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DavarOCR 0.1.0 setup
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‎davarocr/LICENSE

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Copyright 2020-2021 Davar-Lab. All rights reserved.
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‎davarocr/davarocr/__init__.py

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"""
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##################################################################################################
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# Copyright Info : Copyright (c) Davar Lab @ Hikvision Research Institute. All rights reserved.
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# Filename : __init__.py
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# Abstract :
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# Current Version: 1.0.0
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# Date : 2020-05-31
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##################################################################################################
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"""
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from .davar_common import *
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from .davar_det import *
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from .davar_rcg import *
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from .davar_spotting import *
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from .davar_ie import *
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from .mmcv import *
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from .version import __version__
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__all__ = ['__version__']

‎davarocr/davarocr/davar_common/__init__.py

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"""
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##################################################################################################
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# Copyright Info : Copyright (c) Davar Lab @ Hikvision Research Institute. All rights reserved.
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# Filename : __init__.py
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# Abstract :
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# Current Version: 1.0.0
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# Date : 2020-05-31
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##################################################################################################
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"""
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from .models import *
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from .datasets import *
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from .utils import *
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from .apis import *
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from .core import *

‎davarocr/davarocr/davar_common/apis/__init__.py

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"""
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##################################################################################################
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# Copyright Info : Copyright (c) Davar Lab @ Hikvision Research Institute. All rights reserved.
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# Filename : __init__.py
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# Abstract :
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# Current Version: 1.0.0
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# Date : 2020-05-31
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##################################################################################################
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"""
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from .inference import inference_model, init_model
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from .test import single_gpu_test, multi_gpu_test
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from .train import train_model
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__all__ = [
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'inference_model',
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'train_model',
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'init_model',
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'single_gpu_test',
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'multi_gpu_test'
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]

‎davarocr/davarocr/davar_common/apis/inference.py

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"""
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##################################################################################################
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# Copyright Info : Copyright (c) Davar Lab @ Hikvision Research Institute. All rights reserved.
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# Filename : inference.py
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# Abstract : The common inference api for davarocr used in offline testing.
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Support for DETECTOR, RECOGNIZOR, SPOTTER, INFO_EXTRACTOR, etc.
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# Current Version: 1.0.0
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# Date : 2021-05-20
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##################################################################################################
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"""
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import warnings
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import torch
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import numpy as np
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import mmcv
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from mmcv.runner import load_checkpoint
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from mmcv.parallel import collate, scatter
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from mmdet.datasets.pipelines import Compose
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from mmdet.models import build_detector
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from mmdet.core import get_classes
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def init_model(config, checkpoint=None, device='cuda:0', cfg_options=None):
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"""Initialize a model from config file.
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Model types can be 'DETECTOR'(default), 'RECOGNIZOR', 'SPOTTER', 'INFO_EXTRACTOR'
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Args:
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config (str or :obj:`mmcv.Config`): Config file path or the config
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object.
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checkpoint (str, optional): Checkpoint path. If left as None, the model
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will not load any weights.
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cfg_options (dict): Options to override some settings in the used
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config.
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Returns:
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nn.Module: The constructed detector.
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"""
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if isinstance(config, str):
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config = mmcv.Config.fromfile(config)
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elif not isinstance(config, mmcv.Config):
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raise TypeError('config must be a filename or Config object, '
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f'but got {type(config)}')
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if cfg_options is not None:
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config.merge_from_dict(cfg_options)
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config.model.pretrained = None
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config.model.train_cfg = None
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# Can be extended according to the supported model types
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cfg_types = config.get("type", "DETECTOR")
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if cfg_types == "DETECTOR":
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model = build_detector(config.model, test_cfg=config.get('test_cfg'))
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elif cfg_types == "RECOGNIZER":
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from davarocr.davar_rcg.models.builder import build_recognizor
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model = build_recognizor(config.model, test_cfg=config.get('test_cfg'))
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elif cfg_types == "SPOTTER":
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from davarocr.davar_spotting.models.builder import build_spotter
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model = build_spotter(config.model, test_cfg=config.get('test_cfg'))
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else:
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raise NotImplementedError
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if checkpoint is not None:
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map_loc = 'cpu' if device == 'cpu' else None
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checkpoint = load_checkpoint(model, checkpoint, map_location=map_loc)
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if 'CLASSES' in checkpoint.get('meta', {}):
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model.CLASSES = checkpoint['meta']['CLASSES']
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else:
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warnings.simplefilter('once')
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warnings.warn('Class names are not saved in the checkpoint\'s '
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'meta data, use COCO classes by default.')
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model.CLASSES = get_classes('coco')
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# Save the config in the model for convenience
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model.cfg = config
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model.to(device)
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model.eval()
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return model
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def inference_model(model, imgs):
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""" Inference image(s) with the models
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Model types can be 'DETECTOR'(default), 'RECOGNIZOR', 'SPOTTER', 'INFO_EXTRACTOR'
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Args:
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model (nn.Module): The loaded model
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imgs (str | nd.array | list(str|nd.array)): Image files. It can be a filename of np array (single img inference)
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or a list of filenames | np.array (batch imgs inference.
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Returns:
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result (dict): results.
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"""
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cfg = model.cfg
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device = next(model.parameters()).device # model device
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# Build the data pipeline
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test_pipeline = Compose(cfg.data.test.pipeline)
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# Prepare data
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if isinstance(imgs, (str, np.ndarray)):
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# If the input is single image
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data = dict(img=imgs)
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data = test_pipeline(data)
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device = int(str(device).split(":")[-1])
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data = scatter(collate([data], samples_per_gpu=1), [device])[0]
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else:
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# If the input are batch of images
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batch_data = []
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for img in imgs:
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data = dict(img=img)
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data = test_pipeline(data)
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batch_data.append(data)
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data_collate = collate(batch_data, samples_per_gpu=len(batch_data))
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device = int(str(device).split(":")[-1])
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data = scatter(data_collate, [device])[0]
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# Forward inference
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with torch.no_grad():
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result = model(return_loss=False, rescale=True, **data)
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return result

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