mirror of
https://github.com/open-mmlab/mmclassification.git
synced 2025-06-03 21:53:55 +08:00
3a08db9182
* 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
222 lines
6.9 KiB
Python
222 lines
6.9 KiB
Python
import torch
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from mmcls.models.classifiers import ImageClassifier
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def test_image_classifier():
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# Test mixup in ImageClassifier
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model_cfg = dict(
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backbone=dict(
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type='ResNet_CIFAR',
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depth=50,
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num_stages=4,
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out_indices=(3, ),
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style='pytorch'),
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neck=dict(type='GlobalAveragePooling'),
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head=dict(
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type='MultiLabelLinearClsHead',
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num_classes=10,
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in_channels=2048,
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loss=dict(type='CrossEntropyLoss', loss_weight=1.0,
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use_soft=True)),
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train_cfg=dict(
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augments=dict(
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type='BatchMixup', alpha=1., num_classes=10, prob=1.)))
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img_classifier = ImageClassifier(**model_cfg)
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img_classifier.init_weights()
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imgs = torch.randn(16, 3, 32, 32)
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label = torch.randint(0, 10, (16, ))
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losses = img_classifier.forward_train(imgs, label)
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assert losses['loss'].item() > 0
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# Considering BC-breaking
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model_cfg['train_cfg'] = dict(mixup=dict(alpha=1.0, num_classes=10))
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img_classifier = ImageClassifier(**model_cfg)
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img_classifier.init_weights()
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imgs = torch.randn(16, 3, 32, 32)
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label = torch.randint(0, 10, (16, ))
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losses = img_classifier.forward_train(imgs, label)
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assert losses['loss'].item() > 0
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def test_image_classifier_with_cutmix():
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# Test cutmix in ImageClassifier
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model_cfg = dict(
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backbone=dict(
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type='ResNet_CIFAR',
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depth=50,
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num_stages=4,
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out_indices=(3, ),
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style='pytorch'),
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neck=dict(type='GlobalAveragePooling'),
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head=dict(
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type='MultiLabelLinearClsHead',
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num_classes=10,
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in_channels=2048,
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loss=dict(type='CrossEntropyLoss', loss_weight=1.0,
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use_soft=True)),
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train_cfg=dict(
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augments=dict(
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type='BatchCutMix', alpha=1., num_classes=10, prob=1.)))
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img_classifier = ImageClassifier(**model_cfg)
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img_classifier.init_weights()
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imgs = torch.randn(16, 3, 32, 32)
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label = torch.randint(0, 10, (16, ))
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losses = img_classifier.forward_train(imgs, label)
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assert losses['loss'].item() > 0
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# Considering BC-breaking
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model_cfg['train_cfg'] = dict(
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cutmix=dict(alpha=1.0, num_classes=10, cutmix_prob=1.0))
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img_classifier = ImageClassifier(**model_cfg)
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img_classifier.init_weights()
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imgs = torch.randn(16, 3, 32, 32)
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label = torch.randint(0, 10, (16, ))
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losses = img_classifier.forward_train(imgs, label)
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assert losses['loss'].item() > 0
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def test_image_classifier_with_augments():
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imgs = torch.randn(16, 3, 32, 32)
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label = torch.randint(0, 10, (16, ))
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# Test cutmix and mixup in ImageClassifier
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model_cfg = dict(
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backbone=dict(
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type='ResNet_CIFAR',
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depth=50,
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num_stages=4,
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out_indices=(3, ),
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style='pytorch'),
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neck=dict(type='GlobalAveragePooling'),
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head=dict(
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type='MultiLabelLinearClsHead',
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num_classes=10,
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in_channels=2048,
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loss=dict(type='CrossEntropyLoss', loss_weight=1.0,
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use_soft=True)),
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train_cfg=dict(augments=[
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dict(type='BatchCutMix', alpha=1., num_classes=10, prob=0.5),
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dict(type='BatchMixup', alpha=1., num_classes=10, prob=0.3),
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dict(type='Identity', num_classes=10, prob=0.2)
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]))
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img_classifier = ImageClassifier(**model_cfg)
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img_classifier.init_weights()
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losses = img_classifier.forward_train(imgs, label)
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assert losses['loss'].item() > 0
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# Test cutmix with cutmix_minmax in ImageClassifier
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model_cfg['train_cfg'] = dict(
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augments=dict(
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type='BatchCutMix',
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alpha=1.,
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num_classes=10,
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prob=1.,
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cutmix_minmax=[0.2, 0.8]))
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img_classifier = ImageClassifier(**model_cfg)
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img_classifier.init_weights()
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losses = img_classifier.forward_train(imgs, label)
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assert losses['loss'].item() > 0
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# Test not using cutmix and mixup in ImageClassifier
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model_cfg = dict(
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backbone=dict(
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type='ResNet_CIFAR',
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depth=50,
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num_stages=4,
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out_indices=(3, ),
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style='pytorch'),
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neck=dict(type='GlobalAveragePooling'),
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head=dict(
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type='LinearClsHead',
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num_classes=10,
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in_channels=2048,
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loss=dict(type='CrossEntropyLoss', loss_weight=1.0)))
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img_classifier = ImageClassifier(**model_cfg)
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img_classifier.init_weights()
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imgs = torch.randn(16, 3, 32, 32)
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label = torch.randint(0, 10, (16, ))
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losses = img_classifier.forward_train(imgs, label)
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assert losses['loss'].item() > 0
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# Test not using cutmix and mixup in ImageClassifier
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model_cfg['train_cfg'] = dict(augments=None)
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img_classifier = ImageClassifier(**model_cfg)
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img_classifier.init_weights()
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losses = img_classifier.forward_train(imgs, label)
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assert losses['loss'].item() > 0
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def test_image_classifier_with_label_smooth_loss():
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# Test mixup in ImageClassifier
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model_cfg = dict(
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backbone=dict(
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type='ResNet_CIFAR',
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depth=50,
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num_stages=4,
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out_indices=(3, ),
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style='pytorch'),
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neck=dict(type='GlobalAveragePooling'),
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head=dict(
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type='MultiLabelLinearClsHead',
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num_classes=10,
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in_channels=2048,
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loss=dict(type='LabelSmoothLoss', label_smooth_val=0.1)),
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train_cfg=dict(
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augments=dict(
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type='BatchMixup', alpha=1., num_classes=10, prob=1.)))
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img_classifier = ImageClassifier(**model_cfg)
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img_classifier.init_weights()
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imgs = torch.randn(16, 3, 32, 32)
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label = torch.randint(0, 10, (16, ))
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losses = img_classifier.forward_train(imgs, label)
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assert losses['loss'].item() > 0
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def test_image_classifier_vit():
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model_cfg = dict(
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backbone=dict(
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type='VisionTransformer',
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num_layers=12,
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embed_dim=768,
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num_heads=12,
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img_size=224,
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patch_size=16,
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in_channels=3,
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feedforward_channels=3072,
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drop_rate=0.1,
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attn_drop_rate=0.),
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neck=None,
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head=dict(
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type='VisionTransformerClsHead',
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num_classes=1000,
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in_channels=768,
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hidden_dim=3072,
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loss=dict(type='CrossEntropyLoss', loss_weight=1.0, use_soft=True),
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topk=(1, 5),
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),
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train_cfg=dict(
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augments=dict(
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type='BatchMixup', alpha=0.2, num_classes=1000, prob=1.)))
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img_classifier = ImageClassifier(**model_cfg)
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img_classifier.init_weights()
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imgs = torch.randn(2, 3, 224, 224)
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label = torch.randint(0, 1000, (2, ))
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losses = img_classifier.forward_train(imgs, label)
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assert losses['loss'].item() > 0
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