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import sys
import os
import json
import hashlib
from tqdm import tqdm
sys.path.insert(0, "eval_tools")
from eval_tools.vlm.gpt import GPT
class GenModel:
def __init__(self, model_name, save_mode="video") -> None:
self.save_mode = save_mode
if model_name == "vc2":
from eval_models.VC2.vc2_predict import VideoCrafter
self.predictor = VideoCrafter("vc2")
elif model_name == "vc09":
from eval_models.VC09.vc09_predict import VideoCrafter09
self.predictor = VideoCrafter09()
elif model_name == "modelscope":
from eval_models.modelscope.modelscope_predict import ModelScope
self.predictor = ModelScope()
elif model_name == "latte1":
from eval_models.latte.latte_1_predict import Latte1
self.predictor = Latte1()
elif model_name == "cogvideox-2b":
from eval_models.cogvideox.cogvideox_predict import CogVideoX2B
self.predictor = CogVideoX2B()
elif model_name == "cogvideox-5b":
from eval_models.cogvideox.cogvideox_predict import CogVideoX5B
self.predictor = CogVideoX5B()
elif model_name == "show1":
from eval_models.show1.show1_predict import Show1
self.predictor = Show1()
elif model_name == "animatediff":
from eval_models.animatediff.animatediff_predict import AnimateDiffV2
self.predictor = AnimateDiffV2()
elif model_name == "SDXL-1":
from eval_models.SD.sd_predict import SDXL
self.predictor = SDXL()
elif model_name == "SD-21":
from eval_models.SD.sd_predict import SD21
self.predictor = SD21()
elif model_name == "SD-14":
from eval_models.SD.sd_predict import SD14
self.predictor = SD14()
elif model_name == "SD-3":
from eval_models.SD.sd_predict import SD3
self.predictor = SD3()
else:
raise ValueError(f"This {model_name} has not been implemented yet")
def predict(self, prompt, save_path):
os.makedirs(save_path, exist_ok=True)
# Create a safe filename from the prompt
import hashlib
# Clean the prompt for filename use
clean_prompt = prompt.strip().replace(" ", "_")
clean_prompt = "".join(c for c in clean_prompt if c.isalnum() or c in "_-.")
# Truncate to safe length and add hash for uniqueness
max_length = 200 # Leave room for hash and extension
if len(clean_prompt) > max_length:
# Use first part of prompt + hash of full prompt for uniqueness
prompt_hash = hashlib.md5(prompt.encode()).hexdigest()[:8]
name = f"{clean_prompt[:max_length]}_{prompt_hash}"
else:
name = clean_prompt
if self.save_mode == "video":
save_name = os.path.join(save_path, f"{name}.mp4")
elif self.save_mode == "img":
save_name = os.path.join(save_path, f"{name}.png")
else:
raise NotImplementedError(f"Wrong mode -- {self.save_mode}")
self.predictor.predict(prompt, save_name)
return prompt, save_name
class ToolBox:
def __init__(self) -> None:
pass
def call(self, tool_name, video_pairs):
method = getattr(self, tool_name, None)
if callable(method):
return method(video_pairs)
else:
raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{tool_name}'")
def color_binding(self, image_pairs):
sys.path.insert(0, "eval_tools/t2i_comp/BLIPvqa_eval")
from eval_tools.t2i_comp.BLIPvqa_eval.BLIP_vqa_eval_agent import calculate_attribute_binding
results = calculate_attribute_binding(image_pairs)
return results
def shape_binding(self, image_pairs):
sys.path.insert(0, "eval_tools/t2i_comp/BLIPvqa_eval")
from eval_tools.t2i_comp.BLIPvqa_eval.BLIP_vqa_eval_agent import calculate_attribute_binding
results = calculate_attribute_binding(image_pairs)
return results
def texture_binding(self, image_pairs):
sys.path.insert(0, "eval_tools/t2i_comp/BLIPvqa_eval")
from eval_tools.t2i_comp.BLIPvqa_eval.BLIP_vqa_eval_agent import calculate_attribute_binding
results = calculate_attribute_binding(image_pairs)
return results
def non_spatial(self, image_pairs):
sys.path.insert(0, "eval_tools/t2i_comp/CLIPScore_eval")
from eval_tools.t2i_comp.CLIPScore_eval.CLIP_similarity_eval_agent import calculate_clip_score
results = calculate_clip_score(image_pairs)
return results
def overall_consistency(self, video_pairs):
from eval_tools.vbench.overall_consistency import compute_overall_consistency
results = compute_overall_consistency(video_pairs)
return results
def aesthetic_quality(self, video_pairs):
from eval_tools.vbench.aesthetic_quality import compute_aesthetic_quality
results = compute_aesthetic_quality(video_pairs)
return results
def appearance_style(self, video_pairs):
from eval_tools.vbench.appearance_style import compute_appearance_style
results = compute_appearance_style(video_pairs)
return results
def background_consistency(self, video_pairs):
from eval_tools.vbench.background_consistency import compute_background_consistency
results = compute_background_consistency(video_pairs)
return results
def color(self, video_pairs):
from eval_tools.vbench.color import compute_color
results = compute_color(video_pairs)
return results
def dynamic_degree(self, video_pairs):
from eval_tools.vbench.dynamic_degree import compute_dynamic_degree
results = compute_dynamic_degree(video_pairs)
return results
def human_action(self, video_pairs):
from eval_tools.vbench.human_action import compute_human_action
results = compute_human_action(video_pairs)
return results
def imaging_quality(self, video_pairs):
from eval_tools.vbench.imaging_quality import compute_imaging_quality
results = compute_imaging_quality(video_pairs)
return results
def motion_smoothness(self, video_pairs):
from eval_tools.vbench.motion_smoothness import compute_motion_smoothness
results = compute_motion_smoothness(video_pairs)
return results
def multiple_objects(self, video_pairs):
from eval_tools.vbench.multiple_objects import compute_multiple_objects
results = compute_multiple_objects(video_pairs)
return results
def object_class(self, video_pairs):
from eval_tools.vbench.object_class import compute_object_class
results = compute_object_class(video_pairs)
return results
def scene(self, video_pairs):
from eval_tools.vbench.scene import compute_scene
results = compute_scene(video_pairs)
return results
def spatial_relationship(self, video_pairs):
from eval_tools.vbench.spatial_relationship import compute_spatial_relationship
results = compute_spatial_relationship(video_pairs)
return results
def subject_consistency(self, video_pairs):
from eval_tools.vbench.subject_consistency import compute_subject_consistency
results = compute_subject_consistency(video_pairs)
return results
def temporal_style(self, video_pairs):
from eval_tools.vbench.temporal_style import compute_temporal_style
results = compute_temporal_style(video_pairs)
return results
class ToolCalling:
def __init__(self, sample_model, save_mode):
self.gen = GenModel(sample_model, save_mode)
self.eval_tools = ToolBox()
self.vlm_gpt = GPT()
def sample(self, prompts, save_path):
info_list = []
for prompt in tqdm(prompts):
prompt, content = self.gen.predict(prompt, save_path)
info_list.append({
"prompt":prompt,
"content_path":content
})
return info_list
def eval(self, tool_name, video_pairs):
results = self.eval_tools.call(tool_name, video_pairs)
return results
def vlm_eval(self, content_path, question):
response = self.vlm_gpt.predict(content_path, question)
return response
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