update

czczup · Jun 3, 2022 · d841f75 · d841f75
1 parent 8375314
commit d841f75
Show file tree

Hide file tree

Showing 6 changed files with 490 additions and 0 deletions.
diff --git a/segmentation/configs/coco_stuff10k/README.md b/segmentation/configs/coco_stuff10k/README.md
@@ -0,0 +1,14 @@
+# COCO-Stuff-10K
+
+<!-- [ALGORITHM] -->
+
+## Introduction
+
+The Common Objects in COntext-stuff (COCO-stuff) dataset is a dataset for scene understanding tasks like semantic segmentation, object detection and image captioning. It is constructed by annotating the original COCO dataset, which originally annotated things while neglecting stuff annotations. There are 10k images in COCO-Stuff-10K dataset that span over 172 categories including 80 things, 91 stuff, and 1 unlabeled class. It is split into 9,000 and 1,000 images for training and testing.
+
+## Results and Models
+
+| Method      | Backbone      | Pre-train                                                                                                             | Batch Size | Lr schd | Crop Size | mIoU (SS)                                                                                  | mIoU (MS)                                                                                  | #Param | Config                                                                | Download                                                                                                                                                                                                                     |
+|:-----------:|:-------------:|:---------------------------------------------------------------------------------------------------------------------:|:----------:|:-------:|:---------:|:------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:------:|:---------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| UperNet     | ViT-Adapter-L | [BEiT-L](https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_224_pt22k_ft22k.pth) | 8x2        | 80k     | 512       | [51.0](https://drive.google.com/file/d/1xZodiAvOLGaLtMGx_btYVZIMC2VKrDhI/view?usp=sharing) | [51.4](https://drive.google.com/file/d/1bmFG9GA4bRqOEJfqXcO7nWYPwG3wSk2J/view?usp=sharing) | 451M   | [config](./upernet_beit_adapter_large_512_80k_cocostuff10k_ss.py)     | [model](https://github.com/czczup/ViT-Adapter/releases/download/v0.2.4/upernet_beit_adapter_large_512_80k_cocostuff10k.pth.tar) \| [log](https://github.com/czczup/ViT-Adapter/releases/download/v0.2.4/20220505_091358.log) |
+| Mask2Former | ViT-Adapter-L | [BEiT-L](https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_224_pt22k_ft22k.pth) | 8x2        | 40k     | 512       | [53.2](https://drive.google.com/file/d/1Buewc1n7GBAcBDXeia-QarujrDZqc_Sx/view?usp=sharing) | [54.2](https://drive.google.com/file/d/1kQgJUHDeQoO3pPY6QoXRKwyF7heT7wCJ/view?usp=sharing) | 568M   | [config](./mask2former_beit_adapter_large_512_40k_cocostuff10k_ss.py) | [model]() \| [log]()                                                                                                                                                                                                         |
diff --git a/segmentation/configs/coco_stuff10k/mask2former_beit_adapter_large_512_40k_cocostuff10k_ms.py b/segmentation/configs/coco_stuff10k/mask2former_beit_adapter_large_512_40k_cocostuff10k_ms.py
@@ -0,0 +1,149 @@
+# Copyright (c) Shanghai AI Lab. All rights reserved.
+_base_ = [
+    '../_base_/models/mask2former_beit_cocostuff.py',
+    '../_base_/datasets/coco-stuff10k.py',
+    '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_40k.py'
+]
+crop_size = (512, 512)
+# 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=512,
+        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,
+        interaction_indexes=[[0, 5], [6, 11], [12, 17], [18, 23]],
+    ),
+    decode_head=dict(
+        in_channels=[1024, 1024, 1024, 1024],
+        feat_channels=1024,
+        out_channels=1024,
+        num_queries=100,
+        pixel_decoder=dict(
+            type='MSDeformAttnPixelDecoder',
+            num_outs=3,
+            norm_cfg=dict(type='GN', num_groups=32),
+            act_cfg=dict(type='ReLU'),
+            encoder=dict(
+                type='DetrTransformerEncoder',
+                num_layers=6,
+                transformerlayers=dict(
+                    type='BaseTransformerLayer',
+                    attn_cfgs=dict(
+                        type='MultiScaleDeformableAttention',
+                        embed_dims=1024,
+                        num_heads=32,
+                        num_levels=3,
+                        num_points=4,
+                        im2col_step=64,
+                        dropout=0.0,
+                        batch_first=False,
+                        norm_cfg=None,
+                        init_cfg=None),
+                    ffn_cfgs=dict(
+                        type='FFN',
+                        embed_dims=1024,
+                        feedforward_channels=4096,
+                        num_fcs=2,
+                        ffn_drop=0.0,
+                        act_cfg=dict(type='ReLU', inplace=True)),
+                    operation_order=('self_attn', 'norm', 'ffn', 'norm')),
+                init_cfg=None),
+            positional_encoding=dict(
+                type='SinePositionalEncoding', num_feats=512, normalize=True),
+            init_cfg=None),
+        positional_encoding=dict(
+            type='SinePositionalEncoding', num_feats=512, normalize=True),
+        transformer_decoder=dict(
+            type='DetrTransformerDecoder',
+            return_intermediate=True,
+            num_layers=9,
+            transformerlayers=dict(
+                type='DetrTransformerDecoderLayer',
+                attn_cfgs=dict(
+                    type='MultiheadAttention',
+                    embed_dims=1024,
+                    num_heads=32,
+                    attn_drop=0.0,
+                    proj_drop=0.0,
+                    dropout_layer=None,
+                    batch_first=False),
+                ffn_cfgs=dict(
+                    embed_dims=1024,
+                    feedforward_channels=4096,
+                    num_fcs=2,
+                    act_cfg=dict(type='ReLU', inplace=True),
+                    ffn_drop=0.0,
+                    dropout_layer=None,
+                    add_identity=True),
+                feedforward_channels=4096,
+                operation_order=('cross_attn', 'norm', 'self_attn', 'norm',
+                                 'ffn', 'norm')),
+            init_cfg=None)
+    ),
+    test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341))
+)
+# dataset settings
+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, 512), 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='ToMask'),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg', 'gt_masks', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(2048, 512),
+        img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
+        flip=True,
+        transforms=[
+            dict(type='SETR_Resize', keep_ratio=True,
+                 crop_size=crop_size, setr_multi_scale=True),
+            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=4000, metric='mIoU', save_best='mIoU')
diff --git a/segmentation/configs/coco_stuff10k/mask2former_beit_adapter_large_512_40k_cocostuff10k_ss.py b/segmentation/configs/coco_stuff10k/mask2former_beit_adapter_large_512_40k_cocostuff10k_ss.py
@@ -0,0 +1,149 @@
+# Copyright (c) Shanghai AI Lab. All rights reserved.
+_base_ = [
+    '../_base_/models/mask2former_beit_cocostuff.py',
+    '../_base_/datasets/coco-stuff10k.py',
+    '../_base_/default_runtime.py',
+    '../_base_/schedules/schedule_40k.py'
+]
+crop_size = (512, 512)
+# 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=512,
+        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,
+        interaction_indexes=[[0, 5], [6, 11], [12, 17], [18, 23]],
+    ),
+    decode_head=dict(
+        in_channels=[1024, 1024, 1024, 1024],
+        feat_channels=1024,
+        out_channels=1024,
+        num_queries=100,
+        pixel_decoder=dict(
+            type='MSDeformAttnPixelDecoder',
+            num_outs=3,
+            norm_cfg=dict(type='GN', num_groups=32),
+            act_cfg=dict(type='ReLU'),
+            encoder=dict(
+                type='DetrTransformerEncoder',
+                num_layers=6,
+                transformerlayers=dict(
+                    type='BaseTransformerLayer',
+                    attn_cfgs=dict(
+                        type='MultiScaleDeformableAttention',
+                        embed_dims=1024,
+                        num_heads=32,
+                        num_levels=3,
+                        num_points=4,
+                        im2col_step=64,
+                        dropout=0.0,
+                        batch_first=False,
+                        norm_cfg=None,
+                        init_cfg=None),
+                    ffn_cfgs=dict(
+                        type='FFN',
+                        embed_dims=1024,
+                        feedforward_channels=4096,
+                        num_fcs=2,
+                        ffn_drop=0.0,
+                        act_cfg=dict(type='ReLU', inplace=True)),
+                    operation_order=('self_attn', 'norm', 'ffn', 'norm')),
+                init_cfg=None),
+            positional_encoding=dict(
+                type='SinePositionalEncoding', num_feats=512, normalize=True),
+            init_cfg=None),
+        positional_encoding=dict(
+            type='SinePositionalEncoding', num_feats=512, normalize=True),
+        transformer_decoder=dict(
+            type='DetrTransformerDecoder',
+            return_intermediate=True,
+            num_layers=9,
+            transformerlayers=dict(
+                type='DetrTransformerDecoderLayer',
+                attn_cfgs=dict(
+                    type='MultiheadAttention',
+                    embed_dims=1024,
+                    num_heads=32,
+                    attn_drop=0.0,
+                    proj_drop=0.0,
+                    dropout_layer=None,
+                    batch_first=False),
+                ffn_cfgs=dict(
+                    embed_dims=1024,
+                    feedforward_channels=4096,
+                    num_fcs=2,
+                    act_cfg=dict(type='ReLU', inplace=True),
+                    ffn_drop=0.0,
+                    dropout_layer=None,
+                    add_identity=True),
+                feedforward_channels=4096,
+                operation_order=('cross_attn', 'norm', 'self_attn', 'norm',
+                                 'ffn', 'norm')),
+            init_cfg=None)
+    ),
+    test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341))
+)
+# dataset settings
+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, 512), 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='ToMask'),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg', 'gt_masks', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(2048, 512),
+        # 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=4000, metric='mIoU', save_best='mIoU')