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Running on Zero
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fed6c68 | 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 | # Copyright 2025 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 os
import pandas as pd
import torch
import torchvision
from PIL import Image
from torchvision import transforms
from veomni.utils import logging
logger = logging.get_logger(__name__)
class TensorDataset(torch.utils.data.Dataset):
def __init__(self, base_path, metadata_path, datasets_repeat=1):
metadata = pd.read_csv(metadata_path)
self.path = [os.path.join(base_path, "train", file_name) for file_name in metadata["file_name"]]
logger.info_rank0(f"{len(self.path)} videos in metadata.")
self.path = [i + ".tensors.pth" for i in self.path if os.path.exists(i + ".tensors.pth")]
logger.info_rank0(f"{len(self.path)} tensors cached in metadata.")
assert len(self.path) > 0
self.datasets_repeat = datasets_repeat
def __getitem__(self, index):
data_id = (index) % len(self.path) # For fixed seed.
path = self.path[data_id]
data = torch.load(path, weights_only=True, map_location="cpu")
data["latents"] = data["latents"].squeeze(0)
return [data]
def __len__(self):
return len(self.path) * self.datasets_repeat
class Text2ImageDataset(torch.utils.data.Dataset):
def __init__(
self,
dataset_path,
metadata_path,
height=1024,
width=1024,
center_crop=True,
random_flip=False,
datasets_repeat=1,
):
metadata = pd.read_csv(metadata_path)
self.path = [os.path.join(dataset_path, "train", file_name) for file_name in metadata["file_name"]]
self.text = metadata["text"].to_list()
self.height = height
self.width = width
self.image_processor = transforms.Compose(
[
transforms.CenterCrop((height, width)) if center_crop else transforms.RandomCrop((height, width)),
transforms.RandomHorizontalFlip() if random_flip else transforms.Lambda(lambda x: x),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
logger.info_rank0(f"{len(self.path)} tensors cached in metadata.")
assert len(self.path) > 0
self.datasets_repeat = datasets_repeat
def __getitem__(self, index):
data_id = torch.randint(0, len(self.path), (1,))[0]
data_id = (data_id + index) % len(self.path) # For fixed seed.
text = self.text[data_id]
image = Image.open(self.path[data_id]).convert("RGB")
target_height, target_width = self.height, self.width
width, height = image.size
scale = max(target_width / width, target_height / height)
shape = [round(height * scale), round(width * scale)]
image = torchvision.transforms.functional.resize(
image, shape, interpolation=transforms.InterpolationMode.BILINEAR
)
image = self.image_processor(image)
return [{"text": text, "image": image}]
def __len__(self):
return len(self.path) * self.datasets_repeat
def build_tensor_dataset(base_path, metadata_path, datasets_repeat=1):
return TensorDataset(base_path, metadata_path, datasets_repeat)
def build_text_image_dataset(base_path, metadata_path, height, width, center_crop, random_flip, datasets_repeat=1):
return Text2ImageDataset(base_path, metadata_path, height, width, center_crop, random_flip, datasets_repeat)
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