From 0bd9565cc283d10d085e101ed7a20dcd77b2cdcc Mon Sep 17 00:00:00 2001
From: myhloli <moe@myhloli.com>
Date: Thu, 26 Dec 2024 11:18:45 +0800
Subject: [PATCH] refactor(datasets): remove unused video processing code and
 update dependencies

- Remove load_video function and related imports
- Update unimernet version to 0.2.3 in pyproject.toml
- Remove eva-decord dependency
---
 pyproject.toml                   |  3 +--
 unimernet/datasets/data_utils.py | 26 --------------------------
 2 files changed, 1 insertion(+), 28 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index aaae535..8b5e322 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "unimernet"
-version = "0.2.2"
+version = "0.2.3"
 description = 'UniMERNet: A Universal Network for Real-World Mathematical Expression Recognition'
 authors = ["Bin Wang <ictwangbin@gmail.com>"]
 readme = "README.md"
@@ -30,7 +30,6 @@ fairscale = "^0.4.13"
 ftfy = {version = "^6.2.0", python = ">=3.10,<4.0"}
 albumentations = "^1.4.4"
 wand = "^0.6.13"
-eva-decord = "^0.6.1"
 webdataset = "^0.2.86"
 rapidfuzz = "^3.8.1"
 termcolor = "^2.4.0"
diff --git a/unimernet/datasets/data_utils.py b/unimernet/datasets/data_utils.py
index 4e82c90..7a22b46 100644
--- a/unimernet/datasets/data_utils.py
+++ b/unimernet/datasets/data_utils.py
@@ -12,43 +12,17 @@
 import tarfile
 import zipfile
 
-import decord
 import webdataset as wds
 import numpy as np
 import torch
 from torch.utils.data.dataset import IterableDataset, ChainDataset
-from decord import VideoReader
 from unimernet.common.registry import registry
 from unimernet.datasets.datasets.base_dataset import ConcatDataset
 from tqdm import tqdm
 
-decord.bridge.set_bridge("torch")
 MAX_INT = registry.get("MAX_INT")
 
 
-def load_video(video_path, n_frms=MAX_INT, height=-1, width=-1, sampling="uniform"):
-    vr = VideoReader(uri=video_path, height=height, width=width)
-
-    vlen = len(vr)
-    start, end = 0, vlen
-
-    n_frms = min(n_frms, vlen)
-
-    if sampling == "uniform":
-        indices = np.arange(start, end, vlen / n_frms).astype(int)
-    elif sampling == "headtail":
-        indices_h = sorted(rnd.sample(range(vlen // 2), n_frms // 2))
-        indices_t = sorted(rnd.sample(range(vlen // 2, vlen), n_frms // 2))
-        indices = indices_h + indices_t
-    else:
-        raise NotImplementedError
-
-    # get_batch -> T, H, W, C
-    frms = vr.get_batch(indices).permute(3, 0, 1, 2).float()  # (C, T, H, W)
-
-    return frms
-
-
 def apply_to_sample(f, sample):
     if len(sample) == 0:
         return {}