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inf_test.py
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inf_test.py
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import torch
import platform
import math
import numpy as np
import os, cv2, argparse, subprocess
from tqdm import tqdm
from torch import nn
from torch.nn import functional as F
from argparse import Namespace
from torch.utils.data import DataLoader
from python_speech_features import logfbank
from fairseq import checkpoint_utils, utils, tasks
from fairseq.dataclass.utils import convert_namespace_to_omegaconf, populate_dataclass, merge_with_parent
from scipy.io import wavfile
from utils.data_avhubert import collater_audio, emb_roi2im
from models.talklip import TalkLip
def build_encoder(hubert_root, path='config.yaml'):
from omegaconf import OmegaConf
cfg = OmegaConf.load(path)
import sys
sys.path.append(hubert_root)
from avhubert.hubert_asr import HubertEncoderWrapper, AVHubertSeq2SeqConfig
# cfg = merge_with_parent(AVHubertSeq2SeqConfig(), cfg)
arg_overrides = {
"dropout": cfg.dropout,
"activation_dropout": cfg.activation_dropout,
"dropout_input": cfg.dropout_input,
"attention_dropout": cfg.attention_dropout,
"mask_length": cfg.mask_length,
"mask_prob": cfg.mask_prob,
"mask_selection": cfg.mask_selection,
"mask_other": cfg.mask_other,
"no_mask_overlap": cfg.no_mask_overlap,
"mask_channel_length": cfg.mask_channel_length,
"mask_channel_prob": cfg.mask_channel_prob,
"mask_channel_selection": cfg.mask_channel_selection,
"mask_channel_other": cfg.mask_channel_other,
"no_mask_channel_overlap": cfg.no_mask_channel_overlap,
"encoder_layerdrop": cfg.layerdrop,
"feature_grad_mult": cfg.feature_grad_mult,
}
if cfg.w2v_args is None:
state = checkpoint_utils.load_checkpoint_to_cpu(
cfg.w2v_path, arg_overrides
)
w2v_args = state.get("cfg", None)
if w2v_args is None:
w2v_args = convert_namespace_to_omegaconf(state["args"])
cfg.w2v_args = w2v_args
else:
state = None
w2v_args = cfg.w2v_args
if isinstance(w2v_args, Namespace):
cfg.w2v_args = w2v_args = convert_namespace_to_omegaconf(
w2v_args
)
w2v_args.task.data = cfg.data
task_pretrain = tasks.setup_task(w2v_args.task)
task_pretrain.load_state_dict(torch.load('task_state.pt'))
encoder_ = task_pretrain.build_model(w2v_args.model)
encoder = HubertEncoderWrapper(encoder_)
if state is not None and not cfg.no_pretrained_weights:
# set strict=False because we omit some modules
del state['model']['mask_emb']
encoder.w2v_model.load_state_dict(state["model"], strict=False)
encoder.w2v_model.remove_pretraining_modules()
return encoder, encoder.w2v_model.encoder_embed_dim
def parse_filelist(file_list, save_root, check):
with open(file_list) as f:
lines = f.readlines()
if check:
sample_paths = []
for line in lines:
line = line.strip().split()[0]
if not os.path.exists('{}/{}.mp4'.format(save_root, line)):
sample_paths.append(line)
else:
sample_paths = [line.strip().split()[0] for line in lines]
return sample_paths
class Talklipdata(object):
def __init__(self, args):
self.data_root = args.video_root
self.bbx_root = args.bbx_root
self.audio_root = args.audio_root
self.samples = parse_filelist(args.filelist, args.save_root, args.check)
self.stack_order_audio = 4
self.crop_size = 96
def prepare_window(self, window):
# T x 3 x H x W
x = window / 255.
x = x.permute((0, 3, 1, 2))
return x
def croppatch(self, images, bbxs):
patch = np.zeros((images.shape[0], self.crop_size, self.crop_size, 3))
width = images.shape[1]
for i, bbx in enumerate(bbxs):
bbx[2] = min(bbx[2], width)
bbx[3] = min(bbx[3], width)
patch[i] = cv2.resize(images[i, bbx[1]:bbx[3], bbx[0]:bbx[2], :], (self.crop_size, self.crop_size))
return patch
def audio_visual_align(self, audio_feats, video_feats):
diff = len(audio_feats) - len(video_feats)
if diff < 0:
audio_feats = np.concatenate(
[audio_feats, np.zeros([-diff, audio_feats.shape[-1]], dtype=audio_feats.dtype)])
elif diff > 0:
left = diff // 2
right = diff - left
audio_feats = audio_feats[left:-right]
# audio_feats = audio_feats[:-diff]
return audio_feats
def fre_audio(self, wav_data, sample_rate):
def stacker(feats, stack_order):
"""
Concatenating consecutive audio frames, 4 frames of tf forms a new frame of tf
Args:
feats - numpy.ndarray of shape [T, F]
stack_order - int (number of neighboring frames to concatenate
Returns:
feats - numpy.ndarray of shape [T', F']
"""
feat_dim = feats.shape[1]
if len(feats) % stack_order != 0:
res = stack_order - len(feats) % stack_order
res = np.zeros([res, feat_dim]).astype(feats.dtype)
feats = np.concatenate([feats, res], axis=0)
feats = feats.reshape((-1, stack_order, feat_dim)).reshape(-1, stack_order*feat_dim)
return feats
audio_feats = logfbank(wav_data, samplerate=sample_rate).astype(np.float32) # [T, F]
audio_feats = stacker(audio_feats, self.stack_order_audio) # [T/stack_order_audio, F*stack_order_audio]
return audio_feats
def load_video(self, path):
cap = cv2.VideoCapture(path)
imgs = []
while True:
ret, frame = cap.read()
if ret:
imgs.append(frame)
else:
break
cap.release()
return imgs
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
"""
Args:
idx:
Returns:
x (N*6*96*96): concatenation of N identity images (different) and mask images (same)
spectrogram (N_a * 321): spectrogram of whole wav
idAudio ((N*7)): matched audio index
y (N*3*96*96): ground truth images
"""
sample = self.samples[idx]
video_path = '{}/{}.mp4'.format(self.data_root, sample)
bbx_path = '{}/{}.npy'.format(self.bbx_root, sample)
wav_path = '{}/{}.wav'.format(self.audio_root, sample)
bbxs = np.load(bbx_path)
imgs = np.array(self.load_video(video_path))
volume = len(imgs)
sampRate, wav = wavfile.read(wav_path)
spectrogram = self.fre_audio(wav, sampRate)
spectrogram = torch.tensor(spectrogram) # T'* F
with torch.no_grad():
spectrogram = F.layer_norm(spectrogram, spectrogram.shape[1:])
pickedimg = list(range(volume))
poseImgRaw = np.array(pickedimg)
poseImg = self.croppatch(imgs[poseImgRaw], bbxs[poseImgRaw])
idImgRaw = np.zeros(volume, dtype=np.int32)
idImg = self.croppatch(imgs[idImgRaw], bbxs[idImgRaw])
poseImg = torch.tensor(poseImg, dtype=torch.float32) # T*3*96*96
idImg = torch.tensor(idImg, dtype=torch.float32) # T*3*96*96
spectrogram = self.audio_visual_align(spectrogram, imgs)
pose_inp = self.prepare_window(poseImg)
gt = pose_inp.clone()
# mask off the bottom half
pose_inp[:, :, pose_inp.shape[2] // 2:] = 0.
id_inp = self.prepare_window(idImg)
inp = torch.cat([pose_inp, id_inp], dim=1)
pickedimg, bbxs = torch.tensor(pickedimg), torch.tensor(bbxs)
imgs = torch.from_numpy(imgs)
return inp, spectrogram, gt, volume, pickedimg, imgs, bbxs, sample
def collate_fn(dataBatch):
"""
Args:
dataBatch:
Returns:
xBatch: input T_sum*6*96*96, concatenation of all video chips in the time dimension
yBatch: output T_sum*3*96*96
inputLenBatch: bs
inputLenRequire: bs
audioBatch: bs*T'*321 or T_sum*1*80*16
idAudio: (bs*N*7)
targetBatch: bs*L*1
videoBatch: bs*T''*3*96*96
pickedimg: (bs*N*5)
videoBatch: bs*T''*3*96*96
"""
xBatch = torch.cat([data[0] for data in dataBatch], dim=0)
yBatch = torch.cat([data[2] for data in dataBatch], dim=0)
inputLenBatch = [data[3] for data in dataBatch]
audioBatch, padding_mask = collater_audio([data[1] for data in dataBatch], max(inputLenBatch))
audiolen = audioBatch.shape[2]
idAudio = torch.cat([data[4] + audiolen * i for i, data in enumerate(dataBatch)], dim=0)
pickedimg = [data[4] for data in dataBatch]
videoBatch = [data[5] for data in dataBatch]
bbxs = [data[6] for data in dataBatch]
names = [data[7] for data in dataBatch]
return xBatch, audioBatch, idAudio, yBatch, padding_mask, pickedimg, videoBatch, bbxs, names
def get_gpu_memory_map():
result = subprocess.check_output(
[
'nvidia-smi', '--query-gpu=memory.used',
'--format=csv,nounits,noheader'
], encoding='utf-8')
gpu_memory = [int(x) for x in result.strip().split('\n')]
gpu_memory_map = dict(zip(range(len(gpu_memory)), gpu_memory))
return gpu_memory_map
def model_synt(test_data_loader, device, model, args):
tmpvideo = '{}.avi'.format(args.save_root.split('/')[-1])
model.eval()
for inps, spectrogram, idAudio, gt, padding_mask, pickedimg, imgs, bbxs, names in tqdm(test_data_loader): #
inps, gt = inps.to(device), gt.to(device)
spectrogram = spectrogram.to(device)
padding_mask = padding_mask.to(device)
sample = {'net_input': {'source': {'audio': spectrogram, 'video': None}, 'padding_mask': padding_mask, 'prev_output_tokens': None},
'target_lengths': None, 'ntokens': None, 'target': None}
prediction, enc_audio = model(sample, inps, idAudio, spectrogram.shape[0])
file_size = imgs[0].shape[1]
processed_img = emb_roi2im(pickedimg, imgs, bbxs, prediction, device)
for i, video in enumerate(processed_img):
out_path = '{}/{}.mp4'.format(args.save_root, names[i])
if not os.path.exists(os.path.dirname(out_path)):
os.makedirs(os.path.dirname(out_path), exist_ok=True)
out = cv2.VideoWriter(tmpvideo, cv2.VideoWriter_fourcc(*'DIVX'), 25, (file_size, file_size))
for j, im in enumerate(video):
im = im.cpu().clone().detach().numpy().astype(np.uint8)
out.write(im)
out.release()
audio = '{}/{}.wav'.format(args.audio_root, names[i])
command = '{} -y -i {} -i {} -strict -2 -q:v 1 {} -loglevel quiet'.format(args.ffmpeg, audio, tmpvideo, out_path)
subprocess.call(command, shell=platform.system() != 'Windows')
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description='Synthesize videos to be evaluated')
parser.add_argument('--filelist', help="Path of a file list containing all samples' name", required=True, type=str)
parser.add_argument("--video_root", help="Root folder of video", required=True, type=str)
parser.add_argument("--audio_root", help="Root folder of audio", required=True, type=str)
parser.add_argument('--bbx_root', help="Root folder of bounding boxes of faces", required=True, type=str)
parser.add_argument("--save_root", help="a directory to save synthesized videos", required=True, type=str)
parser.add_argument('--ckpt_path', help='pretrained checkpoint', required=True, type=str)
parser.add_argument('--avhubert_root', help='Path of av_hubert root', required=True, type=str)
parser.add_argument('--check', help='whether filter out videos which have been synthesized in save_root', default=False, type=bool)
parser.add_argument('--ffmpeg', default='ffmpeg', type=str)
parser.add_argument('--device', default=0, type=int)
args = parser.parse_args()
device = "cuda:{}".format(args.device) if torch.cuda.is_available() else "cpu"
# Dataset and Dataloader setup
test_dataset = Talklipdata(args)
test_loader = DataLoader(test_dataset, batch_size=4, collate_fn=collate_fn, num_workers=6) #hparams.batch_size, 4,
model = TalkLip(*build_encoder(args.avhubert_root)).to(device)
model.load_state_dict(torch.load(args.ckpt_path, map_location=device)["state_dict"])
with torch.no_grad():
model_synt(test_loader, device, model, args)