import os import shutil import time import numpy as np import torch import torchvision.transforms as T from decord import VideoReader, cpu from PIL import Image from torchvision.transforms.functional import InterpolationMode from transformers import AutoModel, AutoTokenizer # model setting model_path = './' device = torch.device("cuda:0") tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) model = AutoModel.from_pretrained(model_path, trust_remote_code=True).half().to(device).to(torch.bfloat16) IMAGENET_MEAN = (0.485, 0.456, 0.406) IMAGENET_STD = (0.229, 0.224, 0.225) def build_transform(input_size): MEAN, STD = IMAGENET_MEAN, IMAGENET_STD transform = T.Compose([T.Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img), T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC), T.ToTensor(), T.Normalize(mean=MEAN, std=STD)]) return transform def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size): best_ratio_diff = float("inf") best_ratio = (1, 1) area = width * height for ratio in target_ratios: target_aspect_ratio = ratio[0] / ratio[1] ratio_diff = abs(aspect_ratio - target_aspect_ratio) if ratio_diff < best_ratio_diff: best_ratio_diff = ratio_diff best_ratio = ratio elif ratio_diff == best_ratio_diff: if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]: best_ratio = ratio return best_ratio def dynamic_preprocess(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False): orig_width, orig_height = image.size aspect_ratio = orig_width / orig_height # calculate the existing image aspect ratio target_ratios = set((i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if i * j <= max_num and i * j >= min_num) target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) # find the closest aspect ratio to the target target_aspect_ratio = find_closest_aspect_ratio(aspect_ratio, target_ratios, orig_width, orig_height, image_size) # calculate the target width and height target_width = image_size * target_aspect_ratio[0] target_height = image_size * target_aspect_ratio[1] blocks = target_aspect_ratio[0] * target_aspect_ratio[1] # resize the image resized_img = image.resize((target_width, target_height)) processed_images = [] for i in range(blocks): box = ((i % (target_width // image_size)) * image_size, (i // (target_width // image_size)) * image_size, ((i % (target_width // image_size)) + 1) * image_size, ((i // (target_width // image_size)) + 1) * image_size) # split the image split_img = resized_img.crop(box) processed_images.append(split_img) assert len(processed_images) == blocks if use_thumbnail and len(processed_images) != 1: thumbnail_img = image.resize((image_size, image_size)) processed_images.append(thumbnail_img) return processed_images def load_image(image, input_size=448, max_num=6): transform = build_transform(input_size=input_size) images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num) pixel_values = [transform(image) for image in images] pixel_values = torch.stack(pixel_values) return pixel_values def get_index(bound, fps, max_frame, first_idx=0, num_segments=32): if bound: start, end = bound[0], bound[1] else: start, end = -100000, 100000 start_idx = max(first_idx, round(start * fps)) end_idx = min(round(end * fps), max_frame) seg_size = float(end_idx - start_idx) / num_segments frame_indices = np.array([int(start_idx + (seg_size / 2) + np.round(seg_size * idx)) for idx in range(num_segments)]) return frame_indices def get_num_frames_by_duration(duration): local_num_frames = 1 num_segments = int(duration // local_num_frames) if num_segments == 0: num_frames = local_num_frames else: num_frames = local_num_frames * num_segments num_frames = min(512, num_frames) num_frames = max(1, num_frames) return num_frames def load_video(video_path, bound=None, input_size=448, max_num=1, num_segments=32, get_frame_by_duration = False): vr = VideoReader(video_path, ctx=cpu(0), num_threads=1) max_frame = len(vr) - 1 fps = float(vr.get_avg_fps()) video_name = os.path.splitext(os.path.basename(video_path))[0] save_dir = f'./examples/frames/{video_name}' if os.path.exists(save_dir): save_flag = False else: save_flag = True os.makedirs(save_dir, exist_ok=True) destination_path = f'./examples/videos/{os.path.basename(video_path)}' os.makedirs(destination_path, exist_ok=True) shutil.copy(video_path, destination_path) print(f"Video copied to {destination_path}") pixel_values_list, num_patches_list = [], [] transform = build_transform(input_size=input_size) if get_frame_by_duration: duration = max_frame / fps num_segments = get_num_frames_by_duration(duration) frame_indices = get_index(bound, fps, max_frame, first_idx=0, num_segments=num_segments) for i in range(len(frame_indices)): img = Image.fromarray(vr[frame_indices[i]].asnumpy()).convert("RGB") if save_flag: save_path = os.path.join(save_dir, f'frame_{i+1}.png') img.save(save_path) img = dynamic_preprocess(img, image_size=input_size, use_thumbnail=True, max_num=max_num) pixel_values = [transform(tile) for tile in img] pixel_values = torch.stack(pixel_values) num_patches_list.append(pixel_values.shape[0]) pixel_values_list.append(pixel_values) pixel_values = torch.cat(pixel_values_list) return pixel_values, num_patches_list # evaluation setting max_num_frames = 512 generation_config = dict( max_new_tokens=1024, num_beams=1, repetition_penalty = 1.05 ) video_path = "./demo.mp4" temporal_questions = { 2: "Where is the man lying in the video?", 4: "What could be the possible relationship between him and the person next to him?", 8: "What is the woman in the video doing?", 10: "What is the expression of the woman in the video?", 12: "What is his reaction?", 13: "Is there a thermos on the table beside the hospital bed?", 14: "Is there any tissue on the table?", 20: "What color is the woman's clothing?", 26: "How does the color of the bed differ from it?", 41: "What is the girl in the video doing?", 46: "What does the boy in the video say?", 49: "How is his tone when he speaks?", 50: "From what he said, could this woman be his mother?", 64: "What is the expression in the boy's eyes?", 73: "What else does the boy say?", 74: "What animal is this toy?", 75: "What color is the toy in the boy's memory?", 78: "What's the difference between the scene with the door and the scene with the frog toy that appeared before?", 79: "Is there a lock on the door?", 81: "Are there any plants on the hospital room window?", 87: "What's on the boy's back?", 88: "What did the girl do to the scar?", 92: "Does the girl have any special facial expression while wiping the scar?" } with torch.no_grad(): pixel_values, num_patches_list = load_video(video_path, max_num=1, get_frame_by_duration=True) pixel_values = pixel_values.to(torch.bfloat16).to(model.device) batch_frame = 1 start_time = time.time() chat_history = None question = '' for i in range(0, 100, batch_frame): video_frame = "".join([f"Frame-{i+j+1}: \n" for j in range(batch_frame)]) question += video_frame if (i + 1) in temporal_questions: question += temporal_questions[i + 1] + "\n" output_last, chat_history = model.chat( tokenizer, pixel_values[:i+batch_frame, ...], question, generation_config, num_patches_list=num_patches_list[:i+batch_frame], history=chat_history, return_history=True ) print(f"{'Frame' + str(i+1):<15} {'Q: ' + temporal_questions[i+1]:<50}") print(f"{'':<15} {'A: ' + output_last:<50}") question = '' else: print(f"{'Frame' + str(i+1):<15} {'Keep watching...':<50}") end_time = time.time() print("Program runtime:", end_time - start_time, "seconds")