Created appropriated files and linked them for MSD and MSD_Marked dat…

…asets. Adapted the visualization file by overloading the mmsegmentation class_names.py file

SegFormer/demo/__init__.py

@@ -0,0 +1 @@
+from class_names import *

SegFormer/demo/class_names.py

@@ -0,0 +1,166 @@
+import mmcv
+
+
+def cityscapes_classes():
+    """Cityscapes class names for external use."""
+    return [
+        'road', 'sidewalk', 'building', 'wall', 'fence', 'pole',
+        'traffic light', 'traffic sign', 'vegetation', 'terrain', 'sky',
+        'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
+        'bicycle'
+    ]
+
+
+def ade_classes():
+    """ADE20K class names for external use."""
+    return [
+        'wall', 'building', 'sky', 'floor', 'tree', 'ceiling', 'road', 'bed ',
+        'windowpane', 'grass', 'cabinet', 'sidewalk', 'person', 'earth',
+        'door', 'table', 'mountain', 'plant', 'curtain', 'chair', 'car',
+        'water', 'painting', 'sofa', 'shelf', 'house', 'sea', 'mirror', 'rug',
+        'field', 'armchair', 'seat', 'fence', 'desk', 'rock', 'wardrobe',
+        'lamp', 'bathtub', 'railing', 'cushion', 'base', 'box', 'column',
+        'signboard', 'chest of drawers', 'counter', 'sand', 'sink',
+        'skyscraper', 'fireplace', 'refrigerator', 'grandstand', 'path',
+        'stairs', 'runway', 'case', 'pool table', 'pillow', 'screen door',
+        'stairway', 'river', 'bridge', 'bookcase', 'blind', 'coffee table',
+        'toilet', 'flower', 'book', 'hill', 'bench', 'countertop', 'stove',
+        'palm', 'kitchen island', 'computer', 'swivel chair', 'boat', 'bar',
+        'arcade machine', 'hovel', 'bus', 'towel', 'light', 'truck', 'tower',
+        'chandelier', 'awning', 'streetlight', 'booth', 'television receiver',
+        'airplane', 'dirt track', 'apparel', 'pole', 'land', 'bannister',
+        'escalator', 'ottoman', 'bottle', 'buffet', 'poster', 'stage', 'van',
+        'ship', 'fountain', 'conveyer belt', 'canopy', 'washer', 'plaything',
+        'swimming pool', 'stool', 'barrel', 'basket', 'waterfall', 'tent',
+        'bag', 'minibike', 'cradle', 'oven', 'ball', 'food', 'step', 'tank',
+        'trade name', 'microwave', 'pot', 'animal', 'bicycle', 'lake',
+        'dishwasher', 'screen', 'blanket', 'sculpture', 'hood', 'sconce',
+        'vase', 'traffic light', 'tray', 'ashcan', 'fan', 'pier', 'crt screen',
+        'plate', 'monitor', 'bulletin board', 'shower', 'radiator', 'glass',
+        'clock', 'flag'
+    ]
+
+
+def voc_classes():
+    """Pascal VOC class names for external use."""
+    return [
+        'background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
+        'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
+        'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train',
+        'tvmonitor'
+    ]
+
+
+def msd_classes():
+    """MSD class names for external use."""
+    return [
+        'no ailment', 'ailment'
+    ]
+
+
+def cityscapes_palette():
+    """Cityscapes palette for external use."""
+    return [[128, 64, 128], [244, 35, 232], [70, 70, 70], [102, 102, 156],
+            [190, 153, 153], [153, 153, 153], [250, 170, 30], [220, 220, 0],
+            [107, 142, 35], [152, 251, 152], [70, 130, 180], [220, 20, 60],
+            [255, 0, 0], [0, 0, 142], [0, 0, 70], [0, 60, 100], [0, 80, 100],
+            [0, 0, 230], [119, 11, 32]]
+
+
+def ade_palette():
+    """ADE20K palette for external use."""
+    return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
+            [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
+            [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
+            [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
+            [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
+            [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
+            [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
+            [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
+            [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
+            [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
+            [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
+            [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
+            [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
+            [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
+            [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
+            [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
+            [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
+            [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
+            [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
+            [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
+            [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
+            [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
+            [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
+            [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
+            [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
+            [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
+            [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
+            [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
+            [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
+            [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
+            [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
+            [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
+            [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
+            [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
+            [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
+            [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
+            [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
+            [102, 255, 0], [92, 0, 255]]
+
+
+def voc_palette():
+    """Pascal VOC palette for external use."""
+    return [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128],
+            [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0],
+            [192, 0, 0], [64, 128, 0], [192, 128, 0], [64, 0, 128],
+            [192, 0, 128], [64, 128, 128], [192, 128, 128], [0, 64, 0],
+            [128, 64, 0], [0, 192, 0], [128, 192, 0], [0, 64, 128]]
+
+
+def msd_palette():
+    """MSD palette for external use."""
+    return [[128, 128, 128], [0, 64, 0]]
+
+
+dataset_aliases = {
+    'cityscapes': ['cityscapes'],
+    'ade': ['ade', 'ade20k'],
+    'voc': ['voc', 'pascal_voc', 'voc12', 'voc12aug'],
+    'msd': ['msd', 'msd_marked', 'msd_balanced'],
+}
+
+
+def get_classes(dataset):
+    """Get class names of a dataset."""
+    alias2name = {}
+    for name, aliases in dataset_aliases.items():
+        for alias in aliases:
+            alias2name[alias] = name
+    print(alias2name)
+
+    if mmcv.is_str(dataset):
+        if dataset in alias2name:
+            labels = eval(alias2name[dataset] + '_classes()')
+        else:
+            raise ValueError(f'Unrecognized dataset: {dataset}')
+    else:
+        raise TypeError(f'dataset must a str, but got {type(dataset)}')
+    return labels
+
+
+def get_palette(dataset):
+    """Get class palette (RGB) of a dataset."""
+    alias2name = {}
+    for name, aliases in dataset_aliases.items():
+        for alias in aliases:
+            alias2name[alias] = name
+
+    if mmcv.is_str(dataset):
+        if dataset in alias2name:
+            labels = eval(alias2name[dataset] + '_palette()')
+        else:
+            raise ValueError(f'Unrecognized dataset: {dataset}')
+    else:
+        raise TypeError(f'dataset must a str, but got {type(dataset)}')
+    return labels

SegFormer/demo/image_demo.py

@@ -1,7 +1,7 @@
 from argparse import ArgumentParser
 
 from mmseg.apis import inference_segmentor, init_segmentor, show_result_pyplot
-from mmseg.core.evaluation import get_palette
+from class_names import get_palette
 
 
 def main():

SegFormer/local_configs/_base_/datasets/msd.py

@@ -36,19 +36,32 @@
     workers_per_gpu=2,
     train=dict(
         type=dataset_type,
-        data_root=data_root,
+        data_root='../../data/MSD/Task09_Spleen_RGB_2D_512',
         img_dir='images/training',
         ann_dir='annotations/training',
         pipeline=train_pipeline),
     val=dict(
         type=dataset_type,
-        data_root=data_root,
-        img_dir='images/validation',
-        ann_dir='annotations/validation',
+        data_root='../../data/MSD/Task09_Spleen_RGB_2D_512',
+        img_dir='images/training',
+        ann_dir='annotations/training',
         pipeline=test_pipeline),
     test=dict(
         type=dataset_type,
-        data_root=data_root,
-        img_dir='images/validation',
-        ann_dir='annotations/validation',
-        pipeline=test_pipeline))
+        data_root='../../data/MSD/Task09_Spleen_RGB_2D_512',
+        img_dir='images/training',
+        ann_dir='annotations/training',
+        pipeline=test_pipeline)
+    # val=dict(
+    #     type=dataset_type,
+    #     data_root='../../data/MSD/Task09_Spleen_RGB_2D_512',
+    #     img_dir='images/validation',
+    #     ann_dir='annotations/validation',
+    #     pipeline=test_pipeline),
+    # test=dict(
+    #     type=dataset_type,
+    #     data_root='../../data/MSD/Task09_Spleen_RGB_2D_512',,
+    #     img_dir='images/validation',
+    #     ann_dir='annotations/validation',
+    #     pipeline=test_pipeline)
+)

SegFormer/local_configs/_base_/datasets/msd_marked.py

@@ -0,0 +1,67 @@
+# dataset settings
+dataset_type = 'MSDMarkedDataset'
+data_root = 'data/MSD/Task09_Spleen_RGB_2D_512_Marked'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+crop_size = (512, 512)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations'),
+    dict(type='Resize', img_scale=(512, 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='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(512, 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='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_root='../../data/MSD/Task09_Spleen_RGB_2D_512_Marked',
+        img_dir='images/training',
+        ann_dir='annotations/training',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_root='../../data/MSD/Task09_Spleen_RGB_2D_512_Marked',
+        img_dir='images/training',
+        ann_dir='annotations/training',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        data_root='../../data/MSD/Task09_Spleen_RGB_2D_512_Marked',
+        img_dir='images/training',
+        ann_dir='annotations/training',
+        pipeline=test_pipeline)
+    # val=dict(
+    #     type=dataset_type,
+    #     data_root='../../data/MSD/Task09_Spleen_RGB_2D_512_Marked',
+    #     img_dir='images/validation',
+    #     ann_dir='annotations/validation',
+    #     pipeline=test_pipeline),
+    # test=dict(
+    #     type=dataset_type,
+    #     data_root='../../data/MSD/Task09_Spleen_RGB_2D_512_Marked',
+    #     img_dir='images/validation',
+    #     ann_dir='annotations/validation',
+    #     pipeline=test_pipeline)
+)

SegFormer/local_configs/_base_/schedules/schedule_20k.py

@@ -4,6 +4,6 @@
 # learning policy
 lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
 # runtime settings
-runner = dict(type='IterBasedRunner', max_iters=2000)
+runner = dict(type='IterBasedRunner', max_iters=20000)
 checkpoint_config = dict(by_epoch=False, interval=2000)
 evaluation = dict(interval=2000, metric='mIoU')

SegFormer/local_configs/segformer/MSD/segformer.512x512.msd.160k.py

@@ -0,0 +1,47 @@
+_base_ = [
+    '../../_base_/models/segformer.py',
+    '../../_base_/datasets/msd.py',
+    '../../_base_/default_runtime.py',
+    '../../_base_/schedules/schedule_160k_adamw.py'
+]
+
+# model settings
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+find_unused_parameters = True
+model = dict(
+    type='EncoderDecoder',
+    pretrained='../../pretrained/ImageNet-1K/mit_b0.pth',
+    backbone=dict(
+        type='mit_b0',
+        style='pytorch'),
+    decode_head=dict(
+        type='SegFormerHead',
+        in_channels=[32, 64, 160, 256],
+        in_index=[0, 1, 2, 3],
+        feature_strides=[4, 8, 16, 32],
+        channels=128,
+        dropout_ratio=0.1,
+        num_classes=150,
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        decoder_params=dict(embed_dim=256),
+        loss_decode=dict(type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
+    # model training and testing settings
+    train_cfg=dict(),
+    test_cfg=dict(mode='whole'))
+
+# optimizer
+optimizer = dict(_delete_=True, type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01,
+                 paramwise_cfg=dict(custom_keys={'pos_block': dict(decay_mult=0.),
+                                                 'norm': dict(decay_mult=0.),
+                                                 'head': dict(lr_mult=10.)
+                                                 }))
+
+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)
+evaluation = dict(interval=16000, metric='mIoU')

SegFormer/local_configs/segformer/MSD/segformer.512x512.msd.20k.py

@@ -0,0 +1,47 @@
+_base_ = [
+    '../../_base_/models/segformer.py',
+    '../../_base_/datasets/msd.py',
+    '../../_base_/default_runtime.py',
+    '../../_base_/schedules/schedule_20k.py'
+]
+
+# model settings
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+find_unused_parameters = True
+model = dict(
+    type='EncoderDecoder',
+    pretrained='../../pretrained/ImageNet-1K/mit_b0.pth',
+    backbone=dict(
+        type='mit_b0',
+        style='pytorch'),
+    decode_head=dict(
+        type='SegFormerHead',
+        in_channels=[32, 64, 160, 256],
+        in_index=[0, 1, 2, 3],
+        feature_strides=[4, 8, 16, 32],
+        channels=128,
+        dropout_ratio=0.1,
+        num_classes=150,
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        decoder_params=dict(embed_dim=256),
+        loss_decode=dict(type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
+    # model training and testing settings
+    train_cfg=dict(),
+    test_cfg=dict(mode='whole'))
+
+# optimizer
+optimizer = dict(_delete_=True, type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01,
+                 paramwise_cfg=dict(custom_keys={'pos_block': dict(decay_mult=0.),
+                                                 'norm': dict(decay_mult=0.),
+                                                 'head': dict(lr_mult=10.)
+                                                 }))
+
+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)
+evaluation = dict(interval=16000, metric='mIoU')

SegFormer/local_configs/segformer/MSD/segformer.512x512.msd.40k.py

@@ -2,7 +2,7 @@
     '../../_base_/models/segformer.py',
     '../../_base_/datasets/msd.py',
     '../../_base_/default_runtime.py',
-    '../../_base_/schedules/schedule_20k.py'
+    '../../_base_/schedules/schedule_40k_adamw.py'
 ]
 
 # model settings