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# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
import time
from tqdm import tqdm
from glob import glob
import argparse
import math
import random
import numpy as np
from PIL import Image
from collections import defaultdict
import torch
import torch.distributed as dist
from src.flux.generate import seed_everything
from src.utils.data_utils import get_train_config, get_rank_and_worldsize
from src.utils.data_utils import pad_to_square, pad_to_target, json_dump, json_load, split_grid, image_grid, pil2tensor
import shutil
from eval.tools.face_id import FaceID
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--input_dir", type=str, default="../examples")
parser.add_argument("--test_list_name", type=str, default="base_test_list_200")
args = parser.parse_args()
return args
def main():
args = parse_args()
print(args)
local_rank, global_rank, world_size = get_rank_and_worldsize()
print(f"local_rank={local_rank}, global_rank={global_rank}, world_size={world_size}")
is_local_main_process = local_rank == 0
is_main_process = global_rank == 0
torch.cuda.set_device(local_rank)
dtype = torch.bfloat16
device = "cuda"
run_name = time.strftime("%m%d_$H")
face_score_model = FaceID(device)
test_list = json_load(f"eval/tools/{args.test_list_name}.json", 'utf-8')
images = list(glob(f"{args.input_dir}/*.png"))
print(len(test_list), len(images))
assert len(test_list) == len(images)
num_samples = len(test_list)
num_ranks = world_size
assert local_rank == global_rank
if world_size > 1:
num_per_rank = math.ceil(num_samples / num_ranks)
test_list_indices = list(range(num_samples))
random.seed(0)
random.shuffle(test_list_indices)
local_test_list_indices = test_list_indices[local_rank*num_per_rank:(local_rank+1)*num_per_rank]
os.environ['CUDA_VISIBLE_DEVICES'] = str(local_rank % 8)
print(f"[worker {local_rank}] got {len(local_test_list_indices)} local samples")
run_name = time.strftime("%Y%m%d-%H")
temp_dir = os.path.join(args.input_dir, f"eval_id_temp_{run_name}")
if is_main_process:
if os.path.exists(temp_dir):
shutil.rmtree(temp_dir)
os.makedirs(temp_dir)
rank_json = {}
with torch.no_grad():
for i in tqdm(local_test_list_indices):
test_sample = test_list[i]
real_paths, real_faces, real_names = [], [], []
for x in test_sample["modulation"][0]["src_inputs"]:
img_path = x["image_path"]
name = "_".join(img_path.split("/")[-2:])
if name.startswith("human"):
real_paths.append(img_path)
try:
real_faces.append(Image.open(img_path).convert("RGB"))
real_names.append(name)
except Exception as e:
print(f"Failed to open image {img_path}, error message: {e}")
gen_img_path = list(filter(lambda x: x.split("/")[-1].split("_")[0] == str(i), images))[0]
rank_json[i] = []
try:
for j, gen_img in enumerate(split_grid(Image.open(gen_img_path))):
rank_json[i].append({})
if len(real_names) > 0:
gen_bboxes = face_score_model.detect(
(pil2tensor(gen_img).unsqueeze(0) * 255).to(torch.uint8)
)
gen_faces = [gen_img.crop(bbox) for bbox in gen_bboxes]
for k, (real_name, real_face) in enumerate(zip(real_names, real_faces)):
if len(gen_faces) > 0:
score = max([face_score_model(real_face, x) for x in gen_faces])
else:
score = 0
rank_json[i][j][real_name] = score
except Exception as e:
print(f"Failed to process image {gen_img_path}, error message: {e}")
json_dump(rank_json, f"{temp_dir}/scores_{global_rank}.json", "utf-8")
if is_main_process:
while len(glob(f"{temp_dir}/scores_*.json")) < world_size:
time.sleep(5)
time.sleep(5) # wait for the file writting to be finished
merged_json = {}
id_scores = defaultdict(list)
all_scores = []
for rank_path in glob(f"{temp_dir}/scores_*.json"):
rank_json = json_load(rank_path, "utf-8")
merged_json.update(rank_json)
for i, grid_json in merged_json.items():
for img_json in grid_json:
for id_name, id_score in img_json.items():
id_scores[id_name].append(id_score)
for id_name in id_scores:
all_scores += id_scores[id_name]
id_scores[id_name] = np.mean(id_scores[id_name])
print(id_name, id_scores[id_name])
json_dump(merged_json, f"{args.input_dir}/id_scores_{run_name}.json", "utf-8")
final_id_score = np.mean(all_scores)
lines_to_write = [
f"ID Score: {final_id_score:.2f}\n"
]
print(lines_to_write[0])
for id_name, score in id_scores.items():
lines_to_write.append(f"{id_name}: {score:.2f}\n")
with open(f"{args.input_dir}/id_scores_{run_name}.txt", "w") as f:
f.writelines(lines_to_write)
shutil.rmtree(temp_dir)
if __name__ == "__main__":
main() |