From 8966870a05b85ea940a02c4646693ec101ab0575 Mon Sep 17 00:00:00 2001 From: Lupin1998 <1070535169@qq.com> Date: Sat, 15 Oct 2022 04:48:52 +0800 Subject: [PATCH] fix update of issue #25 --- README.md | 3 +- .../imagenet/basic_tta_sz224_4xbs64.py | 64 +++ .../imagenet/deit3_ft_sz224_8xbs128.py | 83 ++++ .../datasets/imagenet/deit3_sz160_8xbs128.py | 64 +++ .../datasets/imagenet/deit3_sz192_8xbs128.py | 64 +++ .../datasets/imagenet/deit3_sz224_8xbs128.py | 64 +++ .../models/deit3/deit3_base_p16_sz192.py | 24 + .../models/deit3/deit3_huge_p16_sz160.py | 24 + .../models/deit3/deit3_large_p16_sz192.py | 24 + .../models/deit3/deit3_small_p16_sz224.py | 24 + ...6_a2_near_lam_cat_switch0_8_8x128_ep300.py | 4 +- ...m_cat_switch0_8_att_ln_8x128_fp16_ep300.py | 4 +- ...2_near_lam_cat_swch0_8_8x128_fp16_ep300.py | 122 +++++ ...am_cat_swch0_8_attn_ln_8x128_fp16_ep300.py | 122 +++++ .../classification/imagenet/deit3/README.md | 38 ++ .../deit3_base_sz192_8xb128_accu2_ep800.py | 43 ++ .../deit3/deit3_base_sz224_ft_4xb128_ep20.py | 53 ++ .../deit3_huge_sz160_8xb64_accu4_ep800.py | 43 ++ .../deit3/deit3_huge_sz224_ft_8xb64_ep20.py | 55 +++ .../deit3_large_sz192_8xb128_accu2_ep800.py | 43 ++ .../deit3/deit3_large_sz224_ft_8xb64_ep20.py | 53 ++ .../deit3/deit3_small_sz224_8xb256_ep800.py | 43 ++ .../resnet/resnet101_4xb64_step_ep100.py | 4 + .../resnet/resnet154_4xb64_step_ep100.py | 4 + .../resnet/resnet18_4xb64_cos_ep100.py | 35 ++ .../resnet/resnet18_4xb64_step_ep100.py | 35 ++ .../resnet/resnet34_4xb64_step_ep100.py | 4 + .../resnet/resnet50_4xb64_cos_ep100.py | 4 + .../resnet/resnet50_4xb64_step_ep100.py | 35 ++ .../resnext101_32x4d_4xb64_step_ep100.py | 11 + .../resnext154_32x4d_4xb64_step_ep100.py | 11 + .../resnext50_32x4d_4xb64_step_ep100.py | 11 + configs/selfsup/cae/RAEDME.md | 2 +- demo/bird.JPEG | Bin 0 -> 74237 bytes demo/cat-dog.png | Bin 0 -> 744894 bytes docs/en/awesome_selfsup/MIM.md | 8 + docs/en/changelog.md | 8 +- docs/en/index.rst | 7 + docs/en/model_zoos/Model_Zoo_sup.md | 2 +- docs/en/tools/analysis.md | 135 +++++ docs/en/tools/visualization.md | 221 +++++++++ openmixup/__init__.py | 57 +++ openmixup/datasets/data_sources/image_list.py | 19 +- openmixup/datasets/multi_view.py | 32 +- openmixup/datasets/pipelines/transforms.py | 16 +- openmixup/models/backbones/__init__.py | 3 +- openmixup/models/backbones/deit3.py | 460 ++++++++++++++++++ openmixup/models/backbones/moganet.py | 10 +- .../models/classifiers/automix_V1plus.py | 65 ++- .../models/classifiers/classification.py | 28 +- .../classifiers/mixup_classification.py | 23 +- openmixup/models/heads/cls_head.py | 28 +- openmixup/models/heads/cls_mixup_head.py | 25 +- openmixup/models/utils/layers/attention.py | 11 +- tools/analysis_tools/gradcam.py | 421 ---------------- tools/visualizations/vis_cam.py | 364 ++++++++++++++ tools/visualizations/vis_lr.py | 333 +++++++++++++ 57 files changed, 3021 insertions(+), 472 deletions(-) create mode 100644 configs/classification/_base_/datasets/imagenet/basic_tta_sz224_4xbs64.py create mode 100644 configs/classification/_base_/datasets/imagenet/deit3_ft_sz224_8xbs128.py create mode 100644 configs/classification/_base_/datasets/imagenet/deit3_sz160_8xbs128.py create mode 100644 configs/classification/_base_/datasets/imagenet/deit3_sz192_8xbs128.py create mode 100644 configs/classification/_base_/datasets/imagenet/deit3_sz224_8xbs128.py create mode 100644 configs/classification/_base_/models/deit3/deit3_base_p16_sz192.py create mode 100644 configs/classification/_base_/models/deit3/deit3_huge_p16_sz160.py create mode 100644 configs/classification/_base_/models/deit3/deit3_large_p16_sz192.py create mode 100644 configs/classification/_base_/models/deit3/deit3_small_p16_sz224.py create mode 100644 configs/classification/imagenet/automix/pvt/pvt_t_l2_a2_near_lam_cat_swch0_8_8x128_fp16_ep300.py create mode 100644 configs/classification/imagenet/automix/pvt/pvt_t_l2_a2_near_lam_cat_swch0_8_attn_ln_8x128_fp16_ep300.py create mode 100644 configs/classification/imagenet/deit3/README.md create mode 100644 configs/classification/imagenet/deit3/deit3_base_sz192_8xb128_accu2_ep800.py create mode 100644 configs/classification/imagenet/deit3/deit3_base_sz224_ft_4xb128_ep20.py create mode 100644 configs/classification/imagenet/deit3/deit3_huge_sz160_8xb64_accu4_ep800.py create mode 100644 configs/classification/imagenet/deit3/deit3_huge_sz224_ft_8xb64_ep20.py create mode 100644 configs/classification/imagenet/deit3/deit3_large_sz192_8xb128_accu2_ep800.py create mode 100644 configs/classification/imagenet/deit3/deit3_large_sz224_ft_8xb64_ep20.py create mode 100644 configs/classification/imagenet/deit3/deit3_small_sz224_8xb256_ep800.py create mode 100644 configs/classification/imagenet/resnet/resnet101_4xb64_step_ep100.py create mode 100644 configs/classification/imagenet/resnet/resnet154_4xb64_step_ep100.py create mode 100644 configs/classification/imagenet/resnet/resnet18_4xb64_cos_ep100.py create mode 100644 configs/classification/imagenet/resnet/resnet18_4xb64_step_ep100.py create mode 100644 configs/classification/imagenet/resnet/resnet34_4xb64_step_ep100.py create mode 100644 configs/classification/imagenet/resnet/resnet50_4xb64_cos_ep100.py create mode 100644 configs/classification/imagenet/resnet/resnet50_4xb64_step_ep100.py create mode 100644 configs/classification/imagenet/resnet/resnext101_32x4d_4xb64_step_ep100.py create mode 100644 configs/classification/imagenet/resnet/resnext154_32x4d_4xb64_step_ep100.py create mode 100644 configs/classification/imagenet/resnet/resnext50_32x4d_4xb64_step_ep100.py create mode 100644 demo/bird.JPEG create mode 100644 demo/cat-dog.png create mode 100644 docs/en/tools/analysis.md create mode 100644 docs/en/tools/visualization.md create mode 100644 openmixup/models/backbones/deit3.py delete mode 100644 tools/analysis_tools/gradcam.py create mode 100644 tools/visualizations/vis_cam.py create mode 100644 tools/visualizations/vis_lr.py diff --git a/README.md b/README.md index ea42022a..84a17835 100644 --- a/README.md +++ b/README.md @@ -41,7 +41,7 @@ The main branch works with **PyTorch 1.8** (required by some self-supervised met ## What's New -[2022-10-12] Update new features and documents of `OpenMixup` v0.2.6 (issue [#24](https://github.com/Westlake-AI/openmixup/issues/24) and issue [#25](https://github.com/Westlake-AI/openmixup/issues/25)). +[2022-10-15] Update new features and documents of `OpenMixup` v0.2.6 (issue [#24](https://github.com/Westlake-AI/openmixup/issues/24) and issue [#25](https://github.com/Westlake-AI/openmixup/issues/25)). [2022-09-14] `OpenMixup` v0.2.6 is released (issue [#20](https://github.com/Westlake-AI/openmixup/issues/20)). @@ -113,6 +113,7 @@ Please refer to [Model Zoos](docs/en/model_zoos) for various backbones, mixup me - [x] [MViTV2](https://arxiv.org/abs/2112.01526) (CVPR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mvit/)] - [x] [RepMLP](https://arxiv.org/abs/2105.01883) (CVPR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/repmlp/)] - [x] [VAN](https://arxiv.org/abs/2202.09741) (ArXiv'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/van/)] + - [x] [DeiT-3](https://arxiv.org/abs/2204.07118) (ECCV'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/deit3/)] - [x] [LITv2](https://arxiv.org/abs/2205.13213) (NIPS'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/lit_v2/)] - [x] [HorNet](https://arxiv.org/abs/2207.14284) (NIPS'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/hornet/)] - [x] [EdgeNeXt](https://arxiv.org/abs/2206.10589) (ECCVW'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/edgenext/)] diff --git a/configs/classification/_base_/datasets/imagenet/basic_tta_sz224_4xbs64.py b/configs/classification/_base_/datasets/imagenet/basic_tta_sz224_4xbs64.py new file mode 100644 index 00000000..2b069634 --- /dev/null +++ b/configs/classification/_base_/datasets/imagenet/basic_tta_sz224_4xbs64.py @@ -0,0 +1,64 @@ +# dataset settings +data_source_cfg = dict(type='ImageNet') +# ImageNet dataset +data_train_list = 'data/meta/ImageNet/train_labeled_full.txt' +data_train_root = 'data/ImageNet/train' +data_test_list = 'data/meta/ImageNet/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, interpolation=3), # bicubic + dict(type='RandomHorizontalFlip'), +] +test_pipeline_1 = [ + dict(type='Resize', size=256, interpolation=3), # 0.85 + dict(type='RandomHorizontalFlip', p=0.5), + dict(type='CenterCrop', size=224), + dict(type='ToTensor'), + dict(type='Normalize', **img_norm_cfg), +] +test_pipeline_2 = [ + dict(type='Resize', size=256, interpolation=3), # 0.85 + dict(type='RandomVerticalFlip', p=0.5), + dict(type='PlaceCrop', size=224, start=[0, 5, 10, 15,]), + dict(type='ToTensor'), + dict(type='Normalize', **img_norm_cfg), +] + +# prefetch +prefetch = True +if not prefetch: + train_pipeline.extend([dict(type='ToTensor'), dict(type='Normalize', **img_norm_cfg)]) + +data = dict( + imgs_per_gpu=64, + workers_per_gpu=8, + train=dict( + type=dataset_type, + data_source=dict( + list_file=data_train_list, root=data_train_root, + **data_source_cfg), + pipeline=train_pipeline, + prefetch=prefetch, + ), + val=dict( + type="MultiViewDataset", # use multi-view for test time augmentations + data_source=dict( + list_file=data_test_list, root=data_test_root, **data_source_cfg), + num_views=[2, 4], + pipelines=[test_pipeline_1, test_pipeline_2], + prefetch=False, + )) + +# validation hook +evaluation = dict( + initial=False, + interval=1, + imgs_per_gpu=128, + workers_per_gpu=4, + eval_param=dict(topk=(1, 5))) + +# checkpoint +checkpoint_config = dict(interval=1, max_keep_ckpts=1) diff --git a/configs/classification/_base_/datasets/imagenet/deit3_ft_sz224_8xbs128.py b/configs/classification/_base_/datasets/imagenet/deit3_ft_sz224_8xbs128.py new file mode 100644 index 00000000..45889f7a --- /dev/null +++ b/configs/classification/_base_/datasets/imagenet/deit3_ft_sz224_8xbs128.py @@ -0,0 +1,83 @@ +# Refers to `_RAND_INCREASING_TRANSFORMS` in pytorch-image-models +rand_increasing_policies = [ + dict(type='AutoContrast'), + dict(type='Equalize'), + dict(type='Invert'), + dict(type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)), + dict(type='Posterize', magnitude_key='bits', magnitude_range=(4, 0)), + dict(type='Solarize', magnitude_key='thr', magnitude_range=(256, 0)), + dict(type='SolarizeAdd', magnitude_key='magnitude', magnitude_range=(0, 110)), + dict(type='ColorTransform', magnitude_key='magnitude', magnitude_range=(0, 0.9)), + dict(type='Contrast', magnitude_key='magnitude', magnitude_range=(0, 0.9)), + dict(type='Brightness', magnitude_key='magnitude', magnitude_range=(0, 0.9)), + dict(type='Sharpness', magnitude_key='magnitude', magnitude_range=(0, 0.9)), + dict(type='Shear', + magnitude_key='magnitude', magnitude_range=(0, 0.3), direction='horizontal'), + dict(type='Shear', + magnitude_key='magnitude', magnitude_range=(0, 0.3), direction='vertical'), + dict(type='Translate', + magnitude_key='magnitude', magnitude_range=(0, 0.45), direction='horizontal'), + dict(type='Translate', + magnitude_key='magnitude', magnitude_range=(0, 0.45), direction='vertical'), +] + +# dataset settings +data_source_cfg = dict(type='ImageNet') +# ImageNet dataset +data_train_list = 'data/meta/ImageNet/train_labeled_full.txt' +data_train_root = 'data/ImageNet/train' +data_test_list = 'data/meta/ImageNet/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, interpolation=3), # bicubic + dict(type='RandomHorizontalFlip'), + dict(type='RandAugment', + policies=rand_increasing_policies, + num_policies=2, total_level=10, + magnitude_level=9, magnitude_std=0.5, + hparams=dict( + pad_val=[104, 116, 124], interpolation='bicubic')), +] +test_pipeline = [ + dict(type='Resize', size=224, interpolation=3), # crop-ratio = 1.0 + dict(type='CenterCrop', size=224), + dict(type='ToTensor'), + dict(type='Normalize', **img_norm_cfg), +] +# prefetch +prefetch = True +if not prefetch: + train_pipeline.extend([dict(type='ToTensor'), dict(type='Normalize', **img_norm_cfg)]) + +data = dict( + imgs_per_gpu=128, + workers_per_gpu=10, + train=dict( + type=dataset_type, + data_source=dict( + list_file=data_train_list, root=data_train_root, + **data_source_cfg), + pipeline=train_pipeline, + prefetch=prefetch, + ), + val=dict( + type=dataset_type, + data_source=dict( + list_file=data_test_list, root=data_test_root, **data_source_cfg), + pipeline=test_pipeline, + prefetch=False, + )) + +# validation hook +evaluation = dict( + initial=False, + interval=1, + imgs_per_gpu=128, + workers_per_gpu=4, + eval_param=dict(topk=(1, 5))) + +# checkpoint +checkpoint_config = dict(interval=1, max_keep_ckpts=1) diff --git a/configs/classification/_base_/datasets/imagenet/deit3_sz160_8xbs128.py b/configs/classification/_base_/datasets/imagenet/deit3_sz160_8xbs128.py new file mode 100644 index 00000000..f626123b --- /dev/null +++ b/configs/classification/_base_/datasets/imagenet/deit3_sz160_8xbs128.py @@ -0,0 +1,64 @@ +# dataset settings +data_source_cfg = dict(type='ImageNet') +# ImageNet dataset +data_train_list = 'data/meta/ImageNet/train_labeled_full.txt' +data_train_root = 'data/ImageNet/train' +data_test_list = 'data/meta/ImageNet/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=160, interpolation=3), # bicubic + dict(type='RandomHorizontalFlip'), + dict(type='RandomAppliedTrans', # 3-Augment in DeiT III + transforms=[ + dict(type='RandomGrayscale', p=1.), + dict(type='Solarization', p=1.), + dict(type='GaussianBlur', sigma_min=0.1, sigma_max=2.0, p=1.), + ], + p=1.0), + dict(type='ColorJitter', + brightness=0.3, contrast=0.3, saturation=0.3), +] +test_pipeline = [ + dict(type='Resize', size=160, interpolation=3), # crop-ratio = 1.0 + dict(type='CenterCrop', size=160), + dict(type='ToTensor'), + dict(type='Normalize', **img_norm_cfg), +] + +# prefetch +prefetch = True +if not prefetch: + train_pipeline.extend([dict(type='ToTensor'), dict(type='Normalize', **img_norm_cfg)]) + +data = dict( + imgs_per_gpu=128, + workers_per_gpu=8, + train=dict( + type=dataset_type, + data_source=dict( + list_file=data_train_list, root=data_train_root, + **data_source_cfg), + pipeline=train_pipeline, + prefetch=prefetch, + ), + val=dict( + type=dataset_type, + data_source=dict( + list_file=data_test_list, root=data_test_root, **data_source_cfg), + pipeline=test_pipeline, + prefetch=False, + )) + +# validation hook +evaluation = dict( + initial=False, + interval=1, + imgs_per_gpu=128, + workers_per_gpu=4, + eval_param=dict(topk=(1, 5))) + +# checkpoint +checkpoint_config = dict(interval=1, max_keep_ckpts=1) diff --git a/configs/classification/_base_/datasets/imagenet/deit3_sz192_8xbs128.py b/configs/classification/_base_/datasets/imagenet/deit3_sz192_8xbs128.py new file mode 100644 index 00000000..f2f7cb03 --- /dev/null +++ b/configs/classification/_base_/datasets/imagenet/deit3_sz192_8xbs128.py @@ -0,0 +1,64 @@ +# dataset settings +data_source_cfg = dict(type='ImageNet') +# ImageNet dataset +data_train_list = 'data/meta/ImageNet/train_labeled_full.txt' +data_train_root = 'data/ImageNet/train' +data_test_list = 'data/meta/ImageNet/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=192, interpolation=3), # bicubic + dict(type='RandomHorizontalFlip'), + dict(type='RandomAppliedTrans', # 3-Augment in DeiT III + transforms=[ + dict(type='RandomGrayscale', p=1.), + dict(type='Solarization', p=1.), + dict(type='GaussianBlur', sigma_min=0.1, sigma_max=2.0, p=1.), + ], + p=1.0), + dict(type='ColorJitter', + brightness=0.3, contrast=0.3, saturation=0.3), +] +test_pipeline = [ + dict(type='Resize', size=192, interpolation=3), # crop-ratio = 1.0 + dict(type='CenterCrop', size=192), + dict(type='ToTensor'), + dict(type='Normalize', **img_norm_cfg), +] + +# prefetch +prefetch = True +if not prefetch: + train_pipeline.extend([dict(type='ToTensor'), dict(type='Normalize', **img_norm_cfg)]) + +data = dict( + imgs_per_gpu=128, + workers_per_gpu=8, + train=dict( + type=dataset_type, + data_source=dict( + list_file=data_train_list, root=data_train_root, + **data_source_cfg), + pipeline=train_pipeline, + prefetch=prefetch, + ), + val=dict( + type=dataset_type, + data_source=dict( + list_file=data_test_list, root=data_test_root, **data_source_cfg), + pipeline=test_pipeline, + prefetch=False, + )) + +# validation hook +evaluation = dict( + initial=False, + interval=1, + imgs_per_gpu=128, + workers_per_gpu=4, + eval_param=dict(topk=(1, 5))) + +# checkpoint +checkpoint_config = dict(interval=1, max_keep_ckpts=1) diff --git a/configs/classification/_base_/datasets/imagenet/deit3_sz224_8xbs128.py b/configs/classification/_base_/datasets/imagenet/deit3_sz224_8xbs128.py new file mode 100644 index 00000000..a8fa0d25 --- /dev/null +++ b/configs/classification/_base_/datasets/imagenet/deit3_sz224_8xbs128.py @@ -0,0 +1,64 @@ +# dataset settings +data_source_cfg = dict(type='ImageNet') +# ImageNet dataset +data_train_list = 'data/meta/ImageNet/train_labeled_full.txt' +data_train_root = 'data/ImageNet/train' +data_test_list = 'data/meta/ImageNet/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, interpolation=3), # bicubic + dict(type='RandomHorizontalFlip'), + dict(type='RandomAppliedTrans', # 3-Augment in DeiT III + transforms=[ + dict(type='RandomGrayscale', p=1.), + dict(type='Solarization', p=1.), + dict(type='GaussianBlur', sigma_min=0.1, sigma_max=2.0, p=1.), + ], + p=1.0), + dict(type='ColorJitter', + brightness=0.3, contrast=0.3, saturation=0.3), +] +test_pipeline = [ + dict(type='Resize', size=224, interpolation=3), # crop-ratio = 1.0 + dict(type='CenterCrop', size=224), + dict(type='ToTensor'), + dict(type='Normalize', **img_norm_cfg), +] + +# prefetch +prefetch = True +if not prefetch: + train_pipeline.extend([dict(type='ToTensor'), dict(type='Normalize', **img_norm_cfg)]) + +data = dict( + imgs_per_gpu=128, + workers_per_gpu=8, + train=dict( + type=dataset_type, + data_source=dict( + list_file=data_train_list, root=data_train_root, + **data_source_cfg), + pipeline=train_pipeline, + prefetch=prefetch, + ), + val=dict( + type=dataset_type, + data_source=dict( + list_file=data_test_list, root=data_test_root, **data_source_cfg), + pipeline=test_pipeline, + prefetch=False, + )) + +# validation hook +evaluation = dict( + initial=False, + interval=1, + imgs_per_gpu=128, + workers_per_gpu=4, + eval_param=dict(topk=(1, 5))) + +# checkpoint +checkpoint_config = dict(interval=1, max_keep_ckpts=1) diff --git a/configs/classification/_base_/models/deit3/deit3_base_p16_sz192.py b/configs/classification/_base_/models/deit3/deit3_base_p16_sz192.py new file mode 100644 index 00000000..4d97709e --- /dev/null +++ b/configs/classification/_base_/models/deit3/deit3_base_p16_sz192.py @@ -0,0 +1,24 @@ +# model settings +model = dict( + type='MixUpClassification', + pretrained=None, + alpha=[0.8, 1.0,], + mix_mode=["mixup", "cutmix",], + mix_args=dict(), + backbone=dict( + type='DeiT3', + arch='base', + img_size=192, + patch_size=16, + drop_path_rate=0.2), + head=dict( + type='VisionTransformerClsHead', + loss=dict(type='CrossEntropyLoss', # mixup BCE loss (one-hot encoding) + use_soft=False, use_sigmoid=True, loss_weight=1.0), + multi_label=True, + in_channels=768, num_classes=1000), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=.02), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + ], +) diff --git a/configs/classification/_base_/models/deit3/deit3_huge_p16_sz160.py b/configs/classification/_base_/models/deit3/deit3_huge_p16_sz160.py new file mode 100644 index 00000000..3be0655c --- /dev/null +++ b/configs/classification/_base_/models/deit3/deit3_huge_p16_sz160.py @@ -0,0 +1,24 @@ +# model settings +model = dict( + type='MixUpClassification', + pretrained=None, + alpha=[0.8, 1.0,], + mix_mode=["mixup", "cutmix",], + mix_args=dict(), + backbone=dict( + type='DeiT3', + arch='huge', + img_size=160, + patch_size=16, + drop_path_rate=0.6), + head=dict( + type='VisionTransformerClsHead', + loss=dict(type='CrossEntropyLoss', # mixup BCE loss (one-hot encoding) + use_soft=False, use_sigmoid=True, loss_weight=1.0), + multi_label=True, + in_channels=1280, num_classes=1000), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=.02), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + ], +) diff --git a/configs/classification/_base_/models/deit3/deit3_large_p16_sz192.py b/configs/classification/_base_/models/deit3/deit3_large_p16_sz192.py new file mode 100644 index 00000000..aa16fa68 --- /dev/null +++ b/configs/classification/_base_/models/deit3/deit3_large_p16_sz192.py @@ -0,0 +1,24 @@ +# model settings +model = dict( + type='MixUpClassification', + pretrained=None, + alpha=[0.8, 1.0,], + mix_mode=["mixup", "cutmix",], + mix_args=dict(), + backbone=dict( + type='DeiT3', + arch='large', + img_size=192, + patch_size=16, + drop_path_rate=0.45), + head=dict( + type='VisionTransformerClsHead', + loss=dict(type='CrossEntropyLoss', # mixup BCE loss (one-hot encoding) + use_soft=False, use_sigmoid=True, loss_weight=1.0), + multi_label=True, + in_channels=1024, num_classes=1000), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=.02), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + ], +) diff --git a/configs/classification/_base_/models/deit3/deit3_small_p16_sz224.py b/configs/classification/_base_/models/deit3/deit3_small_p16_sz224.py new file mode 100644 index 00000000..b0bf4019 --- /dev/null +++ b/configs/classification/_base_/models/deit3/deit3_small_p16_sz224.py @@ -0,0 +1,24 @@ +# model settings +model = dict( + type='MixUpClassification', + pretrained=None, + alpha=[0.8, 1.0,], + mix_mode=["mixup", "cutmix",], + mix_args=dict(), + backbone=dict( + type='DeiT3', + arch='small', + img_size=224, + patch_size=16, + drop_path_rate=0.05), + head=dict( + type='VisionTransformerClsHead', + loss=dict(type='CrossEntropyLoss', # mixup BCE loss (one-hot encoding) + use_soft=False, use_sigmoid=True, loss_weight=1.0), + multi_label=True, + in_channels=384, num_classes=1000), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=.02), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + ], +) diff --git a/configs/classification/imagenet/automix/deit/deit_s_l6_a2_near_lam_cat_switch0_8_8x128_ep300.py b/configs/classification/imagenet/automix/deit/deit_s_l6_a2_near_lam_cat_switch0_8_8x128_ep300.py index 41fa6c7d..77de3bbd 100644 --- a/configs/classification/imagenet/automix/deit/deit_s_l6_a2_near_lam_cat_switch0_8_8x128_ep300.py +++ b/configs/classification/imagenet/automix/deit/deit_s_l6_a2_near_lam_cat_switch0_8_8x128_ep300.py @@ -9,7 +9,7 @@ pretrained=None, alpha=2.0, momentum=0.999, - mask_layer=2, # dowmsampling to 1/8 + mask_layer=2, # dowmsampling to 1/16 mask_loss=0.1, # using loss mask_adjust=0, # none for large datasets lam_margin=0.08, @@ -92,7 +92,7 @@ # Sets `find_unused_parameters`: randomly switch off mixblock find_unused_parameters = True -# apex +# fp16 use_fp16 = False fp16 = dict(type='mmcv', loss_scale='dynamic') optimizer_config = dict( diff --git a/configs/classification/imagenet/automix/deit/deit_s_l6_a2_near_lam_cat_switch0_8_att_ln_8x128_fp16_ep300.py b/configs/classification/imagenet/automix/deit/deit_s_l6_a2_near_lam_cat_switch0_8_att_ln_8x128_fp16_ep300.py index 84cca4ec..d3a4b718 100644 --- a/configs/classification/imagenet/automix/deit/deit_s_l6_a2_near_lam_cat_switch0_8_att_ln_8x128_fp16_ep300.py +++ b/configs/classification/imagenet/automix/deit/deit_s_l6_a2_near_lam_cat_switch0_8_att_ln_8x128_fp16_ep300.py @@ -9,7 +9,7 @@ pretrained=None, alpha=2.0, momentum=0.999, - mask_layer=2, # dowmsampling to 1/8 + mask_layer=2, # dowmsampling to 1/16 mask_loss=0.1, # using loss mask_adjust=0, # none for large datasets lam_margin=0.08, @@ -92,7 +92,7 @@ # Sets `find_unused_parameters`: randomly switch off mixblock find_unused_parameters = True -# apex +# fp16 use_fp16 = True fp16 = dict(type='mmcv', loss_scale='dynamic') optimizer_config = dict( diff --git a/configs/classification/imagenet/automix/pvt/pvt_t_l2_a2_near_lam_cat_swch0_8_8x128_fp16_ep300.py b/configs/classification/imagenet/automix/pvt/pvt_t_l2_a2_near_lam_cat_swch0_8_8x128_fp16_ep300.py new file mode 100644 index 00000000..a883eb8e --- /dev/null +++ b/configs/classification/imagenet/automix/pvt/pvt_t_l2_a2_near_lam_cat_swch0_8_8x128_fp16_ep300.py @@ -0,0 +1,122 @@ +_base_ = [ + '../../../_base_/datasets/imagenet/swin_sz224_4xbs256.py', + '../../../_base_/default_runtime.py', +] + +# model settings +model = dict( + type='AutoMixup', + pretrained=None, + alpha=2.0, + momentum=0.999, + mask_layer=2, # dowmsampling to 1/16 + mask_loss=0.1, # using loss + mask_adjust=0, # none for large datasets + lam_margin=0.08, + switch_off=0.8, # switch off mixblock (fixed) + mask_up_override=None, + debug=True, + backbone=dict( + type='PyramidVisionTransformer', + arch='tiny', + img_size=224, + in_channels=3, + drop_path_rate=0.1, + out_indices=(2,3,), + ), + mix_block = dict( # SAMix + type='PixelMixBlock', + in_channels=320, reduction=2, use_scale=True, + unsampling_mode=['nearest',], # str or list, train & test MixBlock + lam_concat=True, lam_concat_v=False, # AutoMix.V1: lam cat q,k,v + lam_mul=False, lam_residual=False, lam_mul_k=-1, # SAMix lam: none + value_neck_cfg=None, # SAMix: non-linear value + x_qk_concat=False, x_v_concat=False, # SAMix x concat: none + att_norm_cfg=None, # AutoMix: attention norm for fp16 + mask_loss_mode="L1", mask_loss_margin=0.1, # L1 loss, 0.1 + mask_mode="none_v_", + frozen=False), + head_one=dict( + type='VisionTransformerClsHead', # mixup CE + label smooth + loss=dict(type='LabelSmoothLoss', + label_smooth_val=0.1, num_classes=1000, mode='original', loss_weight=1.0), + in_channels=512, num_classes=1000), + head_mix=dict( + type='VisionTransformerClsHead', # mixup CE + label smooth + loss=dict(type='LabelSmoothLoss', + label_smooth_val=0.1, num_classes=1000, mode='original', loss_weight=1.0), + in_channels=512, num_classes=1000), + head_weights=dict( + decent_weight=[], accent_weight=[], + head_mix_q=1, head_one_q=1, head_mix_k=1, head_one_k=1), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], +) + +# dataset +data = dict(imgs_per_gpu=128, workers_per_gpu=10) +# sampler = "RepeatAugSampler" # the official repo uses repeated_aug + +# interval for accumulate gradient +update_interval = 1 # total: 8 x bs128 x 1 accumulates = bs1024 + +custom_hooks = [ + dict(type='SAVEHook', + save_interval=1251 * 20, # plot every 20 ep + iter_per_epoch=1251, + ), + dict(type='CustomCosineAnnealingHook', # 0.1 to 0 + attr_name="mask_loss", attr_base=0.1, min_attr=0., by_epoch=False, # by iter + update_interval=update_interval, + ), + dict(type='CosineScheduleHook', + end_momentum=0.99999, # 0.999 to 0.99999 + adjust_scope=[0.25, 1.0], + warming_up="constant", + update_interval=update_interval, + interval=1) +] + +# optimizer +optimizer = dict( + type='AdamW', + lr=1e-3, # lr = 5e-4 * (256 * 4) * 1 accumulate / 512 = 1e-3 / bs1024 + weight_decay=0.05, eps=1e-8, betas=(0.9, 0.999), + paramwise_options={ + '(bn|ln|gn)(\d+)?.(weight|bias)': dict(weight_decay=0.), + 'norm': dict(weight_decay=0.), + 'bias': dict(weight_decay=0.), + 'cls_token': dict(weight_decay=0.), + 'pos_embed': dict(weight_decay=0.), + 'mix_block': dict(lr=1e-3), + }) +# Sets `find_unused_parameters`: randomly switch off mixblock +find_unused_parameters = True + +# fp16 +use_fp16 = False +fp16 = dict(type='mmcv', loss_scale='dynamic') +optimizer_config = dict( + grad_clip=dict(max_norm=5.0), update_interval=update_interval) + +# lr scheduler +lr_config = dict( + policy='CosineAnnealing', + by_epoch=False, min_lr=1e-5, # 1e-5 yields better performances than 1e-6 + warmup='linear', + warmup_iters=5, warmup_by_epoch=True, + warmup_ratio=1e-6, +) +# additional scheduler +addtional_scheduler = dict( + policy='CosineAnnealing', + by_epoch=False, min_lr=1e-4, + paramwise_options=['mix_block'], + warmup_iters=5, warmup_by_epoch=True, + warmup_ratio=1e-6, +) + +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=300) diff --git a/configs/classification/imagenet/automix/pvt/pvt_t_l2_a2_near_lam_cat_swch0_8_attn_ln_8x128_fp16_ep300.py b/configs/classification/imagenet/automix/pvt/pvt_t_l2_a2_near_lam_cat_swch0_8_attn_ln_8x128_fp16_ep300.py new file mode 100644 index 00000000..3b3e55c4 --- /dev/null +++ b/configs/classification/imagenet/automix/pvt/pvt_t_l2_a2_near_lam_cat_swch0_8_attn_ln_8x128_fp16_ep300.py @@ -0,0 +1,122 @@ +_base_ = [ + '../../../_base_/datasets/imagenet/swin_sz224_4xbs256.py', + '../../../_base_/default_runtime.py', +] + +# model settings +model = dict( + type='AutoMixup', + pretrained=None, + alpha=2.0, + momentum=0.999, + mask_layer=2, # dowmsampling to 1/16 + mask_loss=0.1, # using loss + mask_adjust=0, # none for large datasets + lam_margin=0.08, + switch_off=0.8, # switch off mixblock (fixed) + mask_up_override=None, + debug=True, + backbone=dict( + type='PyramidVisionTransformer', + arch='tiny', + img_size=224, + in_channels=3, + drop_path_rate=0.1, + out_indices=(2,3,), + ), + mix_block = dict( # SAMix + type='PixelMixBlock', + in_channels=320, reduction=2, use_scale=True, + unsampling_mode=['nearest',], # str or list, train & test MixBlock + lam_concat=True, lam_concat_v=False, # AutoMix.V1: lam cat q,k,v + lam_mul=False, lam_residual=False, lam_mul_k=-1, # SAMix lam: none + value_neck_cfg=None, # SAMix: non-linear value + x_qk_concat=False, x_v_concat=False, # SAMix x concat: none + att_norm_cfg=dict(type='LN2d', eps=1e-6), # AutoMix: attention norm for fp16 + mask_loss_mode="L1", mask_loss_margin=0.1, # L1 loss, 0.1 + mask_mode="none_v_", + frozen=False), + head_one=dict( + type='VisionTransformerClsHead', # mixup CE + label smooth + loss=dict(type='LabelSmoothLoss', + label_smooth_val=0.1, num_classes=1000, mode='original', loss_weight=1.0), + in_channels=512, num_classes=1000), + head_mix=dict( + type='VisionTransformerClsHead', # mixup CE + label smooth + loss=dict(type='LabelSmoothLoss', + label_smooth_val=0.1, num_classes=1000, mode='original', loss_weight=1.0), + in_channels=512, num_classes=1000), + head_weights=dict( + decent_weight=[], accent_weight=[], + head_mix_q=1, head_one_q=1, head_mix_k=1, head_one_k=1), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], +) + +# dataset +data = dict(imgs_per_gpu=128, workers_per_gpu=10) +# sampler = "RepeatAugSampler" # the official repo uses repeated_aug + +# interval for accumulate gradient +update_interval = 1 # total: 8 x bs128 x 1 accumulates = bs1024 + +custom_hooks = [ + dict(type='SAVEHook', + save_interval=1251 * 20, # plot every 20 ep + iter_per_epoch=1251, + ), + dict(type='CustomCosineAnnealingHook', # 0.1 to 0 + attr_name="mask_loss", attr_base=0.1, min_attr=0., by_epoch=False, # by iter + update_interval=update_interval, + ), + dict(type='CosineScheduleHook', + end_momentum=0.99999, # 0.999 to 0.99999 + adjust_scope=[0.25, 1.0], + warming_up="constant", + update_interval=update_interval, + interval=1) +] + +# optimizer +optimizer = dict( + type='AdamW', + lr=1e-3, # lr = 5e-4 * (256 * 4) * 1 accumulate / 512 = 1e-3 / bs1024 + weight_decay=0.05, eps=1e-8, betas=(0.9, 0.999), + paramwise_options={ + '(bn|ln|gn)(\d+)?.(weight|bias)': dict(weight_decay=0.), + 'norm': dict(weight_decay=0.), + 'bias': dict(weight_decay=0.), + 'cls_token': dict(weight_decay=0.), + 'pos_embed': dict(weight_decay=0.), + 'mix_block': dict(lr=1e-3), + }) +# Sets `find_unused_parameters`: randomly switch off mixblock +find_unused_parameters = True + +# fp16 +use_fp16 = True +fp16 = dict(type='mmcv', loss_scale='dynamic') +optimizer_config = dict( + grad_clip=dict(max_norm=5.0), update_interval=update_interval) + +# lr scheduler +lr_config = dict( + policy='CosineAnnealing', + by_epoch=False, min_lr=1e-5, # 1e-5 yields better performances than 1e-6 + warmup='linear', + warmup_iters=5, warmup_by_epoch=True, + warmup_ratio=1e-6, +) +# additional scheduler +addtional_scheduler = dict( + policy='CosineAnnealing', + by_epoch=False, min_lr=1e-4, + paramwise_options=['mix_block'], + warmup_iters=5, warmup_by_epoch=True, + warmup_ratio=1e-6, +) + +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=300) diff --git a/configs/classification/imagenet/deit3/README.md b/configs/classification/imagenet/deit3/README.md new file mode 100644 index 00000000..6a72d38e --- /dev/null +++ b/configs/classification/imagenet/deit3/README.md @@ -0,0 +1,38 @@ +# DeiT III + +> [DeiT III: Revenge of the ViT](https://arxiv.org/abs/2204.07118) + +## Abstract + +A Vision Transformer (ViT) is a simple neural architecture amenable to serve several computer vision tasks. It has limited built-in architectural priors, in contrast to more recent architectures that incorporate priors either about the input data or of specific tasks. Recent works show that ViTs benefit from self-supervised pre-training, in particular BerT-like pre-training like BeiT. In this paper, we revisit the supervised training of ViTs. Our procedure builds upon and simplifies a recipe introduced for training ResNet-50. It includes a new simple data-augmentation procedure with only 3 augmentations, closer to the practice in self-supervised learning. Our evaluations on Image classification (ImageNet-1k with and without pre-training on ImageNet-21k), transfer learning and semantic segmentation show that our procedure outperforms by a large margin previous fully supervised training recipes for ViT. It also reveals that the performance of our ViT trained with supervision is comparable to that of more recent architectures. Our results could serve as better baselines for recent self-supervised approaches demonstrated on ViT. + +
+ + +
+ +## Results and models + +### ImageNet-1k + +Notice that DeiT3 models are first trained on small resolutions (224x224, 192x192, 160x160) and then fine-tuned 20 epochs on 224x224 resolutions for the final results on ImageNet-1K. Moreover, DeiT3 models are pre-trained on ImageNet-21K and fine-tuned with 384x384 resolutions on ImageNet-1K. + +| Model | Pre-train resolution | fine-tune resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Pre-train config | Fine-tune config | Download | +|:---------:|:--------------------:|:--------------------:|:---------:|:--------:|:---------:|:---------:|:---------------------------------------------------:|:----------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------:| +| DeiT3-S\* | 224x224 | 224x224 | 22.06 | 4.61 | 81.35 | 95.31 | [config](./deit3_small_sz224_8xb256_ep800.py) | [config](./deit3_small_sz224_8xb256_ep800.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit3/deit3-small-p16_3rdparty_in1k_20221008-0f7c70cf.pth) | +| DeiT3-B\* | 192x192 | 224x224 | 86.59 | 17.58 | 83.80 | 96.55 | [config](./deit3_base_sz192_8xb128_accu2_ep800.py) | [config](./deit3_base_sz224_ft_4xb128_ep20.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit3/deit3-base-p16_3rdparty_in1k_20221008-60b8c8bf.pth) | +| DeiT3-L\* | 192x192 | 224x224 | 304.37 | 61.60 | 84.87 | 97.01 | [config](./deit3_large_sz192_8xb128_accu2_ep800.py) | [config](./deit3_large_sz224_ft_8xb64_ep20.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit3/deit3-large-p16_3rdparty_in1k_20221009-03b427ea.pth) | +| DeiT3-H\* | 160x160 | 224x224 | 632.13 | 167.40 | 85.21 | 97.36 | [config](./deit3_huge_sz160_8xb64_accu4_ep800.py) | [config](./deit3_huge_sz224_ft_8xb64_ep20.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit3/deit3-huge-p14_3rdparty_in1k_20221009-e107bcb7.pth) | + +*Models with * are converted from the [official repo](https://github.com/facebookresearch/deit). The config files of these models are only for validation and we don't ensure these config files' training accuracy.* + +## Citation + +``` +@article{Touvron2022DeiTIR, + title={DeiT III: Revenge of the ViT}, + author={Hugo Touvron and Matthieu Cord and Herve Jegou}, + journal={arXiv preprint arXiv:2204.07118}, + year={2022}, +} +``` diff --git a/configs/classification/imagenet/deit3/deit3_base_sz192_8xb128_accu2_ep800.py b/configs/classification/imagenet/deit3/deit3_base_sz192_8xb128_accu2_ep800.py new file mode 100644 index 00000000..8a7be49a --- /dev/null +++ b/configs/classification/imagenet/deit3/deit3_base_sz192_8xb128_accu2_ep800.py @@ -0,0 +1,43 @@ +_base_ = [ + '../../_base_/models/deit3/deit3_base_p16_sz224.py', + '../../_base_/datasets/imagenet/deit3_sz192_8xbs128.py', + '../../_base_/default_runtime.py', +] + +# data +data = dict(imgs_per_gpu=128, workers_per_gpu=10) + +# additional hooks +update_interval = 2 # 128 x 8gpus x 2 accumulates = bs2048 + +# optimizer +optimizer = dict( + type='LAMB', + lr=3e-3 * 8, # lr = 3e-3 * 2048 / 256 = 2.4e-2 / bs2048 + weight_decay=0.05, eps=1e-8, betas=(0.9, 0.999), + paramwise_options={ + '(bn|ln|gn)(\d+)?.(weight|bias)': dict(weight_decay=0.), + 'norm': dict(weight_decay=0.), + 'bias': dict(weight_decay=0.), + 'cls_token': dict(weight_decay=0.), + 'pos_embed': dict(weight_decay=0.), + 'gamma': dict(weight_decay=0.), + }) + +# fp16 +use_fp16 = True +fp16 = dict(type='mmcv', loss_scale='dynamic') +optimizer_config = dict( + grad_clip=dict(max_norm=5.0), update_interval=update_interval) + +# lr scheduler +lr_config = dict( + policy='CosineAnnealing', + by_epoch=False, min_lr=1e-5, + warmup='linear', + warmup_iters=5, warmup_by_epoch=True, # warmup 5 epochs. + warmup_ratio=1e-6, +) + +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=800) diff --git a/configs/classification/imagenet/deit3/deit3_base_sz224_ft_4xb128_ep20.py b/configs/classification/imagenet/deit3/deit3_base_sz224_ft_4xb128_ep20.py new file mode 100644 index 00000000..2119f6d6 --- /dev/null +++ b/configs/classification/imagenet/deit3/deit3_base_sz224_ft_4xb128_ep20.py @@ -0,0 +1,53 @@ +_base_ = [ + '../../_base_/models/deit3/deit3_base_p16_sz224.py', + '../../_base_/datasets/imagenet/deit3_ft_sz224_8xbs128.py', + '../../_base_/default_runtime.py', +] + +# model settings +model = dict( + backbone=dict(img_size=224), + head=dict( + type='VisionTransformerClsHead', # mixup CE + label smooth + loss=dict(type='LabelSmoothLoss', + label_smooth_val=0.1, num_classes=1000, mode='original', loss_weight=1.0), + in_channels=768, num_classes=1000) +) + +# data +data = dict(imgs_per_gpu=128, workers_per_gpu=10) + +# additional hooks +update_interval = 1 # 128 x 4gpus x 1 accumulates = bs512 + +# optimizer +optimizer = dict( + type='AdamW', + lr=1e-5 * 8, # lr = 8e-5 / bs512 + weight_decay=0.1, eps=1e-8, betas=(0.9, 0.999), + paramwise_options={ + '(bn|ln|gn)(\d+)?.(weight|bias)': dict(weight_decay=0.), + 'norm': dict(weight_decay=0.), + 'bias': dict(weight_decay=0.), + 'cls_token': dict(weight_decay=0.), + 'pos_embed': dict(weight_decay=0.), + 'gamma': dict(weight_decay=0.), + }) + +# fp16 +use_fp16 = True +fp16 = dict(type='mmcv', loss_scale='dynamic') +optimizer_config = dict( + grad_clip=dict(max_norm=5.0), update_interval=update_interval) + +# lr scheduler +lr_config = dict( + policy='CosineAnnealing', + by_epoch=False, min_lr=1e-5, + warmup='linear', + warmup_iters=5, warmup_by_epoch=True, # warmup 5 epochs. + warmup_ratio=1e-6, +) + +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/classification/imagenet/deit3/deit3_huge_sz160_8xb64_accu4_ep800.py b/configs/classification/imagenet/deit3/deit3_huge_sz160_8xb64_accu4_ep800.py new file mode 100644 index 00000000..ac360eca --- /dev/null +++ b/configs/classification/imagenet/deit3/deit3_huge_sz160_8xb64_accu4_ep800.py @@ -0,0 +1,43 @@ +_base_ = [ + '../../_base_/models/deit3/deit3_large_p16_sz224.py', + '../../_base_/datasets/imagenet/deit3_sz192_8xbs128.py', + '../../_base_/default_runtime.py', +] + +# data +data = dict(imgs_per_gpu=64, workers_per_gpu=6) + +# additional hooks +update_interval = 4 # 64 x 8gpus x 4 accumulates = bs2048 + +# optimizer +optimizer = dict( + type='LAMB', + lr=3e-3 * 8, # lr = 3e-3 * 2048 / 256 = 2.4e-2 / bs2048 + weight_decay=0.05, eps=1e-8, betas=(0.9, 0.999), + paramwise_options={ + '(bn|ln|gn)(\d+)?.(weight|bias)': dict(weight_decay=0.), + 'norm': dict(weight_decay=0.), + 'bias': dict(weight_decay=0.), + 'cls_token': dict(weight_decay=0.), + 'pos_embed': dict(weight_decay=0.), + 'gamma': dict(weight_decay=0.), + }) + +# fp16 +use_fp16 = True +fp16 = dict(type='mmcv', loss_scale='dynamic') +optimizer_config = dict( + grad_clip=dict(max_norm=5.0), update_interval=update_interval) + +# lr scheduler +lr_config = dict( + policy='CosineAnnealing', + by_epoch=False, min_lr=1e-5, + warmup='linear', + warmup_iters=5, warmup_by_epoch=True, # warmup 5 epochs. + warmup_ratio=1e-6, +) + +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=800) diff --git a/configs/classification/imagenet/deit3/deit3_huge_sz224_ft_8xb64_ep20.py b/configs/classification/imagenet/deit3/deit3_huge_sz224_ft_8xb64_ep20.py new file mode 100644 index 00000000..6c3ba190 --- /dev/null +++ b/configs/classification/imagenet/deit3/deit3_huge_sz224_ft_8xb64_ep20.py @@ -0,0 +1,55 @@ +_base_ = [ + '../../_base_/models/deit3/deit3_huge_p16_sz224.py', + '../../_base_/datasets/imagenet/deit3_ft_sz224_8xbs128.py', + '../../_base_/default_runtime.py', +] + +# model settings +model = dict( + backbone=dict( + img_size=224, + drop_path_rate=0.55), + head=dict( + type='VisionTransformerClsHead', # mixup CE + label smooth + loss=dict(type='LabelSmoothLoss', + label_smooth_val=0.1, num_classes=1000, mode='original', loss_weight=1.0), + in_channels=1280, num_classes=1000) +) + +# data +data = dict(imgs_per_gpu=64, workers_per_gpu=6) + +# additional hooks +update_interval = 1 # 64 x 8gpus x 1 accumulates = bs512 + +# optimizer +optimizer = dict( + type='AdamW', + lr=1e-5 * 8, # lr = 8e-5 / bs512 + weight_decay=0.1, eps=1e-8, betas=(0.9, 0.999), + paramwise_options={ + '(bn|ln|gn)(\d+)?.(weight|bias)': dict(weight_decay=0.), + 'norm': dict(weight_decay=0.), + 'bias': dict(weight_decay=0.), + 'cls_token': dict(weight_decay=0.), + 'pos_embed': dict(weight_decay=0.), + 'gamma': dict(weight_decay=0.), + }) + +# fp16 +use_fp16 = True +fp16 = dict(type='mmcv', loss_scale='dynamic') +optimizer_config = dict( + grad_clip=dict(max_norm=5.0), update_interval=update_interval) + +# lr scheduler +lr_config = dict( + policy='CosineAnnealing', + by_epoch=False, min_lr=1e-5, + warmup='linear', + warmup_iters=5, warmup_by_epoch=True, # warmup 5 epochs. + warmup_ratio=1e-6, +) + +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/classification/imagenet/deit3/deit3_large_sz192_8xb128_accu2_ep800.py b/configs/classification/imagenet/deit3/deit3_large_sz192_8xb128_accu2_ep800.py new file mode 100644 index 00000000..6e1158fd --- /dev/null +++ b/configs/classification/imagenet/deit3/deit3_large_sz192_8xb128_accu2_ep800.py @@ -0,0 +1,43 @@ +_base_ = [ + '../../_base_/models/deit3/deit3_large_p16_sz224.py', + '../../_base_/datasets/imagenet/deit3_sz192_8xbs128.py', + '../../_base_/default_runtime.py', +] + +# data +data = dict(imgs_per_gpu=128, workers_per_gpu=10) + +# additional hooks +update_interval = 2 # 128 x 8gpus x 2 accumulates = bs2048 + +# optimizer +optimizer = dict( + type='LAMB', + lr=3e-3 * 8, # lr = 3e-3 * 2048 / 256 = 2.4e-2 / bs2048 + weight_decay=0.05, eps=1e-8, betas=(0.9, 0.999), + paramwise_options={ + '(bn|ln|gn)(\d+)?.(weight|bias)': dict(weight_decay=0.), + 'norm': dict(weight_decay=0.), + 'bias': dict(weight_decay=0.), + 'cls_token': dict(weight_decay=0.), + 'pos_embed': dict(weight_decay=0.), + 'gamma': dict(weight_decay=0.), + }) + +# fp16 +use_fp16 = True +fp16 = dict(type='mmcv', loss_scale='dynamic') +optimizer_config = dict( + grad_clip=dict(max_norm=5.0), update_interval=update_interval) + +# lr scheduler +lr_config = dict( + policy='CosineAnnealing', + by_epoch=False, min_lr=1e-5, + warmup='linear', + warmup_iters=5, warmup_by_epoch=True, # warmup 5 epochs. + warmup_ratio=1e-6, +) + +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=800) diff --git a/configs/classification/imagenet/deit3/deit3_large_sz224_ft_8xb64_ep20.py b/configs/classification/imagenet/deit3/deit3_large_sz224_ft_8xb64_ep20.py new file mode 100644 index 00000000..23752ed6 --- /dev/null +++ b/configs/classification/imagenet/deit3/deit3_large_sz224_ft_8xb64_ep20.py @@ -0,0 +1,53 @@ +_base_ = [ + '../../_base_/models/deit3/deit3_large_p16_sz224.py', + '../../_base_/datasets/imagenet/deit3_ft_sz224_8xbs128.py', + '../../_base_/default_runtime.py', +] + +# model settings +model = dict( + backbone=dict(img_size=224), + head=dict( + type='VisionTransformerClsHead', # mixup CE + label smooth + loss=dict(type='LabelSmoothLoss', + label_smooth_val=0.1, num_classes=1000, mode='original', loss_weight=1.0), + in_channels=1024, num_classes=1000) +) + +# data +data = dict(imgs_per_gpu=64, workers_per_gpu=6) + +# additional hooks +update_interval = 1 # 64 x 8gpus x 1 accumulates = bs512 + +# optimizer +optimizer = dict( + type='AdamW', + lr=1e-5 * 8, # lr = 8e-5 / bs512 + weight_decay=0.1, eps=1e-8, betas=(0.9, 0.999), + paramwise_options={ + '(bn|ln|gn)(\d+)?.(weight|bias)': dict(weight_decay=0.), + 'norm': dict(weight_decay=0.), + 'bias': dict(weight_decay=0.), + 'cls_token': dict(weight_decay=0.), + 'pos_embed': dict(weight_decay=0.), + 'gamma': dict(weight_decay=0.), + }) + +# fp16 +use_fp16 = True +fp16 = dict(type='mmcv', loss_scale='dynamic') +optimizer_config = dict( + grad_clip=dict(max_norm=5.0), update_interval=update_interval) + +# lr scheduler +lr_config = dict( + policy='CosineAnnealing', + by_epoch=False, min_lr=1e-5, + warmup='linear', + warmup_iters=5, warmup_by_epoch=True, # warmup 5 epochs. + warmup_ratio=1e-6, +) + +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/classification/imagenet/deit3/deit3_small_sz224_8xb256_ep800.py b/configs/classification/imagenet/deit3/deit3_small_sz224_8xb256_ep800.py new file mode 100644 index 00000000..c870209c --- /dev/null +++ b/configs/classification/imagenet/deit3/deit3_small_sz224_8xb256_ep800.py @@ -0,0 +1,43 @@ +_base_ = [ + '../../_base_/models/deit3/deit3_small_p16_sz224.py', + '../../_base_/datasets/imagenet/deit3_sz224_8xbs128.py', + '../../_base_/default_runtime.py', +] + +# data +data = dict(imgs_per_gpu=64, workers_per_gpu=4) + +# additional hooks +update_interval = 1 # 256 x 8gpus x 1 accumulates = bs2048 + +# optimizer +optimizer = dict( + type='LAMB', + lr=4e-3 * 8, # lr = 4e-3 * 2048 / 256 = 3.2e-2 / bs2048 + weight_decay=0.05, eps=1e-8, betas=(0.9, 0.999), + paramwise_options={ + '(bn|ln|gn)(\d+)?.(weight|bias)': dict(weight_decay=0.), + 'norm': dict(weight_decay=0.), + 'bias': dict(weight_decay=0.), + 'cls_token': dict(weight_decay=0.), + 'pos_embed': dict(weight_decay=0.), + 'gamma': dict(weight_decay=0.), + }) + +# fp16 +use_fp16 = True +fp16 = dict(type='mmcv', loss_scale='dynamic') +optimizer_config = dict( + grad_clip=dict(max_norm=5.0), update_interval=update_interval) + +# lr scheduler +lr_config = dict( + policy='CosineAnnealing', + by_epoch=False, min_lr=1e-5, + warmup='linear', + warmup_iters=5, warmup_by_epoch=True, # warmup 5 epochs. + warmup_ratio=1e-6, +) + +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=800) diff --git a/configs/classification/imagenet/resnet/resnet101_4xb64_step_ep100.py b/configs/classification/imagenet/resnet/resnet101_4xb64_step_ep100.py new file mode 100644 index 00000000..37314b9c --- /dev/null +++ b/configs/classification/imagenet/resnet/resnet101_4xb64_step_ep100.py @@ -0,0 +1,4 @@ +_base_ = "resnet50_4xb64_step_ep100.py" + +# model settings +model = dict(backbone=dict(depth=101)) diff --git a/configs/classification/imagenet/resnet/resnet154_4xb64_step_ep100.py b/configs/classification/imagenet/resnet/resnet154_4xb64_step_ep100.py new file mode 100644 index 00000000..6dcf8de4 --- /dev/null +++ b/configs/classification/imagenet/resnet/resnet154_4xb64_step_ep100.py @@ -0,0 +1,4 @@ +_base_ = "resnet50_4xb64_step_ep100.py" + +# model settings +model = dict(backbone=dict(depth=154)) diff --git a/configs/classification/imagenet/resnet/resnet18_4xb64_cos_ep100.py b/configs/classification/imagenet/resnet/resnet18_4xb64_cos_ep100.py new file mode 100644 index 00000000..d6abc433 --- /dev/null +++ b/configs/classification/imagenet/resnet/resnet18_4xb64_cos_ep100.py @@ -0,0 +1,35 @@ +_base_ = [ + '../../_base_/datasets/imagenet/basic_sz224_4xbs64.py', + '../../_base_/default_runtime.py', +] + +# model settings +model = dict( + type='Classification', + pretrained=None, + backbone=dict( + type='ResNet', # normal + depth=18, + num_stages=4, + out_indices=(3,), # no conv-1, x-1: stage-x + style='pytorch'), + head=dict( + type='ClsHead', # normal CE loss + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + with_avg_pool=True, multi_label=False, in_channels=512, num_classes=1000) +) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) + +# fp16 +use_fp16 = False +fp16 = dict(type='mmcv', loss_scale='dynamic') +# optimizer args +optimizer_config = dict(update_interval=1, grad_clip=None) + +# lr scheduler +lr_config = dict(policy='CosineAnnealing', min_lr=0) + +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=100) diff --git a/configs/classification/imagenet/resnet/resnet18_4xb64_step_ep100.py b/configs/classification/imagenet/resnet/resnet18_4xb64_step_ep100.py new file mode 100644 index 00000000..0833e0ba --- /dev/null +++ b/configs/classification/imagenet/resnet/resnet18_4xb64_step_ep100.py @@ -0,0 +1,35 @@ +_base_ = [ + '../../_base_/datasets/imagenet/basic_sz224_4xbs64.py', + '../../_base_/default_runtime.py', +] + +# model settings +model = dict( + type='Classification', + pretrained=None, + backbone=dict( + type='ResNet', # normal + depth=18, + num_stages=4, + out_indices=(3,), # no conv-1, x-1: stage-x + style='pytorch'), + head=dict( + type='ClsHead', # normal CE loss + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + with_avg_pool=True, multi_label=False, in_channels=512, num_classes=1000) +) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) + +# fp16 +use_fp16 = False +fp16 = dict(type='mmcv', loss_scale='dynamic') +# optimizer args +optimizer_config = dict(update_interval=1, grad_clip=None) + +# lr scheduler +lr_config = dict(policy='step', step=[30, 60, 90,]) + +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=100) diff --git a/configs/classification/imagenet/resnet/resnet34_4xb64_step_ep100.py b/configs/classification/imagenet/resnet/resnet34_4xb64_step_ep100.py new file mode 100644 index 00000000..a0963265 --- /dev/null +++ b/configs/classification/imagenet/resnet/resnet34_4xb64_step_ep100.py @@ -0,0 +1,4 @@ +_base_ = "resnet18_4xb64_step_ep100.py" + +# model settings +model = dict(backbone=dict(depth=34)) diff --git a/configs/classification/imagenet/resnet/resnet50_4xb64_cos_ep100.py b/configs/classification/imagenet/resnet/resnet50_4xb64_cos_ep100.py new file mode 100644 index 00000000..ac1a8216 --- /dev/null +++ b/configs/classification/imagenet/resnet/resnet50_4xb64_cos_ep100.py @@ -0,0 +1,4 @@ +_base_ = "resnet50_4xb64_step_ep100.py" + +# lr scheduler +lr_config = dict(policy='CosineAnnealing', min_lr=0) diff --git a/configs/classification/imagenet/resnet/resnet50_4xb64_step_ep100.py b/configs/classification/imagenet/resnet/resnet50_4xb64_step_ep100.py new file mode 100644 index 00000000..926c43dc --- /dev/null +++ b/configs/classification/imagenet/resnet/resnet50_4xb64_step_ep100.py @@ -0,0 +1,35 @@ +_base_ = [ + '../../_base_/datasets/imagenet/basic_sz224_4xbs64.py', + '../../_base_/default_runtime.py', +] + +# model settings +model = dict( + type='Classification', + pretrained=None, + backbone=dict( + type='ResNet', # normal + depth=50, + num_stages=4, + out_indices=(3,), # no conv-1, x-1: stage-x + style='pytorch'), + head=dict( + type='ClsHead', # normal CE loss + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + with_avg_pool=True, multi_label=False, in_channels=2048, num_classes=1000) +) + +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) + +# fp16 +use_fp16 = False +fp16 = dict(type='mmcv', loss_scale='dynamic') +# optimizer args +optimizer_config = dict(update_interval=1, grad_clip=None) + +# lr scheduler +lr_config = dict(policy='step', step=[30, 60, 90,]) + +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=100) diff --git a/configs/classification/imagenet/resnet/resnext101_32x4d_4xb64_step_ep100.py b/configs/classification/imagenet/resnet/resnext101_32x4d_4xb64_step_ep100.py new file mode 100644 index 00000000..eaad04ba --- /dev/null +++ b/configs/classification/imagenet/resnet/resnext101_32x4d_4xb64_step_ep100.py @@ -0,0 +1,11 @@ +_base_ = "resnet50_4xb64_step_ep100.py" + +# model settings +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, width_per_group=4, # 32x4d + out_indices=(3,), # no conv-1, x-1: stage-x + style='pytorch'), +) diff --git a/configs/classification/imagenet/resnet/resnext154_32x4d_4xb64_step_ep100.py b/configs/classification/imagenet/resnet/resnext154_32x4d_4xb64_step_ep100.py new file mode 100644 index 00000000..279086c2 --- /dev/null +++ b/configs/classification/imagenet/resnet/resnext154_32x4d_4xb64_step_ep100.py @@ -0,0 +1,11 @@ +_base_ = "resnet50_4xb64_step_ep100.py" + +# model settings +model = dict( + backbone=dict( + type='ResNeXt', + depth=154, + groups=32, width_per_group=4, # 32x4d + out_indices=(3,), # no conv-1, x-1: stage-x + style='pytorch'), +) diff --git a/configs/classification/imagenet/resnet/resnext50_32x4d_4xb64_step_ep100.py b/configs/classification/imagenet/resnet/resnext50_32x4d_4xb64_step_ep100.py new file mode 100644 index 00000000..f2412244 --- /dev/null +++ b/configs/classification/imagenet/resnet/resnext50_32x4d_4xb64_step_ep100.py @@ -0,0 +1,11 @@ +_base_ = "resnet50_4xb64_step_ep100.py" + +# model settings +model = dict( + backbone=dict( + type='ResNeXt', + depth=50, + groups=32, width_per_group=4, # 32x4d + out_indices=(3,), # no conv-1, x-1: stage-x + style='pytorch'), +) diff --git a/configs/selfsup/cae/RAEDME.md b/configs/selfsup/cae/RAEDME.md index 1aa060b6..bd1eaba9 100644 --- a/configs/selfsup/cae/RAEDME.md +++ b/configs/selfsup/cae/RAEDME.md @@ -7,7 +7,7 @@ We present a novel masked image modeling (MIM) approach, context autoencoder (CAE), for self-supervised learning. We randomly partition the image into two sets: visible patches and masked patches. The CAE architecture consists of: (i) an encoder that takes visible patches as input and outputs their latent representations, (ii) a latent context regressor that predicts the masked patch representations from the visible patch representations that are not updated in this regressor, (iii) a decoder that takes the estimated masked patch representations as input and makes predictions for the masked patches, and (iv) an alignment module that aligns the masked patch representation estimation with the masked patch representations computed from the encoder. In comparison to previous MIM methods that couple the encoding and decoding roles, e.g., using a single module in BEiT, our approach attempts to separate the encoding role (content understanding) from the decoding role (making predictions for masked patches) using different modules, improving the content understanding capability. In addition, our approach makes predictions from the visible patches to the masked patches in the latent representation space that is expected to take on semantics. In addition, we present the explanations about why contrastive pretraining and supervised pretraining perform similarly and why MIM potentially performs better. We demonstrate the effectiveness of our CAE through superior transfer performance in downstream tasks: semantic segmentation, and object detection and instance segmentation.
- +
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z&gIHB_Qn_DA&*h6CRA!)otD2V$Wo@zxhmi~nO!S?Zza)*fa^c?uo1DrEMccI`_cxO48dp zXzl;E9IuR&H?*KP>FMs#($}lPVa>&6O2*O=i`5h@;7So>WiilSNN-OGR!adr&IY== zThQs%bXZJi41yJd6TM}S(0j#f+v_Jl^5LtQhh4y$`ptFLZcPzj-Qe%<9AMJS>r9&A z&De44dE>nk?ATw-jsu0RsdJR-?n(jDO}o+ECv6a5t~Nv!2Dv zudrhM4aQGcCE!}dInOvst2?Qvw_~*Z8&lV>H0e80>69z?`dv}1`gR>{W}9p7LW84= z76F%XwV}<~?}}!r1fY%5?A?8sT1+^qO>`D$FhrH2x|T@TPsb_Qb(5aU8Ll!7MawVJ zv@-ywe6Q5|gDF_;LDP}Dw75r+w#$>aT^A_v48@#W!Em#MLA?#TMvuNtFlEr-80eIGQsX!<%_z*0GysWvrW7X1bj9 z3=LP&+n-BMUk28$M9j8my1V1(>q=m-JB{JqV)`A`^xB#k?6S~nmd~eYL0g_J$H8>@ z&EAp$x;MRP*Re*O!xC`;d$foAuFjzgl);nVezg93P + +```shell +python tools/analysis_tools/analyze_logs.py plot_curve \ + ${JSON_LOGS} \ + [--keys ${KEYS}] \ + [--title ${TITLE}] \ + [--legend ${LEGEND}] \ + [--backend ${BACKEND}] \ + [--style ${STYLE}] \ + [--out ${OUT_FILE}] \ + [--window-size ${WINDOW_SIZE}] +``` + +**Description of all arguments**： + +- `json_logs` : The paths of the log files, separate multiple files by spaces. +- `--keys` : The fields of the logs to analyze, separate multiple keys by spaces. Defaults to 'loss'. +- `--title` : The title of the figure. Defaults to use the filename. +- `--legend` : The names of legend, the number of which must be equal to `len(${JSON_LOGS}) * len(${KEYS})`. Defaults to use `"${JSON_LOG}-${KEYS}"`. +- `--backend` : The backend of matplotlib. Defaults to auto selected by matplotlib. +- `--style` : The style of the figure. Default to `whitegrid`. +- `--out` : The path of the output picture. If not set, the figure won't be saved. +- `--window-size`: The shape of the display window. The format should be `'W*H'`. Defaults to `'12*7'`. + +```{note} +The `--style` option depends on `seaborn` package, please install it before setting it. +``` + +Examples: + +- Plot the loss curve in training. + + ```shell + python tools/analysis_tools/analyze_logs.py plot_curve your_log_json --keys loss --legend loss + ``` + +- Plot the top-1 accuracy and top-5 accuracy curves, and save the figure to results.jpg. + + ```shell + python tools/analysis_tools/analyze_logs.py plot_curve your_log_json --keys accuracy_top-1 accuracy_top-5 --legend top1 top5 --out results.jpg + ``` + +- Compare the top-1 accuracy of two log files in the same figure. + + ```shell + python tools/analysis_tools/analyze_logs.py plot_curve log1.json log2.json --keys accuracy_top-1 --legend exp1 exp2 + ``` + +```{note} +The tool will automatically select to find keys in training logs or validation logs according to the keys. +Therefore, if you add a custom evaluation metric, please also add the key to `TEST_METRICS` in this tool. +``` + +### Calculate Training Time + +`tools/analysis_tools/analyze_logs.py` can also calculate the training time according to the log files. + +```shell +python tools/analysis_tools/analyze_logs.py cal_train_time \ + ${JSON_LOGS} + [--include-outliers] +``` + +**Description of all arguments**: + +- `json_logs` : The paths of the log files, separate multiple files by spaces. +- `--include-outliers` : If set, include the first iteration in each epoch (Sometimes the time of first iterations is longer). + +Example: + +```shell +python tools/analysis_tools/analyze_logs.py cal_train_time work_dirs/some_exp/20200422_153324.log.json +``` + +The output is expected to be like the below. + +```text +-----Analyze train time of work_dirs/some_exp/20200422_153324.log.json----- +slowest epoch 68, average time is 0.3818 +fastest epoch 1, average time is 0.3694 +time std over epochs is 0.0020 +average iter time: 0.3777 s/iter +``` + +## Model Complexity + +### Get the FLOPs and params (experimental) + +We provide a script adapted from [flops-counter.pytorch](https://github.com/sovrasov/flops-counter.pytorch) to compute the FLOPs and params of a given model. + +```shell +python tools/analysis_tools/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}] +``` + +Description of all arguments: + +- `config` : The path of the model config file. +- `--shape`: Input size, support single value or double value parameter, such as `--shape 256` or `--shape 224 256`. If not set, default to be `224 224`. + +You will get a result like this. + +```text +============================== +Input shape: (3, 224, 224) +Flops: 4.12 GFLOPs +Params: 25.56 M +============================== +``` + +```{warning} +This tool is still experimental and we do not guarantee that the number is correct. You may well use the result for simple comparisons, but double-check it before you adopt it in technical reports or papers. +- FLOPs are related to the input shape while parameters are not. The default input shape is (1, 3, 224, 224). +- Some operators are not counted into FLOPs like GN and custom operators. Refer to [`mmcv.cnn.get_model_complexity_info()`](https://github.com/open-mmlab/mmcv/blob/master/mmcv/cnn/utils/flops_counter.py) for details. +``` + +## FAQs + +- None diff --git a/docs/en/tools/visualization.md b/docs/en/tools/visualization.md new file mode 100644 index 00000000..88052f72 --- /dev/null +++ b/docs/en/tools/visualization.md @@ -0,0 +1,221 @@ +# Visualization + +- [Visualization](#visualization) + - [Learning Rate Schedule Visualization](#learning-rate-schedule-visualization) + - [Class Activation Map Visualization](#class-activation-map-visualization) + - [FAQs](#faqs) + + +## Learning Rate Schedule Visualization + +```bash +python tools/visualizations/vis_lr.py \ + ${CONFIG_FILE} \ + --dataset-size ${DATASET_SIZE} \ + --ngpus ${NUM_GPUs} + --save-path ${SAVE_PATH} \ + --title ${TITLE} \ + --style ${STYLE} \ + --window-size ${WINDOW_SIZE} + --cfg-options +``` + +**Description of all arguments**： + +- `config` : The path of a model config file. +- `dataset-size` : The size of the datasets. If set，`build_dataset` will be skipped and `${DATASET_SIZE}` will be used as the size. Default to use the function `build_dataset`. +- `ngpus` : The number of GPUs used in training, default to be 1. +- `save-path` : The learning rate curve plot save path, default not to save. +- `title` : Title of figure. If not set, default to be config file name. +- `style` : Style of plt. If not set, default to be `whitegrid`. +- `window-size`: The shape of the display window. If not specified, it will be set to `12*7`. If used, it must be in the format `'W*H'`. +- `cfg-options` : Modifications to the configuration file, refer to [Tutorial 1: Learn about Configs](https://openmixup.readthedocs.io/en/latest/tutorials/0_config.html). + +```{note} +Loading annotations maybe consume much time, you can directly specify the size of the dataset with `dataset-size` to save time. +``` + +**Examples**： + +```bash +python tools/visualizations/vis_lr.py configs/classification/imagenet/resnet/resnet50_4xb64_step_ep100.py +``` + +When using ImageNet, directly specify the size of ImageNet, as below: + +```bash +python tools/visualizations/vis_lr.py configs/classification/imagenet/resnet/resnet50_4xb64_step_ep100.py --dataset-size 1281167 --ngpus 4 --save-path ./resnet50_4xb64_step_ep100.jpg +``` + + +## Class Activation Map Visualization + +OpenMixup provides `tools\visualizations\vis_cam.py` tool to visualize class activation map. Please use `pip install "grad-cam>=1.3.6"` command to install [pytorch-grad-cam](https://github.com/jacobgil/pytorch-grad-cam). + +The supported methods are as follows: + +| Method | What it does | +| ------------ | ---------------------------------------------------------------------------------------------------------------------------- | +| GradCAM | Weight the 2D activations by the average gradient | +| GradCAM++ | Like GradCAM but uses second order gradients | +| XGradCAM | Like GradCAM but scale the gradients by the normalized activations | +| EigenCAM | Takes the first principle component of the 2D Activations (no class discrimination, but seems to give great results) | +| EigenGradCAM | Like EigenCAM but with class discrimination: First principle component of Activations\*Grad. Looks like GradCAM, but cleaner | +| LayerCAM | Spatially weight the activations by positive gradients. Works better especially in lower layers | + +**Command**： + +```bash +python tools/visualizations/vis_cam.py \ + ${IMG} \ + ${CONFIG_FILE} \ + ${CHECKPOINT} \ + [--target-layers ${TARGET-LAYERS}] \ + [--preview-model] \ + [--method ${METHOD}] \ + [--target-category ${TARGET-CATEGORY}] \ + [--save-path ${SAVE_PATH}] \ + [--vit-like] \ + [--num-extra-tokens ${NUM-EXTRA-TOKENS}] + [--aug_smooth] \ + [--eigen_smooth] \ + [--device ${DEVICE}] \ + [--cfg-options ${CFG-OPTIONS}] +``` + +**Description of all arguments**： + +- `img` : The target picture path. +- `config` : The path of the model config file. +- `checkpoint` : The path of the checkpoint. +- `--target-layers` : The target layers to get activation maps, one or more network layers can be specified. If not set, use the norm layer of the last block. +- `--preview-model` : Whether to print all network layer names in the model. +- `--method` : Visualization method, supports `GradCAM`, `GradCAM++`, `XGradCAM`, `EigenCAM`, `EigenGradCAM`, `LayerCAM`, which is case insensitive. Defaults to `GradCAM`. +- `--target-category` : Target category, if not set, use the category detected by the given model. +- `--save-path` : The path to save the CAM visualization image. If not set, the CAM image will not be saved. +- `--vit-like` : Whether the network is ViT-like network. +- `--num-extra-tokens` : The number of extra tokens in ViT-like backbones. If not set, use num_extra_tokens the backbone. +- `--aug_smooth` : Whether to use TTA(Test Time Augment) to get CAM. +- `--eigen_smooth` : Whether to use the principal component to reduce noise. +- `--device` : The computing device used. Default to 'cpu'. +- `--cfg-options` : Modifications to the configuration file, refer to [Tutorial 1: Learn about Configs](https://openmixup.readthedocs.io/en/latest/tutorials/0_config.html). + +```{note} +The argument `--preview-model` can view all network layers names in the given model. It will be helpful if you know nothing about the model layers when setting `--target-layers`. +``` + +**Examples(CNN)**： + +Here are some examples of `target-layers` in ResNet-50, which can be any module or layer: + +- `'backbone.layer4'` means the output of the forth ResLayer. +- `'backbone.layer4.2'` means the output of the third BottleNeck block in the forth ResLayer. +- `'backbone.layer4.2.conv1'` means the output of the `conv1` layer in above BottleNeck block. + +```{note} +For `ModuleList` or `Sequential`, you can also use the index to specify which sub-module is the target layer. + +For example, the `backbone.layer4[-1]` is the same as `backbone.layer4.2` since `layer4` is a `Sequential` with three sub-modules. +``` + +1. Use different methods to visualize CAM for `ResNet50`, the `target-category` is the predicted result by the given checkpoint, using the default `target-layers`. + + ```shell + python tools/visualizations/vis_cam.py \ + demo/bird.JPEG \ + configs/classification/imagenet/resnet/resnet50_4xb64_step_ep100.py \ + https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth \ + --method GradCAM + # GradCAM++, XGradCAM, EigenCAM, EigenGradCAM, LayerCAM + ``` + + | Image | GradCAM | GradCAM++ | EigenGradCAM | LayerCAM | + | ------------------------------------ | --------------------------------------- | ----------------------------------------- | -------------------------------------------- | ---------------------------------------- | + |

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| + +2. Use different `target-category` to get CAM from the same picture. In `ImageNet` dataset, the category 238 is 'Greater Swiss Mountain dog', the category 281 is 'tabby, tabby cat'. + + ```shell + python tools/visualizations/vis_cam.py \ + demo/cat-dog.png \ + configs/classification/imagenet/resnet/resnet50_4xb64_step_ep100.py \ + https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth \ + --target-layers 'backbone.layer4.2' \ + --method GradCAM \ + --target-category 238 + # --target-category 281 + ``` + + | Category | Image | GradCAM | XGradCAM | LayerCAM | + | -------- | ---------------------------------------------- | ------------------------------------------------ | ------------------------------------------------- | ------------------------------------------------- | + | Dog |
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| + +3. Use `--eigen-smooth` and `--aug-smooth` to improve visual effects. + + ```shell + python tools/visualizations/vis_cam.py \ + demo/bird.JPEG \ + configs/classification/imagenet/resnet/resnet50_4xb64_step_ep100.py \ + https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth \ + --target-layers 'backbone.layer4.2' \ + --method LayerCAM \ + --eigen-smooth --aug-smooth + ``` + + | Image | LayerCAM | eigen-smooth | aug-smooth | eigen&aug | + | ------------------------------------ | --------------------------------------- | ------------------------------------------- | ----------------------------------------- | ----------------------------------------- | + |
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| + +**Examples(Transformer)**： + +Here are some examples: + +- `'backbone.norm3'` for Swin-Transformer; +- `'backbone.layers[-1].ln1'` for ViT; + +For ViT-like networks, such as ViT, T2T-ViT and Swin-Transformer, the features are flattened. And for drawing the CAM, we need to specify the `--vit-like` argument to reshape the features into square feature maps. + +Besides the flattened features, some ViT-like networks also add extra tokens like the class token in ViT and T2T-ViT, and the distillation token in DeiT. In these networks, the final classification is done on the tokens computed in the last attention block, and therefore, the classification score will not be affected by other features and the gradient of the classification score with respect to them, will be zero. Therefore, you shouldn't use the output of the last attention block as the target layer in these networks. + +To exclude these extra tokens, we need know the number of extra tokens. Almost all transformer-based backbones in MMClassification have the `num_extra_tokens` attribute. If you want to use this tool in a new or third-party network that don't have the `num_extra_tokens` attribute, please specify it the `--num-extra-tokens` argument. + +1. Visualize CAM for `Swin Transformer`, using default `target-layers`: + + ```shell + python tools/visualizations/vis_cam.py \ + demo/bird.JPEG \ + configs/classification/imagenet/swin_transformer/swin_tiny_8xb128_fp16_ep300.py \ + https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925-66df6be6.pth \ + --vit-like + ``` + +2. Visualize CAM for `Vision Transformer(ViT)`: + + ```shell + python tools/visualizations/vis_cam.py \ + demo/bird.JPEG \ + configs/classification/imagenet/deit/deit_base_8xb128_ep300.py \ + https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_pt-16xb64_in1k_20211124-6f40c188.pth \ + --vit-like \ + --target-layers 'backbone.layers[-1].ln1' + ``` + +3. Visualize CAM for `T2T-ViT`: + + ```shell + python tools/visualizations/vis_cam.py \ + demo/bird.JPEG \ + configs/classification/imagenet/t2t_vit/t2t_vit_tiny_14_ema_8xb64_ep310.py \ + https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-14_3rdparty_8xb64_in1k_20210928-b7c09b62.pth \ + --vit-like \ + --target-layers 'backbone.encoder[-1].ln1' + ``` + +| Image | ResNet50 | ViT | Swin | T2T-ViT | +| --------------------------------------- | ------------------------------------------ | -------------------------------------- | --------------------------------------- | ------------------------------------------ | +|
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| + +## FAQs + +- None diff --git a/openmixup/__init__.py b/openmixup/__init__.py index 1c4f7e8f..02c2f777 100644 --- a/openmixup/__init__.py +++ b/openmixup/__init__.py @@ -1,3 +1,60 @@ +import warnings + +import mmcv +from packaging.version import parse + from .version import __version__, short_version + +def digit_version(version_str: str, length: int = 4): + """Convert a version string into a tuple of integers. + + This method is usually used for comparing two versions. For pre-release + versions: alpha < beta < rc. + + Args: + version_str (str): The version string. + length (int): The maximum number of version levels. Default: 4. + + Returns: + tuple[int]: The version info in digits (integers). + """ + version = parse(version_str) + assert version.release, f'failed to parse version {version_str}' + release = list(version.release) + release = release[:length] + if len(release) < length: + release = release + [0] * (length - len(release)) + if version.is_prerelease: + mapping = {'a': -3, 'b': -2, 'rc': -1} + val = -4 + # version.pre can be None + if version.pre: + if version.pre[0] not in mapping: + warnings.warn(f'unknown prerelease version {version.pre[0]}, ' + 'version checking may go wrong') + else: + val = mapping[version.pre[0]] + release.extend([val, version.pre[-1]]) + else: + release.extend([val, 0]) + + elif version.is_postrelease: + release.extend([1, version.post]) + else: + release.extend([0, 0]) + return tuple(release) + + +mmcv_minimum_version = '1.4.2' +mmcv_maximum_version = '1.6.0' +mmcv_version = digit_version(mmcv.__version__) + + +assert (mmcv_version >= digit_version(mmcv_minimum_version) + and mmcv_version <= digit_version(mmcv_maximum_version)), \ + f'MMCV=={mmcv.__version__} is used but incompatible. ' \ + f'Please install mmcv>={mmcv_minimum_version}, <={mmcv_maximum_version}.' + + __all__ = ['__version__', 'short_version'] diff --git a/openmixup/datasets/data_sources/image_list.py b/openmixup/datasets/data_sources/image_list.py index 6e622e8d..cf91b7e0 100644 --- a/openmixup/datasets/data_sources/image_list.py +++ b/openmixup/datasets/data_sources/image_list.py @@ -1,6 +1,8 @@ +import cv2 import os -from PIL import Image import mmcv +import numpy as np +from PIL import Image from ..registry import DATASOURCES @@ -12,6 +14,13 @@ class ImageList(object): The `ImageList` can load an annotation file or a list of files and merge all data records to one list. If data is unlabeled, the gt_label will be set -1. + + Args: + root (str): Path to the dataset. + list_file (str): Path to the txt list file. + splitor (str): Splitor between file names and the class id. + backend (str): Backend of the loader in {'pillow', 'cv2'}. + return_label (bool): Whether to return the class id. """ CLASSES = None @@ -37,6 +46,7 @@ def __init__(self, self.fns = [l.strip() for l in lines] self.fns = [os.path.join(root, fn) for fn in self.fns] self.backend = backend + assert backend in ['cv2', 'pillow'] if self.backend == 'cv2': self.file_client = mmcv.FileClient(backend='disk') @@ -50,8 +60,13 @@ def get_sample(self, idx): else: img_bytes = self.file_client.get(self.fns[idx]) img = mmcv.imfrombytes(img_bytes, flag='color') + if img is None: + img = cv2.cvtColor( + np.array(Image.open(self.fns[idx])), cv2.COLOR_RGB2BGR) + img = Image.fromarray(img.astype(np.uint8)) + if self.has_labels and self.return_label: target = self.labels[idx] - return img, target + return (img, target) else: return img diff --git a/openmixup/datasets/multi_view.py b/openmixup/datasets/multi_view.py index c1dac58c..f217c9f4 100644 --- a/openmixup/datasets/multi_view.py +++ b/openmixup/datasets/multi_view.py @@ -1,7 +1,7 @@ import torch from PIL import Image -from openmixup.utils import build_from_cfg +from openmixup.utils import print_log, build_from_cfg from torchvision.transforms import Compose from .base import BaseDataset @@ -55,6 +55,10 @@ def __init__(self, data_source, num_views, pipelines, prefetch=False): def __getitem__(self, idx): img = self.data_source.get_sample(idx) + if self.data_source.return_label: + img, target = img + else: + target = None assert isinstance(img, Image.Image), \ 'The output from the data source must be an Image, got: {}. \ Please ensure that the list file does not contain labels.'.format( @@ -64,7 +68,27 @@ def __getitem__(self, idx): multi_views = [ torch.from_numpy(to_numpy(img)) for img in multi_views ] - return dict(img=multi_views) + return dict(img=multi_views, gt_label=target, idx=idx) - def evaluate(self, scores, keyword, logger=None): - raise NotImplementedError + def evaluate(self, scores, keyword, logger=None, topk=(1, 5)): + """ (original supervised) classification evaluation """ + eval_res = {} + assert self.data_source.return_label and self.data_source.has_labels + + target = torch.LongTensor(self.data_source.labels) + assert scores.size(0) == target.size(0), \ + "Inconsistent length for results and labels, {} vs {}".format( + scores.size(0), target.size(0)) + num = scores.size(0) + _, pred = scores.topk(max(topk), dim=1, largest=True, sorted=True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) # KxN + for k in topk: + correct_k = correct[:k].contiguous().view(-1).float().sum(0).item() + acc = correct_k * 100.0 / num + eval_res["{}_top{}".format(keyword, k)] = acc + if logger is not None and logger != 'silent': + print_log( + "{}_top{}: {:.03f}".format(keyword, k, acc), + logger=logger) + return eval_res diff --git a/openmixup/datasets/pipelines/transforms.py b/openmixup/datasets/pipelines/transforms.py index c9945be2..3ced998c 100644 --- a/openmixup/datasets/pipelines/transforms.py +++ b/openmixup/datasets/pipelines/transforms.py @@ -7,7 +7,7 @@ import mmcv import numpy as np -from PIL import Image, ImageFilter +from PIL import Image, ImageFilter, ImageOps import torch from torchvision import transforms as _transforms @@ -1744,7 +1744,7 @@ class GaussianBlur(object): p (float, optional): Probability. Defaults to 0.5. """ - def __init__(self, sigma_min, sigma_max, p=0.5): + def __init__(self, sigma_min=0.1, sigma_max=2.0, p=0.5): assert 0 <= p <= 1.0, \ f'The prob should be in range [0,1], got {p} instead.' self.sigma_min = sigma_min @@ -1778,18 +1778,22 @@ class Solarization(object): p (float, optional): Probability. Defaults to 0.5. """ - def __init__(self, threshold=128, p=0.5): + def __init__(self, threshold=128, p=0.5, backend='pillow'): assert 0 <= p <= 1.0, \ f'The prob should be in range [0, 1], got {p} instead.' self.threshold = threshold self.prob = p + self.backend = backend def __call__(self, img): if np.random.rand() > self.prob: return img - img = np.array(img) - img = np.where(img < self.threshold, img, 255 -img) - return Image.fromarray(img.astype(np.uint8)) + if self.backend == 'pillow': + return ImageOps.solarize(img) + else: + img = np.array(img) + img = np.where(img < self.threshold, img, 255 -img) + return Image.fromarray(img.astype(np.uint8)) def __repr__(self): repr_str = self.__class__.__name__ diff --git a/openmixup/models/backbones/__init__.py b/openmixup/models/backbones/__init__.py index 2a5c6216..30390574 100644 --- a/openmixup/models/backbones/__init__.py +++ b/openmixup/models/backbones/__init__.py @@ -2,6 +2,7 @@ from .convmixer import ConvMixer from .convnext import ConvNeXt from .deit import DistilledVisionTransformer +from .deit3 import DeiT3 from .densenet import DenseNet from .edgenext import EdgeNeXt from .efficientformer import EfficientFormer @@ -41,7 +42,7 @@ from .wide_resnet import WideResNet, WideResNet_Mix __all__ = [ - 'AlexNet', 'BEiTViT', 'ConvNeXt', 'ConvMixer', 'DistilledVisionTransformer', 'DenseNet', + 'AlexNet', 'BEiTViT', 'ConvNeXt', 'ConvMixer', 'DistilledVisionTransformer', 'DeiT3', 'DenseNet', 'EdgeNeXt', 'EfficientFormer', 'EfficientNet', 'HorNet', 'InceptionV3', 'LeNet5', 'MAEViT', 'MIMVisionTransformer', 'SimMIMViT', 'SimMIMSwinTransformer', 'MIMResNet', 'LIT', 'MlpMixer', 'MobileNetV2', 'MobileNetV3', 'MogaNet', 'MViT', diff --git a/openmixup/models/backbones/deit3.py b/openmixup/models/backbones/deit3.py new file mode 100644 index 00000000..70a51202 --- /dev/null +++ b/openmixup/models/backbones/deit3.py @@ -0,0 +1,460 @@ +from typing import Sequence + +import numpy as np +import torch +import torch.nn as nn +from mmcv.cnn import Linear, build_activation_layer, build_norm_layer +from mmcv.cnn.bricks.drop import build_dropout +from mmcv.cnn.bricks.transformer import PatchEmbed +from mmcv.cnn.utils.weight_init import constant_init, trunc_normal_init +from mmcv.runner import BaseModule, ModuleList, Sequential +from mmcv.utils import deprecated_api_warning + +from ..utils import LayerScale, MultiheadAttention, resize_pos_embed, to_2tuple +from ..builder import BACKBONES +from .vision_transformer import VisionTransformer + + +class DeiT3FFN(BaseModule): + """FFN for DeiT3. + + The differences between DeiT3FFN & FFN: + 1. Use LayerScale. + + Args: + embed_dims (int): The feature dimension. Same as + `MultiheadAttention`. Defaults: 256. + feedforward_channels (int): The hidden dimension of FFNs. + Defaults: 1024. + num_fcs (int, optional): The number of fully-connected layers in + FFNs. Default: 2. + act_cfg (dict, optional): The activation config for FFNs. + Default: dict(type='ReLU') + ffn_drop (float, optional): Probability of an element to be + zeroed in FFN. Default 0.0. + add_identity (bool, optional): Whether to add the + identity connection. Default: `True`. + dropout_layer (obj:`ConfigDict`): The dropout_layer used + when adding the shortcut. + use_layer_scale (bool): Whether to use layer_scale in + DeiT3FFN. Defaults to True. + init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization. + Default: None. + """ + + @deprecated_api_warning( + { + 'dropout': 'ffn_drop', + 'add_residual': 'add_identity' + }, + cls_name='FFN') + def __init__(self, + embed_dims=256, + feedforward_channels=1024, + num_fcs=2, + act_cfg=dict(type='ReLU', inplace=True), + ffn_drop=0., + dropout_layer=None, + add_identity=True, + use_layer_scale=True, + init_cfg=None, + **kwargs): + super().__init__(init_cfg) + assert num_fcs >= 2, 'num_fcs should be no less ' \ + f'than 2. got {num_fcs}.' + self.embed_dims = embed_dims + self.feedforward_channels = feedforward_channels + self.num_fcs = num_fcs + self.act_cfg = act_cfg + self.activate = build_activation_layer(act_cfg) + + layers = [] + in_channels = embed_dims + for _ in range(num_fcs - 1): + layers.append( + Sequential( + Linear(in_channels, feedforward_channels), self.activate, + nn.Dropout(ffn_drop))) + in_channels = feedforward_channels + layers.append(Linear(feedforward_channels, embed_dims)) + layers.append(nn.Dropout(ffn_drop)) + self.layers = Sequential(*layers) + self.dropout_layer = build_dropout( + dropout_layer) if dropout_layer else torch.nn.Identity() + self.add_identity = add_identity + + if use_layer_scale: + self.gamma2 = LayerScale(embed_dims, data_format='channels_last') + else: + self.gamma2 = nn.Identity() + + @deprecated_api_warning({'residual': 'identity'}, cls_name='FFN') + def forward(self, x, identity=None): + """Forward function for `FFN`. + + The function would add x to the output tensor if residue is None. + """ + out = self.gamma2(self.layers(x)) + if not self.add_identity: + return self.dropout_layer(out) + if identity is None: + identity = x + return identity + self.dropout_layer(out) + + +class DeiT3TransformerEncoderLayer(BaseModule): + """Implements one encoder layer in DeiT3. + + The differences between DeiT3TransformerEncoderLayer and the vanilla + TransformerEncoderLayer: Using LayerScale. + + Args: + embed_dims (int): The feature dimension + num_heads (int): Parallel attention heads + feedforward_channels (int): The hidden dimension for FFNs + drop_rate (float): Probability of an element to be zeroed + after the feed forward layer. Defaults to 0. + attn_drop_rate (float): The drop out rate for attention output weights. + Defaults to 0. + drop_path_rate (float): Stochastic depth rate. Defaults to 0. + num_fcs (int): The number of fully-connected layers for FFNs. + Defaults to 2. + qkv_bias (bool): enable bias for qkv if True. Defaults to True. + use_layer_scale (bool): Whether to use layer_scale in + DeiT3TransformerEncoderLayer. Defaults to True. + act_cfg (dict): The activation config for FFNs. + Defaluts to ``dict(type='GELU')``. + norm_cfg (dict): Config dict for normalization layer. + Defaults to ``dict(type='LN')``. + init_cfg (dict, optional): Initialization config dict. + Defaults to None. + """ + + def __init__(self, + embed_dims, + num_heads, + feedforward_channels, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0., + num_fcs=2, + qkv_bias=True, + use_layer_scale=True, + act_cfg=dict(type='GELU'), + norm_cfg=dict(type='LN'), + init_cfg=None): + super(DeiT3TransformerEncoderLayer, self).__init__(init_cfg=init_cfg) + + self.embed_dims = embed_dims + + self.norm1_name, norm1 = build_norm_layer( + norm_cfg, self.embed_dims, postfix=1) + self.add_module(self.norm1_name, norm1) + + self.attn = MultiheadAttention( + embed_dims=embed_dims, + num_heads=num_heads, + attn_drop=attn_drop_rate, + proj_drop=drop_rate, + dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate), + qkv_bias=qkv_bias, + use_layer_scale=use_layer_scale) + + self.norm2_name, norm2 = build_norm_layer( + norm_cfg, self.embed_dims, postfix=2) + self.add_module(self.norm2_name, norm2) + + self.ffn = DeiT3FFN( + embed_dims=embed_dims, + feedforward_channels=feedforward_channels, + num_fcs=num_fcs, + ffn_drop=drop_rate, + dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate), + act_cfg=act_cfg, + use_layer_scale=use_layer_scale) + + @property + def norm1(self): + return getattr(self, self.norm1_name) + + @property + def norm2(self): + return getattr(self, self.norm2_name) + + def init_weights(self): + super(DeiT3TransformerEncoderLayer, self).init_weights() + for m in self.ffn.modules(): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + nn.init.normal_(m.bias, std=1e-6) + + def forward(self, x): + x = x + self.attn(self.norm1(x)) + x = self.ffn(self.norm2(x), identity=x) + return x + + +@BACKBONES.register_module() +class DeiT3(VisionTransformer): + """DeiT3 backbone. + + A PyTorch implement of : `DeiT III: Revenge of the ViT + `_ + + The differences between DeiT3 & VisionTransformer: + + 1. Use LayerScale. + 2. Concat cls token after adding pos_embed. + + Args: + arch (str | dict): DeiT3 architecture. If use string, + choose from 'small', 'base', 'medium', 'large' and 'huge'. + If use dict, it should have below keys: + + - **embed_dims** (int): The dimensions of embedding. + - **num_layers** (int): The number of transformer encoder layers. + - **num_heads** (int): The number of heads in attention modules. + - **feedforward_channels** (int): The hidden dimensions in + feedforward modules. + + Defaults to 'base'. + img_size (int | tuple): The expected input image shape. Because we + support dynamic input shape, just set the argument to the most + common input image shape. Defaults to 224. + patch_size (int | tuple): The patch size in patch embedding. + Defaults to 16. + in_channels (int): The num of input channels. Defaults to 3. + out_indices (Sequence | int): Output from which stages. + Defaults to -1, means the last stage. + drop_rate (float): Probability of an element to be zeroed. + Defaults to 0. + drop_path_rate (float): stochastic depth rate. Defaults to 0. + qkv_bias (bool): Whether to add bias for qkv in attention modules. + Defaults to True. + norm_cfg (dict): Config dict for normalization layer. + Defaults to ``dict(type='LN')``. + final_norm (bool): Whether to add a additional layer to normalize + final feature map. Defaults to True. + with_cls_token (bool): Whether concatenating class token into image + tokens as transformer input. Defaults to True. + output_cls_token (bool): Whether output the cls_token. If set True, + ``with_cls_token`` must be True. Defaults to True. + use_layer_scale (bool): Whether to use layer_scale in DeiT3. + Defaults to True. + interpolate_mode (str): Select the interpolate mode for position + embeding vector resize. Defaults to "bicubic". + patch_cfg (dict): Configs of patch embeding. Defaults to an empty dict. + layer_cfgs (Sequence | dict): Configs of each transformer layer in + encoder. Defaults to an empty dict. + init_cfg (dict, optional): Initialization config dict. + Defaults to None. + """ + arch_zoo = { + **dict.fromkeys( + ['s', 'small'], { + 'embed_dims': 384, + 'num_layers': 12, + 'num_heads': 6, + 'feedforward_channels': 1536, + }), + **dict.fromkeys( + ['m', 'medium'], { + 'embed_dims': 512, + 'num_layers': 12, + 'num_heads': 8, + 'feedforward_channels': 2048, + }), + **dict.fromkeys( + ['b', 'base'], { + 'embed_dims': 768, + 'num_layers': 12, + 'num_heads': 12, + 'feedforward_channels': 3072 + }), + **dict.fromkeys( + ['l', 'large'], { + 'embed_dims': 1024, + 'num_layers': 24, + 'num_heads': 16, + 'feedforward_channels': 4096 + }), + **dict.fromkeys( + ['h', 'huge'], { + 'embed_dims': 1280, + 'num_layers': 32, + 'num_heads': 16, + 'feedforward_channels': 5120 + }), + } + # not using num_extra_tokens in deit3 because adding cls tokens after + # adding pos_embed + num_extra_tokens = 0 + + def __init__(self, + arch='base', + img_size=224, + patch_size=16, + in_channels=3, + out_indices=-1, + drop_rate=0., + drop_path_rate=0., + qkv_bias=True, + norm_cfg=dict(type='LN', eps=1e-6), + final_norm=True, + with_cls_token=True, + output_cls_token=True, + use_layer_scale=True, + interpolate_mode='bicubic', + patch_cfg=dict(), + layer_cfgs=dict(), + frozen_stages=-1, + norm_eval=False, + init_cfg=None, + **kwargs): + super(VisionTransformer, self).__init__(init_cfg) + + if isinstance(arch, str): + arch = arch.lower() + assert arch in set(self.arch_zoo), \ + f'Arch {arch} is not in default archs {set(self.arch_zoo)}' + self.arch_settings = self.arch_zoo[arch] + else: + essential_keys = { + 'embed_dims', 'num_layers', 'num_heads', 'feedforward_channels' + } + assert isinstance(arch, dict) and essential_keys <= set(arch), \ + f'Custom arch needs a dict with keys {essential_keys}' + self.arch_settings = arch + + self.embed_dims = self.arch_settings['embed_dims'] + self.num_layers = self.arch_settings['num_layers'] + self.img_size = to_2tuple(img_size) + + # Set patch embedding + _patch_cfg = dict( + in_channels=in_channels, + input_size=img_size, + embed_dims=self.embed_dims, + conv_type='Conv2d', + kernel_size=patch_size, + stride=patch_size, + ) + _patch_cfg.update(patch_cfg) + self.patch_embed = PatchEmbed(**_patch_cfg) + self.patch_resolution = self.patch_embed.init_out_size + num_patches = self.patch_resolution[0] * self.patch_resolution[1] + + # Set cls token + if output_cls_token: + assert with_cls_token is True, f'with_cls_token must be True if' \ + f'set output_cls_token to True, but got {with_cls_token}' + self.with_cls_token = with_cls_token + self.output_cls_token = output_cls_token + self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dims)) + + # Set position embedding + self.interpolate_mode = interpolate_mode + self.pos_embed = nn.Parameter( + torch.zeros(1, num_patches, self.embed_dims)) + self._register_load_state_dict_pre_hook(self._prepare_pos_embed) + + self.drop_after_pos = nn.Dropout(p=drop_rate) + + if isinstance(out_indices, int): + out_indices = [out_indices] + assert isinstance(out_indices, Sequence), \ + f'"out_indices" must by a sequence or int, ' \ + f'get {type(out_indices)} instead.' + for i, index in enumerate(out_indices): + if index < 0: + out_indices[i] = self.num_layers + index + assert 0 <= out_indices[i] <= self.num_layers, \ + f'Invalid out_indices {index}' + self.out_indices = out_indices + self.frozen_stages = frozen_stages + self.norm_eval = norm_eval + + # stochastic depth decay rule + dpr = np.linspace(0, drop_path_rate, self.num_layers) + + self.layers = ModuleList() + if isinstance(layer_cfgs, dict): + layer_cfgs = [layer_cfgs] * self.num_layers + for i in range(self.num_layers): + _layer_cfg = dict( + embed_dims=self.embed_dims, + num_heads=self.arch_settings['num_heads'], + feedforward_channels=self. + arch_settings['feedforward_channels'], + drop_rate=drop_rate, + drop_path_rate=dpr[i], + qkv_bias=qkv_bias, + norm_cfg=norm_cfg, + use_layer_scale=use_layer_scale) + _layer_cfg.update(layer_cfgs[i]) + self.layers.append(DeiT3TransformerEncoderLayer(**_layer_cfg)) + + self.final_norm = final_norm + if final_norm: + self.norm1_name, norm1 = build_norm_layer( + norm_cfg, self.embed_dims, postfix=1) + self.add_module(self.norm1_name, norm1) + + def init_weights(self, pretrained=None): + + if pretrained is None: + if self.init_cfg is None: + for m in self.modules(): + if isinstance(m, (nn.Linear)): + trunc_normal_init(m, std=0.02) + elif isinstance(m, ( + nn.LayerNorm, nn.BatchNorm2d, nn.GroupNorm)): + constant_init(m, val=1, bias=0) + # pos_embed & cls_token + nn.init.trunc_normal_(self.pos_embed, mean=0, std=.02) + nn.init.trunc_normal_(self.cls_token, mean=0, std=.02) + + def forward(self, x): + B = x.shape[0] + x, patch_resolution = self.patch_embed(x) + + x = x + resize_pos_embed( + self.pos_embed, + self.patch_resolution, + patch_resolution, + mode=self.interpolate_mode, + num_extra_tokens=self.num_extra_tokens) + x = self.drop_after_pos(x) + + # stole cls_tokens impl from Phil Wang, thanks + cls_tokens = self.cls_token.expand(B, -1, -1) + x = torch.cat((cls_tokens, x), dim=1) + + if not self.with_cls_token: + # Remove class token for transformer encoder input + x = x[:, 1:] + + outs = [] + for i, layer in enumerate(self.layers): + x = layer(x) + + if i == len(self.layers) - 1 and self.final_norm: + x = self.norm1(x) + + if i in self.out_indices: + B, _, C = x.shape + if self.with_cls_token: + patch_token = x[:, 1:].reshape(B, *patch_resolution, C) + patch_token = patch_token.permute(0, 3, 1, 2) + cls_token = x[:, 0] + else: + patch_token = x.reshape(B, *patch_resolution, C) + patch_token = patch_token.permute(0, 3, 1, 2) + cls_token = None + if self.output_cls_token: + out = [patch_token, cls_token] + else: + out = patch_token + outs.append(out) + + return outs diff --git a/openmixup/models/backbones/moganet.py b/openmixup/models/backbones/moganet.py index 53d74c81..b75a7767 100644 --- a/openmixup/models/backbones/moganet.py +++ b/openmixup/models/backbones/moganet.py @@ -588,8 +588,9 @@ def __init__(self, self.frozen_stages = frozen_stages self.norm_eval = norm_eval self.ffn_types = ffn_types + self.attn_force_fp32 = attn_force_fp32 self.use_layer_norm = stem_norm_cfg['type'] == 'LN' - assert stem_norm_cfg['type'] in ['BN', 'LN', 'LN2d',] + assert stem_norm_cfg['type'] in ['BN', 'SyncBN', 'LN', 'LN2d',] assert len(ffn_types) == self.num_stages assert len(patchembed_types) == self.num_stages @@ -703,10 +704,9 @@ def forward(self, x): x = norm(x) if i in self.out_indices: - ########################## - if torch.any(torch.isnan(x)) or torch.any(torch.isinf(x)): - raise ValueError("STOP NAN.") - ########################## + if self.attn_force_fp32: + if torch.any(torch.isnan(x)) or torch.any(torch.isinf(x)): + raise ValueError("Inf or nan value: use FP32 instead.") outs.append(x) return outs diff --git a/openmixup/models/classifiers/automix_V1plus.py b/openmixup/models/classifiers/automix_V1plus.py index 9e57b92a..73dcef1d 100644 --- a/openmixup/models/classifiers/automix_V1plus.py +++ b/openmixup/models/classifiers/automix_V1plus.py @@ -57,6 +57,7 @@ class AutoMixup(BaseModel): Default: -1 (or 0). switch_off (bool or float): Switch off MixBlock updating for fast training. Default to False (or 0). + head_ensemble (bool): Whether to ensemble results of all heads. Default to False. mix_shuffle_no_repeat (bool): Whether to use 'no_repeat' mode to generate mixup shuffle idx. We can ignore this issue in supervised learning. Default: False. @@ -85,6 +86,7 @@ def __init__(self, pre_mix_loss=0., lam_margin=-1, switch_off=0., + head_ensemble=False, save=False, save_name='MixedSamples', debug=False, @@ -105,6 +107,7 @@ def __init__(self, self.pre_mix_loss = float(pre_mix_loss) if float(pre_mix_loss) > 0 else 0 self.lam_margin = float(lam_margin) if float(lam_margin) > 0 else 0 self.switch_off = float(switch_off) if float(switch_off) > 0 else 0 + self.head_ensemble = bool(head_ensemble) self.mask_up_override = mask_up_override \ if isinstance(mask_up_override, (str, list)) else None self.save = bool(save) @@ -558,18 +561,41 @@ def pixel_mixup(self, x, y, lam, index, feature): return results - def forward_test(self, img, **kwargs): - """Forward computation during testing. + def simple_test(self, img, **kwargs): + """Test without augmentation.""" + keys = list() # 'acc_mix_k', 'acc_one_k', 'acc_mix_q', 'acc_one_q' + pred = list() + # backbone + last_k = self.backbone_k(img)[-1] + last_q = self.backbone_q(img)[-1] + # head k + if self.weight_mix_k > 0: + pred.append(self.head_mix_k([last_k])) + keys.append('acc_mix_k') + if self.head_one_k is not None: + pred.append(self.head_one_k([last_k])) + keys.append('acc_one_k') + # head q + pred.append(self.head_mix_q([last_q])) + keys.append('acc_mix_q') + if self.head_one_q is not None: + pred.append(self.head_one_q([last_q])) + keys.append('acc_one_q') + # head ensemble + if self.head_ensemble: + pred.append([torch.stack( + [pred[i][0] ** 2 for i in range(len(pred))]).mean(dim=0)]) + keys.append('acc_avg') - Args: - img (Tensor): Input of a batch of images, (N, C, H, W). + out_tensors = [p[0].cpu() for p in pred] # NxC + return dict(zip(keys, out_tensors)) - Returns: - dict[key, Tensor]: A dictionary of head names (key) and predictions. - """ + def augment_test(self, img, **kwargs): + """Test function with test time augmentation.""" keys = list() # 'acc_mix_k', 'acc_one_k', 'acc_mix_q', 'acc_one_q' pred = list() # backbone + img = img[0] last_k = self.backbone_k(img)[-1] last_q = self.backbone_q(img)[-1] # head k @@ -585,10 +611,31 @@ def forward_test(self, img, **kwargs): if self.head_one_q is not None: pred.append(self.head_one_q([last_q])) keys.append('acc_one_q') - + # head ensemble + if self.head_ensemble: + pred.append([torch.stack( + [pred[i][0] ** 2 for i in range(len(pred))]).mean(dim=0)]) + keys.append('acc_avg') + out_tensors = [p[0].cpu() for p in pred] # NxC return dict(zip(keys, out_tensors)) - + + def forward_test(self, img, **kwargs): + """Forward computation during testing. + + Args: + img (List[Tensor] or Tensor): the outer list indicates the + test-time augmentations and inner Tensor should have a + shape of (N, C, H, W). + + Returns: + dict[key, Tensor]: A dictionary of head names (key) and predictions. + """ + if isinstance(img, list): + return self.augment_test(img) + else: + return self.simple_test(img) + def forward_vis(self, img, gt_label, **kwargs): """" visualization by jupyter notebook """ batch_size = img.size()[0] diff --git a/openmixup/models/classifiers/classification.py b/openmixup/models/classifiers/classification.py index 25c3d54f..12982a2b 100644 --- a/openmixup/models/classifiers/classification.py +++ b/openmixup/models/classifiers/classification.py @@ -66,7 +66,8 @@ def forward_train(self, img, gt_label, **kwargs): losses = self.head.loss(outs, gt_label) return losses - def forward_test(self, img, **kwargs): + def simple_test(self, img): + """Test without augmentation.""" x = self.backbone(img) # tuple if self.with_neck: x = self.neck(x) @@ -74,3 +75,28 @@ def forward_test(self, img, **kwargs): keys = [f'head{i}' for i in range(len(outs))] out_tensors = [out.cpu() for out in outs] # NxC return dict(zip(keys, out_tensors)) + + def augment_test(self, img): + """Test function with test time augmentation.""" + x = list() + for _img in img: + if self.with_neck: + x.append(self.neck(self.backbone(_img))[0]) + else: + x.append(self.backbone(_img)[0]) + outs = self.head(x) + keys = [f'head{i}' for i in range(len(outs))] + out_tensors = [out.cpu() for out in outs] # NxC + return dict(zip(keys, out_tensors)) + + def forward_test(self, img, **kwargs): + """ + Args: + img (List[Tensor] or Tensor): the outer list indicates the + test-time augmentations and inner Tensor should have a + shape of (N, C, H, W). + """ + if isinstance(img, list): + return self.augment_test(img) + else: + return self.simple_test(img) diff --git a/openmixup/models/classifiers/mixup_classification.py b/openmixup/models/classifiers/mixup_classification.py index 4a3a59de..a2b4fe9f 100644 --- a/openmixup/models/classifiers/mixup_classification.py +++ b/openmixup/models/classifiers/mixup_classification.py @@ -318,13 +318,34 @@ def forward_train(self, img, gt_label, **kwargs): return losses - def forward_test(self, img, **kwargs): + def simple_test(self, img): + """Test without augmentation.""" x = self.backbone(img) # tuple outs = self.head(x) keys = ['head{}'.format(i) for i in range(len(outs))] out_tensors = [out.cpu() for out in outs] # NxC return dict(zip(keys, out_tensors)) + def augment_test(self, img): + """Test function with test time augmentation.""" + x = [self.backbone(_img)[0] for _img in img] + outs = self.head(x) + keys = [f'head{i}' for i in range(len(outs))] + out_tensors = [out.cpu() for out in outs] # NxC + return dict(zip(keys, out_tensors)) + + def forward_test(self, img, **kwargs): + """ + Args: + img (List[Tensor] or Tensor): the outer list indicates the + test-time augmentations and inner Tensor should have a + shape of (N, C, H, W). + """ + if isinstance(img, list): + return self.augment_test(img) + else: + return self.simple_test(img) + @force_fp32(apply_to=('img_mixed', )) def plot_mix(self, img_mixed, mix_mode="", lam=None): """ visualize mixup results """ diff --git a/openmixup/models/heads/cls_head.py b/openmixup/models/heads/cls_head.py index 8163856d..85ff66a0 100644 --- a/openmixup/models/heads/cls_head.py +++ b/openmixup/models/heads/cls_head.py @@ -22,6 +22,8 @@ class ClsHead(BaseModule): multi_label (bool): Whether to use one_hot like labels (requiring the multi-label classification loss). Notice that we support the single-label cls task to use the multi-label cls loss. + finetune (bool): Whether to use the finetune mode of ViTs. + aug_test (bool): Whether to perform test time augmentations. frozen (bool): Whether to freeze the parameters. """ @@ -31,8 +33,9 @@ def __init__(self, in_channels=2048, num_classes=1000, multi_label=False, - frozen=False, finetune=False, + aug_test=False, + frozen=False, init_cfg=None): super(ClsHead, self).__init__(init_cfg=init_cfg) self.with_avg_pool = with_avg_pool @@ -40,6 +43,7 @@ def __init__(self, self.num_classes = num_classes self.multi_label = multi_label self.finetune = finetune + self.aug_test = aug_test # loss if loss is not None: @@ -81,11 +85,8 @@ def init_weights(self, init_linear='normal', std=0.01, bias=0.): elif init_linear == 'trunc_normal': trunc_normal_init(m, std=std, bias=bias) - def forward(self, x): - assert isinstance(x, (tuple, list)) and len(x) == 1 - if self.fc is None: - return x - x = x[0] + def forward_head(self, x): + """" forward cls head with x in a shape of (X, \*) """ if self.with_avg_pool: if x.dim() == 3: x = F.adaptive_avg_pool1d(x, 1).view(x.size(0), -1) @@ -94,7 +95,20 @@ def forward(self, x): else: assert x.dim() in [2, 3, 4], \ "Tensor must has 2, 3 or 4 dims, got: {}".format(x.dim()) - return [self.fc(x)] + return self.fc(x) + + def forward(self, x): + assert isinstance(x, (tuple, list)) and len(x) >= 1 + if self.fc is None: + return x + # test-time augmentation + if len(x) > 1 and self.aug_test: + aug_pred = [self.forward_head(_x) for _x in x] + aug_pred = torch.stack(aug_pred).mean(dim=0) + return [aug_pred] + # simple test + else: + return [self.forward_head(x[0])] def loss(self, cls_score, labels, **kwargs): """" cls loss forward diff --git a/openmixup/models/heads/cls_mixup_head.py b/openmixup/models/heads/cls_mixup_head.py index 45142062..d1e99cbd 100644 --- a/openmixup/models/heads/cls_mixup_head.py +++ b/openmixup/models/heads/cls_mixup_head.py @@ -42,6 +42,7 @@ class ClsMixupHead(BaseModule): neg_weight (bool or float): Whether to remove (or reweight) the negative part of loss according to gt_label (should be BCE multi-label loss). Default: 1 (True). + aug_test (bool): Whether to perform test time augmentations. frozen (bool): Whether to freeze the parameters. """ @@ -58,6 +59,7 @@ def __init__(self, lam_idx=1, eta_weight=dict(eta=0, mode="both", thr=0.5), neg_weight=1, + aug_test=False, frozen=False, init_cfg=None): super(ClsMixupHead, self).__init__(init_cfg=init_cfg) @@ -72,6 +74,7 @@ def __init__(self, self.lam_idx = float(lam_idx) self.eta_weight = eta_weight self.neg_weight = float(neg_weight) if float(neg_weight) != 1 else 1 + self.aug_test = aug_test assert lam_scale_mode in ['none', 'pow', 'exp'] assert eta_weight["mode"] in ['more', 'less', 'both'] and \ 0 <= eta_weight["thr"] <= 1 and eta_weight["eta"] < 100 @@ -115,9 +118,8 @@ def init_weights(self, init_linear='normal', std=0.01, bias=0.): elif init_linear == 'trunc_normal': trunc_normal_init(m, std=std, bias=bias) - def forward(self, x): - assert isinstance(x, (tuple, list)) and len(x) == 1 - x = x[0] + def forward_head(self, x): + """" forward cls head with x in a shape of (X, \*) """ if self.with_avg_pool: if x.dim() == 3: x = F.adaptive_avg_pool1d(x, 1).view(x.size(0), -1) @@ -126,8 +128,21 @@ def forward(self, x): else: assert x.dim() in [2, 3, 4], \ "Tensor must has 2, 3 or 4 dims, got: {}".format(x.dim()) - return [self.fc(x)] - + return self.fc(x) + + def forward(self, x): + assert isinstance(x, (tuple, list)) and len(x) >= 1 + if self.fc is None: + return x + # test-time augmentation + if len(x) > 1 and self.aug_test: + aug_pred = [self.forward_head(_x) for _x in x] + aug_pred = torch.stack(aug_pred).mean(dim=0) + return [aug_pred] + # simple test + else: + return [self.forward_head(x[0])] + def lambda_adjust(self, lam, mode="pow", thr=1, idx=1): """ rescale lambda for two-hot label mixup classification diff --git a/openmixup/models/utils/layers/attention.py b/openmixup/models/utils/layers/attention.py index efeb885f..b7a88fb5 100644 --- a/openmixup/models/utils/layers/attention.py +++ b/openmixup/models/utils/layers/attention.py @@ -13,6 +13,7 @@ from mmcv.cnn.utils.weight_init import trunc_normal_ from mmcv.runner.base_module import BaseModule +from .layer_scale import LayerScale from ..helpers import to_2tuple @@ -385,6 +386,8 @@ class MultiheadAttention(BaseModule): v_shortcut (bool): Add a shortcut from value to output. It's usually used if ``input_dims`` is different from ``embed_dims``. Defaults to False. + use_layer_scale (bool): Whether to use the layer init scale. Defaults + to False. init_cfg (dict, optional): The Config for initialization. Defaults to None. """ @@ -401,6 +404,7 @@ def __init__(self, proj_bias=True, attn_scale=False, v_shortcut=False, + use_layer_scale=False, init_cfg=None): super(MultiheadAttention, self).__init__(init_cfg=init_cfg) @@ -424,6 +428,11 @@ def __init__(self, self.out_drop = DROPOUT_LAYERS.build(dropout_layer) + if use_layer_scale: + self.gamma1 = LayerScale(embed_dims, data_format='channels_last') + else: + self.gamma1 = nn.Identity() + def forward(self, x): B, N, _ = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, @@ -446,7 +455,7 @@ def forward(self, x): x = (attn @ v).transpose(1, 2).reshape(B, N, self.embed_dims) x = self.proj(x) - x = self.out_drop(self.proj_drop(x)) + x = self.out_drop(self.gamma1(self.proj_drop(x))) if self.v_shortcut: x = v.squeeze(1) + x diff --git a/tools/analysis_tools/gradcam.py b/tools/analysis_tools/gradcam.py deleted file mode 100644 index 9d735d58..00000000 --- a/tools/analysis_tools/gradcam.py +++ /dev/null @@ -1,421 +0,0 @@ -# reference: https://github.com/jacobgil/pytorch-grad-cam -import os -import cv2 -import numpy as np -import torch -from torch.autograd import Function -from torchvision import models - - -class FeatureExtractor(): - """ Class for extracting activations and - registering gradients from targetted intermediate layers """ - - def __init__(self, model, target_layers): - self.model = model - self.target_layers = target_layers - self.gradients = [] - - def save_gradient(self, grad): - self.gradients.append(grad) - - def __call__(self, x): - outputs = [] - self.gradients = [] - for name, module in self.model._modules.items(): - x = module(x) - if name in self.target_layers: - x.register_hook(self.save_gradient) - outputs += [x] - return outputs, x - - -class ModelOutputs(): - """ Class for making a forward pass, and getting: - 1. The network output. - 2. Activations from intermeddiate targetted layers. - 3. Gradients from intermeddiate targetted layers. """ - - def __init__(self, model, feature_module, target_layers): - self.model = model - self.feature_module = feature_module - self.feature_extractor = FeatureExtractor(self.feature_module, target_layers) - - def get_gradients(self): - return self.feature_extractor.gradients - - def __call__(self, x): - target_activations = [] - for name, module in self.model._modules.items(): - if module == self.feature_module: - target_activations, x = self.feature_extractor(x) - elif "avgpool" in name.lower(): - x = module(x) - x = x.view(x.size(0),-1) - else: - x = module(x) - - return target_activations, x - - -def preprocess_image(img): - # using ImageNet mean, std - means = [0.485, 0.456, 0.406] - stds = [0.229, 0.224, 0.225] - - preprocessed_img = img.copy()[:, :, ::-1] - for i in range(3): - preprocessed_img[:, :, i] = preprocessed_img[:, :, i] - means[i] - preprocessed_img[:, :, i] = preprocessed_img[:, :, i] / stds[i] - preprocessed_img = \ - np.ascontiguousarray(np.transpose(preprocessed_img, (2, 0, 1))) - preprocessed_img = torch.from_numpy(preprocessed_img) - preprocessed_img.unsqueeze_(0) - input = preprocessed_img.requires_grad_(True) - return input - - -def show_cam_on_image(img, mask, save_name, save_path): - heatmap = cv2.applyColorMap(np.uint8(255 * mask), cv2.COLORMAP_JET) - heatmap = np.float32(heatmap) / 255 - cam = heatmap + np.float32(img) - cam = cam / np.max(cam) - cv2.imwrite("{}/{}_cam.jpg".format(save_path, save_name), np.uint8(255 * cam)) - - -class GradCam: - def __init__(self, model, feature_module, target_layer_names, use_cuda): - self.model = model - self.feature_module = feature_module - self.model.eval() - self.cuda = use_cuda - if self.cuda: - self.model = model.cuda() - - self.extractor = ModelOutputs(self.model, self.feature_module, target_layer_names) - - def forward(self, input): - return self.model(input) - - def __call__(self, input, index=None): - if self.cuda: - features, output = self.extractor(input.cuda()) - else: - features, output = self.extractor(input) - - if index == None: - index = np.argmax(output.cpu().data.numpy()) - - one_hot = np.zeros((1, output.size()[-1]), dtype=np.float32) - one_hot[0][index] = 1 - one_hot = torch.from_numpy(one_hot).requires_grad_(True) - if self.cuda: - one_hot = torch.sum(one_hot.cuda() * output) - else: - one_hot = torch.sum(one_hot * output) - - self.feature_module.zero_grad() - self.model.zero_grad() - one_hot.backward(retain_graph=True) - - grads_val = self.extractor.get_gradients()[-1].cpu().data.numpy() - - target = features[-1] - target = target.cpu().data.numpy()[0, :] - - weights = np.mean(grads_val, axis=(2, 3))[0, :] - cam = np.zeros(target.shape[1:], dtype=np.float32) - - for i, w in enumerate(weights): - cam += w * target[i, :, :] - - cam = np.maximum(cam, 0) - cam = cv2.resize(cam, input.shape[2:]) - cam = cam - np.min(cam) - cam = cam / np.max(cam) - return cam - - -class GuidedBackpropReLU(Function): - - @staticmethod - def forward(self, input): - positive_mask = (input > 0).type_as(input) - output = torch.addcmul(torch.zeros(input.size()).type_as(input), input, positive_mask) - self.save_for_backward(input, output) - return output - - @staticmethod - def backward(self, grad_output): - input, output = self.saved_tensors - grad_input = None - - positive_mask_1 = (input > 0).type_as(grad_output) - positive_mask_2 = (grad_output > 0).type_as(grad_output) - grad_input = torch.addcmul(torch.zeros(input.size()).type_as(input), - torch.addcmul(torch.zeros(input.size()).type_as(input), grad_output, - positive_mask_1), positive_mask_2) - return grad_input - - -class GuidedBackpropReLUModel: - def __init__(self, model, use_cuda): - self.model = model - self.model.eval() - self.cuda = use_cuda - if self.cuda: - self.model = model.cuda() - - def recursive_relu_apply(module_top): - for idx, module in module_top._modules.items(): - recursive_relu_apply(module) - if module.__class__.__name__ == 'ReLU': - module_top._modules[idx] = GuidedBackpropReLU.apply - - # replace ReLU with GuidedBackpropReLU - recursive_relu_apply(self.model) - - def forward(self, input): - return self.model(input) - - def __call__(self, input, index=None, target=None): - if self.cuda: - output = self.forward(input.cuda()) - else: - output = self.forward(input) - - cls_res = {} # classification False or True - if index == None: - index = np.argmax(output.cpu().data.numpy()) - if target != None: - assert isinstance(target, int) or isinstance(target, np.int) - cls_res["bool"] = index == target - print("cls={}, index={}, target={}".format(cls_res["bool"], index, target)) - cls_res["pred"] = index - - one_hot = np.zeros((1, output.size()[-1]), dtype=np.float32) - one_hot[0][index] = 1 - one_hot = torch.from_numpy(one_hot).requires_grad_(True) - if self.cuda: - one_hot = torch.sum(one_hot.cuda() * output) - else: - one_hot = torch.sum(one_hot * output) - - # self.model.features.zero_grad() - # self.model.classifier.zero_grad() - one_hot.backward(retain_graph=True) - - output = input.grad.cpu().data.numpy() - output = output[0, :, :, :] - - return output, cls_res - - -def deprocess_image(img): - """ see https://github.com/jacobgil/keras-grad-cam/blob/master/grad-cam.py#L65 """ - img = img - np.mean(img) - img = img / (np.std(img) + 1e-5) - img = img * 0.1 - img = img + 0.5 - img = np.clip(img, 0, 1) - return np.uint8(img*255) - - -def fit_model_state_dict(model, ckpt_path): - ckpt = torch.load(ckpt_path) - ckpt_state_dict = ckpt["state_dict"] - model_state_dict = model.state_dict() - print( len(ckpt_state_dict.keys()), len(model_state_dict.keys()) ) - - pretrained = {} - for k, v in ckpt_state_dict.items(): - if k.find("backbone") != -1: - new_key = k.split("backbone.")[-1] - elif k.find("head.") != -1: # head.fc_cls.weight, head.fc_cls.bias -> 'fc.weight', 'fc.bias' - new_key = "fc." + k.split(".")[-1] - else: - new_key = k - if new_key in model_state_dict.keys(): - pretrained[new_key] = v - else: - print(k) - print("loaded model keys={}".format(len(pretrained.keys()))) - - model_state_dict.update(pretrained) - model.load_state_dict(model_state_dict) - - -def get_sample_cub200_dict(): - return { - "001.Black_footed_Albatross/Black_Footed_Albatross_0046_18.jpg": 0, - "001.Black_footed_Albatross/Black_Footed_Albatross_0002_55.jpg": 0, - "002.Laysan_Albatross/Laysan_Albatross_0002_1027.jpg": 1, - "002.Laysan_Albatross/Laysan_Albatross_0082_524.jpg": 1, - "003.Sooty_Albatross/Sooty_Albatross_0003_1078.jpg": 2, - "003.Sooty_Albatross/Sooty_Albatross_0044_1105.jpg": 2, - "004.Groove_billed_Ani/Groove_Billed_Ani_0068_1538.jpg": 3, - "004.Groove_billed_Ani/Groove_Billed_Ani_0094_1540.jpg": 3, - "005.Crested_Auklet/Crested_Auklet_0006_1813.jpg": 4, - "005.Crested_Auklet/Crested_Auklet_0042_794902.jpg": 4, - "006.Least_Auklet/Least_Auklet_0014_1901.jpg": 5, - "006.Least_Auklet/Least_Auklet_0032_795068.jpg": 5, - "007.Parakeet_Auklet/Parakeet_Auklet_0017_795924.jpg": 6, - "007.Parakeet_Auklet/Parakeet_Auklet_0072_795929.jpg": 6, - "008.Rhinoceros_Auklet/Rhinoceros_Auklet_0042_2101.jpg": 7, - "008.Rhinoceros_Auklet/Rhinoceros_Auklet_0027_797496.jpg": 7, - "009.Brewer_Blackbird/Brewer_Blackbird_0099_2560.jpg": 8, - "009.Brewer_Blackbird/Brewer_Blackbird_0106_2608.jpg": 8, - "010.Red_winged_Blackbird/Red_Winged_Blackbird_0022_4483.jpg": 9, - "010.Red_winged_Blackbird/Red_Winged_Blackbird_0017_4116.jpg": 9, - } - - -def get_sample_pets37_dict(): - return { - "Abyssinian_201.jpg": 0, - "Abyssinian_202.jpg": 0, - "american_bulldog_203.jpg": 1, - "american_bulldog_205.jpg": 1, - "american_pit_bull_terrier_191.jpg": 2, - "american_pit_bull_terrier_192.jpg": 2, - "basset_hound_191.jpg": 3, - "basset_hound_192.jpg": 3, - "beagle_195.jpg": 4, - "beagle_196.jpg": 4, - "Bengal_192.jpg": 5, - "Bengal_193.jpg": 5, - "Birman_191.jpg": 6, - "Birman_192.jpg": 6, - "Bombay_200.jpg": 7, - "Bombay_201.jpg": 7, - "boxer_191.jpg": 8, - "boxer_192.jpg": 8, - "British_Shorthair_212.jpg": 9, - "British_Shorthair_213.jpg": 9, - } - - -def get_sample_dogs120_dict(): - return { - "n02085620-Chihuahua/n02085620_2650.jpg": 0, - "n02085620-Chihuahua/n02085620_4919.jpg": 0, - "n02085782-Japanese_spaniel/n02085782_191.jpg": 1, - "n02085782-Japanese_spaniel/n02085782_1929.jpg": 1, - "n02085936-Maltese_dog/n02085936_2760.jpg": 2, - "n02085936-Maltese_dog/n02085936_8350.jpg": 2, - "n02086079-Pekinese/n02086079_14999.jpg": 3, - "n02086079-Pekinese/n02086079_6333.jpg": 3, - "n02086240-Shih-Tzu/n02086240_5096.jpg": 4, - "n02086240-Shih-Tzu/n02086240_1690.jpg": 4, - "n02086646-Blenheim_spaniel/n02086646_23.jpg": 5, - "n02086646-Blenheim_spaniel/n02086646_2834.jpg": 5, - "n02086910-papillon/n02086910_2670.jpg": 6, - "n02086910-papillon/n02086910_7766.jpg": 6, - "n02087046-toy_terrier/n02087046_430.jpg": 7, - "n02087046-toy_terrier/n02087046_5843.jpg": 7, - "n02087394-Rhodesian_ridgeback/n02087394_1963.jpg": 8, - "n02087394-Rhodesian_ridgeback/n02087394_3619.jpg": 8, - "n02088094-Afghan_hound/n02088094_7106.jpg": 9, - "n02088094-Afghan_hound/n02088094_12664.jpg": 9, - } - - - -if __name__ == '__main__': - """ Usage of grad-CAM visualization - version 01.05 - - Settings: - class_num: set the class num of your dataset - data_name: choose a dataset - model_arch: CNN arth such as "resnet50" in torchvision - model_name: name to save - model_path: path of linear_classification checkpoint - img_path: a dict of img_name (string) and img_class (int) - img_root: dirs of image dataset - - Running: - 1. set a config and data_name. - 2. python tools/gradcam.py - """ - - args = { - # ----- dataset 1: cub200 ------ - "class_num": 200, - "data_name": "cub200", - # ------ dataset 2: pets ------- - # "class_num": 37, - # "data_name": "pets37", - # ----- dataset 3: dogs120 ----- - # "class_num": 120, - # "data_name": "dogs120", - # -------- cnn arch ---------- - "model_arch": "resnet50", - # ------------------------- Example: SL imagenet path ----------------------------- - "model_name": "imagenet_r50_pytorch", # model name 1 - "model_path": "./work_dirs/benchmarks/linear_classification/cub200/r50_last_2gpu_cub200/imagenet_r50_pytorch.pth/epoch_100.pth", # cub200 baseline - # "model_path": "./work_dirs/benchmarks/linear_classification/pets/r50_last_2gpu_pets/imagenet_r50_pytorch.pth/epoch_100.pth", # Pets baseline - # "model_path": "./work_dirs/benchmarks/linear_classification/dogs120/r50_last_2gpu_dogs120/imagenet_r50_pytorch.pth/epoch_100.pth", # Dogs baseline - # "model_name": "imagenet_r50_pytorch_bbox", # model name 2 - # "model_path": "./work_dirs/benchmarks/linear_classification/cub200/r50_last_2gpu_cub200_ablation/imagenet_r50_pytorch.pth/epoch_100.pth", # cub200, no back: bbox - # "model_name": "imagenet_r50_pytorch_mask", # model name 3 - # "model_path": "./work_dirs/benchmarks/linear_classification/pets/r50_last_2gpu_pets_ablation/imagenet_r50_pytorch.pth/epoch_100.pth", # pets, no back: mask - # -------- image dict ---------- - "img_path": dict(), - # ------- dataset root --------- - # "img_root": "/usr/commondata/public/CUB200/CUB_200/images/", # CUB-200, ori img - # "img_root": "/usr/commondata/public/CUB200/CUB_200/images_bbox/", # CUB-200, no back: bbox - # "img_root": "/usr/commondata/public/Pets37/images/", # Pets-37, ori img - "img_root": "/usr/commondata/public/Pets37/images_segmented/", # Pets-37, no back: mask - # "img_root": "/usr/commondata/public/Dogs120/Images/", # Dogs-120, ori img - "use_cuda": torch.cuda.is_available() - } - assert args["data_name"] in ["cub200", "pets37", "dogs120"] - - # get sample dict - try: - args["img_path"] = eval("get_sample_{}_dict()".format(args["data_name"])) - except: - print("Please add 'get_sample_{}_dict()' to load img list!".format(args["data_name"])) - exit() - - i = 0 - for k,v in args["img_path"].items(): - - cnn_arch = models.__dict__[args["model_arch"]] - model = cnn_arch(num_classes=args["class_num"]) - fit_model_state_dict(model, args["model_path"]) - grad_cam = GradCam( - model=model, feature_module=model.layer4, target_layer_names=["2"], use_cuda=args["use_cuda"]) - print(i) - - if len(args["img_root"]) > 1: - img_path = args["img_root"] + k - else: - img_path = k - - img = cv2.imread(img_path, 1) - img = np.float32(cv2.resize(img, (224, 224))) / 255 - input = preprocess_image(img) - - # save to plot dir - save_path = "work_dirs/plot_cam/{}/{}".format(args["data_name"], args["model_name"]) - if not os.path.exists(save_path): - os.makedirs(save_path) - # choose a target label, set None as default - target_index = None - mask = grad_cam(input, target_index) - - gb_model = GuidedBackpropReLUModel(model=model, use_cuda=args["use_cuda"]) - - gb, cls_res = gb_model(input, index=target_index, target=v) - gb = gb.transpose((1, 2, 0)) - cam_mask = cv2.merge([mask, mask, mask]) - cam_gb = deprocess_image(cam_mask * gb) - gb = deprocess_image(gb) - - cls_res_str = str(cls_res["bool"]) if cls_res["bool"] else "{}_pred{}".format(str(cls_res["bool"]), str(cls_res["pred"])) - show_cam_on_image(img, mask, "{}_C{}_id{}_{}".format(args["model_name"], str(v), str(i), cls_res_str), save_path) - # cv2.imwrite('{}/{}_id{}_gb.jpg'.format(save_path, args["model_name"], str(i)), gb) - cv2.imwrite('{}/{}_C{}_id{}_{}_cam_gb.jpg'.format(save_path, args["model_name"], str(v), str(i), cls_res_str), cam_gb) - i += 1 \ No newline at end of file diff --git a/tools/visualizations/vis_cam.py b/tools/visualizations/vis_cam.py new file mode 100644 index 00000000..47a18298 --- /dev/null +++ b/tools/visualizations/vis_cam.py @@ -0,0 +1,364 @@ +import argparse +import math +import pkg_resources +import re +import mmcv +import numpy as np + +from pathlib import Path +from PIL import Image +from mmcv import Config, DictAction +from mmcv.runner import load_checkpoint +from mmcv.utils import to_2tuple +from torch.nn import BatchNorm1d, BatchNorm2d, GroupNorm, LayerNorm +from torchvision.transforms import Compose + +from openmixup import digit_version +from openmixup.datasets.registry import PIPELINES +from openmixup.models import build_model +from openmixup.utils import build_from_cfg + +try: + from pytorch_grad_cam import (EigenCAM, EigenGradCAM, GradCAM, + GradCAMPlusPlus, LayerCAM, XGradCAM) + from pytorch_grad_cam.activations_and_gradients import \ + ActivationsAndGradients + from pytorch_grad_cam.utils.image import show_cam_on_image +except ImportError: + raise ImportError('Please run `pip install "grad-cam>=1.3.6"` to install ' + '3rd party package pytorch_grad_cam.') + +# set of transforms, which just change data format, not change the pictures +FORMAT_TRANSFORMS_SET = {'ToTensor', 'Normalize',} + +# Supported grad-cam type map +METHOD_MAP = { + 'gradcam': GradCAM, + 'gradcam++': GradCAMPlusPlus, + 'xgradcam': XGradCAM, + 'eigencam': EigenCAM, + 'eigengradcam': EigenGradCAM, + 'layercam': LayerCAM, +} + + +def parse_args(): + parser = argparse.ArgumentParser(description='Visualize CAM') + parser.add_argument('img', help='Image file') + parser.add_argument('config', help='Config file') + parser.add_argument('checkpoint', help='Checkpoint file') + parser.add_argument( + '--target-layers', + default=[], + nargs='+', + type=str, + help='The target layers to get CAM, if not set, the tool will ' + 'specify the norm layer in the last block. Backbones ' + 'implemented by users are recommended to manually specify' + ' target layers in commmad statement.') + parser.add_argument( + '--preview-model', + default=False, + action='store_true', + help='To preview all the model layers') + parser.add_argument( + '--method', + default='GradCAM', + help='Type of method to use, supports ' + f'{", ".join(list(METHOD_MAP.keys()))}.') + parser.add_argument( + '--target-category', + default=[], + nargs='+', + type=int, + help='The target category to get CAM, default to use result ' + 'get from given model.') + parser.add_argument( + '--eigen-smooth', + default=False, + action='store_true', + help='Reduce noise by taking the first principle componenet of ' + '``cam_weights*activations``') + parser.add_argument( + '--aug-smooth', + default=False, + action='store_true', + help='Wether to use test time augmentation, default not to use') + parser.add_argument( + '--save-path', + type=Path, + help='The path to save visualize cam image, default not to save.') + parser.add_argument('--device', default='cpu', help='Device to use cpu') + parser.add_argument( + '--vit-like', + action='store_true', + help='Whether the network is a ViT-like network.') + parser.add_argument( + '--num-extra-tokens', + type=int, + help='The number of extra tokens in ViT-like backbones. Defaults to' + ' use num_extra_tokens of the backbone.') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + args = parser.parse_args() + if args.method.lower() not in METHOD_MAP.keys(): + raise ValueError(f'invalid CAM type {args.method},' + f' supports {", ".join(list(METHOD_MAP.keys()))}.') + + return args + + +def build_reshape_transform(model, args): + """Build reshape_transform for `cam.activations_and_grads`, which is + necessary for ViT-like networks.""" + # ViT_based_Transformers have an additional clstoken in features + if not args.vit_like: + + def check_shape(tensor): + assert len(tensor.size()) != 3, \ + (f"The input feature's shape is {tensor.size()}, and it seems " + 'to have been flattened or from a vit-like network. ' + "Please use `--vit-like` if it's from a vit-like network.") + return tensor + + return check_shape + + if args.num_extra_tokens is not None: + num_extra_tokens = args.num_extra_tokens + elif hasattr(model.backbone, 'num_extra_tokens'): + num_extra_tokens = model.backbone.num_extra_tokens + else: + num_extra_tokens = 1 + + def _reshape_transform(tensor): + """reshape_transform helper.""" + assert len(tensor.size()) == 3, \ + (f"The input feature's shape is {tensor.size()}, " + 'and the feature seems not from a vit-like network?') + tensor = tensor[:, num_extra_tokens:, :] + # get heat_map_height and heat_map_width, preset input is a square + heat_map_area = tensor.size()[1] + height, width = to_2tuple(int(math.sqrt(heat_map_area))) + assert height * height == heat_map_area, \ + (f"The input feature's length ({heat_map_area+num_extra_tokens}) " + f'minus num-extra-tokens ({num_extra_tokens}) is {heat_map_area},' + ' which is not a perfect square number. Please check if you used ' + 'a wrong num-extra-tokens.') + result = tensor.reshape(tensor.size(0), height, width, tensor.size(2)) + + # Bring the channels to the first dimension, like in CNNs. + result = result.transpose(2, 3).transpose(1, 2) + return result + + return _reshape_transform + + +def apply_transforms(img_path, pipeline_cfg): + """Apply transforms pipeline and get formatted data.""" + + def split_pipeline_cfg(pipeline_cfg): + """to split the transfoms into image_transforms and + format_transforms.""" + image_transforms_cfg, format_transforms_cfg = [], [] + for transform in pipeline_cfg: + if transform['type'] in FORMAT_TRANSFORMS_SET: + format_transforms_cfg.append(transform) + else: + image_transforms_cfg.append(transform) + return image_transforms_cfg, format_transforms_cfg + + image_transforms, format_transforms = split_pipeline_cfg(pipeline_cfg) + image_transforms = Compose([ + build_from_cfg(p, PIPELINES) for p in image_transforms]) + format_transforms = Compose([ + build_from_cfg(p, PIPELINES) for p in format_transforms]) + + img = Image.open(img_path) + img = img.convert('RGB') + img = image_transforms(img) + format_img = format_transforms(img) + + return (format_img, img) + + +class MMActivationsAndGradients(ActivationsAndGradients): + """Activations and gradients manager for mmcls models.""" + + def __call__(self, x): + self.gradients = [] + self.activations = [] + results = self.model(x, mode='test') + assert len(results) == 1 + return list(results.values())[0] + +def init_cam(method, model, target_layers, use_cuda, reshape_transform): + """Construct the CAM object once, In order to be compatible with mmcls, + here we modify the ActivationsAndGradients object.""" + + GradCAM_Class = METHOD_MAP[method.lower()] + cam = GradCAM_Class( + model=model, target_layers=target_layers, use_cuda=use_cuda) + # Release the original hooks in ActivationsAndGradients to use + # MMActivationsAndGradients. + cam.activations_and_grads.release() + cam.activations_and_grads = MMActivationsAndGradients( + cam.model, cam.target_layers, reshape_transform) + + return cam + + +def get_layer(layer_str, model): + """get model layer from given str.""" + cur_layer = model + layer_names = layer_str.strip().split('.') + + def get_children_by_name(model, name): + try: + return getattr(model, name) + except AttributeError as e: + raise AttributeError( + e.args[0] + + '. Please use `--preview-model` to check keys at first.') + + def get_children_by_eval(model, name): + try: + return eval(f'model{name}', {}, {'model': model}) + except (AttributeError, IndexError) as e: + raise AttributeError( + e.args[0] + + '. Please use `--preview-model` to check keys at first.') + + for layer_name in layer_names: + match_res = re.match('(?P.+?)(?P(\\[.+\\])+)', + layer_name) + if match_res: + layer_name = match_res.groupdict()['name'] + indices = match_res.groupdict()['indices'] + cur_layer = get_children_by_name(cur_layer, layer_name) + cur_layer = get_children_by_eval(cur_layer, indices) + else: + cur_layer = get_children_by_name(cur_layer, layer_name) + + return cur_layer + + +def show_cam_grad(grayscale_cam, src_img, title, out_path=None): + """fuse src_img and grayscale_cam and show or save.""" + grayscale_cam = grayscale_cam[0, :] + src_img = np.float32(src_img) / 255 + visualization_img = show_cam_on_image( + src_img, grayscale_cam, use_rgb=False) + + if out_path: + mmcv.imwrite(visualization_img, str(out_path)) + else: + mmcv.imshow(visualization_img, win_name=title) + + +def get_default_traget_layers(model, args): + """get default target layers from given model, here choose nrom type layer + as default target layer.""" + norm_layers = [] + for m in model.backbone.modules(): + if isinstance(m, (BatchNorm2d, LayerNorm, GroupNorm, BatchNorm1d)): + norm_layers.append(m) + if len(norm_layers) == 0: + raise ValueError( + '`--target-layers` is empty. Please use `--preview-model`' + ' to check keys at first and then specify `target-layers`.') + # if the model is CNN model or Swin model, just use the last norm + # layer as the target-layer, if the model is ViT model, the final + # classification is done on the class token computed in the last + # attention block, the output will not be affected by the 14x14 + # channels in the last layer. The gradient of the output with + # respect to them, will be 0! here use the last 3rd norm layer. + # means the first norm of the last decoder block. + if args.vit_like: + if args.num_extra_tokens: + num_extra_tokens = args.num_extra_tokens + elif hasattr(model.backbone, 'num_extra_tokens'): + num_extra_tokens = model.backbone.num_extra_tokens + else: + raise AttributeError('Please set num_extra_tokens in backbone' + " or using 'num-extra-tokens'") + + # if a vit-like backbone's num_extra_tokens bigger than 0, view it + # as a VisionTransformer backbone, eg. DeiT, T2T-ViT. + if num_extra_tokens >= 1: + print('Automatically choose the last norm layer before the ' + 'final attention block as target_layer..') + return [norm_layers[-3]] + print('Automatically choose the last norm layer as target_layer.') + target_layers = [norm_layers[-1]] + return target_layers + + +def main(): + args = parse_args() + cfg = Config.fromfile(args.config) + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + + # build the model from a config file and a checkpoint file + model = build_model(cfg.model) + if args.checkpoint is not None: + # Mapping the weights to GPU may cause unexpected video memory leak + # which refers to https://github.com/open-mmlab/mmdetection/pull/6405 + load_checkpoint(model, args.checkpoint, map_location='cpu') + model.to(args.device) + model.eval() + + if args.preview_model: + print(model) + print('\n Please remove `--preview-model` to get the CAM.') + return + + # apply transform and perpare data + format_img, src_img = apply_transforms(args.img, cfg.data.val.pipeline) + + # build target layers + if args.target_layers: + target_layers = [ + get_layer(layer, model) for layer in args.target_layers + ] + else: + target_layers = get_default_traget_layers(model, args) + + # init a cam grad calculator + use_cuda = ('cuda' in args.device) + reshape_transform = build_reshape_transform(model, args) + cam = init_cam(args.method, model, target_layers, use_cuda, + reshape_transform) + + # warp the target_category with ClassifierOutputTarget in grad_cam>=1.3.7, + # to fix the bug in #654. + targets = None + if args.target_category: + grad_cam_v = pkg_resources.get_distribution('grad_cam').version + if digit_version(grad_cam_v) >= digit_version('1.3.7'): + from pytorch_grad_cam.utils.model_targets import \ + ClassifierOutputTarget + targets = [ClassifierOutputTarget(c) for c in args.target_category] + else: + targets = args.target_category + + # calculate cam grads and show|save the visualization image + grayscale_cam = cam( + format_img.unsqueeze(0), + targets, + eigen_smooth=args.eigen_smooth, + aug_smooth=args.aug_smooth) + show_cam_grad( + grayscale_cam, src_img, title=args.method, out_path=args.save_path) + + +if __name__ == '__main__': + main() diff --git a/tools/visualizations/vis_lr.py b/tools/visualizations/vis_lr.py new file mode 100644 index 00000000..657148fa --- /dev/null +++ b/tools/visualizations/vis_lr.py @@ -0,0 +1,333 @@ +import argparse +import os.path as osp +import re +import time +from pathlib import Path +from pprint import pformat + +import matplotlib.pyplot as plt +import mmcv +import torch.nn as nn +from mmcv import Config, DictAction, ProgressBar +from mmcv.runner import (EpochBasedRunner, IterBasedRunner, IterLoader, + build_optimizer) +from torch.utils.data import DataLoader + +from openmixup.utils import get_root_logger + + +class DummyEpochBasedRunner(EpochBasedRunner): + """Fake Epoch-based Runner. + + This runner won't train model, and it will only call hooks and return all + learning rate in each iteration. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.progress_bar = ProgressBar(self._max_epochs, start=False) + + def train(self, data_loader, **kwargs): + lr_list = [] + self.model.train() + self.mode = 'train' + self.data_loader = data_loader + self._max_iters = self._max_epochs * len(self.data_loader) + self.call_hook('before_train_epoch') + for i in range(len(self.data_loader)): + self._inner_iter = i + self.call_hook('before_train_iter') + lr_list.append(self.current_lr()) + self.call_hook('after_train_iter') + self._iter += 1 + + self.call_hook('after_train_epoch') + self._epoch += 1 + self.progress_bar.update(1) + return lr_list + + def run(self, data_loaders, workflow, **kwargs): + assert isinstance(data_loaders, list) + assert mmcv.is_list_of(workflow, tuple) + assert len(data_loaders) == len(workflow) + + assert self._max_epochs is not None, ( + 'max_epochs must be specified during instantiation') + + for i, flow in enumerate(workflow): + mode, epochs = flow + if mode == 'train': + self._max_iters = self._max_epochs * len(data_loaders[i]) + break + + self.logger.info('workflow: %s, max: %d epochs', workflow, + self._max_epochs) + self.call_hook('before_run') + + self.progress_bar.start() + lr_list = [] + while self.epoch < self._max_epochs: + for i, flow in enumerate(workflow): + mode, epochs = flow + if isinstance(mode, str): # self.train() + if not hasattr(self, mode): + raise ValueError( + f'runner has no method named "{mode}" to run an ' + 'epoch') + epoch_runner = getattr(self, mode) + else: + raise TypeError( + 'mode in workflow must be a str, but got {}'.format( + type(mode))) + + for _ in range(epochs): + if mode == 'train' and self.epoch >= self._max_epochs: + break + lr_list.extend(epoch_runner(data_loaders[i], **kwargs)) + + self.progress_bar.file.write('\n') + time.sleep(1) # wait for some hooks like loggers to finish + self.call_hook('after_run') + return lr_list + + +class DummyIterBasedRunner(IterBasedRunner): + """Fake Iter-based Runner. + + This runner won't train model, and it will only call hooks and return all + learning rate in each iteration. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.progress_bar = ProgressBar(self._max_iters, start=False) + + def train(self, data_loader, **kwargs): + lr_list = [] + self.model.train() + self.mode = 'train' + self.data_loader = data_loader + self._epoch = data_loader.epoch + next(data_loader) + self.call_hook('before_train_iter') + lr_list.append(self.current_lr()) + self.call_hook('after_train_iter') + self._inner_iter += 1 + self._iter += 1 + self.progress_bar.update(1) + return lr_list + + def run(self, data_loaders, workflow, **kwargs): + assert isinstance(data_loaders, list) + assert mmcv.is_list_of(workflow, tuple) + assert len(data_loaders) == len(workflow) + assert self._max_iters is not None, ( + 'max_iters must be specified during instantiation') + + self.logger.info('workflow: %s, max: %d iters', workflow, + self._max_iters) + self.call_hook('before_run') + + iter_loaders = [IterLoader(x) for x in data_loaders] + + self.call_hook('before_epoch') + + self.progress_bar.start() + lr_list = [] + while self.iter < self._max_iters: + for i, flow in enumerate(workflow): + self._inner_iter = 0 + mode, iters = flow + if not isinstance(mode, str) or not hasattr(self, mode): + raise ValueError( + 'runner has no method named "{}" to run a workflow'. + format(mode)) + iter_runner = getattr(self, mode) + for _ in range(iters): + if mode == 'train' and self.iter >= self._max_iters: + break + lr_list.extend(iter_runner(iter_loaders[i], **kwargs)) + + self.progress_bar.file.write('\n') + time.sleep(1) # wait for some hooks like loggers to finish + self.call_hook('after_epoch') + self.call_hook('after_run') + return lr_list + + +class SimpleModel(nn.Module): + """simple model that do nothing in train_step.""" + + def __init__(self): + super(SimpleModel, self).__init__() + self.conv = nn.Conv2d(1, 1, 1) + + def train_step(self, *args, **kwargs): + pass + + +def parse_args(): + parser = argparse.ArgumentParser( + description='Visualize a Dataset Pipeline') + parser.add_argument('config', help='config file path') + parser.add_argument( + '--dataset-size', + type=int, + help='The size of the dataset. If specify, `build_dataset` will ' + 'be skipped and use this size as the dataset size.') + parser.add_argument( + '--ngpus', + type=int, + default=1, + help='The number of GPUs used in training.') + parser.add_argument('--title', type=str, help='title of figure') + parser.add_argument( + '--style', type=str, default='whitegrid', help='style of plt') + parser.add_argument( + '--save-path', + type=Path, + help='The learning rate curve plot save path') + parser.add_argument( + '--window-size', + default='12*7', + help='Size of the window to display images, in format of "$W*$H".') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + args = parser.parse_args() + if args.window_size != '': + assert re.match(r'\d+\*\d+', args.window_size), \ + "'window-size' must be in format 'W*H'." + + return args + + +def plot_curve(lr_list, args, iters_per_epoch, by_epoch=True): + """Plot learning rate vs iter graph.""" + try: + import seaborn as sns + sns.set_style(args.style) + except ImportError: + print("Attention: The plot style won't be applied because 'seaborn' " + 'package is not installed, please install it if you want better ' + 'show style.') + wind_w, wind_h = args.window_size.split('*') + wind_w, wind_h = int(wind_w), int(wind_h) + plt.figure(figsize=(wind_w, wind_h)) + # if legend is None, use {filename}_{key} as legend + + ax: plt.Axes = plt.subplot() + + ax.plot(lr_list, linewidth=1) + if by_epoch: + ax.xaxis.tick_top() + ax.set_xlabel('Iters') + ax.xaxis.set_label_position('top') + sec_ax = ax.secondary_xaxis( + 'bottom', + functions=(lambda x: x / iters_per_epoch, + lambda y: y * iters_per_epoch)) + sec_ax.set_xlabel('Epochs') + # ticks = range(0, len(lr_list), iters_per_epoch) + # plt.xticks(ticks=ticks, labels=range(len(ticks))) + else: + plt.xlabel('Iters') + plt.ylabel('Learning Rate') + + if args.title is None: + plt.title(f'{osp.basename(args.config)} Learning Rate curve') + else: + plt.title(args.title) + + if args.save_path: + plt.savefig(args.save_path) + print(f'The learning rate graph is saved at {args.save_path}') + plt.show() + + +def simulate_train(data_loader, cfg, by_epoch=True): + # build logger, data_loader, model and optimizer + logger = get_root_logger() + data_loaders = [data_loader] + model = SimpleModel() + optimizer = build_optimizer(model, cfg.optimizer) + + # build runner + if by_epoch: + runner = DummyEpochBasedRunner( + max_epochs=cfg.runner.max_epochs, + model=model, + optimizer=optimizer, + logger=logger) + else: + runner = DummyIterBasedRunner( + max_iters=cfg.runner.max_iters, + model=model, + optimizer=optimizer, + logger=logger) + + # register hooks + runner.register_training_hooks( + lr_config=cfg.lr_config, + custom_hooks_config=cfg.get('custom_hooks', None), + ) + + # only use the first train workflow + workflow = cfg.workflow[:1] + assert workflow[0][0] == 'train' + return runner.run(data_loaders, cfg.workflow) + + +def main(): + args = parse_args() + cfg = Config.fromfile(args.config) + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + + # make sure save_root exists + if args.save_path and not args.save_path.parent.exists(): + raise Exception(f'The save path is {args.save_path}, and directory ' + f"'{args.save_path.parent}' do not exist.") + + # init logger + logger = get_root_logger(log_level=cfg.log_level) + logger.info('Lr config : \n\n' + pformat(cfg.lr_config, sort_dicts=False) + + '\n') + + by_epoch = True if cfg.runner.type == 'EpochBasedRunner' else False + + # prepare data loader + batch_size = cfg.data.samples_per_gpu * args.ngpus + + if args.dataset_size is None and by_epoch: + from mmcls.datasets.builder import build_dataset + dataset_size = len(build_dataset(cfg.data.train)) + else: + dataset_size = args.dataset_size or batch_size + + fake_dataset = list(range(dataset_size)) + data_loader = DataLoader(fake_dataset, batch_size=batch_size) + dataset_info = (f'\nDataset infos:' + f'\n - Dataset size: {dataset_size}' + f'\n - Samples per GPU: {cfg.data.samples_per_gpu}' + f'\n - Number of GPUs: {args.ngpus}' + f'\n - Total batch size: {batch_size}') + if by_epoch: + dataset_info += f'\n - Iterations per epoch: {len(data_loader)}' + logger.info(dataset_info) + + # simulation training process + lr_list = simulate_train(data_loader, cfg, by_epoch) + + plot_curve(lr_list, args, len(data_loader), by_epoch) + + +if __name__ == '__main__': + main()