Pedestrian Attribute Recognition Using Lightweight Attribute-Specific Localization Model

Compression of Convolutional Layers in Attribute-Specific Localization Model using CPD-EPC, SVD Decompositions

Required Environments

• Python 3.7 +
• Pytorch 1.11
• Flopco-pytorch
• Numpy
• Tensorly 0.4.4

Datasets

• PETA: http://mmlab.ie.cuhk.edu.hk/projects/PETA.html
• PA-100K: https://github.com/xh-liu/HydraPlus-Net

Labels

• data_labels/peta
• data_labels/pa-100k

Steps to Reproduce

1. Use "run_main.py" to save best checkpoint for the dataset.
   

    python3 run_main.py --model=FULL --attr_num=26 --experiment=PA-100K --epoch=15 --checkpoint_save=path_to_save_checkpoints
   

2. Use "make_decompositios.py" for full model decomposition using SVD or CPD-EPC.
   

   python3 make_decompositios.py --dpath=path_to_dataset --mpath=path_to_saved_checkpoint --tlabels=path_to_train_labels --vlabels=path_to_val_labels --factors=both --attr_num=26 --experiment=PA-100K --device=cpu
   

   To Submit Slurm jobs for full model decompositions:

   python3 submit_decompositios_slurm.py --dpath=path_to_dataset --mpath=path_to_saved_checkpoint --tlabels=path_to_train_labels --vlabels=path_to_val_labels --factors=both --attr_num=26 --experiment=PA-100K --device=cpu
   

3. Use "create_compressed_model.py" to create "Fully-Compressed" or "Partially-Compressed" Model.

   1. To create a Fully-Compressed Model: (Use create_compressed_model.py script)

      python3 create_compressed_model.py --model_type=full --save_path=path_to_save_model --ranks_dir=directory_to_load_factors&ranks --attr_num=26 or 35
      

   2. To create a Partially-Compressed Model:
      1. Perform step i to create full model.
      2. Use create_compressed_model.py script for partial model creation.
         

         python3 create_compressed_model.py --full_model=path_to_full_model --model_type=partial --save_path=path_to_save_model --ranks_dir=directory_to_load_factors&ranks --attr_num=26 or 35
         

4. Use "run_main.py" to fine-tune compressed model
   

   python3 run_main.py --model=COMPRESSED --attr_num=26 --experiment=PA-100K --epoch=15 --model_path=path_to_compressed_model --checkpoint_save=path_to_save_checkpoints
   

Besides,
layer_decomposer.py can also be used to decompose specific layers.

example:

python3 layer_decomposition.py --layer=main_branch.conv1_7x7_s2 --rank=81 --eps=0.002 --device=cpu --dpath=dataset_path --mpath=model_path --tlabels=data_train_label --vlabels=data_val/test_label --experiment=PA-100K --attr_num=26

Additionally,

1. The pretrained model for BNInception can be found here: https://www.dropbox.com/sh/z4cnegl1p1tsn8j/AADYxmDA5Of24vtjx3PK1o7Ba?dl=0
2. If there are problems installing flopco directly from pip. Clone the repo: git clone https://github.com/juliagusak/flopco-pytorch.git cd /flopco-pytorch mv flopco ../

Test Calibration using Binning:

import numpy
from utils.binning import binning 
num_bins = 10
figsize = (4, 4)

binning(numpy.hstack(preds_original), numpy.hstack(labels), num_bins=num_bins, savename="calibrated_compressed_network.png", figsize=figsize, title="Histogram Binning_PA-100K(Compressed-Caliberated)")

If this work is helpful for your research, please consider citing the following BibTeX entry.

@article{jha2023lightweight,
  title={Lightweight Attribute Localizing Models for Pedestrian Attribute Recognition},
  author={Jha, Ashish and Ermilov, Dimitrii and Sobolev, Konstantin and Phan, Anh Huy and Ahmadi-Asl, Salman and Ahmed, Naveed and Junejo, Imran and Aghbari, Zaher AL and Baker, Thar and Khedr, Ahmed Mohamed and others},
  journal={arXiv preprint arXiv:2306.09822},
  year={2023}
}