Spaces:
Sleeping
Sleeping
File size: 3,328 Bytes
5672777 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 |
# 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 template to train Mask R-CNN."""
from official.legacy.detection.configs import base_config
from official.modeling.hyperparams import params_dict
# pylint: disable=line-too-long
MASKRCNN_CFG = params_dict.ParamsDict(base_config.BASE_CFG)
MASKRCNN_CFG.override({
'type': 'mask_rcnn',
'eval': {
'type': 'box_and_mask',
'num_images_to_visualize': 0,
},
'architecture': {
'parser': 'maskrcnn_parser',
'min_level': 2,
'max_level': 6,
'include_mask': True,
'mask_target_size': 28,
},
'maskrcnn_parser': {
'output_size': [1024, 1024],
'num_channels': 3,
'rpn_match_threshold': 0.7,
'rpn_unmatched_threshold': 0.3,
'rpn_batch_size_per_im': 256,
'rpn_fg_fraction': 0.5,
'aug_rand_hflip': True,
'aug_scale_min': 1.0,
'aug_scale_max': 1.0,
'skip_crowd_during_training': True,
'max_num_instances': 100,
'mask_crop_size': 112,
},
'anchor': {
'num_scales': 1,
'anchor_size': 8,
},
'rpn_head': {
'num_convs': 2,
'num_filters': 256,
'use_separable_conv': False,
'use_batch_norm': False,
},
'frcnn_head': {
'num_convs': 0,
'num_filters': 256,
'use_separable_conv': False,
'num_fcs': 2,
'fc_dims': 1024,
'use_batch_norm': False,
},
'mrcnn_head': {
'num_convs': 4,
'num_filters': 256,
'use_separable_conv': False,
'use_batch_norm': False,
},
'rpn_score_loss': {
'rpn_batch_size_per_im': 256,
},
'rpn_box_loss': {
'huber_loss_delta': 1.0 / 9.0,
},
'frcnn_box_loss': {
'huber_loss_delta': 1.0,
},
'roi_proposal': {
'rpn_pre_nms_top_k': 2000,
'rpn_post_nms_top_k': 1000,
'rpn_nms_threshold': 0.7,
'rpn_score_threshold': 0.0,
'rpn_min_size_threshold': 0.0,
'test_rpn_pre_nms_top_k': 1000,
'test_rpn_post_nms_top_k': 1000,
'test_rpn_nms_threshold': 0.7,
'test_rpn_score_threshold': 0.0,
'test_rpn_min_size_threshold': 0.0,
'use_batched_nms': False,
},
'roi_sampling': {
'num_samples_per_image': 512,
'fg_fraction': 0.25,
'fg_iou_thresh': 0.5,
'bg_iou_thresh_hi': 0.5,
'bg_iou_thresh_lo': 0.0,
'mix_gt_boxes': True,
},
'mask_sampling': {
'num_mask_samples_per_image': 128, # Typically = `num_samples_per_image` * `fg_fraction`.
},
'postprocess': {
'pre_nms_num_boxes': 1000,
},
}, is_strict=False)
MASKRCNN_RESTRICTIONS = [
]
# pylint: enable=line-too-long
|