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Running
on
Zero
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import base64
import os
import mmcv
import numpy as np
import torch
from ts.torch_handler.base_handler import BaseHandler
from mmdet.apis import inference_detector, init_detector
class MMdetHandler(BaseHandler):
threshold = 0.5
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_detector(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):
results = inference_detector(self.model, data)
return results
def postprocess(self, data):
# Format output following the example ObjectDetectionHandler format
output = []
for data_sample in data:
pred_instances = data_sample.pred_instances
bboxes = pred_instances.bboxes.cpu().numpy().astype(
np.float32).tolist()
labels = pred_instances.labels.cpu().numpy().astype(
np.int32).tolist()
scores = pred_instances.scores.cpu().numpy().astype(
np.float32).tolist()
preds = []
for idx in range(len(labels)):
cls_score, bbox, cls_label = scores[idx], bboxes[idx], labels[
idx]
if cls_score >= self.threshold:
class_name = self.model.dataset_meta['classes'][cls_label]
result = dict(
class_label=cls_label,
class_name=class_name,
bbox=bbox,
score=cls_score)
preds.append(result)
output.append(preds)
return output
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