def __init__(self,
encoder=dict(type='TransformerEncoder'),
decoder=dict(type='ABIVisionDecoder'),
init_cfg=dict(type='Xavier', layer='Conv2d'),
**kwargs):
super().__init__(init_cfg=init_cfg)
self.encoder = build_encoder(encoder)
self.decoder = build_decoder(decoder)
def __init__(self,
preprocessor=None,
backbone=None,
encoder=None,
decoder=None,
loss=None,
label_convertor=None,
train_cfg=None,
test_cfg=None,
max_seq_len=40,
pretrained=None,
init_cfg=None):
super().__init__(init_cfg=init_cfg)
# Label convertor (str2tensor, tensor2str)
assert label_convertor is not None
label_convertor.update(max_seq_len=max_seq_len)
self.label_convertor = build_convertor(label_convertor)
# Preprocessor module, e.g., TPS
self.preprocessor = None
if preprocessor is not None:
self.preprocessor = build_preprocessor(preprocessor)
# Backbone
assert backbone is not None
self.backbone = build_backbone(backbone)
# Encoder module
self.encoder = None
if encoder is not None:
self.encoder = build_encoder(encoder)
# Decoder module
assert decoder is not None
decoder.update(num_classes=self.label_convertor.num_classes())
decoder.update(start_idx=self.label_convertor.start_idx)
decoder.update(padding_idx=self.label_convertor.padding_idx)
decoder.update(max_seq_len=max_seq_len)
self.decoder = build_decoder(decoder)
# Loss
assert loss is not None
loss.update(ignore_index=self.label_convertor.padding_idx)
self.loss = build_loss(loss)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self.max_seq_len = max_seq_len
if pretrained is not None:
warnings.warn('DeprecationWarning: pretrained is a deprecated \
key, please consider using init_cfg')
self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
def __init__(self,
preprocessor=None,
backbone=None,
encoder=None,
decoder=None,
loss=None,
label_convertor=None,
train_cfg=None,
test_cfg=None,
max_seq_len=40,
pretrained=None):
super().__init__()
# Label convertor (str2tensor, tensor2str)
assert label_convertor is not None
label_convertor.update(max_seq_len=max_seq_len)
self.label_convertor = build_convertor(label_convertor)
# Preprocessor module, e.g., TPS
self.preprocessor = None
if preprocessor is not None:
self.preprocessor = build_preprocessor(preprocessor)
# Backbone
assert backbone is not None
self.backbone = build_backbone(backbone)
# Encoder module
self.encoder = None
if encoder is not None:
self.encoder = build_encoder(encoder)
# Decoder module
assert decoder is not None
decoder.update(num_classes=self.label_convertor.num_classes())
decoder.update(start_idx=self.label_convertor.start_idx)
decoder.update(padding_idx=self.label_convertor.padding_idx)
decoder.update(max_seq_len=max_seq_len)
self.decoder = build_decoder(decoder)
# Loss
assert loss is not None
loss.update(ignore_index=self.label_convertor.padding_idx)
self.loss = build_loss(loss)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self.max_seq_len = max_seq_len
self.init_weights(pretrained=pretrained)
def __init__(self,
encoder,
decoder,
loss,
label_convertor,
train_cfg=None,
test_cfg=None,
init_cfg=None):
super().__init__(init_cfg=init_cfg)
self.label_convertor = build_convertor(label_convertor)
self.encoder = build_encoder(encoder)
decoder.update(num_labels=self.label_convertor.num_labels)
self.decoder = build_decoder(decoder)
loss.update(num_labels=self.label_convertor.num_labels)
self.loss = build_loss(loss)