Awesome

Official code for 'How does topology influence gradient propagation and model performance of deep networks with DenseNet-type skip connections?' (CVPR 2021) ArXiv link

Usage

For all kinds of models in the model_zoo:
- access model.nn_mass will return the NN_Mass value of the model

MLP

NN_Mass vs. Test Accuracy

Evaluate the test accuracy of MLPs with random concatenation-type skip connections
usage:
- python train_mlp.py [arguments]

optional arguments	Description
-h, --help	show this help message and exit
--batch_size	Number of samples per mini-batch
--epochs	Number of epoch to train
--lr	Learning rate
--depth	the depth (number of FC layers) of the MLP
--width	the width (number of neurons per layers) of the MLP
--num_seg	the number of segmentation for the synthetic dataset (currently we support 'linear' and 'circle' dataset)
--tc	maximum number of candidate channels/neurons from previous layers that can supply concatenation-type skip connections
--dataset	the type of dataset
--make_dataset	generate/regenerate the synthetic dataset or not
--train_log_file	the name of file used to record the training/test record of MLPs
--res_log_file	the name of file used to record the training/test record of MLPs
--iter_times	the number of iteration times to train the same architecture

Example: train a 8-layer MLP with 8 neurons per layer and tc=10 on MNIST dataset
- python train_mlp.py --depth=8 --width=8 --tc=10 --dataset='MNIST'

NN_Mass vs. LDI

Calculate the LDI (mean singular value of Jacobians) of MLPs with random concatenation-type skip connections.
Currenlty, our code only could calculate the LDI of MLP on MNIST dataset.
The usage is similar to train_mlp.py
- python ldi.py [arguments]

optional arguments	Description
-h,	show this help message and exit
--batch_size	Number of samples per mini-batch
--epochs	Number of epoch to train
--depth	the depth (number of FC layers) of the MLP
--width	the width (number of neurons per layers) of the MLP
--num_seg	the number of segmentation for the synthetic dataset
--tc	maximum number of candidate channels/neurons from previous layers that can supply concatenation-type skip connections
--dataset DAASET	the type of dataset
--sigma_log_file	the name of file used to record the LDI record of MLPs
--iter_times	the number of iteration times to calculate the LDI of the same architecture

Example: Calculate the LDI of a 8-layer MLP with 8 neurons per layer and tc=10 on MNIST dataset
- python ldi.py --depth=8 --width=8 --tc=10 --dataset='MNIST'

CNN

CIFAR-10/100

Train and evaluate the test accuracy of MLP with self-defined topology/architecture
Currently we support DensetNet-type CNNs ('regular_dense') and DensetNet-type +Depthwisw Conv CNNs. ('dense_depth')
usage:
- python train_cifar.py [arguments]

optional arguments	Description
-h, --help	show this help message and exit
--arch	network architecture
--wm	width multipler of CNN cells
--num_cells	number of cells
--cell_depth	number of layers for each cell
--tc1	within the first cell, maximum number of candidate channels/neurons from previous layers that can supply concatenation-type skip connections
--tc2	within the second cell, maximum number of candidate channels/neurons from previous layers that can supply concatenation-type skip connections
--tc3	within the third cell, maximum number of candidate channels/neurons from previous layers that can supply concatenation-type skip connections
--tc4	within the fourth cell, maximum number of candidate channels/neurons from previous layers that can supply concatenation-type skip connections
--dataset	dataset

Example: train a 3-cell wm=2 8-layer per cell DensetNet-type CNN with tc[10,20,30] for each cell on CIFAR10 dataset
- python train_cifar.py --num_cells=3 --cell_depth=8 --wm=2 --tc1=10 --tc2=20 --tc3=40 --dataset='cifar10' --arch='regular_dense'

ImageNet

We reuse some code from mobilenetv2.pytorch
Currently, we support the following networks (use python train_imagenet.py -a network_name to select the networks):

mobilenet_v2
resnet18
resnet34
resnet50
resnet101
resnet152
resnext50_32x4d
resnext101_32x8d
wide_resnet50_2
wide_resnet101_2

Example: Train MobileNet-v2

python train_imagenet.py \
    -a mobilenetv2 \
    -d <path-to-ILSVRC2012-data> \
    --epochs 150 \
    --lr-decay cos \
    --lr 0.05 \
    --wd 4e-5 \
    -c <path-to-save-checkpoints> \
    --width-mult <width-multiplier> \
    --input-size <input-resolution> \
    -j <num-workers>

Example: Test MobileNet-v2

python train_imagenet.py \
    -a mobilenetv2 \
    -d <path-to-ILSVRC2012-data> \
    --weight <pretrained-pth-file> \
    --width-mult <width-multiplier> \
    --input-size <input-resolution> \
    -e

Dependency

Please check the environment.sh
Note: the installation of pytorch depends on your OS version and GPU types.

if you find our code useful, please consider citing our paper:

@article{bhardwaj2019does,
  title={How does topology influence gradient propagation and model performance of deep networks with DenseNet-type skip connections?},
  author={Bhardwaj, Kartikeya and Li, Guihong and Marculescu, Radu},
  journal={arXiv preprint arXiv:1910.00780},
  year={2019}
}