course/training_example/CNN/ResNet

• ResNet
• • Performance
  • • Precision comparison line chart
  • pretrained models
  • Training
  • • Parameter description
  • Examples
  • • Train
    • Eval
    • Infer

ResNet

---

ResNet (residual neural network) was proposed by Kaiming He and other four Chinese of Microsoft Research Institute. Traditional convolution network or full connection network will have more or less information loss. At the same time, it will lead to the disappearance or explosion of gradient, which leads to the failure of deep network training. ResNet solves this problem to a certain extent. By passing the input information to the output, the integrity of the information is protected. The whole network only needs to learn the part of the difference between input and output, which simplifies the learning objectives and difficulties.The structure of ResNet can accelerate the training of neural network very quickly, and the accuracy of the model is also greatly improved.

The architectural definition of each network refers to the following papers:

[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep Residual Learning for Image Recognition. arXiv preprint arXiv: 1512.03385, 2015b.

Performance

---

Precision comparison line chart

The following line chart shows the accuracy comparison between resnet network and pytorch and paper in Top1 and top5.

pretrained models

---

All resnet checkpoints were trained with image resolution 224x224. Each model verifies the accuracy
of Top-1 and Top-5, and compares it with that of pytorch and the paper. The table is as follows:

		MindSpore	MindSpore	Pytorch	Pytorch	Paper	Paper
Model	Dataset	Top-1 (%)	Top-5 (%)	Top-1 (%)	Top-5 (%)	Top-1 (%)	Top-5 (%)	Download	Config
Resnet18	ImageNet	70.078	89.236	69.758	89.078	/	/	model	config
Resnet34	ImageNet	73.72	91.56	73.314	91.42	74.97	92.24	model	config
Resnet50	ImageNet	76.6	93.078	76.13	92.862	77.15	93.29	model	config
Resnet50	Cifar10	93.79	99.85	/	/	/	/	model	config
Resnet101	ImageNet	77.62	93.632	77.374	93.546	78.25	93.95	model	config
Resnet152	ImageNet	78.636	94.137	78.312	94.046	78.57	94.29	model	config

Training

---

Parameter description

Parameter	Default	Description
model		Model Type
device_target	GPU	Hardware device
data_url		Path to training dataset
pretrained	False	Load pretrained model
run_distribute	True	Distributed parallel training
num_parallel_workers	8	Number of parallel workers
dataset_sink_mode	True	Data sinking mode
num_classes	1000	Number of dataset classifications
batch_size	64	Number of batch size
repeat_num	1	Number of data repetitions
momentum	0.9	Momentum parameter
epoch_size	90	Number of epoch
keep_checkpoint_max	10	Maximum number of checkpoints saved
ckpt_save_dir	'./resnet'	Save path of checkpoint
decay_epoch	90	Number of decay epoch
lr_decay_mode	cosine_decay_lr	Learning rate decay mode
smooth_factor	0.1	Label smoothing factor
max_lr	0.1	maximum learning rate
min_lr	0.0	minimum learning rate
milestone		A list of milestone
learning_rates		A list of learning rates
resize	224	Resize the image

Examples

---

Train

• The following configuration uses 8 GPUs for training.

  mpirun -n 8 python resnet_imagenet_train.py --model resnet50 --data_url ./dataset/imagenet --lr_decay_mode cosine_decay_lr
  

  output:

  Epoch:[0/90], step:[2502/2502], loss:[4.573/4.573], time:1095567.210, lr:0.10000
  Epoch time:1106613.360, per step time:442.292, avg loss:4.573
  Epoch:[1/90], step:[2502/2502], loss:[4.375/4.375], time:1091808.393, lr:0.09997
  Epoch time:1091811.163, per step time:436.375, avg loss:4.375
  Epoch:[2/90], step:[2502/2502], loss:[3.904/3.904], time:1094093.746, lr:0.09988
  Epoch time:1094096.121, per step time:437.289, avg loss:3.904
  Epoch:[3/90], step:[2502/2502], loss:[3.745/3.745], time:1071049.615, lr:0.09973
  Epoch time:1071054.215, per step time:428.079, avg loss:3.745
  Epoch:[4/90], step:[2502/2502], loss:[3.125/3.125], time:1073788.254, lr:0.09951
  Epoch time:1073791.104, per step time:429.173, avg loss:3.125
  
  ...
  

• The following configuration uses yaml file for training.

  mpirun -n 8 python examples/classification/tools/train_config.py -c mindvision/classification/config/resnet/resnet50_imagenet2012.yaml
  

  output:

  Epoch:[0/90], step:[2502/2502], loss:[4.819/4.819], time:1095563.976, lr:0.10000
  Epoch time:1106607.839, per step time:442.289, avg loss:4.819
  Epoch:[1/90], step:[2502/2502], loss:[4.658/4.658], time:1091867.306, lr:0.09997
  Epoch time:1091869.861, per step time:436.339, avg loss:4.658
  Epoch:[2/90], step:[2502/2502], loss:[4.020/4.020], time:1093903.073, lr:0.09988
  Epoch time:1093905.668, per step time:437.212, avg loss:4.020
  Epoch:[3/90], step:[2502/2502], loss:[3.494/3.494], time:1071292.745, lr:0.09973
  Epoch time:1071295.067, per step time:428.175, avg loss:3.494
  Epoch:[4/90], step:[2502/2502], loss:[3.007/3.007], time:1073570.003, lr:0.09951
  Epoch time:1073572.577, per step time:429.086, avg loss:3.007
  
  ...
  

Eval

• The following configuration for eval.

  python resnet_imagenet_eval.py --model resnet50 --data_url ./dataset/imagenet --pretrained True
  

  output:

  {'Top_1_Accuracy': 0.7658050576184379, 'Top_5_Accuracy': 0.9306378040973111}  
  

• The following configuration uses yaml file for eval.

  python examples/classification/tools/eval_config.py -c mindvision/classification/config/resnet/resnet50_imagenet2012.yaml
  

  output:

  {'Top_1_Accuracy': 0.7658450704225352, 'Top_5_Accuracy': 0.9307778489116517}
  

Infer

• The following configuration for infer.

  python resnet_imagenet_infer.py --model resnet50 --pretrained True --data_url ./infer
  

  output:

  {151: 'Chihuahua'}