Skip to content

Unofficial implementation of DeepPack in PyTorch. DeepPack is a deep reinforcement learning based algorithm dealing with 2D online bin packing problem.

License

Notifications You must be signed in to change notification settings

JeepWay/DeepPack

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

20 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Deep-Pack in PyTorch

GitHub license

This repository contains a Pytorch unofficial implementation of the algorithm presented in the paper Deep-Pack: A Vision-Based 2D Online Bin Packing Algorithm with Deep Reinforcement Learning

Modified CNN Architecture

Because the dimension of the proposed concatenated state (WĂ—2H) inputted to the model doesn't satisfyied the dimension requirements of the CNN architecture mentioned in the paper, thus, this repository use the modified CNN architecture to train.

⬇️ CNN architecture for 4x4 bin size

CNN for 4x4 bin size

⬇️ CNN architecture for 5x5 bin size

CNN for 5x5 bin size

Dataset

The all training and testing dataset are generated from generate_train.py with random seed 777 and generate_test.py with random seed 666.

To reduce the size of this repository, we don't upload the training and testing data except for 4x4 bin size with retangular item.

If you want to see the training and testing data, you can run generate_train.py and generate_test.py to generate it.

Task type

  • Bin size
    • 3x3
    • 4x4
    • 5x5
  • Items type
    • Unit square (1x1)
    • Only square
    • Rectangular and square

Environments

  • OS: Window 10
  • Visual Studio Code
  • Anaconda3
  • Python: 3.9.15
  • PyTorch: 2.X.X+cu118

Installation

  • If you want to use your own env, then run:
    pip install -r install/requirements.txt
    
  • If you want to create new env, then run:
    conda env create -f install/environment.yml
    
    or run:
    .\install\install.bat
    

Usage example

  • run.bat
    • Modify this command call D:\anaconda3\Scripts\activate.bat in run.bat, if your anaconda3 path is not in D drive.
    • Modify this command call conda activate deeppack in run.bat, if your anaconda3 env name D is not deeppack.
    • Execute the command in the .bat file, including training and testing parts.
  • python main.py --task rectangular --bin_w 4 --bin_h 4 --iterations 300000
    • Execute to train the doubleDQN model.
  • python test.py --task rectangular --bin_w 4 --bin_h 4 --sequence_type type1 --max_sequence 4 --iterations 5000
    • Execute to test the result of the trained doubleDQN model.

Result Folder

The all training and testing result and log will be placed in (items_name)_(bin_w)x(bin_h)_result folder.

To reduce the size of this repository, we don't upload the result folders.

If you want to see the traing log and testing result and dynamic demo gifs, you can run run.bat or other command lines, describes in Usage example section, then you will get the whole information.

├── (itemsName)_(binW)x(binH)_result
|   ├── img
|   |   ├── train_record.png
|   |   ├── test_record.png
|   |   ├── test_type*_iter*.gif
|   ├── test
|   |   ├── log.txt
|   |   ├── test_result.txt
|   ├── train
|   |   ├── log.txt
|   ├── hyperparameter.txt
|   ├── (itemsName)_(binW)x(binH).pt

Experimental Results

Train

We only show the result of 4x4 and 5x5 bin size, just like the original paper. If you want to see the result of 3x3 bin size, you can generate 3x3 training data and then train it with suitable hyper-parameters and CNN settings.

We plot the training curves with moving average (window size 50).

Training result of 4x4 bin size

loss reward PE
unit_square_4x4 unit_square_4x4 unit_square_4x4
square_4x4 square_4x4 square_4x4
rectangular_4x4 rectangular_4x4 rectangular_4x4

Training result of 5x5 bin size

loss reward PE
unit_square_5x5 unit_square_5x5 unit_square_5x5
square_5x5 square_5x5 square_5x5
rectangular_5x5 rectangular_5x5 rectangular_5x5

Test

In test section, we only show the result of 4x4 and 5x5 bin size, just like the original paper, but we have modified some wrong sequence types of the original paper.

Because the training data which are randomly generated may not contain the specific test sequence types, leading to the model doesn't see that pattern, therefore, the result of the test may not perform as good as the training do.

We guess the the training data in the original paper contain the specific test sequence types, thus, the test result of the the original paper is awesome, not like our experiments.

test_result

Dynamic Demo Example

The red dot indicates the placement location of the item.

Unit square 4x4 bin with type1 sequence

'type1': [(16, (1, 1))]
iter1 iter2
type1_iter1 type1_iter2

Square 4x4 bin with random sequence

items are all random, just like training
iter1 iter2
random_iter1 random_iter2
iter3 iter4
random_iter3 random_iter4

Square 4x4 bin with type1 sequence

'type1': [(1, (4, 4))]
iter1 iter2
type1_iter1 type1_iter2

Square 4x4 bin with type2 sequence

'type2': [(1, (3, 3)), (7, (1, 1))]
iter1 iter2
type2_iter1 type2_iter2
iter3 iter4
type2_iter3 type2_iter4

Square 4x4 bin with type3 sequence

'type3': [(3, (2, 2)), (4, (1, 1))]
iter1 iter2
type3_iter1 type3_iter2
iter3 iter4
type3_iter3 type3_iter4

Rectangular 4x4 bin with random sequence

items are all random, just like training
iter1 iter2
random_iter1 random_iter2
iter3 iter4
random_iter3 random_iter4

Rectangular 4x4 bin with type1 sequence

'type1': [(1, (3, 2)), (1, (1, 4)), (1, (2, 2)), (1, (1, 2))]
iter1 iter2
type1_iter1 type1_iter2
iter3 iter4
type1_iter3 type1_iter4

Rectangular 4x4 bin with type2 sequence

'type2': [(1, (1, 1)), (1, (1, 3)), (1, (3, 1)), (1, (3, 3))]
iter1 iter2
type2_iter1 type2_iter2
iter3 iter4
type2_iter3 type2_iter4

Rectangular 4x4 bin with type3 sequence

'type3': [(1, (4, 2)), (2, (2, 2))]
iter1 iter2
type3_iter1 type3_iter2
iter3 iter4
type3_iter3 type3_iter4

Unit square 5x5 bin with type1 sequence

'type1': [(25, (1, 1))]
iter1 iter2
type1_iter1 type1_iter2

Square 5x5 bin with random sequence

items are all random, just like training
iter1 iter2
type1_iter1 type1_iter2

Square 5x5 bin with type1 sequence

'type1': [(1, (5, 5))]
iter1 iter2
type1_iter1 type1_iter2

Square 5x5 bin with type2 sequence

'type2': [(4, (2, 2)), (9, (1, 1))]
iter1 iter2
type2_iter1 type2_iter2
iter3 iter4
type2_iter3 type2_iter4

Square 5x5 bin with type3 sequence

'type3': [(1, (3, 3)), (3, (2, 2)), (4, (1, 1))]
iter1 iter2
type3_iter1 type3_iter2
iter3 iter4
type3_iter3 type3_iter4

Rectangular 5x5 bin with random sequence

items are all random, just like training
iter1 iter2
random_iter1 random_iter2
iter3 iter4
random_iter3 random_iter4

Rectangular 5x5 bin with type1 sequence

'type1': [(4, (2, 2)), (1, (1, 4)), (1, (4, 1)), (1, (1, 1))]
iter1 iter2
type1_iter1 type1_iter2
iter3 iter4
type1_iter3 type1_iter4

Rectangular 5x5 bin with type2 sequence

'type2': [(1, (2, 5)), (1, (3, 3)), (1, (3, 2))]
iter1 iter2
type2_iter1 type2_iter2
iter3 iter4
type2_iter3 type2_iter4

Rectangular 5x5 bin with type3 sequence

'type3': [(1, (4, 4)), (1, (1, 4)), (1, (4, 1)), (1, (1, 1))]
iter1 iter2
type3_iter1 type3_iter2
iter3 iter4
type3_iter3 type3_iter4

References

About

Unofficial implementation of DeepPack in PyTorch. DeepPack is a deep reinforcement learning based algorithm dealing with 2D online bin packing problem.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published