| dataset_type = 'CustomDataset' | |
| data_root = 'data/FoodSeg103/Images/' | |
| img_norm_cfg = dict( | |
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | |
| crop_size = (512, 1024) | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations'), | |
| dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)), | |
| dict(type='RandomCrop', crop_size=(512, 1024), cat_max_ratio=0.75), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict(type='PhotoMetricDistortion'), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| to_rgb=True), | |
| dict(type='Pad', size=(512, 1024), pad_val=0, seg_pad_val=255), | |
| dict(type='DefaultFormatBundle'), | |
| dict(type='Collect', keys=['img', 'gt_semantic_seg']) | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(2048, 1024), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict(type='RandomFlip'), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| to_rgb=True), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img']) | |
| ]) | |
| ] | |
| data = dict( | |
| samples_per_gpu=2, | |
| workers_per_gpu=2, | |
| train=dict( | |
| type='CustomDataset', | |
| data_root='data/FoodSeg103/Images/', | |
| img_dir='img_dir/train', | |
| ann_dir='ann_dir/train', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations'), | |
| dict( | |
| type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)), | |
| dict(type='RandomCrop', crop_size=(512, 1024), cat_max_ratio=0.75), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict(type='PhotoMetricDistortion'), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| to_rgb=True), | |
| dict(type='Pad', size=(512, 1024), pad_val=0, seg_pad_val=255), | |
| dict(type='DefaultFormatBundle'), | |
| dict(type='Collect', keys=['img', 'gt_semantic_seg']) | |
| ]), | |
| val=dict( | |
| type='CustomDataset', | |
| data_root='data/FoodSeg103/Images/', | |
| img_dir='img_dir/test', | |
| ann_dir='ann_dir/test', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(2048, 1024), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict(type='RandomFlip'), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| to_rgb=True), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img']) | |
| ]) | |
| ]), | |
| test=dict( | |
| type='CustomDataset', | |
| data_root='data/FoodSeg103/Images/', | |
| img_dir='img_dir/test', | |
| ann_dir='ann_dir/test', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=(2048, 1024), | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict(type='RandomFlip'), | |
| dict( | |
| type='Normalize', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| to_rgb=True), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img']) | |
| ]) | |
| ])) | |
| log_config = dict( | |
| interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)]) | |
| dist_params = dict(backend='nccl') | |
| log_level = 'INFO' | |
| load_from = None | |
| resume_from = None | |
| workflow = [('train', 1)] | |
| cudnn_benchmark = True | |
| norm_cfg = dict(type='SyncBN', requires_grad=True) | |
| model = dict( | |
| type='EncoderDecoder', | |
| pretrained='pretrained_model/recipe1MP_R50.pth', | |
| backbone=dict( | |
| type='ResNet', | |
| depth=50, | |
| num_stages=4, | |
| out_indices=(0, 1, 2, 3), | |
| dilations=(1, 1, 2, 4), | |
| strides=(1, 2, 1, 1), | |
| norm_cfg=dict(type='SyncBN', requires_grad=True), | |
| norm_eval=False, | |
| style='pytorch', | |
| contract_dilation=True), | |
| decode_head=dict( | |
| type='CCHead', | |
| in_channels=2048, | |
| in_index=3, | |
| channels=512, | |
| recurrence=2, | |
| dropout_ratio=0.1, | |
| num_classes=104, | |
| norm_cfg=dict(type='SyncBN', requires_grad=True), | |
| align_corners=False, | |
| loss_decode=dict( | |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), | |
| auxiliary_head=dict( | |
| type='FCNHead', | |
| in_channels=1024, | |
| in_index=2, | |
| channels=256, | |
| num_convs=1, | |
| concat_input=False, | |
| dropout_ratio=0.1, | |
| num_classes=104, | |
| norm_cfg=dict(type='SyncBN', requires_grad=True), | |
| align_corners=False, | |
| loss_decode=dict( | |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | |
| train_cfg=dict(), | |
| test_cfg=dict(mode='whole')) | |
| optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) | |
| optimizer_config = dict() | |
| lr_config = dict(policy='poly', power=0.9, min_lr=0.0001, by_epoch=False) | |
| runner = dict(type='IterBasedRunner', max_iters=80000) | |
| checkpoint_config = dict(by_epoch=False, interval=4000) | |
| evaluation = dict(interval=4000, metric='mIoU') | |
| work_dir = 'checkpoints/ccnet/recipe1m_train2' | |
| gpu_ids = range(0, 1) | |