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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. | |
| """Config to train shapemask on COCO.""" | |
| from official.legacy.detection.configs import base_config | |
| from official.modeling.hyperparams import params_dict | |
| SHAPEMASK_RESNET_FROZEN_VAR_PREFIX = r'(conv2d(|_([1-9]|10))|batch_normalization(|_([1-9]|10)))\/' | |
| SHAPEMASK_CFG = params_dict.ParamsDict(base_config.BASE_CFG) | |
| SHAPEMASK_CFG.override({ | |
| 'type': 'shapemask', | |
| 'architecture': { | |
| 'parser': 'shapemask_parser', | |
| 'backbone': 'resnet', | |
| 'multilevel_features': 'fpn', | |
| 'outer_box_scale': 1.25, | |
| }, | |
| 'train': { | |
| 'total_steps': 45000, | |
| 'learning_rate': { | |
| 'learning_rate_steps': [30000, 40000], | |
| }, | |
| 'frozen_variable_prefix': SHAPEMASK_RESNET_FROZEN_VAR_PREFIX, | |
| 'regularization_variable_regex': None, | |
| }, | |
| 'eval': { | |
| 'type': 'shapemask_box_and_mask', | |
| 'mask_eval_class': 'all', # 'all', 'voc', or 'nonvoc'. | |
| }, | |
| 'shapemask_parser': { | |
| 'output_size': [640, 640], | |
| 'num_channels': 3, | |
| 'match_threshold': 0.5, | |
| 'unmatched_threshold': 0.5, | |
| 'aug_rand_hflip': True, | |
| 'aug_scale_min': 0.8, | |
| 'aug_scale_max': 1.2, | |
| 'skip_crowd_during_training': True, | |
| 'max_num_instances': 100, | |
| # Shapemask specific parameters | |
| 'mask_train_class': 'all', # 'all', 'voc', or 'nonvoc'. | |
| 'use_category': True, | |
| 'outer_box_scale': 1.25, | |
| 'num_sampled_masks': 8, | |
| 'mask_crop_size': 32, | |
| 'mask_min_level': 3, | |
| 'mask_max_level': 5, | |
| 'box_jitter_scale': 0.025, | |
| 'upsample_factor': 4, | |
| }, | |
| 'retinanet_head': { | |
| 'num_convs': 4, | |
| 'num_filters': 256, | |
| 'use_separable_conv': False, | |
| 'use_batch_norm': True, | |
| }, | |
| 'shapemask_head': { | |
| 'num_downsample_channels': 128, | |
| 'mask_crop_size': 32, | |
| 'use_category_for_mask': True, | |
| 'num_convs': 4, | |
| 'upsample_factor': 4, | |
| 'shape_prior_path': '', | |
| }, | |
| 'retinanet_loss': { | |
| 'focal_loss_alpha': 0.4, | |
| 'focal_loss_gamma': 1.5, | |
| 'huber_loss_delta': 0.15, | |
| 'box_loss_weight': 50, | |
| }, | |
| 'shapemask_loss': { | |
| 'shape_prior_loss_weight': 0.1, | |
| 'coarse_mask_loss_weight': 1.0, | |
| 'fine_mask_loss_weight': 1.0, | |
| }, | |
| }, is_strict=False) | |
| SHAPEMASK_RESTRICTIONS = [ | |
| 'shapemask_head.mask_crop_size == shapemask_parser.mask_crop_size', | |
| 'shapemask_head.upsample_factor == shapemask_parser.upsample_factor', | |
| 'shapemask_parser.outer_box_scale == architecture.outer_box_scale', | |
| ] | |
| # pylint: enable=line-too-long | |