HVE-Imagine

1. Train the Model

• In terminal, run cd Imagine

• Edit script.sh. Set the path of dataset and output file.

#!/usr/bin/env bash

FEATURE=texture # choose from texture, color, shape

python main.py --cuda 0, 1 \
               --mode train \
               --batch_size 16 \
               --dataset_path /lab/tmpig8d/u/yao_data/human_simulation_engine/V3_${FEATURE}_dataset \
               --output_path out/deeper/${FEATURE}

• run sh script.sh

2. Run Generation

• Edit test.sh.
  • Set the path of dataset and output file.
  • Set the test checkpoint file
  • If want to use the mismatch shape, texture, color as input, set --mismatch
  • Example

FEATURE=texture # choose from texture, color, shape

python main.py --cuda 0 \
               --mode predict \
               --batch_size 16 \
               --dataset_path /lab/tmpig8d/u/yao_data/human_simulation_engine/V3_${FEATURE}_dataset \
               --output_path out/deeper/${FEATURE} \
               --test_epoch 269 \

• run sh test.sh

3. Calculate the FID

• Install pytorch-fid

pip install pytorch-fid

• Resize the groughtruth image

For example:

#!/usr/bin/env bash
FEATURE=texture # choose from texture, color, shape

# Ground truth images dir
dataset_path=/lab/tmpig8d/u/yao_data/human_simulation_engine/V3_${FEATURE}_dataset/ori/valid/

# Processed gt images dir
process_path=out/deeper_deeper_res_new_texture/${FEATURE}/gt

python create_dataset.py --ori_path ${dataset_path} --path ${process_path}

• Run Fid code:

#!/usr/bin/env bash
FEATURE=texture # choose from texture, color, shape

# Processed gt images dir
process_path=out/deeper_deeper_res_new_texture/${FEATURE}/gt

# Generation result dir
output_path=out/deeper_deeper_res_new_texture/${FEATURE}/result_mismatch

python -m pytorch_fid ${process_path} ${output_path} --device cuda:1 --batch-size 128