File size: 2,806 Bytes
d670799 |
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 |
# Copyright (c) OpenMMLab. All rights reserved.
import base64
import os
import os.path as osp
import warnings
import decord
import numpy as np
import torch
from mmaction.apis import inference_recognizer, init_recognizer # noqa: F401
try:
from ts.torch_handler.base_handler import BaseHandler
except ImportError:
raise ImportError('`ts` is required. Try: pip install ts.')
class MMActionHandler(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')
mapping_file_path = osp.join(model_dir, 'label_map.txt')
if not os.path.isfile(mapping_file_path):
warnings.warn('Missing the label_map.txt file. '
'Inference output will not include class name.')
self.mapping = None
else:
lines = open(mapping_file_path).readlines()
self.mapping = [x.strip() for x in lines]
self.model = init_recognizer(self.config_file, checkpoint, self.device)
self.initialized = True
def preprocess(self, data):
videos = []
for row in data:
video = row.get('data') or row.get('body')
if isinstance(video, str):
video = base64.b64decode(video)
# First save the bytes as a tmp file
with open('/tmp/tmp.mp4', 'wb') as fout:
fout.write(video)
video = decord.VideoReader('/tmp/tmp.mp4')
frames = [x.asnumpy() for x in video]
videos.append(np.stack(frames))
return videos
def inference(self, data, *args, **kwargs):
results = [inference_recognizer(self.model, item) for item in data]
return results
def postprocess(self, data):
# Format output following the example ObjectDetectionHandler format
output = []
for video_idx, video_result in enumerate(data):
output.append([])
assert isinstance(video_result, list)
output[video_idx] = {
self.mapping[x[0]] if self.mapping else x[0]: float(x[1])
for x in video_result
}
return output
|