def deep_mask_head_rcnn_resnetfpn_coco() -> cfg.ExperimentConfig:
"""COCO object detection with Mask R-CNN with deep mask heads."""
global_batch_size = 64
steps_per_epoch = int(retinanet_config.COCO_TRAIN_EXAMPLES /
global_batch_size)
coco_val_samples = 5000
config = cfg.ExperimentConfig(
runtime=cfg.RuntimeConfig(mixed_precision_dtype='bfloat16'),
task=DeepMaskHeadRCNNTask(
init_checkpoint=
'gs://cloud-tpu-checkpoints/vision-2.0/resnet50_imagenet/ckpt-28080',
init_checkpoint_modules='backbone',
annotation_file=os.path.join(maskrcnn_config.COCO_INPUT_PATH_BASE,
'instances_val2017.json'),
model=DeepMaskHeadRCNN(num_classes=91,
input_size=[1024, 1024, 3],
include_mask=True), # pytype: disable=wrong-keyword-args
losses=maskrcnn_config.Losses(l2_weight_decay=0.00004),
train_data=maskrcnn_config.DataConfig(
input_path=os.path.join(maskrcnn_config.COCO_INPUT_PATH_BASE,
'train*'),
is_training=True,
global_batch_size=global_batch_size,
parser=maskrcnn_config.Parser(aug_rand_hflip=True,
aug_scale_min=0.8,
aug_scale_max=1.25)),
validation_data=maskrcnn_config.DataConfig(input_path=os.path.join(
maskrcnn_config.COCO_INPUT_PATH_BASE, 'val*'),
is_training=False,
global_batch_size=8)), # pytype: disable=wrong-keyword-args
trainer=cfg.TrainerConfig(
train_steps=22500,
validation_steps=coco_val_samples // 8,
validation_interval=steps_per_epoch,
steps_per_loop=steps_per_epoch,
summary_interval=steps_per_epoch,
checkpoint_interval=steps_per_epoch,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9
}
},
'learning_rate': {
'type': 'stepwise',
'stepwise': {
'boundaries': [15000, 20000],
'values': [0.12, 0.012, 0.0012],
}
},
'warmup': {
'type': 'linear',
'linear': {
'warmup_steps': 500,
'warmup_learning_rate': 0.0067
}
}
})),
restrictions=[
'task.train_data.is_training != None',
'task.validation_data.is_training != None'
])
return config
def deep_mask_head_rcnn_spinenet_coco() -> cfg.ExperimentConfig:
"""COCO object detection with Mask R-CNN with SpineNet backbone."""
steps_per_epoch = 463
coco_val_samples = 5000
train_batch_size = 256
eval_batch_size = 8
config = cfg.ExperimentConfig(
runtime=cfg.RuntimeConfig(mixed_precision_dtype='bfloat16'),
task=DeepMaskHeadRCNNTask(
annotation_file=os.path.join(maskrcnn_config.COCO_INPUT_PATH_BASE,
'instances_val2017.json'), # pytype: disable=wrong-keyword-args
model=DeepMaskHeadRCNN(
backbone=backbones.Backbone(type='spinenet',
spinenet=backbones.SpineNet(
model_id='49',
min_level=3,
max_level=7,
)),
decoder=decoders.Decoder(type='identity',
identity=decoders.Identity()),
anchor=maskrcnn_config.Anchor(anchor_size=3),
norm_activation=common.NormActivation(use_sync_bn=True),
num_classes=91,
input_size=[640, 640, 3],
min_level=3,
max_level=7,
include_mask=True), # pytype: disable=wrong-keyword-args
losses=maskrcnn_config.Losses(l2_weight_decay=0.00004),
train_data=maskrcnn_config.DataConfig(
input_path=os.path.join(maskrcnn_config.COCO_INPUT_PATH_BASE,
'train*'),
is_training=True,
global_batch_size=train_batch_size,
parser=maskrcnn_config.Parser(aug_rand_hflip=True,
aug_scale_min=0.5,
aug_scale_max=2.0)),
validation_data=maskrcnn_config.DataConfig(
input_path=os.path.join(maskrcnn_config.COCO_INPUT_PATH_BASE,
'val*'),
is_training=False,
global_batch_size=eval_batch_size,
drop_remainder=False)), # pytype: disable=wrong-keyword-args
trainer=cfg.TrainerConfig(
train_steps=steps_per_epoch * 350,
validation_steps=coco_val_samples // eval_batch_size,
validation_interval=steps_per_epoch,
steps_per_loop=steps_per_epoch,
summary_interval=steps_per_epoch,
checkpoint_interval=steps_per_epoch,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9
}
},
'learning_rate': {
'type': 'stepwise',
'stepwise': {
'boundaries':
[steps_per_epoch * 320, steps_per_epoch * 340],
'values': [0.32, 0.032, 0.0032],
}
},
'warmup': {
'type': 'linear',
'linear': {
'warmup_steps': 2000,
'warmup_learning_rate': 0.0067
}
}
})),
restrictions=[
'task.train_data.is_training != None',
'task.validation_data.is_training != None',
'task.model.min_level == task.model.backbone.spinenet.min_level',
'task.model.max_level == task.model.backbone.spinenet.max_level',
])
return config