_base_ = ['../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/publicdataset_autolaparo.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_300e_val1_check10.py']

norm_cfg = {'type': 'BN'}

crop_size = (512, 512)

data_preprocessor = {'size': (512, 512), 'mean': [123.62464353460942, 85.34836259209033, 82.31539425671558], 'std': [47.172211618459315, 47.08256715323592, 48.135121265163605], 'bgr_to_rgb': False}

model = {'pretrained': './My_Local_Model/open_mmlab/resnet18_v1c.pth', 'backbone': {'depth': 18, 'type': 'ResNetV1c'}, 'data_preprocessor': {'size': (512, 512), 'mean': [123.62464353460942, 85.34836259209033, 82.31539425671558], 'std': [47.172211618459315, 47.08256715323592, 48.135121265163605], 'bgr_to_rgb': False}, 'decode_head': {'in_channels': 512, 'channels': 128, 'num_classes': 10, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN'}}, 'auxiliary_head': {'in_channels': 256, 'channels': 64, 'num_classes': 10, 'loss_decode': {'type': 'DiceLoss', 'use_sigmoid': False, 'loss_weight': 1.0}, 'norm_cfg': {'type': 'BN'}}}

test_cfg = {'crop_size': (512, 512)}

optim_wrapper = {'type': 'OptimWrapper', '_delete_': True, 'optimizer': {'type': 'AdamW', 'lr': 0.0001, 'weight_decay': 0.0005}, 'clip_grad': {'max_norm': 1, 'norm_type': 2}}

param_scheduler = [{'type': 'LinearLR', 'start_factor': 1e-06, 'by_epoch': True, 'begin': 0, 'end': 10}, {'type': 'PolyLR', 'power': 0.9, 'begin': 10, 'end': 300, 'eta_min': 1e-05, 'by_epoch': True}]

vis_backends = [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_pspnet_r18_Pre_g1-300e_val1_check10_publicdataset_autolaparo-512x512'}}]

visualizer = {'name': 'visualizer', 'type': 'SegLocalVisualizer', 'vis_backends': [{'type': 'LocalVisBackend'}, {'type': 'TensorboardVisBackend'}, {'type': 'WandbVisBackend', 'init_kwargs': {'project': 'Seg_MMSeg_Test', 'name': 'my_pspnet_r18_Pre_g1-300e_val1_check10_publicdataset_autolaparo-512x512'}}]}

