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# Copyright (c) OpenMMLab. All rights reserved.
import base64
import os
import cv2
import mmcv
import torch
from mmengine.model.utils import revert_sync_batchnorm
from ts.torch_handler.base_handler import BaseHandler
from mmseg.apis import inference_model, init_model
class MMsegHandler(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_model(self.config_file, checkpoint, self.device)
self.model = revert_sync_batchnorm(self.model)
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):
results = [inference_model(self.model, img) for img in data]
return results
def postprocess(self, data):
output = []
for image_result in data:
_, buffer = cv2.imencode('.png', image_result[0].astype('uint8'))
content = buffer.tobytes()
output.append(content)
return output
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