File size: 9,091 Bytes
cf49179 | 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 | """Custom Inference Endpoint handler for ViTPose+ (top-down pose).
ViTPose is not a catalog / pipeline task, so Hugging Face Inference Endpoints
need this EndpointHandler. It detects people (RT-DETR) then estimates COCO-17
keypoints. Pass `boxes` to skip the detector.
Request JSON:
{"inputs": "<base64 or URL>", "parameters": {"threshold": 0.3, "dataset_index": 0}}
{"inputs": "<base64>", "boxes": [[x, y, w, h], ...]} # COCO xywh, skips detector
dataset_index (ViTPose+ MoE experts):
0 COCO, 1 AIC, 2 MPII, 3 AP-10K, 4 APT-36K, 5 COCO-WholeBody
"""
from __future__ import annotations
import base64
import io
from typing import Any
from urllib.parse import urlparse
import numpy as np
import requests
import torch
from PIL import Image
from transformers import AutoProcessor, RTDetrForObjectDetection, VitPoseForPoseEstimation
DETECTOR_ID = "PekingU/rtdetr_r50vd_coco_o365"
POSE_FALLBACK_ID = "usyd-community/vitpose-plus-base"
def _as_float(value: Any) -> float:
if hasattr(value, "item"):
return float(value.item())
return float(value)
def _as_list(value: Any) -> list:
if hasattr(value, "detach"):
value = value.detach().cpu().numpy()
if hasattr(value, "tolist"):
return value.tolist()
return list(value)
class EndpointHandler:
def __init__(self, path: str = "") -> None:
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.dtype = torch.float16 if self.device.type == "cuda" else torch.float32
pose_id = path or POSE_FALLBACK_ID
self.processor = AutoProcessor.from_pretrained(pose_id)
self.model = VitPoseForPoseEstimation.from_pretrained(pose_id)
self.model.to(device=self.device, dtype=self.dtype)
self.model.eval()
backbone = getattr(self.model.config, "backbone_config", None)
self.num_experts = int(getattr(backbone, "num_experts", 1) or 1)
self.id2label = {int(k): v for k, v in self.model.config.id2label.items()}
self.det_processor = AutoProcessor.from_pretrained(DETECTOR_ID)
self.det_model = RTDetrForObjectDetection.from_pretrained(DETECTOR_ID)
self.det_model.to(device=self.device, dtype=self.dtype)
self.det_model.eval()
self.person_label_ids = {
int(i)
for i, name in self.det_model.config.id2label.items()
if str(name).lower() == "person"
} or {0}
def __call__(self, data: dict[str, Any]) -> dict[str, Any]:
payload = dict(data or {})
parameters = payload.pop("parameters", None) or {}
if not isinstance(parameters, dict):
parameters = {}
raw = payload.pop("inputs", payload)
boxes = payload.pop("boxes", parameters.get("boxes"))
boxes_format = str(payload.pop("boxes_format", parameters.get("boxes_format", "xywh"))).lower()
threshold = float(payload.pop("threshold", parameters.get("threshold", 0.3)))
detect_threshold = float(
payload.pop("detect_threshold", parameters.get("detect_threshold", 0.3))
)
dataset_index = int(payload.pop("dataset_index", parameters.get("dataset_index", 0)))
if isinstance(raw, dict):
boxes = raw.get("boxes", boxes)
boxes_format = str(raw.get("boxes_format", boxes_format)).lower()
threshold = float(raw.get("threshold", threshold))
detect_threshold = float(raw.get("detect_threshold", detect_threshold))
dataset_index = int(raw.get("dataset_index", dataset_index))
raw = raw.get("image", raw.get("inputs", raw))
image = self._load_image(raw)
person_boxes = self._resolve_boxes(image, boxes, boxes_format, detect_threshold)
if person_boxes.shape[0] == 0:
return {
"people": [],
"width": image.width,
"height": image.height,
"dataset_index": dataset_index,
}
inputs = self.processor(image, boxes=[person_boxes], return_tensors="pt")
inputs = {
k: v.to(self.device, dtype=self.dtype) if torch.is_floating_point(v) else v.to(self.device)
for k, v in inputs.items()
}
if self.num_experts > 1:
inputs["dataset_index"] = torch.tensor([dataset_index], device=self.device)
with torch.inference_mode():
outputs = self.model(**inputs)
pose_results = self.processor.post_process_pose_estimation(
outputs, boxes=[person_boxes], threshold=threshold
)
image_pose_result = pose_results[0] if pose_results else []
people: list[dict[str, Any]] = []
for i, person_pose in enumerate(image_pose_result):
box = person_boxes[i].tolist() if i < len(person_boxes) else None
keypoints = []
for keypoint, label, score in zip(
person_pose["keypoints"], person_pose["labels"], person_pose["scores"]
):
label_id = int(_as_float(label))
xy = _as_list(keypoint)
keypoints.append(
{
"name": self.id2label.get(label_id, str(label_id)),
"label": label_id,
"x": float(xy[0]),
"y": float(xy[1]),
"score": _as_float(score),
}
)
people.append({"box": box, "keypoints": keypoints})
return {
"people": people,
"width": image.width,
"height": image.height,
"dataset_index": dataset_index,
}
def _load_image(self, image_input: Any) -> Image.Image:
if isinstance(image_input, Image.Image):
return image_input.convert("RGB")
if isinstance(image_input, (bytes, bytearray, memoryview)):
return Image.open(io.BytesIO(bytes(image_input))).convert("RGB")
if isinstance(image_input, np.ndarray):
if image_input.ndim == 3:
return Image.fromarray(image_input.astype("uint8")).convert("RGB")
raise ValueError("ndarray image must be HWC uint8")
if isinstance(image_input, list) and image_input and isinstance(image_input[0], int):
return Image.open(io.BytesIO(bytes(image_input))).convert("RGB")
if not isinstance(image_input, str):
raise ValueError("inputs must be a PIL image, base64 string, URL, or bytes")
text = image_input.strip()
if not text:
raise ValueError("empty image input")
parsed = urlparse(text)
if parsed.scheme in ("http", "https"):
response = requests.get(text, timeout=30)
response.raise_for_status()
return Image.open(io.BytesIO(response.content)).convert("RGB")
if text.startswith("data:") and "," in text:
text = text.split(",", 1)[1]
try:
raw = base64.b64decode(text, validate=False)
except Exception as exc:
raise ValueError("inputs string is not a valid image URL or base64 payload") from exc
return Image.open(io.BytesIO(raw)).convert("RGB")
def _resolve_boxes(
self,
image: Image.Image,
boxes: Any,
boxes_format: str,
detect_threshold: float,
) -> np.ndarray:
if boxes is not None:
arr = np.asarray(boxes, dtype=np.float32)
if arr.size == 0:
return np.zeros((0, 4), dtype=np.float32)
if arr.ndim == 1:
arr = arr.reshape(1, 4)
if arr.shape[-1] != 4:
raise ValueError("boxes must be [x, y, w, h] or [x1, y1, x2, y2]")
if boxes_format in ("xyxy", "voc"):
arr = arr.copy()
arr[:, 2] = arr[:, 2] - arr[:, 0]
arr[:, 3] = arr[:, 3] - arr[:, 1]
return arr
det_inputs = self.det_processor(images=image, return_tensors="pt")
det_inputs = {
k: v.to(self.device, dtype=self.dtype) if torch.is_floating_point(v) else v.to(self.device)
for k, v in det_inputs.items()
}
with torch.inference_mode():
det_outputs = self.det_model(**det_inputs)
results = self.det_processor.post_process_object_detection(
det_outputs,
target_sizes=torch.tensor([(image.height, image.width)], device=self.device),
threshold=detect_threshold,
)
result = results[0]
labels = result["labels"]
mask = torch.zeros_like(labels, dtype=torch.bool)
for person_id in self.person_label_ids:
mask |= labels == person_id
person_boxes = result["boxes"][mask].detach().cpu().numpy().astype(np.float32)
if person_boxes.size == 0:
return np.zeros((0, 4), dtype=np.float32)
person_boxes[:, 2] = person_boxes[:, 2] - person_boxes[:, 0]
person_boxes[:, 3] = person_boxes[:, 3] - person_boxes[:, 1]
return person_boxes
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