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import warnings |
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import torch.nn as nn |
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from mmcv.cnn import ACTIVATION_LAYERS as MMCV_ACTIVATION_LAYERS |
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from mmcv.cnn import UPSAMPLE_LAYERS as MMCV_UPSAMPLE_LAYERS |
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from mmcv.utils import Registry, build_from_cfg |
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from mmdet.models.builder import BACKBONES as MMDET_BACKBONES |
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CONVERTORS = Registry('convertor') |
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ENCODERS = Registry('encoder') |
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DECODERS = Registry('decoder') |
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PREPROCESSOR = Registry('preprocessor') |
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POSTPROCESSOR = Registry('postprocessor') |
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UPSAMPLE_LAYERS = Registry('upsample layer', parent=MMCV_UPSAMPLE_LAYERS) |
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BACKBONES = Registry('models', parent=MMDET_BACKBONES) |
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LOSSES = BACKBONES |
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DETECTORS = BACKBONES |
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ROI_EXTRACTORS = BACKBONES |
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HEADS = BACKBONES |
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NECKS = BACKBONES |
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FUSERS = BACKBONES |
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RECOGNIZERS = BACKBONES |
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ACTIVATION_LAYERS = Registry('activation layer', parent=MMCV_ACTIVATION_LAYERS) |
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def build_recognizer(cfg, train_cfg=None, test_cfg=None): |
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"""Build recognizer.""" |
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return build_from_cfg(cfg, RECOGNIZERS, |
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dict(train_cfg=train_cfg, test_cfg=test_cfg)) |
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def build_convertor(cfg): |
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"""Build label convertor for scene text recognizer.""" |
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return build_from_cfg(cfg, CONVERTORS) |
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def build_encoder(cfg): |
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"""Build encoder for scene text recognizer.""" |
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return build_from_cfg(cfg, ENCODERS) |
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def build_decoder(cfg): |
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"""Build decoder for scene text recognizer.""" |
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return build_from_cfg(cfg, DECODERS) |
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def build_preprocessor(cfg): |
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"""Build preprocessor for scene text recognizer.""" |
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return build_from_cfg(cfg, PREPROCESSOR) |
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def build_postprocessor(cfg): |
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"""Build postprocessor for scene text detector.""" |
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return build_from_cfg(cfg, POSTPROCESSOR) |
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def build_roi_extractor(cfg): |
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"""Build roi extractor.""" |
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return ROI_EXTRACTORS.build(cfg) |
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def build_loss(cfg): |
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"""Build loss.""" |
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return LOSSES.build(cfg) |
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def build_backbone(cfg): |
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"""Build backbone.""" |
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return BACKBONES.build(cfg) |
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def build_head(cfg): |
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"""Build head.""" |
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return HEADS.build(cfg) |
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def build_neck(cfg): |
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"""Build neck.""" |
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return NECKS.build(cfg) |
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def build_fuser(cfg): |
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"""Build fuser.""" |
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return FUSERS.build(cfg) |
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def build_upsample_layer(cfg, *args, **kwargs): |
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"""Build upsample layer. |
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Args: |
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cfg (dict): The upsample layer config, which should contain: |
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- type (str): Layer type. |
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- scale_factor (int): Upsample ratio, which is not applicable to |
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deconv. |
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- layer args: Args needed to instantiate a upsample layer. |
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args (argument list): Arguments passed to the ``__init__`` |
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method of the corresponding conv layer. |
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kwargs (keyword arguments): Keyword arguments passed to the |
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``__init__`` method of the corresponding conv layer. |
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Returns: |
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nn.Module: Created upsample layer. |
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""" |
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if not isinstance(cfg, dict): |
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raise TypeError(f'cfg must be a dict, but got {type(cfg)}') |
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if 'type' not in cfg: |
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raise KeyError( |
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f'the cfg dict must contain the key "type", but got {cfg}') |
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cfg_ = cfg.copy() |
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layer_type = cfg_.pop('type') |
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if layer_type not in UPSAMPLE_LAYERS: |
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raise KeyError(f'Unrecognized upsample type {layer_type}') |
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else: |
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upsample = UPSAMPLE_LAYERS.get(layer_type) |
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if upsample is nn.Upsample: |
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cfg_['mode'] = layer_type |
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layer = upsample(*args, **kwargs, **cfg_) |
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return layer |
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def build_activation_layer(cfg): |
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"""Build activation layer. |
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Args: |
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cfg (dict): The activation layer config, which should contain: |
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- type (str): Layer type. |
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- layer args: Args needed to instantiate an activation layer. |
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Returns: |
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nn.Module: Created activation layer. |
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""" |
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return build_from_cfg(cfg, ACTIVATION_LAYERS) |
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def build_detector(cfg, train_cfg=None, test_cfg=None): |
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"""Build detector.""" |
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if train_cfg is not None or test_cfg is not None: |
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warnings.warn( |
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'train_cfg and test_cfg is deprecated, ' |
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'please specify them in model', UserWarning) |
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assert cfg.get('train_cfg') is None or train_cfg is None, \ |
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'train_cfg specified in both outer field and model field ' |
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assert cfg.get('test_cfg') is None or test_cfg is None, \ |
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'test_cfg specified in both outer field and model field ' |
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return DETECTORS.build( |
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cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg)) |
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