Performance of 'topiq_nr-face' metric

[diego0718]

Good day. First of all, thanks for all work done in this repo!.

I have noticed the topiq_nr-face metrics performance decays along time. i try 30k images of various dimentions. It starts with 9 it/s and finally decays to 2.5 it/s. I share the code i used for the test: import os import torch import time import pyiqa import pandas as pd import torch.distributed as dist from torch.nn.parallel import DistributedDataParallel as DDP from torch.utils.data import Dataset, DataLoader from torch.utils.data.distributed import DistributedSampler from tqdm import tqdm from torch.utils.data import Dataset,DataLoader import torchvision.transforms as transforms from PIL import Image

class ImageDataset(Dataset): def init(self, image_paths): self.image_paths = image_paths # Create the Compose object # self.transforms=transforms.Compose( # [transforms.Resize((960, 960)), # transforms.ToTensor()])

def __len__(self):
    return len(self.image_paths)

def __getitem__(self, idx):
    img_path=self.image_paths[idx]
    #img = Image.open(img_path).convert("RGB")
    #img_tensor = self.transforms(img)
    return img_path#,img_tensor

def main(): # Initialize the distributed environment. dist.init_process_group(backend='nccl') rank = int(os.environ["LOCAL_RANK"]) print(rank)

df = pd.read_csv('dataset.csv')
inference_set = df['image_path'].tolist()

# Create dataset and dataloader
dataset = ImageDataset(inference_set)
sampler = DistributedSampler(dataset)
dataloader = DataLoader(dataset, batch_size=1,pin_memory=True, sampler=sampler)

METRIC=''topiq_nr-face'
print(f'Metric:{METRIC}')

# Create model and move it to GPU with id rank
device = torch.device(f"cuda:{rank}" if torch.cuda.is_available() else "cpu")
torch.cuda.set_device(device)
iqa_metric = pyiqa.create_metric(METRIC,device=device)
iqa_metric = iqa_metric.to(device)
ddp_model = DDP(iqa_metric, device_ids=[rank])
# Create a list to store results
results = []

# Wrap your iterable with tqdm() for a progress bar
for img_path in tqdm(dataloader):
    try:
        #source = source.unsqueeze(0)
        #source = source.to(rank)
        score_fr = ddp_model(img_path[0]).item()
        results.append({'image_path': img_path[0], METRIC: score_fr})
    except:
        results.append({'image_path': img_path[0], METRIC: -1})

# Create DataFrame from the list of results
result_df = pd.DataFrame(results)
# Check if the CSV file already exists

csv_filename = 'dataset_with_metric.csv'
if os.path.exists(csv_filename):
    # If it exists, load the existing DataFrame and append the new results
    existing_df = pd.read_csv(csv_filename)
    result_df = pd.concat([existing_df, result_df], ignore_index=True)

# Save the updated DataFrame to CSV
result_df.to_csv(csv_filename, index=False)
# Clean up distributed data parallel environment
dist.destroy_process_group()

if name == "main": marktime = time.time() main() print(time.time() - marktime)

Dont care about pandas ops. Do you see something wrong?. Someone with same escenario and solution? Thank you in advance! Have a nice day