| _base_ = './htc_without_semantic_r50_fpn_1x_coco.py' | |
| model = dict( | |
| roi_head=dict( | |
| semantic_roi_extractor=dict( | |
| type='SingleRoIExtractor', | |
| roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), | |
| out_channels=256, | |
| featmap_strides=[8]), | |
| semantic_head=dict( | |
| type='FusedSemanticHead', | |
| num_ins=5, | |
| fusion_level=1, | |
| num_convs=4, | |
| in_channels=256, | |
| conv_out_channels=256, | |
| num_classes=183, | |
| loss_seg=dict( | |
| type='CrossEntropyLoss', ignore_index=255, loss_weight=0.2)))) | |
| data_root = 'data/coco/' | |
| img_norm_cfg = dict( | |
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True), | |
| dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), | |
| dict(type='RandomFlip', flip_ratio=0.5), | |
| dict(type='Normalize', **img_norm_cfg), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='SegRescale', scale_factor=1 / 8), | |
| dict(type='DefaultFormatBundle'), | |
| dict( | |
| type='Collect', | |
| keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']), | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(1333, 800), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict(type='RandomFlip', flip_ratio=0.5), | |
| dict(type='Normalize', **img_norm_cfg), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img']), | |
| ]) | |
| ] | |
| data = dict( | |
| train=dict( | |
| seg_prefix=data_root + 'stuffthingmaps/train2017/', | |
| pipeline=train_pipeline), | |
| val=dict(pipeline=test_pipeline), | |
| test=dict(pipeline=test_pipeline)) | |