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[PaddlePaddle Hackathon] add AlexNet (#36058)
* add alexnet
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# 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. | ||
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from __future__ import absolute_import | ||
from __future__ import division | ||
from __future__ import print_function | ||
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import math | ||
import paddle | ||
import paddle.nn as nn | ||
import paddle.nn.functional as F | ||
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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 | ||
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model_urls = { | ||
"alexnet": ( | ||
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparams", | ||
"7f0f9f737132e02732d75a1459d98a43", ) | ||
} | ||
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__all__ = [] | ||
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class ConvPoolLayer(nn.Layer): | ||
def __init__(self, | ||
input_channels, | ||
output_channels, | ||
filter_size, | ||
stride, | ||
padding, | ||
stdv, | ||
groups=1, | ||
act=None): | ||
super(ConvPoolLayer, self).__init__() | ||
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self.relu = ReLU() if act == "relu" else None | ||
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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) | ||
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def forward(self, inputs): | ||
x = self._conv(inputs) | ||
if self.relu is not None: | ||
x = self.relu(x) | ||
x = self._pool(x) | ||
return x | ||
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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() | ||
""" | ||
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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") | ||
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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))) | ||
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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))) | ||
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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) | ||
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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) | ||
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return x | ||
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def _alexnet(arch, pretrained, **kwargs): | ||
model = AlexNet(**kwargs) | ||
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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]) | ||
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param = paddle.load(weight_path) | ||
model.load_dict(param) | ||
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return model | ||
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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) |