We use python files as configs, incorporate modular and inheritance design into our config system, which is convenient to conduct various experiments.
You can find all the provided configs under $MMPose/configs. If you wish to inspect the config file,
you may run python tools/analysis/print_config.py /PATH/TO/CONFIG to see the complete config.
When submitting jobs using "tools/train.py" or "tools/test.py", you may specify --cfg-options to in-place modify the config.
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Update config keys of dict chains.
The config options can be specified following the order of the dict keys in the original config. For example,
--cfg-options model.backbone.norm_eval=Falsechanges the all BN modules in model backbones totrainmode. -
Update keys inside a list of configs.
Some config dicts are composed as a list in your config. For example, the training pipeline
data.train.pipelineis normally a list e.g.[dict(type='LoadImageFromFile'), dict(type='TopDownRandomFlip', flip_prob=0.5), ...]. If you want to change'flip_prob=0.5'to'flip_prob=0.0'in the pipeline, you may specify--cfg-options data.train.pipeline.1.flip_prob=0.0. -
Update values of list/tuples.
If the value to be updated is a list or a tuple. For example, the config file normally sets
workflow=[('train', 1)]. If you want to change this key, you may specify--cfg-options workflow="[(train,1),(val,1)]". Note that the quotation mark " is necessary to support list/tuple data types, and that NO white space is allowed inside the quotation marks in the specified value.
We follow the style below to name config files. Contributors are advised to follow the same style.
configs/{topic}/{task}/{algorithm}/{dataset}/{backbone}_[model_setting]_{dataset}_[input_size]_[technique].py
{xxx} is required field and [yyy] is optional.
{topic}: topic type, e.g.body,face,hand,animal, etc.{task}: task type,[2d | 3d]_[kpt | mesh]_[sview | mview]_[rgb | rgbd]_[img | vid]. The task is categorized in 5: (1) 2D or 3D pose estimation, (2) representation type: keypoint (kpt), mesh, or DensePose (dense). (3) Single-view (sview) or multi-view (mview), (4) RGB or RGBD, and (5) Image (img) or Video (vid). e.g.2d_kpt_sview_rgb_img,3d_kpt_sview_rgb_vid, etc.{algorithm}: algorithm type, e.g.associative_embedding,deeppose, etc.{dataset}: dataset name, e.g.coco, etc.{backbone}: backbone type, e.g.res50(ResNet-50), etc.[model setting]: specific setting for some models.[input_size]: input size of the model.[technique]: some specific techniques, including losses, augmentation and tricks, e.g.wingloss,udp,fp16.
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An Example of 2D Top-down Heatmap-based Human Pose Estimation
To help the users have a basic idea of a complete config structure and the modules in the config system, we make brief comments on 'https://github.com/open-mmlab/mmpose/tree/e1ec589884235bee875c89102170439a991f8450/configs/top_down/resnet/coco/res50_coco_256x192.py' as the following. For more detailed usage and alternative for per parameter in each module, please refer to the API documentation.
# runtime settings log_level = 'INFO' # The level of logging load_from = None # load models as a pre-trained model from a given path. This will not resume training resume_from = None # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved dist_params = dict(backend='nccl') # Parameters to setup distributed training, the port can also be set workflow = [('train', 1)] # Workflow for runner. [('train', 1)] means there is only one workflow and the workflow named 'train' is executed once checkpoint_config = dict( # Config to set the checkpoint hook, Refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py for implementation interval=10) # Interval to save checkpoint evaluation = dict( # Config of evaluation during training interval=10, # Interval to perform evaluation metric='mAP', # Metrics to be performed save_best='AP') # set `AP` as key indicator to save best checkpoint # optimizer optimizer = dict( # Config used to build optimizer, support (1). All the optimizers in PyTorch # whose arguments are also the same as those in PyTorch. (2). Custom optimizers # which are builed on `constructor`, referring to "tutorials/4_new_modules.md" # for implementation. type='Adam', # Type of optimizer, refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/optimizer/default_constructor.py#L13 for more details lr=5e-4, # Learning rate, see detail usages of the parameters in the documentation of PyTorch ) optimizer_config = dict(grad_clip=None) # Do not use gradient clip # learning policy lr_config = dict( # Learning rate scheduler config used to register LrUpdater hook policy='step', # Policy of scheduler, also support CosineAnnealing, Cyclic, etc. Refer to details of supported LrUpdater from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py#L9 warmup='linear', # Type of warmup used. It can be None(use no warmup), 'constant', 'linear' or 'exp'. warmup_iters=500, # The number of iterations or epochs that warmup warmup_ratio=0.001, # LR used at the beginning of warmup equals to warmup_ratio * initial_lr step=[170, 200]) # Steps to decay the learning rate total_epochs = 210 # Total epochs to train the model log_config = dict( # Config to register logger hook interval=50, # Interval to print the log hooks=[ dict(type='TextLoggerHook'), # The logger used to record the training process # dict(type='TensorboardLoggerHook') # The Tensorboard logger is also supported ]) channel_cfg = dict( num_output_channels=17, # The output channels of keypoint head dataset_joints=17, # Number of joints in the dataset dataset_channel=[ # Dataset supported channels [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], ], inference_channel=[ # Channels to output 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 ]) # model settings model = dict( # Config of the model type='TopDown', # Type of the model pretrained='torchvision://resnet50', # The url/site of the pretrained model backbone=dict( # Dict for backbone type='ResNet', # Name of the backbone depth=50), # Depth of ResNet model keypoint_head=dict( # Dict for keypoint head type='TopdownHeatmapSimpleHead', # Name of keypoint head in_channels=2048, # The input channels of keypoint head out_channels=channel_cfg['num_output_channels'], # The output channels of keypoint head loss_keypoint=dict( # Dict for keypoint loss type='JointsMSELoss', # Name of keypoint loss use_target_weight=True)), # Whether to consider target_weight during loss calculation train_cfg=dict(), # Config of training hyper-parameters test_cfg=dict( # Config of testing hyper-parameters flip_test=True, # Whether to use flip-test during inference post_process='default', # Use 'default' post-processing approach. shift_heatmap=True, # Shift and align the flipped heatmap to achieve higher performance modulate_kernel=11)) # Gaussian kernel size for modulation. Only used for "post_process='unbiased'" data_cfg = dict( image_size=[192, 256], # Size of model input resolution heatmap_size=[48, 64], # Size of the output heatmap num_output_channels=channel_cfg['num_output_channels'], # Number of output channels num_joints=channel_cfg['dataset_joints'], # Number of joints dataset_channel=channel_cfg['dataset_channel'], # Dataset supported channels inference_channel=channel_cfg['inference_channel'], # Channels to output soft_nms=False, # Whether to perform soft-nms during inference nms_thr=1.0, # Threshold for non maximum suppression. oks_thr=0.9, # Threshold of oks (object keypoint similarity) score during nms vis_thr=0.2, # Threshold of keypoint visibility use_gt_bbox=False, # Whether to use ground-truth bounding box during testing det_bbox_thr=0.0, # Threshold of detected bounding box score. Used when 'use_gt_bbox=True' bbox_file='data/coco/person_detection_results/' # Path to the bounding box detection file 'COCO_val2017_detections_AP_H_56_person.json', ) train_pipeline = [ dict(type='LoadImageFromFile'), # Loading image from file dict(type='TopDownRandomFlip', # Perform random flip augmentation flip_prob=0.5), # Probability of implementing flip dict( type='TopDownHalfBodyTransform', # Config of TopDownHalfBodyTransform data-augmentation num_joints_half_body=8, # Threshold of performing half-body transform. prob_half_body=0.3), # Probability of implementing half-body transform dict( type='TopDownGetRandomScaleRotation', # Config of TopDownGetRandomScaleRotation rot_factor=40, # Rotating to ``[-2*rot_factor, 2*rot_factor]``. scale_factor=0.5), # Scaling to ``[1-scale_factor, 1+scale_factor]``. dict(type='TopDownAffine', # Affine transform the image to make input. use_udp=False), # Do not use unbiased data processing. dict(type='ToTensor'), # Convert other types to tensor type pipeline dict( type='NormalizeTensor', # Normalize input tensors mean=[0.485, 0.456, 0.406], # Mean values of different channels to normalize std=[0.229, 0.224, 0.225]), # Std values of different channels to normalize dict(type='TopDownGenerateTarget', # Generate heatmap target. Different encoding types supported. sigma=2), # Sigma of heatmap gaussian dict( type='Collect', # Collect pipeline that decides which keys in the data should be passed to the detector keys=['img', 'target', 'target_weight'], # Keys of input meta_keys=[ # Meta keys of input 'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', 'rotation', 'bbox_score', 'flip_pairs' ]), ] val_pipeline = [ dict(type='LoadImageFromFile'), # Loading image from file dict(type='TopDownAffine'), # Affine transform the image to make input. dict(type='ToTensor'), # Config of ToTensor dict( type='NormalizeTensor', mean=[0.485, 0.456, 0.406], # Mean values of different channels to normalize std=[0.229, 0.224, 0.225]), # Std values of different channels to normalize dict( type='Collect', # Collect pipeline that decides which keys in the data should be passed to the detector keys=['img'], # Keys of input meta_keys=[ # Meta keys of input 'image_file', 'center', 'scale', 'rotation', 'bbox_score', 'flip_pairs' ]), ] test_pipeline = val_pipeline data_root = 'data/coco' # Root of the dataset data = dict( # Config of data samples_per_gpu=64, # Batch size of each single GPU during training workers_per_gpu=2, # Workers to pre-fetch data for each single GPU val_dataloader=dict(samples_per_gpu=32), # Batch size of each single GPU during validation test_dataloader=dict(samples_per_gpu=32), # Batch size of each single GPU during testing train=dict( # Training dataset config type='TopDownCocoDataset', # Name of dataset ann_file=f'{data_root}/annotations/person_keypoints_train2017.json', # Path to annotation file img_prefix=f'{data_root}/train2017/', data_cfg=data_cfg, pipeline=train_pipeline), val=dict( # Validation dataset config type='TopDownCocoDataset', # Name of dataset ann_file=f'{data_root}/annotations/person_keypoints_val2017.json', # Path to annotation file img_prefix=f'{data_root}/val2017/', data_cfg=data_cfg, pipeline=val_pipeline), test=dict( # Testing dataset config type='TopDownCocoDataset', # Name of dataset ann_file=f'{data_root}/annotations/person_keypoints_val2017.json', # Path to annotation file img_prefix=f'{data_root}/val2017/', data_cfg=data_cfg, pipeline=val_pipeline), )
Some intermediate variables are used in the config files, like train_pipeline/val_pipeline/test_pipeline etc.
For Example, we would like to first define train_pipeline/val_pipeline/test_pipeline and pass them into data.
Thus, train_pipeline/val_pipeline/test_pipeline are intermediate variable.