ai-toolkit/scripts/add_mask_dataset.py

257 lines
8.9 KiB
Python

import os
import sys
import queue
import random
import argparse
import threading
import traceback
import torch
from tqdm import tqdm
# allow importing from project root
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from toolkit.control_generator import ControlGenerator, img_ext_list
def control_exists(img_path, control_type):
# mirrors the lookup in ControlGenerator.get_control_path so we can skip
# images another instance has already finished
controls_folder = os.path.join(os.path.dirname(img_path), "_controls")
file_name_no_ext = os.path.splitext(os.path.basename(img_path))[0]
file_name_no_ext_control = f"{file_name_no_ext}.{control_type}"
for ext in img_ext_list:
if os.path.exists(os.path.join(controls_folder, file_name_no_ext_control + ext)):
return True
return False
# sentinel pushed through the queues to tell workers to stop
_DONE = object()
def run_pipeline(control_gen, img_list, control_type, regen, n_load, n_save):
# Three-stage pipeline so the GPU never waits on disk/CPU work:
# loaders (N threads) -> read + exif + resize + preprocess to a CPU tensor
# gpu worker (1 thread) -> model forward only (kept single so VRAM is bounded)
# savers (M threads) -> postprocess (resize/alpha) + encode + write
# The heavy CPU work (resize/normalize on input, resize/convert on output) is
# pushed onto the loader/saver threads so the GPU thread does almost nothing
# but the forward pass. Bounded queues apply backpressure so we don't load the
# whole dataset into RAM.
infer_q = queue.Queue(maxsize=n_load * 2)
save_q = queue.Queue(maxsize=n_save * 2)
path_q = queue.Queue()
for img_path in img_list:
path_q.put(img_path)
# miniters=1 disables tqdm's dynamic-miniters heuristic (which otherwise
# raises the redraw threshold after a fast burst and makes the bar look
# frozen); mininterval keeps redraws time-based and cheap.
pbar = tqdm(total=len(img_list), desc=f"Generating {control_type}",
miniters=1, mininterval=0.5)
pbar_lock = threading.Lock()
# set on completion OR on Ctrl-C; every blocking call below uses a timeout and
# re-checks this so the worker threads can actually be shut down.
stop_event = threading.Event()
def put(q, item):
# interruptible put: blocks until there's room, but wakes periodically so
# a stop request (or KeyboardInterrupt on the main thread) is honored.
while not stop_event.is_set():
try:
q.put(item, timeout=0.2)
return
except queue.Full:
continue
def loader():
while not stop_event.is_set():
try:
img_path = path_q.get_nowait()
except queue.Empty:
break
try:
if not regen and control_exists(img_path, control_type):
# another instance (or a previous run) already did it
with pbar_lock:
pbar.update(1)
continue
image = control_gen.load_image(img_path)
payload = control_gen.preprocess(image, control_type)
put(infer_q, (img_path, image, payload))
except Exception:
traceback.print_exc()
with pbar_lock:
pbar.update(1)
def saver():
while not stop_event.is_set():
try:
item = save_q.get(timeout=0.2)
except queue.Empty:
continue
if item is _DONE:
break
img_path, image, result = item
try:
out_image = control_gen.postprocess(result, image, control_type)
save_path = control_gen.control_save_path(img_path, control_type)
control_gen.save_control(out_image, save_path)
except Exception:
traceback.print_exc()
finally:
with pbar_lock:
pbar.update(1)
loaders = [threading.Thread(target=loader, daemon=True) for _ in range(n_load)]
savers = [threading.Thread(target=saver, daemon=True) for _ in range(n_save)]
for t in loaders + savers:
t.start()
# GPU stage runs on the main thread: pull preprocessed tensors, run the
# forward pass, hand the raw result off to the savers. We stop once every
# loader has exited and nothing is left queued for inference.
interrupted = False
try:
while not stop_event.is_set():
if not any(t.is_alive() for t in loaders) and infer_q.empty():
break
try:
img_path, image, payload = infer_q.get(timeout=0.1)
except queue.Empty:
continue
try:
result = control_gen.run_inference(payload, control_type)
put(save_q, (img_path, image, result))
except Exception:
traceback.print_exc()
with pbar_lock:
pbar.update(1)
except KeyboardInterrupt:
interrupted = True
print("\nInterrupted, shutting down...")
if interrupted:
# abort: tell every worker to stop; pending items are dropped
stop_event.set()
else:
# normal finish: let savers drain whatever is still queued, then stop
for _ in savers:
save_q.put(_DONE)
# join with a timeout so a stuck worker can never wedge shutdown; threads are
# daemons, so anything still alive is torn down when we return.
for t in savers:
t.join(timeout=5)
pbar.close()
if interrupted:
raise KeyboardInterrupt
def main():
parser = argparse.ArgumentParser(
description="Generate masks for a dataset using the ControlGenerator"
)
parser.add_argument("img_dir", type=str, help="Path to image directory")
parser.add_argument(
"--control",
type=str,
default="mask",
choices=["mask", "inpaint", "depth", "pose", "line", "sapiens2_mask"],
help="Control type to generate (default: mask)",
)
parser.add_argument(
"--device", type=str, default="cuda", help="Device to run on (default: cuda)"
)
parser.add_argument("--debug", action="store_true", help="Enable debug mode")
parser.add_argument(
"--regen",
action="store_true",
help="Regenerate controls even if they already exist",
)
parser.add_argument(
"--shuffle",
action="store_true",
help="Shuffle image order so multiple instances on the same dataset "
"don't chase the same images",
)
parser.add_argument(
"--load-workers",
type=int,
default=16,
help="Number of threads for loading/resizing images (default: 4)",
)
parser.add_argument(
"--save-workers",
type=int,
default=16,
help="Number of threads for saving controls (default: 4)",
)
args = parser.parse_args()
img_dir = args.img_dir
if not os.path.isdir(img_dir):
print(f"Error: {img_dir} is not a directory")
sys.exit(1)
# find images, skipping existing _controls folders and hidden files
img_list = []
for root, dirs, files in os.walk(img_dir):
if "_controls" in root:
continue
for file in files:
if file.startswith("."):
continue
if file.lower().endswith(tuple(img_ext_list)):
img_list.append(os.path.join(root, file))
if len(img_list) == 0:
print(f"Error: no images found in {img_dir}")
sys.exit(1)
# filter out images that already have controls up front so the progress bar
# reflects only real work (otherwise it races through thousands of skips and
# the rate/ETA are meaningless). The loader still re-checks just before
# processing to handle the multi-instance race.
if not args.regen:
total = len(img_list)
img_list = [p for p in img_list if not control_exists(p, args.control)]
skipped = total - len(img_list)
if skipped:
print(f"Skipping {skipped} images that already have '{args.control}' controls")
if len(img_list) == 0:
print("All images already have controls. Nothing to do.")
return
if args.shuffle:
random.shuffle(img_list)
control_gen = ControlGenerator(torch.device(args.device))
control_gen.debug = args.debug
control_gen.regen = args.regen
interrupted = False
try:
run_pipeline(
control_gen,
img_list,
args.control,
args.regen,
max(1, args.load_workers),
max(1, args.save_workers),
)
except KeyboardInterrupt:
interrupted = True
finally:
control_gen.cleanup()
if interrupted:
sys.exit(130)
print("Done")
if __name__ == "__main__":
main()