wehub-resource-sync
899 lines
28 KiB
Python
899 lines
28 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
|
|
|
|
import paddle
|
|
from paddle import nn
|
|
from paddle.utils.download import get_weights_path_from_url
|
|
|
|
if TYPE_CHECKING:
|
|
from collections.abc import Callable
|
|
from typing import Literal, TypedDict
|
|
|
|
from typing_extensions import NotRequired, Unpack
|
|
|
|
from paddle import Tensor
|
|
from paddle._typing import Size2
|
|
|
|
_ResNetArch = Literal[
|
|
'resnet18',
|
|
'resnet34',
|
|
'resnet50',
|
|
'resnet101',
|
|
'resnet152',
|
|
'resnext50_32x4d',
|
|
'resnext50_64x4d',
|
|
'resnext101_32x4d',
|
|
'resnext101_64x4d',
|
|
'resnext152_32x4d',
|
|
'resnext152_64x4d',
|
|
'wide_resnet50_2',
|
|
'wide_resnet101_2',
|
|
]
|
|
|
|
class _ResNetOptions(TypedDict):
|
|
width: NotRequired[int]
|
|
num_classes: NotRequired[int]
|
|
with_pool: NotRequired[bool]
|
|
groups: NotRequired[int]
|
|
|
|
|
|
__all__ = []
|
|
|
|
model_urls: dict[str, tuple[str, str]] = {
|
|
'resnet18': (
|
|
'https://paddle-hapi.bj.bcebos.com/models/resnet18.pdparams',
|
|
'cf548f46534aa3560945be4b95cd11c4',
|
|
),
|
|
'resnet34': (
|
|
'https://paddle-hapi.bj.bcebos.com/models/resnet34.pdparams',
|
|
'8d2275cf8706028345f78ac0e1d31969',
|
|
),
|
|
'resnet50': (
|
|
'https://paddle-hapi.bj.bcebos.com/models/resnet50.pdparams',
|
|
'ca6f485ee1ab0492d38f323885b0ad80',
|
|
),
|
|
'resnet101': (
|
|
'https://paddle-hapi.bj.bcebos.com/models/resnet101.pdparams',
|
|
'02f35f034ca3858e1e54d4036443c92d',
|
|
),
|
|
'resnet152': (
|
|
'https://paddle-hapi.bj.bcebos.com/models/resnet152.pdparams',
|
|
'7ad16a2f1e7333859ff986138630fd7a',
|
|
),
|
|
'resnext50_32x4d': (
|
|
'https://paddle-hapi.bj.bcebos.com/models/resnext50_32x4d.pdparams',
|
|
'dc47483169be7d6f018fcbb7baf8775d',
|
|
),
|
|
"resnext50_64x4d": (
|
|
'https://paddle-hapi.bj.bcebos.com/models/resnext50_64x4d.pdparams',
|
|
'063d4b483e12b06388529450ad7576db',
|
|
),
|
|
'resnext101_32x4d': (
|
|
'https://paddle-hapi.bj.bcebos.com/models/resnext101_32x4d.pdparams',
|
|
'967b090039f9de2c8d06fe994fb9095f',
|
|
),
|
|
'resnext101_64x4d': (
|
|
'https://paddle-hapi.bj.bcebos.com/models/resnext101_64x4d.pdparams',
|
|
'98e04e7ca616a066699230d769d03008',
|
|
),
|
|
'resnext152_32x4d': (
|
|
'https://paddle-hapi.bj.bcebos.com/models/resnext152_32x4d.pdparams',
|
|
'18ff0beee21f2efc99c4b31786107121',
|
|
),
|
|
'resnext152_64x4d': (
|
|
'https://paddle-hapi.bj.bcebos.com/models/resnext152_64x4d.pdparams',
|
|
'77c4af00ca42c405fa7f841841959379',
|
|
),
|
|
'wide_resnet50_2': (
|
|
'https://paddle-hapi.bj.bcebos.com/models/wide_resnet50_2.pdparams',
|
|
'0282f804d73debdab289bd9fea3fa6dc',
|
|
),
|
|
'wide_resnet101_2': (
|
|
'https://paddle-hapi.bj.bcebos.com/models/wide_resnet101_2.pdparams',
|
|
'd4360a2d23657f059216f5d5a1a9ac93',
|
|
),
|
|
}
|
|
|
|
|
|
class BasicBlock(nn.Layer):
|
|
expansion: int = 1
|
|
|
|
def __init__(
|
|
self,
|
|
inplanes: int,
|
|
planes: int,
|
|
stride: Size2 = 1,
|
|
downsample: nn.Layer | None = None,
|
|
groups: int = 1,
|
|
base_width: int = 64,
|
|
dilation: int = 1,
|
|
norm_layer: Callable[..., nn.Layer] | None = None,
|
|
) -> None:
|
|
super().__init__()
|
|
if norm_layer is None:
|
|
norm_layer: type[nn.BatchNorm2D] = nn.BatchNorm2D
|
|
|
|
if dilation > 1:
|
|
raise NotImplementedError(
|
|
"Dilation > 1 not supported in BasicBlock"
|
|
)
|
|
|
|
self.conv1 = nn.Conv2D(
|
|
inplanes, planes, 3, padding=1, stride=stride, bias_attr=False
|
|
)
|
|
self.bn1 = norm_layer(planes)
|
|
self.relu = nn.ReLU()
|
|
self.conv2 = nn.Conv2D(planes, planes, 3, padding=1, bias_attr=False)
|
|
self.bn2 = norm_layer(planes)
|
|
self.downsample = downsample
|
|
self.stride = stride
|
|
|
|
def forward(self, x: Tensor) -> Tensor:
|
|
identity = x
|
|
|
|
out = self.conv1(x)
|
|
out = self.bn1(out)
|
|
out = self.relu(out)
|
|
|
|
out = self.conv2(out)
|
|
out = self.bn2(out)
|
|
|
|
if self.downsample is not None:
|
|
identity = self.downsample(x)
|
|
|
|
out += identity
|
|
out = self.relu(out)
|
|
|
|
return out
|
|
|
|
|
|
class BottleneckBlock(nn.Layer):
|
|
expansion: int = 4
|
|
|
|
def __init__(
|
|
self,
|
|
inplanes: int,
|
|
planes: int,
|
|
stride: Size2 = 1,
|
|
downsample: nn.Layer | None = None,
|
|
groups: int = 1,
|
|
base_width: int = 64,
|
|
dilation: int = 1,
|
|
norm_layer: Callable[..., nn.Layer] | None = None,
|
|
) -> None:
|
|
super().__init__()
|
|
if norm_layer is None:
|
|
norm_layer = nn.BatchNorm2D
|
|
width = int(planes * (base_width / 64.0)) * groups
|
|
|
|
self.conv1 = nn.Conv2D(inplanes, width, 1, bias_attr=False)
|
|
self.bn1 = norm_layer(width)
|
|
|
|
self.conv2 = nn.Conv2D(
|
|
width,
|
|
width,
|
|
3,
|
|
padding=dilation,
|
|
stride=stride,
|
|
groups=groups,
|
|
dilation=dilation,
|
|
bias_attr=False,
|
|
)
|
|
self.bn2 = norm_layer(width)
|
|
|
|
self.conv3 = nn.Conv2D(
|
|
width, planes * self.expansion, 1, bias_attr=False
|
|
)
|
|
self.bn3 = norm_layer(planes * self.expansion)
|
|
self.relu = nn.ReLU()
|
|
self.downsample = downsample
|
|
self.stride = stride
|
|
|
|
def forward(self, x: Tensor) -> Tensor:
|
|
identity = x
|
|
|
|
out = self.conv1(x)
|
|
out = self.bn1(out)
|
|
out = self.relu(out)
|
|
|
|
out = self.conv2(out)
|
|
out = self.bn2(out)
|
|
out = self.relu(out)
|
|
|
|
out = self.conv3(out)
|
|
out = self.bn3(out)
|
|
|
|
if self.downsample is not None:
|
|
identity = self.downsample(x)
|
|
|
|
out += identity
|
|
out = self.relu(out)
|
|
|
|
return out
|
|
|
|
|
|
class ResNet(nn.Layer):
|
|
"""ResNet model from
|
|
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
|
|
|
|
Args:
|
|
Block (BasicBlock|BottleneckBlock): Block module of model.
|
|
depth (int, optional): Layers of ResNet, Default: 50.
|
|
width (int, optional): Base width per convolution group for each convolution block, Default: 64.
|
|
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 fc layer or not. Default: True.
|
|
groups (int, optional): Number of groups for each convolution block, Default: 1.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_nn_Layer`. An instance of ResNet model.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.vision.models import ResNet
|
|
>>> from paddle.vision.models.resnet import (
|
|
... BottleneckBlock,
|
|
... BasicBlock,
|
|
... )
|
|
|
|
>>> # build ResNet with 18 layers
|
|
>>> resnet18 = ResNet(BasicBlock, 18)
|
|
|
|
>>> # build ResNet with 50 layers
|
|
>>> resnet50 = ResNet(BottleneckBlock, 50)
|
|
|
|
>>> # build Wide ResNet model
|
|
>>> wide_resnet50_2 = ResNet(BottleneckBlock, 50, width=64 * 2)
|
|
|
|
>>> # build ResNeXt model
|
|
>>> resnext50_32x4d = ResNet(BottleneckBlock, 50, width=4, groups=32)
|
|
|
|
>>> x = paddle.rand([1, 3, 224, 224])
|
|
>>> out = resnet18(x)
|
|
|
|
>>> print(out.shape)
|
|
paddle.Size([1, 1000])
|
|
"""
|
|
|
|
groups: int
|
|
base_width: int
|
|
num_classes: int
|
|
with_pool: bool
|
|
inplanes: int
|
|
dilation: int
|
|
|
|
def __init__(
|
|
self,
|
|
block: type[BasicBlock | BottleneckBlock],
|
|
depth: int = 50,
|
|
width: int = 64,
|
|
num_classes: int = 1000,
|
|
with_pool: bool = True,
|
|
groups: int = 1,
|
|
) -> None:
|
|
super().__init__()
|
|
layer_cfg = {
|
|
18: [2, 2, 2, 2],
|
|
34: [3, 4, 6, 3],
|
|
50: [3, 4, 6, 3],
|
|
101: [3, 4, 23, 3],
|
|
152: [3, 8, 36, 3],
|
|
}
|
|
layers = layer_cfg[depth]
|
|
self.groups = groups
|
|
self.base_width = width
|
|
self.num_classes = num_classes
|
|
self.with_pool = with_pool
|
|
self._norm_layer = nn.BatchNorm2D
|
|
|
|
self.inplanes = 64
|
|
self.dilation = 1
|
|
|
|
self.conv1 = nn.Conv2D(
|
|
3,
|
|
self.inplanes,
|
|
kernel_size=7,
|
|
stride=2,
|
|
padding=3,
|
|
bias_attr=False,
|
|
)
|
|
self.bn1 = self._norm_layer(self.inplanes)
|
|
self.relu = nn.ReLU()
|
|
self.maxpool = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
|
|
self.layer1 = self._make_layer(block, 64, layers[0])
|
|
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
|
|
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
|
|
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
|
|
if with_pool:
|
|
self.avgpool = nn.AdaptiveAvgPool2D((1, 1))
|
|
|
|
if num_classes > 0:
|
|
self.fc = nn.Linear(512 * block.expansion, num_classes)
|
|
|
|
def _make_layer(
|
|
self,
|
|
block: type[BasicBlock | BottleneckBlock],
|
|
planes: int,
|
|
blocks: int,
|
|
stride: int = 1,
|
|
dilate: bool = False,
|
|
) -> nn.Sequential:
|
|
norm_layer = self._norm_layer
|
|
downsample = None
|
|
previous_dilation = self.dilation
|
|
if dilate:
|
|
self.dilation *= stride
|
|
stride = 1
|
|
if stride != 1 or self.inplanes != planes * block.expansion:
|
|
downsample = nn.Sequential(
|
|
nn.Conv2D(
|
|
self.inplanes,
|
|
planes * block.expansion,
|
|
1,
|
|
stride=stride,
|
|
bias_attr=False,
|
|
),
|
|
norm_layer(planes * block.expansion),
|
|
)
|
|
|
|
layers = []
|
|
layers.append(
|
|
block(
|
|
self.inplanes,
|
|
planes,
|
|
stride,
|
|
downsample,
|
|
self.groups,
|
|
self.base_width,
|
|
previous_dilation,
|
|
norm_layer,
|
|
)
|
|
)
|
|
self.inplanes = planes * block.expansion
|
|
for _ in range(1, blocks):
|
|
layers.append(
|
|
block(
|
|
self.inplanes,
|
|
planes,
|
|
groups=self.groups,
|
|
base_width=self.base_width,
|
|
norm_layer=norm_layer,
|
|
)
|
|
)
|
|
|
|
return nn.Sequential(*layers)
|
|
|
|
def forward(self, x: Tensor) -> Tensor:
|
|
x = self.conv1(x)
|
|
x = self.bn1(x)
|
|
x = self.relu(x)
|
|
x = self.maxpool(x)
|
|
x = self.layer1(x)
|
|
x = self.layer2(x)
|
|
x = self.layer3(x)
|
|
x = self.layer4(x)
|
|
|
|
if self.with_pool:
|
|
x = self.avgpool(x)
|
|
|
|
if self.num_classes > 0:
|
|
x = paddle.flatten(x, 1)
|
|
x = self.fc(x)
|
|
|
|
return x
|
|
|
|
|
|
def _resnet(
|
|
arch: _ResNetArch,
|
|
Block: type[BasicBlock | BottleneckBlock],
|
|
depth: int,
|
|
pretrained: bool,
|
|
**kwargs: Unpack[_ResNetOptions],
|
|
) -> ResNet:
|
|
model = ResNet(Block, depth, **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.set_dict(param)
|
|
|
|
return model
|
|
|
|
|
|
def resnet18(pretrained=False, **kwargs: Unpack[_ResNetOptions]) -> ResNet:
|
|
"""ResNet 18-layer model from
|
|
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
|
|
|
|
Args:
|
|
pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
|
on ImageNet. Default: False.
|
|
**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_models_ResNet>`.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_nn_Layer`. An instance of ResNet 18-layer model.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.vision.models import resnet18
|
|
|
|
>>> # build model
|
|
>>> model = resnet18()
|
|
|
|
>>> # build model and load imagenet pretrained weight
|
|
>>> # model = resnet18(pretrained=True)
|
|
|
|
>>> x = paddle.rand([1, 3, 224, 224])
|
|
>>> out = model(x)
|
|
|
|
>>> print(out.shape)
|
|
paddle.Size([1, 1000])
|
|
"""
|
|
return _resnet('resnet18', BasicBlock, 18, pretrained, **kwargs)
|
|
|
|
|
|
def resnet34(
|
|
pretrained: bool = False, **kwargs: Unpack[_ResNetOptions]
|
|
) -> ResNet:
|
|
"""ResNet 34-layer model from
|
|
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
|
|
|
|
Args:
|
|
pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
|
on ImageNet. Default: False.
|
|
**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_models_ResNet>`.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_nn_Layer`. An instance of ResNet 34-layer model.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.vision.models import resnet34
|
|
|
|
>>> # build model
|
|
>>> model = resnet34()
|
|
|
|
>>> # build model and load imagenet pretrained weight
|
|
>>> # model = resnet34(pretrained=True)
|
|
|
|
>>> x = paddle.rand([1, 3, 224, 224])
|
|
>>> out = model(x)
|
|
|
|
>>> print(out.shape)
|
|
paddle.Size([1, 1000])
|
|
"""
|
|
return _resnet('resnet34', BasicBlock, 34, pretrained, **kwargs)
|
|
|
|
|
|
def resnet50(
|
|
pretrained: bool = False, **kwargs: Unpack[_ResNetOptions]
|
|
) -> ResNet:
|
|
"""ResNet 50-layer model from
|
|
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
|
|
|
|
Args:
|
|
pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
|
on ImageNet. Default: False.
|
|
**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_models_ResNet>`.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_nn_Layer`. An instance of ResNet 50-layer model.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.vision.models import resnet50
|
|
|
|
>>> # build model
|
|
>>> model = resnet50()
|
|
|
|
>>> # build model and load imagenet pretrained weight
|
|
>>> # model = resnet50(pretrained=True)
|
|
|
|
>>> x = paddle.rand([1, 3, 224, 224])
|
|
>>> out = model(x)
|
|
|
|
>>> print(out.shape)
|
|
paddle.Size([1, 1000])
|
|
"""
|
|
return _resnet('resnet50', BottleneckBlock, 50, pretrained, **kwargs)
|
|
|
|
|
|
def resnet101(
|
|
pretrained: bool = False, **kwargs: Unpack[_ResNetOptions]
|
|
) -> ResNet:
|
|
"""ResNet 101-layer model from
|
|
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
|
|
|
|
Args:
|
|
pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
|
on ImageNet. Default: False.
|
|
**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_models_ResNet>`.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_nn_Layer`. An instance of ResNet 101-layer.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.vision.models import resnet101
|
|
|
|
>>> # build model
|
|
>>> model = resnet101()
|
|
|
|
>>> # build model and load imagenet pretrained weight
|
|
>>> # model = resnet101(pretrained=True)
|
|
|
|
>>> x = paddle.rand([1, 3, 224, 224])
|
|
>>> out = model(x)
|
|
|
|
>>> print(out.shape)
|
|
paddle.Size([1, 1000])
|
|
"""
|
|
return _resnet('resnet101', BottleneckBlock, 101, pretrained, **kwargs)
|
|
|
|
|
|
def resnet152(
|
|
pretrained: bool = False, **kwargs: Unpack[_ResNetOptions]
|
|
) -> ResNet:
|
|
"""ResNet 152-layer model from
|
|
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
|
|
|
|
Args:
|
|
pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
|
on ImageNet. Default: False.
|
|
**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_models_ResNet>`.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_nn_Layer`. An instance of ResNet 152-layer model.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.vision.models import resnet152
|
|
|
|
>>> # build model
|
|
>>> model = resnet152()
|
|
|
|
>>> # build model and load imagenet pretrained weight
|
|
>>> # model = resnet152(pretrained=True)
|
|
|
|
>>> x = paddle.rand([1, 3, 224, 224])
|
|
>>> out = model(x)
|
|
|
|
>>> print(out.shape)
|
|
paddle.Size([1, 1000])
|
|
"""
|
|
return _resnet('resnet152', BottleneckBlock, 152, pretrained, **kwargs)
|
|
|
|
|
|
def resnext50_32x4d(
|
|
pretrained: bool = False, **kwargs: Unpack[_ResNetOptions]
|
|
) -> ResNet:
|
|
"""ResNeXt-50 32x4d model from
|
|
`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
|
|
|
|
Args:
|
|
pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
|
on ImageNet. Default: False.
|
|
**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_models_ResNet>`.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_nn_Layer`. An instance of ResNeXt-50 32x4d model.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.vision.models import resnext50_32x4d
|
|
|
|
>>> # build model
|
|
>>> model = resnext50_32x4d()
|
|
|
|
>>> # build model and load imagenet pretrained weight
|
|
>>> # model = resnext50_32x4d(pretrained=True)
|
|
|
|
>>> x = paddle.rand([1, 3, 224, 224])
|
|
>>> out = model(x)
|
|
|
|
>>> print(out.shape)
|
|
paddle.Size([1, 1000])
|
|
"""
|
|
kwargs['groups'] = 32
|
|
kwargs['width'] = 4
|
|
return _resnet('resnext50_32x4d', BottleneckBlock, 50, pretrained, **kwargs)
|
|
|
|
|
|
def resnext50_64x4d(
|
|
pretrained: bool = False, **kwargs: Unpack[_ResNetOptions]
|
|
) -> ResNet:
|
|
"""ResNeXt-50 64x4d model from
|
|
`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
|
|
|
|
Args:
|
|
pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
|
on ImageNet. Default: False.
|
|
**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_models_ResNet>`.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_nn_Layer`. An instance of ResNeXt-50 64x4d model.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.vision.models import resnext50_64x4d
|
|
|
|
>>> # build model
|
|
>>> model = resnext50_64x4d()
|
|
|
|
>>> # build model and load imagenet pretrained weight
|
|
>>> # model = resnext50_64x4d(pretrained=True)
|
|
|
|
>>> x = paddle.rand([1, 3, 224, 224])
|
|
>>> out = model(x)
|
|
|
|
>>> print(out.shape)
|
|
paddle.Size([1, 1000])
|
|
"""
|
|
kwargs['groups'] = 64
|
|
kwargs['width'] = 4
|
|
return _resnet('resnext50_64x4d', BottleneckBlock, 50, pretrained, **kwargs)
|
|
|
|
|
|
def resnext101_32x4d(
|
|
pretrained: bool = False, **kwargs: Unpack[_ResNetOptions]
|
|
) -> ResNet:
|
|
"""ResNeXt-101 32x4d model from
|
|
`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
|
|
|
|
Args:
|
|
pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
|
on ImageNet. Default: False.
|
|
**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_models_ResNet>`.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_nn_Layer`. An instance of ResNeXt-101 32x4d model.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.vision.models import resnext101_32x4d
|
|
|
|
>>> # build model
|
|
>>> model = resnext101_32x4d()
|
|
|
|
>>> # build model and load imagenet pretrained weight
|
|
>>> # model = resnext101_32x4d(pretrained=True)
|
|
|
|
>>> x = paddle.rand([1, 3, 224, 224])
|
|
>>> out = model(x)
|
|
|
|
>>> print(out.shape)
|
|
paddle.Size([1, 1000])
|
|
"""
|
|
kwargs['groups'] = 32
|
|
kwargs['width'] = 4
|
|
return _resnet(
|
|
'resnext101_32x4d', BottleneckBlock, 101, pretrained, **kwargs
|
|
)
|
|
|
|
|
|
def resnext101_64x4d(
|
|
pretrained: bool = False, **kwargs: Unpack[_ResNetOptions]
|
|
) -> ResNet:
|
|
"""ResNeXt-101 64x4d model from
|
|
`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
|
|
|
|
Args:
|
|
pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
|
on ImageNet. Default: False.
|
|
**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_models_ResNet>`.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_nn_Layer`. An instance of ResNeXt-101 64x4d model.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.vision.models import resnext101_64x4d
|
|
|
|
>>> # build model
|
|
>>> model = resnext101_64x4d()
|
|
|
|
>>> # build model and load imagenet pretrained weight
|
|
>>> # model = resnext101_64x4d(pretrained=True)
|
|
|
|
>>> x = paddle.rand([1, 3, 224, 224])
|
|
>>> out = model(x)
|
|
|
|
>>> print(out.shape)
|
|
paddle.Size([1, 1000])
|
|
"""
|
|
kwargs['groups'] = 64
|
|
kwargs['width'] = 4
|
|
return _resnet(
|
|
'resnext101_64x4d', BottleneckBlock, 101, pretrained, **kwargs
|
|
)
|
|
|
|
|
|
def resnext152_32x4d(
|
|
pretrained: bool = False, **kwargs: Unpack[_ResNetOptions]
|
|
) -> ResNet:
|
|
"""ResNeXt-152 32x4d model from
|
|
`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
|
|
|
|
Args:
|
|
pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
|
on ImageNet. Default: False.
|
|
**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_models_ResNet>`.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_nn_Layer`. An instance of ResNeXt-152 32x4d model.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.vision.models import resnext152_32x4d
|
|
|
|
>>> # build model
|
|
>>> model = resnext152_32x4d()
|
|
|
|
>>> # build model and load imagenet pretrained weight
|
|
>>> # model = resnext152_32x4d(pretrained=True)
|
|
|
|
>>> x = paddle.rand([1, 3, 224, 224])
|
|
>>> out = model(x)
|
|
|
|
>>> print(out.shape)
|
|
paddle.Size([1, 1000])
|
|
"""
|
|
kwargs['groups'] = 32
|
|
kwargs['width'] = 4
|
|
return _resnet(
|
|
'resnext152_32x4d', BottleneckBlock, 152, pretrained, **kwargs
|
|
)
|
|
|
|
|
|
def resnext152_64x4d(
|
|
pretrained: bool = False, **kwargs: Unpack[_ResNetOptions]
|
|
) -> ResNet:
|
|
"""ResNeXt-152 64x4d model from
|
|
`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
|
|
|
|
Args:
|
|
pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
|
on ImageNet. Default: False.
|
|
**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_models_ResNet>`.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_nn_Layer`. An instance of ResNeXt-152 64x4d model.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.vision.models import resnext152_64x4d
|
|
|
|
>>> # build model
|
|
>>> model = resnext152_64x4d()
|
|
|
|
>>> # build model and load imagenet pretrained weight
|
|
>>> # model = resnext152_64x4d(pretrained=True)
|
|
|
|
>>> x = paddle.rand([1, 3, 224, 224])
|
|
>>> out = model(x)
|
|
|
|
>>> print(out.shape)
|
|
paddle.Size([1, 1000])
|
|
"""
|
|
kwargs['groups'] = 64
|
|
kwargs['width'] = 4
|
|
return _resnet(
|
|
'resnext152_64x4d', BottleneckBlock, 152, pretrained, **kwargs
|
|
)
|
|
|
|
|
|
def wide_resnet50_2(
|
|
pretrained: bool = False, **kwargs: Unpack[_ResNetOptions]
|
|
) -> ResNet:
|
|
"""Wide ResNet-50-2 model from
|
|
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
|
|
|
|
Args:
|
|
pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
|
on ImageNet. Default: False.
|
|
**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_models_ResNet>`.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_nn_Layer`. An instance of Wide ResNet-50-2 model.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.vision.models import wide_resnet50_2
|
|
|
|
>>> # build model
|
|
>>> model = wide_resnet50_2()
|
|
|
|
>>> # build model and load imagenet pretrained weight
|
|
>>> # model = wide_resnet50_2(pretrained=True)
|
|
|
|
>>> x = paddle.rand([1, 3, 224, 224])
|
|
>>> out = model(x)
|
|
|
|
>>> print(out.shape)
|
|
paddle.Size([1, 1000])
|
|
"""
|
|
kwargs['width'] = 64 * 2
|
|
return _resnet('wide_resnet50_2', BottleneckBlock, 50, pretrained, **kwargs)
|
|
|
|
|
|
def wide_resnet101_2(
|
|
pretrained: bool = False, **kwargs: Unpack[_ResNetOptions]
|
|
) -> ResNet:
|
|
"""Wide ResNet-101-2 model from
|
|
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
|
|
|
|
Args:
|
|
pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
|
on ImageNet. Default: False.
|
|
**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ResNet <api_paddle_vision_models_ResNet>`.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_nn_Layer`. An instance of Wide ResNet-101-2 model.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.vision.models import wide_resnet101_2
|
|
|
|
>>> # build model
|
|
>>> model = wide_resnet101_2()
|
|
|
|
>>> # build model and load imagenet pretrained weight
|
|
>>> # model = wide_resnet101_2(pretrained=True)
|
|
|
|
>>> x = paddle.rand([1, 3, 224, 224])
|
|
>>> out = model(x)
|
|
|
|
>>> print(out.shape)
|
|
paddle.Size([1, 1000])
|
|
"""
|
|
kwargs['width'] = 64 * 2
|
|
return _resnet(
|
|
'wide_resnet101_2', BottleneckBlock, 101, pretrained, **kwargs
|
|
)
|