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@szha @piiswrong @mli @larroy
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
# coding: utf-8
# pylint: disable= arguments-differ
"""Alexnet, implemented in Gluon."""
__all__ = ['AlexNet', 'alexnet']
import os
from ....context import cpu
from ...block import HybridBlock
from ... import nn
from .... import base
# Net
class AlexNet(HybridBlock):
r"""AlexNet model from the `"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.
Parameters
----------
classes : int, default 1000
Number of classes for the output layer.
"""
def __init__(self, classes=1000, **kwargs):
super(AlexNet, self).__init__(**kwargs)
with self.name_scope():
self.features = nn.HybridSequential(prefix='')
with self.features.name_scope():
self.features.add(nn.Conv2D(64, kernel_size=11, strides=4,
padding=2, activation='relu'))
self.features.add(nn.MaxPool2D(pool_size=3, strides=2))
self.features.add(nn.Conv2D(192, kernel_size=5, padding=2,
activation='relu'))
self.features.add(nn.MaxPool2D(pool_size=3, strides=2))
self.features.add(nn.Conv2D(384, kernel_size=3, padding=1,
activation='relu'))
self.features.add(nn.Conv2D(256, kernel_size=3, padding=1,
activation='relu'))
self.features.add(nn.Conv2D(256, kernel_size=3, padding=1,
activation='relu'))
self.features.add(nn.MaxPool2D(pool_size=3, strides=2))
self.features.add(nn.Flatten())
self.features.add(nn.Dense(4096, activation='relu'))
self.features.add(nn.Dropout(0.5))
self.features.add(nn.Dense(4096, activation='relu'))
self.features.add(nn.Dropout(0.5))
self.output = nn.Dense(classes)
def hybrid_forward(self, F, x):
x = self.features(x)
x = self.output(x)
return x
# Constructor
def alexnet(pretrained=False, ctx=cpu(),
root=os.path.join(base.data_dir(), 'models'), **kwargs):
r"""AlexNet model from the `"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.
Parameters
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default $MXNET_HOME/models
Location for keeping the model parameters.
"""
net = AlexNet(**kwargs)
if pretrained:
from ..model_store import get_model_file
net.load_parameters(get_model_file('alexnet', root=root), ctx=ctx)
return net
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