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import subprocess
from typing import List, Dict, Any
from dataclasses import dataclass
from abc import ABC, abstractmethod
from PIL import Image
from pathlib import Path
import numpy as np
import cv2
import clip
import torch
from torch import nn
import torch.nn.functional as F
from typing import Tuple
import os
import json
from diffusers import CogVideoXPipeline
from diffusers.utils import export_to_video
from video_generation_evaluation.toolkit.fvd import get_dataset_features, I3DFeatureExtractor
from numpy import cov
from numpy import mean
from scipy.linalg import sqrtm
from video_generation_evaluation.evaluate import task2dimension
class BaseTask(ABC):
def __init__(self, task_data: str, model):
self.task_data = task_data
self.model = model
self.data = self._parse_data(task_data)
@abstractmethod
def _parse_data(self, task_data: Dict[str, Any]):
pass
@abstractmethod
def evaluate(self) -> Dict[str, float]:
pass
@abstractmethod
def run_inference(self):
pass
class T2VTask(BaseTask):
def _parse_result_file(self, output_dir: Path) -> float | None:
for jsonfile in output_dir.iterdir():
if "eval" in jsonfile.name:
with open(jsonfile.as_posix(), "r") as file:
data = json.load(file)
return float(data[self.taskname][0])
def _parse_data(self, task_data):
with open(task_data, "r") as file:
annos = json.load(file)
taskname = annos["task"].replace(" ", "")
self.taskname = taskname
self.save_root = os.path.join("General-Bench", "Video-Generation", taskname)
return annos["data"]
def run_inference(self):
for d in self.data:
prompt = d["input"]["prompt"]
for i in range(5):
video = self.model(prompt, generator=torch.Generator(self.model.device).manual_seed(i)).frames[0]
save_name = prompt + "-" + str(i) + ".mp4"
save_path = os.path.join(self.save_root, save_name)
export_to_video(video, save_path, fps=8)
class FVDEval(T2VTask):
def evaluate(self, real_video_root):
model = I3DFeatureExtractor().cuda().eval()
real_features = get_dataset_features(real_video_root, model)
generated_features = get_dataset_features(self.save_root, model)
mu_real = mean(real_features, axis=0)
mu_generated = mean(generated_features, axis=0)
sigma_real = cov(real_features, rowvar=False)
sigma_generated = cov(generated_features, rowvar=False)
diff = mu_real - mu_generated
covmean, _ = sqrtm(sigma_real.dot(sigma_generated), disp=False)
if np.iscomplexobj(covmean):
covmean = covmean.real
fvd = diff.dot(diff) + np.trace(sigma_real + sigma_generated - 2 * covmean)
print(f"{self.taskname} score: {fvd}")
return fvd
class ThirdPartyEval(T2VTask):
def evaluate(self):
videos_path = Path(self.save_root).resolve()
dimension = task2dimension[self.taskname]
full_info = Path("./full_info_t2v.json").resolve()
output_dir = Path("./evaluation_results").resolve()
output_dir = output_dir.joinpath(self.taskname)
output_dir.mkdir(parents=True, exist_ok=True)
cmd = [
"python", "-W", "ignore", "evaluate.py",
"--full_json_dir", str(full_info),
"--videos_path", str(videos_path),
"--dimension", dimension,
"--output_path", str(output_dir)
]
try:
subprocess.run(cmd, check=True)
except subprocess.CalledProcessError as exc:
raise RuntimeError(f"Evaluation failed: {exc}") from exc
score = self._parse_result_file(Path(output_dir))
print(f"{self.taskname} score: {score}")
return score
class I2VTask(BaseTask):
def _parse_result_file(self, output_dir: Path) -> float | None:
score = 0
for jsonfile in output_dir.iterdir():
if "eval" in jsonfile.name:
with open(jsonfile.as_posix(), "r") as file:
data: dict = json.load(file)
score += list(data.values())[0][0]
return score
def _parse_data(self, task_data):
self.dirpath = os.path.dirname(task_data)
with open(task_data, "r") as file:
annos = json.load(file)
taskname = annos["task"].replace(" ", "")
self.taskname = taskname
self.dimensions = ("subject_consistency", "overall_consistency", "motion_smoothness", "dynamic_degree")
self.save_root = os.path.join("General-Bench", "Video-Generation", taskname)
def run_inference(self):
for d in self.data:
prompt = d["input"]["prompt"]
image = d["input"]["image"]
image = os.path.join(self.dirpath, image)
for i in range(5):
video = self.model(
prompt=prompt,
image=image,
generator=torch.Generator(self.model.device).manual_seed(i)
).frames[0]
save_name = prompt + "-" + str(i) + ".mp4"
save_path = os.path.join(self.save_root, save_name)
export_to_video(video, save_path, fps=8)
def evaluate(self):
taskname = self.taskname
full_info = Path("./full_info_i2v.json").resolve()
output_dir = Path("./evaluation_results").resolve()
output_dir = output_dir.joinpath(taskname)
output_dir.mkdir(parents=True, exist_ok=True)
for dimension in self.dimensions:
cmd = [
"python", "-W", "ignore", "evaluate.py",
"--full_json_dir", str(full_info),
"--videos_path", str(self.save_root),
"--dimension", dimension,
"--output_path", str(output_dir)
]
try:
subprocess.run(cmd, check=True)
except subprocess.CalledProcessError as exc:
raise RuntimeError(f"Evaluation failed: {exc}") from exc
score = self._parse_result_file(Path(output_dir))
print(f"{self.taskname} score: {score}")
return score
class AthleticsT2V(FVDEval): pass
class HumanT2V(FVDEval): pass
class ConcertT2V(FVDEval): pass
class TerrestrialAnimalT2V(FVDEval): pass
class WaterSportsT2V(FVDEval): pass
class ActionT2V(ThirdPartyEval): pass
class ArtisticT2V(ThirdPartyEval): pass
class BackgroundConsistency(ThirdPartyEval): pass
class CameraMotionT2V(ThirdPartyEval): pass
class ClassConditionedT2V(ThirdPartyEval): pass
class ColorT2V(ThirdPartyEval): pass
class DynamicT2V(ThirdPartyEval): pass
class MaterialT2V(ThirdPartyEval): pass
class MultiClassConditionedT2V(ThirdPartyEval): pass
class SceneT2V(ThirdPartyEval): pass
class SpatialRelationT2V(ThirdPartyEval): pass
class StaticT2V(ThirdPartyEval): pass
class StyleT2V(ThirdPartyEval): pass
class ArchitectureI2V(I2VTask): pass
class ClothI2V(I2VTask): pass
class FoodI2V(I2VTask): pass
class FurnitureI2V(I2VTask): pass
class HumanI2V(I2VTask): pass
class PetI2V(I2VTask): pass
class PlantI2V(I2VTask): pass
class SceneI2V(I2VTask): pass
class VehicleI2V(I2VTask): pass
class WeatherI2V(I2VTask): pass
class WildAnimalI2V(I2VTask): pass
if __name__ == "__main__":
root = Path("General-Bench-Openset/video/generation")
task_type = "T2V"
model = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-2b", torch_dtype=torch.bfloat16).to("cuda")
task_files = [
"AthleticsT2V",
"HumanT2V",
"ConcertT2V",
"TerrestrialAnimalT2V",
"WaterSportsT2V",
"ActionT2V",
"ArtisticT2V",
"BackgroundConsistency",
"CameraMotionT2V",
"ClassConditionedT2V",
"ColorT2V",
"DynamicT2V",
"MaterialT2V",
"MultiClassConditionedT2V",
"SceneT2V",
"SpatialRelationT2V",
"StaticT2V",
"StyleT2V",
"ArchitectureI2V",
"ClothI2V",
"FoodI2V",
"FurnitureI2V",
"HumanI2V",
"PetI2V",
"PlantI2V",
"SceneI2V",
"VehicleI2V",
"WeatherI2V",
"WildAnimalI2V",
]
task_files = [root.joinpath(task, "annotation.json") for task in task_files]
for idx, file in enumerate(task_files):
if file.exists():
continue
with open(file.as_posix(), 'r', encoding='utf-8') as f:
task_data = json.load(f)
task_name = task_data["task"]
print(f"Running evaluation for task {idx + 1}: {task_name}")
TASK_MAPPING = {
"AthleticsT2V": AthleticsT2V,
"HumanT2V": HumanT2V,
"ConcertT2V": ConcertT2V,
"TerrestrialAnimalT2V": TerrestrialAnimalT2V,
"WaterSportsT2V": WaterSportsT2V,
"ActionT2V": ActionT2V,
"ArtisticT2V": ArtisticT2V,
"BackgroundConsistency": BackgroundConsistency,
"CameraMotionT2V": CameraMotionT2V,
"ClassConditionedT2V": ClassConditionedT2V,
"ColorT2V": ColorT2V,
"DynamicT2V": DynamicT2V,
"MaterialT2V": MaterialT2V,
"MultiClassConditionedT2V": MultiClassConditionedT2V,
"SceneT2V": SceneT2V,
"SpatialRelationT2V": SpatialRelationT2V,
"StaticT2V": StaticT2V,
"StyleT2V": StyleT2V,
"ArchitectureI2V": ArchitectureI2V,
"ClothI2V": ClothI2V,
"FoodI2V": FoodI2V,
"FurnitureI2V": FurnitureI2V,
"HumanI2V": HumanI2V,
"PetI2V": PetI2V,
"PlantI2V": PlantI2V,
"SceneI2V": SceneI2V,
"VehicleI2V": VehicleI2V,
"WeatherI2V": WeatherI2V,
"WildAnimalI2V": WildAnimalI2V,
}
clean_task_name = task_name.replace(" ", "")
task_class = TASK_MAPPING.get(clean_task_name)
if task_class is None:
raise NotImplementedError
elif task_type not in clean_task_name:
continue
else:
task = task_class(file.as_posix(), model)
task.run_inference()
metrics = task.evaluate()
print("Task name: ", task_name, "Task type: ", task_type, "Evaluation results:", metrics) |