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b/VideoX-Fun/visualization/qwen25vl_gradient_heatmap.py @@ -0,0 +1,519 @@ +import argparse +import json +import os +import re +import math +from torchvision import io, transforms +from torchvision.transforms import InterpolationMode + +from pathlib import Path +import pdb +import matplotlib.pyplot as plt +import numpy as np +import torch +import torch.nn.functional as F +from tqdm import tqdm +from transformers import AutoModelForImageTextToText, AutoProcessor +try: + import cv2 +except ImportError: + cv2 = None +try: + from scipy.ndimage import gaussian_filter +except ImportError: + gaussian_filter = None + + +def smart_resize(height, width, factor=28, min_pixels=56 * 56, max_pixels=14 * 14 * 4 * 1280): + import math + + if max(height, width) / min(height, width) > 200: + raise ValueError(f"absolute aspect ratio must be smaller than 200, got {max(height, width) / min(height, width)}") + h_bar = round(height / factor) * factor + w_bar = round(width / factor) * factor + if h_bar * w_bar > max_pixels: + beta = math.sqrt((height * width) / max_pixels) + h_bar = max(factor, math.floor(height / beta / factor) * factor) + w_bar = max(factor, math.floor(width / beta / factor) * factor) + elif h_bar * w_bar < min_pixels: + beta = math.sqrt(min_pixels / (height * width)) + h_bar = math.ceil(height * beta / factor) * factor + w_bar = math.ceil(width * beta / factor) * factor + return int(h_bar), int(w_bar) + +def smart_resize( + height: int, width: int, factor: int = 28, min_pixels: int = 56 * 56, max_pixels: int = 14 * 14 * 4 * 1280 +): + """Rescales the image so that the following conditions are met: + + 1. Both dimensions (height and width) are divisible by 'factor'. + + 2. The total number of pixels is within the range ['min_pixels', 'max_pixels']. + + 3. The aspect ratio of the image is maintained as closely as possible. + + """ + if max(height, width) / min(height, width) > 200: + raise ValueError( + f"absolute aspect ratio must be smaller than 200, got {max(height, width) / min(height, width)}" + ) + h_bar = round(height / factor) * factor + w_bar = round(width / factor) * factor + if h_bar * w_bar > max_pixels: + beta = math.sqrt((height * width) / max_pixels) + h_bar = max(factor, math.floor(height / beta / factor) * factor) + w_bar = max(factor, math.floor(width / beta / factor) * factor) + elif h_bar * w_bar < min_pixels: + beta = math.sqrt(min_pixels / (height * width)) + h_bar = math.ceil(height * beta / factor) * factor + w_bar = math.ceil(width * beta / factor) * factor + return h_bar, w_bar + +def resize_frames(frames, max_frame_pixels): + + B, C, T, H, W = frames.shape + x = frames.permute(0, 2, 1, 3, 4) # [B, T, C, H, W] + resized_height, resized_width = smart_resize( + H, + W, + factor=28, # image factor + min_pixels=16384, # 128*128 + max_pixels=max_frame_pixels, + ) + frames_resized = [] + for v in x: + v_r = transforms.functional.resize( + v, + [resized_height, resized_width], + interpolation=InterpolationMode.BICUBIC, + antialias=True, + ).float() + frames_resized.append(v_r) + + frames_resized = torch.stack(frames_resized) + + return frames_resized + +def read_video(video_path, num_frames, resize_factor, min_pixels, max_pixels): + try: + import decord + from decord import VideoReader, cpu + except ImportError as e: + raise ImportError("decord is required for reading videos. Please install decord.") from e + decord.bridge.set_bridge("torch") + + vr = VideoReader(video_path, ctx=cpu(0)) + total_frames = len(vr) + if total_frames == 0: + raise ValueError(f"Empty video: {video_path}") + idx = torch.linspace(0, total_frames - 1, num_frames).round().long().tolist() + video = vr.get_batch(idx).permute(0, 3, 1, 2).float() / 255.0 + _, _, h0, w0 = video.shape + video = resize_frames(video.unsqueeze(0), max_pixels).permute(0, 2, 1, 3, 4)[0] # [B, C, T, H, W] -> [B, T, C, H, W] + + return video + + +def extract_yes_no_from_gt(gt_text): + if not gt_text: + return None + try: + obj = json.loads(gt_text) + ans = str(obj.get("answer", "")).strip().lower() + if ans in {"yes", "no"}: + return ans + except Exception: + pass + m = re.search(r'"answer"\s*:\s*"(Yes|No|yes|no)"', gt_text) + if m: + return m.group(1).lower() + return None + + +def token_id_set_for_yes_no(tokenizer, label=None): + token_texts = [] + if label == "yes": + token_texts = ["Yes", "yes", " Yes", " yes"] + elif label == "no": + token_texts = ["No", "no", " No", " no"] + else: + token_texts = ["Yes", "yes", " Yes", " yes", "No", "no", " No", " no"] + + token_ids = set() + for t in token_texts: + ids = tokenizer.encode(t, add_special_tokens=False) + if ids: + token_ids.add(ids[0]) + return token_ids + + +def get_yes_token_id(tokenizer): + for tok in ["Yes", " Yes", "yes", " yes"]: + ids = tokenizer.encode(tok, add_special_tokens=False) + if ids: + return ids[0] + raise RuntimeError("Cannot find a valid Yes token id in tokenizer.") + + +def find_yes_no_index_at_end(input_ids_1d, tokenizer, answer_label=None, search_window=256): + seq = input_ids_1d.tolist() if hasattr(input_ids_1d, "tolist") else list(input_ids_1d) + im_start_id = tokenizer.convert_tokens_to_ids("<|im_start|>") + target_ids = token_id_set_for_yes_no(tokenizer, label=answer_label) + + # Align with preprocess logic: search backward near the end. + # pdb.set_trace() + start_pos = len(seq) - 1 + end_pos = max(0, len(seq) - search_window) + for i in range(start_pos, end_pos, -1): + token_id = seq[i] + if token_id == im_start_id: + break + if token_id in target_ids: + return i + return None + + +def infer_token_grid(video_grid_thw, token_count, merge_size): + grid_t, grid_h, grid_w = [int(x) for x in video_grid_thw] + cands = [] + if merge_size > 0 and grid_h % merge_size == 0 and grid_w % merge_size == 0: + cands.append((grid_t, grid_h // merge_size, grid_w // merge_size)) + cands.append((grid_t, grid_h, grid_w)) + + for t, h, w in cands: + if t * h * w == token_count: + return t, h, w + raise ValueError( + f"Cannot match token_count={token_count} with video_grid={video_grid_thw} and merge_size={merge_size}" + ) + + +def plot_smooth_heatmap_with_merge_correction( + input_video, + video_grid, + token_grads, + merge_size=2, + blur_sigma=5.0, + save_path="heatmap_smooth.png" +): + """ + blur_sigma: 高斯模糊的半径。 + 如果是一个 float (e.g., 5.0)，则在时间、高度、宽度上应用相同的模糊。 + 如果是一个 tuple (e.g., (1.5, 8.0, 8.0))，则分别对应 (T, H, W) 方向的模糊程度。 + """ + T_in, C, H_in, W_in = input_video.shape + grid_t, grid_h, grid_w = video_grid + + token_t = grid_t + token_h = grid_h // merge_size + token_w = grid_w // merge_size + + assert token_grads.numel() == token_t * token_h * token_w + + grads = token_grads.detach().cpu().float() + if grads.dim() > 1: + grads = grads.norm(dim=-1) + grads = (grads - grads.min()) / (grads.max() - grads.min() + 1e-8) + heatmap_small = grads.view(1, 1, token_t, token_h, token_w) + + heatmap_upsampled = F.interpolate( + heatmap_small, + size=(T_in, H_in, W_in), + mode='trilinear', + align_corners=False + ).squeeze() + + heatmap_np = heatmap_upsampled.numpy() + + if isinstance(blur_sigma, float) or isinstance(blur_sigma, int): + sigma_spatial = blur_sigma + sigma_temporal = max(0.5, blur_sigma / 3.0) + current_sigma = (sigma_temporal, sigma_spatial, sigma_spatial) + else: + current_sigma = blur_sigma + + heatmap_smooth = gaussian_filter(heatmap_np, sigma=current_sigma) + heatmap_smooth = (heatmap_smooth - heatmap_smooth.min()) / (heatmap_smooth.max() - heatmap_smooth.min() + 1e-8) + + video_np = input_video.permute(0, 2, 3, 1).cpu().numpy() + video_np = (video_np - video_np.min()) / (video_np.max() - video_np.min()) + + fig, axes = plt.subplots(2, T_in, figsize=(T_in * 2, 5)) + plt.subplots_adjust(wspace=0.02, hspace=0.02, left=0.01, right=0.99) + + for t in range(T_in): + img = video_np[t] + hm = heatmap_smooth[t] + + ax_top = axes[0, t] + ax_top.imshow(img) + ax_top.imshow(hm, cmap='jet', alpha=0.5, vmin=0, vmax=1) + ax_top.axis('off') + + ax_bot = axes[1, t] + ax_bot.imshow(img) + ax_bot.axis('off') + + if t == 0: + ax_top.set_title("Smooth Heatmap", fontsize=10, x=-0.1, y=0.5, rotation=90) + ax_bot.set_title("Original", fontsize=10, x=-0.1, y=0.5, rotation=90) + ax_bot.text(0.5, -0.05, f"t={t}", transform=ax_bot.transAxes, ha='center', fontsize=8) + + plt.savefig(save_path, dpi=150, bbox_inches='tight') + plt.close() + return heatmap_smooth + + +def save_overlay_png(video_uint8, heatmap, out_png): + t_num = video_uint8.shape[0] + fig, axes = plt.subplots(2, t_num, figsize=(max(10, t_num * 1.8), 4.5)) + if t_num == 1: + axes = np.array(axes).reshape(2, 1) + plt.subplots_adjust(wspace=0.02, hspace=0.02, left=0.01, right=0.99) + for t in range(t_num): + ax0 = axes[0, t] + ax0.imshow(video_uint8[t]) + ax0.imshow(heatmap[t], cmap="jet", alpha=0.5, vmin=0, vmax=1) + ax0.axis("off") + + ax1 = axes[1, t] + ax1.imshow(video_uint8[t]) + ax1.axis("off") + ax1.text(0.5, -0.06, f"t={t}", transform=ax1.transAxes, ha="center", fontsize=8) + + axes[0, 0].set_title("Grad Heatmap", fontsize=10, x=-0.08, y=0.5, rotation=90) + axes[1, 0].set_title("Original", fontsize=10, x=-0.08, y=0.5, rotation=90) + plt.savefig(out_png, dpi=140, bbox_inches="tight") + plt.close() + + +def save_overlay_mp4(video_uint8, heatmap, out_mp4, fps=4): + if cv2 is None: + return False + t_num, h, w, _ = video_uint8.shape + writer = cv2.VideoWriter(out_mp4, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) + for t in range(t_num): + hm = plt.get_cmap("jet")(heatmap[t])[..., :3] + frame = (0.5 * (video_uint8[t] / 255.0) + 0.5 * hm) * 255.0 + frame = frame.astype(np.uint8) + writer.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)) + writer.release() + return True + + +def build_messages(question): + return [ + { + "role": "user", + "content": [ + {"type": "video", "video": "