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import os
import glob
from functools import partial
from tqdm import tqdm, trange
from multiprocessing import Pool
from PIL import Image
import cv2
import mlxu
from natsort import natsorted
import numpy as np
import einops
import torch
from vqlm_demo.inference import MultiProcessInferenceModel
from vqlm_demo.utils import (
is_video, random_square_crop,
read_frames_from_dir, read_frames_from_video
)
FLAGS, _ = mlxu.define_flags_with_default(
checkpoint='',
input_files='',
frame_input=False,
read_file_list='',
output_dir='',
center_crop=1.0,
n_context_frames=12,
n_new_frames=4,
n_candidates=8,
temperature=1.0,
top_p=1.0,
n_workers=8,
stride=8,
batch_size=32,
torch_devices='',
shuffle=False,
max_examples=0,
)
def save_image(args):
image, filename = args
base = FLAGS.input_files.split('*')[0]
filename = filename[len(base):].replace('/', '_') + '.png'
Image.fromarray(image).save(os.path.join(FLAGS.output_dir, filename))
class VideoDataset(torch.utils.data.Dataset):
def __init__(self, videos, frame_input=False, n_frames=8, stride=1):
self.videos = videos
self.frame_input = frame_input
self.n_frames = n_frames
self.stride = stride
def __getitem__(self, index):
if self.frame_input:
frames = read_frames_from_dir(
self.videos[index], self.n_frames, self.stride,
center_crop=FLAGS.center_crop,
)
else:
frames = read_frames_from_video(
self.videos[index], self.n_frames, self.stride,
center_crop=FLAGS.center_crop,
)
if frames is None:
return self[np.random.randint(0, len(self))]
return frames, self.videos[index]
def __len__(self):
return len(self.videos)
def main(_):
assert FLAGS.checkpoint != '' and FLAGS.output_dir != ''
assert FLAGS.read_file_list != '' or FLAGS.input_files != ''
os.makedirs(FLAGS.output_dir, exist_ok=True)
if FLAGS.read_file_list != '':
with open(FLAGS.read_file_list, 'r') as f:
videos = [x.strip() for x in f.readlines()]
else:
videos = glob.glob(FLAGS.input_files)
if FLAGS.frame_input:
videos = [x for x in videos if os.path.isdir(x)]
else:
videos = [x for x in videos if is_video(x)]
if FLAGS.shuffle:
np.random.shuffle(videos)
if FLAGS.max_examples > 0:
videos = videos[:FLAGS.max_examples]
dataset = VideoDataset(
videos,
frame_input=FLAGS.frame_input,
n_frames=FLAGS.n_context_frames,
stride=FLAGS.stride
)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=FLAGS.batch_size,
shuffle=False,
num_workers=FLAGS.n_workers,
prefetch_factor=4,
drop_last=True,
)
if FLAGS.torch_devices == '':
torch_devices = None
else:
torch_devices = [f'cuda:{x}' for x in FLAGS.torch_devices.split(',')]
model = MultiProcessInferenceModel(
checkpoint=FLAGS.checkpoint, torch_devices=torch_devices,
)
save_img_pool = Pool(FLAGS.n_workers)
for batch, filenames in tqdm(dataloader, ncols=0):
batch = batch.numpy()
generated = model(
batch,
n_new_frames=FLAGS.n_new_frames,
n_candidates=FLAGS.n_candidates,
temperature=FLAGS.temperature,
top_p=FLAGS.top_p,
)
generated = np.array(generated)
output_batch = einops.repeat(
batch,
'b s h w c -> b n s h w c',
n=FLAGS.n_candidates,
)
combined = einops.rearrange(
np.concatenate([output_batch, generated], axis=2),
'b n s h w c -> b (n h) (s w) c'
)
combined = (np.clip(combined, 0, 1) * 255).astype(np.uint8)
save_img_pool.imap(save_image, zip(combined, filenames))
if __name__ == '__main__':
mlxu.run(main) |