_base_ = [ '../_base_/models/segmenter_vit-b16_mask.py', '../_base_/datasets/ade20k_640x640.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' ] crop_size = (640, 640) data_preprocessor = dict(size=crop_size) checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segmenter/vit_large_p16_384_20220308-d4efb41d.pth' # noqa model = dict( data_preprocessor=data_preprocessor, pretrained=checkpoint, backbone=dict( type='VisionTransformer', img_size=(640, 640), embed_dims=1024, num_layers=24, num_heads=16), decode_head=dict( type='SegmenterMaskTransformerHead', in_channels=1024, channels=1024, num_heads=16, embed_dims=1024), test_cfg=dict(mode='slide', crop_size=(640, 640), stride=(608, 608))) optimizer = dict(lr=0.001, weight_decay=0.0) optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer) train_dataloader = dict( # num_gpus: 8 -> batch_size: 8 batch_size=1) val_dataloader = dict(batch_size=1)