class ResNetModelConfig(base_configs.ModelConfig):
"""Configuration for the ResNet model."""
name: str = 'ResNet'
num_classes: int = 1000
model_params: base_config.Config = dataclasses.field(
default_factory=lambda: {
'num_classes': 1000,
'batch_size': None,
'use_l2_regularizer': True,
'rescale_inputs': False,
})
loss: base_configs.LossConfig = base_configs.LossConfig(
name='sparse_categorical_crossentropy')
optimizer: base_configs.OptimizerConfig = base_configs.OptimizerConfig(
name='momentum',
decay=0.9,
epsilon=0.001,
momentum=0.9,
moving_average_decay=None)
learning_rate: base_configs.LearningRateConfig = (
base_configs.LearningRateConfig(
name='stepwise',
initial_lr=0.1,
examples_per_epoch=1281167,
boundaries=[30, 60, 80],
warmup_epochs=5,
scale_by_batch_size=1. / 256.,
multipliers=[0.1 / 256, 0.01 / 256, 0.001 / 256, 0.0001 / 256]))
def test_get_loss_scale(self, loss_scale, dtype, expected):
config = base_configs.ExperimentConfig(
model=base_configs.ModelConfig(
loss=base_configs.LossConfig(loss_scale=loss_scale)),
train_dataset=dataset_factory.DatasetConfig(dtype=dtype))
ls = classifier_trainer.get_loss_scale(config, fp16_default=128)
self.assertEqual(ls, expected)
class ResNetModelConfig(base_configs.ModelConfig):
"""Configuration for the ResNet model."""
name: str = 'ResNet'
num_classes: int = 1000
model_params: base_config.Config = dataclasses.field(
default_factory=lambda: {
'num_classes': 1000,
'batch_size': None,
'use_l2_regularizer': True,
'rescale_inputs': False,
})
loss: base_configs.LossConfig = base_configs.LossConfig(
name='sparse_categorical_crossentropy')
optimizer: base_configs.OptimizerConfig = base_configs.OptimizerConfig(
name='momentum',
decay=0.9,
epsilon=0.001,
momentum=0.9,
moving_average_decay=None)
learning_rate: base_configs.LearningRateConfig = (
base_configs.LearningRateConfig(
name='piecewise_constant_with_warmup',
examples_per_epoch=1281167,
warmup_epochs=_RESNET_LR_WARMUP_EPOCHS,
boundaries=_RESNET_LR_BOUNDARIES,
multipliers=_RESNET_LR_MULTIPLIERS))
class EfficientNetModelConfig(base_configs.ModelConfig):
"""Configuration for the EfficientNet model.
This configuration will default to settings used for training efficientnet-b0
on a v3-8 TPU on ImageNet.
Attributes:
name: The name of the model. Defaults to 'EfficientNet'.
num_classes: The number of classes in the model.
model_params: A dictionary that represents the parameters of the
EfficientNet model. These will be passed in to the "from_name" function.
loss: The configuration for loss. Defaults to a categorical cross entropy
implementation.
optimizer: The configuration for optimizations. Defaults to an RMSProp
configuration.
learning_rate: The configuration for learning rate. Defaults to an
exponential configuration.
"""
name: str = 'EfficientNet'
num_classes: int = 1000
model_params: base_config.Config = dataclasses.field(
default_factory=lambda: {
'model_name': 'efficientnet-b0',
'model_weights_path': '',
'weights_format': 'saved_model',
'overrides': {
'batch_norm': 'default',
'rescale_input': True,
'num_classes': 1000,
'activation': 'swish',
'dtype': 'float32',
}
})
loss: base_configs.LossConfig = base_configs.LossConfig(
name='categorical_crossentropy', label_smoothing=0.1)
optimizer: base_configs.OptimizerConfig = base_configs.OptimizerConfig(
name='rmsprop',
decay=0.9,
epsilon=0.001,
momentum=0.9,
moving_average_decay=None)
learning_rate: base_configs.LearningRateConfig = base_configs.LearningRateConfig( # pylint: disable=line-too-long
name='exponential',
initial_lr=0.008,
decay_epochs=2.4,
decay_rate=0.97,
warmup_epochs=5,
scale_by_batch_size=1. / 128.,
staircase=True)
def test_initialize(self, dtype):
config = base_configs.ExperimentConfig(
runtime=base_configs.RuntimeConfig(
enable_eager=False,
enable_xla=False,
gpu_threads_enabled=True,
per_gpu_thread_count=1,
gpu_thread_mode='gpu_private',
num_gpus=1,
dataset_num_private_threads=1,
),
train_dataset=dataset_factory.DatasetConfig(dtype=dtype),
model=base_configs.ModelConfig(
loss=base_configs.LossConfig(loss_scale='dynamic')),
)
classifier_trainer.initialize(config)
def test_initialize(self, dtype):
config = base_configs.ExperimentConfig(
runtime=base_configs.RuntimeConfig(
run_eagerly=False,
enable_xla=False,
gpu_threads_enabled=True,
per_gpu_thread_count=1,
gpu_thread_mode='gpu_private',
num_gpus=1,
dataset_num_private_threads=1,
),
train_dataset=dataset_factory.DatasetConfig(dtype=dtype),
model=base_configs.ModelConfig(
loss=base_configs.LossConfig(loss_scale='dynamic')),
)
class EmptyClass:
pass
fake_ds_builder = EmptyClass()
fake_ds_builder.dtype = dtype
fake_ds_builder.config = EmptyClass()
fake_ds_builder.config.data_format = None
classifier_trainer.initialize(config, fake_ds_builder)