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f7e25b2 | 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 | import os, glob, sys
import torch
from torchvision.io import read_video
from torchvision.utils import save_image
ROOT = "workspace/outputs/gen_4chunk_motion_delta_vs_multires"
OUT = "workspace/outputs/gen_4chunk_motion_delta_vs_multires/_grids"
os.makedirs(OUT, exist_ok=True)
FRAMES = [0, 6, 12, 18] # sample across the 21-frame clip
def load(path):
v, _, _ = read_video(path, pts_unit="sec", output_format="TCHW")
return v.float() / 255.0
# key prompts (substring match), across train+ood
keys = ["skier", "golden retriever", "Mercedes", "flyby video", "Aerial drone"]
pairs = []
for s in ["train", "ood"]:
for mp in sorted(glob.glob(os.path.join(ROOT, "motion_delta", s, "*.mp4"))):
name = os.path.basename(mp)
if any(k.lower() in name.lower() for k in keys):
rp = os.path.join(ROOT, "multires", s, name)
if os.path.exists(rp):
pairs.append((s, name, mp, rp))
for s, name, mp, rp in pairs:
vm, vr = load(mp), load(rp)
n = min(vm.shape[0], vr.shape[0])
idx = [i for i in FRAMES if i < n]
rows = torch.cat([vm[idx], vr[idx]], dim=0) # top: motion_delta, bottom: multires
tag = (s + "_" + name[:32]).replace(" ", "_").replace("/", "_").replace(".", "")
outp = os.path.join(OUT, tag + ".png")
save_image(rows, outp, nrow=len(idx))
print(f"{outp} frames={idx} (top=motion_delta bottom=multires)")
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