Awesome
Crowd Counting via Adversarial Cross-Scale Consistency Pursuit IN PYTORCH (Unofficial)
Implementation of CVPR 2018 paper Crowd Counting via Adversarial Cross-Scale
1. Environment
We used the following enviroment:
- Python 3
- PyTorch
- OpenCV
- Numpy
- MatPlotLib
- Ubuntu 16.04
You can also run the code using the docker image of ufoym/deepo.
2. Preparing data
We make available UCF-CC-50 and Shanghai Tech datasets here, download and unzip it into the root of the repo. Directories should have the following hierarchy:
ROOT_OF_REPO
data
ucf_cc_50
UCF_CC_50
images
labels
ShanghaiTech
part_A
train_data
images
ground-truth
test_data
images
ground-truth
part_B
train_data
images
ground-truth
test_data
images
ground-truth
The code was developed such that data augmentation is computed before every other step and the results are stored in the hard drive. Thus, the first time you run the code it will take quite a long time. Augmented data is stored with the following hierarchy:
ROOT_OF_REPO
data
ucf_cc_50
people_thr_0_gt_mode_same
ShanghaiTech
part_A
people_thr_0_gt_mode_same
part_B
people_thr_0_gt_mode_same
3. Training
To train using UCF-CC-50 (with all folds) and save the results log in log/ACSCP
you can run:
python3 train.py -d ucf-cc-50 --gt-mode same --people-thr 0 --train-batch 24 --save-dir log/ACSCP
In case you want to run a specific fold or part you can use flag --units
, check the Makefile
for more examples.
The training log is stored in log_train.txt
inside the corresponding log/fold/part directory.
4. Testing
After training you can re-load the trained weights (using flag --resume
) and use them for testing:
python3 train.py -d ucf-cc-50 --save-dir log/multi-stream --resume log/ACSCP/ucf-cc-50_people_thr_0_gt_mode_same --evaluate-only
The testing log is stored in log_test.txt
inside the corresponding log/fold/part directory. You can also generate the plots of the predictions using flag --save-plots
, results are stored in the directory plot-results-test
inside the corresponding log/fold/part directory.
5. Final notes / TODO:
- Results for UCF_CC_50 with this code are MAE 281,73 MSE 415,56 (--people-thr 20). Reported results by the authors are MAE 291.0 MSE 404.6.
- Validation for other dataset may be done in the future.
- Batch normalization is not used because of inestable learning.
- Ground thruth is generated using a gaussian of fixed size.
- Number of channels of autoencoder in the middle layer is changed to 3, instead of 4.
- Network receives images of 1 channel, instead of 3.
- You can use the flag
--overlap-test
to overlap the sliding windows used for testing (as implemented by the authors).