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import base64 |
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import os |
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import mmcv |
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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 image_index, image_result in enumerate(data): |
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output.append([]) |
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if isinstance(image_result, tuple): |
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bbox_result, segm_result = image_result |
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if isinstance(segm_result, tuple): |
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segm_result = segm_result[0] |
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else: |
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bbox_result, segm_result = image_result, None |
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for class_index, class_result in enumerate(bbox_result): |
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class_name = self.model.CLASSES[class_index] |
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for bbox in class_result: |
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bbox_coords = bbox[:-1].tolist() |
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score = float(bbox[-1]) |
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if score >= self.threshold: |
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output[image_index].append({ |
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'class_name': class_name, |
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'bbox': bbox_coords, |
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'score': score |
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}) |
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return output |
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