-hapi.bj.bcebos.com/models/vgg19.pdparams',
|
|
'23b18bb13d8894f60f54e642be79a0dd',
|
|
),
|
|
}
|
|
|
|
|
|
class VGG(nn.Layer):
|
|
"""VGG model from
|
|
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_.
|
|
|
|
Args:
|
|
features (nn.Layer): Vgg features create by function make_layers.
|
|
num_classes (int, optional): Output dim of last fc layer. If num_classes <= 0, last fc layer
|
|
will not be defined. Default: 1000.
|
|
with_pool (bool, optional): Use pool before the last three fc layer or not. Default: True.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_nn_Layer`. An instance of VGG model.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.vision.models import VGG
|
|
>>> from paddle.vision.models.vgg import make_layers
|
|
|
|
>>> vgg11_cfg = [
|
|
... 64,
|
|
... 'M',
|
|
... 128,
|
|
... 'M',
|
|
... 256,
|
|
... 256,
|
|
... 'M',
|
|
... 512,
|
|
... 512,
|
|
... 'M',
|
|
... 512,
|
|
... 512,
|
|
... 'M',
|
|
... ]
|
|
|
|
>>> features = make_layers(vgg11_cfg) # type: ignore
|
|
|
|
>>> vgg11 = VGG(features)
|
|
|
|
>>> x = paddle.rand([1, 3, 224, 224])
|
|
>>> out = vgg11(x)
|
|
|
|
>>> print(out.shape)
|
|
paddle.Size([1, 1000])
|
|
"""
|
|
|
|
num_classes: int
|
|
with_pool: bool
|
|
|
|
def __init__(
|
|
self, features: Layer, num_classes: int = 1000, with_pool: bool = True
|
|
) -> None:
|
|
super().__init__()
|
|
self.features = features
|
|
self.num_classes = num_classes
|
|
self.with_pool = with_pool
|
|
|
|
if with_pool:
|
|
self.avgpool = nn.AdaptiveAvgPool2D((7, 7))
|
|
|
|
if num_classes > 0:
|
|
self.classifier = nn.Sequential(
|
|
nn.Linear(512 * 7 * 7, 4096),
|
|
nn.ReLU(),
|
|
nn.Dropout(),
|
|
nn.Linear(4096, 4096),
|
|
nn.ReLU(),
|
|
nn.Dropout(),
|
|
nn.Linear(4096, num_classes),
|
|
)
|
|
|
|
def forward(self, x: Tensor) -> Tensor:
|
|
x = self.features(x)
|
|
|
|
if self.with_pool:
|
|
x = self.avgpool(x)
|
|
|
|
if self.num_classes > 0:
|
|
x = paddle.flatten(x, 1)
|
|
x = self.classifier(x)
|
|
|
|
return x
|
|
|
|
|
|
def make_layers(
|
|
cfg: list[int | Literal['M']], batch_norm: bool = False
|
|
) -> Sequential:
|
|
layers = []
|
|
in_channels = 3
|
|
for v in cfg:
|
|
if v == 'M':
|
|
layers += [nn.MaxPool2D(kernel_size=2, stride=2)]
|
|
else:
|
|
conv2d = nn.Conv2D(in_channels, v, kernel_size=3, padding=1)
|
|
if batch_norm:
|
|
layers += [conv2d, nn.BatchNorm2D(v), nn.ReLU()]
|
|
else:
|
|
layers += [conv2d, nn.ReLU()]
|
|
in_channels = v
|
|
return nn.Sequential(*layers)
|
|
|
|
|
|
cfgs = {
|
|
'A': [
|
|
64, 'M',
|
|
128, 'M',
|
|
256, 256, 'M',
|
|
512, 512, 'M',
|
|
512, 512, 'M',
|
|
],
|
|
'B': [
|
|
64, 64, 'M',
|
|
128, 128, 'M',
|
|
256, 256, 'M',
|
|
512, 512, 'M',
|
|
512, 512, 'M',
|
|
],
|
|
'D': [
|
|
64, 64, 'M',
|
|
128, 128, 'M',
|
|
256, 256, 256, 'M',
|
|
512, 512, 512, 'M',
|
|
512, 512, 512, 'M',
|
|
],
|
|
'E': [
|
|
64, 64, 'M',
|
|
128, 128, 'M',
|
|
256, 256, 256, 256, 'M',
|
|
512, 512, 512, 512, 'M',
|
|
512, 512, 512, 512, 'M',
|
|
],
|
|
} # fmt: skip
|
|
|
|
|
|
def _vgg(
|
|
arch: str,
|
|
cfg: Literal["A", "B", "D", "E"],
|
|
batch_norm: bool,
|
|
pretrained: bool,
|
|
**kwargs: Unpack[_VGGOptions],
|
|
) -> VGG:
|
|
model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **kwargs)
|
|
|
|
if pretrained:
|
|
assert arch in model_urls, (
|
|
f"{arch} model do not have a pretrained model now, you should set pretrained=False"
|
|
)
|
|
weight_path = get_weights_path_from_url(
|
|
model_urls[arch][0], model_urls[arch][1]
|
|
)
|
|
|
|
param = paddle.load(weight_path)
|
|
model.load_dict(param)
|
|
|
|
return model
|
|
|
|
|
|
def vgg11(
|
|
pretrained: bool = False,
|
|
batch_norm: bool = False,
|
|
**kwargs: Unpack[_VGGOptions],
|
|
) -> VGG:
|
|
"""VGG 11-layer model from
|
|
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_.
|
|
|
|
Args:
|
|
pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
|
on ImageNet. Default: False.
|
|
batch_norm (bool, optional): If True, returns a model with batch_norm layer. Default: False.
|
|
**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`VGG <api_paddle_vision_models_VGG>`.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_nn_Layer`. An instance of VGG 11-layer model.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.vision.models import vgg11
|
|
|
|
>>> # build model
|
|
>>> model = vgg11()
|
|
|
|
>>> # build vgg11 model with batch_norm
|
|
>>> model = vgg11(batch_norm=True)
|
|
|
|
>>> x = paddle.rand([1, 3, 224, 224])
|
|
>>> out = model(x)
|
|
|
|
>>> print(out.shape)
|
|
paddle.Size([1, 1000])
|
|
"""
|
|
model_name = 'vgg11'
|
|
if batch_norm:
|
|
model_name += '_bn'
|
|
return _vgg(model_name, 'A', batch_norm, pretrained, **kwargs)
|
|
|
|
|
|
def vgg13(
|
|
pretrained: bool = False,
|
|
batch_norm: bool = False,
|
|
**kwargs: Unpack[_VGGOptions],
|
|
) -> VGG:
|
|
"""VGG 13-layer model from
|
|
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_.
|
|
|
|
Args:
|
|
pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
|
on ImageNet. Default: False.
|
|
batch_norm (bool): If True, returns a model with batch_norm layer. Default: False.
|
|
**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`VGG <api_paddle_vision_models_VGG>`.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_nn_Layer`. An instance of VGG 13-layer model.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.vision.models import vgg13
|
|
|
|
>>> # build model
|
|
>>> model = vgg13()
|
|
|
|
>>> # build vgg13 model with batch_norm
|
|
>>> model = vgg13(batch_norm=True)
|
|
|
|
>>> x = paddle.rand([1, 3, 224, 224])
|
|
>>> out = model(x)
|
|
|
|
>>> print(out.shape)
|
|
paddle.Size([1, 1000])
|
|
"""
|
|
model_name = 'vgg13'
|
|
if batch_norm:
|
|
model_name += '_bn'
|
|
return _vgg(model_name, 'B', batch_norm, pretrained, **kwargs)
|
|
|
|
|
|
def vgg16(
|
|
pretrained: bool = False,
|
|
batch_norm: bool = False,
|
|
**kwargs: Unpack[_VGGOptions],
|
|
) -> VGG:
|
|
"""VGG 16-layer model from
|
|
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_.
|
|
|
|
Args:
|
|
pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
|
on ImageNet. Default: False.
|
|
batch_norm (bool, optional): If True, returns a model with batch_norm layer. Default: False.
|
|
**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`VGG <api_paddle_vision_models_VGG>`.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_nn_Layer`. An instance of VGG 16-layer model.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.vision.models import vgg16
|
|
|
|
>>> # build model
|
|
>>> model = vgg16()
|
|
|
|
>>> # build vgg16 model with batch_norm
|
|
>>> model = vgg16(batch_norm=True)
|
|
|
|
>>> x = paddle.rand([1, 3, 224, 224])
|
|
>>> out = model(x)
|
|
|
|
>>> print(out.shape)
|
|
paddle.Size([1, 1000])
|
|
"""
|
|
model_name = 'vgg16'
|
|
if batch_norm:
|
|
model_name += '_bn'
|
|
return _vgg(model_name, 'D', batch_norm, pretrained, **kwargs)
|
|
|
|
|
|
def vgg19(
|
|
pretrained: bool = False,
|
|
batch_norm: bool = False,
|
|
**kwargs: Unpack[_VGGOptions],
|
|
) -> VGG:
|
|
"""VGG 19-layer model from
|
|
`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_.
|
|
|
|
Args:
|
|
pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
|
on ImageNet. Default: False.
|
|
batch_norm (bool, optional): If True, returns a model with batch_norm layer. Default: False.
|
|
**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`VGG <api_paddle_vision_models_VGG>`.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_nn_Layer`. An instance of VGG 19-layer model.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.vision.models import vgg19
|
|
|
|
>>> # build model
|
|
>>> model = vgg19()
|
|
|
|
>>> # build vgg19 model with batch_norm
|
|
>>> model = vgg19(batch_norm=True)
|
|
|
|
>>> x = paddle.rand([1, 3, 224, 224])
|
|
>>> out = model(x)
|
|
|
|
>>> print(out.shape)
|
|
paddle.Size([1, 1000])
|
|
"""
|
|
model_name = 'vgg19'
|
|
if batch_norm:
|
|
model_name += '_bn'
|
|
return _vgg(model_name, 'E', batch_norm, pretrained, **kwargs)
|