.. _sphx_glr_build_examples_segmentation_train_fcn.py:

2. Train FCN on Pascal VOC Dataset
==================================

This is a semantic segmentation tutorial using Gluon Vison, a step-by-step example.
The readers should have basic knowledge of deep learning and should be familiar with Gluon API.
New users may first go through `A 60-minute Gluon Crash Course <http://gluon-crash-course.mxnet.io/>`_.
You can `Start Training Now`_ or `Dive into Deep`_.

Start Training Now
~~~~~~~~~~~~~~~~~~

.. hint::

    Feel free to skip the tutorial because the training script is self-complete and ready to launch.

    :download:`Download Full Python Script: train.py<../../../scripts/segmentation/train.py>`

    Example training command::

        # First training on augmented set
        CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset pascal_aug --model fcn --backbone resnet50 --lr 0.001 --checkname mycheckpoint
        # Finetuning on original set
        CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset pascal_voc --model fcn --backbone resnet50 --lr 0.0001 --checkname mycheckpoint --resume runs/pascal_aug/fcn/mycheckpoint/checkpoint.params

    For more training command options, please run ``python train.py -h``
    Please checkout the `model_zoo <../model_zoo/index.html#semantic-segmentation>`_ for training commands of reproducing the pretrained model.

Dive into Deep
~~~~~~~~~~~~~~



.. code-block:: python

    import numpy as np
    import mxnet as mx
    from mxnet import gluon, autograd

    import gluoncv






Fully Convolutional Network
---------------------------

.. image:: https://cdn-images-1.medium.com/max/800/1*wRkj6lsQ5ckExB5BoYkrZg.png
    :width: 70%
    :align: center

(figure redit to `Long et al. <https://arxiv.org/pdf/1411.4038.pdf>`_ )

State-of-the-art approaches of semantic segmentation are typically based on
Fully Convolutional Network (FCN) [Long15]_.
The key idea of a fully convolutional network is that it is "fully convolutional",
which means it does have any fully connected layers. Therefore, the network can
accept arbitrary input size and make dense per-pixel predictions.
Base/Encoder network is typically pre-trained on ImageNet, because the features
learned from diverse set of images contain rich contextual information, which
can be beneficial for semantic segmentation.




Model Dilation
--------------

The adaption of base network pre-trained on ImageNet leads to loss spatial resolution,
because these networks are originally designed for classification task.
Following standard implementation in recent works of semantic segmentation,
we apply dilation strategy to the
stage 3 and stage 4 of the pre-trained networks, which produces stride of 8
featuremaps (models are provided in
:class:`gluoncv.model_zoo.dilatedresnetv0.DilatedResNetV0`).
Visualization of dilated/atrous convoution
(figure credit to `conv_arithmetic <https://github.com/vdumoulin/conv_arithmetic>`_ ):

.. image:: https://raw.githubusercontent.com/vdumoulin/conv_arithmetic/master/gif/dilation.gif
    :width: 40%
    :align: center

Loading a dilated ResNet50 is simply:




.. code-block:: python

    pretrained_net = gluoncv.model_zoo.dilatedresnetv0.dilated_resnet50(pretrained=True)







For convenience, we provide a base model for semantic segmentation, which automatically
load the pre-trained dilated ResNet :class:`gluoncv.model_zoo.SegBaseModel`
with a convenient method ``base_forward(input)`` to get stage 3 & 4 featuremaps:




.. code-block:: python

    basemodel = gluoncv.model_zoo.SegBaseModel(nclass=10, aux=False)
    x = mx.nd.random.uniform(shape=(1, 3, 224, 224))
    c3, c4 = basemodel.base_forward(x)
    print('Shapes of c3 & c4 featuremaps are ', c3.shape, c4.shape)





.. rst-class:: sphx-glr-script-out

 Out::

    Shapes of c3 & c4 featuremaps are  (1, 1024, 28, 28) (1, 2048, 28, 28)


FCN Model
---------

We build a fully convolutional "head" on top of the base network,
the FCNHead is defined as::

    class _FCNHead(HybridBlock):
        def __init__(self, in_channels, channels, norm_layer, **kwargs):
            super(_FCNHead, self).__init__()
            with self.name_scope():
                self.block = nn.HybridSequential()
                inter_channels = in_channels // 4
                with self.block.name_scope():
                    self.block.add(nn.Conv2D(in_channels=in_channels, channels=inter_channels,
                                             kernel_size=3, padding=1))
                    self.block.add(norm_layer(in_channels=inter_channels))
                    self.block.add(nn.Activation('relu'))
                    self.block.add(nn.Dropout(0.1))
                    self.block.add(nn.Conv2D(in_channels=inter_channels, channels=channels,
                                             kernel_size=1))

    def hybrid_forward(self, F, x):
        return self.block(x)

FCN model is provided in :class:`gluoncv.model_zoo.FCN`. To get
FCN model using ResNet50 base network for Pascal VOC dataset:



.. code-block:: python

    model = gluoncv.model_zoo.get_fcn(dataset='pascal_voc', backbone='resnet50', pretrained=False)
    print(model)





.. rst-class:: sphx-glr-script-out

 Out::

    FCN(
      (conv1): Conv2D(3 -> 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
      (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64)
      (relu): Activation(relu)
      (maxpool): MaxPool2D(size=(3, 3), stride=(2, 2), padding=(1, 1), ceil_mode=False)
      (layer1): HybridSequential(
        (0): DilatedBottleneckV0(
          (conv1): Conv2D(64 -> 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64)
          (conv2): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64)
          (conv3): Conv2D(64 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
          (relu): Activation(relu)
          (downsample): HybridSequential(
            (0): Conv2D(64 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
          )
        )
        (1): DilatedBottleneckV0(
          (conv1): Conv2D(256 -> 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64)
          (conv2): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64)
          (conv3): Conv2D(64 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
          (relu): Activation(relu)
        )
        (2): DilatedBottleneckV0(
          (conv1): Conv2D(256 -> 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64)
          (conv2): Conv2D(64 -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=64)
          (conv3): Conv2D(64 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
          (relu): Activation(relu)
        )
      )
      (layer2): HybridSequential(
        (0): DilatedBottleneckV0(
          (conv1): Conv2D(256 -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128)
          (conv2): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128)
          (conv3): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512)
          (relu): Activation(relu)
          (downsample): HybridSequential(
            (0): Conv2D(256 -> 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
            (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512)
          )
        )
        (1): DilatedBottleneckV0(
          (conv1): Conv2D(512 -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128)
          (conv2): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128)
          (conv3): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512)
          (relu): Activation(relu)
        )
        (2): DilatedBottleneckV0(
          (conv1): Conv2D(512 -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128)
          (conv2): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128)
          (conv3): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512)
          (relu): Activation(relu)
        )
        (3): DilatedBottleneckV0(
          (conv1): Conv2D(512 -> 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128)
          (conv2): Conv2D(128 -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=128)
          (conv3): Conv2D(128 -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512)
          (relu): Activation(relu)
        )
      )
      (layer3): HybridSequential(
        (0): DilatedBottleneckV0(
          (conv1): Conv2D(512 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
          (conv2): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
          (conv3): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024)
          (relu): Activation(relu)
          (downsample): HybridSequential(
            (0): Conv2D(512 -> 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024)
          )
        )
        (1): DilatedBottleneckV0(
          (conv1): Conv2D(1024 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
          (conv2): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
          (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
          (conv3): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024)
          (relu): Activation(relu)
        )
        (2): DilatedBottleneckV0(
          (conv1): Conv2D(1024 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
          (conv2): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
          (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
          (conv3): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024)
          (relu): Activation(relu)
        )
        (3): DilatedBottleneckV0(
          (conv1): Conv2D(1024 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
          (conv2): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
          (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
          (conv3): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024)
          (relu): Activation(relu)
        )
        (4): DilatedBottleneckV0(
          (conv1): Conv2D(1024 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
          (conv2): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
          (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
          (conv3): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024)
          (relu): Activation(relu)
        )
        (5): DilatedBottleneckV0(
          (conv1): Conv2D(1024 -> 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
          (conv2): Conv2D(256 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
          (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
          (conv3): Conv2D(256 -> 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=1024)
          (relu): Activation(relu)
        )
      )
      (layer4): HybridSequential(
        (0): DilatedBottleneckV0(
          (conv1): Conv2D(1024 -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512)
          (conv2): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False)
          (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512)
          (conv3): Conv2D(512 -> 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=2048)
          (relu): Activation(relu)
          (downsample): HybridSequential(
            (0): Conv2D(1024 -> 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=2048)
          )
        )
        (1): DilatedBottleneckV0(
          (conv1): Conv2D(2048 -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512)
          (conv2): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)
          (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512)
          (conv3): Conv2D(512 -> 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=2048)
          (relu): Activation(relu)
        )
        (2): DilatedBottleneckV0(
          (conv1): Conv2D(2048 -> 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512)
          (conv2): Conv2D(512 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4), bias=False)
          (bn2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512)
          (conv3): Conv2D(512 -> 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (bn3): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=2048)
          (relu): Activation(relu)
        )
      )
      (head): _FCNHead(
        (block): HybridSequential(
          (0): Conv2D(2048 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=512)
          (2): Activation(relu)
          (3): Dropout(p = 0.1, axes=())
          (4): Conv2D(512 -> 22, kernel_size=(1, 1), stride=(1, 1))
        )
      )
      (auxlayer): _FCNHead(
        (block): HybridSequential(
          (0): Conv2D(1024 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=256)
          (2): Activation(relu)
          (3): Dropout(p = 0.1, axes=())
          (4): Conv2D(256 -> 22, kernel_size=(1, 1), stride=(1, 1))
        )
      )
    )


Dataset and Data Augmentation
-----------------------------

image transform for color normalization



.. code-block:: python

    from mxnet.gluon.data.vision import transforms
    input_transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize([.485, .456, .406], [.229, .224, .225]),
    ])







We provide semantic segmentation datasets in :class:`gluoncv.data`.
For example, we can easily get the Pascal VOC 2012 dataset:



.. code-block:: python

    trainset = gluoncv.data.VOCSegmentation(split='train', transform=input_transform)
    print('Training images:', len(trainset))
    # Create Training Loader
    train_data = gluon.data.DataLoader(
        trainset, 4, shuffle=True, last_batch='rollover',
        num_workers=4)





.. rst-class:: sphx-glr-script-out

 Out::

    Training images: 2913


For data augmentation, 
we follow the standard data augmentation routine to transform the input image
and the ground truth label map synchronously. (*Note that "nearest"
mode upsample are applied to the label maps to avoid messing up the boundaries.*)
We first randomly scale the input image from 0.5 to 2.0 times, then rotate
the image from -10 to 10 degrees, and crop the image with padding if needed.
Finally a random Gaussian blurring is applied.

Random pick one example for visualization:



.. code-block:: python

    from random import randint
    idx = randint(0, len(trainset))
    img, mask = trainset[idx]
    from gluoncv.utils.viz import get_color_pallete, DeNormalize
    # get color pallete for visualize mask
    mask = get_color_pallete(mask.asnumpy(), dataset='pascal_voc')
    mask.save('mask.png')
    # denormalize the image
    img = DeNormalize([.485, .456, .406], [.229, .224, .225])(img)
    img = np.transpose((img.asnumpy()*255).astype(np.uint8), (1, 2, 0))







Plot the image and mask



.. code-block:: python

    from matplotlib import pyplot as plt
    import matplotlib.image as mpimg
    # subplot 1 for img
    fig = plt.figure()
    fig.add_subplot(1,2,1)

    plt.imshow(img)
    # subplot 2 for the mask
    mmask = mpimg.imread('mask.png')
    fig.add_subplot(1,2,2)
    plt.imshow(mmask)
    # display
    plt.show()




.. image:: /build/examples_segmentation/images/sphx_glr_train_fcn_001.png
    :align: center




Training Details
----------------

- Training Losses:

    We apply a standard per-pixel Softmax Cross Entropy Loss to train FCN. For Pascal
    VOC dataset, we ignore the loss from boundary class (number 22).
    Additionally, an Auxiliary Loss as in PSPNet [Zhao17]_ at Stage 3 can be enabled when
    training with command ``--aux``. This will create an additional FCN "head" after Stage 3.




.. code-block:: python

    from gluoncv.model_zoo.segbase import SoftmaxCrossEntropyLossWithAux
    criterion = SoftmaxCrossEntropyLossWithAux(aux=True)







- Learning Rate and Scheduling:

    We use different learning rate for FCN "head" and the base network. For the FCN "head",
    we use :math:`10\times` base learning rate, because those layers are learned from scratch.
    We use a poly-like learning rate scheduler for FCN training, provided in :class:`gluoncv.utils.PolyLRScheduler`.
    The learning rate is given by :math:`lr = baselr \times (1-iter)^{power}`




.. code-block:: python

    lr_scheduler = gluoncv.utils.PolyLRScheduler(0.001, niters=len(train_data), 
                                                     nepochs=50)







- Dataparallel for multi-gpu training



.. code-block:: python

    from gluoncv.utils.parallel import *
    ctx_list = [mx.gpu(0), mx.gpu(1)]
    model = DataParallelModel(model, ctx_list)
    criterion = DataParallelCriterion(criterion, ctx_list)







- Create SGD solver



.. code-block:: python

    kv = mx.kv.create('device')
    optimizer = gluon.Trainer(model.module.collect_params(), 'sgd',
                              {'lr_scheduler': lr_scheduler,
                               'wd':0.0001,
                               'momentum': 0.9,
                               'multi_precision': True},
                              kvstore = kv)







The training loop
-----------------




.. code-block:: python

    train_loss = 0.0
    epoch = 0
    for i, (data, target) in enumerate(train_data):
        lr_scheduler.update(i, epoch)
        with autograd.record(True):
            outputs = model(data)
            losses = criterion(outputs, target)
            mx.nd.waitall()
            autograd.backward(losses)
        optimizer.step(4)
        for loss in losses:
            train_loss += loss.asnumpy()[0] / len(losses)
        print('Epoch %d, training loss %.3f'%(epoch, train_loss/(i+1)))
        # just demo for 20 iters
        if i > 20:
            break






.. rst-class:: sphx-glr-script-out

 Out::

    Epoch 0, training loss 4.125
    Epoch 0, training loss 3.925
    Epoch 0, training loss 3.612
    Epoch 0, training loss 3.307
    Epoch 0, training loss 3.234
    Epoch 0, training loss 2.959
    Epoch 0, training loss 2.939
    Epoch 0, training loss 2.961
    Epoch 0, training loss 2.748
    Epoch 0, training loss 2.594
    Epoch 0, training loss 2.860
    Epoch 0, training loss 2.956
    Epoch 0, training loss 2.961
    Epoch 0, training loss 2.808
    Epoch 0, training loss 2.882
    Epoch 0, training loss 2.862
    Epoch 0, training loss 2.807
    Epoch 0, training loss 2.793
    Epoch 0, training loss 2.763
    Epoch 0, training loss 2.692
    Epoch 0, training loss 2.724
    Epoch 0, training loss 2.724


You can `Start Training Now`_.

References
----------

.. [Long15] Long, Jonathan, Evan Shelhamer, and Trevor Darrell. \
    "Fully convolutional networks for semantic segmentation." \
    Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.

.. [Zhao17] Zhao, Hengshuang, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. \
    "Pyramid scene parsing network." IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). 2017.



**Total running time of the script:** ( 0 minutes  24.714 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

     :download:`Download Python source code: train_fcn.py <train_fcn.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: train_fcn.ipynb <train_fcn.ipynb>`


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