base.py for semi 1percent

configs/benchmarks/semi_classification/imagenet_1percent/base.py

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+_base_ = '../../../base.py'
+# model settings
+model = dict(
+    type='Classification',
+    pretrained=None,
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        out_indices=[4],  # 0: conv-1, x: stage-x
+        norm_cfg=dict(type='SyncBN')),
+    head=dict(
+        type='ClsHead', with_avg_pool=True, in_channels=2048,
+        num_classes=1000))
+# dataset settings
+data_source_cfg = dict(
+    type='ImageNet',
+    memcached=True,
+    mclient_path='/mnt/lustre/share/memcached_client')
+data_train_list = 'data/imagenet/meta/train_labeled_1percent.txt'
+data_train_root = 'data/imagenet/train'
+data_test_list = 'data/imagenet/meta/val_labeled.txt'
+data_test_root = 'data/imagenet/val'
+dataset_type = 'ClassificationDataset'
+img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+train_pipeline = [
+    dict(type='RandomResizedCrop', size=224),
+    dict(type='RandomHorizontalFlip'),
+    dict(type='ToTensor'),
+    dict(type='Normalize', **img_norm_cfg),
+]
+test_pipeline = [
+    dict(type='Resize', size=256),
+    dict(type='CenterCrop', size=224),
+    dict(type='ToTensor'),
+    dict(type='Normalize', **img_norm_cfg),
+]
+data = dict(
+    imgs_per_gpu=64,  # total 256
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_source=dict(
+            list_file=data_train_list, root=data_train_root,
+            **data_source_cfg),
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_source=dict(
+            list_file=data_test_list, root=data_test_root, **data_source_cfg),
+        pipeline=test_pipeline))
+# additional hooks
+custom_hooks = [
+    dict(
+        type='ValidateHook',
+        dataset=data['val'],
+        initial=False,
+        interval=20,
+        imgs_per_gpu=32,
+        workers_per_gpu=2,
+        eval_param=dict(topk=(1, 5)))
+]
+# learning policy
+lr_config = dict(policy='step', step=[12, 16], gamma=0.2)
+checkpoint_config = dict(interval=20)
+log_config = dict(
+    interval=10,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        dict(type='TensorboardLoggerHook')
+    ])
+# runtime settings
+total_epochs = 20