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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.
"""Model architecture factory."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from official.legacy.detection.modeling.architecture import fpn
from official.legacy.detection.modeling.architecture import heads
from official.legacy.detection.modeling.architecture import identity
from official.legacy.detection.modeling.architecture import nn_ops
from official.legacy.detection.modeling.architecture import resnet
from official.legacy.detection.modeling.architecture import spinenet
def norm_activation_generator(params):
return nn_ops.norm_activation_builder(
momentum=params.batch_norm_momentum,
epsilon=params.batch_norm_epsilon,
trainable=params.batch_norm_trainable,
activation=params.activation)
def backbone_generator(params):
"""Generator function for various backbone models."""
if params.architecture.backbone == 'resnet':
resnet_params = params.resnet
backbone_fn = resnet.Resnet(
resnet_depth=resnet_params.resnet_depth,
activation=params.norm_activation.activation,
norm_activation=norm_activation_generator(
params.norm_activation))
elif params.architecture.backbone == 'spinenet':
spinenet_params = params.spinenet
backbone_fn = spinenet.SpineNetBuilder(model_id=spinenet_params.model_id)
else:
raise ValueError('Backbone model `{}` is not supported.'
.format(params.architecture.backbone))
return backbone_fn
def multilevel_features_generator(params):
"""Generator function for various FPN models."""
if params.architecture.multilevel_features == 'fpn':
fpn_params = params.fpn
fpn_fn = fpn.Fpn(
min_level=params.architecture.min_level,
max_level=params.architecture.max_level,
fpn_feat_dims=fpn_params.fpn_feat_dims,
use_separable_conv=fpn_params.use_separable_conv,
activation=params.norm_activation.activation,
use_batch_norm=fpn_params.use_batch_norm,
norm_activation=norm_activation_generator(
params.norm_activation))
elif params.architecture.multilevel_features == 'identity':
fpn_fn = identity.Identity()
else:
raise ValueError('The multi-level feature model `{}` is not supported.'
.format(params.architecture.multilevel_features))
return fpn_fn
def retinanet_head_generator(params):
"""Generator function for RetinaNet head architecture."""
head_params = params.retinanet_head
anchors_per_location = params.anchor.num_scales * len(
params.anchor.aspect_ratios)
return heads.RetinanetHead(
params.architecture.min_level,
params.architecture.max_level,
params.architecture.num_classes,
anchors_per_location,
head_params.num_convs,
head_params.num_filters,
head_params.use_separable_conv,
norm_activation=norm_activation_generator(params.norm_activation))
def rpn_head_generator(params):
"""Generator function for RPN head architecture."""
head_params = params.rpn_head
anchors_per_location = params.anchor.num_scales * len(
params.anchor.aspect_ratios)
return heads.RpnHead(
params.architecture.min_level,
params.architecture.max_level,
anchors_per_location,
head_params.num_convs,
head_params.num_filters,
head_params.use_separable_conv,
params.norm_activation.activation,
head_params.use_batch_norm,
norm_activation=norm_activation_generator(params.norm_activation))
def oln_rpn_head_generator(params):
"""Generator function for OLN-proposal (OLN-RPN) head architecture."""
head_params = params.rpn_head
anchors_per_location = params.anchor.num_scales * len(
params.anchor.aspect_ratios)
return heads.OlnRpnHead(
params.architecture.min_level,
params.architecture.max_level,
anchors_per_location,
head_params.num_convs,
head_params.num_filters,
head_params.use_separable_conv,
params.norm_activation.activation,
head_params.use_batch_norm,
norm_activation=norm_activation_generator(params.norm_activation))
def fast_rcnn_head_generator(params):
"""Generator function for Fast R-CNN head architecture."""
head_params = params.frcnn_head
return heads.FastrcnnHead(
params.architecture.num_classes,
head_params.num_convs,
head_params.num_filters,
head_params.use_separable_conv,
head_params.num_fcs,
head_params.fc_dims,
params.norm_activation.activation,
head_params.use_batch_norm,
norm_activation=norm_activation_generator(params.norm_activation))
def oln_box_score_head_generator(params):
"""Generator function for Scoring Fast R-CNN head architecture."""
head_params = params.frcnn_head
return heads.OlnBoxScoreHead(
params.architecture.num_classes,
head_params.num_convs,
head_params.num_filters,
head_params.use_separable_conv,
head_params.num_fcs,
head_params.fc_dims,
params.norm_activation.activation,
head_params.use_batch_norm,
norm_activation=norm_activation_generator(params.norm_activation))
def mask_rcnn_head_generator(params):
"""Generator function for Mask R-CNN head architecture."""
head_params = params.mrcnn_head
return heads.MaskrcnnHead(
params.architecture.num_classes,
params.architecture.mask_target_size,
head_params.num_convs,
head_params.num_filters,
head_params.use_separable_conv,
params.norm_activation.activation,
head_params.use_batch_norm,
norm_activation=norm_activation_generator(params.norm_activation))
def oln_mask_score_head_generator(params):
"""Generator function for Scoring Mask R-CNN head architecture."""
head_params = params.mrcnn_head
return heads.OlnMaskScoreHead(
params.architecture.num_classes,
params.architecture.mask_target_size,
head_params.num_convs,
head_params.num_filters,
head_params.use_separable_conv,
params.norm_activation.activation,
head_params.use_batch_norm,
norm_activation=norm_activation_generator(params.norm_activation))
def shapeprior_head_generator(params):
"""Generator function for shape prior head architecture."""
head_params = params.shapemask_head
return heads.ShapemaskPriorHead(
params.architecture.num_classes,
head_params.num_downsample_channels,
head_params.mask_crop_size,
head_params.use_category_for_mask,
head_params.shape_prior_path)
def coarsemask_head_generator(params):
"""Generator function for ShapeMask coarse mask head architecture."""
head_params = params.shapemask_head
return heads.ShapemaskCoarsemaskHead(
params.architecture.num_classes,
head_params.num_downsample_channels,
head_params.mask_crop_size,
head_params.use_category_for_mask,
head_params.num_convs,
norm_activation=norm_activation_generator(params.norm_activation))
def finemask_head_generator(params):
"""Generator function for Shapemask fine mask head architecture."""
head_params = params.shapemask_head
return heads.ShapemaskFinemaskHead(
params.architecture.num_classes,
head_params.num_downsample_channels,
head_params.mask_crop_size,
head_params.use_category_for_mask,
head_params.num_convs,
head_params.upsample_factor)
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