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import base64 |
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import os |
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import mmcv |
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import numpy as np |
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import torch |
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from ts.torch_handler.base_handler import BaseHandler |
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from mmdet.apis import inference_detector, init_detector |
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class MMdetHandler(BaseHandler): |
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threshold = 0.5 |
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def initialize(self, context): |
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properties = context.system_properties |
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self.map_location = 'cuda' if torch.cuda.is_available() else 'cpu' |
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self.device = torch.device(self.map_location + ':' + |
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str(properties.get('gpu_id')) if torch.cuda. |
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is_available() else self.map_location) |
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self.manifest = context.manifest |
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model_dir = properties.get('model_dir') |
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serialized_file = self.manifest['model']['serializedFile'] |
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checkpoint = os.path.join(model_dir, serialized_file) |
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self.config_file = os.path.join(model_dir, 'config.py') |
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self.model = init_detector(self.config_file, checkpoint, self.device) |
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self.initialized = True |
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def preprocess(self, data): |
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images = [] |
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for row in data: |
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image = row.get('data') or row.get('body') |
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if isinstance(image, str): |
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image = base64.b64decode(image) |
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image = mmcv.imfrombytes(image) |
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images.append(image) |
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return images |
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def inference(self, data, *args, **kwargs): |
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results = inference_detector(self.model, data) |
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return results |
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def postprocess(self, data): |
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output = [] |
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for data_sample in data: |
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pred_instances = data_sample.pred_instances |
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bboxes = pred_instances.bboxes.cpu().numpy().astype( |
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np.float32).tolist() |
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labels = pred_instances.labels.cpu().numpy().astype( |
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np.int32).tolist() |
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scores = pred_instances.scores.cpu().numpy().astype( |
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np.float32).tolist() |
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preds = [] |
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for idx in range(len(labels)): |
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cls_score, bbox, cls_label = scores[idx], bboxes[idx], labels[ |
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idx] |
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if cls_score >= self.threshold: |
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class_name = self.model.dataset_meta['classes'][cls_label] |
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result = dict( |
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class_label=cls_label, |
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class_name=class_name, |
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bbox=bbox, |
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score=cls_score) |
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preds.append(result) |
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output.append(preds) |
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return output |
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