Merge pull request #46 from guochengqian/patch-1

Create r50_withoutsobel.py for deepclustering experiment

configs/selfsup/deepcluster/r50_withoutsobel.py

@@ -0,0 +1,88 @@
+_base_ = '../../base.py'
+# model settings
+num_classes = 10000
+model = dict(
+    type='DeepCluster',
+    pretrained=None,
+    with_sobel=False,
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        in_channels=3,
+        out_indices=[4],  # 0: conv-1, x: stage-x
+        norm_cfg=dict(type='BN')),
+    neck=dict(type='AvgPoolNeck'),
+    head=dict(
+        type='ClsHead',
+        with_avg_pool=False,  # already has avgpool in the neck
+        in_channels=2048,
+        num_classes=num_classes))
+# dataset settings
+data_source_cfg = dict(
+    type='ImageNet',
+    memcached=True,
+    mclient_path='/mnt/lustre/share/memcached_client')
+data_train_list = 'data/imagenet/meta/train.txt'
+data_train_root = 'data/imagenet/jpeg/train'
+dataset_type = 'DeepClusterDataset'
+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='RandomRotation', degrees=2),
+    dict(
+        type='ColorJitter',
+        brightness=0.4,
+        contrast=0.4,
+        saturation=1.0,
+        hue=0.5),
+    dict(type='RandomGrayscale', p=0.2),
+    dict(type='ToTensor'),
+    dict(type='Normalize', **img_norm_cfg),
+]
+extract_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,    # 32
+    workers_per_gpu=4,
+    train=dict(
+        type=dataset_type,
+        data_source=dict(
+            list_file=data_train_list, root=data_train_root,
+            **data_source_cfg),
+        pipeline=train_pipeline))
+# additional hooks
+custom_hooks = [
+    dict(
+        type='DeepClusterHook',
+        extractor=dict(
+            imgs_per_gpu=128,
+            workers_per_gpu=8,
+            dataset=dict(
+                type=dataset_type,
+                data_source=dict(
+                    list_file=data_train_list,
+                    root=data_train_root,
+                    **data_source_cfg),
+                pipeline=extract_pipeline)),
+        clustering=dict(type='Kmeans', k=num_classes, pca_dim=256),
+        unif_sampling=True,
+        reweight=False,
+        reweight_pow=0.5,
+        initial=True,  # call initially
+        interval=1)
+]
+# optimizer
+optimizer = dict(
+    type='SGD', lr=0.3, momentum=0.9, weight_decay=0.00001,
+    nesterov=False,
+    paramwise_options={'\Ahead.': dict(momentum=0.)})
+# learning policy
+lr_config = dict(policy='step', step=[400])
+checkpoint_config = dict(interval=10)
+# runtime settings
+total_epochs = 200