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# 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
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