添加openmmlab测试用例

openmmlab_test/mmclassification-speed-benchmark/configs/vgg/README.md

+# Very Deep Convolutional Networks for Large-Scale Image Recognition
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+```latex
+@article{simonyan2014very,
+  title={Very deep convolutional networks for large-scale image recognition},
+  author={Simonyan, Karen and Zisserman, Andrew},
+  journal={arXiv preprint arXiv:1409.1556},
+  year={2014}
+}
+
+```
+
+## Results and models
+
+### ImageNet
+
+|         Model         | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
+|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:|
+| VGG-11 | 132.86 | 7.63 | 68.75 | 88.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.log.json) |
+| VGG-13 | 133.05 | 11.34 | 70.02 | 89.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.log.json) |
+| VGG-16 | 138.36 | 15.5 | 71.62 | 90.49 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.log.json) |
+| VGG-19 | 143.67 | 19.67 | 72.41 | 90.80 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.log.json)|
+| VGG-11-BN | 132.87 | 7.64 | 70.75 | 90.12 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11bn_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.log.json) |
+| VGG-13-BN | 133.05 | 11.36 | 72.15 | 90.71 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13bn_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.log.json) |
+| VGG-16-BN | 138.37 | 15.53 | 73.72 | 91.68 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.log.json) |
+| VGG-19-BN | 143.68 | 19.7 | 74.70 | 92.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19bn_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.log.json)|

openmmlab_test/mmclassification-speed-benchmark/configs/vgg/metafile.yml

+Collections:
+  - Name: VGG
+    Metadata:
+      Training Data: ImageNet
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x Xp GPUs
+      Epochs: 100
+      Batch Size: 256
+      Architecture:
+        - VGG
+    Paper: https://arxiv.org/abs/1409.1556
+    README: configs/vgg/README.md
+
+Models:
+- Config: configs/vgg/vgg11_b32x8_imagenet.py
+  In Collection: VGG
+  Metadata:
+    FLOPs: 7630000000
+    Parameters: 132860000
+  Name: vgg11_b32x8_imagenet
+  Results:
+  - Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 68.75
+      Top 5 Accuracy: 88.87
+    Task: Image Classification
+  Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.pth
+- Config: configs/vgg/vgg13_b32x8_imagenet.py
+  In Collection: VGG
+  Metadata:
+    FLOPs: 11340000000
+    Parameters: 133050000
+  Name: vgg13_b32x8_imagenet
+  Results:
+  - Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 70.02
+      Top 5 Accuracy: 89.46
+    Task: Image Classification
+  Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.pth
+- Config: configs/vgg/vgg16_b32x8_imagenet.py
+  In Collection: VGG
+  Metadata:
+    FLOPs: 15500000000
+    Parameters: 138360000
+  Name: vgg16_b32x8_imagenet
+  Results:
+  - Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 71.62
+      Top 5 Accuracy: 90.49
+    Task: Image Classification
+  Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.pth
+- Config: configs/vgg/vgg19_b32x8_imagenet.py
+  In Collection: VGG
+  Metadata:
+    FLOPs: 19670000000
+    Parameters: 143670000
+  Name: vgg19_b32x8_imagenet
+  Results:
+  - Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 72.41
+      Top 5 Accuracy: 90.8
+    Task: Image Classification
+  Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth
+- Config: configs/vgg/vgg11bn_b32x8_imagenet.py
+  In Collection: VGG
+  Metadata:
+    FLOPs: 7640000000
+    Parameters: 132870000
+  Name: vgg11bn_b32x8_imagenet
+  Results:
+  - Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 70.75
+      Top 5 Accuracy: 90.12
+    Task: Image Classification
+  Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.pth
+- Config: configs/vgg/vgg13bn_b32x8_imagenet.py
+  In Collection: VGG
+  Metadata:
+    FLOPs: 11360000000
+    Parameters: 133050000
+  Name: vgg13bn_b32x8_imagenet
+  Results:
+  - Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 72.15
+      Top 5 Accuracy: 90.71
+    Task: Image Classification
+  Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.pth
+- Config: configs/vgg/vgg16_b32x8_imagenet.py
+  In Collection: VGG
+  Metadata:
+    FLOPs: 15530000000
+    Parameters: 138370000
+  Name: vgg16_b32x8_imagenet
+  Results:
+  - Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 73.72
+      Top 5 Accuracy: 91.68
+    Task: Image Classification
+  Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.pth
+- Config: configs/vgg/vgg19bn_b32x8_imagenet.py
+  In Collection: VGG
+  Metadata:
+    FLOPs: 19700000000
+    Parameters: 143680000
+  Name: vgg19bn_b32x8_imagenet
+  Results:
+  - Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 74.7
+      Top 5 Accuracy: 92.24
+    Task: Image Classification
+  Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth

openmmlab_test/mmclassification-speed-benchmark/configs/vgg/vgg11_b32x8_imagenet.py

+_base_ = [
+    '../_base_/models/vgg11.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py',
+    '../_base_/default_runtime.py',
+]
+optimizer = dict(lr=0.01)

openmmlab_test/mmclassification-speed-benchmark/configs/vgg/vgg11bn_b32x8_imagenet.py

+_base_ = [
+    '../_base_/models/vgg11bn.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

openmmlab_test/mmclassification-speed-benchmark/configs/vgg/vgg13_b32x8_imagenet.py

+_base_ = [
+    '../_base_/models/vgg13.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]
+optimizer = dict(lr=0.01)

openmmlab_test/mmclassification-speed-benchmark/configs/vgg/vgg13bn_b32x8_imagenet.py

+_base_ = [
+    '../_base_/models/vgg13bn.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

openmmlab_test/mmclassification-speed-benchmark/configs/vgg/vgg16_b16x8_voc.py

+_base_ = ['../_base_/datasets/voc_bs16.py', '../_base_/default_runtime.py']
+
+# use different head for multilabel task
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(type='VGG', depth=16, num_classes=20),
+    neck=None,
+    head=dict(
+        type='MultiLabelClsHead',
+        loss=dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)))
+
+# load model pretrained on imagenet
+load_from = 'https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.pth'  # noqa
+
+# optimizer
+optimizer = dict(
+    type='SGD',
+    lr=0.001,
+    momentum=0.9,
+    weight_decay=0,
+    paramwise_cfg=dict(custom_keys={'.backbone.classifier': dict(lr_mult=10)}))
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(policy='step', step=20, gamma=0.1)
+runner = dict(type='EpochBasedRunner', max_epochs=40)

openmmlab_test/mmclassification-speed-benchmark/configs/vgg/vgg16_b32x8_imagenet.py

+_base_ = [
+    '../_base_/models/vgg16.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]
+optimizer = dict(lr=0.01)

openmmlab_test/mmclassification-speed-benchmark/configs/vgg/vgg16bn_b32x8_imagenet.py

+_base_ = [
+    '../_base_/models/vgg16bn.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

openmmlab_test/mmclassification-speed-benchmark/configs/vgg/vgg19_b32x8_imagenet.py

+_base_ = [
+    '../_base_/models/vgg19.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]
+optimizer = dict(lr=0.01)

openmmlab_test/mmclassification-speed-benchmark/configs/vgg/vgg19bn_b32x8_imagenet.py

+_base_ = [
+    '../_base_/models/vgg19bn.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

openmmlab_test/mmclassification-speed-benchmark/configs/vision_transformer/vit_base_patch16_224_finetune_imagenet.py

+# Refer to pytorch-image-models
+_base_ = [
+    '../_base_/models/vit_base_patch16_224_finetune.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256_epochstep.py',
+    '../_base_/default_runtime.py'
+]
+
+img_norm_cfg = dict(
+    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)

openmmlab_test/mmclassification-speed-benchmark/configs/vision_transformer/vit_base_patch16_224_pretrain_imagenet.py

+_base_ = [
+    '../_base_/models/vit_base_patch16_224_pretrain.py',
+    '../_base_/datasets/imagenet_bs64_pil_resize.py',
+    '../_base_/schedules/imagenet_bs4096_AdamW.py',
+    '../_base_/default_runtime.py'
+]
+
+policies = [
+    [
+        dict(type='Posterize', bits=4, prob=0.4),
+        dict(type='Rotate', angle=30., prob=0.6)
+    ],
+    [
+        dict(type='Solarize', thr=256 / 9 * 4, prob=0.6),
+        dict(type='AutoContrast', prob=0.5)
+    ],
+    [dict(type='Equalize', prob=0.8),
+     dict(type='Equalize', prob=0.6)],
+    [
+        dict(type='Posterize', bits=5, prob=0.6),
+        dict(type='Posterize', bits=5, prob=0.6)
+    ],
+    [
+        dict(type='Equalize', prob=0.4),
+        dict(type='Solarize', thr=256 / 9 * 5, prob=0.2)
+    ],
+    [
+        dict(type='Equalize', prob=0.4),
+        dict(type='Rotate', angle=30 / 9 * 8, prob=0.8)
+    ],
+    [
+        dict(type='Solarize', thr=256 / 9 * 6, prob=0.6),
+        dict(type='Equalize', prob=0.6)
+    ],
+    [dict(type='Posterize', bits=6, prob=0.8),
+     dict(type='Equalize', prob=1.)],
+    [
+        dict(type='Rotate', angle=10., prob=0.2),
+        dict(type='Solarize', thr=256 / 9, prob=0.6)
+    ],
+    [
+        dict(type='Equalize', prob=0.6),
+        dict(type='Posterize', bits=5, prob=0.6)
+    ],
+    [
+        dict(type='Rotate', angle=30 / 9 * 8, prob=0.8),
+        dict(type='ColorTransform', magnitude=0., prob=0.4)
+    ],
+    [
+        dict(type='Rotate', angle=30., prob=0.4),
+        dict(type='Equalize', prob=0.6)
+    ],
+    [dict(type='Equalize', prob=0.0),
+     dict(type='Equalize', prob=0.8)],
+    [dict(type='Invert', prob=0.6),
+     dict(type='Equalize', prob=1.)],
+    [
+        dict(type='ColorTransform', magnitude=0.4, prob=0.6),
+        dict(type='Contrast', magnitude=0.8, prob=1.)
+    ],
+    [
+        dict(type='Rotate', angle=30 / 9 * 8, prob=0.8),
+        dict(type='ColorTransform', magnitude=0.2, prob=1.)
+    ],
+    [
+        dict(type='ColorTransform', magnitude=0.8, prob=0.8),
+        dict(type='Solarize', thr=256 / 9 * 2, prob=0.8)
+    ],
+    [
+        dict(type='Sharpness', magnitude=0.7, prob=0.4),
+        dict(type='Invert', prob=0.6)
+    ],
+    [
+        dict(
+            type='Shear',
+            magnitude=0.3 / 9 * 5,
+            prob=0.6,
+            direction='horizontal'),
+        dict(type='Equalize', prob=1.)
+    ],
+    [
+        dict(type='ColorTransform', magnitude=0., prob=0.4),
+        dict(type='Equalize', prob=0.6)
+    ],
+    [
+        dict(type='Equalize', prob=0.4),
+        dict(type='Solarize', thr=256 / 9 * 5, prob=0.2)
+    ],
+    [
+        dict(type='Solarize', thr=256 / 9 * 4, prob=0.6),
+        dict(type='AutoContrast', prob=0.6)
+    ],
+    [dict(type='Invert', prob=0.6),
+     dict(type='Equalize', prob=1.)],
+    [
+        dict(type='ColorTransform', magnitude=0.4, prob=0.6),
+        dict(type='Contrast', magnitude=0.8, prob=1.)
+    ],
+    [dict(type='Equalize', prob=0.8),
+     dict(type='Equalize', prob=0.6)],
+]
+
+dataset_type = 'ImageNet'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='RandomResizedCrop', size=224),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(type='AutoAugment', policies=policies),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=(256, -1), backend='pillow'),
+    dict(type='CenterCrop', crop_size=224),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=64,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline),
+    test=dict(
+        # replace `data/val` with `data/test` for standard test
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='accuracy')

openmmlab_test/mmclassification-speed-benchmark/configs/vision_transformer/vit_base_patch16_384_finetune_imagenet.py

+# Refer to pytorch-image-models
+_base_ = [
+    '../_base_/models/vit_base_patch16_384_finetune.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256_epochstep.py',
+    '../_base_/default_runtime.py'
+]
+
+img_norm_cfg = dict(
+    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=(384, -1), backend='pillow'),
+    dict(type='CenterCrop', crop_size=384),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+
+data = dict(test=dict(pipeline=test_pipeline))

openmmlab_test/mmclassification-speed-benchmark/configs/vision_transformer/vit_base_patch32_384_finetune_imagenet.py

+# Refer to pytorch-image-models
+_base_ = [
+    '../_base_/models/vit_base_patch32_384_finetune.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256_epochstep.py',
+    '../_base_/default_runtime.py'
+]
+
+img_norm_cfg = dict(
+    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=(384, -1), backend='pillow'),
+    dict(type='CenterCrop', crop_size=384),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+
+data = dict(test=dict(pipeline=test_pipeline))

openmmlab_test/mmclassification-speed-benchmark/configs/vision_transformer/vit_large_patch16_224_finetune_imagenet.py

+# Refer to pytorch-image-models
+_base_ = [
+    '../_base_/models/vit_large_patch16_224_finetune.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256_epochstep.py',
+    '../_base_/default_runtime.py'
+]
+
+img_norm_cfg = dict(
+    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)

openmmlab_test/mmclassification-speed-benchmark/configs/vision_transformer/vit_large_patch16_384_finetune_imagenet.py

+# Refer to pytorch-image-models
+_base_ = [
+    '../_base_/models/vit_large_patch16_384_finetune.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256_epochstep.py',
+    '../_base_/default_runtime.py'
+]
+
+img_norm_cfg = dict(
+    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=(384, -1), backend='pillow'),
+    dict(type='CenterCrop', crop_size=384),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+
+data = dict(test=dict(pipeline=test_pipeline))

openmmlab_test/mmclassification-speed-benchmark/configs/vision_transformer/vit_large_patch32_384_finetune_imagenet.py

+# Refer to pytorch-image-models
+_base_ = [
+    '../_base_/models/vit_large_patch32_384_finetune.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256_epochstep.py',
+    '../_base_/default_runtime.py'
+]
+
+img_norm_cfg = dict(
+    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=(384, -1), backend='pillow'),
+    dict(type='CenterCrop', crop_size=384),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+
+data = dict(test=dict(pipeline=test_pipeline))

openmmlab_test/mmclassification-speed-benchmark/demo/image_demo.py

+from argparse import ArgumentParser
+
+from mmcls.apis import inference_model, init_model, show_result_pyplot
+
+
+def main():
+    parser = ArgumentParser()
+    parser.add_argument('img', help='Image file')
+    parser.add_argument('config', help='Config file')
+    parser.add_argument('checkpoint', help='Checkpoint file')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+    args = parser.parse_args()
+
+    # build the model from a config file and a checkpoint file
+    model = init_model(args.config, args.checkpoint, device=args.device)
+    # test a single image
+    result = inference_model(model, args.img)
+    # show the results
+    show_result_pyplot(model, args.img, result)
+
+
+if __name__ == '__main__':
+    main()