class SqueezeNetModelConfig(base_configs.ModelConfig):
"""Configuration for the SqueezeNet model."""
name: str = 'SqueezeNet'
num_classes: int = 1000
model_params: Mapping[str,
Any] = dataclasses.field(default_factory=lambda: {
'num_classes': 1000,
'batch_size': None,
})
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=_LR_WARMUP_EPOCHS,
boundaries=_LR_BOUNDARIES,
multipliers=_LR_MULTIPLIERS))
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()
classifier_trainer.initialize(config, fake_ds_builder)