def test_configure_optimizer(self, mixed_precision_dtype, loss_scale):
config = cfg.ExperimentConfig(
runtime=cfg.RuntimeConfig(
mixed_precision_dtype=mixed_precision_dtype, loss_scale=loss_scale),
trainer=cfg.TrainerConfig(
optimizer_config=cfg.OptimizationConfig({
'optimizer': {
'type': 'sgd'
},
'learning_rate': {
'type': 'constant'
}
})))
trainer = self.create_test_trainer(config)
if mixed_precision_dtype != 'float16':
self.assertIsInstance(trainer.optimizer, tf.keras.optimizers.SGD)
elif mixed_precision_dtype == 'float16' and loss_scale is None:
self.assertIsInstance(trainer.optimizer, tf.keras.optimizers.SGD)
else:
self.assertIsInstance(
trainer.optimizer,
tf.keras.mixed_precision.experimental.LossScaleOptimizer)
metrics = trainer.train(tf.convert_to_tensor(5, dtype=tf.int32))
self.assertIn('training_loss', metrics)
def test_export_best_ckpt(self, distribution):
config = cfg.ExperimentConfig(
trainer=cfg.TrainerConfig(
best_checkpoint_export_subdir='best_ckpt',
best_checkpoint_eval_metric='acc',
optimizer_config=cfg.OptimizationConfig({
'optimizer': {
'type': 'sgd'
},
'learning_rate': {
'type': 'constant'
}
})))
model_dir = self.get_temp_dir()
task = mock_task.MockTask(config.task, logging_dir=model_dir)
ckpt_exporter = train_lib.maybe_create_best_ckpt_exporter(config, model_dir)
trainer = trainer_lib.Trainer(
config,
task,
model=task.build_model(),
checkpoint_exporter=ckpt_exporter)
trainer.train(tf.convert_to_tensor(1, dtype=tf.int32))
trainer.evaluate(tf.convert_to_tensor(1, dtype=tf.int32))
self.assertTrue(
tf.io.gfile.exists(os.path.join(model_dir, 'best_ckpt', 'info.json')))
def basnet() -> cfg.ExperimentConfig:
"""BASNet general."""
return cfg.ExperimentConfig(task=BASNetModel(),
trainer=cfg.TrainerConfig(),
restrictions=[
'task.train_data.is_training != None',
'task.validation_data.is_training != None'
])
def image_classification() -> cfg.ExperimentConfig:
"""Image classification general."""
return cfg.ExperimentConfig(task=ImageClassificationTask(),
trainer=cfg.TrainerConfig(),
restrictions=[
'task.train_data.is_training != None',
'task.validation_data.is_training != None'
])
def semantic_segmentation() -> cfg.ExperimentConfig:
"""Semantic segmentation general."""
return cfg.ExperimentConfig(task=SemanticSegmentationModel(),
trainer=cfg.TrainerConfig(),
restrictions=[
'task.train_data.is_training != None',
'task.validation_data.is_training != None'
])
def seg_unet3d_test() -> cfg.ExperimentConfig:
"""Image segmentation on a dummy dataset with 3D UNet for testing purpose."""
train_batch_size = 2
eval_batch_size = 2
steps_per_epoch = 10
config = cfg.ExperimentConfig(
task=SemanticSegmentation3DTask(
model=SemanticSegmentationModel3D(
num_classes=2,
input_size=[32, 32, 32],
num_channels=2,
backbone=backbones.Backbone(
type='unet_3d', unet_3d=backbones.UNet3D(model_id=2)),
decoder=decoders.Decoder(
type='unet_3d_decoder',
unet_3d_decoder=decoders.UNet3DDecoder(model_id=2)),
head=SegmentationHead3D(num_convs=0, num_classes=2),
norm_activation=common.NormActivation(
activation='relu', use_sync_bn=False)),
train_data=DataConfig(
input_path='train.tfrecord',
num_classes=2,
input_size=[32, 32, 32],
num_channels=2,
is_training=True,
global_batch_size=train_batch_size),
validation_data=DataConfig(
input_path='val.tfrecord',
num_classes=2,
input_size=[32, 32, 32],
num_channels=2,
is_training=False,
global_batch_size=eval_batch_size),
losses=Losses(loss_type='adaptive')),
trainer=cfg.TrainerConfig(
steps_per_loop=steps_per_epoch,
summary_interval=steps_per_epoch,
checkpoint_interval=steps_per_epoch,
train_steps=10,
validation_steps=10,
validation_interval=steps_per_epoch,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'sgd',
},
'learning_rate': {
'type': 'constant',
'constant': {
'learning_rate': 0.000001
}
}
})),
restrictions=[
'task.train_data.is_training != None',
'task.validation_data.is_training != None'
])
return config
def video_classification() -> cfg.ExperimentConfig:
"""Video classification general."""
return cfg.ExperimentConfig(
task=VideoClassificationTask(),
trainer=cfg.TrainerConfig(),
restrictions=[
'task.train_data.is_training != None',
'task.validation_data.is_training != None',
'task.train_data.num_classes == task.validation_data.num_classes',
])
def video_classification() -> cfg.ExperimentConfig:
"""Video classification general."""
return cfg.ExperimentConfig(
runtime=cfg.RuntimeConfig(mixed_precision_dtype='bfloat16'),
task=VideoClassificationTask(),
trainer=cfg.TrainerConfig(),
restrictions=[
'task.train_data.is_training != None',
'task.validation_data.is_training != None',
'task.train_data.num_classes == task.validation_data.num_classes',
])
def setUp(self):
super().setUp()
self._config = cfg.ExperimentConfig(trainer=cfg.TrainerConfig(
optimizer_config=cfg.OptimizationConfig({
'optimizer': {
'type': 'sgd'
},
'learning_rate': {
'type': 'constant'
}
})))
def add_trainer(experiment: cfg.ExperimentConfig,
train_batch_size: int,
eval_batch_size: int,
learning_rate: float = 1.6,
train_epochs: int = 44,
warmup_epochs: int = 5):
"""Add and config a trainer to the experiment config."""
if experiment.task.train_data.num_examples <= 0:
raise ValueError('Wrong train dataset size {!r}'.format(
experiment.task.train_data))
if experiment.task.validation_data.num_examples <= 0:
raise ValueError('Wrong validation dataset size {!r}'.format(
experiment.task.validation_data))
experiment.task.train_data.global_batch_size = train_batch_size
experiment.task.validation_data.global_batch_size = eval_batch_size
steps_per_epoch = experiment.task.train_data.num_examples // train_batch_size
experiment.trainer = cfg.TrainerConfig(
steps_per_loop=steps_per_epoch,
summary_interval=steps_per_epoch,
checkpoint_interval=steps_per_epoch,
train_steps=train_epochs * steps_per_epoch,
validation_steps=experiment.task.validation_data.num_examples //
eval_batch_size,
validation_interval=steps_per_epoch,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9,
'nesterov': True,
}
},
'learning_rate': {
'type': 'cosine',
'cosine': {
'initial_learning_rate': learning_rate,
'decay_steps': train_epochs * steps_per_epoch,
}
},
'warmup': {
'type': 'linear',
'linear': {
'warmup_steps': warmup_epochs * steps_per_epoch,
'warmup_learning_rate': 0
}
}
}))
return experiment
def maskrcnn_resnetfpn_coco() -> cfg.ExperimentConfig:
"""COCO object detection with Mask R-CNN."""
steps_per_epoch = 500
coco_val_samples = 5000
config = cfg.ExperimentConfig(
runtime=cfg.RuntimeConfig(mixed_precision_dtype='bfloat16'),
task=MaskRCNNTask(
init_checkpoint='gs://cloud-tpu-checkpoints/vision-2.0/resnet50_imagenet/ckpt-28080',
init_checkpoint_modules='backbone',
annotation_file=os.path.join(COCO_INPUT_PATH_BASE,
'instances_val2017.json'),
model=MaskRCNN(
num_classes=91,
input_size=[1024, 1024, 3],
include_mask=True),
losses=Losses(l2_weight_decay=0.00004),
train_data=DataConfig(
input_path=os.path.join(COCO_INPUT_PATH_BASE, 'train*'),
is_training=True,
global_batch_size=64,
parser=Parser(
aug_rand_hflip=True, aug_scale_min=0.8, aug_scale_max=1.25)),
validation_data=DataConfig(
input_path=os.path.join(COCO_INPUT_PATH_BASE, 'val*'),
is_training=False,
global_batch_size=8)),
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 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 maskrcnn_spinenet_coco() -> cfg.ExperimentConfig:
"""COCO object detection with Mask R-CNN with SpineNet backbone."""
steps_per_epoch = 463
coco_val_samples = 5000
config = cfg.ExperimentConfig(
runtime=cfg.RuntimeConfig(mixed_precision_dtype='bfloat16'),
task=MaskRCNNTask(
annotation_file=os.path.join(COCO_INPUT_PATH_BASE,
'instances_val2017.json'),
model=MaskRCNN(
backbone=backbones.Backbone(
type='spinenet', spinenet=backbones.SpineNet(model_id='49')),
decoder=decoders.Decoder(
type='identity', identity=decoders.Identity()),
anchor=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),
losses=Losses(l2_weight_decay=0.00004),
train_data=DataConfig(
input_path=os.path.join(COCO_INPUT_PATH_BASE, 'train*'),
is_training=True,
global_batch_size=256,
parser=Parser(
aug_rand_hflip=True, aug_scale_min=0.5, aug_scale_max=2.0)),
validation_data=DataConfig(
input_path=os.path.join(COCO_INPUT_PATH_BASE, 'val*'),
is_training=False,
global_batch_size=8)),
trainer=cfg.TrainerConfig(
train_steps=steps_per_epoch * 350,
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': [
steps_per_epoch * 320, steps_per_epoch * 340
],
'values': [0.28, 0.028, 0.0028],
}
},
'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'
])
return config
def detector_yolo() -> cfg.ExperimentConfig:
"""YOLO on custom datasets"""
config = cfg.ExperimentConfig(
task=YoloTask(
model=YoloModel(
num_classes=6,
input_size=[256, 256, 3],
backbone=backbones.Backbone(
type='hardnet', hardnet=backbones.HardNet(model_id=70)),
decoder=decoders.Decoder(
type='pan', pan=decoders.PAN(levels=3)),
head=YoloHead(
anchor_per_scale=3,
strides=[16, 32, 64],
anchors=[12,16, 19,36, 40,28, 36,75, 76,55, 72,146, 142,110, 192,243, 459,401],
xy_scale=[1.2, 1.1, 1.05]
),
norm_activation=common.NormActivation(
activation='relu',
norm_momentum=0.9997,
norm_epsilon=0.001,
use_sync_bn=True)),
losses=YoloLosses(l2_weight_decay=1e-4,
iou_loss_thres=0.5),
train_data=DataConfig(
input_path='D:/data/whizz_tf/detect_env*',
output_size=[256, 256],
is_training=True,
global_batch_size=1),
validation_data=DataConfig(
input_path='D:/data/whizz_tf/detect_env*',
output_size=[256, 256],
is_training=False,
global_batch_size=1,
drop_remainder=False),
# init_checkpoint=None
init_checkpoint_modules='backbone'),
trainer=cfg.TrainerConfig(
steps_per_loop=2,
summary_interval=2,
checkpoint_interval=2,
train_steps=20,
validation_steps=20,
validation_interval=2,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9
}
},
'learning_rate': {
'type': 'polynomial',
'polynomial': {
'initial_learning_rate': 0.007,
'decay_steps': 20,
'end_learning_rate': 0.0,
'power': 0.9
}
},
'warmup': {
'type': 'linear',
'linear': {
'warmup_steps': 2,
'warmup_learning_rate': 0
}
}
})),
restrictions=[
'task.train_data.is_training != None',
'task.validation_data.is_training != None'
])
return config
def image_classification_imagenet() -> cfg.ExperimentConfig:
"""Image classification on imagenet with resnet."""
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='resnet', resnet=backbones.ResNet(model_id=50)),
norm_activation=common.NormActivation(norm_momentum=0.9,
norm_epsilon=1e-5)),
losses=Losses(l2_weight_decay=1e-4),
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=90 * steps_per_epoch,
validation_steps=IMAGENET_VAL_EXAMPLES // eval_batch_size,
validation_interval=steps_per_epoch,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9
}
},
'learning_rate': {
'type': 'stepwise',
'stepwise': {
'boundaries': [
30 * steps_per_epoch, 60 * steps_per_epoch,
80 * steps_per_epoch
],
'values': [
0.1 * train_batch_size / 256,
0.01 * train_batch_size / 256,
0.001 * train_batch_size / 256,
0.0001 * train_batch_size / 256,
]
}
},
'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 mnv2_deeplabv3_cityscapes() -> cfg.ExperimentConfig:
"""Image segmentation on cityscapes with mobilenetv2 deeplabv3."""
train_batch_size = 16
eval_batch_size = 16
steps_per_epoch = CITYSCAPES_TRAIN_EXAMPLES // train_batch_size
output_stride = 16
aspp_dilation_rates = []
pool_kernel_size = [512, 1024]
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='mobilenet',
mobilenet=backbones.MobileNet(
model_id='MobileNetV2',
output_stride=output_stride)),
decoder=decoders.Decoder(
type='aspp',
aspp=decoders.ASPP(level=level,
dilation_rates=aspp_dilation_rates,
pool_kernel_size=pool_kernel_size)),
head=SegmentationHead(level=level, num_convs=0),
norm_activation=common.NormActivation(activation='relu',
norm_momentum=0.99,
norm_epsilon=1e-3,
use_sync_bn=True)),
losses=Losses(l2_weight_decay=4e-5),
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),
# Coco pre-trained mobilenetv2 checkpoint
init_checkpoint=
'gs://tf_model_garden/cloud/vision-2.0/deeplab/deeplabv3_mobilenetv2_coco/best_ckpt-63',
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=100000,
validation_steps=CITYSCAPES_VAL_EXAMPLES // eval_batch_size,
validation_interval=steps_per_epoch,
best_checkpoint_eval_metric='mean_iou',
best_checkpoint_export_subdir='best_ckpt',
best_checkpoint_metric_comp='higher',
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9
}
},
'learning_rate': {
'type': 'polynomial',
'polynomial': {
'initial_learning_rate': 0.01,
'decay_steps': 100000,
'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 basnet_duts() -> cfg.ExperimentConfig:
"""Image segmentation on duts with basnet."""
train_batch_size = 16
eval_batch_size = 16
steps_per_epoch = DUTS_TRAIN_EXAMPLES // train_batch_size
config = cfg.ExperimentConfig(
task=BASNetTask(
model=BASNetModel(
input_size=[224, 224, 3], # Resize to 256, 256
backbone=backbones.Backbone(
type='basnet_en', basnet_en=backbones.BASNet_En(
)),
decoder=decoders.Decoder(
type='basnet_de', basnet_de=decoders.BASNet_De(
)),
norm_activation=common.NormActivation(
activation='relu',
norm_momentum=0.99,
norm_epsilon=1e-3,
use_sync_bn=True)),
losses=Losses(l2_weight_decay=0),
train_data=DataConfig(
input_path=os.path.join(DUTS_INPUT_PATH_BASE_TR, 'DUTS-TR-*'),
is_training=True,
global_batch_size=train_batch_size,
),
validation_data=DataConfig(
input_path=os.path.join(DUTS_INPUT_PATH_BASE_VAL, 'DUTS-TE-*'),
is_training=False,
global_batch_size=eval_batch_size,
),
#init_checkpoint='',
#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=300 * steps_per_epoch,
validation_steps=DUTS_VAL_EXAMPLES // eval_batch_size,
validation_interval=steps_per_epoch,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'adam',
'adam': {
'beta_1': 0.9,
'beta_2': 0.999,
'epsilon': 1e-8,
}
},
'learning_rate': {
'type': 'stepwise',
'stepwise': {'boundaries': [200*steps_per_epoch],
'values': [0.001, 0.001]}
}
})),
restrictions=[
'task.train_data.is_training != None',
'task.validation_data.is_training != None'
])
return config
def mock_experiment() -> cfg.ExperimentConfig:
config = cfg.ExperimentConfig(task=MockTaskConfig(),
trainer=cfg.TrainerConfig())
return config
def seg_deeplabv2_pascal() -> cfg.ExperimentConfig:
"""Image segmentation on imagenet with vggnet & resnet deeplabv2."""
train_batch_size = 16
eval_batch_size = 8
steps_per_epoch = PASCAL_TRAIN_EXAMPLES // train_batch_size
# for Large FOV
fov_dilation_rates = 12
kernel_size = 3
# for ASPP
aspp_dilation_rates = [6, 12, 18, 24]
output_stride = 16
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_vggnet',
dilated_vggnet=backbones.DilatedVGGNet(model_id=16)),
decoder=decoders.Decoder(
type='aspp',
aspp=decoders.ASPP(level=level,
dilation_rates=aspp_dilation_rates,
stem_type='v2',
num_filters=1024,
use_sync_bn=True)),
head=SegmentationHead(level=level,
num_convs=0,
low_level_num_filters=1024,
feature_fusion='deeplabv2'),
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=1.5),
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='/home/gunho1123/ckpt_vggnet16_deeplab/',
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 image_classification_imagenet_mobilenet() -> cfg.ExperimentConfig:
"""Image classification on imagenet with mobilenet."""
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,
dropout_rate=0.2,
input_size=[224, 224, 3],
backbone=backbones.Backbone(type='mobilenet',
mobilenet=backbones.MobileNet(
model_id='MobileNetV2',
filter_size_scale=1.0)),
norm_activation=common.NormActivation(norm_momentum=0.997,
norm_epsilon=1e-3)),
losses=Losses(l2_weight_decay=1e-5, label_smoothing=0.1),
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=500 * steps_per_epoch,
validation_steps=IMAGENET_VAL_EXAMPLES // eval_batch_size,
validation_interval=steps_per_epoch,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'rmsprop',
'rmsprop': {
'rho': 0.9,
'momentum': 0.9,
'epsilon': 0.002,
}
},
'learning_rate': {
'type': 'exponential',
'exponential': {
'initial_learning_rate':
0.008 * (train_batch_size // 128),
'decay_steps': int(2.5 * steps_per_epoch),
'decay_rate': 0.98,
'staircase': True
}
},
'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 retinanet_resnetfpn_coco() -> cfg.ExperimentConfig:
"""COCO object detection with RetinaNet."""
train_batch_size = 256
eval_batch_size = 8
steps_per_epoch = COCO_TRIAN_EXAMPLES // train_batch_size
config = cfg.ExperimentConfig(
runtime=cfg.RuntimeConfig(mixed_precision_dtype='bfloat16'),
task=RetinaNetTask(
init_checkpoint=
'gs://cloud-tpu-checkpoints/vision-2.0/resnet50_imagenet/ckpt-28080',
init_checkpoint_modules='backbone',
annotation_file=os.path.join(COCO_INPUT_PATH_BASE,
'instances_val2017.json'),
model=RetinaNet(num_classes=91,
input_size=[640, 640, 3],
min_level=3,
max_level=7),
losses=Losses(l2_weight_decay=1e-4),
train_data=DataConfig(input_path=os.path.join(
COCO_INPUT_PATH_BASE, 'train*'),
is_training=True,
global_batch_size=train_batch_size,
parser=Parser(aug_rand_hflip=True,
aug_scale_min=0.5,
aug_scale_max=2.0)),
validation_data=DataConfig(input_path=os.path.join(
COCO_INPUT_PATH_BASE, 'val*'),
is_training=False,
global_batch_size=eval_batch_size)),
trainer=cfg.TrainerConfig(
train_steps=72 * steps_per_epoch,
validation_steps=COCO_VAL_EXAMPLES // 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':
[57 * steps_per_epoch, 67 * steps_per_epoch],
'values': [
0.28 * train_batch_size / 256.0,
0.028 * train_batch_size / 256.0,
0.0028 * train_batch_size / 256.0
],
}
},
'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 seg_resnetfpn_pascal() -> cfg.ExperimentConfig:
"""Image segmentation on imagenet with resnet-fpn."""
train_batch_size = 256
eval_batch_size = 32
steps_per_epoch = PASCAL_TRAIN_EXAMPLES // train_batch_size
config = cfg.ExperimentConfig(
task=SemanticSegmentationTask(
model=SemanticSegmentationModel(
num_classes=21,
input_size=[512, 512, 3],
min_level=3,
max_level=7,
backbone=backbones.Backbone(
type='resnet', resnet=backbones.ResNet(model_id=50)),
decoder=decoders.Decoder(type='fpn', fpn=decoders.FPN()),
head=SegmentationHead(level=3, num_convs=3),
norm_activation=common.NormActivation(activation='swish',
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*'),
is_training=True,
global_batch_size=train_batch_size,
aug_scale_min=0.2,
aug_scale_max=1.5),
validation_data=DataConfig(input_path=os.path.join(
PASCAL_INPUT_PATH_BASE, 'val*'),
is_training=False,
global_batch_size=eval_batch_size,
resize_eval_groundtruth=False,
groundtruth_padded_size=[512, 512],
drop_remainder=False),
),
trainer=cfg.TrainerConfig(
steps_per_loop=steps_per_epoch,
summary_interval=steps_per_epoch,
checkpoint_interval=steps_per_epoch,
train_steps=450 * 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': 450 * 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
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_ade20k_32(backbone: str,
init_backbone: bool = True
) -> cfg.ExperimentConfig:
"""Semantic segmentation on ADE20K dataset with deeplabv3+."""
epochs = 200
train_batch_size = 128
eval_batch_size = 32
image_size = 512
steps_per_epoch = ADE20K_TRAIN_EXAMPLES // train_batch_size
aspp_dilation_rates = [5, 10, 15]
pretrained_checkpoint_path = BACKBONE_PRETRAINED_CHECKPOINT[
backbone] if init_backbone else None
config = cfg.ExperimentConfig(
task=CustomSemanticSegmentationTaskConfig(
model=base_cfg.SemanticSegmentationModel(
# ADE20K uses only 32 semantic classes for train/evaluation.
# The void (background) class is ignored in train and evaluation.
num_classes=32,
input_size=[None, None, 3],
backbone=Backbone(
type='mobilenet_edgetpu',
mobilenet_edgetpu=MobileNetEdgeTPU(
model_id=backbone,
pretrained_checkpoint_path=pretrained_checkpoint_path,
freeze_large_filters=500,
)),
decoder=decoders.Decoder(
type='aspp',
aspp=decoders.ASPP(
level=BACKBONE_HEADPOINT[backbone],
use_depthwise_convolution=True,
dilation_rates=aspp_dilation_rates,
pool_kernel_size=[256, 256],
num_filters=128,
dropout_rate=0.3,
)),
head=base_cfg.SegmentationHead(
level=BACKBONE_HEADPOINT[backbone],
num_convs=2,
num_filters=256,
use_depthwise_convolution=True,
feature_fusion='deeplabv3plus',
low_level=BACKBONE_LOWER_FEATURES[backbone],
low_level_num_filters=48),
norm_activation=common.NormActivation(
activation='relu',
norm_momentum=0.99,
norm_epsilon=2e-3,
use_sync_bn=False)),
train_data=base_cfg.DataConfig(
input_path=os.path.join(ADE20K_INPUT_PATH_BASE, 'train-*'),
output_size=[image_size, image_size],
is_training=True,
global_batch_size=train_batch_size),
validation_data=base_cfg.DataConfig(
input_path=os.path.join(ADE20K_INPUT_PATH_BASE, 'val-*'),
output_size=[image_size, image_size],
is_training=False,
global_batch_size=eval_batch_size,
resize_eval_groundtruth=True,
drop_remainder=False),
evaluation=base_cfg.Evaluation(report_train_mean_iou=False),
),
trainer=cfg.TrainerConfig(
steps_per_loop=steps_per_epoch,
summary_interval=steps_per_epoch,
checkpoint_interval=steps_per_epoch,
train_steps=epochs * steps_per_epoch,
validation_steps=ADE20K_VAL_EXAMPLES // eval_batch_size,
validation_interval=steps_per_epoch,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'adam',
},
'learning_rate': {
'type': 'polynomial',
'polynomial': {
'initial_learning_rate': 0.0001,
'decay_steps': epochs * steps_per_epoch,
'end_learning_rate': 0.0,
'power': 0.9
}
},
'warmup': {
'type': 'linear',
'linear': {
'warmup_steps': 4 * steps_per_epoch,
'warmup_learning_rate': 0
}
}
})),
restrictions=[
'task.train_data.is_training != None',
'task.validation_data.is_training != None'
])
return config
def retinanet_spinenet_coco() -> cfg.ExperimentConfig:
"""COCO object detection with RetinaNet using SpineNet backbone."""
train_batch_size = 256
eval_batch_size = 8
steps_per_epoch = COCO_TRIAN_EXAMPLES // train_batch_size
input_size = 640
config = cfg.ExperimentConfig(
runtime=cfg.RuntimeConfig(mixed_precision_dtype='float32'),
task=RetinaNetTask(
annotation_file=os.path.join(COCO_INPUT_PATH_BASE,
'instances_val2017.json'),
model=RetinaNet(
backbone=backbones.Backbone(
type='spinenet',
spinenet=backbones.SpineNet(model_id='49')),
decoder=decoders.Decoder(type='identity',
identity=decoders.Identity()),
anchor=Anchor(anchor_size=3),
norm_activation=common.NormActivation(use_sync_bn=True),
num_classes=91,
input_size=[input_size, input_size, 3],
min_level=3,
max_level=7),
losses=Losses(l2_weight_decay=4e-5),
train_data=DataConfig(input_path=os.path.join(
COCO_INPUT_PATH_BASE, 'train*'),
is_training=True,
global_batch_size=train_batch_size,
parser=Parser(aug_rand_hflip=True,
aug_scale_min=0.5,
aug_scale_max=2.0)),
validation_data=DataConfig(input_path=os.path.join(
COCO_INPUT_PATH_BASE, 'val*'),
is_training=False,
global_batch_size=eval_batch_size)),
trainer=cfg.TrainerConfig(
train_steps=350 * steps_per_epoch,
validation_steps=COCO_VAL_EXAMPLES // 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':
[320 * steps_per_epoch, 340 * steps_per_epoch],
'values': [
0.28 * train_batch_size / 256.0,
0.028 * train_batch_size / 256.0,
0.0028 * train_batch_size / 256.0
],
}
},
'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'
])
return config
def yolo_v4_coco() -> cfg.ExperimentConfig:
"""COCO object detection with YOLO."""
train_batch_size = 1
eval_batch_size = 1
base_default = 1200000
num_batches = 1200000 * 64 / train_batch_size
config = cfg.ExperimentConfig(
runtime=cfg.RuntimeConfig(
# mixed_precision_dtype='float16',
# loss_scale='dynamic',
num_gpus=2),
task=YoloTask(
model=Yolo(base='v4'),
train_data=DataConfig( # input_path=os.path.join(
# COCO_INPUT_PATH_BASE, 'train*'),
is_training=True,
global_batch_size=train_batch_size,
parser=Parser(),
shuffle_buffer_size=2),
validation_data=DataConfig(
# input_path=os.path.join(COCO_INPUT_PATH_BASE,
# 'val*'),
is_training=False,
global_batch_size=eval_batch_size,
shuffle_buffer_size=2)),
trainer=cfg.TrainerConfig(
steps_per_loop=2000,
summary_interval=8000,
checkpoint_interval=10000,
train_steps=num_batches,
validation_steps=1000,
validation_interval=10,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9
}
},
'learning_rate': {
'type': 'stepwise',
'stepwise': {
'boundaries': [
int(400000 / base_default * num_batches),
int(450000 / base_default * num_batches)
],
'values': [
0.00261 * train_batch_size / 64,
0.000261 * train_batch_size / 64,
0.0000261 * train_batch_size / 64
]
}
},
'warmup': {
'type': 'linear',
'linear': {
'warmup_steps': 1000 * 64 // num_batches,
'warmup_learning_rate': 0
}
}
})),
restrictions=[
'task.train_data.is_training != None',
'task.validation_data.is_training != None'
])
return config
def basnet_duts() -> cfg.ExperimentConfig:
"""Image segmentation on duts with basnet."""
train_batch_size = 64
eval_batch_size = 16
steps_per_epoch = DUTS_TRAIN_EXAMPLES // train_batch_size
config = cfg.ExperimentConfig(
task=BASNetTask(
model=BASNetModel(input_size=[None, None, 3],
use_bias=True,
norm_activation=common.NormActivation(
activation='relu',
norm_momentum=0.99,
norm_epsilon=1e-3,
use_sync_bn=True)),
losses=Losses(l2_weight_decay=0),
train_data=DataConfig(
input_path=os.path.join(DUTS_INPUT_PATH_BASE_TR,
'tf_record_train'),
file_type='tfrecord',
crop_size=[224, 224],
output_size=[256, 256],
is_training=True,
global_batch_size=train_batch_size,
),
validation_data=DataConfig(
input_path=os.path.join(DUTS_INPUT_PATH_BASE_VAL,
'tf_record_test'),
file_type='tfrecord',
output_size=[256, 256],
is_training=False,
global_batch_size=eval_batch_size,
),
init_checkpoint=
'gs://cloud-basnet-checkpoints/basnet_encoder_imagenet/ckpt-340306',
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=300 * steps_per_epoch,
validation_steps=DUTS_VAL_EXAMPLES // eval_batch_size,
validation_interval=steps_per_epoch,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'adam',
'adam': {
'beta_1': 0.9,
'beta_2': 0.999,
'epsilon': 1e-8,
}
},
'learning_rate': {
'type': 'constant',
'constant': {
'learning_rate': 0.001
}
}
})),
restrictions=[
'task.train_data.is_training != None',
'task.validation_data.is_training != None'
])
return config
def basnet_duts() -> cfg.ExperimentConfig:
"""Image segmentation on imagenet with resnet deeplabv3."""
train_batch_size = 16
eval_batch_size = 16
steps_per_epoch = DUTS_TRAIN_EXAMPLES // train_batch_size
config = cfg.ExperimentConfig(
task=BASNetTask(
model=BASNetModel(
#num_classes=21,
# TODO(arashwan): test changing size to 513 to match deeplab.
input_size=[224, 224, 3], # Resize to 256, 256
backbone=backbones.Backbone(
type='basnet_en', basnet_en=backbones.BASNet_En(
)),
decoder=decoders.Decoder(
type='basnet_de', basnet_de=decoders.BASNet_De(
)),
#head=BASNetHead(level=3, num_convs=0),
norm_activation=common.NormActivation(
activation='relu',
norm_momentum=0.99,
norm_epsilon=1e-3,
use_sync_bn=True)),
losses=Losses(l2_weight_decay=0),
train_data=DataConfig(
#input_path=os.path.join(PASCAL_INPUT_PATH_BASE, 'train_aug*'), # Dataset Path #
input_path=os.path.join(DUTS_INPUT_PATH_BASE_TR, 'DUTS-TR-*'),
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(DUTS_INPUT_PATH_BASE_VAL, 'DUTS-TE-*'),
is_training=False,
global_batch_size=eval_batch_size,
),
#init_checkpoint='',
#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, # (gunho) more epochs
validation_steps=DUTS_VAL_EXAMPLES // eval_batch_size, # No validation in BASNet
validation_interval=steps_per_epoch,
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'adam', #BASNet
'adam': {
'beta_1': 0.9,
'beta_2': 0.999,
'epsilon': 1e-8,
}
},
'learning_rate': {
'type': 'stepwise',
'stepwise': {'boundaries': [70*steps_per_epoch, 100*steps_per_epoch, 150*steps_per_epoch],
'values': [0.01, 0.001, 0.001, 0.0001]}
}
})),
restrictions=[
'task.train_data.is_training != None',
'task.validation_data.is_training != None'
])
return config
