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import autoroot
import os
import warnings
from pathlib import Path
from typing import Any, Optional
import numpy as np
import open_clip
import torch
import torchvision
import webdataset as wds
from einops import rearrange
from ipdb import set_trace as st
from torch.utils.data import Dataset
from gen import DEFAULT_PROMPT, MOVI_DATASET_PATH, MOVI_MEDIUM_PATH, MOVI_OVERFIT_DATASET_PATH
from gen.configs.utils import inherit_parent_args
from gen.datasets.augmentation.kornia_augmentation import Augmentation, Data
from gen.datasets.base_dataset import AbstractDataset, Split
from gen.datasets.utils import get_open_clip_transforms_v2, get_stable_diffusion_transforms
torchvision.disable_beta_transforms_warning()
import torchvision.transforms.v2 as transforms
from torchvision.tv_tensors import BoundingBoxes, BoundingBoxFormat, Image, Mask
from gen.utils.tokenization_utils import get_tokens
@inherit_parent_args
class MoviDataset(AbstractDataset, Dataset):
def __init__(
self,
*,
tokenizer: Optional[Any] = None,
path: Path = MOVI_DATASET_PATH,
resolution: int = 512,
override_text: bool = True,
dataset: str = "movi_e",
num_frames: int = 24,
num_objects: int = 23,
augmentation: Optional[Augmentation] = Augmentation(),
custom_split: Optional[str] = None,
subset: Optional[tuple[str]] = None,
return_video: bool = False,
fake_return_n: Optional[int] = None,
use_single_mask: bool = False, # Force using a single mask with all 1s
num_cameras: int = 1,
multi_camera_format: bool = False,
**kwargs,
):
# Note: The super __init__ is handled by inherit_parent_args
self.tokenizer = tokenizer
self.root = path # Path to the dataset containing folders of "movi_a", "movi_e", etc.
self.dataset = dataset # str of dataset name (e.g. "movi_a")
self.resolution = resolution
self.return_video = return_video
self.fake_return_n = fake_return_n
self.use_single_mask = use_single_mask
self.new_format = multi_camera_format
self.num_cameras = num_cameras
if num_cameras > 1: assert multi_camera_format
local_split = ("train" if self.split == Split.TRAIN else "validation")
local_split = local_split if custom_split is None else custom_split
self.root_dir = self.root / self.dataset / local_split
if subset is not None:
self.files = subset
else:
self.files = os.listdir(self.root_dir)
self.files.sort()
self.num_frames = num_frames
self.num_classes = num_objects
self.augmentation = augmentation
if self.split == Split.VALIDATION:
self.augmentation.set_validation()
self.override_text = override_text
if self.override_text:
warnings.warn(f"Overriding text captions with {DEFAULT_PROMPT}")
def get_dataset(self):
return self
def collate_fn(self, batch):
return torch.utils.data.default_collate(batch)
def map_idx(self, idx):
file_idx = idx // (self.num_cameras * self.num_frames)
camera_idx = (idx % (self.num_cameras * self.num_frames)) // self.num_frames
frame_idx = idx % self.num_frames
return file_idx, camera_idx, frame_idx
def __getitem__(self, index):
file_idx, camera_idx, frame_idx = self.map_idx(index)
if self.fake_return_n:
file_idx = 0
try:
path = self.files[file_idx]
except IndexError:
print(f"Index {file_idx} is out of bounds for dataset of size {len(self.files)} for dir: {self.root_dir}")
raise
ret = {}
if self.new_format:
data = np.load(self.root_dir / path / "data.npz")
rgb = data["rgb"][camera_idx, frame_idx]
instance = data["segment"][camera_idx, frame_idx]
quaternions = data["quaternions"][camera_idx, frame_idx] # (23, 4)
positions = data["positions"][camera_idx, frame_idx] # (23, 3)
valid = data["valid"][camera_idx, :].squeeze(0) # (23, )
categories = data["categories"][camera_idx, :].squeeze(0) # (23, )
ret.update({
"quaternions": quaternions,
"positions": positions,
"valid": valid,
"categories": categories,
})
else:
assert self.num_cameras == 1 and camera_idx == 0
rgb = os.path.join(self.root_dir, os.path.join(path, "rgb.npy"))
instance = os.path.join(self.root_dir, os.path.join(path, "segment.npy"))
bbx = os.path.join(self.root_dir, os.path.join(path, "bbox.npy"))
rgb = np.load(rgb)
bbx = np.load(bbx)
instance = np.load(instance)
if self.num_frames == 1 and rgb.shape[0] > 1:
# Get middle frame
frame_idx = rgb.shape[0] // 2
rgb = rgb[frame_idx]
instance = instance[frame_idx]
bbx = bbx[frame_idx]
bbx[..., [0, 1]] = bbx[..., [1, 0]]
bbx[..., [2, 3]] = bbx[..., [3, 2]]
bbx[..., [0, 2]] *= rgb.shape[1]
bbx[..., [1, 3]] *= rgb.shape[0]
bbx = torch.from_numpy(bbx)
assert rgb.shape[0] == rgb.shape[1]
rgb = rearrange(rgb, "... h w c -> ... c h w") / 255.0 # [0, 1]
source_data, target_data = self.augmentation(
source_data=Data(image=torch.from_numpy(rgb[None]).float(), segmentation=torch.from_numpy(instance[None].squeeze(-1)).float()),
target_data=Data(image=torch.from_numpy(rgb[None]).float(), segmentation=torch.from_numpy(instance[None].squeeze(-1)).float()),
)
# We have -1 as invalid so we simply add 1 to all the labels to make it start from 0 and then later remove the 1st channel
source_data.image = source_data.image.squeeze(0)
source_data.segmentation = torch.nn.functional.one_hot(source_data.segmentation.squeeze(0).long() + 1, num_classes=self.num_classes + 2)[..., 1:]
target_data.image = target_data.image.squeeze(0)
target_data.segmentation = torch.nn.functional.one_hot(target_data.segmentation.squeeze(0).long() + 1, num_classes=self.num_classes + 2)[..., 1:]
if self.use_single_mask:
source_data.segmentation = torch.ones_like(source_data.segmentation)[..., [0]]
target_data.segmentation = torch.ones_like(target_data.segmentation)[..., [0]]
ret.update({
"gen_pixel_values": target_data.image,
"gen_grid": target_data.grid,
"gen_segmentation": target_data.segmentation,
"disc_pixel_values": source_data.image,
"disc_grid": source_data.grid,
"disc_segmentation": source_data.segmentation,
"input_ids": get_tokens(self.tokenizer),
})
if self.return_video:
ret["video"] = path
return ret
def __len__(self):
init_size = len(self.files) * self.num_frames * self.num_cameras
return init_size * self.fake_return_n if self.fake_return_n else init_size
if __name__ == "__main__":
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32")
dataset = MoviDataset(
cfg=None,
split=Split.TRAIN,
num_workers=0,
batch_size=1,
shuffle=True,
subset_size=None,
dataset="movi_e",
num_frames=24,
tokenizer=tokenizer,
path=MOVI_OVERFIT_DATASET_PATH,
num_objects=1,
augmentation=Augmentation(minimal_source_augmentation=True, enable_crop=True, enable_horizontal_flip=True),
return_video=True
)
new_dataset = MoviDataset(
cfg=None,
split=Split.TRAIN,
num_workers=0,
batch_size=1,
shuffle=True,
subset_size=None,
dataset="movi_e",
tokenizer=tokenizer,
path=MOVI_MEDIUM_PATH,
num_objects=23,
num_frames=8,
num_cameras=2,
augmentation=Augmentation(target_resolution=256, minimal_source_augmentation=True, enable_crop=True, enable_horizontal_flip=True),
return_video=True,
multi_camera_format=True,
)
dataloader = new_dataset.get_dataloader()
for batch in dataloader:
from image_utils import Im, get_layered_image_from_binary_mask
gen_ = Im.concat_vertical(Im((batch['gen_pixel_values'][0] + 1) / 2), Im(get_layered_image_from_binary_mask(batch['gen_segmentation'].squeeze(0))))
disc_ = Im.concat_vertical(Im((batch['disc_pixel_values'][0] + 1) / 2), Im(get_layered_image_from_binary_mask(batch['disc_segmentation'].squeeze(0))))
print(batch['gen_segmentation'].sum() / batch['gen_segmentation'][0, ..., 0].numel(), batch['disc_segmentation'].sum() / batch['disc_segmentation'][0, ..., 0].numel())
Im.concat_horizontal(gen_, disc_).save(batch['video'][0])
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