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README.md

Unsupervised Learning of Video Representations via Dense Trajectory Clustering

This is an implementation of the Unsupervised Learning of Video Representations via Dense Trajectory Clustering algorithm.

The codebased is built upon Local Aggregation and 3D ResNet.

Prerequisites

  • Linux
  • Pytorch 1.2.0
  • Faiss
  • tqdm
  • dotmap
  • tensorboardX
  • sklearn
  • pandas

Unsupervised representation learning

Dataset preprocessing

Training is done on the Kinetics-400 dataset. Download it and preprocess as follows.

cd 3D-ResNet
  • Convert from avi to jpg files using utils/video_jpg_kinetics.py
python utils/video_jpg_kinetics.py AVI_VIDEO_DIRECTORY JPG_VIDEO_DIRECTORY
  • Generate n_frames files using utils/n_frames_kinetics.py
python utils/n_frames_kinetics.py JPG_VIDEO_DIRECTORY
  • Generate annotation file in json format using utils/kinetics_json.py
    • The CSV files (kinetics_{train, val, test}.csv) are included in the crawler.
python utils/kinetics_json.py TRAIN_CSV_PATH VAL_CSV_PATH TEST_CSV_PATH DST_JSON_APTH

If you want to use our precomuted IDT clusters for training, please use the Kinetics annotation file provided in this codebase (splits.json). If you find that some videos are missing in you local copy of Kinetics, then you'll have to recompute the clusters using the cluster_fv.py script below, otherwise the correspondence between cluster labels and videos will be broken.

Runtime Setup

cd LocalAggregation
source init_env.sh

Pretrained models

We provide several models trained using our Video LA + IDT prior objective, as well as precomputed clusters for the training set of Kinetics-400, under this link (for the varaints trained on 370k videos we skipped the last tuning stage due to memory issues). In addition, this archive contains models finetuned on UCF101 and HMDB51, which are reported in the state-of-the-art comparison section of the paper.

Training using precomputed IDT descriptors

Begin with training a 3D ResNet with an IR objective for 40 epochs. This is done as a warmup step. Don't forget to update data and experiment paths in the config file.

CUDA_VISIBLE_DEVICES=0 python scripts/instance.py ./config/kinetics_ir.json 

Then specify instance_exp_dir in ./config/kinetics_la.json to point to the IR model you've just trained, and run the following command to trasfer IDT representations to the 3D ResNet via non-parametric clustering:

CUDA_VISIBLE_DEVICES=0,1,2 python scripts/localagg.py ./config/kinetics_la.json

To run the final fine-tuning stage, specify instance_exp_dir in ./config/kinetics_la_tune.json to point to the model trained with IDTs, and run the following command:

CUDA_VISIBLE_DEVICES=0,1,2 python scripts/localagg.py ./config/kinetics_la_tune.json

Recomputing and clustering IDT descriptors

We provide precomputed Fisher vector-encoded IDT descriptors for the Kinetics dataset under this link.

If you wish to recompute them, you will need to first download and install the original IDT implementation. This codes takes person detections as input. You can download the detections we used here.

Next, estimate the model (PCA, GMM) parameters used in Fisher vector encoding. To this end, first sample 3500 videos from Kinetics at random, and compute IDTs for them, using the script bellow (don't forget to update paths to the IDT implementation).

sh src/idt/run_idt.sh PATH_TO_VIDEO PATH_TO_BOXES OUTPUT_NAME PATH_TO_IDTS

Then run the following script to estimate model parameters based on the computed IDTs. The parameters will be saved to the same directory as the IDTs.

python src/idt/compute_fv_models.py --idt_path PATH_TO_IDTS

Now you can compute the Fisher vector encoded IDT descriptors for training set of Kinetics. The following script takes a category as input, so the in can be parallelized 400-way on a CPU cluster (pleas update the path to a temporary folder insight the script, which is used to store raw IDTs).

python src/idt/extract_idt.py --category CATEGORY_NAME --model_path PATH_TO_IDTS --videos_path PATH_TO_TRAIN_VIDEOS --boxes_path PATH_TO_BOXES --out_path FV_OUTPUT_PATH

Finally, to cluster descriptors, run the following script.

python src/idt/cluster_fv.py --k 6000 --num_c 3 --frames_path PATH_TO_FRAMES --annotation_path PATH_TO_ANNOTATIONS_JSON --fv_path FV_OUTPUT_PATH --clusters_path PATH_TO_OUTPUT_CLUSTERS_DIRECTORY --processed_annotation_path PATH_TO_OUTPUT_ANNOTATIONS_JSON --gpu 0 1

This script produces a clustering assignement for the training set videos, and a new annotation file. Make sure to use this file in all the config files to ensure correct correspondence between videos and cluster labels.

Transfer learning

cd 3D-ResNet

Dataset preprocessing

Download and pre-process UCF101 and HMDB51 datasets as follows.

python utils/video_jpg_ucf101_hmdb51.py AVI_VIDEO_DIRECTORY JPG_VIDEO_DIRECTORY
  • Generate n_frames files using utils/n_frames_ucf101_hmdb51.py
python utils/n_frames_ucf101_hmdb51.py JPG_VIDEO_DIRECTORY
  • Generate annotation file in json format using utils/ucf101_json.py and utils/hmdb51_json.py
python utils/ucf101_json.py ANNOTATION_DIR_PATH
python utils/hmdb51_json.py ANNOTATION_DIR_PATH

Finetuning

On UCF101:

python main.py --video_path PATH_TO_FRAMES --annotation_path PATH_TO_ANNOTATION --result_path OUTPUT_MODEL_PATH --dataset ucf101 --n_finetune_classes 101 --model resnet --model_depth 18 --resnet_shortcut B --batch_size 128 --n_threads 16 --gpu 0 --pretrain_path PATH_TO_PRETRAINED_MODEL  --checkpoint 10 --ft_begin_index 2 --n_epochs 40 --lr_patience 5  --n_scales 2 --train_crop random

On HMDB51:

python main.py --video_path PATH_TO_FRAMES --annotation_path PATH_TO_ANNOTATION --result_path OUTPUT_MODEL_PATH --dataset hmdb51 --n_finetune_classes 101 --model resnet --model_depth 18 --resnet_shortcut B --batch_size 128 --n_threads 16 --gpu 0 --pretrain_path PATH_TO_PRETRAINED_MODEL  --checkpoint 10 --ft_begin_index 3 --n_epochs 30 --lr_patience 5  --n_scales 2 --train_crop random

Evaluation

On UCF101:

python main.py --video_path PATH_TO_FRAMES --annotation_path PATH_TO_ANNOTATION --dataset ucf101 --n_classes 101 --model resnet --model_depth 18 --resnet_shortcut B --batch_size 128 --n_threads 16 --gpu 0 --test --no_train --no_val --resume_path OUTPUT_MODEL_PATH/save_40.pth

On HMDB51:

python main.py --video_path PATH_TO_FRAMES --annotation_path PATH_TO_ANNOTATION --dataset hmdb51 --n_classes 101 --model resnet --model_depth 18 --resnet_shortcut B --batch_size 128 --n_threads 16 --gpu 0 --test --no_train --no_val --resume_path OUTPUT_MODEL_PATH/save_30.pth

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This is an implementation of the Unsupervised Learning of Video Representations via Dense Trajectory Clustering algorithm.

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