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import ast
import random
from pathlib import Path
import pandas as pd
import torch
from lightning import LightningDataModule
from PIL import Image
from torch.utils.data import DataLoader, Dataset
from src.data.transforms import transform_test, transform_train
from src.data.utils import FrameLoader, id2int, pre_caption
from src.tools.files import write_txt
from src.tools.utils import print_dist
Image.MAX_IMAGE_PIXELS = None # Disable DecompressionBombWarning
class WebVidCoVRDataModuleRuleBased(LightningDataModule):
def __init__(
self,
batch_size: int,
num_workers: int = 4,
pin_memory: bool = True,
annotation: dict = {"train": "", "val": ""},
vid_dirs: dict = {"train": "", "val": ""},
emb_dirs: dict = {"train": "", "val": ""},
image_size: int = 384,
emb_pool: str = "query",
iterate: str = "pth2",
vid_query_method: str = "middle",
vid_frames: int = 1,
**kwargs, # type: ignore
) -> None:
super().__init__()
# this line allows to access init params with 'self.hparams' attribute
# also ensures init params will be stored in ckpt
self.save_hyperparameters(logger=False)
self.batch_size = batch_size
self.num_workers = num_workers
self.pin_memory = pin_memory
self.emb_pool = emb_pool
self.iterate = iterate
self.vid_query_method = vid_query_method
self.vid_frames = vid_frames
self.transform_train = transform_train(image_size)
self.transform_test = transform_test(image_size)
self.data_train = WebVidCoVRDatasetRuleBased(
transform=self.transform_train,
annotation=annotation["train"],
vid_dir=vid_dirs["train"],
emb_dir=emb_dirs["train"],
split="train",
emb_pool=self.emb_pool,
iterate=self.iterate,
vid_query_method=self.vid_query_method,
vid_frames=self.vid_frames,
)
self.data_val = WebVidCoVRDatasetRuleBased(
transform=self.transform_test,
annotation=annotation["val"],
vid_dir=vid_dirs["val"],
emb_dir=emb_dirs["val"],
split="val",
emb_pool=self.emb_pool,
iterate=self.iterate,
vid_query_method=self.vid_query_method,
vid_frames=self.vid_frames,
)
def prepare_data(self):
# things to do on 1 GPU/TPU (not on every GPU/TPU in DDP)
# download data, pre-process, split, save to disk, etc...
pass
def train_dataloader(self):
return DataLoader(
dataset=self.data_train,
batch_size=self.batch_size,
num_workers=self.num_workers,
pin_memory=self.pin_memory,
shuffle=True,
drop_last=True,
)
def val_dataloader(self):
return DataLoader(
dataset=self.data_val,
batch_size=self.batch_size,
num_workers=self.num_workers,
pin_memory=self.pin_memory,
shuffle=False,
drop_last=False,
)
class WebVidCoVRDatasetRuleBased(Dataset):
def __init__(
self,
transform,
annotation: str,
vid_dir: str,
emb_dir: str,
split: str,
max_words: int = 30,
emb_pool: str = "query",
iterate: str = "pth2",
vid_query_method: str = "middle",
vid_frames: int = 1,
) -> None:
super().__init__()
self.transform = transform
self.annotation_pth = annotation
assert Path(annotation).exists(), f"Annotation file {annotation} does not exist"
self.df = pd.read_csv(annotation)
self.vid_dir = Path(vid_dir)
self.emb_dir = Path(emb_dir)
assert self.vid_dir.exists(), f"Image directory {self.vid_dir} does not exist"
assert self.emb_dir.exists(), f"Embedding directory {emb_dir} does not exist"
assert split in [
"train",
"val",
"test",
], f"Invalid split: {split}, must be one of train, val, or test"
self.split = split
vid_pths = self.vid_dir.glob("*/*.mp4")
emb_pths = self.emb_dir.glob("*/*.pth")
id2vidpth = {
vid_pth.parent.stem + "/" + vid_pth.stem: vid_pth for vid_pth in vid_pths
}
id2embpth = {
emb_pth.parent.stem + "/" + emb_pth.stem: emb_pth for emb_pth in emb_pths
}
assert len(id2vidpth) > 0, f"No videos found in {vid_dir}"
assert len(id2embpth) > 0, f"No embeddings found in {emb_dir}"
self.df["path1"] = self.df["pth1"].apply(lambda x: id2vidpth.get(x, None)) # type: ignore
self.df["path2"] = self.df["pth2"].apply(lambda x: id2embpth.get(x, None)) # type: ignore
# Count unique missing paths
missing_pth1 = self.df[self.df["path1"].isna()]["pth1"].unique().tolist()
missing_pth1.sort()
total_pth1 = self.df["pth1"].nunique()
missing_pth2 = self.df[self.df["path2"].isna()]["pth2"].unique().tolist()
missing_pth2.sort()
total_pth2 = self.df["pth2"].nunique()
if len(missing_pth1) > 0:
print_dist(
f"Missing {len(missing_pth1)} pth1's ({len(missing_pth1)/total_pth1 * 100:.1f}%), saving them to missing_pth1-{split}.txt"
)
write_txt(missing_pth1, f"missing_pth1-{split}.txt")
if len(missing_pth2) > 0:
print_dist(
f"Missing {len(missing_pth2)} pth2's ({len(missing_pth2)/total_pth2 * 100:.1f}%), saving them to missing_pth2-{split}.txt"
)
write_txt(missing_pth2, f"missing_pth2-{split}.txt")
# Remove missing paths
self.df = self.df[self.df["path1"].notna()]
self.df = self.df[self.df["path2"].notna()]
self.df.reset_index(drop=True, inplace=True)
self.max_words = max_words
assert emb_pool in [
"middle",
"mean",
"query",
], f"Invalid emb_pool: {emb_pool}, must be one of middle, mean, or query"
self.emb_pool = emb_pool
if iterate in ["idx", "triplets"]:
iterate = "idx"
self.df["idx"] = self.df.index
self.iterate = iterate
self.target_txts = self.df[iterate].unique()
assert iterate in self.df.columns, f"{iterate} not in {Path(annotation).stem}"
self.df.sort_values(iterate, inplace=True)
self.df.reset_index(drop=True, inplace=True)
self.df["int1"] = self.df["pth1"].apply(lambda x: id2int(x, sub="0"))
self.df["int2"] = self.df["pth2"].apply(lambda x: id2int(x, sub="0"))
self.pairid2ref = self.df["int1"].to_dict()
assert (
self.df["int1"].nunique() == self.df["pth1"].nunique()
), "int1 is not unique"
assert (
self.df["int2"].nunique() == self.df["pth2"].nunique()
), "int2 is not unique"
# int2id is a dict with key: int1, value: pth1
self.int2id = self.df.groupby("int1")["pth1"].apply(set).to_dict()
self.int2id = {k: list(v)[0] for k, v in self.int2id.items()}
self.pairid2tar = self.df["int2"].to_dict()
self.df.set_index(iterate, inplace=True)
self.df[iterate] = self.df.index
self.df = add_different_words(self.df)
if split == "test":
assert (
len(self.target_txts) == self.df.shape[0]
), "Test split should have one caption per row"
assert vid_query_method in [
"middle",
"random",
"sample",
], f"Invalid vid_query_method: {vid_query_method}, must be one of middle, random, or sample"
self.frame_loader = FrameLoader(
transform=self.transform, method=vid_query_method, frames_video=vid_frames
)
def __len__(self) -> int:
return len(self.target_txts)
def __getitem__(self, index):
target_txt = self.target_txts[index]
ann = self.df.loc[target_txt]
if ann.ndim > 1:
ann = ann.sample()
ann = ann.iloc[0]
reference_pth = str(ann["path1"])
reference_vid = self.frame_loader(reference_pth)
caption = self.generate_rule_based_edit(ann["diff_txt1"], ann["diff_txt1"])
caption = pre_caption(caption, self.max_words)
target_pth = str(ann["path2"])
target_emb = torch.load(target_pth).cpu()
if self.emb_pool == "middle":
target_emb = target_emb[len(target_emb) // 2]
elif self.emb_pool == "mean":
target_emb = target_emb.mean(0)
elif self.emb_pool == "query":
vid_scores = ast.literal_eval(str(ann["scores"]))
if len(vid_scores) == 0:
vid_scores = [1.0] * len(target_emb)
vid_scores = torch.Tensor(vid_scores)
vid_scores = (vid_scores / 0.1).softmax(dim=0)
target_emb = torch.einsum("f,fe->e", vid_scores, target_emb)
return reference_vid, target_emb, caption, index
@staticmethod
def generate_rule_based_edit(txt1, txt2):
templates = [
"Remove {txt1}",
"Take out {txt1} and add {txt2}",
"Change {txt1} for {txt2}",
"Replace {txt1} with {txt2}",
"Replace {txt1} by {txt2}",
"Replace {txt1} with {txt2}",
"Make the {txt1} into {txt2}",
"Add {txt2}",
"Change it to {txt2}",
]
template = random.choice(templates)
sentence = template.format(txt1=txt1, txt2=txt2)
return sentence
def get_different_word_in_each_sentence(sentence1, sentence2):
sentence1_words = sentence1.lower().replace(".", "").replace(",", "").split()
sentence2_words = sentence2.lower().replace(".", "").replace(",", "").split()
different_word_in_sentence1 = None
different_word_in_sentence2 = None
for w1, w2 in zip(sentence1_words, sentence2_words):
if w1 != w2:
different_word_in_sentence1 = w1
different_word_in_sentence2 = w2
break
return different_word_in_sentence1, different_word_in_sentence2
def add_different_words(df):
diff_txt1s = []
diff_txt2s = []
for row in df.itertuples():
diff_txt1, diff_txt2 = get_different_word_in_each_sentence(row.txt1, row.txt2)
diff_txt1s.append(diff_txt1)
diff_txt2s.append(diff_txt2)
df["diff_txt1"] = diff_txt1s
df["diff_txt2"] = diff_txt2s
df = df[df["diff_txt1"].apply(lambda x: isinstance(x, str))]
df = df[df["diff_txt2"].apply(lambda x: isinstance(x, str))]
return df
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