---
tags: machine-learning
---
# Inception-V2/V3: Summary and Implementation
<div style="text-align: center">
<img src="https://raw.githubusercontent.com/valoxe/image-storage-1/master/research-paper-summary/inception-v2-v3/0.png?token=AMAXSKIACB6JIGNFBCAZ33C6WMHBG">
</div>
>This post is divided into 2 sections: Summary and Implementation.
>
>We are going to have an in-depth review of [Rethinking the Inception Architecture for Computer Vision](https://arxiv.org/pdf/1512.00567v3.pdf) paper which introduces the Inception-V2/Inception-V3 architecture.
>
> The implementation uses Pytorch as framework. To see full implementation, please refer to this [repository](https://github.com/3outeille/Research-Paper-Summary/tree/master/src/architecture/inception_v2_v3/pytorch).
>
> Also, if you want to read other "Summary and Implementation", feel free to check them at my [blog](https://ferdinandmom.engineer/deep-learning/).
# I) Summary
- The paper [Rethinking the Inception Architecture for Computer Vision](https://arxiv.org/pdf/1512.00567v3.pdf) proposed a number of upgrades which increase the accuracy and reduce the computational complexity of a deep convolutional network. The authors studied it in the context of the Inception architecture.
- They also studied how factorizing convolutions and aggressive dimension reductions inside neural network can result in networks with relatively low computational cost while maintaining high quality.
- They benchmarked their results on the ILSVRC 2012 classification challenge validation with a single model that has:
- less than 25 million parameters
- 5 billion multiply-adds operations (for a single inference).
- top-1 and top-5 error rate of 21.2% and 5.6%.
## General design principles
- ==They proposed few general design principles and optimization ideas that proved to be useful for scaling up convolution networks in efficient ways. The author issues to use these ideas judiciously!==
- **Avoid representational bottlenecks, especially early in the network**. Down scale the input image and the feature maps gently.
- **The more different filters you have, the more different feature maps** (higher dimensional representations) **you will have and the faster your network will learn** because it will have access to more different informations leading to detect which features are salient earlier in the training.
- **Spatial aggregation (dimension reduction) can be done over lower dimensional embeddings without much or any loss in representational power.** It means that in the inception module, performing a 1x1 convolution before a 3x3 convolution will not cause serious adverse effects. This will even promotes faster learning.
- **Optimal performance of the network can be reach by a careful balance between the width and depth**.
## Factorizing convolutions
- ==They have studied how factorizing convolutions and aggressive dimension reductions inside neural network can result in networks with relatively low computational cost while maintaining high quality.==
- They factorize 5x5 convolution into two stacked 3x3 convolution which result in a $(9+9)/25$ computation load reduction with a relative gain of $28\%$ by this factorization.
<div style="text-align:center">
<img src="https://raw.githubusercontent.com/valoxe/image-storage-1/master/research-paper-summary/inception-v2-v3/1.png?token=AMAXSKP4RL3EFOND74EO3ZK6WMHBK">
</div>
- To get these numbers, let's say we have a 5x5 input image and after a convolution, we want to produce a 5x5 output image (use of padding). The goal is to compare the number of operations between a 5x5 convolution and 2 stacked 3x3 convolutions.
- With a 5x5 convolution, we need a padding of 2.
- \# of operations = 5x5x5x5 = 25x25.
- With 2 stacked 3x3 convolution, we need a padding of 1 for each convolution.
- \# of operations = 5x5x3x3 + 5x5x3x3 = 25x9 + 25x9 = 25x18.
- Thus, 2 stacked 3x3 convolutions reduce the computation load by $(9+9)/25$ ((25x18)/(25x25) = 18/25 = (9+9)/25) which result in a computation saving of $28\%$ by this factorization ((1 - 18/25)*100 = 28\%).
- Moreover, it is worth noticing that 2 stacked 3x3 filters give the same receptive field as as a 5x5 filter.
- They factorize n x n convolution into a combination of 1 x n convolution and n x 1 convolution. They call it "asymmetric convolution". For example, a 3x3 convolution is equivalent to first performing a 1x3 convolution, and then performing a 3x1 convolution on its output. They found it to be $33\%$ cheaper than a single 3x3 convolution.
- Same experience as above but this time with a 3x3 input/output image:
- with a 3x3 convolution, we need a padding of 1:
- \# of operations = 3x3x3x3 = 81.
- with combination of 1x3 / 3x1 convolutions, we need a padding of 1 for each convolution:
- \# of operations = 3x3x1x3 + 3x3x3x1 = 54.
- Thus, a combination of 1x3 and 3x1 convolutions result in a computation saving of $33\%$ ((1 - (54/81))*100 = 33\%) by this factorization.
<br>
<div style="text-align:center">
<img src="https://raw.githubusercontent.com/valoxe/image-storage-1/master/research-paper-summary/inception-v2-v3/2.png?token=AMAXSKMO7EP4PFH4WRVP7EK6WMHBM" width="60%">
</div>
## Grid size reduction
- Traditionally, convolutional networks use some pooling before convolution operations to reduce the gride size of the feature maps. Problem is, it can introduce a representational bottleneck.
- ==The authors think that increasing the number of filters (expand the filter bank) remove the representational bottleneck. This is achieved by the inception module.==
<div style="text-align:center">
<img src="https://raw.githubusercontent.com/valoxe/image-storage-1/master/research-paper-summary/inception-v2-v3/3.png?token=AMAXSKPNZA7AZ6Y5E4AEGO26WMHBM">
</div>
<br>
- In the left picture, we are introducing a representational bottleneck by first reducing the grid size and then expanding the filter bank which is the other way around in the right picture.
- To get the intuition behind it, let's follow this simple example. Suppose you are a primary student split between choosing either general study or technical study.
- It makes more sense to choose general study to learn first different stuffs and then specialize in what you like (expand first, reduce after). By specializing first (reduce first), you will close yourself some doors (loss of information) so if you don't like what you chose and try to look for other fields after (expand after), then it will be harder to find something different.
- However, the right side is more expensive so they proposed another solution that reduces the computational cost while eliminating the bottleneck (by using 2 parallel stride 2 pooling/convolution blocks).
<div style="text-align:center">
<img src="https://raw.githubusercontent.com/valoxe/image-storage-1/master/research-paper-summary/inception-v2-v3/4.png?token=AMAXSKI6UZ3LV4OR3OHDE2K6WMHBM">
<figcaption> The diagram on the right represents the same <br> solution but from the perspective of grid sizes<br> rather than the operations.</figcaption>
</div>
<br>
## Inception-V2
Whole network is 42 layers deep, computational cost is 2.5 times higher than GoogLeNet.
<div style="text-align:center">
<img src="https://raw.githubusercontent.com/valoxe/image-storage-1/master/research-paper-summary/inception-v2-v3/5.png?token=AMAXSKPSD7DR5VHQYTUPU226WMHBO">
</div>
<br>
<div style="text-align:center">
<img src="https://raw.githubusercontent.com/valoxe/image-storage-1/master/research-paper-summary/inception-v2-v3/6.png?token=AMAXSKIR4IVW7WZXOXVJJQS6WMHBO">
</div>
<br>
<ins>**Remark:**</ins> Authors say they use variations of reduction technique (picture below) to reduce the grid sizes between the Inception blocks whenever applicable. They also add an auxilary classifier on top of the last 17×17 layer.
<div style="text-align:center">
<img src="https://raw.githubusercontent.com/valoxe/image-storage-1/master/research-paper-summary/inception-v2-v3/7.png?token=AMAXSKPYQVYCN6S2TOKX4OS6WMHBQ">
</div>
## Utility of Auxiliary Classifiers
- Authors say that they didn't contribute much but act more as regularizer if used with batch normalization or dropout.
## Label smoothing
- In CNN, the label is a vector. If you have 3 class, the one-hot labels are [0, 0, 1] or [0, 1, 0] or [1, 0, 0]. Each of the vector stands for a class at the output layer. Label smoothing, in my understanding, is to use a relatvely smooth vector to represent a ground truth label. Say [0, 0, 1] can be represented as [0.1, 0.1, 0.8]. It is used when the loss function is the cross entropy function.
- According to the author:
- First, it (using unsmoothed label) may result in overfitting: if the model learns to assign full probability to the ground-truth label for each training example, it is not guaranteed to generalize.
- Second, it encourages the differences between the largest logit and all others to become large, and this, combined with the bounded gradient, reduces the ability of the model to adapt. Intuitively, this happens because the model becomes too confident about its predictions.
- They claim that by using label smoothing, the top-1 and top-5 error rate are reduced by 0.2%.
## Inception V3
Inception Net v3 incorporated all of the above upgrades stated for Inception v2, and in addition used the following:
- RMSProp Optimizer.
- Factorized 7x7 convolutions.
- BatchNorm in the Auxillary Classifiers.
- Label Smoothing.
## Architecture
<div style="text-align:center">
<img src="https://raw.githubusercontent.com/valoxe/image-storage-1/master/research-paper-summary/inception-v2-v3/8.png?token=AMAXSKKXGJIZBQVTLC2ITOK6WMHBS">
</div>
<div style="text-align:center">
<img src="https://raw.githubusercontent.com/valoxe/image-storage-1/master/research-paper-summary/inception-v2-v3/9.png?token=AMAXSKK4DNL2Q3V4NLAEYPC6WMHBS">
</div>
<div style="text-align:center">
<img src="https://raw.githubusercontent.com/valoxe/image-storage-1/master/research-paper-summary/inception-v2-v3/10.png?token=AMAXSKPZIJVYHT4VKNENXIS6WMHHS">
</div>
<div style="text-align:center">
<img src="https://raw.githubusercontent.com/valoxe/image-storage-1/master/research-paper-summary/inception-v2-v3/11.png?token=AMAXSKPKXIW6K6RBX6HLV7S6WMHHU">
</div>
# II) Implementation
We will implement Inception-V2. According to the paper,
```
The detailed structure of the network, including the sizes of filter
banks inside the Inception modules, is given in the supplementary
material, given in the model.txt
```
However, no ```model.txt``` was found so we have to refer to the tensorflow implementation.
### 1) Architecture build
```python
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding):
super(ConvBlock, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)
self.bn = nn.BatchNorm2d(out_channels)
self.act = nn.ReLU()
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.act(x)
return x
```
```python
class InceptionF5(nn.Module):
"""
From the paper, figure 5 inception module.
"""
def __init__(self, in_channels):
super(InceptionF5, self).__init__()
self.branch1 = nn.Sequential(
ConvBlock(in_channels, 64, kernel_size=1, stride=1, padding=0),
ConvBlock(64, 96, kernel_size=3, stride=1, padding=1),
ConvBlock(96, 96, kernel_size=3, stride=1, padding=1)
)
self.branch2 = nn.Sequential(
ConvBlock(in_channels, 48, kernel_size=1, stride=1, padding=0),
ConvBlock(48, 64, kernel_size=3, stride=1, padding=1)
)
self.branch3 = nn.Sequential(
nn.MaxPool2d(3, stride=1, padding=1),
ConvBlock(in_channels, 64, kernel_size=1, stride=1, padding=0)
)
self.branch4 = nn.Sequential(
ConvBlock(in_channels, 64, kernel_size=1, stride=1, padding=0)
)
def forward(self, x):
branch1 = self.branch1(x)
branch2 = self.branch2(x)
branch3 = self.branch3(x)
branch4 = self.branch4(x)
return torch.cat([branch1, branch2, branch3, branch4], 1)
```
```python
class InceptionF6(nn.Module):
"""
From the paper, figure 6 inception module.
"""
def __init__(self, in_channels, f_7x7):
super(InceptionF6, self).__init__()
self.branch1 = nn.Sequential(
ConvBlock(in_channels, f_7x7, kernel_size=1, stride=1, padding=0),
ConvBlock(f_7x7, f_7x7, kernel_size=(1,7), stride=1, padding=(0,3)),
ConvBlock(f_7x7, f_7x7, kernel_size=(7,1), stride=1, padding=(3,0)),
ConvBlock(f_7x7, f_7x7, kernel_size=(1,7), stride=1, padding=(0,3)),
ConvBlock(f_7x7, 192, kernel_size=(7,1), stride=1, padding=(3,0))
)
self.branch2 = nn.Sequential(
ConvBlock(in_channels, f_7x7, kernel_size=1, stride=1, padding=0),
ConvBlock(f_7x7, f_7x7, kernel_size=(1,7), stride=1, padding=(0,3)),
ConvBlock(f_7x7, 192, kernel_size=(7,1), stride=1, padding=(3,0))
)
self.branch3 = nn.Sequential(
nn.MaxPool2d(3, stride=1, padding=1),
ConvBlock(in_channels, 192, kernel_size=1, stride=1, padding=0)
)
self.branch4 = nn.Sequential(
ConvBlock(in_channels, 192, kernel_size=1, stride=1, padding=0)
)
def forward(self, x):
branch1 = self.branch1(x)
branch2 = self.branch2(x)
branch3 = self.branch3(x)
branch4 = self.branch4(x)
return torch.cat([branch1, branch2, branch3, branch4], 1)
```
```python
class InceptionF7(nn.Module):
"""
From the paper, figure 7 inception module.
"""
def __init__(self, in_channels):
super(InceptionF7, self).__init__()
self.branch1 = nn.Sequential(
ConvBlock(in_channels, 448, kernel_size=1, stride=1, padding=0),
ConvBlock(448, 384, kernel_size=(3,3), stride=1, padding=1)
)
self.branch1_top = ConvBlock(384, 384, kernel_size=(1,3), stride=1, padding=(0,1))
self.branch1_bot = ConvBlock(384, 384, kernel_size=(3,1), stride=1, padding=(1,0))
self.branch2 = ConvBlock(in_channels, 384, kernel_size=1, stride=1, padding=0)
self.branch2_top = ConvBlock(384, 384, kernel_size=(1,3), stride=1, padding=(0,1))
self.branch2_bot = ConvBlock(384, 384, kernel_size=(3,1), stride=1, padding=(1,0))
self.branch3 = nn.Sequential(
nn.MaxPool2d(3, stride=1, padding=1),
ConvBlock(in_channels, 192, kernel_size=1, stride=1, padding=0)
)
self.branch4 = nn.Sequential(
ConvBlock(in_channels, 320, kernel_size=1, stride=1, padding=0)
)
def forward(self, x):
branch1 = self.branch1(x)
branch1 = torch.cat([self.branch1_top(branch1), self.branch1_bot(branch1)], 1)
branch2 = self.branch2(x)
branch2 = torch.cat([self.branch2_top(branch2), self.branch2_bot(branch2)], 1)
branch3 = self.branch3(x)
branch4 = self.branch4(x)
return torch.cat([branch1, branch2, branch3, branch4], 1)
```
```python
class InceptionRed(nn.Module):
"""
From the paper, figure 10 improved pooling operation.
"""
def __init__(self, in_channels, f_3x3_r, add_ch=0):
super(InceptionRed, self).__init__()
self.branch1 = nn.Sequential(
ConvBlock(in_channels, f_3x3_r, kernel_size=1, stride=1, padding=0),
ConvBlock(f_3x3_r, 178 + add_ch, kernel_size=3, stride=1, padding=1),
ConvBlock(178 + add_ch, 178 + add_ch, kernel_size=3, stride=2, padding=0)
)
self.branch2 = nn.Sequential(
ConvBlock(in_channels, f_3x3_r, kernel_size=1, stride=1, padding=0),
ConvBlock(f_3x3_r, 302 + add_ch, kernel_size=3, stride=2, padding=0)
)
self.branch3 = nn.Sequential(
nn.MaxPool2d(3, stride=2, padding=0)
)
def forward(self, x):
branch1 = self.branch1(x)
branch2 = self.branch2(x)
branch3 = self.branch3(x)
return torch.cat([branch1, branch2, branch3], 1)
```
```python
class InceptionAux(nn.Module):
"""
From the paper, auxilary classifier
"""
def __init__(self, in_channels, num_classes):
super(InceptionAux, self).__init__()
self.pool = nn.AdaptiveAvgPool2d((4,4))
self.conv = nn.Conv2d(in_channels, 128, kernel_size=1, stride=1, padding=0)
self.act = nn.ReLU()
self.fc1 = nn.Linear(2048, 1024)
self.dropout = nn.Dropout(0.7)
self.fc2 = nn.Linear(1024, num_classes)
def forward(self, x):
x = self.pool(x)
x = self.conv(x)
x = self.act(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = self.act(x)
x = self.dropout(x)
x = self.fc2(x)
return x
```
```python
class InceptionV2(nn.Module):
def __init__(self, num_classes = 10):
super(InceptionV2, self).__init__()
self.conv1 = ConvBlock(3, 32, kernel_size=3, stride=2, padding=0)
self.conv2 = ConvBlock(32, 32, kernel_size=3, stride=1, padding=0)
self.conv3 = ConvBlock(32, 64, kernel_size=3, stride=1, padding=1)
self.pool1 = nn.MaxPool2d(3, stride=2, padding=0)
self.conv4 = ConvBlock(64, 80, kernel_size=3, stride=1, padding=0)
self.conv5 = ConvBlock(80, 192, kernel_size=3, stride=2, padding=0)
self.conv6 = ConvBlock(192, 288, kernel_size=3, stride=1, padding=1)
self.inception3a = InceptionF5(288)
self.inception3b = InceptionF5(288)
self.inception3c = InceptionF5(288)
self.inceptionRed1 = InceptionRed(288,f_3x3_r=64, add_ch=0)
self.inception4a = InceptionF6(768, f_7x7=128)
self.inception4b = InceptionF6(768, f_7x7=160)
self.inception4c = InceptionF6(768, f_7x7=160)
self.inception4d = InceptionF6(768, f_7x7=160)
self.inception4e = InceptionF6(768, f_7x7=192)
self.inceptionRed2 = InceptionRed(768,f_3x3_r=192, add_ch=16)
self.aux = InceptionAux(768, num_classes)
self.inception5a = InceptionF7(1280)
self.inception5b = InceptionF7(2048)
self.pool6 = nn.AdaptiveAvgPool2d((1,1))
self.dropout = nn.Dropout(0.4)
self.fc = nn.Linear(2048, num_classes)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.pool1(x)
x = self.conv4(x)
x = self.conv5(x)
x = self.conv6(x)
x = self.inception3a(x)
x = self.inception3b(x)
x = self.inception3c(x)
x = self.inceptionRed1(x)
x = self.inception4a(x)
x = self.inception4b(x)
x = self.inception4c(x)
x = self.inception4d(x)
x = self.inception4e(x)
aux = self.aux(x)
x = self.inceptionRed2(x)
x = self.inception5a(x)
x = self.inception5b(x)
x = self.pool6(x)
x = self.dropout(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x, aux
```
### 2) Training on CIFAR-10
```
train_costs, val_costs = train_model()
```
```
[Epoch 1/15]: train-loss = 2.559885 | train-acc = 0.259 | val-loss = 2.215568 | val-acc = 0.359
[Epoch 2/15]: train-loss = 1.992639 | train-acc = 0.424 | val-loss = 1.846719 | val-acc = 0.472
[Epoch 3/15]: train-loss = 1.640984 | train-acc = 0.537 | val-loss = 1.591195 | val-acc = 0.558
[Epoch 4/15]: train-loss = 1.385598 | train-acc = 0.619 | val-loss = 1.439533 | val-acc = 0.614
[Epoch 5/15]: train-loss = 1.168221 | train-acc = 0.684 | val-loss = 1.305238 | val-acc = 0.655
[Epoch 6/15]: train-loss = 1.002453 | train-acc = 0.731 | val-loss = 1.214881 | val-acc = 0.681
[Epoch 7/15]: train-loss = 0.851118 | train-acc = 0.771 | val-loss = 1.217857 | val-acc = 0.687
[Epoch 8/15]: train-loss = 0.711145 | train-acc = 0.811 | val-loss = 1.147356 | val-acc = 0.711
[Epoch 9/15]: train-loss = 0.608349 | train-acc = 0.838 | val-loss = 1.140132 | val-acc = 0.724
[Epoch 10/15]: train-loss = 0.515004 | train-acc = 0.863 | val-loss = 1.154486 | val-acc = 0.735
[Epoch 11/15]: train-loss = 0.437409 | train-acc = 0.885 | val-loss = 1.180348 | val-acc = 0.741
[Epoch 12/15]: train-loss = 0.386996 | train-acc = 0.899 | val-loss = 1.190746 | val-acc = 0.742
[Epoch 13/15]: train-loss = 0.339299 | train-acc = 0.911 | val-loss = 1.171836 | val-acc = 0.749
[Epoch 14/15]: train-loss = 0.293521 | train-acc = 0.923 | val-loss = 1.245975 | val-acc = 0.751
[Epoch 15/15]: train-loss = 0.235749 | train-acc = 0.938 | val-loss = 1.239976 | val-acc = 0.761
```
### 3) Evaluating model
```python
nb_test_examples = 10000
correct = 0
model.eval().cuda()
with torch.no_grad():
for inputs, labels in test_loader:
inputs, labels = inputs.to(device), labels.to(device)
# Make predictions.
prediction, _, _ = model(inputs)
# Retrieve predictions indexes.
_, predicted_class = torch.max(prediction.data, 1)
# Compute number of correct predictions.
correct += (predicted_class == labels).float().sum().item()
test_accuracy = correct / nb_test_examples
print('Test accuracy: {}'.format(test_accuracy))
```
```
Test accuracy: 0.7418
```
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