We consider the following questions:. MobileNet v1 is smart enough to decompose the standard convolution operation into two separate operations: depth-wise or channel-wise convolution and point-wise convolution.
With decomposition, the two separate operation parts lead to output feature maps with exactly the same size as the standard counterpart does while with much less computation cost. How does that works? OK, depth-wise convolution takes as input a single channel and output a single channel for each channel of the input volume, and then concatenates the output channels for the second stage, in which the point-wise convolution takes place.
According to this, its corresponding computation cost is:. Since we have dealt with the input volume with a channel-by-channel strategy at first, so the purpose of point-wise operation is to combine the information of different channels and fuse them to new features. The point-wise operation costs. In this way, both computation cost and model size can be considerably reduced.
If you are not clear, the following is the whole MobielNet v1 structure with all the bells and whistles. Finally, BR means Batch normalization and Relu layers after a certain filter. What surprised me was that there is no residual module at all, what if we add some residuals or shortcuts like ResNet? Afterall, the author got his purpose and the accuracy on ImageNet classification task is comparable to the one using the standard convolution filters instead as well as other famous CNN models.
We can see from the table that the only highlight of SqueezeNet is its model size. It is not ignorable that we also need the speed of computation when we embed our model into resource-restricted devices like Mobile phones.
Take a look at it basic unit a fire module :. The basic idea behind SqueezeNet comes from three principles. First, using 1x1 filters as possible as we can; Second, decreasing the number of input channels to 3x3 filters. The last pinciple is to downsample feature maps after the merging operation of residual blocks so that to keep more activations. These two kinds of filters become the very basic tools for most of the following works focusing on network compression and speeding up, including MobileNet v2, ShuffleNet v1 and v2.
For analysis, we take part of it as the whole structure is stacked with similar components.
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In this illustration, the green unit means a residual block while the orange one means a normal block without residual with stride 2 to do downsampling. The main characteristic of MobileNet v2 architecture is for every unit or block, it first expands the number of channels by point-wise convolutions, then applies depth-wise convolutions with kernel size 3x3 on the expanded space, and finally projects back to low-channel feature spaces using point-wise convolutions again.
Now, I have the following questions:. For question 1, there is a intuition when designing MobileNet v2: bottlenecks actually contains all the necessary informations.
So it would not cause information loss if we do like that. ReLu cause information collapse. However, the higher the dimension of the input, the less the degree information collapses. So the high dimension in the middle of block is to avoid information loss. And intuitively, more channels usually means more powerful representative features thus to enhance the discriminability of a model.
We can use the same explanation to attack ResNet, which indeed use ReLU on the low-dimensional features. So why is it still so effective? This would attribute to its high dimensions of input and output ends of a ResNet block, which ensure its representative ability even with the ReLU layer in the bottleneck. The design art of MobileNet v2 is to keep few number of channels for the input and output of each block, while doing more complicated feature extraction inside the block with enough channels.
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They can run their models on fat desktop GPUs or compute clusters. The best way to measure the speed of a model is to run it a number of times in a row and take the average elapsed time. The time you measure for any single run may have a fairly large margin of error — the CPU or GPU may be busy doing other tasks drawing the screen, for example — but when you average over multiple runs this will significantly shrink that error.
For their V2 layers they used a depth multiplier of 1. It turns out my hunch was right — the V2 model was in fact slower! One way to get an idea of the speed of your model is to simply count how many computations it does. Why multiply-accumulate? Many of the computations in neural networks are dot productssuch as this:. Here, w and x are two vectors, and the result y is a scalar a single number.
Typically a layer will have multiple outputs, and so we compute many of these dot products. The above formula has n of these MACCs. Note: Technically speaking there are only n - 1 additions in the above formula, one less than the number of multiplications. Think of the number of MACCs as being an approximation, just like Big-O notation is an approximation of the complexity of an algorithm.
In a fully-connected layer, all the inputs are connected to all the outputs. The computation performed by a fully-connected layer is:. The result y contains the output values computed by the layer and is also a vector of size J. To compute the number of MACCs, we look at where the dot products happen. For a fully-connected layer that is in the matrix multiplication matmul x, W. A matrix multiply is simply a whole bunch of dot products. Each dot product is between the input x and one column in the matrix W.
Recall that a dot product has one less addition than multiplication anyway, so adding this bias value simply gets absorbed in that final multiply-accumulate. Note: Sometimes the formula for the fully-connected layer is written without an explicit bias value. If the fully-connected layer directly follows a convolutional layer, its input size may not be specified as a single vector length I but perhaps as a feature map with a shape such as7, 7.
Usually a layer is followed by a non-linear activation function, such as a ReLU or a sigmoid. Naturally, it takes time to compute these activation functions. Some activation functions are more difficult to compute than others.
For example, a ReLU is just:. This is a single operation on the GPU. The activation function is only applied to the output of the layer. Most convolutional layers used today have square kernels.
Something we should not ignore is the stride of the layer, as well as any dilation factors, padding, etc. Gotta keep that GPU busy…. A depthwise-separable convolution is a factorization of a regular convolution into two smaller operations.
Together they take up a lot less memory fewer weights and are much faster. These kinds of layers work very well on mobile devices and are the foundation of MobileNet, but also of larger models such as Xception. This first operation is the depthwise convolution.
There are always the same number of output channels as input channels.A little less than a year ago I wrote about MobileNetsa neural network architecture that runs very efficiently on mobile devices. Recently researchers at Google announced MobileNet version 2. This is mostly a refinement of V1 that makes it even more efficient and powerful. Naturally, I made an implementation using Metal Performance Shaders and I can confirm it lives up to the promise.
The big idea behind MobileNet V1 is that convolutional layers, which are essential to computer vision tasks but are quite expensive to compute, can be replaced by so-called depthwise separable convolutions. It does approximately the same thing as traditional convolution but is much faster. There are no pooling layers in between these depthwise separable blocks. Instead, some of the depthwise layers have a stride of 2 to reduce the spatial dimensions of the data. When that happens, the corresponding pointwise layer also doubles the number of output channels.
As is common in modern architectures, the convolution layers are followed by batch normalization. This is like the well-known ReLU but it prevents activations from becoming too big:.
There is actually more than one MobileNet. It was designed to be a family of neural network architectures. There are several hyperparameters that let you play with different architecture trade-offs. This changes how many channels are in each layer. Using a depth multiplier of 0. It is therefore much faster than the full model but also less accurate. Thanks to the innovation of depthwise separable convolutions, MobileNet has to do about 9 times less work than comparable neural nets with the same accuracy.
For a more in-depth look, check out my previous blog post or the original paper. MobileNet V2 still uses depthwise separable convolutions, but its main building block now looks like this:. This time there are three convolutional layers in the block. In V1 the pointwise convolution either kept the number of channels the same or doubled them.
In V2 it does the opposite: it makes the number of channels smaller. This is why this layer is now known as the projection layer — it projects data with a high number of dimensions channels into a tensor with a much lower number of dimensions.
I want to design a convolutional neural network which occupy GPU resource no more than Alexnet.
Is there any tools to do it,please? This supports most wide known layers. For custom layers you will have to calculate yourself. For future visitors, if you use Keras and TensorFlow as Backend then you can try the following example. Even if not using Keras, it may be worth it to recreate your nets in Keras just so you can get the flops counts. Learn more. Asked 2 years, 11 months ago. Active 2 years, 4 months ago. Viewed 14k times. StalkerMuse StalkerMuse 2 2 gold badges 8 8 silver badges 21 21 bronze badges.
Shai: that doesn't answer the question. The resolution of that link is that half the problem is an open request in TF. This is Caffe. Active Oldest Votes. As of the day of this comment, this webpage dgschwend. RunMetadata with tf. Graph as sess: K. Tobias Scheck Tobias Scheck 5 5 silver badges 14 14 bronze badges. How it's related to en. If I run this on Mobilenet V2, I get flops of 7.
I've changed the code to fit the tf 2. If I use the implementation gist. The flops are multiplications and additions, to get the MACs value you should divide the result by 2. Alex I Alex I Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password.
Review: G-RMI — Winner in 2016 COCO Detection (Object Detection)
Post as a guest Name.Read this paper on arXiv. Currently, the neural network architecture design is mostly guided by the indirect metric of computation complexity, i. However, the direct metric, e. Thus, this work proposes to evaluate the direct metric on the target platform, beyond only considering FLOPs.
Based on a series of controlled experiments, this work derives several practical guidelines for efficient network design. Accordingly, a new architecture is presented, called ShuffleNet V2.
Comprehensive ablation experiments verify that our model is the state-of-the-art in terms of speed and accuracy tradeoff. The architecture of deep convolutional neutral networks CNNs has evolved for years, becoming more accurate and faster. Besides accuracy, computation complexity is another important consideration. Real world tasks often aim at obtaining best accuracy under a limited computational budget, given by target platform e.MACHINE LEARNING MONDAY - MobileNet V2 SSD Lite on Raspberry Pi 4
Group convolution and depth-wise convolution are crucial in these works. However, FLOPs is an indirect metric. It is an approximation of, but usually not equivalent to the direct metric that we really care about, such as speed or latency. Therefore, using FLOPs as the only metric for computation complexity is insufficient and could lead to sub-optimal design.
The discrepancy between the indirect FLOPs and direct speed metrics can be attributed to two main reasons. First, several important factors that have considerable affection on speed are not taken into account by FLOPs.
One such factor is memory access cost MAC. Such cost constitutes a large portion of runtime in certain operations like group convolution. It could be bottleneck on devices with strong computing power, e. This cost should not be simply ignored during network architecture design. Another one is degree of parallelism. A model with high degree of parallelism could be much faster than another one with low degree of parallelism, under the same FLOPs.
Second, operations with the same FLOPs could have different running time, depending on the platform. With these observations, we propose that two principles should be considered for effective network architecture design. First, the direct metric e.
Second, such metric should be evaluated on the target platform. In this work, we follow the two principles and propose a more effective network architecture. Then, we derive four guidelines for efficient network design, which are beyond only considering FLOPs. While these guidelines are platform independent, we perform a series of controlled experiments to validate them on two different platforms GPU and ARM with dedicated code optimization, ensuring that our conclusions are state-of-the-art.
Our study is performed on two widely adopted hardwares with industry-level optimization of CNN library. We note that our CNN library is more efficient than most open source libraries. Thus, we ensure that our observations and conclusions are solid and of significance for practice in industry.Efficient networks optimized for speed and memory, with residual blocks. All pre-trained models expect input images normalized in the same way, i.
The MobileNet v2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input. MobileNet v2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, non-linearities in the narrow layers were removed in order to maintain representational power. To analyze traffic and optimize your experience, we serve cookies on this site.
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