Final configs

.gitignore

@@ -152,3 +152,4 @@ logs/*.log
 /pretrained/ImageNet-1K/mit_b5.pth
 /data/MSD/
 /pretrained/ImageNet-1K/mit_b0.pth
+/pretrained/ImageNet-1K/mit_b3.pth

SegFormer/demo/image_demo.py

@@ -31,11 +31,10 @@ def show_result_pyplot(model, img, result, train_or_test='train', palette=None,
     plt.figure(figsize=fig_size)
     fig, ax = plt.subplots(1, 1, figsize=fig_size)
     ax.imshow(mmcv.bgr2rgb(img), alpha=1.0)
-    if train_or_test == 'train':
-        img_annot = img_path.replace("images", "annotations")
-        img_annot = imread(img_annot)
-        ax.imshow(img_annot, cmap=my_cmap, interpolation='none',
-                  clim=[0.9, 1], alpha=.4)
+    img_annot = img_path.replace("images", "annotations")
+    img_annot = imread(img_annot)
+    ax.imshow(img_annot, cmap=my_cmap, interpolation='none',
+              clim=[0.9, 1], alpha=.4)
     plt.show()
 
 

SegFormer/local_configs/_base_/datasets/msd_lungs_balanced.py

@@ -49,7 +49,7 @@
     test=dict(
         type=dataset_type,
         data_root='../../data/MSD/Task06_Lungs_RGB_2D_512_Balanced',
-        img_dir='images/training',
+        img_dir='images/validation',
         ann_dir='annotations/validation',
         pipeline=test_pipeline)
 )

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

@@ -21,7 +21,7 @@
         feature_strides=[4, 8, 16, 32],
         channels=128,
         dropout_ratio=0.1,
-        num_classes=150,
+        num_classes=2,
         norm_cfg=norm_cfg,
         align_corners=False,
         decoder_params=dict(embed_dim=256),

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

@@ -21,7 +21,7 @@
         feature_strides=[4, 8, 16, 32],
         channels=128,
         dropout_ratio=0.1,
-        num_classes=150,
+        num_classes=2,
         norm_cfg=norm_cfg,
         align_corners=False,
         decoder_params=dict(embed_dim=256),

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

@@ -21,7 +21,7 @@
         feature_strides=[4, 8, 16, 32],
         channels=128,
         dropout_ratio=0.1,
-        num_classes=150,
+        num_classes=2,
         norm_cfg=norm_cfg,
         align_corners=False,
         decoder_params=dict(embed_dim=256),

SegFormer/local_configs/segformer/MSD/segformer.512x512.msd_balanced.20k.B3.py

@@ -0,0 +1,49 @@
+_base_ = [
+    '../../_base_/models/segformer.py',
+    '../../_base_/datasets/msd_balanced.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_b3.pth',
+    backbone=dict(
+        type='mit_b3',
+        style='pytorch'),
+    decode_head=dict(
+        type='SegFormerHead',
+        in_channels=[64, 128, 320, 512],
+        in_index=[0, 1, 2, 3],
+        feature_strides=[4, 8, 16, 32],
+        channels=128,
+        dropout_ratio=0.1,
+        num_classes=2,
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        decoder_params=dict(embed_dim=768),
+        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_balanced.20k.B5.py

@@ -0,0 +1,47 @@
+_base_ = [
+    '../../_base_/models/segformer.py',
+    '../../_base_/datasets/msd_balanced.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_b5.pth',
+    backbone=dict(
+        type='mit_b5',
+        style='pytorch'),
+    decode_head=dict(
+        type='SegFormerHead',
+        in_channels=[64, 128, 320, 512],
+        in_index=[0, 1, 2, 3],
+        feature_strides=[4, 8, 16, 32],
+        channels=128,
+        dropout_ratio=0.1,
+        num_classes=2,
+        norm_cfg=norm_cfg,
+        align_corners=False,
+        decoder_params=dict(embed_dim=768),
+        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_balanced.20k.py

@@ -21,7 +21,7 @@
         feature_strides=[4, 8, 16, 32],
         channels=128,
         dropout_ratio=0.1,
-        num_classes=150,
+        num_classes=2,
         norm_cfg=norm_cfg,
         align_corners=False,
         decoder_params=dict(embed_dim=256),

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

@@ -21,7 +21,7 @@
         feature_strides=[4, 8, 16, 32],
         channels=128,
         dropout_ratio=0.1,
-        num_classes=150,
+        num_classes=2,
         norm_cfg=norm_cfg,
         align_corners=False,
         decoder_params=dict(embed_dim=256),

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

@@ -21,7 +21,7 @@
         feature_strides=[4, 8, 16, 32],
         channels=128,
         dropout_ratio=0.1,
-        num_classes=150,
+        num_classes=2,
         norm_cfg=norm_cfg,
         align_corners=False,
         decoder_params=dict(embed_dim=256),

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

@@ -21,7 +21,7 @@
         feature_strides=[4, 8, 16, 32],
         channels=128,
         dropout_ratio=0.1,
-        num_classes=150,
+        num_classes=2,
         norm_cfg=norm_cfg,
         align_corners=False,
         decoder_params=dict(embed_dim=256),

SegFormer/mmseg/datasets/custom.py

@@ -321,7 +321,6 @@ def evaluate(self,
         Returns:
             dict[str, float]: Default metrics.
         """
-
         if isinstance(metric, str):
             metric = [metric]
         allowed_metrics = ['mIoU', 'mDice']
@@ -341,7 +340,8 @@ def evaluate(self,
             self.ignore_index,
             metric,
             label_map=self.label_map,
-            reduce_zero_label=self.reduce_zero_label)
+            nan_to_num=1,
+            reduce_zero_label=False)
         class_table_data = [['Class'] + [m[1:] for m in metric] + ['Acc']]
         if self.CLASSES is None:
             class_names = tuple(range(num_classes))