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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.dataloader import maskrcnn_parser
from official.legacy.detection.dataloader import olnmask_parser
from official.legacy.detection.dataloader import retinanet_parser
from official.legacy.detection.dataloader import shapemask_parser
def parser_generator(params, mode):
"""Generator function for various dataset parser."""
if params.architecture.parser == 'retinanet_parser':
anchor_params = params.anchor
parser_params = params.retinanet_parser
parser_fn = retinanet_parser.Parser(
output_size=parser_params.output_size,
min_level=params.architecture.min_level,
max_level=params.architecture.max_level,
num_scales=anchor_params.num_scales,
aspect_ratios=anchor_params.aspect_ratios,
anchor_size=anchor_params.anchor_size,
match_threshold=parser_params.match_threshold,
unmatched_threshold=parser_params.unmatched_threshold,
aug_rand_hflip=parser_params.aug_rand_hflip,
aug_scale_min=parser_params.aug_scale_min,
aug_scale_max=parser_params.aug_scale_max,
use_autoaugment=parser_params.use_autoaugment,
autoaugment_policy_name=parser_params.autoaugment_policy_name,
skip_crowd_during_training=parser_params.skip_crowd_during_training,
max_num_instances=parser_params.max_num_instances,
use_bfloat16=params.architecture.use_bfloat16,
mode=mode)
elif params.architecture.parser == 'maskrcnn_parser':
anchor_params = params.anchor
parser_params = params.maskrcnn_parser
parser_fn = maskrcnn_parser.Parser(
output_size=parser_params.output_size,
min_level=params.architecture.min_level,
max_level=params.architecture.max_level,
num_scales=anchor_params.num_scales,
aspect_ratios=anchor_params.aspect_ratios,
anchor_size=anchor_params.anchor_size,
rpn_match_threshold=parser_params.rpn_match_threshold,
rpn_unmatched_threshold=parser_params.rpn_unmatched_threshold,
rpn_batch_size_per_im=parser_params.rpn_batch_size_per_im,
rpn_fg_fraction=parser_params.rpn_fg_fraction,
aug_rand_hflip=parser_params.aug_rand_hflip,
aug_scale_min=parser_params.aug_scale_min,
aug_scale_max=parser_params.aug_scale_max,
skip_crowd_during_training=parser_params.skip_crowd_during_training,
max_num_instances=parser_params.max_num_instances,
include_mask=params.architecture.include_mask,
mask_crop_size=parser_params.mask_crop_size,
use_bfloat16=params.architecture.use_bfloat16,
mode=mode)
elif params.architecture.parser == 'olnmask_parser':
anchor_params = params.anchor
parser_params = params.olnmask_parser
parser_fn = olnmask_parser.Parser(
output_size=parser_params.output_size,
min_level=params.architecture.min_level,
max_level=params.architecture.max_level,
num_scales=anchor_params.num_scales,
aspect_ratios=anchor_params.aspect_ratios,
anchor_size=anchor_params.anchor_size,
rpn_match_threshold=parser_params.rpn_match_threshold,
rpn_unmatched_threshold=parser_params.rpn_unmatched_threshold,
rpn_batch_size_per_im=parser_params.rpn_batch_size_per_im,
rpn_fg_fraction=parser_params.rpn_fg_fraction,
aug_rand_hflip=parser_params.aug_rand_hflip,
aug_scale_min=parser_params.aug_scale_min,
aug_scale_max=parser_params.aug_scale_max,
skip_crowd_during_training=parser_params.skip_crowd_during_training,
max_num_instances=parser_params.max_num_instances,
include_mask=params.architecture.include_mask,
mask_crop_size=parser_params.mask_crop_size,
use_bfloat16=params.architecture.use_bfloat16,
mode=mode,
has_centerness=parser_params.has_centerness,
rpn_center_match_iou_threshold=(
parser_params.rpn_center_match_iou_threshold),
rpn_center_unmatched_iou_threshold=(
parser_params.rpn_center_unmatched_iou_threshold),
rpn_num_center_samples_per_im=(
parser_params.rpn_num_center_samples_per_im),
class_agnostic=parser_params.class_agnostic,
train_class=parser_params.train_class,)
elif params.architecture.parser == 'shapemask_parser':
anchor_params = params.anchor
parser_params = params.shapemask_parser
parser_fn = shapemask_parser.Parser(
output_size=parser_params.output_size,
min_level=params.architecture.min_level,
max_level=params.architecture.max_level,
num_scales=anchor_params.num_scales,
aspect_ratios=anchor_params.aspect_ratios,
anchor_size=anchor_params.anchor_size,
use_category=parser_params.use_category,
outer_box_scale=parser_params.outer_box_scale,
box_jitter_scale=parser_params.box_jitter_scale,
num_sampled_masks=parser_params.num_sampled_masks,
mask_crop_size=parser_params.mask_crop_size,
mask_min_level=parser_params.mask_min_level,
mask_max_level=parser_params.mask_max_level,
upsample_factor=parser_params.upsample_factor,
match_threshold=parser_params.match_threshold,
unmatched_threshold=parser_params.unmatched_threshold,
aug_rand_hflip=parser_params.aug_rand_hflip,
aug_scale_min=parser_params.aug_scale_min,
aug_scale_max=parser_params.aug_scale_max,
skip_crowd_during_training=parser_params.skip_crowd_during_training,
max_num_instances=parser_params.max_num_instances,
use_bfloat16=params.architecture.use_bfloat16,
mask_train_class=parser_params.mask_train_class,
mode=mode)
else:
raise ValueError('Parser %s is not supported.' % params.architecture.parser)
return parser_fn
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