- python2
- Numpy
- Scipy
- Skimage
- Pandas
- Tensorflow
- Keras
- tqdm
In orger to train a model:
- Download market dataset https://drive.google.com/file/d/0B8-rUzbwVRk0c054eEozWG9COHM/view. Put it in data folder. Rename this folder to data/market-dataset. Rename bounding_box_test and bounding_box_train with test and train.
1.1 Download deep fasion dataset http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html (In-shop Clothes Retrieval Benchmark). You will need to ask a pasword from dataset maintainers. Move img/ to data folder and rename it fasion/. Download key-point estimations from (https://yadi.sk/d/suymftBy3S7oKD) for fasion. Run script split_fasion_data.py in data/ folder. Go to the step 3.
- Download pose estimator (conversion of this https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation) pose_estimator.h5. Launch
python compute_cordinates.py.It will compute human keypoints. Alternativlly you can download key points estimations from (https://yadi.sk/d/suymftBy3S7oKD). - Create pairs dataset with
python create_pairs_dataset.py. It define pairs for training. - Run
python train.py(see list of parameters in cmd.py) To replicate the experiment in the paper use following setup:
CUDA_VISIBLE_DEVICES=0 python train.py --output_dir output/baseline --checkpoints_dir output/baseline --warp_skip none --dataset market --l1_penalty_weight 10 --batch_size 4 --number_of_epochs 90
CUDA_VISIBLE_DEVICES=0 python train.py --output_dir output/dsc --checkpoints_dir output/dsc --warp_skip mask --dataset market --l1_penalty_weight 10 --batch_size 4 --number_of_epochs 90
CUDA_VISIBLE_DEVICES=0 python train.py --output_dir output/full --checkpoints_dir output/full --warp_skip mask --dataset market --l1_penalty_weight 0.01 --nn_loss_area_size 3 --batch_size 4 --content_loss_layer block1_conv2 --number_of_epochs 90
CUDA_VISIBLE_DEVICES=0 python train.py --output_dir output/fm --checkpoints_dir output/fm --warp_skip mask --dataset market --l1_penalty_weight 0.5 --batch_size 4 --content_loss_layer block2_conv1 --number_of_epochs 90
CUDA_VISIBLE_DEVICES=0 python train.py --output_dir output/baseline --checkpoints_dir output/baseline --warp_skip none --dataset fasion --l1_penalty_weight 10 --batch_size 4 --number_of_epochs 90
CUDA_VISIBLE_DEVICES=0 python train.py --output_dir output/dsc --checkpoints_dir output/dsc --warp_skip mask --dataset fasion --l1_penalty_weight 10 --batch_size 4 --number_of_epochs 90
CUDA_VISIBLE_DEVICES=0 python train.py --output_dir output/full --checkpoints_dir output/full --warp_skip mask --dataset fasion --l1_penalty_weight 0.01 --nn_loss_area_size 5 --batch_size 2 --content_loss_layer block1_conv2 --number_of_epochs 90
CUDA_VISIBLE_DEVICES=0 python train.py --output_dir output/fm --checkpoints_dir output/fm --warp_skip mask --dataset fasion --l1_penalty_weight 0.5 --batch_size 4 --content_loss_layer block2_conv1 --number_of_epochs 90
- Download checkpoints (https://yadi.sk/d/dxVvYxBw3QuUT9).
- Run
python test.py --generator_checkpoint path/to/generator/checkpoint(and same parameters as in train.py). It generate images and compute inception score, SSIM score and their masked versions. - To compute ssd_score. Download pretrained on VOC 300x300 model from https://github.com/weiliu89/caffe/tree/ssd. Put it in the ssd_score forlder. Run
python compute_ssd_score.py --input_dir path/to/generated/images --img_index 2
Both training and testing require large amount of disk space, because compute_pose_map_batch in pose_dataset.py store intermediate pose_maps on disk. This help to improve both training and testing time. If you don't have enogh space comment lines 64,65,66 and 69 in pose_dataset.py