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Tutorial 1: Finetuning Models

Detectors pre-trained on the COCO dataset can serve as a good pre-trained model for other datasets, e.g., COCO-WholeBody Dataset. This tutorial provides instruction for users to use the models provided in the Model Zoo for other datasets to obtain better performance.

Outline

There are two steps to finetune a model on a new dataset.

To finetune on the custom datasets, the users need to modify four parts in the config.

Modify Head

Then the new config needs to modify the model according to the keypoint numbers of the new datasets. By only changing out_channels in the keypoint_head. For example, we have 133 keypoints for COCO-WholeBody, and we have 17 keypoints for COCO.

channel_cfg = dict(
    num_output_channels=133,  # changing from 17 to 133
    dataset_joints=133,  # changing from 17 to 133
    dataset_channel=[
        list(range(133)),  # changing from 17 to 133
    ],
    inference_channel=list(range(133)))  # changing from 17 to 133

# model settings
model = dict(
    type='TopDown',
    pretrained='https://download.openmmlab.com/mmpose/'
    'pretrain_models/hrnet_w48-8ef0771d.pth',
    backbone=dict(
        type='HRNet',
        in_channels=3,
        extra=dict(
            stage1=dict(
                num_modules=1,
                num_branches=1,
                block='BOTTLENECK',
                num_blocks=(4, ),
                num_channels=(64, )),
            stage2=dict(
                num_modules=1,
                num_branches=2,
                block='BASIC',
                num_blocks=(4, 4),
                num_channels=(48, 96)),
            stage3=dict(
                num_modules=4,
                num_branches=3,
                block='BASIC',
                num_blocks=(4, 4, 4),
                num_channels=(48, 96, 192)),
            stage4=dict(
                num_modules=3,
                num_branches=4,
                block='BASIC',
                num_blocks=(4, 4, 4, 4),
                num_channels=(48, 96, 192, 384))),
    ),
    keypoint_head=dict(
        type='TopdownHeatmapSimpleHead',
        in_channels=48,
        out_channels=channel_cfg['num_output_channels'], # modify this
        num_deconv_layers=0,
        extra=dict(final_conv_kernel=1, ),
        loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
    train_cfg=dict(),
    test_cfg=dict(
        flip_test=True,
        post_process='unbiased',
        shift_heatmap=True,
        modulate_kernel=17))

Note that the pretrained='https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w48-8ef0771d.pth' setting is used for initializing backbone. If you are training a new model from ImageNet-pretrained weights, this is for you. However, this setting is not related to our task at hand. What we need is load_from, which will be discussed later.

Modify dataset

The users may also need to prepare the dataset and write the configs about dataset. MMPose supports multiple (10+) dataset, including COCO, COCO-WholeBody and MPII-TRB.

data_root = 'data/coco'
data = dict(
    samples_per_gpu=32,
    workers_per_gpu=2,
    val_dataloader=dict(samples_per_gpu=32),
    test_dataloader=dict(samples_per_gpu=32),
    train=dict(
        type='TopDownCocoWholeBodyDataset', # modify the name of the dataset
        ann_file=f'{data_root}/annotations/coco_wholebody_train_v1.0.json', # modify the path to the annotation file
        img_prefix=f'{data_root}/train2017/',
        data_cfg=data_cfg,
        pipeline=train_pipeline),
    val=dict(
        type='TopDownCocoWholeBodyDataset', # modify the name of the dataset
        ann_file=f'{data_root}/annotations/coco_wholebody_val_v1.0.json', # modify the path to the annotation file
        img_prefix=f'{data_root}/val2017/',
        data_cfg=data_cfg,
        pipeline=val_pipeline),
    test=dict(
        type='TopDownCocoWholeBodyDataset', # modify the name of the dataset
        ann_file=f'{data_root}/annotations/coco_wholebody_val_v1.0.json', # modify the path to the annotation file
        img_prefix=f'{data_root}/val2017/',
        data_cfg=data_cfg,
        pipeline=val_pipeline)
)

Modify training schedule

The finetuning hyperparameters vary from the default schedule. It usually requires smaller learning rate and less training epochs

optimizer = dict(
    type='Adam',
    lr=5e-4, # reduce it
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[170, 200]) # reduce it
total_epochs = 210 # reduce it

Use pre-trained model

Users can load a pre-trained model by setting the load_from field of the config to the model's path or link. The users might need to download the model weights before training to avoid the download time during training.

# use the pre-trained model for the whole HRNet
load_from = 'https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_384x288_dark-741844ba_20200812.pth'  # model path can be found in model zoo