CrowdDiff: Multi-hypothesis Crowd Density Estimation using Diffusion Models

This repository contains the codes for the PyTorch implementation of the paper [Diffuse-Denoise-Count: Accurate Crowd Counting with Diffusion Models]

Method

Visualized demos for density maps

Visualized demos for crowd maps and stochastic generation

        Ground Truth: 361               Trial 1: 349                   Trial 2: 351

        Final Prediction: 359             Trial 3: 356                   Trial 4: 360

Installing

• Install python dependencies. We use python 3.9.7 and PyTorch 1.13.1.
  

pip install -r requirements.txt

Dataset preparation

• Run the preprocessing script.
  

python cc_utils/preprocess_shtech.py \
    --data_dir path/to/data \
    --output_dir path/to/save \
    --dataset dataset \
    --mode test \
    --image_size 256 \
    --ndevices 1 \
    --sigma '0.5' \
    --kernel_size '3' \

Training

• Download the pre-trained weights.
• Run the training script.
  

DATA_DIR="--data_dir path/to/train/data --val_samples_dir path/to/val/data"
LOG_DIR="--log_dir path/to/results --resume_checkpoint path/to/pre-trained/weights"
TRAIN_FLAGS="--normalizer 0.8 --pred_channels 1 --batch_size 8 --save_interval 10000 --lr 1e-4"
MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond False --diffusion_steps 1000 --large_size 256  --small_size 256 --learn_sigma True --noise_schedule linear --num_channels 192 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"

CUDA_VISIBLE_DEVICES=0 python scripts/super_res_train.py $DATA_DIR $LOG_DIR $TRAIN_FLAGS $MODEL_FLAGS

Testing

• Download the pre-trained weights.
• Run the testing script.
  

DATA_DIR="--data_dir path/to/test/data"
LOG_DIR="--log_dir path/to/results --model_path path/to/model"
TRAIN_FLAGS="--normalizer 0.8 --pred_channels 1 --batch_size 1 --per_samples 1"
MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond False --diffusion_steps 1000 --large_size 256  --small_size 256 --learn_sigma True --noise_schedule linear --num_channels 192 --num_head_channels 64 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True"

CUDA_VISIBLE_DEVICES=0 python scripts/super_res_sample.py $DATA_DIR $LOG_DIR $TRAIN_FLAGS $MODEL_FLAGS

Acknowledgement:

Part of the codes are borrowed from guided-diffusion codebase.