未验证 提交 caa2003a 编写于 作者: F fuqianya 提交者: GitHub

[PaddlePaddle Hackathon] add AlexNet (#36058)

* add alexnet
上级 90457d8c
...@@ -52,7 +52,9 @@ class TestPretrainedModel(unittest.TestCase): ...@@ -52,7 +52,9 @@ class TestPretrainedModel(unittest.TestCase):
np.testing.assert_allclose(res['dygraph'], res['static']) np.testing.assert_allclose(res['dygraph'], res['static'])
def test_models(self): def test_models(self):
arches = ['mobilenet_v1', 'mobilenet_v2', 'resnet18', 'vgg16'] arches = [
'mobilenet_v1', 'mobilenet_v2', 'resnet18', 'vgg16', 'alexnet'
]
for arch in arches: for arch in arches:
self.infer(arch) self.infer(arch)
......
...@@ -11,7 +11,6 @@ ...@@ -11,7 +11,6 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
import unittest import unittest
import numpy as np import numpy as np
...@@ -71,6 +70,9 @@ class TestVisonModels(unittest.TestCase): ...@@ -71,6 +70,9 @@ class TestVisonModels(unittest.TestCase):
def test_resnet152(self): def test_resnet152(self):
self.models_infer('resnet152') self.models_infer('resnet152')
def test_alexnet(self):
self.models_infer('alexnet')
def test_vgg16_num_classes(self): def test_vgg16_num_classes(self):
vgg16 = models.__dict__['vgg16'](pretrained=False, num_classes=10) vgg16 = models.__dict__['vgg16'](pretrained=False, num_classes=10)
......
...@@ -44,6 +44,8 @@ from .models import vgg13 # noqa: F401 ...@@ -44,6 +44,8 @@ from .models import vgg13 # noqa: F401
from .models import vgg16 # noqa: F401 from .models import vgg16 # noqa: F401
from .models import vgg19 # noqa: F401 from .models import vgg19 # noqa: F401
from .models import LeNet # noqa: F401 from .models import LeNet # noqa: F401
from .models import AlexNet # noqa: F401
from .models import alexnet # noqa: F401
from .transforms import BaseTransform # noqa: F401 from .transforms import BaseTransform # noqa: F401
from .transforms import Compose # noqa: F401 from .transforms import Compose # noqa: F401
from .transforms import Resize # noqa: F401 from .transforms import Resize # noqa: F401
......
...@@ -28,6 +28,8 @@ from .vgg import vgg13 # noqa: F401 ...@@ -28,6 +28,8 @@ from .vgg import vgg13 # noqa: F401
from .vgg import vgg16 # noqa: F401 from .vgg import vgg16 # noqa: F401
from .vgg import vgg19 # noqa: F401 from .vgg import vgg19 # noqa: F401
from .lenet import LeNet # noqa: F401 from .lenet import LeNet # noqa: F401
from .alexnet import AlexNet # noqa: F401
from .alexnet import alexnet # noqa: F401
__all__ = [ #noqa __all__ = [ #noqa
'ResNet', 'ResNet',
...@@ -45,5 +47,7 @@ __all__ = [ #noqa ...@@ -45,5 +47,7 @@ __all__ = [ #noqa
'mobilenet_v1', 'mobilenet_v1',
'MobileNetV2', 'MobileNetV2',
'mobilenet_v2', 'mobilenet_v2',
'LeNet' 'LeNet',
'AlexNet',
'alexnet'
] ]
# copyright (c) 2021 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.nn import Linear, Dropout, ReLU
from paddle.nn import Conv2D, MaxPool2D
from paddle.nn.initializer import Uniform
from paddle.fluid.param_attr import ParamAttr
from paddle.utils.download import get_weights_path_from_url
model_urls = {
"alexnet": (
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparams",
"7f0f9f737132e02732d75a1459d98a43", )
}
__all__ = []
class ConvPoolLayer(nn.Layer):
def __init__(self,
input_channels,
output_channels,
filter_size,
stride,
padding,
stdv,
groups=1,
act=None):
super(ConvPoolLayer, self).__init__()
self.relu = ReLU() if act == "relu" else None
self._conv = Conv2D(
in_channels=input_channels,
out_channels=output_channels,
kernel_size=filter_size,
stride=stride,
padding=padding,
groups=groups,
weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)),
bias_attr=ParamAttr(initializer=Uniform(-stdv, stdv)))
self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
def forward(self, inputs):
x = self._conv(inputs)
if self.relu is not None:
x = self.relu(x)
x = self._pool(x)
return x
class AlexNet(nn.Layer):
"""AlexNet model from
`"ImageNet Classification with Deep Convolutional Neural Networks"
<https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf>`_
Args:
num_classes (int): Output dim of last fc layer. Default: 1000.
Examples:
.. code-block:: python
from paddle.vision.models import AlexNet
alexnet = AlexNet()
"""
def __init__(self, num_classes=1000):
super(AlexNet, self).__init__()
self.num_classes = num_classes
stdv = 1.0 / math.sqrt(3 * 11 * 11)
self._conv1 = ConvPoolLayer(3, 64, 11, 4, 2, stdv, act="relu")
stdv = 1.0 / math.sqrt(64 * 5 * 5)
self._conv2 = ConvPoolLayer(64, 192, 5, 1, 2, stdv, act="relu")
stdv = 1.0 / math.sqrt(192 * 3 * 3)
self._conv3 = Conv2D(
192,
384,
3,
stride=1,
padding=1,
weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)),
bias_attr=ParamAttr(initializer=Uniform(-stdv, stdv)))
stdv = 1.0 / math.sqrt(384 * 3 * 3)
self._conv4 = Conv2D(
384,
256,
3,
stride=1,
padding=1,
weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)),
bias_attr=ParamAttr(initializer=Uniform(-stdv, stdv)))
stdv = 1.0 / math.sqrt(256 * 3 * 3)
self._conv5 = ConvPoolLayer(256, 256, 3, 1, 1, stdv, act="relu")
if self.num_classes > 0:
stdv = 1.0 / math.sqrt(256 * 6 * 6)
self._drop1 = Dropout(p=0.5, mode="downscale_in_infer")
self._fc6 = Linear(
in_features=256 * 6 * 6,
out_features=4096,
weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)),
bias_attr=ParamAttr(initializer=Uniform(-stdv, stdv)))
self._drop2 = Dropout(p=0.5, mode="downscale_in_infer")
self._fc7 = Linear(
in_features=4096,
out_features=4096,
weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)),
bias_attr=ParamAttr(initializer=Uniform(-stdv, stdv)))
self._fc8 = Linear(
in_features=4096,
out_features=num_classes,
weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)),
bias_attr=ParamAttr(initializer=Uniform(-stdv, stdv)))
def forward(self, inputs):
x = self._conv1(inputs)
x = self._conv2(x)
x = self._conv3(x)
x = F.relu(x)
x = self._conv4(x)
x = F.relu(x)
x = self._conv5(x)
if self.num_classes > 0:
x = paddle.flatten(x, start_axis=1, stop_axis=-1)
x = self._drop1(x)
x = self._fc6(x)
x = F.relu(x)
x = self._drop2(x)
x = self._fc7(x)
x = F.relu(x)
x = self._fc8(x)
return x
def _alexnet(arch, pretrained, **kwargs):
model = AlexNet(**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.load_dict(param)
return model
def alexnet(pretrained=False, **kwargs):
"""AlexNet model
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet. Default: False.
Examples:
.. code-block:: python
from paddle.vision.models import alexnet
# build model
model = alexnet()
# build model and load imagenet pretrained weight
# model = alexnet(pretrained=True)
"""
return _alexnet('alexnet', pretrained, **kwargs)
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