Convolutional Network for Visual Recognition Tasks

In this section, you will learn how to write

  • A small convolutional network with a model class that is inherited from Chain,
  • A large convolutional network that has several building block networks with ChainList.

After reading this section, you will be able to:

  • Write your own original convolutional network in Chainer

A convolutional network (ConvNet) is mainly comprised of convolutional layers. This type of network is commonly used for various visual recognition tasks, e.g., classifying hand-written digits or natural images into given object classes, detecting objects from an image, and labeling all pixels of an image with the object classes (semantic segmentation), and so on.

In such tasks, a typical ConvNet takes a set of images whose shape is \((N, C, H, W)\), where

  • \(N\) denotes the number of images in a mini-batch,
  • \(C\) denotes the number of channels of those images,
  • \(H\) and \(W\) denote the height and width of those images,

respectively. Then, it typically outputs a fixed-sized vector as membership probabilities over the target object classes. It also can output a set of feature maps that have the corresponding size to the input image for a pixel labeling task, etc.

In the example code of this tutorial, we assume for simplicity that the following symbols are already imported.
import numpy as np
import chainer
from chainer import backend
from chainer import backends
from chainer.backends import cuda
from chainer import Function, gradient_check, report, training, utils, Variable
from chainer import datasets, iterators, optimizers, serializers
from chainer import Link, Chain, ChainList
import chainer.functions as F
import chainer.links as L
from chainer.training import extensions

LeNet5

Here, let’s start by defining LeNet5 [LeCun98] in Chainer. In this example, we show a simplified version of LeNet5 introduced in Deep Learning Tutorials. This is a ConvNet model that has 5 layers comprised of 3 convolutional layers and 2 fully-connected layers. This was proposed to classify hand-written digit images in 1998. In Chainer, the model can be written as follows:

class LeNet5(Chain):
    def __init__(self):
        super(LeNet5, self).__init__()
        with self.init_scope():
            self.conv1 = L.Convolution2D(
                in_channels=1, out_channels=6, ksize=5, stride=1)
            self.conv2 = L.Convolution2D(
                in_channels=6, out_channels=16, ksize=5, stride=1)
            self.conv3 = L.Convolution2D(
                in_channels=16, out_channels=120, ksize=4, stride=1)
            self.fc4 = L.Linear(None, 84)
            self.fc5 = L.Linear(84, 10)

    def forward(self, x):
        h = F.sigmoid(self.conv1(x))
        h = F.max_pooling_2d(h, 2, 2)
        h = F.sigmoid(self.conv2(h))
        h = F.max_pooling_2d(h, 2, 2)
        h = F.sigmoid(self.conv3(h))
        h = F.sigmoid(self.fc4(h))
        if chainer.config.train:
            return self.fc5(h)
        return F.softmax(self.fc5(h))

A typical way to write your network is creating a new class inherited from Chain class. When defining your model in this way, typically, all the layers which have trainable parameters are registered to the model by assigning the objects of Link as an attribute.

The model class is instantiated before the forward and backward computations. To give input images and label vectors simply by calling the model object like a function, forward() is usually defined in the model class. This method performs the forward computation of the model. Chainer uses the powerful autograd system for any computational graphs written with FunctionNodes and Links (actually a Link calls a corresponding FunctionNode inside of it), so that you don’t need to explicitly write the code for backward computations in the model. Just prepare the data, then give it to the model. The way this works is the resulting output Variable from the forward computation has a backward() method to perform autograd. In the above model, forward() has a if statement at the end to switch its behavior by the Chainer’s running mode, i.e., training mode or not. Chainer presents the running mode as a global variable chainer.config.train. When it’s in training mode, forward() returns the output value of the last layer as is to compute the loss later on, otherwise it returns a prediction result by calculating softmax().

It is recommended to use the global configuration chainer.config.train to switch the running mode.

If you don’t want to write conv1 and the other layers more than once, you can also write the same model like in this way:

from functools import partial

class LeNet5(Chain):
    def __init__(self):
        super(LeNet5, self).__init__()
        net = [('conv1', L.Convolution2D(1, 6, 5, 1))]
        net += [('_sigm1', F.sigmoid)]
        net += [('_mpool1', partial(F.max_pooling_2d, ksize=2, stride=2))]
        net += [('conv2', L.Convolution2D(6, 16, 5, 1))]
        net += [('_sigm2', F.sigmoid)]
        net += [('_mpool2', partial(F.max_pooling_2d, ksize=2, stride=2))]
        net += [('conv3', L.Convolution2D(16, 120, 4, 1))]
        net += [('_sigm3', F.sigmoid)]
        net += [('_mpool3', partial(F.max_pooling_2d, ksize=2, stride=2))]
        net += [('fc4', L.Linear(None, 84))]
        net += [('_sigm4', F.sigmoid)]
        net += [('fc5', L.Linear(84, 10))]
        net += [('_sigm5', F.sigmoid)]
        with self.init_scope():
            for n in net:
                if not n[0].startswith('_'):
                    setattr(self, n[0], n[1])
        self.layers = net

    def forward(self, x):
        for n, f in self.layers:
            if not n.startswith('_'):
                x = getattr(self, n)(x)
            else:
                x = f(x)
        if chainer.config.train:
            return x
        return F.softmax(x)

Note

You can also use Sequential to write the above model more simply. Please note that Sequential is an experimental feature introduced in Chainer v4 and its interface may be changed in the future versions.

This code creates a list of pairs of component name (e.g., conv1, _sigm1, etc.) and all Links and functions (e.g., F.sigmoid, which internally invokes FunctionNode) after calling its superclass’s constructor. In this case, components whose name start with _ are functions (FunctionNode), which doesn’t have any trainable parameters, so that we don’t register (setattr) it to the model. Others (conv1, fc4, etc.) are Links, which are trainable layers that hold parameters. This operation can be freely replaced with many other ways because those component names are just designed to select Links only from the list net easily. The list net is stored as an attribute layers to refer it in forward(). In forward(), it retrieves all layers in the network from self.forward sequentially and gives the input variable or the intermediate output from the previous layer to the current layer. The last part of the forward() to switch its behavior by the training/inference mode is the same as the former way.

Ways to calculate loss

When you train the model with label vector t, the loss should be calculated using the output from the model. There also are several ways to calculate the loss:

model = LeNet5()

# Input data and label
x = np.random.rand(32, 1, 28, 28).astype(np.float32)
t = np.random.randint(0, 10, size=(32,)).astype(np.int32)

# Forward computation
y = model(x)

# Loss calculation
loss = F.softmax_cross_entropy(y, t)

This is a primitive way to calculate a loss value from the output of the model. On the other hand, the loss computation can be included in the model itself by wrapping the model object (Chain or ChainList object) with a class inherited from Chain. The outer Chain should take the model defined above and register it with init_scope(). Chain is actually inherited from Link, so that Chain itself can also be registered as a trainable Link to another Chain. Actually, Classifier class to wrap the model and add the loss computation to the model already exists. Actually, there is already a Classifier class that can be used to wrap the model and include the loss computation as well. It can be used like this:

model = L.Classifier(LeNet5())

# Foward & Loss calculation
loss = model(x, t)

This class takes a model object as an input argument and registers it to a predictor property as a trained parameter. As shown above, the returned object can then be called like a function in which we pass x and t as the input arguments and the resulting loss value (which we recall is a Variable) is returned.

See the detailed implementation of Classifier from here: chainer.links.Classifier and check the implementation by looking at the source.

From the above examples, we can see that Chainer provides the flexibility to write our original network in many different ways. Such flexibility intends to make it intuitive for users to design new and complex models.

VGG16

Next, let’s write some larger models in Chainer. When you write a large network consisting of several building block networks, ChainList is useful. First, let’s see how to write a VGG16 [Simonyan14] model.

class VGG16(chainer.ChainList):
    def __init__(self):
        super(VGG16, self).__init__(
            VGGBlock(64),
            VGGBlock(128),
            VGGBlock(256, 3),
            VGGBlock(512, 3),
            VGGBlock(512, 3, True))

    def forward(self, x):
        for f in self.children():
            x = f(x)
        if chainer.config.train:
            return x
        return F.softmax(x)


class VGGBlock(chainer.Chain):
    def __init__(self, n_channels, n_convs=2, fc=False):
        w = chainer.initializers.HeNormal()
        super(VGGBlock, self).__init__()
        with self.init_scope():
            self.conv1 = L.Convolution2D(None, n_channels, 3, 1, 1, initialW=w)
            self.conv2 = L.Convolution2D(
                n_channels, n_channels, 3, 1, 1, initialW=w)
            if n_convs == 3:
                self.conv3 = L.Convolution2D(
                    n_channels, n_channels, 3, 1, 1, initialW=w)
            if fc:
                self.fc4 = L.Linear(None, 4096, initialW=w)
                self.fc5 = L.Linear(4096, 4096, initialW=w)
                self.fc6 = L.Linear(4096, 1000, initialW=w)

        self.n_convs = n_convs
        self.fc = fc

    def forward(self, x):
        h = F.relu(self.conv1(x))
        h = F.relu(self.conv2(h))
        if self.n_convs == 3:
            h = F.relu(self.conv3(h))
        h = F.max_pooling_2d(h, 2, 2)
        if self.fc:
            h = F.dropout(F.relu(self.fc4(h)))
            h = F.dropout(F.relu(self.fc5(h)))
            h = self.fc6(h)
        return h

That’s it. VGG16 is a model which won the 1st place in classification + localization task at ILSVRC 2014, and since then, has become one of the standard models for many different tasks as a pre-trained model. This has 16-layers, so it’s called “VGG-16”, but we can write this model without writing all layers independently. Since this model consists of several building blocks that have the same architecture, we can build the whole network by re-using the building block definition. Each part of the network is consisted of 2 or 3 convolutional layers and activation function (relu()) following them, and max_pooling_2d() operations. This block is written as VGGBlock in the above example code. And the whole network just calls this block one by one in sequential manner.

ResNet152

How about ResNet? ResNet [He16] came in the following year’s ILSVRC. It is a much deeper model than VGG16, having up to 152 layers. This sounds super laborious to build, but it can be implemented in almost same manner as VGG16. In the other words, it’s easy. One possible way to write ResNet-152 is:

class ResNet152(chainer.Chain):
    def __init__(self, n_blocks=[3, 8, 36, 3]):
        w = chainer.initializers.HeNormal()
        super(ResNet152, self).__init__()
        with self.init_scope():
            self.conv1 = L.Convolution2D(None, 64, 7, 2, 3, initialW=w, nobias=True)
            self.bn1 = L.BatchNormalization(64)
            self.res2 = ResBlock(n_blocks[0], 64, 64, 256, 1)
            self.res3 = ResBlock(n_blocks[1], 256, 128, 512)
            self.res4 = ResBlock(n_blocks[2], 512, 256, 1024)
            self.res5 = ResBlock(n_blocks[3], 1024, 512, 2048)
            self.fc6 = L.Linear(2048, 1000)

    def forward(self, x):
        h = self.bn1(self.conv1(x))
        h = F.max_pooling_2d(F.relu(h), 2, 2)
        h = self.res2(h)
        h = self.res3(h)
        h = self.res4(h)
        h = self.res5(h)
        h = F.average_pooling_2d(h, h.shape[2:], stride=1)
        h = self.fc6(h)
        if chainer.config.train:
            return h
        return F.softmax(h)


class ResBlock(chainer.ChainList):
    def __init__(self, n_layers, n_in, n_mid, n_out, stride=2):
        super(ResBlock, self).__init__()
        self.add_link(BottleNeck(n_in, n_mid, n_out, stride, True))
        for _ in range(n_layers - 1):
            self.add_link(BottleNeck(n_out, n_mid, n_out))

    def forward(self, x):
        for f in self.children():
            x = f(x)
        return x


class BottleNeck(chainer.Chain):
    def __init__(self, n_in, n_mid, n_out, stride=1, proj=False):
        w = chainer.initializers.HeNormal()
        super(BottleNeck, self).__init__()
        with self.init_scope():
            self.conv1x1a = L.Convolution2D(
                n_in, n_mid, 1, stride, 0, initialW=w, nobias=True)
            self.conv3x3b = L.Convolution2D(
                n_mid, n_mid, 3, 1, 1, initialW=w, nobias=True)
            self.conv1x1c = L.Convolution2D(
                n_mid, n_out, 1, 1, 0, initialW=w, nobias=True)
            self.bn_a = L.BatchNormalization(n_mid)
            self.bn_b = L.BatchNormalization(n_mid)
            self.bn_c = L.BatchNormalization(n_out)
            if proj:
                self.conv1x1r = L.Convolution2D(
                    n_in, n_out, 1, stride, 0, initialW=w, nobias=True)
                self.bn_r = L.BatchNormalization(n_out)
        self.proj = proj

    def forward(self, x):
        h = F.relu(self.bn_a(self.conv1x1a(x)))
        h = F.relu(self.bn_b(self.conv3x3b(h)))
        h = self.bn_c(self.conv1x1c(h))
        if self.proj:
            x = self.bn_r(self.conv1x1r(x))
        return F.relu(h + x)

In the BottleNeck class, depending on the value of the proj argument supplied to the initializer, it will conditionally compute a convolutional layer conv1x1r which will extend the number of channels of the input x to be equal to the number of channels of the output of conv1x1c, and followed by a batch normalization layer before the final ReLU layer. Writing the building block in this way improves the re-usability of a class. It switches not only the behavior in __class__() by flags but also the parameter registration. In this case, when proj is False, the BottleNeck doesn’t have conv1x1r and bn_r layers, so the memory usage would be efficient compared to the case when it registers both anyway and just ignore them if proj is False.

Using nested Chains and ChainList for sequential part enables us to write complex and very deep models easily.

Use Pre-trained Models

Various ways to write your models were described above. It turns out that VGG16 and ResNet are very useful as general feature extractors for many kinds of tasks, including but not limited to image classification. So, Chainer provides you with the pre-trained VGG16 and ResNet-50/101/152 models with a simple API. You can use these models as follows:

from chainer.links import VGG16Layers

model = VGG16Layers()

When VGG16Layers is instantiated, the pre-trained parameters are automatically downloaded from the author’s server. So you can immediately start to use VGG16 with pre-trained weight as a good image feature extractor. See the details of this model here: chainer.links.VGG16Layers.

In the case of ResNet models, there are three variations differing in the number of layers. We have chainer.links.ResNet50Layers, chainer.links.ResNet101Layers, and chainer.links.ResNet152Layers models with easy parameter loading feature. ResNet’s pre-trained parameters are not available for direct downloading, so you need to download the weight from the author’s web page first, and then place it into the dir $CHAINER_DATSET_ROOT/pfnet/chainer/models or your favorite place. Once the preparation is finished, the usage is the same as VGG16:

from chainer.links import ResNet152Layers

model = ResNet152Layers()
Traceback (most recent call last):
OSError: The pre-trained caffemodel does not exist. Please download it from 'https://github.com/KaimingHe/deep-residual-networks', and place it on ...

Please see the details of usage and how to prepare the pre-trained weights for ResNet here: chainer.links.ResNet50Layers

References

[LeCun98]Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324, 1998.
[Simonyan14]Simonyan, K. and Zisserman, A., Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv preprint arXiv:1409.1556, 2014.
[He16]Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. Deep Residual Learning for Image Recognition. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, 2016.