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cbc5e69 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 | #!/usr/bin/env python3
"""Analyze pose ViT features from video2text checkpoint outputs."""
import argparse
import json
import os
from pathlib import Path
import cv2
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import torch
from PIL import Image
from torchvision import transforms
from smkd.pretrained.video2text import (
MultimodalPose2TextDiffusion,
MultimodalVideo2TextDiffusion,
)
INPUT_DIMS = {
"dwpose": 384,
"mediapipe_pose": 258,
"primedepth_depth": 576,
"sapiens_segmentation": 576,
"smplerx": 165,
}
def parse_args():
parser = argparse.ArgumentParser(description="Pose-dimensional ablation via video2text checkpoint.")
parser.add_argument("--video", required=True, help="Path to input video (mp4).")
parser.add_argument(
"--checkpoint",
default="smkd/pretrained/video2text_checkpoint_epoch_14.pth",
help="Path to video2text checkpoint.",
)
parser.add_argument(
"--output-dir",
default="pose_vit_feature_analysis",
help="Directory to store extracted features/plots.",
)
parser.add_argument("--num-frames", type=int, default=32, help="Number of frames sampled from video.")
parser.add_argument("--device", default="cuda", choices=["cuda", "cpu"], help="Torch device preference.")
parser.add_argument("--topk", type=int, default=32, help="Top dimensions to visualize.")
return parser.parse_args()
def prepare_device(device_pref: str) -> torch.device:
if device_pref == "cuda" and torch.cuda.is_available():
return torch.device("cuda")
return torch.device("cpu")
def ensure_dir(path: str) -> Path:
path_obj = Path(path)
path_obj.mkdir(parents=True, exist_ok=True)
return path_obj
def load_video_frames(video_path: str, num_frames: int, transform) -> torch.Tensor:
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
raise RuntimeError(f"Failed to open video: {video_path}")
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
if total_frames <= 0:
raise RuntimeError(f"No frames found in video: {video_path}")
indices = np.linspace(0, max(total_frames - 1, 0), num=num_frames, dtype=np.int32)
frames = []
for idx in indices:
cap.set(cv2.CAP_PROP_POS_FRAMES, int(idx))
ok, frame = cap.read()
if not ok:
continue
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frames.append(transform(Image.fromarray(frame)))
cap.release()
if not frames:
raise RuntimeError(f"No decodable frames in video: {video_path}")
while len(frames) < num_frames:
frames.append(frames[-1].clone())
return torch.stack(frames[:num_frames], dim=0)
def compute_dimension_stats(pose_2048: torch.Tensor):
with torch.no_grad():
scores = pose_2048.abs().mean(dim=(0, 1))
normalized = scores / (scores.max() + 1e-8)
return scores.cpu().numpy(), normalized.cpu().numpy()
def plot_top_dims(scores, top_indices, output_path):
plt.figure(figsize=(max(8, len(top_indices) * 0.35), 4))
plt.bar(range(len(top_indices)), scores[top_indices], color="#1f77b4")
plt.xticks(range(len(top_indices)), [str(i) for i in top_indices], rotation=60)
plt.ylabel("Mean |value|")
plt.xlabel("Dimension")
plt.title("Top pose dimensions")
plt.tight_layout()
plt.savefig(output_path, dpi=240)
plt.close()
def plot_heatmap(norm_scores, output_path):
rows = 32
usable = (norm_scores.shape[0] // rows) * rows
reshaped = norm_scores[:usable].reshape(rows, -1)
plt.figure(figsize=(12, 4))
plt.imshow(reshaped, aspect="auto", cmap="magma")
plt.colorbar(label="Normalized importance")
plt.xlabel("Chunk index")
plt.ylabel("Row")
plt.title("Pose dimension importance heatmap")
plt.tight_layout()
plt.savefig(output_path, dpi=240)
plt.close()
def plot_cumulative(scores, output_path):
sorted_scores = np.sort(scores)[::-1]
cumsum = np.cumsum(sorted_scores)
coverage = cumsum / cumsum[-1]
dims = np.arange(1, len(sorted_scores) + 1)
plt.figure(figsize=(8, 4))
plt.plot(dims, coverage, color="#ff7f0e")
plt.xlabel("Top-k dimensions")
plt.ylabel("Cumulative coverage")
plt.grid(alpha=0.3)
plt.tight_layout()
plt.savefig(output_path, dpi=240)
plt.close()
return coverage
def save_csv(scores, norm_scores, output_path):
with open(output_path, "w", encoding="utf-8") as handle:
handle.write("dimension,score,normalized\n")
for idx, (score, norm) in enumerate(zip(scores, norm_scores)):
handle.write(f"{idx},{score:.8f},{norm:.6f}\n")
def write_report(video_path, checkpoint, scores, norm_scores, coverage, top_indices, output_path):
levels = [0.25, 0.5, 0.9]
with open(output_path, "w", encoding="utf-8") as handle:
handle.write("Pose ViT dimensional analysis\n")
handle.write("=" * 60 + "\n\n")
handle.write(f"Video : {video_path}\n")
handle.write(f"Checkpoint : {checkpoint}\n")
handle.write(f"Total dims : {scores.shape[0]}\n\n")
handle.write("Top dimensions:\n")
for rank, dim_idx in enumerate(top_indices, 1):
handle.write(
f"{rank:02d}. dim {dim_idx:04d} | score={scores[dim_idx]:.6f} "
f"| normalized={norm_scores[dim_idx]:.4f}\n"
)
handle.write("\nCoverage milestones:\n")
for level in levels:
required = int(np.argmax(coverage >= level) + 1)
handle.write(f" - Top {required:4d} dims explain {level:.0%} of energy\n")
handle.write("\nScores computed as mean absolute activations on pose_2048.\n")
def main():
args = parse_args()
video_path = os.path.abspath(args.video)
checkpoint_path = os.path.abspath(args.checkpoint)
output_dir = ensure_dir(args.output_dir)
if not os.path.exists(video_path):
raise FileNotFoundError(f"Video not found: {video_path}")
if not os.path.exists(checkpoint_path):
raise FileNotFoundError(f"Checkpoint not found: {checkpoint_path}")
device = prepare_device(args.device)
print(f"[INFO] Using device: {device}")
pose2text = MultimodalPose2TextDiffusion(
input_dims=INPUT_DIMS,
hidden_dim=2048,
device=device,
codebook=None,
)
video2text = MultimodalVideo2TextDiffusion(
pose2text_model=pose2text,
device=device,
)
checkpoint = torch.load(checkpoint_path, map_location=device)
load_result = video2text.load_state_dict(checkpoint["model_state_dict"], strict=False)
if load_result.missing_keys:
print(f"[WARN] Missing keys ({len(load_result.missing_keys)}): {load_result.missing_keys[:5]}...")
if load_result.unexpected_keys:
print(f"[WARN] Unexpected keys ({len(load_result.unexpected_keys)}): {load_result.unexpected_keys[:5]}...")
video2text.eval()
frame_transform = transforms.Compose(
[
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
]
)
frames = load_video_frames(video_path, args.num_frames, frame_transform)
video_tensor = frames.unsqueeze(0).to(device)
with torch.no_grad():
pose_features = video2text.video2pose(video_tensor)
concatenated = pose2text.pose_encoder(pose_features)
B, F, D = concatenated.shape
flattened = concatenated.reshape(B * F, D)
pose_2048 = pose2text.dim_match(flattened).view(B, F, -1)
pose_numpy = pose_2048.cpu().numpy()
np.save(output_dir / "pose_2048.npy", pose_numpy)
scores, norm_scores = compute_dimension_stats(pose_2048)
save_csv(scores, norm_scores, output_dir / "dimension_scores.csv")
topk = min(args.topk, scores.shape) |