[Feature] Add ResNeSt configs (#332)

* add configs for resnest training

* fix format

configs/_base_/models/resnest101.py

@@ -13,6 +13,12 @@
         type='LinearClsHead',
         num_classes=1000,
         in_channels=2048,
-        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        loss=dict(
+            type='LabelSmoothLoss',
+            label_smooth_val=0.1,
+            num_classes=1000,
+            reduction='mean',
+            loss_weight=1.0),
         topk=(1, 5),
-    ))
+        cal_acc=False))
+train_cfg = dict(mixup=dict(alpha=0.2, num_classes=1000))

configs/_base_/models/resnest200.py

@@ -13,6 +13,12 @@
         type='LinearClsHead',
         num_classes=1000,
         in_channels=2048,
-        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        loss=dict(
+            type='LabelSmoothLoss',
+            label_smooth_val=0.1,
+            num_classes=1000,
+            reduction='mean',
+            loss_weight=1.0),
         topk=(1, 5),
-    ))
+        cal_acc=False))
+train_cfg = dict(mixup=dict(alpha=0.2, num_classes=1000))

configs/_base_/models/resnest269.py

@@ -13,6 +13,12 @@
         type='LinearClsHead',
         num_classes=1000,
         in_channels=2048,
-        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        loss=dict(
+            type='LabelSmoothLoss',
+            label_smooth_val=0.1,
+            num_classes=1000,
+            reduction='mean',
+            loss_weight=1.0),
         topk=(1, 5),
-    ))
+        cal_acc=False))
+train_cfg = dict(mixup=dict(alpha=0.2, num_classes=1000))

configs/_base_/models/resnest50.py

@@ -12,6 +12,12 @@
         type='LinearClsHead',
         num_classes=1000,
         in_channels=2048,
-        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
+        loss=dict(
+            type='LabelSmoothLoss',
+            label_smooth_val=0.1,
+            num_classes=1000,
+            reduction='mean',
+            loss_weight=1.0),
         topk=(1, 5),
-    ))
+        cal_acc=False))
+train_cfg = dict(mixup=dict(alpha=0.2, num_classes=1000))

configs/resnest/resnest101_b64x32_imagenet.py

@@ -0,0 +1,181 @@
+_base_ = ['../_base_/models/resnest101.py', '../_base_/default_runtime.py']
+
+# dataset settings
+dataset_type = 'ImageNet'
+img_lighting_cfg = dict(
+    eigval=[55.4625, 4.7940, 1.1475],
+    eigvec=[[-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.8140],
+            [-0.5836, -0.6948, 0.4203]],
+    alphastd=0.1,
+    to_rgb=True)
+policies = [
+    dict(type='AutoContrast', prob=0.5),
+    dict(type='Equalize', prob=0.5),
+    dict(type='Invert', prob=0.5),
+    dict(
+        type='Rotate',
+        magnitude_key='angle',
+        magnitude_range=(0, 30),
+        pad_val=0,
+        prob=0.5,
+        random_negative_prob=0.5),
+    dict(
+        type='Posterize',
+        magnitude_key='bits',
+        magnitude_range=(0, 4),
+        prob=0.5),
+    dict(
+        type='Solarize',
+        magnitude_key='thr',
+        magnitude_range=(0, 256),
+        prob=0.5),
+    dict(
+        type='SolarizeAdd',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 110),
+        thr=128,
+        prob=0.5),
+    dict(
+        type='ColorTransform',
+        magnitude_key='magnitude',
+        magnitude_range=(-0.9, 0.9),
+        prob=0.5,
+        random_negative_prob=0.),
+    dict(
+        type='Contrast',
+        magnitude_key='magnitude',
+        magnitude_range=(-0.9, 0.9),
+        prob=0.5,
+        random_negative_prob=0.),
+    dict(
+        type='Brightness',
+        magnitude_key='magnitude',
+        magnitude_range=(-0.9, 0.9),
+        prob=0.5,
+        random_negative_prob=0.),
+    dict(
+        type='Sharpness',
+        magnitude_key='magnitude',
+        magnitude_range=(-0.9, 0.9),
+        prob=0.5,
+        random_negative_prob=0.),
+    dict(
+        type='Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        pad_val=0,
+        prob=0.5,
+        direction='horizontal',
+        random_negative_prob=0.5),
+    dict(
+        type='Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        pad_val=0,
+        prob=0.5,
+        direction='vertical',
+        random_negative_prob=0.5),
+    dict(
+        type='Cutout',
+        magnitude_key='shape',
+        magnitude_range=(1, 41),
+        pad_val=0,
+        prob=0.5),
+    dict(
+        type='Translate',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        pad_val=0,
+        prob=0.5,
+        direction='horizontal',
+        random_negative_prob=0.5,
+        interpolation='bicubic'),
+    dict(
+        type='Translate',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        pad_val=0,
+        prob=0.5,
+        direction='vertical',
+        random_negative_prob=0.5,
+        interpolation='bicubic')
+]
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandAugment',
+        policies=policies,
+        num_policies=2,
+        magnitude_level=12),
+    dict(
+        type='RandomResizedCrop',
+        size=256,
+        efficientnet_style=True,
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
+    dict(type='Lighting', **img_lighting_cfg),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=False),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='CenterCrop',
+        crop_size=256,
+        efficientnet_style=True,
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=64,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline),
+    test=dict(
+        # replace `data/val` with `data/test` for standard test
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='accuracy')
+
+# optimizer
+optimizer = dict(
+    type='SGD',
+    lr=0.8,
+    momentum=0.9,
+    weight_decay=1e-4,
+    paramwise_cfg=dict(bias_decay_mult=0., norm_decay_mult=0.))
+optimizer_config = dict(grad_clip=None)
+
+# learning policy
+lr_config = dict(
+    policy='CosineAnnealing',
+    min_lr=0,
+    warmup='linear',
+    warmup_iters=5,
+    warmup_ratio=1e-6,
+    warmup_by_epoch=True)
+runner = dict(type='EpochBasedRunner', max_epochs=270)