mmpretrain/configs/_base_/models/resnet50_cifar_mixup.py

# model settings
model = dict(
    type='ImageClassifier',
    backbone=dict(
        type='ResNet_CIFAR',
        depth=50,
        num_stages=4,
        out_indices=(3, ),
        style='pytorch'),
    neck=dict(type='GlobalAveragePooling'),
    head=dict(
        type='MultiLabelLinearClsHead',
        num_classes=10,
        in_channels=2048,
        loss=dict(type='CrossEntropyLoss', loss_weight=1.0, use_soft=True)),
    train_cfg=dict(
        augments=dict(type='BatchMixup', alpha=1., num_classes=10, prob=1.)))
[Feature] Add "mixup" from Bag of Tricks (#160) * Add mixup option * Modify the structure of mixup and add configs * Clean configs * Add test for mixup and SoftCrossEntropyLoss * Add simple test for ImageClassifier * Fix bug in test_losses.py * Add assertion in CrossEntropyLoss 2021-02-25 14:06:58 +08:00			`# model settings`
			`model = dict(`
			`type='ImageClassifier',`
			`backbone=dict(`
			`type='ResNet_CIFAR',`
			`depth=50,`
			`num_stages=4,`
			`out_indices=(3, ),`
			`style='pytorch'),`
			`neck=dict(type='GlobalAveragePooling'),`
			`head=dict(`
			`type='MultiLabelLinearClsHead',`
			`num_classes=10,`
			`in_channels=2048,`
			`loss=dict(type='CrossEntropyLoss', loss_weight=1.0, use_soft=True)),`
[Feature]Add augments to models/utils (#278) * add mytrain.py for test * test before layers * test attr in layers * test classifier * delete mytrain.py * add rand_bbox_minmax rand_bbox and cutmix_bbox_and_lam to BaseCutMixLayer * add mixup_prob to BatchMixupLayer * add cutmixup * add cutmixup to __init__ * test classifier with cutmixup * delete some comments * set mixup_prob default to 1.0 * add cutmixup to classifier * use cutmixup * use cutmixup * fix bugs * test cutmixup * move mixup and cutmix to augment * inherit from BaseAugment * add BaseAugment * inherit from BaseAugment * rename identity.py * add @ * build augment * register module * rename to augment.py * delete cutmixup.py * do not inherit from BaseAugment * add augments * use augments in classifier * prob default to 1.0 * add comments * use augments * use augments * assert sum of augmentation probabilities should equal to 1 * augmentation probabilities equal to 1 * calculate Identity prob * replace xxx with self.xxx * add comments * sync with augments * for BC-breaking * delete useless comments in mixup.py 2021-06-20 09:44:51 +08:00			`train_cfg=dict(`
			`augments=dict(type='BatchMixup', alpha=1., num_classes=10, prob=1.)))`