From db2da8fd3ca045af9e1e1598b86e48e78b0164f7 Mon Sep 17 00:00:00 2001 From: AmirMasoud Nourollah Date: Mon, 29 Aug 2022 22:22:29 +0430 Subject: [PATCH 1/3] [Docs] Update Config File to Added WandBLogger Hook Details --- docs/en/tutorials/0_config.md | 12 +++++++++++- docs/zh_cn/tutorials/0_config.md | 17 +++++++++++------ 2 files changed, 22 insertions(+), 7 deletions(-) diff --git a/docs/en/tutorials/0_config.md b/docs/en/tutorials/0_config.md index 694ce56ace..0aa188febd 100644 --- a/docs/en/tutorials/0_config.md +++ b/docs/en/tutorials/0_config.md @@ -94,7 +94,17 @@ configs/{topic}/{task}/{algorithm}/{dataset}/{backbone}_[model_setting]_{dataset 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 + intervlog_config = dict( # Config to register logger hook + interval=50, # Interval to print the log + hooks=[ + dict(type='TextLoggerHook', by_epoch=False), # The logger used to record the training process + dict(type='TensorboardLoggerHook', by_epoch=False), # The Tensorboard logger to record log offline + dict(type='WandbLoggerHook', by_epoch=False, # The Wandb logger is also supported, It requires `wandb` to be installed. + init_kwargs={'entity': "OpenMMLab", # The entity used to log on Wandb + 'project': "MMPose", # Project name in WandB + 'config': cfg_dict}), # Check https://docs.wandb.ai/ref/python/init for more init arguments. + ]) # ClearMLLoggerHook, DvcliveLoggerHook, MlflowLoggerHook, NeptuneLoggerHook, PaviLoggerHook, SegmindLoggerHook are also supported based on MMCV implementation. +al=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 diff --git a/docs/zh_cn/tutorials/0_config.md b/docs/zh_cn/tutorials/0_config.md index cf256bb607..2a46538b3a 100644 --- a/docs/zh_cn/tutorials/0_config.md +++ b/docs/zh_cn/tutorials/0_config.md @@ -92,12 +92,17 @@ configs/{topic}/{task}/{algorithm}/{dataset}/{backbone}_[model_setting]_{dataset warmup_ratio=0.001, # 预热开始时使用的学习率，等于预热比 (warmup_ratio) * 初始学习率 step=[170, 200]) # 降低学习率的步数　 total_epochs = 210 # 训练模型的总轮数 - log_config = dict( # 注册日志记录器钩子的配置 - interval=50, # 打印日志的间隔 - hooks=[ - dict(type='TextLoggerHook'), # 用来记录训练过程的日志记录器 - # dict(type='TensorboardLoggerHook') # 也支持 Tensorboard 日志记录器 - ]) + log_config = dict( # 注册日志钩子的设置 + interval=20, # 打印日志间隔 + hooks=[ # 训练期间执行的钩子 + dict(type='TextLoggerHook', by_epoch=False), + dict(type='TensorboardLoggerHook', by_epoch=False), + dict(type='WandbLoggerHook', by_epoch=False, # 还支持 Wandb 记录器，它需要安装 `wandb`。 + init_kwargs={'entity': "entity", # 用于登录wandb的实体 + 'project': "project", # WandB中的项目名称 + 'config': cfg_dict}), # 检查 https://docs.wandb.ai/ref/python/init 以获取更多初始化参数 + ]) + channel_cfg = dict( num_output_channels=17, # 关键点头部的输出通道数 From aa6f29fb37d52c19b3f6e1814937f6e0ac54bf8e Mon Sep 17 00:00:00 2001 From: AmirMasoud Nourollah Date: Fri, 2 Sep 2022 17:05:06 +0430 Subject: [PATCH 2/3] [Docs] lint test passed --- docs/en/tutorials/0_config.md | 264 ++++++++++++++++--------------- docs/zh_cn/tutorials/0_config.md | 2 +- 2 files changed, 135 insertions(+), 131 deletions(-) diff --git a/docs/en/tutorials/0_config.md b/docs/en/tutorials/0_config.md index 0aa188febd..fbfc6346aa 100644 --- a/docs/en/tutorials/0_config.md +++ b/docs/en/tutorials/0_config.md @@ -101,139 +101,142 @@ configs/{topic}/{task}/{algorithm}/{dataset}/{backbone}_[model_setting]_{dataset dict(type='TensorboardLoggerHook', by_epoch=False), # The Tensorboard logger to record log offline dict(type='WandbLoggerHook', by_epoch=False, # The Wandb logger is also supported, It requires `wandb` to be installed. init_kwargs={'entity': "OpenMMLab", # The entity used to log on Wandb - 'project': "MMPose", # Project name in WandB + 'project': "MMPose", # Project name in WandB 'config': cfg_dict}), # Check https://docs.wandb.ai/ref/python/init for more init arguments. - ]) # ClearMLLoggerHook, DvcliveLoggerHook, MlflowLoggerHook, NeptuneLoggerHook, PaviLoggerHook, SegmindLoggerHook are also supported based on MMCV implementation. -al=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', - ) + ])) # ClearMLLoggerHook, DvcliveLoggerHook, MlflowLoggerHook, NeptuneLoggerHook, PaviLoggerHook, SegmindLoggerHook are also supported based on MMCV implementation. + ``` - 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), - ) +al=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), +) - ``` +``` ## FAQ @@ -243,3 +246,4 @@ Some intermediate variables are used in the config files, like `train_pipeline`/ 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. +``` diff --git a/docs/zh_cn/tutorials/0_config.md b/docs/zh_cn/tutorials/0_config.md index 2a46538b3a..8896409b4e 100644 --- a/docs/zh_cn/tutorials/0_config.md +++ b/docs/zh_cn/tutorials/0_config.md @@ -95,7 +95,7 @@ configs/{topic}/{task}/{algorithm}/{dataset}/{backbone}_[model_setting]_{dataset log_config = dict( # 注册日志钩子的设置 interval=20, # 打印日志间隔 hooks=[ # 训练期间执行的钩子 - dict(type='TextLoggerHook', by_epoch=False), + dict(type='TextLoggerHook', by_epoch=False), dict(type='TensorboardLoggerHook', by_epoch=False), dict(type='WandbLoggerHook', by_epoch=False, # 还支持 Wandb 记录器，它需要安装 `wandb`。 init_kwargs={'entity': "entity", # 用于登录wandb的实体 From a410dbd5623b79c8e394ff1f9201190243dda044 Mon Sep 17 00:00:00 2001 From: AmirMasoud Nourollah Date: Tue, 6 Sep 2022 17:35:20 +0430 Subject: [PATCH 3/3] [Docs] Removed redundant text --- docs/en/tutorials/0_config.md | 5 ----- 1 file changed, 5 deletions(-) diff --git a/docs/en/tutorials/0_config.md b/docs/en/tutorials/0_config.md index fbfc6346aa..3cb8bd986a 100644 --- a/docs/en/tutorials/0_config.md +++ b/docs/en/tutorials/0_config.md @@ -106,11 +106,6 @@ configs/{topic}/{task}/{algorithm}/{dataset}/{backbone}_[model_setting]_{dataset ])) # ClearMLLoggerHook, DvcliveLoggerHook, MlflowLoggerHook, NeptuneLoggerHook, PaviLoggerHook, SegmindLoggerHook are also supported based on MMCV implementation. ``` -al=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