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MILK-BIOSLynnL4
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LynnL4
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Refractor: Remove mmcls
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‎configs/_base_/default_runtime_cls.py‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -26,7 +26,7 @@
2626
# set sampler seed in distributed evrionment.
2727
sampler_seed=dict(type='DistSamplerSeedHook'),
2828
# validation results visualization, set True to enable it.
29-
visualization=dict(type='mmcls.VisualizationHook', enable=False),
29+
visualization=dict(type='sscma.ClsVisualizationHook', enable=False),
3030
)
3131

3232
# configure environment
@@ -41,7 +41,7 @@
4141

4242
# set visualizer
4343
vis_backends = [dict(type='LocalVisBackend')]
44-
visualizer = dict(type='mmcls.ClsVisualizer', vis_backends=vis_backends)
44+
visualizer = dict(type='sscma.ClsVisualizer', vis_backends=vis_backends)
4545

4646
# set log level
4747
log_level = 'INFO'

‎configs/accelerometer/3axes_accelerometer_62.5Hz_1s_classify.py‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -39,7 +39,7 @@
3939
),
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head=dict(
4141
type='sscma.AxesClsHead',
42-
loss=dict(type='mmcls.CrossEntropyLoss', loss_weight=1.0),
42+
loss=dict(type='sscma.CrossEntropyLoss', loss_weight=1.0),
4343
topk=(1, 5) if num_classes > 5 else 1,
4444
),
4545
)
@@ -88,7 +88,7 @@
8888
sampler=dict(type='DefaultSampler', shuffle=True),
8989
)
9090

91-
val_evaluator = dict(type='mmcls.Accuracy', topk=(1, 5) if num_classes > 5 else 1)
91+
val_evaluator = dict(type='sscma.Accuracy', topk=(1, 5) if num_classes > 5 else 1)
9292

9393

9494
# If you want standard test, please manually configure the test dataset

‎configs/anomaly/loda.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -76,7 +76,7 @@
7676
sampler=dict(type='DefaultSampler', shuffle=True),
7777
)
7878

79-
val_evaluator = dict(type='mmcls.Accuracy', topk=(1,))
79+
val_evaluator = dict(type='sscma.Accuracy', topk=(1,))
8080

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8282
# If you want standard test, please manually configure the test dataset

‎configs/audio_classify/ali_classiyf_small_8k_8192.py‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -162,7 +162,7 @@
162162
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
163163

164164
# evaluator
165-
val_evaluator = dict(type='mmcls.Accuracy', topk=(1, 5) if num_classes > 5 else 1)
165+
val_evaluator = dict(type='sscma.Accuracy', topk=(1, 5) if num_classes > 5 else 1)
166166
test_evaluator = val_evaluator
167167

168168
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=1000)

‎configs/classification/base.py‎

Lines changed: 9 additions & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -10,7 +10,7 @@
1010

1111
gray = False
1212
# DATA
13-
dataset_type = 'mmcls.CustomDataset'
13+
dataset_type = 'sscma.CustomClsDataset'
1414
# datasets link: https://public.roboflow.com/classification/rock-paper-scissors
1515
data_root = 'https://public.roboflow.com/ds/dTMAyuzrmY?key=VbTbUwLEYG'
1616
train_data = 'train/'
@@ -44,13 +44,13 @@
4444
mean=[0.0] if gray else [0.0, 0.0, 0.0],
4545
std=[255.0] if gray else [255.0, 255.0, 255.0],
4646
),
47-
backbone=dict(type='MobileNetv2', widen_factor=widen_factor),
48-
neck=dict(type='mmcls.GlobalAveragePooling'),
47+
backbone=dict(type='MobileNetV2', widen_factor=widen_factor),
48+
neck=dict(type='sscma.GlobalAveragePooling'),
4949
head=dict(
50-
type='mmcls.LinearClsHead',
50+
type='sscma.LinearClsHead',
5151
in_channels=32,
5252
num_classes=num_classes,
53-
loss=dict(type='mmcls.CrossEntropyLoss', loss_weight=1.0),
53+
loss=dict(type='sscma.CrossEntropyLoss', loss_weight=1.0),
5454
topk=(1, 5) if num_classes > 5 else 1,
5555
),
5656
)
@@ -75,13 +75,13 @@
7575
keymap={'img': 'image'},
7676
),
7777
dict(type='mmengine.Resize', scale=imgsz),
78-
dict(type='mmcls.Rotate', angle=30.0, prob=0.5),
79-
dict(type='mmcls.PackClsInputs'),
78+
dict(type='sscma.RandomRotate', angle=30.0, prob=0.5),
79+
dict(type='sscma.PackClsInputs'),
8080
]
8181

8282
test_pipeline = [
8383
dict(type='mmengine.Resize', scale=imgsz),
84-
dict(type='mmcls.PackClsInputs'),
84+
dict(type='sscma.PackClsInputs'),
8585
]
8686
if gray:
8787
train_pipeline.insert(-2, dict(type='Color2Gray', one_channel=True))
@@ -121,7 +121,7 @@
121121
test_dataloader = val_dataloader
122122

123123
# evaluator
124-
val_evaluator = dict(type='mmcls.Accuracy', topk=(1, 5) if num_classes > 5 else 1)
124+
val_evaluator = dict(type='sscma.Accuracy', topk=(1, 5) if num_classes > 5 else 1)
125125
test_evaluator = val_evaluator
126126

127127

‎configs/classification/efficinet_1.0_rep_1bx16_300e_custom.py‎

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -14,10 +14,10 @@
1414
backbone=dict(
1515
type='EfficientNet', arch='b0', input_channels=1 if gray else 3, out_indices=(6,), rep=True, _delete_=True
1616
),
17-
neck=dict(type='mmcls.GlobalAveragePooling'),
17+
neck=dict(type='sscma.GlobalAveragePooling'),
1818
head=dict(
19-
type='mmcls.LinearClsHead',
19+
type='sscma.LinearClsHead',
2020
in_channels=320,
21-
loss=dict(type='mmcls.CrossEntropyLoss', loss_weight=1.0),
21+
loss=dict(type='sscma.CrossEntropyLoss', loss_weight=1.0),
2222
),
2323
)

‎configs/classification/mobnetv2_0.35_rep_1bx16_300e_cifar10.py‎

Lines changed: 5 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -6,17 +6,17 @@
66
# ========================Suggested optional parameters========================
77
# MODEL
88
gray = False
9-
widen_factor=0.35
9+
widen_factor = 0.35
1010

1111
# ================================END=================================
1212

1313
model = dict(
1414
type='sscma.ImageClassifier',
15-
backbone=dict(type='MobileNetv2', gray_input=gray, widen_factor=widen_factor, out_indices=(2,), rep=True),
16-
neck=dict(type='mmcls.GlobalAveragePooling'),
15+
backbone=dict(type='MobileNetV2', gray_input=gray, widen_factor=widen_factor, out_indices=(2,), rep=True),
16+
neck=dict(type='sscma.GlobalAveragePooling'),
1717
head=dict(
18-
type='mmcls.LinearClsHead',
18+
type='sscma.LinearClsHead',
1919
in_channels=32,
20-
loss=dict(type='mmcls.CrossEntropyLoss', loss_weight=1.0),
20+
loss=dict(type='sscma.CrossEntropyLoss', loss_weight=1.0),
2121
),
2222
)

‎configs/classification/mobnetv2_0.35_rep_1bx16_300e_cifar100.py‎

Lines changed: 6 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -1,5 +1,5 @@
11
# Copyright (c) Seeed Technology Co.,Ltd. All rights reserved.
2-
_base_ = '../_base_/default_runtime_cls.py'
2+
_base_ = './mobnetv2_1.0_1bx16_300e_cifar100.py'
33
default_scope = 'sscma'
44
custom_imports = dict(imports=['sscma'], allow_failed_imports=False)
55

@@ -11,11 +11,11 @@
1111

1212
model = dict(
1313
type='sscma.ImageClassifier',
14-
backbone=dict(type='MobileNetv2', widen_factor=widen_factor, out_indices=(2,), rep=True),
15-
neck=dict(type='mmcls.GlobalAveragePooling'),
14+
backbone=dict(type='MobileNetV2', widen_factor=widen_factor, out_indices=(2,), rep=True),
15+
neck=dict(type='sscma.GlobalAveragePooling'),
1616
head=dict(
17-
type='mmcls.LinearClsHead',
18-
in_channels=16,
19-
loss=dict(type='mmcls.CrossEntropyLoss', loss_weight=1.0),
17+
type='sscma.LinearClsHead',
18+
in_channels=32,
19+
loss=dict(type='sscma.CrossEntropyLoss', loss_weight=1.0),
2020
),
2121
)

‎configs/classification/mobnetv2_0.35_rep_1bx16_300e_custom.py‎

Lines changed: 4 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -10,11 +10,11 @@
1010
# ================================END=================================
1111
model = dict(
1212
type='sscma.ImageClassifier',
13-
backbone=dict(type='MobileNetv2', widen_factor=widen_factor, out_indices=(2,), rep=True),
14-
neck=dict(type='mmcls.GlobalAveragePooling', dim=2),
13+
backbone=dict(type='MobileNetV2', widen_factor=widen_factor, out_indices=(2,), rep=True),
14+
neck=dict(type='sscma.GlobalAveragePooling', dim=2),
1515
head=dict(
16-
type='mmcls.LinearClsHead',
16+
type='sscma.LinearClsHead',
1717
in_channels=32,
18-
loss=dict(type='mmcls.CrossEntropyLoss', loss_weight=1.0),
18+
loss=dict(type='sscma.CrossEntropyLoss', loss_weight=1.0),
1919
),
2020
)

‎configs/classification/mobnetv2_0.35_rep_1bx16_300e_mnist.py‎

Lines changed: 12 additions & 12 deletions
Original file line numberDiff line numberDiff line change
@@ -7,10 +7,10 @@
77
# ========================Suggested optional parameters========================
88
# MODEL
99
gray = True
10-
widen_factor=0.35
10+
widen_factor = 0.35
1111

1212
# DATA
13-
dataset_type = 'mmcls.MNIST'
13+
dataset_type = 'sscma.MNIST'
1414
height = 32
1515
width = 32
1616
imgsz = (width, height)
@@ -23,12 +23,12 @@
2323
# TRAIN
2424
batch = 128
2525
workers = 16
26-
val_batch=batch
27-
val_workers=workers
26+
val_batch = batch
27+
val_workers = workers
2828
persistent_workers = True
2929
# ================================END=================================
3030
data_preprocessor = dict(
31-
type='mmcls.ClsDataPreprocessor',
31+
type='sscma.ClsDataPreprocessor',
3232
mean=[0, 0, 0],
3333
std=[255.0, 255.0, 255.0],
3434
to_rgb=True,
@@ -40,24 +40,24 @@
4040
mean=[0.0] if gray else [0.0, 0.0, 0.0],
4141
std=[255.0] if gray else [255.0, 255.0, 255.0],
4242
),
43-
backbone=dict(type='MobileNetv2', gray_input=gray, widen_factor=widen_factor, out_indices=(2,), rep=True),
44-
neck=dict(type='mmcls.GlobalAveragePooling'),
43+
backbone=dict(type='MobileNetV2', gray_input=gray, widen_factor=widen_factor, out_indices=(2,), rep=True),
44+
neck=dict(type='sscma.GlobalAveragePooling'),
4545
head=dict(
46-
type='mmcls.LinearClsHead',
46+
type='sscma.LinearClsHead',
4747
in_channels=32,
48-
loss=dict(type='mmcls.CrossEntropyLoss', loss_weight=1.0),
48+
loss=dict(type='sscma.CrossEntropyLoss', loss_weight=1.0),
4949
),
5050
)
5151

5252
train_pipeline = [
5353
dict(type='mmengine.Resize', scale=imgsz),
54-
dict(type='mmcls.Rotate', angle=10.0, prob=0.5),
55-
dict(type='mmcls.PackClsInputs'),
54+
dict(type='sscma.RandomRotate', angle=10.0, prob=0.5),
55+
dict(type='sscma.PackClsInputs'),
5656
]
5757

5858
test_pipeline = [
5959
dict(type='mmengine.Resize', scale=imgsz),
60-
dict(type='mmcls.PackClsInputs'),
60+
dict(type='sscma.PackClsInputs'),
6161
]
6262

6363
train_dataloader = dict(

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