def build(config, is_training):
if not isinstance(config, bidirectional_rnn_pb2.BidirectionalRnn):
raise ValueError(
'config not of type bidirectional_rnn_pb2.BidirectionalRnn')
if config.static:
brnn_class = bidirectional_rnn.StaticBidirectionalRnn
else:
brnn_class = bidirectional_rnn.DynamicBidirectionalRnn
fw_cell_object = rnn_cell_builder.build(config.fw_bw_rnn_cell)
bw_cell_object = rnn_cell_builder.build(config.fw_bw_rnn_cell)
rnn_regularizer_object = hyperparams_builder._build_regularizer(
config.rnn_regularizer)
fc_hyperparams_object = None
if config.num_output_units > 0:
if config.fc_hyperparams.op != hyperparams_pb2.Hyperparams.FC:
raise ValueError('op type must be FC')
fc_hyperparams_object = hyperparams_builder.build(
config.fc_hyperparams, is_training)
return brnn_class(fw_cell_object,
bw_cell_object,
rnn_regularizer=rnn_regularizer_object,
num_output_units=config.num_output_units,
fc_hyperparams=fc_hyperparams_object,
summarize_activations=config.summarize_activations)
def _build_stn_resnet(config, is_training):
if not isinstance(config, convnet_pb2.StnResnet):
raise ValueError('config is not of type convnet_pb2.StnResnet')
return resnet.ResnetForSTN(
conv_hyperparams=hyperparams_builder.build(config.conv_hyperparams,
is_training),
summarize_activations=config.summarize_activations,
is_training=is_training)
def _build_stn_convnet(config, is_training):
if not isinstance(config, convnet_pb2.StnConvnet):
raise ValueError('config is not of type convnet_pb2.StnConvnet')
convnet_class = stn_convnet.StnConvnet
if config.tiny == True:
convnet_class = stn_convnet.StnConvnetTiny
return convnet_class(conv_hyperparams=hyperparams_builder.build(
config.conv_hyperparams, is_training),
summarize_activations=config.summarize_activations,
is_training=is_training)
def build(config, is_training):
if not isinstance(config, spatial_transformer_pb2.SpatialTransformer):
raise ValueError('config not of type spatial_transformer_pb2.SpatialTransformer')
convnet_object = convnet_builder.build(config.convnet, is_training)
fc_hyperparams_object = hyperparams_builder.build(config.fc_hyperparams, is_training)
return spatial_transformer.SpatialTransformer(
convnet=convnet_object,
fc_hyperparams=fc_hyperparams_object,
localization_image_size=(config.localization_h, config.localization_w),
output_image_size=(config.output_h, config.output_w),
num_control_points=config.num_control_points,
init_bias_pattern=config.init_bias_pattern,
margins=(config.margin_x, config.margin_y),
activation=config.activation,
summarize_activations=config.summarize_activations
)
def _build_resnet(config, is_training):
if not isinstance(config, convnet_pb2.ResNet):
raise ValueError('config is not of type convnet_pb2.ResNet')
if config.net_type != convnet_pb2.ResNet.SINGLE_BRANCH:
raise ValueError('Only SINGLE_BRANCH is supported for ResNet')
resnet_depth = config.net_depth
if resnet_depth == convnet_pb2.ResNet.RESNET_50:
resnet_class = resnet.Resnet50Layer
else:
raise ValueError('Unknown resnet depth: {}'.format(resnet_depth))
conv_hyperparams = hyperparams_builder.build(config.conv_hyperparams,
is_training)
return resnet_class(
conv_hyperparams=conv_hyperparams,
summarize_activations=config.summarize_activations,
is_training=is_training,
)
def _build_crnn_net(config, is_training):
if not isinstance(config, convnet_pb2.CrnnNet):
raise ValueError('config is not of type convnet_pb2.CrnnNet')
if config.net_type == convnet_pb2.CrnnNet.SINGLE_BRANCH:
crnn_net_class = crnn_net.CrnnNet
elif config.net_type == convnet_pb2.CrnnNet.TWO_BRANCHES:
crnn_net_class = crnn_net.CrnnNetTwoBranches
elif config.net_type == convnet_pb2.CrnnNet.THREE_BRANCHES:
crnn_net_class = crnn_net.CrnnNetThreeBranches
else:
raise ValueError('Unknown net_type: {}'.format(config.net_type))
if config.tiny == True:
crnn_net_class = crnn_net.CrnnNetTiny
hyperparams_object = hyperparams_builder.build(config.conv_hyperparams,
is_training)
return crnn_net_class(conv_hyperparams=hyperparams_object,
summarize_activations=config.summarize_activations,
is_training=is_training)