PiCANet-Implementation

Pytorch Implementation of PiCANet: Learning Pixel-wise Contextual Attention for Saliency Detection

New method on implementing PiCANet

• Issue#9
• Conv3d version is deleted.

• batchsize 1
• batchsize 4

(DUTS) Top 10 Performance Test with F-score (beta-square = 0.3)

batchsize:4

• Issue#9

Execution Guideline

Requirements

Pillow==4.3.0
pytorch==0.4.1
tensorboardX==1.1
torchvision==0.2.1
numpy==1.14.2

My Environment

S/W
Windows 10
CUDA 9.0
cudnn 7.0
python 3.5
H/W
AMD Ryzen 1700
Nvidia gtx 1080ti
32GB RAM

Execution Guide

• For training,
• Please check the Detailed Guideline if you want to know the dataset structure.

    usage: train.py [-h] [--load LOAD] --dataset DATASET [--cuda CUDA]
                    [--batch_size BATCH_SIZE] [--epoch EPOCH] [-lr LEARNING_RATE]
                    [--lr_decay LR_DECAY] [--decay_step DECAY_STEP]
                    [--display_freq DISPLAY_FREQ]
    optional arguments:
      -h, --help            show this help message and exit
      --load LOAD           Directory of pre-trained model, you can download at
                            https://drive.google.com/file/d/109a0hLftRZ5at5hwpteRfO1A6xLzf8Na/view?usp=sharing
                            None --> Do not use pre-trained model. Training will start from random initialized model
      --dataset DATASET     Directory of your Dataset
      --cuda CUDA           'cuda' for cuda, 'cpu' for cpu, default = cuda
      --batch_size BATCH_SIZE
                            batchsize, default = 1
      --epoch EPOCH         # of epochs. default = 20
      -lr LEARNING_RATE, --learning_rate LEARNING_RATE
                            learning_rate. default = 0.001
      --lr_decay LR_DECAY   Learning rate decrease by lr_decay time per decay_step, default = 0.1
      --decay_step DECAY_STEP
                            Learning rate decrease by lr_decay time per decay_step,  default = 7000
      --display_freq DISPLAY_FREQ
                            display_freq to display result image on Tensorboard

• For inference,
• dataset should contain image files only.
• You do not need masks or images folder. If you want to run with PairDataset structure, use argument like
  --dataset [DATAROOT]/images
• You should specify either logdir (for TensorBoard output) or save_dir (for Image file output).
• If you use logdir, you can see the whole images by run tensorboard with --samples_per_plugin images=0 option

    usage: image_test.py [-h] [--model_dir MODEL_DIR] --dataset DATASET
                     [--cuda CUDA] [--batch_size BATCH_SIZE] [--logdir LOGDIR]
                     [--save_dir SAVE_DIR]

    optional arguments:
      -h, --help            show this help message and exit
      --model_dir MODEL_DIR
                            Directory of pre-trained model, you can download at
                            https://drive.google.com/drive/folders/1s4M-_SnCPMj_2rsMkSy3pLnLQcgRakAe?usp=sharing
      --dataset DATASET     Directory of your test_image ""folder""
      --cuda CUDA           cuda for cuda, cpu for cpu, default = cuda
      --batch_size BATCH_SIZE
                            batchsize, default = 4
      --logdir LOGDIR       logdir, log on tensorboard
      --save_dir SAVE_DIR   save result images as .jpg file. If None -> Not save

• To report score,
• dataset should contain masks and images folder.
• You should specify logdir to get PR-Curve.
• The Scores will be printed out on your stdout.
• You should have model files below the model_dir.
• Only supports model files named like "[N]epo_[M]step.ckpt" format.

    usage: measure_test.py [-h] --model_dir MODEL_DIR --dataset DATASET
                       [--cuda CUDA] [--batch_size BATCH_SIZE]
                       [--logdir LOGDIR] [--which_iter WHICH_ITER]
                       [--cont CONT] [--step STEP]

    optional arguments:
      -h, --help            show this help message and exit
      --model_dir MODEL_DIR
                            Directory of folder which contains pre-trained models, you can download at
                            https://drive.google.com/drive/folders/1s4M-_SnCPMj_2rsMkSy3pLnLQcgRakAe?usp=sharing
      --dataset DATASET     Directory of your test_image ""folder""
      --cuda CUDA           cuda for cuda, cpu for cpu, default = cuda
      --batch_size BATCH_SIZE
                            batchsize, default = 4
      --logdir LOGDIR       logdir, log on tensorboard
      --which_iter WHICH_ITER
                            Specific Iter to measure
      --cont CONT           Measure scores from this iter
      --step STEP           Measure scores per this iter step

Detailed Guideline

Pretrained Model

You can download pre-trained models from https://drive.google.com/drive/folders/1s4M-_SnCPMj_2rsMkSy3pLnLQcgRakAe?usp=sharing

Dataset

PairDataset Class

• You can use CustomDataset.
• Your custom dataset should contain images, masks folder.
  • In each folder, the filenames should be matched.
  • eg. images/a.jpg masks/a.jpg

DUTS

You can download dataset from http://saliencydetection.net/duts/#outline-container-orgab269ec.

• Caution: You should check the dataset's Image and GT are matched or not. (ex. # of images, name, ...)
• You can match the file names and automatically remove un-matched datas by using DUTSDataset.arrange(self) method
• Please rename the folders to images and masks.

Directory & Name Format of .ckpt files

• The step is accumulated step from epoch 0.