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| import argparse | |
| import torch | |
| from q_align.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN | |
| from q_align.conversation import conv_templates, SeparatorStyle | |
| from q_align.model.builder import load_pretrained_model | |
| from q_align.mm_utils import process_images, tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria | |
| from PIL import Image | |
| import requests | |
| from PIL import Image | |
| from io import BytesIO | |
| from transformers import TextStreamer | |
| from decord import VideoReader | |
| import json | |
| from tqdm import tqdm | |
| from collections import defaultdict | |
| import os | |
| def disable_torch_init(): | |
| """ | |
| Disable the redundant torch default initialization to accelerate model creation. | |
| """ | |
| import torch | |
| setattr(torch.nn.Linear, "reset_parameters", lambda self: None) | |
| setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None) | |
| def load_video(video_file): | |
| vr = VideoReader(video_file) | |
| # Get video frame rate | |
| fps = vr.get_avg_fps() | |
| # Calculate frame indices for 1fps | |
| frame_indices = [int(fps * i) for i in range(int(len(vr) / fps))] | |
| frames = vr.get_batch(frame_indices).asnumpy() | |
| return [Image.fromarray(frames[i]) for i in range(int(len(vr) / fps))] | |
| def main(args): | |
| # Model | |
| disable_torch_init() | |
| model_name = get_model_name_from_path(args.model_path) | |
| tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, args.load_8bit, args.load_4bit, device=args.device) | |
| import json | |
| image_paths = [ | |
| "playground/data/", | |
| "playground/data/", | |
| "playground/data/KoNViD_1k_videos/", | |
| "playground/data/maxwell/", | |
| ] | |
| json_prefix = "playground/data/test_jsons/" | |
| jsons = [ | |
| json_prefix + "test_lsvq.json", | |
| json_prefix + "test_lsvq_1080p.json", | |
| json_prefix + "konvid.json", | |
| json_prefix + "maxwell_test.json", | |
| ] | |
| os.makedirs(f"results/{args.model_path}/", exist_ok=True) | |
| conv_mode = "mplug_owl2" | |
| inp = "How would you rate the quality of this image?" | |
| conv = conv_templates[conv_mode].copy() | |
| inp = inp + "\n" + DEFAULT_IMAGE_TOKEN | |
| conv.append_message(conv.roles[0], inp) | |
| image = None | |
| conv.append_message(conv.roles[1], None) | |
| prompt = conv.get_prompt() + " The quality of the image is" | |
| toks = ["good", "poor", "high", "fair", "low", "excellent", "bad", "fine", "moderate", "decent", "average", "medium", "acceptable"] | |
| print(toks) | |
| ids_ = [id_[1] for id_ in tokenizer(toks)["input_ids"]] | |
| print(ids_) | |
| input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(args.device) | |
| for image_path, json_ in zip(image_paths, jsons): | |
| with open(json_) as f: | |
| iqadata = json.load(f) | |
| try: | |
| for i, llddata in enumerate(tqdm(iqadata, desc="Evaluating [{}]".format(json_.split("/")[-1]))): | |
| filename = llddata["img_path"] | |
| llddata["logits"] = defaultdict(float) | |
| image = load_video(image_path + filename) | |
| def expand2square(pil_img, background_color): | |
| width, height = pil_img.size | |
| if width == height: | |
| return pil_img | |
| elif width > height: | |
| result = Image.new(pil_img.mode, (width, width), background_color) | |
| result.paste(pil_img, (0, (width - height) // 2)) | |
| return result | |
| else: | |
| result = Image.new(pil_img.mode, (height, height), background_color) | |
| result.paste(pil_img, ((height - width) // 2, 0)) | |
| return result | |
| image = [expand2square(img, tuple(int(x*255) for x in image_processor.image_mean)) for img in image] | |
| image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].half().to(args.device) | |
| if True: | |
| with torch.inference_mode(): | |
| output_logits = model(input_ids.repeat(image_tensor.shape[0], 1), | |
| images=image_tensor)["logits"][:,-1] | |
| for tok, id_ in zip(toks, ids_): | |
| llddata["logits"][tok] += output_logits.mean(0)[id_].item() | |
| # print(llddata) | |
| json_ = json_.replace("combined/", "combined-") | |
| with open(f"results/{args.model_path}/{json_.split('/')[-1]}", "a") as wf: | |
| json.dump(llddata, wf) | |
| except: | |
| continue | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--model-path", type=str, default="q-future/q-align-image") | |
| parser.add_argument("--model-base", type=str, default=None) | |
| parser.add_argument("--device", type=str, default="cuda:0") | |
| parser.add_argument("--conv-mode", type=str, default=None) | |
| parser.add_argument("--temperature", type=float, default=0.2) | |
| parser.add_argument("--max-new-tokens", type=int, default=512) | |
| parser.add_argument("--load-8bit", action="store_true") | |
| parser.add_argument("--load-4bit", action="store_true") | |
| parser.add_argument("--debug", action="store_true") | |
| parser.add_argument("--image-aspect-ratio", type=str, default='pad') | |
| args = parser.parse_args() | |
| main(args) |