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better logging in inference.py 😁 #72

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better logging in inference.py 😁 #72

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18 changes: 14 additions & 4 deletions scripts/evaluation/inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,13 @@
from funcs import load_model_checkpoint, load_prompts, load_image_batch, get_filelist, save_videos
from funcs import batch_ddim_sampling
from utils.utils import instantiate_from_config
import logging

def setup_logging():
"""Set up basic configuration for logging."""
logging.basicConfig(filename='inference_log.txt', level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
logging.info("Logging is configured. Starting the program...")

def get_parser():
parser = argparse.ArgumentParser()
Expand Down Expand Up @@ -61,6 +67,8 @@ def run_inference(args, gpu_num, gpu_no, **kwargs):
## saving folders
os.makedirs(args.savedir, exist_ok=True)

logging.info(f"[rank:{gpu_no}] Model and configuration are loaded.")

## step 2: load data
## -----------------------------------------------------------------
assert os.path.exists(args.prompt_file), "Error: prompt file NOT Found!"
Expand All @@ -70,7 +78,7 @@ def run_inference(args, gpu_num, gpu_no, **kwargs):

samples_split = num_samples // gpu_num
residual_tail = num_samples % gpu_num
print(f'[rank:{gpu_no}] {samples_split}/{num_samples} samples loaded.')
logging.info(f"[rank:{gpu_no}] {samples_split}/{num_samples} samples loaded.")
indices = list(range(samples_split*gpu_no, samples_split*(gpu_no+1)))
if gpu_no == 0 and residual_tail != 0:
indices = indices + list(range(num_samples-residual_tail, num_samples))
Expand All @@ -86,13 +94,14 @@ def run_inference(args, gpu_num, gpu_no, **kwargs):

filename_list_rank = [filename_list[i] for i in indices]


## step 3: run over samples
## -----------------------------------------------------------------
start = time.time()
n_rounds = len(prompt_list_rank) // args.bs
n_rounds = n_rounds+1 if len(prompt_list_rank) % args.bs != 0 else n_rounds
for idx in range(0, n_rounds):
print(f'[rank:{gpu_no}] batch-{idx+1} ({args.bs})x{args.n_samples} ...')
logging.info(f'[rank:{gpu_no}] batch-{idx+1} ({args.bs})x{args.n_samples} ...')
idx_s = idx*args.bs
idx_e = min(idx_s+args.bs, len(prompt_list_rank))
batch_size = idx_e - idx_s
Expand Down Expand Up @@ -124,12 +133,13 @@ def run_inference(args, gpu_num, gpu_no, **kwargs):
## b,samples,c,t,h,w
save_videos(batch_samples, args.savedir, filenames, fps=args.savefps)

print(f"Saved in {args.savedir}. Time used: {(time.time() - start):.2f} seconds")
logging.info(f"Saved in {args.savedir}. Time used: {(time.time() - start):.2f} seconds")


if __name__ == '__main__':
setup_logging()
now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
print("@CoLVDM Inference: %s"%now)
logging.info(f"@CoLVDM Inference: {now}")
parser = get_parser()
args = parser.parse_args()
seed_everything(args.seed)
Expand Down