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67ddbf8 f5651ba 67ddbf8 7a05005 67ddbf8 f5651ba 67ddbf8 f5651ba 67ddbf8 f5651ba 67ddbf8 7a05005 67ddbf8 f5651ba 67ddbf8 f5651ba 67ddbf8 f5651ba 67ddbf8 7a05005 67ddbf8 f5651ba 67ddbf8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 | # Copyright (c) 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import sys
from tqdm.auto import tqdm
import torch
import torch.nn as nn
from einops import rearrange
from latentsync.models.stable_syncnet import StableSyncNet
from latentsync.data.syncnet_dataset import SyncNetDataset
from diffusers import AutoencoderKL
from omegaconf import OmegaConf
from accelerate.utils import set_seed
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from config import MODELS_DIR
def main(config):
set_seed(config.run.seed)
device = "cuda" if torch.cuda.is_available() else "cpu"
if config.data.latent_space:
vae = AutoencoderKL.from_pretrained(
"runwayml/stable-diffusion-inpainting",
subfolder="vae",
revision="fp16",
torch_dtype=torch.float16,
cache_dir=MODELS_DIR,
)
vae.requires_grad_(False)
vae.to(device)
# Dataset and Dataloader setup
dataset = SyncNetDataset(
config.data.val_data_dir, config.data.val_fileslist, config
)
test_dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=config.data.batch_size,
shuffle=False,
num_workers=config.data.num_workers,
drop_last=False,
worker_init_fn=dataset.worker_init_fn,
)
# Model
syncnet = StableSyncNet(OmegaConf.to_container(config.model)).to(device)
print(f"Load checkpoint from: {config.ckpt.inference_ckpt_path}")
checkpoint = torch.load(
config.ckpt.inference_ckpt_path, map_location=device, weights_only=True
)
syncnet.load_state_dict(checkpoint["state_dict"])
syncnet.to(dtype=torch.float16)
syncnet.requires_grad_(False)
syncnet.eval()
global_step = 0
num_val_batches = config.data.num_val_samples // config.data.batch_size
progress_bar = tqdm(range(0, num_val_batches), initial=0, desc="Testing accuracy")
num_correct_preds = 0
num_total_preds = 0
while True:
for step, batch in enumerate(test_dataloader):
### >>>> Test >>>> ###
frames = batch["frames"].to(device, dtype=torch.float16)
audio_samples = batch["audio_samples"].to(device, dtype=torch.float16)
y = batch["y"].to(device, dtype=torch.float16).squeeze(1)
if config.data.latent_space:
frames = rearrange(frames, "b f c h w -> (b f) c h w")
with torch.no_grad():
frames = vae.encode(frames).latent_dist.sample() * 0.18215
frames = rearrange(
frames, "(b f) c h w -> b (f c) h w", f=config.data.num_frames
)
else:
frames = rearrange(frames, "b f c h w -> b (f c) h w")
if config.data.lower_half:
height = frames.shape[2]
frames = frames[:, :, height // 2 :, :]
with torch.no_grad():
vision_embeds, audio_embeds = syncnet(frames, audio_samples)
sims = nn.functional.cosine_similarity(vision_embeds, audio_embeds)
preds = (sims > 0.5).to(dtype=torch.float16)
num_correct_preds += (preds == y).sum().item()
num_total_preds += len(sims)
progress_bar.update(1)
global_step += 1
if global_step >= num_val_batches:
progress_bar.close()
print(
f"SyncNet Accuracy: {num_correct_preds / num_total_preds * 100:.2f}%"
)
return
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Code to test the accuracy of SyncNet")
parser.add_argument(
"--config_path", type=str, default="configs/syncnet/syncnet_16_latent.yaml"
)
args = parser.parse_args()
# Load a configuration file
config = OmegaConf.load(args.config_path)
main(config)
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