Source code for

# 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
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.

# coding: utf-8
# pylint: disable=wildcard-import, arguments-differ
r"""Module for pre-defined neural network models.

This module contains definitions for the following model architectures:
-  `AlexNet`_
-  `DenseNet`_
-  `Inception V3`_
-  `ResNet V1`_
-  `ResNet V2`_
-  `SqueezeNet`_
-  `VGG`_
-  `MobileNet`_
-  `MobileNetV2`_

You can construct a model with random weights by calling its constructor:

.. code::

    from mxnet.gluon.model_zoo import vision
    resnet18 = vision.resnet18_v1()
    alexnet = vision.alexnet()
    squeezenet = vision.squeezenet1_0()
    densenet = vision.densenet_161()

We provide pre-trained models for all the listed models.
These models can constructed by passing ``pretrained=True``:

.. code::

    from mxnet.gluon.model_zoo import vision
    resnet18 = vision.resnet18_v1(pretrained=True)
    alexnet = vision.alexnet(pretrained=True)

All pre-trained models expect input images normalized in the same way,
i.e. mini-batches of 3-channel RGB images of shape (N x 3 x H x W),
where N is the batch size, and H and W are expected to be at least 224.
The images have to be loaded in to a range of [0, 1] and then normalized
using ``mean = [0.485, 0.456, 0.406]`` and ``std = [0.229, 0.224, 0.225]``.
The transformation should preferrably happen at preprocessing. You can use
``mx.image.color_normalize`` for such transformation::

    image = image/255
    normalized = mx.image.color_normalize(image,
                                [0.485, 0.456, 0.406]),
                                [0.229, 0.224, 0.225]))

.. _AlexNet:
.. _DenseNet:
.. _Inception V3:
.. _ResNet V1:
.. _ResNet V2:
.. _SqueezeNet:
.. _VGG:
.. _MobileNet:
.. _MobileNetV2:

from .alexnet import *

from .densenet import *

from .inception import *

from .resnet import *

from .squeezenet import *

from .vgg import *

from .mobilenet import *

[docs]def get_model(name, **kwargs): """Returns a pre-defined model by name Parameters ---------- name : str Name of the model. pretrained : bool Whether to load the pretrained weights for model. classes : int Number of classes for the output layer. 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. Returns ------- gluon.HybridBlock The model. """ models = {'resnet18_v1': resnet18_v1, 'resnet34_v1': resnet34_v1, 'resnet50_v1': resnet50_v1, 'resnet101_v1': resnet101_v1, 'resnet152_v1': resnet152_v1, 'resnet18_v2': resnet18_v2, 'resnet34_v2': resnet34_v2, 'resnet50_v2': resnet50_v2, 'resnet101_v2': resnet101_v2, 'resnet152_v2': resnet152_v2, 'vgg11': vgg11, 'vgg13': vgg13, 'vgg16': vgg16, 'vgg19': vgg19, 'vgg11_bn': vgg11_bn, 'vgg13_bn': vgg13_bn, 'vgg16_bn': vgg16_bn, 'vgg19_bn': vgg19_bn, 'alexnet': alexnet, 'densenet121': densenet121, 'densenet161': densenet161, 'densenet169': densenet169, 'densenet201': densenet201, 'squeezenet1.0': squeezenet1_0, 'squeezenet1.1': squeezenet1_1, 'inceptionv3': inception_v3, 'mobilenet1.0': mobilenet1_0, 'mobilenet0.75': mobilenet0_75, 'mobilenet0.5': mobilenet0_5, 'mobilenet0.25': mobilenet0_25, 'mobilenetv2_1.0': mobilenet_v2_1_0, 'mobilenetv2_0.75': mobilenet_v2_0_75, 'mobilenetv2_0.5': mobilenet_v2_0_5, 'mobilenetv2_0.25': mobilenet_v2_0_25 } name = name.lower() if name not in models: raise ValueError( "Model {} is not supported. Available options are\n\t{}".format(name, '\n\t'.join(sorted(models.keys())))) return models[name](**kwargs)