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_model_initializing(self, init_checkpoint_modules):
shared_backbone = ('segmentation_backbone' not in init_checkpoint_modules)
shared_decoder = ('segmentation_decoder' not in init_checkpoint_modules and
shared_backbone)
task_config = cfg.PanopticMaskRCNNTask(
model=cfg.PanopticMaskRCNN(
num_classes=2,
input_size=[640, 640, 3],
segmentation_model=segmentation_cfg.SemanticSegmentationModel(
decoder=decoder_cfg.Decoder(type='fpn')),
shared_backbone=shared_backbone,
shared_decoder=shared_decoder))
task = panoptic_maskrcnn.PanopticMaskRCNNTask(task_config)
model = task.build_model()
ckpt = tf.train.Checkpoint(**model.checkpoint_items)
ckpt_save_dir = self.create_tempdir().full_path
ckpt.save(os.path.join(ckpt_save_dir, 'ckpt'))
if (init_checkpoint_modules == ['all'] or
'backbone' in init_checkpoint_modules):
task._task_config.init_checkpoint = ckpt_save_dir
if ('segmentation_backbone' in init_checkpoint_modules or
'segmentation_decoder' in init_checkpoint_modules):
task._task_config.segmentation_init_checkpoint = ckpt_save_dir
task._task_config.init_checkpoint_modules = init_checkpoint_modules
task.initialize(model)
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 PanopticMaskRCNN(maskrcnn.MaskRCNN):
"""Panoptic Mask R-CNN model config."""
segmentation_model: semantic_segmentation.SemanticSegmentationModel = (
semantic_segmentation.SemanticSegmentationModel(num_classes=2))
shared_backbone: bool = True
shared_decoder: bool = True
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
