Upload config.json

config.json

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+# Copyright (c) Shanghai AI Lab. All rights reserved.
+_base_ = [
+    '../_base_/models/upernet_beit.py',
+    '../_base_/datasets/ade20k.py',
+    '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_160k.py'
+]
+crop_size = (640, 640)
+# pretrained = 'https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_224_pt22k_ft22k.pth'
+pretrained = 'pretrained/beit_large_patch16_224_pt22k_ft22k.pth'
+model = dict(
+    pretrained=pretrained,
+    backbone=dict(
+        type='BEiTAdapter',
+        img_size=640,
+        patch_size=16,
+        embed_dim=1024,
+        depth=24,
+        num_heads=16,
+        mlp_ratio=4,
+        qkv_bias=True,
+        use_abs_pos_emb=False,
+        use_rel_pos_bias=True,
+        init_values=1e-6,
+        drop_path_rate=0.3,
+        conv_inplane=64,
+        n_points=4,
+        deform_num_heads=16,
+        cffn_ratio=0.25,
+        deform_ratio=0.5,
+        with_cp=True,  # set with_cp=True to save memory
+        interaction_indexes=[[0, 5], [6, 11], [12, 17], [18, 23]],
+    ),
+    decode_head=dict(
+        in_channels=[1024, 1024, 1024, 1024],
+        num_classes=150,
+        channels=1024,
+    ),
+    auxiliary_head=dict(
+        in_channels=1024,
+        num_classes=150
+    ),
+    test_cfg = dict(mode='slide', crop_size=crop_size, stride=(426, 426))
+)
+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', reduce_zero_label=True),
+    dict(type='Resize', img_scale=(2048, 640), ratio_range=(0.5, 2.0)),
+    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=crop_size, 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, 640),
+        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='ResizeToMultiple', size_divisor=32),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+optimizer = dict(_delete_=True, type='AdamW', lr=2e-5, betas=(0.9, 0.999), weight_decay=0.05,
+                 constructor='LayerDecayOptimizerConstructor',
+                 paramwise_cfg=dict(num_layers=24, layer_decay_rate=0.90))
+lr_config = dict(_delete_=True, policy='poly',
+                 warmup='linear',
+                 warmup_iters=1500,
+                 warmup_ratio=1e-6,
+                 power=1.0, min_lr=0.0, by_epoch=False)
+data=dict(samples_per_gpu=2,
+          train=dict(pipeline=train_pipeline),
+          val=dict(pipeline=test_pipeline),
+          test=dict(pipeline=test_pipeline))
+runner = dict(type='IterBasedRunner')
+checkpoint_config = dict(by_epoch=False, interval=1000, max_keep_ckpts=1)
+evaluation = dict(interval=16000, metric='mIoU', save_best='mIoU')