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# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Backbones configurations."""
import dataclasses
from typing import List, Optional, Tuple
from official.modeling import hyperparams
@dataclasses.dataclass
class Transformer(hyperparams.Config):
"""Transformer config."""
mlp_dim: int = 1
num_heads: int = 1
num_layers: int = 1
attention_dropout_rate: float = 0.0
dropout_rate: float = 0.0
@dataclasses.dataclass
class VisionTransformer(hyperparams.Config):
"""VisionTransformer config."""
model_name: str = 'vit-b16'
# pylint: disable=line-too-long
pooler: str = 'token' # 'token', 'gap' or 'none'. If set to 'token', an extra classification token is added to sequence.
# pylint: enable=line-too-long
representation_size: int = 0
hidden_size: int = 1
patch_size: int = 16
transformer: Transformer = dataclasses.field(default_factory=Transformer)
init_stochastic_depth_rate: float = 0.0
original_init: bool = True
pos_embed_shape: Optional[Tuple[int, int]] = None
# If output encoded tokens sequence when pooler is `none`.
output_encoded_tokens: bool = True
# If output encoded tokens 2D feature map.
output_2d_feature_maps: bool = False
# Adding Layerscale to each Encoder block https://arxiv.org/abs/2204.07118
layer_scale_init_value: float = 0.0
# Transformer encoder spatial partition dimensions.
transformer_partition_dims: Optional[Tuple[int, int, int, int]] = None
@dataclasses.dataclass
class ResNet(hyperparams.Config):
"""ResNet config."""
model_id: int = 50
depth_multiplier: float = 1.0
stem_type: str = 'v0'
se_ratio: float = 0.0
stochastic_depth_drop_rate: float = 0.0
scale_stem: bool = True
resnetd_shortcut: bool = False
replace_stem_max_pool: bool = False
bn_trainable: bool = True
@dataclasses.dataclass
class DilatedResNet(hyperparams.Config):
"""DilatedResNet config."""
model_id: int = 50
output_stride: int = 16
multigrid: Optional[List[int]] = None
stem_type: str = 'v0'
last_stage_repeats: int = 1
se_ratio: float = 0.0
stochastic_depth_drop_rate: float = 0.0
resnetd_shortcut: bool = False
replace_stem_max_pool: bool = False
@dataclasses.dataclass
class EfficientNet(hyperparams.Config):
"""EfficientNet config."""
model_id: str = 'b0'
se_ratio: float = 0.0
stochastic_depth_drop_rate: float = 0.0
@dataclasses.dataclass
class MobileNet(hyperparams.Config):
"""Mobilenet config."""
model_id: str = 'MobileNetV2'
filter_size_scale: float = 1.0
stochastic_depth_drop_rate: float = 0.0
output_stride: Optional[int] = None
output_intermediate_endpoints: bool = False
@dataclasses.dataclass
class SpineNet(hyperparams.Config):
"""SpineNet config."""
model_id: str = '49'
stochastic_depth_drop_rate: float = 0.0
min_level: int = 3
max_level: int = 7
@dataclasses.dataclass
class SpineNetMobile(hyperparams.Config):
"""SpineNet config."""
model_id: str = '49'
stochastic_depth_drop_rate: float = 0.0
se_ratio: float = 0.2
expand_ratio: int = 6
min_level: int = 3
max_level: int = 7
# If use_keras_upsampling_2d is True, model uses UpSampling2D keras layer
# instead of optimized custom TF op. It makes model be more keras style. We
# set this flag to True when we apply QAT from model optimization toolkit
# that requires the model should use keras layers.
use_keras_upsampling_2d: bool = False
@dataclasses.dataclass
class RevNet(hyperparams.Config):
"""RevNet config."""
# Specifies the depth of RevNet.
model_id: int = 56
@dataclasses.dataclass
class MobileDet(hyperparams.Config):
"""Mobiledet config."""
model_id: str = 'MobileDetCPU'
filter_size_scale: float = 1.0
@dataclasses.dataclass
class Backbone(hyperparams.OneOfConfig):
"""Configuration for backbones.
Attributes:
type: 'str', type of backbone be used, one of the fields below.
resnet: resnet backbone config.
dilated_resnet: dilated resnet backbone for semantic segmentation config.
revnet: revnet backbone config.
efficientnet: efficientnet backbone config.
spinenet: spinenet backbone config.
spinenet_mobile: mobile spinenet backbone config.
mobilenet: mobilenet backbone config.
mobiledet: mobiledet backbone config.
vit: vision transformer backbone config.
"""
type: Optional[str] = None
resnet: ResNet = dataclasses.field(default_factory=ResNet)
dilated_resnet: DilatedResNet = dataclasses.field(
default_factory=DilatedResNet
)
revnet: RevNet = dataclasses.field(default_factory=RevNet)
efficientnet: EfficientNet = dataclasses.field(default_factory=EfficientNet)
spinenet: SpineNet = dataclasses.field(default_factory=SpineNet)
spinenet_mobile: SpineNetMobile = dataclasses.field(
default_factory=SpineNetMobile
)
mobilenet: MobileNet = dataclasses.field(default_factory=MobileNet)
mobiledet: MobileDet = dataclasses.field(default_factory=MobileDet)
vit: VisionTransformer = dataclasses.field(default_factory=VisionTransformer)
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