CONet: Channel Optimization for Convolutional Neural Networks

Exciting News! CONet has been accepted to ICCV 2021 Workshop: Neural Architectures - Past, Present and Future.

CONet: Channel Optimization for Convolutional Neural Networks,
Mahdi S. Hosseini, Jia Shu Zhang, Zhe Liu, Andre Fu, Jingxuan Su, Mathieu Tuli, Konstantinos N. Plataniotis
In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV2021-NeurArch)

CONet is a NAS algorithm for optimizing the channel sizes of Convolutional Neural Networks. CONet expands/shrinks the channels of convolutional layers based on local indicators after a few epochs of training.

CIFAR10/100 Results

Comparison of CONet results optimizing the DARTS and ResNet34 models on CIFAR10 (left) and CIFAR100 (right).

ImageNet Results

Comparison of CONet results for optimizing the DARTS model. All DARTS models shown use 14 cells.

The Delta and Dataset columns refer to the delta threshold values and datasets used for CONet respectively. Baseline refers to the original DARTS model.

Delta	Dataset	Params (M)	Top-1 Acc (%)	Top-5 Acc (%)
Baseline	-	4.7	73.3	91.3
0.0075	CIFAR10	1.8	67.2	87.6
0.0005	CIFAR10	4.8	74.0	91.8
0.0005	CIFAR100	9.0	76.6	93.2

Table of Contents

• Requirements
• Usage
  • Training Options
  • Important Config Options
    • Config: dataset
    • Config: network
    • Config: full_train_only
    • Config: delta_threshold
    • Config: adapt_trials
    • Config: epochs_per_trial
    • Config: mapping_condition_threshold
    • Config: factor scale
    • Config: max_epoch
  • Setting Channel Sizes
    • Channel Sizes for ResNet34
    • Channel Sizes for DARTS/DARTS+
  • Training Outputs
    • XLSX Output
    • Channel Size Output
    • Checkpoints
  • Code Structure
    • Main Function
    • Training Functions
    • CONet Scaling Algorithm
    • CONet Model Creation
    • Helper functions

Requirements

We use Python 3.7

Per requirements.txt, the following Python packages are required:

certifi==2020.6.20
cycler==0.10.0
et-xmlfile==1.0.1
future==0.18.2
graphviz==0.14.2
jdcal==1.4.1
kiwisolver==1.2.0
matplotlib==3.3.2
memory-profiler==0.57.0
numpy==1.19.2
openpyxl==3.0.5
pandas==1.1.3
Pillow==8.0.0
pip==18.1
pkg-resources==0.0.0
psutil==5.7.2
ptflops==0.6.2
pyparsing==2.4.7
python-dateutil==2.8.1
pytz ==2020.1
PyYAML==5.3.1
scipy==1.5.2
setuptools==40.8.0
six==1.15.0
torch==1.6.0
torchvision==0.7.0
torchviz==0.0.1
wheel==0.35.1
xlrd==1.2.0

Usage

You can run the code by typing

cd CONet
python NAS_main_search.py --...

Where --... represents the options for training (see below)

By default this will initiate a channel size search and an evaluation of the searched model.

The dataset, model, and hyperparameters can be adjusted by changing the configurations in CONet/config.yaml. The description and recommended setting for each configuration option can also be found in config.yaml. We list the important options below.

Training Options

Please see the training options below. Please note that for ImageNet, the --data option must be used to specify the location of the ImageNet dataset as it can no longer be downloaded through Pytorch.

--root ROOT            # Set root path of project that parents all others:
                         Default = '.'
--data DATA_PATH       # Set the path to the dataset, 
                         Default = '.adas-data'
--output OUTPUT_PATH   # Set the directory for output files,  
                         Default = 'adas_search'
--checkpoint CKPT_PATH # Set the directory for model checkpoints,
                         Default = ".adas-checkpoint"

Important Config Options

In the following sections we list the key configuration options available to the user. A more comprehensive description of all options can be found in CONet/config.yaml.

• General Settings
  • dataset
  • network
  • full_train_only
• Channel Search Settings
  • delta_threshold
  • adapt_trials
  • epochs_per_trial
  • mapping_condition_threshold
  • factor_scale
• Model Evaluation Settings
  • max_epoch

---

Config: dataset

Currently only the datasets are supported:

• CIFAR10
• CIFAR100
• ImageNet (Must provide dataset on local storage)

---

Config: network

The following networks are supported:

• ResNet34
• DARTS
• DARTS+

DARTS and DARTS+ can be instantiated with 7, 14, or 20 cells.

---

Config: full_train_only

Set to false to perform channel search and model evaluation.

Set to true to only evaluate a model with the channel sizes specified in CONet\global_vars.py. We will explain how to set channel sizes in global_vars.py below.

---

Config: delta_threshold

Sets the rank slope target for shrinking or expanding channel sizes.

Referred to as "delta" in algorithm 2 in the CONet paper.

This is sensitive to the model space. For DARTS/DARTS+, we recommend values between 0.0025 to 0.01. For ResNet, recommend values between 0.01 to 0.025.

---

Config: adapt_trials

Sets the number of trials for channel search. Channel sizes usually converges around 20 to 25 trials.

---

Config: epochs_per_trial

Sets the number of epochs per trial.

---

Config: mapping_condition_threshold

Sets the mapping condition limit. Layers that exceed that limit are forced to shrink.

Referred to as "mu" in algorithm 2 in the CONet paper.

---

Config: factor scale

Sets the initial percentage to scale channel sizes.

Referred to as "phi" in algorithm 2 in the CONet paper.

---

Config: max_epoch

Sets the number of epochs to evaluate the model. We recommend 250 epochs for CIFAR datasets and 200 epochs for ImageNet.

---

Setting Channel Sizes

Channel sizes can be manually set in CONet\global_vars.py. The code will use the channel sizes in global_vars.py as the initial channel sizes for channel searching and model evaluation. By default, all channels are set to 32 at the beginning of channel search.

Channel Size: ResNet34

The following variables in global_vars.py determines the channel sizes when using ResNet34:

• super1_idx
• super2_idx
• super3_idx
• super4_idx

Each variable corresponds to a superblock in ResNet. The channel sizes correspond directly to the node size in the Directed Acyclic Graph representation of ResNet34 as outlined in section 2 in the CONet paper. For example, the first element in super1_idx corresponds to size of the first node.

Channel Size: DARTS/DARTS+

The following variables in global_vars.py determines the channel sizes when using DARTS/DARTS+ models:

• DARTS_cell_list_7
• DARTS_sep_conv_list_7
• DARTS_cell_list_14
• DARTS_sep_conv_list_14
• DARTS_cell_list_20
• DARTS_sep_conv_list_20
• DARTSPlus_cell_list_7
• DARTSPlus_sep_conv_list_7
• DARTSPlus_cell_list_14
• DARTSPlus_sep_conv_list_14
• DARTSPlus_cell_list_20
• DARTSPlus_sep_conv_list_20

We identified the per cell channel size constraints within the DAG representations of DARTS and DARTS+ models.

Each {model}_cell_list_{cell_num} variable contains one value for each independently adjustable channel size for each cell. Please note that we count the initial stem layers as "cell 0". An illustration of the unique channel sizes within DARTS and DARTS+ cells can be found in CONet\figures\...

Please note that each sep_conv operator in DARTS\DARTS+ model actually represents two separable convolutions in a row. This leaves an intermediate adjustable channel size for all sep_conv connections.

The {model}_sep_conv_list_{cell_num} variables stores the intermediate channel sizes for each sep_conv connection in DARTS/DARTS+ cells.

Training Outputs

XLSX Output

Please note that training progress is conveyed through console outputs, where per-epoch statements are outputed to indicate epoch time and train/test loss/accuracy.

Please note also that a per-epoch updating .xlsx file is created for every training session. This file reports performance metrics of the CNN during training for both channel search and model evaluation.

The location of the output .xlsx file depends on the -root and --output option during training, and naming of the file is determined by the config.yaml file's contents.

Channel Size Output

After completing a channel search trial, the final channel sizes can be found in the following files in the output directory.

ResNet34: Trials\adapted_architectures.xlsx

DARTS/DARTS+: Trials\cell_architecture.xlsx and Trials\sep_conv_architecture.xlsx

These .xlsx files contains the channel size of the mode for each trial in separate rows. To instantiate a model with these channel sizes, copy the values for an entire row into a list and set the corresponding variable in CONet\global_vars.py to that list. The variables are outlined in the previous section.

Checkpoints

Checkpoints are saved to the path specified by the -root and --checkpoint option. A file or directory may be passed. If a directory path is specified, the filename for the checkpoint defaults to ckpt.pth.

Code Structure

This sections outlines the code structure for this CONet package.

Due to differences between ResNet34 and DARTS/DARTS+ architectures, the code for performing channel search and model evaluation are kept separately.

For each architecture (ResNet, DARTS, DARTS+), there are three specific files:

• Training*: Sets up model training for channel search and model evaluation for that architecture
• CONet Algorithm: Implements algorithm 2 for that specific architecture
• Model Creation*: Creates a CONet version of that model with adjustable channel sizes

Please note that users can run all architectures through a common main function in NAS_main_search.py. The main function will read the selected architecture from config.yaml and call the appropriate arch-specific functions.

* Due to similarities between DARTS and DARTS+, their training and model creation codes are combined in train_DARTS.py and model.py respectively.

Main Function

The main function is in CONet\NAS_main_search.py.

Calling the main function will do the following

• Load settings from config.yaml
• Builds necessary directories for output, checkpoints, etc.
• Launch channel search (if full_train_only is set to false)
• Launch model evaluation

Training Functions

The training functions for ResNet34 and DARTS/DARTS+ channel search and model evaluation can be found in CONet\train_ResNet.py and CONet\train_DARTS.py respectively.

In both files, the training functions implement the following:

• Instantiate the necessary components for training (e.g. model, optimizer, etc.)
• For channel search:
  • Train model for a few epochs to generate local indicator metrics
  • Pass metrics to CONet scaling algorithm for that architecture
  • Update model with new channel sizes
• For model evaluation:
  • Fully train model
  • Report relevant metrics (e.g. best acc, etc.)

CONet Scaling Algorithm

The implementation of CONet Scaling algorithm for ResNet, DARTS, and DARTS+ can be found in
CONet\resnet_scaling_algorithm.py, CONet\darts_scaling_algorithm.py, and CONet\dartsplus_scaling_algorithm.py respectively.

These files implement lines 3-14 of algorithm 2 in the CONet paper for each architecture. The scaling algorithm functions implement the following:

• Maps the local indicator metrics from the .xlsx outputs to the channel size variables as listed in CONet\global_vars.py
• Calculate metrics (lines 3-5 in algorithm 2) for each channel size
• Calculate new channel sizes based on metrics

CONet Model Instantiation

The model creation function for ResNet and DARTS/DARTS+ can be found in CONet\models\own_network.py and CONet\model.py respectively.

The model creation functions implement the following:

• Takes in a list of channel size values for that model
• Instantiate a model with the specified channel sizes.

Helper Functions

The following files contain common helper functions that may be of interest to users : CONet\train_help.py, CONet\data.py, CONet\dataset.py

train_help.py contains functions for:

• Instantiating optimizers/LR schedulers for training
• Common training loop (e.g. given a model, train for n epochs)
• Collection of metrics (e.g. accuracy, rank slope, etc.)

data.py contains function for:

• Creating dataloaders for various datasets
• Applying common augmentations

datasets.py contains function for:

• Loading the ImageNet dataset