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visualize.py
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import random
import numpy as np
from tqdm import tqdm
import os
import cv2
import argparse
import torch
from torchvision.io import write_video, write_png
from fastvqa import FragmentVideoDataset, BaseEvaluator
def get_vis_dataset(args, model_type="fast"):
dataset_path = f"{args.pdpath}/{args.dataset}"
inference_set = FragmentVideoDataset(
f"{dataset_path}/labels.txt",
dataset_path,
fragments=7 if model_type == "fast" else 4,
clip_len=32 if model_type == "fast" else 16,
nfrags=1,
num_clips=1,
aligned=32 if model_type == "fast" else 8,
phase="train",
)
return inference_set
def t_rescale(pr, gt=None):
if gt is None:
pr = (pr - pr.mean()) / pr.std()
else:
pr = (pr - pr.mean()) / pr.std() * gt.std() + gt.mean()
return pr
def save_visualizations(args, inference_set, model=None, device="cpu"):
os.makedirs(
f"{args.save_dir}/{args.dataset.lower()}_{args.model_type}", exist_ok=True
)
mean, std = np.array([123.675, 116.28, 103.53]), np.array([58.395, 57.12, 57.375])
results = []
for _ in tqdm(range(args.vs)):
q = random.randrange(len(inference_set))
# q = 1679
data = inference_set.__getitem__(q, need_original_frames=True)
vfrag, video = data["video"], data["original_video"]
if model is not None:
vfrag = vfrag.to(device)
with torch.no_grad():
vr = model(vfrag)
result = torch.nn.functional.interpolate(
vr, scale_factor=(2, 32, 32), mode="nearest"
).cpu()
vresult = torch.nn.functional.interpolate(
vr, size=video.shape[2:], mode="trilinear"
).cpu()
results.append(
(
vfrag,
video,
result,
vresult,
data["original_shape"],
data["gt_label"],
q,
)
)
else:
results.append(
(vfrag, video, None, None, data["original_shape"], data["gt_label"], q)
)
if results[0][2] is not None:
res_res = torch.cat([t_rescale(r[2]) for r in results], 0)
vres_res = [t_rescale(r[3]) for r in results]
else:
res_res = None
vres_res = None
for i, result in enumerate(tqdm(results)):
vfrag, video, _, _, shape, label, q = result
if res_res is not None:
result = (
torch.cat(
(
res_res[i],
-res_res[i],
torch.zeros_like(res_res[i]),
),
0,
)
.permute(1, 2, 3, 0)
.cpu()
.numpy()
)
vresult = torch.cat(
(
vres_res[i][0],
-vres_res[i][0],
torch.zeros_like(vres_res[i][0]),
),
0,
)
vresult = torch.nn.functional.interpolate(
vresult, scale_factor=1 / (min(vresult.shape[2:]) / 540)
)
vresult = vresult.permute(1, 2, 3, 0).cpu().numpy()
frag = vfrag.squeeze(0).permute(1, 2, 3, 0).cpu().numpy() * std + mean
video = video.squeeze(0)
scale = min(video.shape[2:]) / 540
video = torch.nn.functional.interpolate(video.float(), scale_factor=1 / scale)
video = video.permute(1, 2, 3, 0).cpu().numpy()
if res_res is not None:
frag = np.concatenate((frag, -result * 80), 2).clip(0, 255)
video = (
np.concatenate((video, video - vresult * video.mean() / 2), 2)
.clip(0, 255)
.astype(np.uint8)
)
# video = np.concatenate([cv2.resize(video[i])],0)
save_dir = f"{args.save_dir}/{args.dataset.lower()}_{args.model_type}/{q}_{label:.2f}_{shape}"
os.makedirs(save_dir, exist_ok=True)
write_video(f"{save_dir}/fr.mp4", frag, 15)
# for j in range(32):
# write_png(torch.from_numpy(video[j]).permute(2,0,1), f"{save_dir}/vr_{j}.png", 1)
write_video(f"{save_dir}/vr.mp4", video, 15)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"-d",
"--dataset",
type=str,
default="LIVE_VQC",
help="the inference dataset name, can add XXX,a,b to evaluate XXX from [",
)
parser.add_argument(
"--pdpath", type=str, default="../datasets/", help="the inference dataset path"
)
parser.add_argument(
"-v", "--vs", type=int, default=16, help="num of visualizations"
)
parser.add_argument(
"--save_dir",
type=str,
default="demo_",
help="results_dir",
)
parser.add_argument(
"-m",
"--model_type",
type=str,
default="fast",
help="choose whether to use FAST-VQA or the FASTER-VQA",
)
parser.add_argument(
"-nm",
"--need_model",
action="store_true",
help="need the rendering of local quality maps on fragments",
)
args = parser.parse_args()
if torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
if args.model_type == "fast":
backbone_hp = dict(window_size=(8, 7, 7), frag_biases=[True, True, True, False])
else:
backbone_hp = dict(
window_size=(4, 4, 4), frag_biases=[False, False, True, False]
)
if args.need_model:
model = BaseEvaluator(backbone_hp).to(device)
load_path = f"pretrained_weights/{args.model_type}_vqa_v0_3.pth"
state_dict = torch.load(load_path, map_location=device)
if "state_dict" in state_dict:
state_dict = state_dict["state_dict"]
from collections import OrderedDict
i_state_dict = OrderedDict()
for key in state_dict.keys():
if "cls" in key:
tkey = key.replace("cls", "vqa")
i_state_dict[tkey] = state_dict[key]
else:
i_state_dict[key] = state_dict[key]
model.load_state_dict(i_state_dict)
dataset = get_vis_dataset(args, args.model_type)
if args.need_model:
save_visualizations(args, dataset, model=model, device=device)
else:
save_visualizations(args, dataset)
if __name__ == "__main__":
main()