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
- A large convolutional network that has several building block networks with
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.
The below example code assumes that some packages are already imported.
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.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
Here, let’s start by defining LeNet5 [LeCun98] in Chainer. 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 __call__(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,
__call__() 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
Links (actually a
Link calls a corresponding
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,
__call__() 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
When it’s in training mode,
__call__() returns the output value of the
last layer as is to compute the loss later on, otherwise it returns a
prediction result by calculating
In Chainer v1, if a function or link behaved differently in training and other modes, it was common that it held an attribute that represented its running mode or was provided with the mode from outside as an argument. In Chainer v2, 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 model like in this way:
class LeNet5(Chain): def __init__(self): super(LeNet5, self).__init__() net = [('conv1', L.Convolution2D(1, 6, 5, 1))] net += [('_sigm1', F.Sigmoid())] net += [('_mpool1', F.MaxPooling2D(2, 2))] net += [('conv2', L.Convolution2D(6, 16, 5, 1))] net += [('_sigm2', F.Sigmoid())] net += [('_mpool2', F.MaxPooling2D(2, 2))] net += [('conv3', L.Convolution2D(16, 120, 4, 1))] net += [('_sigm3', F.Sigmoid())] net += [('_mpool3', F.MaxPooling2D(2, 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.startswith('_'): setattr(self, n, n) self.forward = net def __call__(self, x): for n, f in self.forward: if not n.startswith('_'): x = getattr(self, n)(x) else: x = f(x) if chainer.config.train: return x return F.softmax(x)
This code creates a list of all
FunctionNodes after calling its superclass’s constructor.
Then the elements of the list are registered to this model as
trainable layers when the name of an element doesn’t start with
character. This operation can be freely replaced with many other ways because
those names are just designed to select
Links only from the
FunctionNode doesn’t have any trainable
parameters, so that we can’t register it to the model, but we want to use
FunctionNodes for constructing a forward path. The list
net is stored as an attribute
forward to refer it in
__call__(), it retrieves all layers in the network
self.forward sequentially regardless of what types of object (
FunctionNode) it is, and gives the
input variable or the intermediate output from the previous layer to the
current layer. The last part of the
__call__() 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
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 (
ChainList object) with a class inherited from
Chain. The outer
Chain should take the
model defined above and register it with
Chain is actually
Link, so that
can also be registered as a trainable
Link to another
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
predictor property as a trained parameter. As shown above, the returned
object can then be called like a function in which we pass
the input arguments and the resulting loss value (which we recall is a
Variable) is returned.
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.
Next, let’s write some larger models in Chainer. When you write a large network
consisting of several building block networks,
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 __call__(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 __call__(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
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.
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, 64, 64, 256, 1) self.res3 = ResBlock(n_blocks, 256, 128, 512) self.res4 = ResBlock(n_blocks, 512, 256, 1024) self.res5 = ResBlock(n_blocks, 1024, 512, 2048) self.fc6 = L.Linear(2048, 1000) def __call__(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 __call__(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 __call__(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)
BottleNeck class, depending on the value of the proj argument
supplied to the initializer, it will conditionally compute a convolutional
conv1x1r which will extend the number of channels of the input
to be equal to the number of channels of the output of
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
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
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()
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:
In the case of ResNet models, there are three variations differing in the number
of layers. We have
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()
Please see the details of usage and how to prepare the pre-trained weights for
|[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.|