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main.py
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main.py
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#! /usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2021 Imperial College London (Pingchuan Ma)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import os
import torch
import argparse
from metrics.measures import get_wer
from metrics.measures import get_cer
from lipreading.utils import save2npz
from lipreading.utils import save2avi
from lipreading.utils import AverageMeter
from lipreading.subroutines import LipreadingPipeline
def load_args(default_config=None):
parser = argparse.ArgumentParser(description='Lipreading Project')
parser.add_argument(
"--config-filename",
type=str,
default=None,
help="Model configuration with ini format",
)
# --
parser.add_argument("--data-filename",
default=None,
type=str,
help="The filename for sequence.",
)
parser.add_argument(
"--landmarks-filename",
default="",
type=str,
help="The filename for tracked landmarks.",
)
parser.add_argument(
"--dst-filename",
type=str,
default=None,
help="The filename of the saved mouth patches or embedding.",
)
# -- for benchmark evaluation
parser.add_argument("--data-dir",
default=None,
type=str,
help="The directory for sequence.",
)
parser.add_argument(
"--landmarks-dir",
default="",
type=str,
help="The directory for tracked landmarks.",
)
parser.add_argument("--labels-filename",
default=None,
type=str,
help="The filename for labels.",
)
parser.add_argument(
"--dst-dir",
type=str,
default=None,
help="The directory of saved mouth patches or embeddings.",
)
# -- feature extraction
parser.add_argument(
"--feats-position",
default="",
choices=["", "mouth", "resnet", "conformer"],
help="Specify the position for feature extraction.",
)
# -- utils
parser.add_argument(
"--video-ext",
default=".mp4",
type=str,
help="The extension for video files.",
)
parser.add_argument(
"--landmarks-ext",
default=".pkl",
type=str,
help="The extension for landmarks file.",
)
parser.add_argument(
"--gpu-idx",
default=-1,
type=int,
help="Inference in GPU when gpu_idx >= 0 or in CPU when gpu_idx < 0.",
)
args = parser.parse_args()
return args
args = load_args()
def benchmark_inference(lipreader, data_dir, landmarks_dir, lines, dst_dir=""):
"""benchmark_inference.
:param lipreader: LipreadingPipeline object, contains the function for \
facial tracking[option], facial pre-processing and lipreading inference.
:param data_dir: str, the directory for tracked landmarks.
:param landmarks_dir: str, the directory for tracked landmarks.
:param lines: List, the list of basename for each sequence.
:param dst_dir: str, The directory of the saved mouth patch or embeddings.
"""
wer = AverageMeter()
cer = AverageMeter()
for idx, line in enumerate(lines):
basename, groundtruth = line.split()[0], " ".join(line.split()[1:])
data_filename = os.path.join(data_dir, basename+args.video_ext)
landmarks_filename = os.path.join(landmarks_dir, basename+args.landmarks_ext)
output = lipreader(data_filename, landmarks_filename)
if isinstance(output, str):
print(f"hyp: {output}")
if groundtruth is not None:
print(f"ref: {groundtruth}")
wer.update( get_wer(output, groundtruth), len(groundtruth.split()))
cer.update( get_cer(output, groundtruth), len(groundtruth.replace(" ", "")))
print(
f"progress: {idx+1}/{len(lines)}\tcur WER: {wer.val*100:.1f}\t"
f"cur CER: {cer.val*100:.1f}\t"
f"avg WER: {wer.avg*100:.1f}\tavg CER: {cer.avg*100:.1f}"
)
elif isinstance(output, tuple):
print(
f"filename: {basename} has been saved to "
f"{os.path.join(dst_dir, basename+'.avi')}."
)
save2avi(
os.path.join(dst_dir, basename+".avi"),
data=output[0],
fps=output[1],
)
else:
print(
f"filename: {basename} has been saved to "
f"{os.path.join(dst_dir, basename+'.npz')}."
)
save2npz(
os.path.join(dst_dir, basename+".npz"),
data=output.cpu().detach().numpy()
)
return
def one_step_inference(lipreader, data_filename, landmarks_filename, dst_filename=""):
"""one_step_inference.
:param lipreader: LipreadingPipeline object, contains the function for \
facial tracking[option], facial pre-processing and lipreading inference.
:param data_filename: str, the filename for tracked landmarks.
:param landmarks_filename: str, the filename for tracked landmarks.
:param dst_filename: str, the filename of the saved mouth patch or embedding.
"""
output = lipreader(data_filename, landmarks_filename)
if isinstance(output, str):
print(f"hyp: {output}")
elif isinstance(output, tuple):
assert dst_filename[-4:] == ".avi", f"the ext of {dst_filename} should be .avi"
print(f"mouth patch is saved to {dst_filename}.")
save2avi(dst_filename, data=output[0], fps=output[1])
else:
assert dst_filename[-4:] == ".npz", f"the ext of {dst_filename} should be .npz"
print(f"embedding is saved to {dst_filename}.")
save2npz(dst_filename, data=output.cpu().detach().numpy())
return
def main():
# -- pick device for inference.
if torch.cuda.is_available() and args.gpu_idx >= 0:
device = torch.device(f"cuda:{args.gpu_idx}")
else:
device = "cpu"
lipreader = LipreadingPipeline(
config_filename=args.config_filename,
feats_position=args.feats_position,
device=device,
face_track=not args.landmarks_filename and not args.landmarks_dir,
)
if args.data_filename is not None:
one_step_inference(
lipreader,
args.data_filename,
args.landmarks_filename,
args.dst_filename,
)
else:
assert os.path.isdir(args.data_dir), \
f"{args.data_dir} is not a directory."
assert os.path.isfile(args.labels_filename), \
f"{args.labels_filename} does not exist."
benchmark_inference(
lipreader,
args.data_dir,
args.landmarks_dir,
open(args.labels_filename).read().splitlines(),
args.dst_dir,
)
if __name__ == '__main__':
main()