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 $MMAction2/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.
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='SampleFrames'), ...]. If you want to change'SampleFrames'to'DenseSampleFrames'in the pipeline, you may specify--cfg-options data.train.pipeline.0.type=DenseSampleFrames. -
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.
There are 3 basic component types under config/_base_, model, schedule, default_runtime.
Many methods could be easily constructed with one of each like TSN, I3D, SlowOnly, etc.
The configs that are composed by components from _base_ are called primitive.
For all configs under the same folder, it is recommended to have only one primitive config. All other configs should inherit from the primitive config. In this way, the maximum of inheritance level is 3.
For easy understanding, we recommend contributors to inherit from exiting methods.
For example, if some modification is made base on TSN, users may first inherit the basic TSN structure by specifying _base_ = ../tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py, then modify the necessary fields in the config files.
If you are building an entirely new method that does not share the structure with any of the existing methods, you may create a folder under configs/TASK.
Please refer to mmcv for detailed documentation.
We follow the style below to name config files. Contributors are advised to follow the same style.
{model}_[model setting]_{backbone}_[misc]_{data setting}_[gpu x batch_per_gpu]_{schedule}_{dataset}_{modality}
{xxx} is required field and [yyy] is optional.
{model}: model type, e.g.tsn,i3d, etc.[model setting]: specific setting for some models.{backbone}: backbone type, e.g.r50(ResNet-50), etc.[misc]: miscellaneous setting/plugins of model, e.g.dense,320p,video, etc.{data setting}: frame sample setting in{clip_len}x{frame_interval}x{num_clips}format.[gpu x batch_per_gpu]: GPUs and samples per GPU.{schedule}: training schedule, e.g.20emeans 20 epochs.{dataset}: dataset name, e.g.kinetics400,mmit, etc.{modality}: frame modality, e.g.rgb,flow, etc.
We incorporate modular design into our config system, which is convenient to conduct various experiments.
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An Example of BMN
To help the users have a basic idea of a complete config structure and the modules in an action localization system, we make brief comments on the config of BMN as the following. For more detailed usage and alternative for per parameter in each module, please refer to the API documentation.
# model settings model = dict( # Config of the model type='BMN', # Type of the localizer temporal_dim=100, # Total frames selected for each video boundary_ratio=0.5, # Ratio for determining video boundaries num_samples=32, # Number of samples for each proposal num_samples_per_bin=3, # Number of bin samples for each sample feat_dim=400, # Dimension of feature soft_nms_alpha=0.4, # Soft NMS alpha soft_nms_low_threshold=0.5, # Soft NMS low threshold soft_nms_high_threshold=0.9, # Soft NMS high threshold post_process_top_k=100) # Top k proposals in post process # model training and testing settings train_cfg = None # Config of training hyperparameters for BMN test_cfg = dict(average_clips='score') # Config for testing hyperparameters for BMN # dataset settings dataset_type = 'ActivityNetDataset' # Type of dataset for training, validation and testing data_root = 'data/activitynet_feature_cuhk/csv_mean_100/' # Root path to data for training data_root_val = 'data/activitynet_feature_cuhk/csv_mean_100/' # Root path to data for validation and testing ann_file_train = 'data/ActivityNet/anet_anno_train.json' # Path to the annotation file for training ann_file_val = 'data/ActivityNet/anet_anno_val.json' # Path to the annotation file for validation ann_file_test = 'data/ActivityNet/anet_anno_test.json' # Path to the annotation file for testing train_pipeline = [ # List of training pipeline steps dict(type='LoadLocalizationFeature'), # Load localization feature pipeline dict(type='GenerateLocalizationLabels'), # Generate localization labels pipeline dict( # Config of Collect type='Collect', # Collect pipeline that decides which keys in the data should be passed to the localizer keys=['raw_feature', 'gt_bbox'], # Keys of input meta_name='video_meta', # Meta name meta_keys=['video_name']), # Meta keys of input dict( # Config of ToTensor type='ToTensor', # Convert other types to tensor type pipeline keys=['raw_feature']), # Keys to be converted from image to tensor dict( # Config of ToDataContainer type='ToDataContainer', # Pipeline to convert the data to DataContainer fields=[dict(key='gt_bbox', stack=False, cpu_only=True)]) # Required fields to be converted with keys and attributes ] val_pipeline = [ # List of validation pipeline steps dict(type='LoadLocalizationFeature'), # Load localization feature pipeline dict(type='GenerateLocalizationLabels'), # Generate localization labels pipeline dict( # Config of Collect type='Collect', # Collect pipeline that decides which keys in the data should be passed to the localizer keys=['raw_feature', 'gt_bbox'], # Keys of input meta_name='video_meta', # Meta name meta_keys=[ 'video_name', 'duration_second', 'duration_frame', 'annotations', 'feature_frame' ]), # Meta keys of input dict( # Config of ToTensor type='ToTensor', # Convert other types to tensor type pipeline keys=['raw_feature']), # Keys to be converted from image to tensor dict( # Config of ToDataContainer type='ToDataContainer', # Pipeline to convert the data to DataContainer fields=[dict(key='gt_bbox', stack=False, cpu_only=True)]) # Required fields to be converted with keys and attributes ] test_pipeline = [ # List of testing pipeline steps dict(type='LoadLocalizationFeature'), # Load localization feature pipeline dict( # Config of Collect type='Collect', # Collect pipeline that decides which keys in the data should be passed to the localizer keys=['raw_feature'], # Keys of input meta_name='video_meta', # Meta name meta_keys=[ 'video_name', 'duration_second', 'duration_frame', 'annotations', 'feature_frame' ]), # Meta keys of input dict( # Config of ToTensor type='ToTensor', # Convert other types to tensor type pipeline keys=['raw_feature']), # Keys to be converted from image to tensor ] data = dict( # Config of data videos_per_gpu=8, # Batch size of each single GPU workers_per_gpu=8, # Workers to pre-fetch data for each single GPU train_dataloader=dict( # Additional config of train dataloader drop_last=True), # Whether to drop out the last batch of data in training val_dataloader=dict( # Additional config of validation dataloader videos_per_gpu=1), # Batch size of each single GPU during evaluation test_dataloader=dict( # Additional config of test dataloader videos_per_gpu=2), # Batch size of each single GPU during testing test=dict( # Testing dataset config type=dataset_type, ann_file=ann_file_test, pipeline=test_pipeline, data_prefix=data_root_val), val=dict( # Validation dataset config type=dataset_type, ann_file=ann_file_val, pipeline=val_pipeline, data_prefix=data_root_val), train=dict( # Training dataset config type=dataset_type, ann_file=ann_file_train, pipeline=train_pipeline, data_prefix=data_root)) # 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 built on `constructor`, referring to "tutorials/5_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=0.001, # Learning rate, see detail usages of the parameters in the documentation of PyTorch weight_decay=0.0001) # Weight decay of Adam optimizer_config = dict( # Config used to build the optimizer hook grad_clip=None) # Most of the methods 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 step=7) # Steps to decay the learning rate total_epochs = 9 # Total epochs to train the model 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=1) # Interval to save checkpoint evaluation = dict( # Config of evaluation during training interval=1, # Interval to perform evaluation metrics=['AR@AN']) # Metrics to be performed log_config = dict( # Config to register logger hook interval=50, # Interval to print the log hooks=[ # Hooks to be implemented during training dict(type='TextLoggerHook'), # The logger used to record the training process # dict(type='TensorboardLoggerHook'), # The Tensorboard logger is also supported ]) # runtime settings dist_params = dict(backend='nccl') # Parameters to setup distributed training, the port can also be set log_level = 'INFO' # The level of logging work_dir = './work_dirs/bmn_400x100_2x8_9e_activitynet_feature/' # Directory to save the model checkpoints and logs for the current experiments 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 workflow = [('train', 1)] # Workflow for runner. [('train', 1)] means there is only one workflow and the workflow named 'train' is executed once output_config = dict( # Config of localization output out=f'{work_dir}/results.json', # Path to output file output_format='json') # File format of output file
We incorporate modular design into our config system, which is convenient to conduct various experiments.
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An Example of TSN
To help the users have a basic idea of a complete config structure and the modules in an action recognition system, we make brief comments on the config of TSN as the following. For more detailed usage and alternative for per parameter in each module, please refer to the API documentation.
# model settings model = dict( # Config of the model type='Recognizer2D', # Type of the recognizer backbone=dict( # Dict for backbone type='ResNet', # Name of the backbone pretrained='torchvision://resnet50', # The url/site of the pretrained model depth=50, # Depth of ResNet model norm_eval=False), # Whether to set BN layers to eval mode when training cls_head=dict( # Dict for classification head type='TSNHead', # Name of classification head num_classes=400, # Number of classes to be classified. in_channels=2048, # The input channels of classification head. spatial_type='avg', # Type of pooling in spatial dimension consensus=dict(type='AvgConsensus', dim=1), # Config of consensus module dropout_ratio=0.4, # Probability in dropout layer init_std=0.01), # Std value for linear layer initiation # model training and testing settings train_cfg=None, # Config of training hyperparameters for TSN test_cfg=dict(average_clips=None)) # Config for testing hyperparameters for TSN. # dataset settings dataset_type = 'RawframeDataset' # Type of dataset for training, validation and testing data_root = 'data/kinetics400/rawframes_train/' # Root path to data for training data_root_val = 'data/kinetics400/rawframes_val/' # Root path to data for validation and testing ann_file_train = 'data/kinetics400/kinetics400_train_list_rawframes.txt' # Path to the annotation file for training ann_file_val = 'data/kinetics400/kinetics400_val_list_rawframes.txt' # Path to the annotation file for validation ann_file_test = 'data/kinetics400/kinetics400_val_list_rawframes.txt' # Path to the annotation file for testing img_norm_cfg = dict( # Config of image normalization used in data pipeline mean=[123.675, 116.28, 103.53], # Mean values of different channels to normalize std=[58.395, 57.12, 57.375], # Std values of different channels to normalize to_bgr=False) # Whether to convert channels from RGB to BGR train_pipeline = [ # List of training pipeline steps dict( # Config of SampleFrames type='SampleFrames', # Sample frames pipeline, sampling frames from video clip_len=1, # Frames of each sampled output clip frame_interval=1, # Temporal interval of adjacent sampled frames num_clips=3), # Number of clips to be sampled dict( # Config of RawFrameDecode type='RawFrameDecode'), # Load and decode Frames pipeline, picking raw frames with given indices dict( # Config of Resize type='Resize', # Resize pipeline scale=(-1, 256)), # The scale to resize images dict( # Config of MultiScaleCrop type='MultiScaleCrop', # Multi scale crop pipeline, cropping images with a list of randomly selected scales input_size=224, # Input size of the network scales=(1, 0.875, 0.75, 0.66), # Scales of width and height to be selected random_crop=False, # Whether to randomly sample cropping bbox max_wh_scale_gap=1), # Maximum gap of w and h scale levels dict( # Config of Resize type='Resize', # Resize pipeline scale=(224, 224), # The scale to resize images keep_ratio=False), # Whether to resize with changing the aspect ratio dict( # Config of Flip type='Flip', # Flip Pipeline flip_ratio=0.5), # Probability of implementing flip dict( # Config of Normalize type='Normalize', # Normalize pipeline **img_norm_cfg), # Config of image normalization dict( # Config of FormatShape type='FormatShape', # Format shape pipeline, Format final image shape to the given input_format input_format='NCHW'), # Final image shape format dict( # Config of Collect type='Collect', # Collect pipeline that decides which keys in the data should be passed to the recognizer keys=['imgs', 'label'], # Keys of input meta_keys=[]), # Meta keys of input dict( # Config of ToTensor type='ToTensor', # Convert other types to tensor type pipeline keys=['imgs', 'label']) # Keys to be converted from image to tensor ] val_pipeline = [ # List of validation pipeline steps dict( # Config of SampleFrames type='SampleFrames', # Sample frames pipeline, sampling frames from video clip_len=1, # Frames of each sampled output clip frame_interval=1, # Temporal interval of adjacent sampled frames num_clips=3, # Number of clips to be sampled test_mode=True), # Whether to set test mode in sampling dict( # Config of RawFrameDecode type='RawFrameDecode'), # Load and decode Frames pipeline, picking raw frames with given indices dict( # Config of Resize type='Resize', # Resize pipeline scale=(-1, 256)), # The scale to resize images dict( # Config of CenterCrop type='CenterCrop', # Center crop pipeline, cropping the center area from images crop_size=224), # The size to crop images dict( # Config of Flip type='Flip', # Flip pipeline flip_ratio=0), # Probability of implementing flip dict( # Config of Normalize type='Normalize', # Normalize pipeline **img_norm_cfg), # Config of image normalization dict( # Config of FormatShape type='FormatShape', # Format shape pipeline, Format final image shape to the given input_format input_format='NCHW'), # Final image shape format dict( # Config of Collect type='Collect', # Collect pipeline that decides which keys in the data should be passed to the recognizer keys=['imgs', 'label'], # Keys of input meta_keys=[]), # Meta keys of input dict( # Config of ToTensor type='ToTensor', # Convert other types to tensor type pipeline keys=['imgs']) # Keys to be converted from image to tensor ] test_pipeline = [ # List of testing pipeline steps dict( # Config of SampleFrames type='SampleFrames', # Sample frames pipeline, sampling frames from video clip_len=1, # Frames of each sampled output clip frame_interval=1, # Temporal interval of adjacent sampled frames num_clips=25, # Number of clips to be sampled test_mode=True), # Whether to set test mode in sampling dict( # Config of RawFrameDecode type='RawFrameDecode'), # Load and decode Frames pipeline, picking raw frames with given indices dict( # Config of Resize type='Resize', # Resize pipeline scale=(-1, 256)), # The scale to resize images dict( # Config of TenCrop type='TenCrop', # Ten crop pipeline, cropping ten area from images crop_size=224), # The size to crop images dict( # Config of Flip type='Flip', # Flip pipeline flip_ratio=0), # Probability of implementing flip dict( # Config of Normalize type='Normalize', # Normalize pipeline **img_norm_cfg), # Config of image normalization dict( # Config of FormatShape type='FormatShape', # Format shape pipeline, Format final image shape to the given input_format input_format='NCHW'), # Final image shape format dict( # Config of Collect type='Collect', # Collect pipeline that decides which keys in the data should be passed to the recognizer keys=['imgs', 'label'], # Keys of input meta_keys=[]), # Meta keys of input dict( # Config of ToTensor type='ToTensor', # Convert other types to tensor type pipeline keys=['imgs']) # Keys to be converted from image to tensor ] data = dict( # Config of data videos_per_gpu=32, # Batch size of each single GPU workers_per_gpu=2, # Workers to pre-fetch data for each single GPU train_dataloader=dict( # Additional config of train dataloader drop_last=True), # Whether to drop out the last batch of data in training val_dataloader=dict( # Additional config of validation dataloader videos_per_gpu=1), # Batch size of each single GPU during evaluation test_dataloader=dict( # Additional config of test dataloader videos_per_gpu=2), # Batch size of each single GPU during testing train=dict( # Training dataset config type=dataset_type, ann_file=ann_file_train, data_prefix=data_root, pipeline=train_pipeline), val=dict( # Validation dataset config type=dataset_type, ann_file=ann_file_val, data_prefix=data_root_val, pipeline=val_pipeline), test=dict( # Testing dataset config type=dataset_type, ann_file=ann_file_test, data_prefix=data_root_val, pipeline=test_pipeline)) # 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 built on `constructor`, referring to "tutorials/5_new_modules.md" # for implementation. type='SGD', # Type of optimizer, refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/optimizer/default_constructor.py#L13 for more details lr=0.01, # Learning rate, see detail usages of the parameters in the documentation of PyTorch momentum=0.9, # Momentum, weight_decay=0.0001) # Weight decay of SGD optimizer_config = dict( # Config used to build the optimizer hook grad_clip=dict(max_norm=40, norm_type=2)) # 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 step=[40, 80]) # Steps to decay the learning rate total_epochs = 100 # Total epochs to train the model 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=5) # Interval to save checkpoint evaluation = dict( # Config of evaluation during training interval=5, # Interval to perform evaluation metrics=['top_k_accuracy', 'mean_class_accuracy'], # Metrics to be performed metric_options=dict(top_k_accuracy=dict(topk=(1, 3))), # Set top-k accuracy to 1 and 3 during validation save_best='top_k_accuracy') # set `top_k_accuracy` as key indicator to save best checkpoint eval_config = dict( metric_options=dict(top_k_accuracy=dict(topk=(1, 3)))) # Set top-k accuracy to 1 and 3 during testing. You can also use `--eval top_k_accuracy` to assign evaluation metrics log_config = dict( # Config to register logger hook interval=20, # Interval to print the log hooks=[ # Hooks to be implemented during training dict(type='TextLoggerHook'), # The logger used to record the training process # dict(type='TensorboardLoggerHook'), # The Tensorboard logger is also supported ]) # runtime settings dist_params = dict(backend='nccl') # Parameters to setup distributed training, the port can also be set log_level = 'INFO' # The level of logging work_dir = './work_dirs/tsn_r50_1x1x3_100e_kinetics400_rgb/' # Directory to save the model checkpoints and logs for the current experiments 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 workflow = [('train', 1)] # Workflow for runner. [('train', 1)] means there is only one workflow and the workflow named 'train' is executed once
We incorporate modular design into our config system, which is convenient to conduct various experiments.
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An Example of FastRCNN
To help the users have a basic idea of a complete config structure and the modules in a spatio-temporal action detection system, we make brief comments on the config of FastRCNN as the following. For more detailed usage and alternative for per parameter in each module, please refer to the API documentation.
# model setting model = dict( # Config of the model type='FastRCNN', # Type of the detector backbone=dict( # Dict for backbone type='ResNet3dSlowOnly', # Name of the backbone depth=50, # Depth of ResNet model pretrained=None, # The url/site of the pretrained model pretrained2d=False, # If the pretrained model is 2D lateral=False, # If the backbone is with lateral connections num_stages=4, # Stages of ResNet model conv1_kernel=(1, 7, 7), # Conv1 kernel size conv1_stride_t=1, # Conv1 temporal stride pool1_stride_t=1, # Pool1 temporal stride spatial_strides=(1, 2, 2, 1)), # The spatial stride for each ResNet stage roi_head=dict( # Dict for roi_head type='AVARoIHead', # Name of the roi_head bbox_roi_extractor=dict( # Dict for bbox_roi_extractor type='SingleRoIExtractor3D', # Name of the bbox_roi_extractor roi_layer_type='RoIAlign', # Type of the RoI op output_size=8, # Output feature size of the RoI op with_temporal_pool=True), # If temporal dim is pooled bbox_head=dict( # Dict for bbox_head type='BBoxHeadAVA', # Name of the bbox_head in_channels=2048, # Number of channels of the input feature num_classes=81, # Number of action classes + 1 multilabel=True, # If the dataset is multilabel dropout_ratio=0.5)), # The dropout ratio used # model training and testing settings train_cfg=dict( # Training config of FastRCNN rcnn=dict( # Dict for rcnn training config assigner=dict( # Dict for assigner type='MaxIoUAssignerAVA', # Name of the assigner pos_iou_thr=0.9, # IoU threshold for positive examples, > pos_iou_thr -> positive neg_iou_thr=0.9, # IoU threshold for negative examples, < neg_iou_thr -> negative min_pos_iou=0.9), # Minimum acceptable IoU for positive examples sampler=dict( # Dict for sample type='RandomSampler', # Name of the sampler num=32, # Batch Size of the sampler pos_fraction=1, # Positive bbox fraction of the sampler neg_pos_ub=-1, # Upper bound of the ratio of num negative to num positive add_gt_as_proposals=True), # Add gt bboxes as proposals pos_weight=1.0, # Loss weight of positive examples debug=False)), # Debug mode test_cfg=dict( # Testing config of FastRCNN rcnn=dict( # Dict for rcnn testing config action_thr=0.002))) # The threshold of an action # dataset settings dataset_type = 'AVADataset' # Type of dataset for training, validation and testing data_root = 'data/ava/rawframes' # Root path to data anno_root = 'data/ava/annotations' # Root path to annotations ann_file_train = f'{anno_root}/ava_train_v2.1.csv' # Path to the annotation file for training ann_file_val = f'{anno_root}/ava_val_v2.1.csv' # Path to the annotation file for validation exclude_file_train = f'{anno_root}/ava_train_excluded_timestamps_v2.1.csv' # Path to the exclude annotation file for training exclude_file_val = f'{anno_root}/ava_val_excluded_timestamps_v2.1.csv' # Path to the exclude annotation file for validation label_file = f'{anno_root}/ava_action_list_v2.1_for_activitynet_2018.pbtxt' # Path to the label file proposal_file_train = f'{anno_root}/ava_dense_proposals_train.FAIR.recall_93.9.pkl' # Path to the human detection proposals for training examples proposal_file_val = f'{anno_root}/ava_dense_proposals_val.FAIR.recall_93.9.pkl' # Path to the human detection proposals for validation examples img_norm_cfg = dict( # Config of image normalization used in data pipeline mean=[123.675, 116.28, 103.53], # Mean values of different channels to normalize std=[58.395, 57.12, 57.375], # Std values of different channels to normalize to_bgr=False) # Whether to convert channels from RGB to BGR train_pipeline = [ # List of training pipeline steps dict( # Config of SampleFrames type='AVASampleFrames', # Sample frames pipeline, sampling frames from video clip_len=4, # Frames of each sampled output clip frame_interval=16), # Temporal interval of adjacent sampled frames dict( # Config of RawFrameDecode type='RawFrameDecode'), # Load and decode Frames pipeline, picking raw frames with given indices dict( # Config of RandomRescale type='RandomRescale', # Randomly rescale the shortedge by a given range scale_range=(256, 320)), # The shortedge size range of RandomRescale dict( # Config of RandomCrop type='RandomCrop', # Randomly crop a patch with the given size size=256), # The size of the cropped patch dict( # Config of Flip type='Flip', # Flip Pipeline flip_ratio=0.5), # Probability of implementing flip dict( # Config of Normalize type='Normalize', # Normalize pipeline **img_norm_cfg), # Config of image normalization dict( # Config of FormatShape type='FormatShape', # Format shape pipeline, Format final image shape to the given input_format input_format='NCTHW', # Final image shape format collapse=True), # Collapse the dim N if N == 1 dict( # Config of Rename type='Rename', # Rename keys mapping=dict(imgs='img')), # The old name to new name mapping dict( # Config of ToTensor type='ToTensor', # Convert other types to tensor type pipeline keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']), # Keys to be converted from image to tensor dict( # Config of ToDataContainer type='ToDataContainer', # Convert other types to DataContainer type pipeline fields=[ # Fields to convert to DataContainer dict( # Dict of fields key=['proposals', 'gt_bboxes', 'gt_labels'], # Keys to Convert to DataContainer stack=False)]), # Whether to stack these tensor dict( # Config of Collect type='Collect', # Collect pipeline that decides which keys in the data should be passed to the detector keys=['img', 'proposals', 'gt_bboxes', 'gt_labels'], # Keys of input meta_keys=['scores', 'entity_ids']), # Meta keys of input ] val_pipeline = [ # List of validation pipeline steps dict( # Config of SampleFrames type='AVASampleFrames', # Sample frames pipeline, sampling frames from video clip_len=4, # Frames of each sampled output clip frame_interval=16) # Temporal interval of adjacent sampled frames dict( # Config of RawFrameDecode type='RawFrameDecode'), # Load and decode Frames pipeline, picking raw frames with given indices dict( # Config of Resize type='Resize', # Resize pipeline scale=(-1, 256)), # The scale to resize images dict( # Config of Normalize type='Normalize', # Normalize pipeline **img_norm_cfg), # Config of image normalization dict( # Config of FormatShape type='FormatShape', # Format shape pipeline, Format final image shape to the given input_format input_format='NCTHW', # Final image shape format collapse=True), # Collapse the dim N if N == 1 dict( # Config of Rename type='Rename', # Rename keys mapping=dict(imgs='img')), # The old name to new name mapping dict( # Config of ToTensor type='ToTensor', # Convert other types to tensor type pipeline keys=['img', 'proposals']), # Keys to be converted from image to tensor dict( # Config of ToDataContainer type='ToDataContainer', # Convert other types to DataContainer type pipeline fields=[ # Fields to convert to DataContainer dict( # Dict of fields key=['proposals'], # Keys to Convert to DataContainer stack=False)]), # Whether to stack these tensor dict( # Config of Collect type='Collect', # Collect pipeline that decides which keys in the data should be passed to the detector keys=['img', 'proposals'], # Keys of input meta_keys=['scores', 'entity_ids'], # Meta keys of input nested=True) # Whether to wrap the data in a nested list ] data = dict( # Config of data videos_per_gpu=16, # Batch size of each single GPU workers_per_gpu=2, # Workers to pre-fetch data for each single GPU val_dataloader=dict( # Additional config of validation dataloader videos_per_gpu=1), # Batch size of each single GPU during evaluation train=dict( # Training dataset config type=dataset_type, ann_file=ann_file_train, exclude_file=exclude_file_train, pipeline=train_pipeline, label_file=label_file, proposal_file=proposal_file_train, person_det_score_thr=0.9, data_prefix=data_root), val=dict( # Validation dataset config type=dataset_type, ann_file=ann_file_val, exclude_file=exclude_file_val, pipeline=val_pipeline, label_file=label_file, proposal_file=proposal_file_val, person_det_score_thr=0.9, data_prefix=data_root)) data['test'] = data['val'] # Set test_dataset as val_dataset # 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 built on `constructor`, referring to "tutorials/5_new_modules.md" # for implementation. type='SGD', # Type of optimizer, refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/optimizer/default_constructor.py#L13 for more details lr=0.2, # Learning rate, see detail usages of the parameters in the documentation of PyTorch (for 8gpu) momentum=0.9, # Momentum, weight_decay=0.00001) # Weight decay of SGD optimizer_config = dict( # Config used to build the optimizer hook grad_clip=dict(max_norm=40, norm_type=2)) # Use gradient clip 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 step=[40, 80], # Steps to decay the learning rate warmup='linear', # Warmup strategy warmup_by_epoch=True, # Warmup_iters indicates iter num or epoch num warmup_iters=5, # Number of iters or epochs for warmup warmup_ratio=0.1) # The initial learning rate is warmup_ratio * lr total_epochs = 20 # Total epochs to train the model 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=1) # Interval to save checkpoint workflow = [('train', 1)] # Workflow for runner. [('train', 1)] means there is only one workflow and the workflow named 'train' is executed once evaluation = dict( # Config of evaluation during training interval=1, save_best='[email protected]') # Interval to perform evaluation and the key for saving best checkpoint log_config = dict( # Config to register logger hook interval=20, # Interval to print the log hooks=[ # Hooks to be implemented during training dict(type='TextLoggerHook'), # The logger used to record the training process ]) # runtime settings dist_params = dict(backend='nccl') # Parameters to setup distributed training, the port can also be set log_level = 'INFO' # The level of logging work_dir = ('./work_dirs/ava/' # Directory to save the model checkpoints and logs for the current experiments 'slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb') load_from = ('https://download.openmmlab.com/mmaction/recognition/slowonly/' # load models as a pre-trained model from a given path. This will not resume training 'slowonly_r50_4x16x1_256e_kinetics400_rgb/' 'slowonly_r50_4x16x1_256e_kinetics400_rgb_20200704-a69556c6.pth') resume_from = None # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved
Some intermediate variables are used in the config files, like train_pipeline/val_pipeline/test_pipeline,
ann_file_train/ann_file_val/ann_file_test, img_norm_cfg 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.
we also define ann_file_train/ann_file_val/ann_file_test and data_root/data_root_val to provide data pipeline some
basic information.
In addition, we use img_norm_cfg as intermediate variables to construct data augmentation components.
...
dataset_type = 'RawframeDataset'
data_root = 'data/kinetics400/rawframes_train'
data_root_val = 'data/kinetics400/rawframes_val'
ann_file_train = 'data/kinetics400/kinetics400_train_list_rawframes.txt'
ann_file_val = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
ann_file_test = 'data/kinetics400/kinetics400_val_list_rawframes.txt'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
train_pipeline = [
dict(type='SampleFrames', clip_len=32, frame_interval=2, num_clips=1),
dict(type='RawFrameDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(
type='MultiScaleCrop',
input_size=224,
scales=(1, 0.8),
random_crop=False,
max_wh_scale_gap=0),
dict(type='Resize', scale=(224, 224), keep_ratio=False),
dict(type='Flip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs', 'label'])
]
val_pipeline = [
dict(
type='SampleFrames',
clip_len=32,
frame_interval=2,
num_clips=1,
test_mode=True),
dict(type='RawFrameDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='CenterCrop', crop_size=224),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs'])
]
test_pipeline = [
dict(
type='SampleFrames',
clip_len=32,
frame_interval=2,
num_clips=10,
test_mode=True),
dict(type='RawFrameDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='ThreeCrop', crop_size=256),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs'])
]
data = dict(
videos_per_gpu=8,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=ann_file_train,
data_prefix=data_root,
pipeline=train_pipeline),
val=dict(
type=dataset_type,
ann_file=ann_file_val,
data_prefix=data_root_val,
pipeline=val_pipeline),
test=dict(
type=dataset_type,
ann_file=ann_file_val,
data_prefix=data_root_val,
pipeline=test_pipeline))