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未验证 提交 ba4e7c7e 编写于 作者: N Nyakku Shigure 提交者： GitHub
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reimplement ResNeXt based on ResNet (#40588) 

* refactor resnext
上级 6721376b
隐藏空白更改
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Showing with 256 addition and 405 deletion
python/paddle/vision/__init__.py
浏览文件 @ ba4e7c7e
......@@ -34,6 +34,12 @@ from .models import resnet34 # noqa: F401
from .models import resnet50 # noqa: F401
from .models import resnet101 # noqa: F401
from .models import resnet152 # noqa: F401
from .models import resnext50_32x4d # noqa: F401
from .models import resnext50_64x4d # noqa: F401
from .models import resnext101_32x4d # noqa: F401
from .models import resnext101_64x4d # noqa: F401
from .models import resnext152_32x4d # noqa: F401
from .models import resnext152_64x4d # noqa: F401
from .models import wide_resnet50_2 # noqa: F401
from .models import wide_resnet101_2 # noqa: F401
from .models import MobileNetV1 # noqa: F401
......@@ -61,13 +67,6 @@ from .models import densenet201 # noqa: F401
from .models import densenet264 # noqa: F401
from .models import AlexNet # noqa: F401
from .models import alexnet # noqa: F401
from .models import ResNeXt # noqa: F401
from .models import resnext50_32x4d # noqa: F401
from .models import resnext50_64x4d # noqa: F401
from .models import resnext101_32x4d # noqa: F401
from .models import resnext101_64x4d # noqa: F401
from .models import resnext152_32x4d # noqa: F401
from .models import resnext152_64x4d # noqa: F401
from .models import InceptionV3 # noqa: F401
from .models import inception_v3 # noqa: F401
from .models import GoogLeNet # noqa: F401
......
python/paddle/vision/models/__init__.py
浏览文件 @ ba4e7c7e
......@@ -18,6 +18,12 @@ from .resnet import resnet34 # noqa: F401
from .resnet import resnet50 # noqa: F401
from .resnet import resnet101 # noqa: F401
from .resnet import resnet152 # noqa: F401
from .resnet import resnext50_32x4d # noqa: F401
from .resnet import resnext50_64x4d # noqa: F401
from .resnet import resnext101_32x4d # noqa: F401
from .resnet import resnext101_64x4d # noqa: F401
from .resnet import resnext152_32x4d # noqa: F401
from .resnet import resnext152_64x4d # noqa: F401
from .resnet import wide_resnet50_2 # noqa: F401
from .resnet import wide_resnet101_2 # noqa: F401
from .mobilenetv1 import MobileNetV1 # noqa: F401
......@@ -42,13 +48,6 @@ from .densenet import densenet201 # noqa: F401
from .densenet import densenet264 # noqa: F401
from .alexnet import AlexNet # noqa: F401
from .alexnet import alexnet # noqa: F401
from .resnext import ResNeXt # noqa: F401
from .resnext import resnext50_32x4d # noqa: F401
from .resnext import resnext50_64x4d # noqa: F401
from .resnext import resnext101_32x4d # noqa: F401
from .resnext import resnext101_64x4d # noqa: F401
from .resnext import resnext152_32x4d # noqa: F401
from .resnext import resnext152_64x4d # noqa: F401
from .inceptionv3 import InceptionV3 # noqa: F401
from .inceptionv3 import inception_v3 # noqa: F401
from .squeezenet import SqueezeNet # noqa: F401
......@@ -72,6 +71,12 @@ __all__ = [ #noqa
'resnet50',
'resnet101',
'resnet152',
'resnext50_32x4d',
'resnext50_64x4d',
'resnext101_32x4d',
'resnext101_64x4d',
'resnext152_32x4d',
'resnext152_64x4d',
'wide_resnet50_2',
'wide_resnet101_2',
'VGG',
......@@ -96,13 +101,6 @@ __all__ = [ #noqa
'densenet264',
'AlexNet',
'alexnet',
'ResNeXt',
'resnext50_32x4d',
'resnext50_64x4d',
'resnext101_32x4d',
'resnext101_64x4d',
'resnext152_32x4d',
'resnext152_64x4d',
'InceptionV3',
'inception_v3',
'SqueezeNet',
......
python/paddle/vision/models/resnet.py
浏览文件 @ ba4e7c7e
......@@ -33,12 +33,30 @@ model_urls = {
'02f35f034ca3858e1e54d4036443c92d'),
'resnet152': ('https://paddle-hapi.bj.bcebos.com/models/resnet152.pdparams',
'7ad16a2f1e7333859ff986138630fd7a'),
'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'),
'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'),
}
......@@ -158,11 +176,12 @@ class ResNet(nn.Layer):
Args:
Block (BasicBlock|BottleneckBlock): block module of model.
depth (int): layers of resnet, default: 50.
width (int): base width of resnet, default: 64.
num_classes (int): output dim of last fc layer. If num_classes <=0, last fc layer
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): use pool before the last fc layer or not. Default: True.
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.
Examples:
.. code-block:: python
......@@ -171,16 +190,23 @@ class ResNet(nn.Layer):
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)
resnet18 = ResNet(BasicBlock, 18)
# 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)
# [1, 1000]
"""
......@@ -189,7 +215,8 @@ class ResNet(nn.Layer):
depth=50,
width=64,
num_classes=1000,
with_pool=True):
with_pool=True,
groups=1):
super(ResNet, self).__init__()
layer_cfg = {
18: [2, 2, 2, 2],
......@@ -199,7 +226,7 @@ class ResNet(nn.Layer):
152: [3, 8, 36, 3]
}
layers = layer_cfg[depth]
self.groups = 1
self.groups = groups
self.base_width = width
self.num_classes = num_classes
self.with_pool = with_pool
......@@ -300,7 +327,7 @@ def resnet18(pretrained=False, **kwargs):
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
pretrained (bool, optional): If True, returns a model pre-trained on ImageNet. Default: False.
Examples:
.. code-block:: python
......@@ -318,6 +345,7 @@ def resnet18(pretrained=False, **kwargs):
out = model(x)
print(out.shape)
# [1, 1000]
"""
return _resnet('resnet18', BasicBlock, 18, pretrained, **kwargs)
......@@ -327,7 +355,7 @@ def resnet34(pretrained=False, **kwargs):
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
pretrained (bool, optional): If True, returns a model pre-trained on ImageNet. Default: False.
Examples:
.. code-block:: python
......@@ -345,6 +373,7 @@ def resnet34(pretrained=False, **kwargs):
out = model(x)
print(out.shape)
# [1, 1000]
"""
return _resnet('resnet34', BasicBlock, 34, pretrained, **kwargs)
......@@ -354,7 +383,7 @@ def resnet50(pretrained=False, **kwargs):
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
pretrained (bool, optional): If True, returns a model pre-trained on ImageNet. Default: False.
Examples:
.. code-block:: python
......@@ -372,6 +401,7 @@ def resnet50(pretrained=False, **kwargs):
out = model(x)
print(out.shape)
# [1, 1000]
"""
return _resnet('resnet50', BottleneckBlock, 50, pretrained, **kwargs)
......@@ -381,7 +411,7 @@ def resnet101(pretrained=False, **kwargs):
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
pretrained (bool, optional): If True, returns a model pre-trained on ImageNet. Default: False.
Examples:
.. code-block:: python
......@@ -399,6 +429,7 @@ def resnet101(pretrained=False, **kwargs):
out = model(x)
print(out.shape)
# [1, 1000]
"""
return _resnet('resnet101', BottleneckBlock, 101, pretrained, **kwargs)
......@@ -408,7 +439,7 @@ def resnet152(pretrained=False, **kwargs):
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
pretrained (bool, optional): If True, returns a model pre-trained on ImageNet. Default: False.
Examples:
.. code-block:: python
......@@ -426,16 +457,201 @@ def resnet152(pretrained=False, **kwargs):
out = model(x)
print(out.shape)
# [1, 1000]
"""
return _resnet('resnet152', BottleneckBlock, 152, pretrained, **kwargs)
def resnext50_32x4d(pretrained=False, **kwargs):
"""ResNeXt-50 32x4d model from
`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
Args:
pretrained (bool, optional): If True, returns a model pre-trained on ImageNet. Default: False.
Examples:
.. code-block:: python
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)
# [1, 1000]
"""
kwargs['groups'] = 32
kwargs['width'] = 4
return _resnet('resnext50_32x4d', BottleneckBlock, 50, pretrained, **kwargs)
def resnext50_64x4d(pretrained=False, **kwargs):
"""ResNeXt-50 64x4d model from
`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
Args:
pretrained (bool, optional): If True, returns a model pre-trained on ImageNet. Default: False.
Examples:
.. code-block:: python
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)
# [1, 1000]
"""
kwargs['groups'] = 64
kwargs['width'] = 4
return _resnet('resnext50_64x4d', BottleneckBlock, 50, pretrained, **kwargs)
def resnext101_32x4d(pretrained=False, **kwargs):
"""ResNeXt-101 32x4d model from
`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
Args:
pretrained (bool, optional): If True, returns a model pre-trained on ImageNet. Default: False.
Examples:
.. code-block:: python
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)
# [1, 1000]
"""
kwargs['groups'] = 32
kwargs['width'] = 4
return _resnet('resnext101_32x4d', BottleneckBlock, 101, pretrained,
**kwargs)
def resnext101_64x4d(pretrained=False, **kwargs):
"""ResNeXt-101 64x4d model from
`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
Args:
pretrained (bool, optional): If True, returns a model pre-trained on ImageNet. Default: False.
Examples:
.. code-block:: python
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)
# [1, 1000]
"""
kwargs['groups'] = 64
kwargs['width'] = 4
return _resnet('resnext101_64x4d', BottleneckBlock, 101, pretrained,
**kwargs)
def resnext152_32x4d(pretrained=False, **kwargs):
"""ResNeXt-152 32x4d model from
`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
Args:
pretrained (bool, optional): If True, returns a model pre-trained on ImageNet. Default: False.
Examples:
.. code-block:: python
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)
# [1, 1000]
"""
kwargs['groups'] = 32
kwargs['width'] = 4
return _resnet('resnext152_32x4d', BottleneckBlock, 152, pretrained,
**kwargs)
def resnext152_64x4d(pretrained=False, **kwargs):
"""ResNeXt-152 64x4d model from
`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
Args:
pretrained (bool, optional): If True, returns a model pre-trained on ImageNet. Default: False.
Examples:
.. code-block:: python
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)
# [1, 1000]
"""
kwargs['groups'] = 64
kwargs['width'] = 4
return _resnet('resnext152_64x4d', BottleneckBlock, 152, pretrained,
**kwargs)
def wide_resnet50_2(pretrained=False, **kwargs):
"""Wide ResNet-50-2 model from
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
pretrained (bool, optional): If True, returns a model pre-trained on ImageNet. Default: False.
Examples:
.. code-block:: python
......@@ -453,6 +669,7 @@ def wide_resnet50_2(pretrained=False, **kwargs):
out = model(x)
print(out.shape)
# [1, 1000]
"""
kwargs['width'] = 64 * 2
return _resnet('wide_resnet50_2', BottleneckBlock, 50, pretrained, **kwargs)
......@@ -463,7 +680,7 @@ def wide_resnet101_2(pretrained=False, **kwargs):
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
pretrained (bool, optional): If True, returns a model pre-trained on ImageNet. Default: False.
Examples:
.. code-block:: python
......@@ -481,6 +698,7 @@ def wide_resnet101_2(pretrained=False, **kwargs):
out = model(x)
print(out.shape)
# [1, 1000]
"""
kwargs['width'] = 64 * 2
return _resnet('wide_resnet101_2', BottleneckBlock, 101, pretrained,
......
python/paddle/vision/models/resnext.py 已删除 100644 → 0
浏览文件 @ 6721376b
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.fluid.param_attr import ParamAttr
from paddle.nn import AdaptiveAvgPool2D, BatchNorm, Conv2D, Linear, MaxPool2D
from paddle.nn.initializer import Uniform
from paddle.utils.download import get_weights_path_from_url
__all__ = []
model_urls = {
'resnext50_32x4d':
('https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_32x4d_pretrained.pdparams',
'bf04add2f7fd22efcbe91511bcd1eebe'),
"resnext50_64x4d":
('https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_64x4d_pretrained.pdparams',
'46307df0e2d6d41d3b1c1d22b00abc69'),
'resnext101_32x4d':
('https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x4d_pretrained.pdparams',
'078ca145b3bea964ba0544303a43c36d'),
'resnext101_64x4d':
('https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_64x4d_pretrained.pdparams',
'4edc0eb32d3cc5d80eff7cab32cd5c64'),
'resnext152_32x4d':
('https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_32x4d_pretrained.pdparams',
'7971cc994d459af167c502366f866378'),
'resnext152_64x4d':
('https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_64x4d_pretrained.pdparams',
'836943f03709efec364d486c57d132de'),
}
class ConvBNLayer(nn.Layer):
def __init__(self,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
act=None):
super(ConvBNLayer, self).__init__()
self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
bias_attr=False)
self._batch_norm = BatchNorm(num_filters, act=act)
def forward(self, inputs):
x = self._conv(inputs)
x = self._batch_norm(x)
return x
class BottleneckBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
stride,
cardinality,
shortcut=True):
super(BottleneckBlock, self).__init__()
self.conv0 = ConvBNLayer(
num_channels=num_channels,
num_filters=num_filters,
filter_size=1,
act='relu')
self.conv1 = ConvBNLayer(
num_channels=num_filters,
num_filters=num_filters,
filter_size=3,
groups=cardinality,
stride=stride,
act='relu')
self.conv2 = ConvBNLayer(
num_channels=num_filters,
num_filters=num_filters * 2 if cardinality == 32 else num_filters,
filter_size=1,
act=None)
if not shortcut:
self.short = ConvBNLayer(
num_channels=num_channels,
num_filters=num_filters * 2
if cardinality == 32 else num_filters,
filter_size=1,
stride=stride)
self.shortcut = shortcut
def forward(self, inputs):
x = self.conv0(inputs)
conv1 = self.conv1(x)
conv2 = self.conv2(conv1)
if self.shortcut:
short = inputs
else:
short = self.short(inputs)
x = paddle.add(x=short, y=conv2)
x = F.relu(x)
return x
class ResNeXt(nn.Layer):
"""ResNeXt model from
`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
Args:
depth (int, optional): depth of resnext. Default: 50.
cardinality (int, optional): cardinality of resnext. Default: 32.
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.
Examples:
.. code-block:: python
import paddle
from paddle.vision.models import ResNeXt
resnext50_32x4d = ResNeXt(depth=50, cardinality=32)
"""
def __init__(self,
depth=50,
cardinality=32,
num_classes=1000,
with_pool=True):
super(ResNeXt, self).__init__()
self.depth = depth
self.cardinality = cardinality
self.num_classes = num_classes
self.with_pool = with_pool
supported_depth = [50, 101, 152]
assert depth in supported_depth, \
"supported layers are {} but input layer is {}".format(
supported_depth, depth)
supported_cardinality = [32, 64]
assert cardinality in supported_cardinality, \
"supported cardinality is {} but input cardinality is {}" \
.format(supported_cardinality, cardinality)
layer_cfg = {50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 152: [3, 8, 36, 3]}
layers = layer_cfg[depth]
num_channels = [64, 256, 512, 1024]
num_filters = [128, 256, 512,
1024] if cardinality == 32 else [256, 512, 1024, 2048]
self.conv = ConvBNLayer(
num_channels=3, num_filters=64, filter_size=7, stride=2, act='relu')
self.pool2d_max = MaxPool2D(kernel_size=3, stride=2, padding=1)
self.block_list = []
for block in range(len(layers)):
shortcut = False
for i in range(layers[block]):
bottleneck_block = self.add_sublayer(
'bb_%d_%d' % (block, i),
BottleneckBlock(
num_channels=num_channels[block] if i == 0 else
num_filters[block] * int(64 // self.cardinality),
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
cardinality=self.cardinality,
shortcut=shortcut))
self.block_list.append(bottleneck_block)
shortcut = True
if with_pool:
self.pool2d_avg = AdaptiveAvgPool2D(1)
if num_classes > 0:
self.pool2d_avg_channels = num_channels[-1] * 2
stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0)
self.out = Linear(
self.pool2d_avg_channels,
num_classes,
weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)))
def forward(self, inputs):
with paddle.static.amp.fp16_guard():
x = self.conv(inputs)
x = self.pool2d_max(x)
for block in self.block_list:
x = block(x)
if self.with_pool:
x = self.pool2d_avg(x)
if self.num_classes > 0:
x = paddle.reshape(x, shape=[-1, self.pool2d_avg_channels])
x = self.out(x)
return x
def _resnext(arch, depth, cardinality, pretrained, **kwargs):
model = ResNeXt(depth=depth, cardinality=cardinality, **kwargs)
if pretrained:
assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format(
arch)
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 resnext50_32x4d(pretrained=False, **kwargs):
"""ResNeXt-50 32x4d model from
`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
Examples:
.. code-block:: python
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)
"""
return _resnext('resnext50_32x4d', 50, 32, pretrained, **kwargs)
def resnext50_64x4d(pretrained=False, **kwargs):
"""ResNeXt-50 64x4d model from
`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
Examples:
.. code-block:: python
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)
"""
return _resnext('resnext50_64x4d', 50, 64, pretrained, **kwargs)
def resnext101_32x4d(pretrained=False, **kwargs):
"""ResNeXt-101 32x4d model from
`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
Examples:
.. code-block:: python
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)
"""
return _resnext('resnext101_32x4d', 101, 32, pretrained, **kwargs)
def resnext101_64x4d(pretrained=False, **kwargs):
"""ResNeXt-101 64x4d model from
`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
Examples:
.. code-block:: python
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)
"""
return _resnext('resnext101_64x4d', 101, 64, pretrained, **kwargs)
def resnext152_32x4d(pretrained=False, **kwargs):
"""ResNeXt-152 32x4d model from
`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
Examples:
.. code-block:: python
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)
"""
return _resnext('resnext152_32x4d', 152, 32, pretrained, **kwargs)
def resnext152_64x4d(pretrained=False, **kwargs):
"""ResNeXt-152 64x4d model from
`"Aggregated Residual Transformations for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
Examples:
.. code-block:: python
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)
"""
return _resnext('resnext152_64x4d', 152, 64, pretrained, **kwargs)
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