def __init__(self, opt, device):
super(STR, self).__init__()
self.opt = opt
# Trans
# self.Trans = Trans.TPS_SpatialTransformerNetwork(F = opt.num_fiducial,
# i_size = (opt.imgH, opt.imgW),
# i_r_size= (opt.imgH, opt.imgW),
# i_channel_num=opt.input_channel,
# device = device)
#Extract
if self.opt.extract =='RCNN':
self.Extract = self.Extract = Extract.RCNN_extractor(opt.input_channel, opt.output_channel)
elif 'efficientnet' in self.opt.extract :
self.Extract = Extract.EfficientNet(opt)
elif 'resnet' in self.opt.extract :
self.Extract = Extract.ResNet_FeatureExtractor(opt.input_channel, opt.output_channel)
else:
raise print('invalid extract model name!')
# self.Extract = Extract.RCNN_extractor(opt.input_channel, opt.output_channel)
# self.Extract = Extract.ResNet_FeatureExtractor(opt.input_channel, opt.output_channel)
self.FeatureExtraction_output = opt.output_channel # (imgH/16 -1 )* 512
self.AdaptiveAvgPool = nn.AdaptiveAvgPool2d((None,1)) # imgH/16-1 -> 1
# Sequence
self.Seq = nn.Sequential(
BidirectionalLSTM(self.FeatureExtraction_output, opt.hidden_size, opt.hidden_size),
# BidirectionalLSTM(1536, opt.hidden_size, opt.hidden_size),
BidirectionalLSTM(opt.hidden_size, opt.hidden_size, opt.hidden_size))
self.Seq_output = opt.hidden_size
#Pred
self.Pred = Pred.Attention(self.Seq_output, opt.hidden_size, opt.num_classes, device=device)
def __init__(self, opt, device):
super(model, self).__init__()
self.opt = opt
#Trans
self.Trans = Trans.TPS_SpatialTransformerNetwork(
F=opt.num_fiducial,
i_size=(opt.imgH, opt.imgW),
i_r_size=(opt.imgH, opt.imgW),
i_channel_num=opt.input_channel,
device=device)
#Extract
if self.opt.extract == 'RCNN':
self.Extract = self.Extract = Extract.RCNN_extractor(
opt.input_channel, opt.output_channel)
elif 'efficientnet' in self.opt.extract:
self.Extract = Extract.EfficientNet(opt)
elif 'resnet' in self.opt.extract:
self.Extract = Extract.ResNet_FeatureExtractor(
opt.input_channel, opt.output_channel)
else:
raise print('invalid extract model name!')
# self.AdaptiveAvgPool = nn.AdaptiveAvgPool2d((None,1)) # imgH/16-1 -> 1
# Position aware module
self.PAM = PositionEnhancement.PositionAwareModule(
opt.output_channel, opt.hidden_size, opt.output_channel, 2)
self.PAttnM_bot = PositionEnhancement.AttnModule(
opt, opt.hidden_size, opt.bot_n_cls, device)
self.PAttnM_mid = PositionEnhancement.AttnModule(
opt, opt.hidden_size, opt.mid_n_cls, device)
self.PAttnM_top = PositionEnhancement.AttnModule(
opt, opt.hidden_size, opt.top_n_cls, device)
# Hybrid branch
self.Hybrid_bot = Hybrid.HybridBranch(opt.output_channel,
opt.batch_max_length + 1,
opt.bot_n_cls, device)
self.Hybrid_mid = Hybrid.HybridBranch(opt.output_channel,
opt.batch_max_length + 1,
opt.mid_n_cls, device)
self.Hybrid_top = Hybrid.HybridBranch(opt.output_channel,
opt.batch_max_length + 1,
opt.top_n_cls, device)
# # Dynamically fusing module
self.Dynamic_fuser_top = PositionEnhancement.DynamicallyFusingModule(
opt.top_n_cls)
self.Dynamic_fuser_mid = PositionEnhancement.DynamicallyFusingModule(
opt.mid_n_cls)
self.Dynamic_fuser_bot = PositionEnhancement.DynamicallyFusingModule(
opt.bot_n_cls)
