def image_classification_imagenet_vit_pretrain() -> cfg.ExperimentConfig:
"""Image classification on imagenet with vision transformer."""
train_batch_size = 4096
eval_batch_size = 4096
steps_per_epoch = IMAGENET_TRAIN_EXAMPLES // train_batch_size
config = cfg.ExperimentConfig(
task=ImageClassificationTask(
model=ImageClassificationModel(
num_classes=1001,
input_size=[224, 224, 3],
backbone=backbones.Backbone(
type='vit',
vit=backbones.VisionTransformer(
model_name='vit-b16', representation_size=768))),
losses=Losses(l2_weight_decay=0.0),
train_data=DataConfig(
input_path=os.path.join(IMAGENET_INPUT_PATH_BASE, 'train*'),
is_training=True,
global_batch_size=train_batch_size),
validation_data=DataConfig(
input_path=os.path.join(IMAGENET_INPUT_PATH_BASE, 'valid*'),
is_training=False,
global_batch_size=eval_batch_size)),
trainer=cfg.TrainerConfig(
steps_per_loop=steps_per_epoch,
summary_interval=steps_per_epoch,
checkpoint_interval=steps_per_epoch,
train_steps=300 * steps_per_epoch,
validation_steps=IMAGENET_VAL_EXAMPLES // eval_batch_size,
validation_interval=steps_per_epoch,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'adamw',
'adamw': {
'weight_decay_rate': 0.3,
'include_in_weight_decay': r'.*(kernel|weight):0$',
}
},
'learning_rate': {
'type': 'cosine',
'cosine': {
'initial_learning_rate': 0.003,
'decay_steps': 300 * steps_per_epoch,
}
},
'warmup': {
'type': 'linear',
'linear': {
'warmup_steps': 10000,
'warmup_learning_rate': 0
}
}
})),
restrictions=[
'task.train_data.is_training != None',
'task.validation_data.is_training != None'
])
return config
class ImageClassificationModel(hyperparams.Config):
"""The model config."""
num_classes: int = 0
input_size: List[int] = dataclasses.field(default_factory=list)
backbone: backbones.Backbone = backbones.Backbone(
type='vit', vit=backbones.VisionTransformer())
dropout_rate: float = 0.0
norm_activation: common.NormActivation = common.NormActivation(
use_sync_bn=False)
# Adds a BatchNormalization layer pre-GlobalAveragePooling in classification
add_head_batch_norm: bool = False
def image_classification_imagenet_vit_finetune() -> cfg.ExperimentConfig:
"""Image classification on imagenet with vision transformer."""
train_batch_size = 512
eval_batch_size = 512
steps_per_epoch = IMAGENET_TRAIN_EXAMPLES // train_batch_size
config = cfg.ExperimentConfig(
task=ImageClassificationTask(
model=ImageClassificationModel(
num_classes=1001,
input_size=[384, 384, 3],
backbone=backbones.Backbone(
type='vit',
vit=backbones.VisionTransformer(model_name='vit-b16'))),
losses=Losses(l2_weight_decay=0.0),
train_data=DataConfig(input_path=os.path.join(
IMAGENET_INPUT_PATH_BASE, 'train*'),
is_training=True,
global_batch_size=train_batch_size),
validation_data=DataConfig(input_path=os.path.join(
IMAGENET_INPUT_PATH_BASE, 'valid*'),
is_training=False,
global_batch_size=eval_batch_size)),
trainer=cfg.TrainerConfig(
steps_per_loop=steps_per_epoch,
summary_interval=steps_per_epoch,
checkpoint_interval=steps_per_epoch,
train_steps=20000,
validation_steps=IMAGENET_VAL_EXAMPLES // eval_batch_size,
validation_interval=steps_per_epoch,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9,
'global_clipnorm': 1.0,
}
},
'learning_rate': {
'type': 'cosine',
'cosine': {
'initial_learning_rate': 0.003,
'decay_steps': 20000,
}
}
})),
restrictions=[
'task.train_data.is_training != None',
'task.validation_data.is_training != None'
])
return config
def image_classification_imagenet_deit_pretrain() -> cfg.ExperimentConfig:
"""Image classification on imagenet with vision transformer."""
train_batch_size = 4096 # originally was 1024 but 4096 better for tpu v3-32
eval_batch_size = 4096 # originally was 1024 but 4096 better for tpu v3-32
num_classes = 1001
label_smoothing = 0.1
steps_per_epoch = IMAGENET_TRAIN_EXAMPLES // train_batch_size
config = cfg.ExperimentConfig(
task=ImageClassificationTask(
model=ImageClassificationModel(
num_classes=num_classes,
input_size=[224, 224, 3],
kernel_initializer='zeros',
backbone=backbones.Backbone(
type='vit',
vit=backbones.VisionTransformer(
model_name='vit-b16',
representation_size=768,
init_stochastic_depth_rate=0.1,
original_init=False,
transformer=backbones.Transformer(
dropout_rate=0.0, attention_dropout_rate=0.0)))),
losses=Losses(l2_weight_decay=0.0,
label_smoothing=label_smoothing,
one_hot=False,
soft_labels=True),
train_data=DataConfig(
input_path=os.path.join(IMAGENET_INPUT_PATH_BASE, 'train*'),
is_training=True,
global_batch_size=train_batch_size,
aug_type=common.Augmentation(type='randaug',
randaug=common.RandAugment(
magnitude=9,
exclude_ops=['Cutout'])),
mixup_and_cutmix=common.MixupAndCutmix(
label_smoothing=label_smoothing)),
validation_data=DataConfig(input_path=os.path.join(
IMAGENET_INPUT_PATH_BASE, 'valid*'),
is_training=False,
global_batch_size=eval_batch_size)),
trainer=cfg.TrainerConfig(
steps_per_loop=steps_per_epoch,
summary_interval=steps_per_epoch,
checkpoint_interval=steps_per_epoch,
train_steps=300 * steps_per_epoch,
validation_steps=IMAGENET_VAL_EXAMPLES // eval_batch_size,
validation_interval=steps_per_epoch,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'adamw',
'adamw': {
'weight_decay_rate': 0.05,
'include_in_weight_decay': r'.*(kernel|weight):0$',
'gradient_clip_norm': 0.0
}
},
'learning_rate': {
'type': 'cosine',
'cosine': {
'initial_learning_rate':
0.0005 * train_batch_size / 512,
'decay_steps': 300 * steps_per_epoch,
}
},
'warmup': {
'type': 'linear',
'linear': {
'warmup_steps': 5 * steps_per_epoch,
'warmup_learning_rate': 0
}
}
})),
restrictions=[
'task.train_data.is_training != None',
'task.validation_data.is_training != None'
])
return config