class DetrTask(cfg.TaskConfig):
model: Detr = Detr()
train_data: cfg.DataConfig = cfg.DataConfig()
validation_data: cfg.DataConfig = cfg.DataConfig()
losses: Losses = Losses()
init_checkpoint: Optional[str] = None
init_checkpoint_modules: Union[str, List[str]] = 'all' # all, backbone
annotation_file: Optional[str] = None
per_category_metrics: bool = False
class TranslationConfig(cfg.TaskConfig): """The translation task config.""" model: ModelConfig = ModelConfig() train_data: cfg.DataConfig = cfg.DataConfig() validation_data: cfg.DataConfig = cfg.DataConfig() # Tokenization sentencepiece_model_path: str = "" # Evaluation. print_translations: Optional[bool] = None
class DualEncoderConfig(cfg.TaskConfig):
"""The model config."""
# At most one of `init_checkpoint` and `hub_module_url` can
# be specified.
init_checkpoint: str = ''
hub_module_url: str = ''
# Defines the concrete model config at instantiation time.
model: ModelConfig = ModelConfig()
train_data: cfg.DataConfig = cfg.DataConfig()
validation_data: cfg.DataConfig = cfg.DataConfig()
class MaskedLMConfig(cfg.TaskConfig):
"""The model config."""
model: bert.PretrainerConfig = bert.PretrainerConfig(cls_heads=[
bert.ClsHeadConfig(inner_dim=768,
num_classes=2,
dropout_rate=0.1,
name='next_sentence')
])
train_data: cfg.DataConfig = cfg.DataConfig()
validation_data: cfg.DataConfig = cfg.DataConfig()
class BertDistillationTaskConfig(cfg.TaskConfig):
"""Defines the teacher/student model architecture and training data."""
teacher_model: bert.PretrainerConfig = bert.PretrainerConfig(
encoder=encoders.EncoderConfig(type='mobilebert'))
student_model: bert.PretrainerConfig = bert.PretrainerConfig(
encoder=encoders.EncoderConfig(type='mobilebert'))
# The path to the teacher model checkpoint or its directory.
teacher_model_init_checkpoint: str = ''
train_data: cfg.DataConfig = cfg.DataConfig()
validation_data: cfg.DataConfig = cfg.DataConfig()
class QuestionAnsweringConfig(cfg.TaskConfig): """The model config.""" # At most one of `init_checkpoint` and `hub_module_url` can be specified. init_checkpoint: str = '' hub_module_url: str = '' n_best_size: int = 20 max_answer_length: int = 30 null_score_diff_threshold: float = 0.0 model: ModelConfig = ModelConfig() train_data: cfg.DataConfig = cfg.DataConfig() validation_data: cfg.DataConfig = cfg.DataConfig()
class MaskedLMConfig(cfg.TaskConfig):
"""The model config."""
model: bert.PretrainerConfig = bert.PretrainerConfig(cls_heads=[
bert.ClsHeadConfig(
inner_dim=768, num_classes=2, dropout_rate=0.1, name='next_sentence')
])
# TODO(b/154564893): Mathematically, scale_loss should be True.
# However, it works better with scale_loss being False.
scale_loss: bool = False
train_data: cfg.DataConfig = cfg.DataConfig()
validation_data: cfg.DataConfig = cfg.DataConfig()
class SentencePredictionConfig(cfg.TaskConfig): """The model config.""" # At most one of `init_checkpoint` and `hub_module_url` can # be specified. init_checkpoint: str = '' init_cls_pooler: bool = False hub_module_url: str = '' metric_type: str = 'accuracy' # Defines the concrete model config at instantiation time. model: ModelConfig = ModelConfig() train_data: cfg.DataConfig = cfg.DataConfig() validation_data: cfg.DataConfig = cfg.DataConfig()
class TaggingConfig(cfg.TaskConfig):
"""The model config."""
# At most one of `init_checkpoint` and `hub_module_url` can be specified.
init_checkpoint: str = ''
hub_module_url: str = ''
model: ModelConfig = ModelConfig()
# The real class names, the order of which should match real label id.
# Note that a word may be tokenized into multiple word_pieces tokens, and
# we asssume the real label id (non-negative) is assigned to the first token
# of the word, and a negative label id is assigned to the remaining tokens.
# The negative label id will not contribute to loss and metrics.
class_names: Optional[List[str]] = None
train_data: cfg.DataConfig = cfg.DataConfig()
validation_data: cfg.DataConfig = cfg.DataConfig()
def testSegmentationInputReader(self, input_size, num_classes,
num_channels):
params = cfg.DataConfig(input_path=self._data_path,
global_batch_size=2,
is_training=False)
decoder = segmentation_input_3d.Decoder()
parser = segmentation_input_3d.Parser(input_size=input_size,
num_classes=num_classes,
num_channels=num_channels)
reader = input_reader.InputReader(
params,
dataset_fn=dataset_fn.pick_dataset_fn('tfrecord'),
decoder_fn=decoder.decode,
parser_fn=parser.parse_fn(params.is_training))
dataset = reader.read()
iterator = iter(dataset)
image, labels = next(iterator)
# Checks image shape.
self.assertEqual(
list(image.numpy().shape),
[2, input_size[0], input_size[1], input_size[2], num_channels])
self.assertEqual(
list(labels.numpy().shape),
[2, input_size[0], input_size[1], input_size[2], num_classes])
class DetectionConfig(cfg.TaskConfig):
"""The translation task config."""
train_data: cfg.DataConfig = cfg.DataConfig()
validation_data: cfg.DataConfig = cfg.DataConfig()
lambda_cls: float = 1.0
lambda_box: float = 5.0
lambda_giou: float = 2.0
init_ckpt: str = ''
num_classes: int = 81 # 0: background
background_cls_weight: float = 0.1
num_encoder_layers: int = 6
num_decoder_layers: int = 6
# Make DETRConfig.
num_queries: int = 100
num_hidden: int = 256
per_category_metrics: bool = False
def unified_detector() -> cfg.ExperimentConfig:
"""Configurations for trainer of unified detector."""
total_train_steps = 100000
summary_interval = steps_per_loop = 200
checkpoint_interval = 2000
warmup_steps = 1000
config = cfg.ExperimentConfig(
# Input pipeline and model are configured through Gin.
task=OcrTaskConfig(train_data=cfg.DataConfig(is_training=True)),
trainer=cfg.TrainerConfig(
train_steps=total_train_steps,
steps_per_loop=steps_per_loop,
summary_interval=summary_interval,
checkpoint_interval=checkpoint_interval,
max_to_keep=1,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'adamw',
'adamw': {
'weight_decay_rate':
0.05,
'include_in_weight_decay': [
'^((?!depthwise).)*(kernel|weights):0$',
],
'exclude_from_weight_decay': [
'(^((?!kernel).)*:0)|(depthwise_kernel)',
],
'gradient_clip_norm':
10.,
},
},
'learning_rate': {
'type': 'cosine',
'cosine': {
'initial_learning_rate': 1e-3,
'decay_steps': total_train_steps - warmup_steps,
'alpha': 1e-2,
'offset': warmup_steps,
},
},
'warmup': {
'type': 'linear',
'linear': {
'warmup_learning_rate': 1e-5,
'warmup_steps': warmup_steps,
}
},
}),
),
)
return config
class TeamsPretrainTaskConfig(cfg.TaskConfig):
"""The model config."""
model: teams.TeamsPretrainerConfig = teams.TeamsPretrainerConfig()
train_data: cfg.DataConfig = cfg.DataConfig()
validation_data: cfg.DataConfig = cfg.DataConfig()
class OcrTaskConfig(cfg.TaskConfig):
train_data: cfg.DataConfig = cfg.DataConfig()
model_call_needs_labels: bool = False
