def seg_deeplabv3_pascal() -> cfg.ExperimentConfig:
"""Image segmentation on imagenet with resnet deeplabv3."""
train_batch_size = 16
eval_batch_size = 8
steps_per_epoch = PASCAL_TRAIN_EXAMPLES // train_batch_size
output_stride = 16
aspp_dilation_rates = [12, 24, 36] # [6, 12, 18] if output_stride = 16
multigrid = [1, 2, 4]
stem_type = 'v1'
level = int(np.math.log2(output_stride))
config = cfg.ExperimentConfig(
task=SemanticSegmentationTask(
model=SemanticSegmentationModel(
num_classes=21,
input_size=[None, None, 3],
backbone=backbones.Backbone(
type='dilated_resnet',
dilated_resnet=backbones.DilatedResNet(
model_id=101,
output_stride=output_stride,
multigrid=multigrid,
stem_type=stem_type)),
decoder=decoders.Decoder(
type='aspp',
aspp=decoders.ASPP(level=level,
dilation_rates=aspp_dilation_rates)),
head=SegmentationHead(level=level, num_convs=0),
norm_activation=common.NormActivation(activation='swish',
norm_momentum=0.9997,
norm_epsilon=1e-3,
use_sync_bn=True)),
losses=Losses(l2_weight_decay=1e-4),
train_data=DataConfig(
input_path=os.path.join(PASCAL_INPUT_PATH_BASE, 'train_aug*'),
# TODO(arashwan): test changing size to 513 to match deeplab.
output_size=[512, 512],
is_training=True,
global_batch_size=train_batch_size,
aug_scale_min=0.5,
aug_scale_max=2.0),
validation_data=DataConfig(input_path=os.path.join(
PASCAL_INPUT_PATH_BASE, 'val*'),
output_size=[512, 512],
is_training=False,
global_batch_size=eval_batch_size,
resize_eval_groundtruth=False,
groundtruth_padded_size=[512, 512],
drop_remainder=False),
# resnet101
init_checkpoint=
'gs://cloud-tpu-checkpoints/vision-2.0/deeplab/deeplab_resnet101_imagenet/ckpt-62400',
init_checkpoint_modules='backbone'),
trainer=cfg.TrainerConfig(
steps_per_loop=steps_per_epoch,
summary_interval=steps_per_epoch,
checkpoint_interval=steps_per_epoch,
train_steps=45 * steps_per_epoch,
validation_steps=PASCAL_VAL_EXAMPLES // eval_batch_size,
validation_interval=steps_per_epoch,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9
}
},
'learning_rate': {
'type': 'polynomial',
'polynomial': {
'initial_learning_rate': 0.007,
'decay_steps': 45 * steps_per_epoch,
'end_learning_rate': 0.0,
'power': 0.9
}
},
'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
def seg_deeplabv3plus_cityscapes() -> cfg.ExperimentConfig:
"""Image segmentation on imagenet with resnet deeplabv3+."""
train_batch_size = 16
eval_batch_size = 16
steps_per_epoch = CITYSCAPES_TRAIN_EXAMPLES // train_batch_size
output_stride = 16
aspp_dilation_rates = [6, 12, 18]
multigrid = [1, 2, 4]
stem_type = 'v1'
level = int(np.math.log2(output_stride))
config = cfg.ExperimentConfig(
task=SemanticSegmentationTask(
model=SemanticSegmentationModel(
# Cityscapes uses only 19 semantic classes for train/evaluation.
# The void (background) class is ignored in train and evaluation.
num_classes=19,
input_size=[None, None, 3],
backbone=backbones.Backbone(
type='dilated_resnet',
dilated_resnet=backbones.DilatedResNet(
model_id=101,
output_stride=output_stride,
stem_type=stem_type,
multigrid=multigrid)),
decoder=decoders.Decoder(
type='aspp',
aspp=decoders.ASPP(level=level,
dilation_rates=aspp_dilation_rates,
pool_kernel_size=[512, 1024])),
head=SegmentationHead(level=level,
num_convs=2,
feature_fusion='deeplabv3plus',
low_level=2,
low_level_num_filters=48),
norm_activation=common.NormActivation(activation='swish',
norm_momentum=0.99,
norm_epsilon=1e-3,
use_sync_bn=True)),
losses=Losses(l2_weight_decay=1e-4),
train_data=DataConfig(input_path=os.path.join(
CITYSCAPES_INPUT_PATH_BASE, 'train_fine**'),
crop_size=[512, 1024],
output_size=[1024, 2048],
is_training=True,
global_batch_size=train_batch_size,
aug_scale_min=0.5,
aug_scale_max=2.0),
validation_data=DataConfig(input_path=os.path.join(
CITYSCAPES_INPUT_PATH_BASE, 'val_fine*'),
output_size=[1024, 2048],
is_training=False,
global_batch_size=eval_batch_size,
resize_eval_groundtruth=True,
drop_remainder=False),
# resnet101
init_checkpoint=
'gs://cloud-tpu-checkpoints/vision-2.0/deeplab/deeplab_resnet101_imagenet/ckpt-62400',
init_checkpoint_modules='backbone'),
trainer=cfg.TrainerConfig(
steps_per_loop=steps_per_epoch,
summary_interval=steps_per_epoch,
checkpoint_interval=steps_per_epoch,
train_steps=500 * steps_per_epoch,
validation_steps=CITYSCAPES_VAL_EXAMPLES // eval_batch_size,
validation_interval=steps_per_epoch,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9
}
},
'learning_rate': {
'type': 'polynomial',
'polynomial': {
'initial_learning_rate': 0.01,
'decay_steps': 500 * steps_per_epoch,
'end_learning_rate': 0.0,
'power': 0.9
}
},
'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
def seg_deeplabv3plus_scooter() -> cfg.ExperimentConfig:
"""Image segmentation on scooter dataset with resnet deeplabv3+.
Barebones config for testing purpose (modify batch size, initial lr, steps per epoch, train input path, val input path)
"""
scooter_path_glob = 'D:/data/test_data/val**'
steps_per_epoch = 1
output_stride = 16
aspp_dilation_rates = [6, 12, 18]
multigrid = [1, 2, 4]
stem_type = 'v1'
level = int(np.math.log2(output_stride))
config = cfg.ExperimentConfig(
task=SemanticSegmentationTask(
model=SemanticSegmentationModel(
num_classes=19,
input_size=[512, 512, 3], # specifying this speeds up model inference, no change in size
backbone=backbones.Backbone(
type='dilated_resnet', dilated_resnet=backbones.DilatedResNet(
model_id=101, output_stride=output_stride,
stem_type=stem_type, multigrid=multigrid)),
decoder=decoders.Decoder(
type='aspp',
aspp=decoders.ASPP(
level=level, dilation_rates=aspp_dilation_rates)),
head=SegmentationHead(
level=level,
num_convs=2,
feature_fusion='deeplabv3plus',
low_level=2,
low_level_num_filters=48),
norm_activation=common.NormActivation(
activation='swish',
norm_momentum=0.99,
norm_epsilon=1e-3,
use_sync_bn=True)),
losses=Losses(
l2_weight_decay=1e-4,
ignore_label=250),
train_data=DataConfig(
input_path=scooter_path_glob,
output_size=[512, 512],
is_training=True,
global_batch_size=1,
aug_scale_min=0.5,
aug_scale_max=2.0),
validation_data=DataConfig(
input_path=scooter_path_glob,
output_size=[512, 512],
is_training=False,
global_batch_size=1,
resize_eval_groundtruth=True,
drop_remainder=False)),
# resnet101
# init_checkpoint='D:/repos/data_root/test_data/deeplab_cityscapes_pretrained/model.ckpt',
# init_checkpoint_modules='all'),
# init_checkpoint='gs://cloud-tpu-checkpoints/vision-2.0/deeplab/deeplab_resnet101_imagenet/ckpt-62400',
# init_checkpoint_modules='backbone'),
trainer=cfg.TrainerConfig(
steps_per_loop=steps_per_epoch,
summary_interval=steps_per_epoch,
checkpoint_interval=steps_per_epoch,
train_steps=500 * steps_per_epoch,
validation_steps=1021,
validation_interval=steps_per_epoch,
continuous_eval_timeout=1,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9
}
},
'learning_rate': {
'type': 'polynomial',
'polynomial': {
'initial_learning_rate': 0.007,
'decay_steps': 500 * steps_per_epoch,
'end_learning_rate': 0.0,
'power': 0.9
}
},
'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
