mirror of https://github.com/open-mmlab/mmpose
81 lines
2.6 KiB
Python
81 lines
2.6 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import base64
|
|
import os
|
|
|
|
import mmcv
|
|
import torch
|
|
|
|
from mmpose.apis import (inference_bottom_up_pose_model,
|
|
inference_top_down_pose_model, init_pose_model)
|
|
from mmpose.models.detectors import AssociativeEmbedding, TopDown
|
|
|
|
try:
|
|
from ts.torch_handler.base_handler import BaseHandler
|
|
except ImportError:
|
|
raise ImportError('Please install torchserve.')
|
|
|
|
|
|
class MMPoseHandler(BaseHandler):
|
|
|
|
def initialize(self, context):
|
|
properties = context.system_properties
|
|
self.map_location = 'cuda' if torch.cuda.is_available() else 'cpu'
|
|
self.device = torch.device(self.map_location + ':' +
|
|
str(properties.get('gpu_id')) if torch.cuda.
|
|
is_available() else self.map_location)
|
|
self.manifest = context.manifest
|
|
|
|
model_dir = properties.get('model_dir')
|
|
serialized_file = self.manifest['model']['serializedFile']
|
|
checkpoint = os.path.join(model_dir, serialized_file)
|
|
self.config_file = os.path.join(model_dir, 'config.py')
|
|
|
|
self.model = init_pose_model(self.config_file, checkpoint, self.device)
|
|
self.initialized = True
|
|
|
|
def preprocess(self, data):
|
|
images = []
|
|
|
|
for row in data:
|
|
image = row.get('data') or row.get('body')
|
|
if isinstance(image, str):
|
|
image = base64.b64decode(image)
|
|
image = mmcv.imfrombytes(image)
|
|
images.append(image)
|
|
|
|
return images
|
|
|
|
def inference(self, data, *args, **kwargs):
|
|
if isinstance(self.model, TopDown):
|
|
results = self._inference_top_down_pose_model(data)
|
|
elif isinstance(self.model, (AssociativeEmbedding, )):
|
|
results = self._inference_bottom_up_pose_model(data)
|
|
else:
|
|
raise NotImplementedError(
|
|
f'Model type {type(self.model)} is not supported.')
|
|
|
|
return results
|
|
|
|
def _inference_top_down_pose_model(self, data):
|
|
results = []
|
|
for image in data:
|
|
# use dummy person bounding box
|
|
preds, _ = inference_top_down_pose_model(
|
|
self.model, image, person_results=None)
|
|
results.append(preds)
|
|
return results
|
|
|
|
def _inference_bottom_up_pose_model(self, data):
|
|
results = []
|
|
for image in data:
|
|
preds, _ = inference_bottom_up_pose_model(self.model, image)
|
|
results.append(preds)
|
|
return results
|
|
|
|
def postprocess(self, data):
|
|
output = [[{
|
|
'keypoints': pred['keypoints'].tolist()
|
|
} for pred in preds] for preds in data]
|
|
|
|
return output
|