def test_spinenet_creation(self, model_id):
"""Test creation of SpineNet models."""
input_size = 128
min_level = 3
max_level = 7
input_specs = tf.keras.layers.InputSpec(
shape=[None, input_size, input_size, 3])
network = backbones.SpineNet(input_specs=input_specs,
min_level=min_level,
max_level=max_level,
norm_momentum=0.99,
norm_epsilon=1e-5)
backbone_config = backbones_cfg.Backbone(
type='spinenet',
spinenet=backbones_cfg.SpineNet(model_id=model_id))
norm_activation_config = common_cfg.NormActivation(norm_momentum=0.99,
norm_epsilon=1e-5,
use_sync_bn=False)
factory_network = factory.build_backbone(
input_specs=tf.keras.layers.InputSpec(
shape=[None, input_size, input_size, 3]),
backbone_config=backbone_config,
norm_activation_config=norm_activation_config)
network_config = network.get_config()
factory_network_config = factory_network.get_config()
self.assertEqual(network_config, factory_network_config)
def test_resnet_creation(self, model_id):
"""Test creation of ResNet models."""
network = backbones.ResNet(model_id=model_id,
se_ratio=0.0,
norm_momentum=0.99,
norm_epsilon=1e-5)
backbone_config = backbones_cfg.Backbone(type='resnet',
resnet=backbones_cfg.ResNet(
model_id=model_id,
se_ratio=0.0))
norm_activation_config = common_cfg.NormActivation(norm_momentum=0.99,
norm_epsilon=1e-5,
use_sync_bn=False)
model_config = retinanet_cfg.RetinaNet(
backbone=backbone_config, norm_activation=norm_activation_config)
factory_network = factory.build_backbone(
input_specs=tf.keras.layers.InputSpec(shape=[None, None, None, 3]),
model_config=model_config)
network_config = network.get_config()
factory_network_config = factory_network.get_config()
self.assertEqual(network_config, factory_network_config)
def test_efficientnet_creation(self, model_id, se_ratio):
"""Test creation of EfficientNet models."""
network = backbones.EfficientNet(model_id=model_id,
se_ratio=se_ratio,
norm_momentum=0.99,
norm_epsilon=1e-5)
backbone_config = backbones_cfg.Backbone(
type='efficientnet',
efficientnet=backbones_cfg.EfficientNet(model_id=model_id,
se_ratio=se_ratio))
norm_activation_config = common_cfg.NormActivation(norm_momentum=0.99,
norm_epsilon=1e-5,
use_sync_bn=False)
factory_network = factory.build_backbone(
input_specs=tf.keras.layers.InputSpec(shape=[None, None, None, 3]),
backbone_config=backbone_config,
norm_activation_config=norm_activation_config)
network_config = network.get_config()
factory_network_config = factory_network.get_config()
self.assertEqual(network_config, factory_network_config)
def test_mobilenet_creation(self, model_id, filter_size_scale):
"""Test creation of Mobilenet models."""
network = backbones.MobileNet(model_id=model_id,
filter_size_scale=filter_size_scale,
norm_momentum=0.99,
norm_epsilon=1e-5)
backbone_config = backbones_cfg.Backbone(
type='mobilenet',
mobilenet=backbones_cfg.MobileNet(
model_id=model_id, filter_size_scale=filter_size_scale))
norm_activation_config = common_cfg.NormActivation(norm_momentum=0.99,
norm_epsilon=1e-5,
use_sync_bn=False)
factory_network = factory.build_backbone(
input_specs=tf.keras.layers.InputSpec(shape=[None, None, None, 3]),
backbone_config=backbone_config,
norm_activation_config=norm_activation_config)
network_config = network.get_config()
factory_network_config = factory_network.get_config()
self.assertEqual(network_config, factory_network_config)
def test_deeplabv3_builder(self, backbone_type, input_size, weight_decay):
num_classes = 21
input_specs = tf.keras.layers.InputSpec(
shape=[None, input_size[0], input_size[1], 3])
model_config = semantic_segmentation_cfg.SemanticSegmentationModel(
num_classes=num_classes,
backbone=backbones.Backbone(type=backbone_type,
mobilenet=backbones.MobileNet(
model_id='MobileNetV2',
output_stride=16)),
decoder=decoders.Decoder(type='aspp',
aspp=decoders.ASPP(level=4,
num_filters=256,
dilation_rates=[],
spp_layer_version='v1',
output_tensor=True)),
head=semantic_segmentation_cfg.SegmentationHead(
level=4,
low_level=2,
num_convs=1,
upsample_factor=2,
use_depthwise_convolution=True))
l2_regularizer = (tf.keras.regularizers.l2(weight_decay)
if weight_decay else None)
model = factory.build_segmentation_model(input_specs=input_specs,
model_config=model_config,
l2_regularizer=l2_regularizer)
quantization_config = common.Quantization()
_ = qat_factory.build_qat_segmentation_model(
model=model,
quantization=quantization_config,
input_specs=input_specs)
def test_builder(self, backbone_type, input_size, has_att_heads):
num_classes = 2
input_specs = tf.keras.layers.InputSpec(
shape=[None, input_size[0], input_size[1], 3])
if has_att_heads:
attribute_heads_config = [
retinanet_cfg.AttributeHead(name='att1'),
retinanet_cfg.AttributeHead(name='att2',
type='classification',
size=2),
]
else:
attribute_heads_config = None
model_config = retinanet_cfg.RetinaNet(
num_classes=num_classes,
backbone=backbones.Backbone(type=backbone_type),
head=retinanet_cfg.RetinaNetHead(
attribute_heads=attribute_heads_config))
l2_regularizer = tf.keras.regularizers.l2(5e-5)
_ = factory.build_retinanet(input_specs=input_specs,
model_config=model_config,
l2_regularizer=l2_regularizer)
if has_att_heads:
self.assertEqual(model_config.head.attribute_heads[0].as_dict(),
dict(name='att1', type='regression', size=1))
self.assertEqual(model_config.head.attribute_heads[1].as_dict(),
dict(name='att2', type='classification', size=2))
class ImageClassificationModel(hyperparams.Config):
num_classes: int = 0
input_size: List[int] = dataclasses.field(default_factory=list)
backbone: backbones.Backbone = backbones.Backbone(
type='resnet', resnet=backbones.ResNet())
dropout_rate: float = 0.0
norm_activation: common.NormActivation = common.NormActivation()
# Adds a BatchNormalization layer pre-GlobalAveragePooling in classification
add_head_batch_norm: bool = False
class MultiHeadModel(hyperparams.Config):
"""Multi head multi task model config, similar to other models but
with input, backbone, activation and weight decay shared."""
input_size: List[int] = dataclasses.field(default_factory=list)
backbone: backbones.Backbone = backbones.Backbone(
type='resnet', resnet=backbones.ResNet())
norm_activation: common.NormActivation = common.NormActivation()
heads: List[Submodel] = dataclasses.field(default_factory=list)
l2_weight_decay: float = 0.0
class YoloModel(hyperparams.Config):
num_classes: int = 0
input_size: List[int] = dataclasses.field(default_factory=list)
min_level: int = 3 # only for FPN or NASFPN
max_level: int = 6 # only for FPN or NASFPN
head: hyperparams.Config = YoloHead()
backbone: backbones.Backbone = backbones.Backbone(
type='resnet', resnet=backbones.ResNet())
decoder: decoders.Decoder = decoders.Decoder(type='identity')
norm_activation: common.NormActivation = common.NormActivation()
class SemanticSegmentationModel(hyperparams.Config):
"""Semantic segmentation model config."""
num_classes: int = 0
input_size: List[int] = dataclasses.field(default_factory=list)
min_level: int = 3
max_level: int = 6
head: SegmentationHead = SegmentationHead()
backbone: backbones.Backbone = backbones.Backbone(
type='resnet', resnet=backbones.ResNet())
decoder: decoders.Decoder = decoders.Decoder(type='identity')
norm_activation: common.NormActivation = common.NormActivation()
def test_builder(self, backbone_type, input_size):
num_classes = 2
input_specs = tf.keras.layers.InputSpec(
shape=[None, input_size[0], input_size[1], 3])
model_config = maskrcnn_cfg.MaskRCNN(
num_classes=num_classes,
backbone=backbones.Backbone(type=backbone_type))
l2_regularizer = tf.keras.regularizers.l2(5e-5)
_ = factory.build_maskrcnn(input_specs=input_specs,
model_config=model_config,
l2_regularizer=l2_regularizer)
class BASNetModel(hyperparams.Config):
"""BASNet model config."""
num_classes: int = 0
input_size: List[int] = dataclasses.field(default_factory=list)
#min_level: int = 3
#max_level: int = 6
#head: BASNetHead = BASNetHead()
backbone: backbones.Backbone = backbones.Backbone(
type='basnet_en', basnet_en=backbones.BASNet_En())
decoder: decoders.Decoder = decoders.Decoder(type='basnet_de')
norm_activation: common.NormActivation = common.NormActivation()
def test_builder(self, backbone_type, input_size, weight_decay):
num_classes = 2
input_specs = tf.keras.layers.InputSpec(
shape=[None, input_size[0], input_size[1], 3])
model_config = classification_cfg.ImageClassificationModel(
num_classes=num_classes,
backbone=backbones.Backbone(type=backbone_type))
l2_regularizer = (tf.keras.regularizers.l2(weight_decay)
if weight_decay else None)
_ = factory.build_classification_model(input_specs=input_specs,
model_config=model_config,
l2_regularizer=l2_regularizer)
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='resnet', resnet=backbones.ResNet())
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
kernel_initializer: str = 'random_uniform'
class SimCLRModel(hyperparams.Config):
"""SimCLR model config."""
input_size: List[int] = dataclasses.field(default_factory=list)
backbone: backbones.Backbone = backbones.Backbone(
type='resnet', resnet=backbones.ResNet())
projection_head: ProjectionHead = ProjectionHead(proj_output_dim=128,
num_proj_layers=3,
ft_proj_idx=1)
supervised_head: SupervisedHead = SupervisedHead(num_classes=1001)
norm_activation: common.NormActivation = common.NormActivation(
norm_momentum=0.9, norm_epsilon=1e-5, use_sync_bn=False)
mode: str = simclr_model.PRETRAIN
backbone_trainable: bool = True
class RetinaNet(hyperparams.Config):
num_classes: int = 0
input_size: List[int] = dataclasses.field(default_factory=list)
min_level: int = 3
max_level: int = 7
anchor: Anchor = Anchor()
backbone: backbones.Backbone = backbones.Backbone(
type='resnet', resnet=backbones.ResNet())
decoder: decoders.Decoder = decoders.Decoder(type='fpn',
fpn=decoders.FPN())
head: RetinaNetHead = RetinaNetHead()
detection_generator: DetectionGenerator = DetectionGenerator()
norm_activation: common.NormActivation = common.NormActivation()
def test_builder(self, backbone_type, input_size,
segmentation_backbone_type, segmentation_decoder_type,
fusion_type):
num_classes = 2
input_specs = tf.keras.layers.InputSpec(
shape=[None, input_size[0], input_size[1], 3])
segmentation_output_stride = 16
level = int(np.math.log2(segmentation_output_stride))
segmentation_model = semantic_segmentation.SemanticSegmentationModel(
num_classes=2,
backbone=backbones.Backbone(type=segmentation_backbone_type),
decoder=decoders.Decoder(type=segmentation_decoder_type),
head=semantic_segmentation.SegmentationHead(level=level))
model_config = panoptic_maskrcnn_cfg.PanopticMaskRCNN(
num_classes=num_classes,
segmentation_model=segmentation_model,
backbone=backbones.Backbone(type=backbone_type),
shared_backbone=segmentation_backbone_type is None,
shared_decoder=segmentation_decoder_type is None)
l2_regularizer = tf.keras.regularizers.l2(5e-5)
_ = factory.build_panoptic_maskrcnn(input_specs=input_specs,
model_config=model_config,
l2_regularizer=l2_regularizer)
class SimCLRMTModelConfig(hyperparams.Config):
"""Model config for multi-task SimCLR model."""
input_size: List[int] = dataclasses.field(default_factory=list)
backbone: backbones.Backbone = backbones.Backbone(
type='resnet', resnet=backbones.ResNet())
backbone_trainable: bool = True
projection_head: simclr_configs.ProjectionHead = simclr_configs.ProjectionHead(
proj_output_dim=128, num_proj_layers=3, ft_proj_idx=1)
norm_activation: common.NormActivation = common.NormActivation(
norm_momentum=0.9, norm_epsilon=1e-5, use_sync_bn=False)
heads: Tuple[SimCLRMTHeadConfig, ...] = ()
# L2 weight decay is used in the model, not in task.
# Note that this can not be used together with lars optimizer.
l2_weight_decay: float = 0.0
class AutosegEdgeTPUModelConfig(hyperparams.Config):
"""Autoseg-EdgeTPU segmentation model config."""
num_classes: int = 0
input_size: List[int] = dataclasses.field(default_factory=list)
backbone: backbones.Backbone = backbones.Backbone()
head: BiFPNHeadConfig = BiFPNHeadConfig()
model_params: Mapping[str, Any] = dataclasses.field(
default_factory=lambda: { # pylint: disable=g-long-lambda
'model_name': 'autoseg_edgetpu_backbone_s',
'checkpoint_format': 'tf_checkpoint',
'overrides': {
'batch_norm': 'tpu',
'rescale_input': False,
'backbone_only': True,
'resolution': 512
}
})
class MaskRCNN(hyperparams.Config):
num_classes: int = 0
input_size: List[int] = dataclasses.field(default_factory=list)
min_level: int = 2
max_level: int = 6
anchor: Anchor = Anchor()
include_mask: bool = True
backbone: backbones.Backbone = backbones.Backbone(
type='resnet', resnet=backbones.ResNet())
decoder: decoders.Decoder = decoders.Decoder(type='fpn',
fpn=decoders.FPN())
rpn_head: RPNHead = RPNHead()
detection_head: DetectionHead = DetectionHead()
roi_generator: ROIGenerator = ROIGenerator()
roi_sampler: ROISampler = ROISampler()
roi_aligner: ROIAligner = ROIAligner()
detection_generator: DetectionGenerator = DetectionGenerator()
mask_head: Optional[MaskHead] = MaskHead()
mask_sampler: Optional[MaskSampler] = MaskSampler()
mask_roi_aligner: Optional[MaskROIAligner] = MaskROIAligner()
norm_activation: common.NormActivation = common.NormActivation(
norm_momentum=0.997, norm_epsilon=0.0001, use_sync_bn=True)
def test_builder(self, backbone_type, input_size, has_attribute_heads):
num_classes = 2
input_specs = tf.keras.layers.InputSpec(
shape=[None, input_size[0], input_size[1], 3])
if has_attribute_heads:
attribute_heads_config = [
retinanet_cfg.AttributeHead(name='att1'),
retinanet_cfg.AttributeHead(name='att2',
type='classification',
size=2),
]
else:
attribute_heads_config = None
model_config = retinanet_cfg.RetinaNet(
num_classes=num_classes,
backbone=backbones.Backbone(
type=backbone_type,
spinenet_mobile=backbones.SpineNetMobile(
model_id='49',
stochastic_depth_drop_rate=0.2,
min_level=3,
max_level=7,
use_keras_upsampling_2d=True)),
head=retinanet_cfg.RetinaNetHead(
attribute_heads=attribute_heads_config))
l2_regularizer = tf.keras.regularizers.l2(5e-5)
quantization_config = common.Quantization()
model = factory.build_retinanet(input_specs=input_specs,
model_config=model_config,
l2_regularizer=l2_regularizer)
_ = qat_factory.build_qat_retinanet(model=model,
quantization=quantization_config,
model_config=model_config)
if has_attribute_heads:
self.assertEqual(model_config.head.attribute_heads[0].as_dict(),
dict(name='att1', type='regression', size=1))
self.assertEqual(model_config.head.attribute_heads[1].as_dict(),
dict(name='att2', type='classification', size=2))
def cascadercnn_spinenet_coco() -> cfg.ExperimentConfig:
"""COCO object detection with Cascade RCNN-RS 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=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',
min_level=3,
max_level=7,
)),
decoder=decoders.Decoder(
type='identity', identity=decoders.Identity()),
roi_sampler=ROISampler(cascade_iou_thresholds=[0.6, 0.7]),
detection_head=DetectionHead(
class_agnostic_bbox_pred=True, cascade_class_ensemble=True),
anchor=Anchor(anchor_size=3),
norm_activation=common.NormActivation(
use_sync_bn=True, activation='swish'),
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=train_batch_size,
parser=Parser(
aug_rand_hflip=True, aug_scale_min=0.1, aug_scale_max=2.5)),
validation_data=DataConfig(
input_path=os.path.join(COCO_INPUT_PATH_BASE, 'val*'),
is_training=False,
global_batch_size=eval_batch_size,
drop_remainder=False)),
trainer=cfg.TrainerConfig(
train_steps=steps_per_epoch * 500,
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 * 475, steps_per_epoch * 490
],
'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
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 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 image_classification_imagenet_revnet() -> cfg.ExperimentConfig:
"""Returns a revnet config for image classification on imagenet."""
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='revnet', revnet=backbones.RevNet(model_id=56)),
norm_activation=common.NormActivation(
norm_momentum=0.9, norm_epsilon=1e-5, use_sync_bn=False),
add_head_batch_norm=True),
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': [0.8, 0.08, 0.008, 0.0008]
}
},
'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_resnetrs() -> cfg.ExperimentConfig:
"""Image classification on imagenet with resnet-rs."""
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=[160, 160, 3],
backbone=backbones.Backbone(
type='resnet',
resnet=backbones.ResNet(
model_id=50,
stem_type='v1',
resnetd_shortcut=True,
replace_stem_max_pool=True,
se_ratio=0.25,
stochastic_depth_drop_rate=0.0)),
dropout_rate=0.25,
norm_activation=common.NormActivation(
norm_momentum=0.0,
norm_epsilon=1e-5,
use_sync_bn=False,
activation='swish')),
losses=Losses(l2_weight_decay=4e-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,
aug_type=common.Augmentation(
type='randaug', randaug=common.RandAugment(magnitude=10))),
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=350 * 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
}
},
'ema': {
'average_decay': 0.9999,
'trainable_weights_only': False,
},
'learning_rate': {
'type': 'cosine',
'cosine': {
'initial_learning_rate': 1.6,
'decay_steps': 350 * 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
def retinanet_spinenet_mobile_coco() -> cfg.ExperimentConfig:
"""COCO object detection with RetinaNet using Mobile SpineNet backbone."""
train_batch_size = 256
eval_batch_size = 8
steps_per_epoch = COCO_TRAIN_EXAMPLES // train_batch_size
input_size = 384
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_mobile',
spinenet_mobile=backbones.SpineNetMobile(
model_id='49',
stochastic_depth_drop_rate=0.2,
min_level=3,
max_level=7)),
decoder=decoders.Decoder(type='identity',
identity=decoders.Identity()),
head=RetinaNetHead(num_filters=48, use_separable_conv=True),
anchor=Anchor(anchor_size=3),
norm_activation=common.NormActivation(use_sync_bn=True,
activation='swish'),
num_classes=91,
input_size=[input_size, input_size, 3],
min_level=3,
max_level=7),
losses=Losses(l2_weight_decay=3e-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=0.1,
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=600 * 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':
[575 * steps_per_epoch, 590 * steps_per_epoch],
'values': [
0.32 * train_batch_size / 256.0,
0.032 * train_batch_size / 256.0,
0.0032 * 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',
'task.model.min_level == task.model.backbone.spinenet_mobile.min_level',
'task.model.max_level == task.model.backbone.spinenet_mobile.max_level',
])
return config
def multitask_vision() -> multi_cfg.MultiTaskExperimentConfig:
"""
Vision task with single backbone and multiple heads.
Each head can be a segmenter, detector or classifier.
TODO: use same num_class and input_size in both task and model definition
multi_cfg.MultiTaskConfig:
- Retains each task_name, entire task, eval_steps and weights,
- Entire_task used in respective multitask trainers for train_step
- Weights used in task_sampler
multi_cfg.MultiTaskTrainerConfig:
- trainer_type and task_sampler used to configure task sampling in train_lib
- Normal multi_cfg.TrainerConfig params used directly in train_lib
"""
input_path_segmentation = ''
input_path_classification = ''
input_path_yolo = ''
steps_per_epoch = 6915 + 2486 + 600
train_batch_size = 1
eval_batch_size = 1
validation_steps = 1021 + 621 + 600
segmentation_routine = multi_cfg.TaskRoutine(
task_name='segmentation',
task_config=SemanticSegmentationSubtask(
model=SemanticSegmentationModelSpecs(num_classes=19,
input_size=[256, 256, 3]),
losses=SegmentationLosses(ignore_label=250,
top_k_percent_pixels=0.3),
train_data=SegmentationDataConfig(
output_size=[256, 256],
input_path=input_path_segmentation,
global_batch_size=train_batch_size,
is_training=True,
aug_scale_min=0.5,
aug_scale_max=2.0,
preserve_aspect_ratio=False,
aug_policy='randaug',
randaug_magnitude=5,
randaug_available_ops=[
'AutoContrast', 'Equalize', 'Invert', 'Rotate',
'Posterize', 'Solarize', 'Color', 'Contrast', 'Brightness',
'Sharpness', 'Cutout', 'SolarizeAdd'
]),
validation_data=SegmentationDataConfig(
output_size=[256, 256],
input_path=input_path_segmentation,
global_batch_size=eval_batch_size,
is_training=False,
resize_eval_groundtruth=True,
drop_remainder=False)),
eval_steps=603, # check where eval steps is used
task_weight=1.0)
classification_routine = multi_cfg.TaskRoutine(
task_name='classification',
task_config=ImageClassificationSubtask(
model=ImageClassificationModelSpecs(num_classes=4,
input_size=[256, 256, 3]),
losses=ClassificationLosses(label_smoothing=0.1),
train_data=ClassificationDataConfig(
input_path=input_path_classification,
is_training=True,
global_batch_size=train_batch_size,
aug_policy='randaug',
randaug_magnitude=5),
validation_data=ClassificationDataConfig(
input_path=input_path_classification,
is_training=False,
global_batch_size=eval_batch_size,
drop_remainder=False)),
eval_steps=621, # check where eval steps is used
task_weight=1.0)
yolo_routine = multi_cfg.TaskRoutine(
task_name='yolo',
task_config=YoloSubtask(
model=YoloModelSpecs(num_classes=4,
input_size=[256, 256, 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])),
losses=YoloLosses(l2_weight_decay=1e-4, iou_loss_thres=0.5),
train_data=YoloDataConfig(input_path=input_path_yolo,
is_training=True,
global_batch_size=train_batch_size,
aug_policy='randaug',
randaug_magnitude=5),
validation_data=YoloDataConfig(input_path=input_path_yolo,
is_training=False,
global_batch_size=eval_batch_size,
drop_remainder=False)),
eval_steps=600, # check where eval steps is used
task_weight=1.0)
model_config = MultiHeadModel(
input_size=[256, 256, 3],
backbone=backbones.Backbone(type='hardnet',
hardnet=backbones.HardNet(model_id=70)),
norm_activation=common.NormActivation(activation='relu',
norm_momentum=0.9997,
norm_epsilon=0.001,
use_sync_bn=True),
heads=[
Submodel(
name='classification',
num_classes=4,
head=ImageClassificationHead(
level=0, # decoder is identity function
num_convs=2,
num_filters=256,
add_head_batch_norm=False,
dropout_rate=0.2)),
Submodel(name='segmentation',
num_classes=19,
decoder=decoders.Decoder(
type='hardnet',
hardnet=decoders.HardNet(model_id=70)),
head=SegmentationHead(level=0,
num_convs=0,
feature_fusion=None,
low_level=0,
low_level_num_filters=0)),
Submodel(name='yolo',
num_classes=4,
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]))
],
l2_weight_decay=1e-4)
return multi_cfg.MultiTaskExperimentConfig(
task=multi_cfg.MultiTaskConfig(model=model_config,
init_checkpoint=None,
task_routines=(segmentation_routine,
classification_routine,
yolo_routine)),
trainer=multi_cfg.MultiTaskTrainerConfig(
trainer_type="interleaving",
task_sampler=multi_cfg.TaskSamplingConfig(
type="proportional",
proportional=multi_cfg.ProportionalSampleConfig(
alpha=1.0)), # uniform, proportional or annealing
steps_per_loop=steps_per_epoch,
summary_interval=steps_per_epoch,
checkpoint_interval=steps_per_epoch,
train_steps=45 * steps_per_epoch,
validation_steps=validation_steps,
validation_interval=steps_per_epoch,
best_checkpoint_eval_metric='mean_iou',
continuous_eval_timeout=3600,
max_to_keep=5,
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
}
}
})))
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, use_sync_bn=False)),
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 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
