Under Development
- ! ! ! It needs an evaluation on ImageNet
This project is an unofficial implementation of AlexNet, using C Program Language Without Any 3rd Library, according to the paper "ImageNet Classification with Deep Convolutional Neural Networks" by Alex Krizhevsky,et al.
Only support CPU now
- Effective matrix multiply, w.r.t L1/L2 cache
- img2col implementation of convolutional layer
- Multi-thread CPU Apply MT in operator's forward computation and backward computation to boost speed
- Efficient Memory Usage & Minimal Memory Occupation Memory is allocated on demand for efficient memory usage. If an array isn't necessary for computations afterward, it's memory will be freed instantly to minimize memory occupation.
- Modular layer Define each layer seperately
----------------------------------------------------------------
Layer Output Shape Param #
================================================================
Conv2d-1 [N, 64, 55, 55] 23,296
ReLU-2 [N, 64, 55, 55] 0
MaxPool2d-3 [N, 64, 27, 27] 0
Conv2d-4 [N, 192, 27, 27] 307,392
ReLU-5 [N, 192, 27, 27] 0
MaxPool2d-6 [N, 192, 13, 13] 0
Conv2d-7 [N, 384, 13, 13] 663,936
ReLU-8 [N, 384, 13, 13] 0
Conv2d-9 [N, 256, 13, 13] 884,992
ReLU-10 [N, 256, 13, 13] 0
Conv2d-11 [N, 256, 13, 13] 590,080
ReLU-12 [N, 256, 13, 13] 0
MaxPool2d-13 [N, 256, 6, 6] 0
Dropout-14 [N, 9216] 0
Linear-15 [N, 4096] 37,752,832
ReLU-16 [N, 4096] 0
Dropout-17 [N, 4096] 0
Linear-18 [N, 4096] 16,781,312
ReLU-19 [N, 4096] 0
Linear-20 [N, 1000] 4,097,000
================================================================
("N" stands for "batch size")
Total params: 61,100,840
Trainable params: 61,100,840
Non-trainable params: 0
----------------------------------------------------------------
git clone [email protected]:Dynmi/AlexNet.git
cd AlexNet
make clean && make all
( The data loader only supports .jpeg and .png$ images now. For image dataset, go to http://www.image-net.org/)
- Create file
images.listin the directory./, each line contains info of one image, like this:class_id image_path.
For example:
0 /home/haris/Documents/AlexNet/images/0/1.jpeg
1 /home/haris/Documents/AlexNet/images/1/1.jpeg
2 /home/haris/Documents/AlexNet/images/2/1.jpeg
3 /home/haris/Documents/AlexNet/images/3/1.jpeg
4 /home/haris/Documents/AlexNet/images/4/1.jpeg
5 /home/haris/Documents/AlexNet/images/5/1.jpeg
- Run the command for training
./alexnet train -batchsize <batch-size> -epochs <epochs> -load_pretrained <weights-path> -save <weights-path>
For example:
./alexnet train -batchsize 8 -epochs 10 -load_pretrained ./alexnet_pretrained.weights -save ./temp.weights
$./alexnet inference -input <image-path> -load <weights-path>
For example:
./alexnet inference -input ./0001.jpeg -load ./alexnet_pretrained.weights
-
Try & Compare on ImageNet
-
CUDA speed boosting
Experiments were done on a laptop --- Huawei MagicBook14
| this | DarkNet | |
|---|---|---|
| Scene1 | 0.13s | 0.44s |
| Scene2 | 0.21s | 0.66s |
Scene1: Input shape [4,224,224,3], weight shape [64,3,11,11]
Scene2: Input shape [4,57,57,128], weight shape [256,128,3,3]
| this | DarkNet | |
|---|---|---|
| Scene1 | 0.07s | 0.24s |
| Scene2 | 0.11s | 0.52s |
Scene1: Input shape [4,2048], weight shape [2048,1024]
Scene2: Input shape [4,4096], weight shape [4096,4096]
divide the learning rate by 10 when the validation error rate stopped improving with the current learning rate. The learning rate was initialized at 0.01 and reduced three times prior to termination. We trained the network for roughly 90 cycles through the training set of 1.2 million images.
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