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Commit 5b3e36dc authored by Sugon_ldc's avatar Sugon_ldc
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add model TSM

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configs/detection/acrn/slowfast_acrn_kinetics_pretrained_r50_8x8x1_cosine_10e_ava_rgb.py 0 → 100644
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model = dict(
type='FastRCNN',
backbone=dict(
type='ResNet3dSlowFast',
pretrained=None,
resample_rate=4,
speed_ratio=4,
channel_ratio=8,
slow_pathway=dict(
type='resnet3d',
depth=50,
pretrained=None,
lateral=True,
fusion_kernel=7,
conv1_kernel=(1, 7, 7),
dilations=(1, 1, 1, 1),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
spatial_strides=(1, 2, 2, 1)),
fast_pathway=dict(
type='resnet3d',
depth=50,
pretrained=None,
lateral=False,
base_channels=8,
conv1_kernel=(5, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
spatial_strides=(1, 2, 2, 1))),
roi_head=dict(
type='AVARoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor3D',
roi_layer_type='RoIAlign',
output_size=8,
with_temporal_pool=True,
temporal_pool_mode='max'),
shared_head=dict(type='ACRNHead', in_channels=4608, out_channels=2304),
bbox_head=dict(
type='BBoxHeadAVA',
dropout_ratio=0.5,
in_channels=2304,
num_classes=81,
multilabel=True)),
train_cfg=dict(
rcnn=dict(
assigner=dict(
type='MaxIoUAssignerAVA',
pos_iou_thr=0.9,
neg_iou_thr=0.9,
min_pos_iou=0.9),
sampler=dict(
type='RandomSampler',
num=32,
pos_fraction=1,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=1.0,
debug=False)),
test_cfg=dict(rcnn=dict(action_thr=0.002)))
dataset_type = 'AVADataset'
data_root = 'data/ava/rawframes'
anno_root = 'data/ava/annotations'
ann_file_train = f'{anno_root}/ava_train_v2.1.csv'
ann_file_val = f'{anno_root}/ava_val_v2.1.csv'
exclude_file_train = f'{anno_root}/ava_train_excluded_timestamps_v2.1.csv'
exclude_file_val = f'{anno_root}/ava_val_excluded_timestamps_v2.1.csv'
label_file = f'{anno_root}/ava_action_list_v2.1_for_activitynet_2018.pbtxt'
proposal_file_train = (f'{anno_root}/ava_dense_proposals_train.FAIR.'
'recall_93.9.pkl')
proposal_file_val = f'{anno_root}/ava_dense_proposals_val.FAIR.recall_93.9.pkl'
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='SampleAVAFrames', clip_len=32, frame_interval=2),
dict(type='RawFrameDecode'),
dict(type='RandomRescale', scale_range=(256, 320)),
dict(type='RandomCrop', size=256),
dict(type='Flip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
dict(
type='ToDataContainer',
fields=[
dict(key=['proposals', 'gt_bboxes', 'gt_labels'], stack=False)
]),
dict(
type='Collect',
keys=['img', 'proposals', 'gt_bboxes', 'gt_labels'],
meta_keys=['scores', 'entity_ids'])
]
# The testing is w/o. any cropping / flipping
val_pipeline = [
dict(
type='SampleAVAFrames', clip_len=32, frame_interval=2, test_mode=True),
dict(type='RawFrameDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals']),
dict(type='ToDataContainer', fields=[dict(key='proposals', stack=False)]),
dict(
type='Collect',
keys=['img', 'proposals'],
meta_keys=['scores', 'img_shape'],
nested=True)
]
data = dict(
videos_per_gpu=6,
workers_per_gpu=2,
val_dataloader=dict(videos_per_gpu=1),
test_dataloader=dict(videos_per_gpu=1),
train=dict(
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(
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']
# optimizer
optimizer = dict(type='SGD', lr=0.075, momentum=0.9, weight_decay=0.00001)
# this lr is used for 8 gpus
optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
# learning policy
lr_config = dict(
policy='CosineAnnealing',
by_epoch=False,
min_lr=0,
warmup='linear',
warmup_by_epoch=True,
warmup_iters=2,
warmup_ratio=0.1)
total_epochs = 10
checkpoint_config = dict(interval=1)
workflow = [('train', 1)]
evaluation = dict(interval=1)
log_config = dict(
interval=20, hooks=[
dict(type='TextLoggerHook'),
])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/slowfast_acrn_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb' # noqa: E501
load_from = 'https://download.openmmlab.com/mmaction/recognition/slowfast/slowfast_r50_8x8x1_256e_kinetics400_rgb/slowfast_r50_8x8x1_256e_kinetics400_rgb_20200716-73547d2b.pth' # noqa: E501
resume_from = None
find_unused_parameters = False
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configs/detection/ava/README_zh-CN.md 0 → 100644
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Collections:
- Name: AVA
README: configs/detection/ava/README.md
Paper:
URL: https://arxiv.org/abs/1705.08421
Title: "AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions"
Models:
- Config: configs/detection/ava/slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb.py
In Collection: AVA
Metadata:
Architecture: ResNet50
Batch Size: 16
Epochs: 20
Input: 4x16
Pretrained: Kinetics-400
Resolution: short-side 256
Training Data: AVA v2.1
Training Resources: 8 GPUs
Modality: RGB
Name: slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb
Results:
- Dataset: AVA v2.1
Metrics:
mAP: 20.1
Task: Spatial Temporal Action Detection
Training Json Log: https://download.openmmlab.com/mmaction/detection/ava/slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb/slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb_20201127.json
Training Log: https://download.openmmlab.com/mmaction/detection/ava/slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb/slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb_20201127.log
Weights: https://download.openmmlab.com/mmaction/detection/ava/slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb/slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb_20201217-40061d5f.pth
- Config: configs/detection/ava/slowonly_omnisource_pretrained_r50_4x16x1_20e_ava_rgb.py
In Collection: AVA
Metadata:
Architecture: ResNet50
Batch Size: 16
Epochs: 20
Input: 4x16
Pretrained: OmniSource
Resolution: short-side 256
Training Data: AVA v2.1
Training Resources: 8 GPUs
Modality: RGB
Name: slowonly_omnisource_pretrained_r50_4x16x1_20e_ava_rgb
Results:
- Dataset: AVA v2.1
Metrics:
mAP: 21.8
Task: Spatial Temporal Action Detection
Training Json Log: https://download.openmmlab.com/mmaction/detection/ava/slowonly_omnisource_pretrained_r50_4x16x1_20e_ava_rgb/slowonly_omnisource_pretrained_r50_4x16x1_20e_ava_rgb_20201127.json
Training Log: https://download.openmmlab.com/mmaction/detection/ava/slowonly_omnisource_pretrained_r50_4x16x1_20e_ava_rgb/slowonly_omnisource_pretrained_r50_4x16x1_20e_ava_rgb_20201127.log
Weights: https://download.openmmlab.com/mmaction/detection/ava/slowonly_omnisource_pretrained_r50_4x16x1_20e_ava_rgb/slowonly_omnisource_pretrained_r50_4x16x1_20e_ava_rgb_20201217-0c6d2e98.pth
- Config: configs/detection/ava/slowonly_nl_kinetics_pretrained_r50_4x16x1_10e_ava_rgb.py
In Collection: AVA
Metadata:
Architecture: ResNet50
Batch Size: 12
Epochs: 10
Input: 4x16
Pretrained: Kinetics-400
Resolution: short-side 256
Training Data: AVA v2.1
Training Resources: 8 GPUs
Modality: RGB
Name: slowonly_nl_kinetics_pretrained_r50_4x16x1_10e_ava_rgb
Results:
- Dataset: AVA v2.1
Metrics:
mAP: 21.75
Task: Spatial Temporal Action Detection
Training Json Log: https://download.openmmlab.com/mmaction/detection/ava/slowonly_nl_kinetics_pretrained_r50_4x16x1_10e_ava_rgb/20210316_122517.log.json
Training Log: https://download.openmmlab.com/mmaction/detection/ava/slowonly_nl_kinetics_pretrained_r50_4x16x1_10e_ava_rgb/20210316_122517.log
Weights: https://download.openmmlab.com/mmaction/detection/ava/slowonly_nl_kinetics_pretrained_r50_4x16x1_10e_ava_rgb/slowonly_nl_kinetics_pretrained_r50_4x16x1_10e_ava_rgb_20210316-959829ec.pth
- Config: configs/detection/ava/slowonly_nl_kinetics_pretrained_r50_8x8x1_10e_ava_rgb.py
In Collection: AVA
Metadata:
Architecture: ResNet50
Batch Size: 6
Epochs: 10
Input: 8x8
Pretrained: Kinetics-400
Resolution: short-side 256
Training Data: AVA v2.1
Training Resources: 16 GPUs
Modality: RGB
Name: slowonly_nl_kinetics_pretrained_r50_8x8x1_10e_ava_rgb
Results:
- Dataset: AVA v2.1
Metrics:
mAP: 23.79
Task: Spatial Temporal Action Detection
Training Json Log: https://download.openmmlab.com/mmaction/detection/ava/slowonly_nl_kinetics_pretrained_r50_8x8x1_10e_ava_rgb/20210316_122517.log.json
Training Log: https://download.openmmlab.com/mmaction/detection/ava/slowonly_nl_kinetics_pretrained_r50_8x8x1_10e_ava_rgb/20210316_122517.log
Weights: https://download.openmmlab.com/mmaction/detection/ava/slowonly_nl_kinetics_pretrained_r50_8x8x1_10e_ava_rgb/slowonly_nl_kinetics_pretrained_r50_8x8x1_10e_ava_rgb_20210316-5742e4dd.pth
- Config: configs/detection/ava/slowonly_kinetics_pretrained_r101_8x8x1_20e_ava_rgb.py
In Collection: AVA
Metadata:
Architecture: ResNet101
Batch Size: 6
Epochs: 20
Input: 8x8
Pretrained: Kinetics-400
Resolution: short-side 256
Training Data: AVA v2.1
Training Resources: 16 GPUs
Modality: RGB
Name: slowonly_kinetics_pretrained_r101_8x8x1_20e_ava_rgb
Results:
- Dataset: AVA v2.1
Metrics:
mAP: 24.6
Task: Spatial Temporal Action Detection
Training Json Log: https://download.openmmlab.com/mmaction/detection/ava/slowonly_kinetics_pretrained_r101_8x8x1_20e_ava_rgb/slowonly_kinetics_pretrained_r101_8x8x1_20e_ava_rgb_20201127.json
Training Log: https://download.openmmlab.com/mmaction/detection/ava/slowonly_kinetics_pretrained_r101_8x8x1_20e_ava_rgb/slowonly_kinetics_pretrained_r101_8x8x1_20e_ava_rgb_20201127.log
Weights: https://download.openmmlab.com/mmaction/detection/ava/slowonly_kinetics_pretrained_r101_8x8x1_20e_ava_rgb/slowonly_kinetics_pretrained_r101_8x8x1_20e_ava_rgb_20201217-1c9b4117.pth
- Config: configs/detection/ava/slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb.py
In Collection: AVA
Metadata:
Architecture: ResNet101
Batch Size: 6
Epochs: 20
Input: 8x8
Pretrained: OmniSource
Resolution: short-side 256
Training Data: AVA v2.1
Training Resources: 16 GPUs
Modality: RGB
Name: slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb
Results:
- Dataset: AVA v2.1
Metrics:
mAP: 25.9
Task: Spatial Temporal Action Detection
Training Json Log: https://download.openmmlab.com/mmaction/detection/ava/slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb/slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb_20201127.json
Training Log: https://download.openmmlab.com/mmaction/detection/ava/slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb/slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb_20201127.log
Weights: https://download.openmmlab.com/mmaction/detection/ava/slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb/slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb_20201217-16378594.pth
- Config: configs/detection/ava/slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb.py
In Collection: AVA
Metadata:
Architecture: ResNet50
Batch Size: 9
Epochs: 20
Input: 32x2
Pretrained: Kinetics-400
Resolution: short-side 256
Training Data: AVA v2.1
Training Resources: 16 GPUs
Modality: RGB
Name: slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb
Results:
- Dataset: AVA v2.1
Metrics:
mAP: 24.4
Task: Spatial Temporal Action Detection
Training Json Log: https://download.openmmlab.com/mmaction/detection/ava/slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb/slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb_20201217.json
Training Log: https://download.openmmlab.com/mmaction/detection/ava/slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb/slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb_20201217.log
Weights: https://download.openmmlab.com/mmaction/detection/ava/slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb/slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb_20201217-6e7c704d.pth
- Config: configs/detection/ava/slowfast_context_kinetics_pretrained_r50_4x16x1_20e_ava_rgb.py
In Collection: AVA
Metadata:
Architecture: ResNet50
Batch Size: 9
Epochs: 20
Input: 32x2
Pretrained: Kinetics-400
Resolution: short-side 256
Training Data: AVA v2.1
Training Resources: 16 GPUs
Modality: RGB
Name: slowfast_context_kinetics_pretrained_r50_4x16x1_20e_ava_rgb
Results:
- Dataset: AVA v2.1
Metrics:
mAP: 25.4
Task: Spatial Temporal Action Detection
Training Json Log: https://download.openmmlab.com/mmaction/detection/ava/slowfast_context_kinetics_pretrained_r50_4x16x1_20e_ava_rgb/slowfast_context_kinetics_pretrained_r50_4x16x1_20e_ava_rgb_20201222.json
Training Log: https://download.openmmlab.com/mmaction/detection/ava/slowfast_context_kinetics_pretrained_r50_4x16x1_20e_ava_rgb/slowfast_context_kinetics_pretrained_r50_4x16x1_20e_ava_rgb_20201222.log
Weights: https://download.openmmlab.com/mmaction/detection/ava/slowfast_context_kinetics_pretrained_r50_4x16x1_20e_ava_rgb/slowfast_context_kinetics_pretrained_r50_4x16x1_20e_ava_rgb_20201222-f4d209c9.pth
- Config: configs/detection/ava/slowfast_kinetics_pretrained_r50_8x8x1_20e_ava_rgb.py
In Collection: AVA
Metadata:
Architecture: ResNet50
Batch Size: 5
Epochs: 20
Input: 32x2
Pretrained: Kinetics-400
Resolution: short-side 256
Training Data: AVA v2.1
Training Resources: 16 GPUs
Modality: RGB
Name: slowfast_kinetics_pretrained_r50_8x8x1_20e_ava_rgb
Results:
- Dataset: AVA v2.1
Metrics:
mAP: 25.5
Task: Spatial Temporal Action Detection
Training Json Log: https://download.openmmlab.com/mmaction/detection/ava/slowfast_kinetics_pretrained_r50_8x8x1_20e_ava_rgb/slowfast_kinetics_pretrained_r50_8x8x1_20e_ava_rgb_20201217.json
Training Log: https://download.openmmlab.com/mmaction/detection/ava/slowfast_kinetics_pretrained_r50_8x8x1_20e_ava_rgb/slowfast_kinetics_pretrained_r50_8x8x1_20e_ava_rgb_20201217.log
Weights: https://download.openmmlab.com/mmaction/detection/ava/slowfast_kinetics_pretrained_r50_8x8x1_20e_ava_rgb/slowfast_kinetics_pretrained_r50_8x8x1_20e_ava_rgb_20201217-ae225e97.pth
- Config: configs/detection/ava/slowfast_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb.py
In Collection: AVA
Metadata:
Architecture: ResNet50
Batch Size: 6
Epochs: 10
Input: 32x2
Pretrained: Kinetics-400
Resolution: short-side 256
Training Data: AVA v2.2
Training Resources: 8 GPUs
Modality: RGB
Name: slowfast_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb
Results:
- Dataset: AVA v2.2
Metrics:
mAP: 26.1
Task: Spatial Temporal Action Detection
Training Json Log: https://download.openmmlab.com/mmaction/detection/ava/slowfast_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb/slowfast_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb.json
Training Log: https://download.openmmlab.com/mmaction/detection/ava/slowfast_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb/slowfast_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb.log
Weights: https://download.openmmlab.com/mmaction/detection/ava/slowfast_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb/slowfast_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb-b987b516.pth
- Config: configs/detection/ava/slowfast_temporal_max_focal_alpha3_gamma1_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb.py
In Collection: AVA
Metadata:
Architecture: ResNet50
Batch Size: 6
Epochs: 10
Input: 32x2
Pretrained: Kinetics-400
Resolution: short-side 256
Training Data: AVA v2.2
Training Resources: 8 GPUs
Modality: RGB
Name: slowfast_temporal_max_focal_alpha3_gamma1_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb
Results:
- Dataset: AVA v2.2
Metrics:
mAP: 26.8
Task: Spatial Temporal Action Detection
Training Json Log: https://download.openmmlab.com/mmaction/detection/ava/slowfast_temporal_max_focal_alpha3_gamma1_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb/slowfast_temporal_max_focal_alpha3_gamma1_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb.json
Training Log: https://download.openmmlab.com/mmaction/detection/ava/slowfast_temporal_max_focal_alpha3_gamma1_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb/slowfast_temporal_max_focal_alpha3_gamma1_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb.log
Weights: https://download.openmmlab.com/mmaction/detection/ava/slowfast_temporal_max_focal_alpha3_gamma1_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb/slowfast_temporal_max_focal_alpha3_gamma1_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb-345618cd.pth
- Config: configs/detection/ava/slowfast_temporal_max_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb.py
In Collection: AVA
Metadata:
Architecture: ResNet50
Batch Size: 6
Epochs: 10
Input: 32x2
Pretrained: Kinetics-400
Resolution: short-side 256
Training Data: AVA v2.2
Training Resources: 8 GPUs
Modality: RGB
Name: slowfast_temporal_max_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb
Results:
- Dataset: AVA v2.2
Metrics:
mAP: 26.4
Task: Spatial Temporal Action Detection
Training Json Log: https://download.openmmlab.com/mmaction/detection/ava/slowfast_temporal_max_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb/slowfast_temporal_max_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb.json
Training Log: https://download.openmmlab.com/mmaction/detection/ava/slowfast_temporal_max_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb/slowfast_temporal_max_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb.log
Weights: https://download.openmmlab.com/mmaction/detection/ava/slowfast_temporal_max_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb/slowfast_temporal_max_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb-874e0845.pth
configs/detection/ava/slowfast_context_kinetics_pretrained_r50_4x16x1_20e_ava_rgb.py 0 → 100644
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# model setting
model = dict(
type='FastRCNN',
backbone=dict(
type='ResNet3dSlowFast',
pretrained=None,
resample_rate=8,
speed_ratio=8,
channel_ratio=8,
slow_pathway=dict(
type='resnet3d',
depth=50,
pretrained=None,
lateral=True,
conv1_kernel=(1, 7, 7),
dilations=(1, 1, 1, 1),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
spatial_strides=(1, 2, 2, 1)),
fast_pathway=dict(
type='resnet3d',
depth=50,
pretrained=None,
lateral=False,
base_channels=8,
conv1_kernel=(5, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
spatial_strides=(1, 2, 2, 1))),
roi_head=dict(
type='AVARoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor3D',
roi_layer_type='RoIAlign',
output_size=8,
with_temporal_pool=True,
with_global=True),
bbox_head=dict(
type='BBoxHeadAVA',
in_channels=4608,
num_classes=81,
multilabel=True,
dropout_ratio=0.5)),
train_cfg=dict(
rcnn=dict(
assigner=dict(
type='MaxIoUAssignerAVA',
pos_iou_thr=0.9,
neg_iou_thr=0.9,
min_pos_iou=0.9),
sampler=dict(
type='RandomSampler',
num=32,
pos_fraction=1,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=1.0,
debug=False)),
test_cfg=dict(rcnn=dict(action_thr=0.002)))
dataset_type = 'AVADataset'
data_root = 'data/ava/rawframes'
anno_root = 'data/ava/annotations'
ann_file_train = f'{anno_root}/ava_train_v2.1.csv'
ann_file_val = f'{anno_root}/ava_val_v2.1.csv'
exclude_file_train = f'{anno_root}/ava_train_excluded_timestamps_v2.1.csv'
exclude_file_val = f'{anno_root}/ava_val_excluded_timestamps_v2.1.csv'
label_file = f'{anno_root}/ava_action_list_v2.1_for_activitynet_2018.pbtxt'
proposal_file_train = (f'{anno_root}/ava_dense_proposals_train.FAIR.'
'recall_93.9.pkl')
proposal_file_val = f'{anno_root}/ava_dense_proposals_val.FAIR.recall_93.9.pkl'
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='SampleAVAFrames', clip_len=32, frame_interval=2),
dict(type='RawFrameDecode'),
dict(type='RandomRescale', scale_range=(256, 320)),
dict(type='RandomCrop', size=256),
dict(type='Flip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
# Rename is needed to use mmdet detectors
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
dict(
type='ToDataContainer',
fields=[
dict(key=['proposals', 'gt_bboxes', 'gt_labels'], stack=False)
]),
dict(
type='Collect',
keys=['img', 'proposals', 'gt_bboxes', 'gt_labels'],
meta_keys=['scores', 'entity_ids'])
]
# The testing is w/o. any cropping / flipping
val_pipeline = [
dict(
type='SampleAVAFrames', clip_len=32, frame_interval=2, test_mode=True),
dict(type='RawFrameDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
# Rename is needed to use mmdet detectors
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals']),
dict(type='ToDataContainer', fields=[dict(key='proposals', stack=False)]),
dict(
type='Collect',
keys=['img', 'proposals'],
meta_keys=['scores', 'img_shape'],
nested=True)
]
data = dict(
videos_per_gpu=9,
workers_per_gpu=2,
val_dataloader=dict(videos_per_gpu=1),
test_dataloader=dict(videos_per_gpu=1),
train=dict(
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(
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']
optimizer = dict(type='SGD', lr=0.1125, momentum=0.9, weight_decay=0.00001)
# this lr is used for 8 gpus
optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
step=[10, 15],
warmup='linear',
warmup_by_epoch=True,
warmup_iters=5,
warmup_ratio=0.1)
total_epochs = 20
checkpoint_config = dict(interval=1)
workflow = [('train', 1)]
evaluation = dict(interval=1, save_best='mAP@0.5IOU')
log_config = dict(
interval=20, hooks=[
dict(type='TextLoggerHook'),
])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = ('./work_dirs/ava/'
'slowfast_context_kinetics_pretrained_r50_4x16x1_20e_ava_rgb')
load_from = ('https://download.openmmlab.com/mmaction/recognition/slowfast/'
'slowfast_r50_4x16x1_256e_kinetics400_rgb/'
'slowfast_r50_4x16x1_256e_kinetics400_rgb_20200704-bcde7ed7.pth')
resume_from = None
find_unused_parameters = False
configs/detection/ava/slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb.py 0 → 100644
View file @ 5b3e36dc
# model setting
model = dict(
type='FastRCNN',
backbone=dict(
type='ResNet3dSlowFast',
pretrained=None,
resample_rate=8,
speed_ratio=8,
channel_ratio=8,
slow_pathway=dict(
type='resnet3d',
depth=50,
pretrained=None,
lateral=True,
conv1_kernel=(1, 7, 7),
dilations=(1, 1, 1, 1),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
spatial_strides=(1, 2, 2, 1)),
fast_pathway=dict(
type='resnet3d',
depth=50,
pretrained=None,
lateral=False,
base_channels=8,
conv1_kernel=(5, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
spatial_strides=(1, 2, 2, 1))),
roi_head=dict(
type='AVARoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor3D',
roi_layer_type='RoIAlign',
output_size=8,
with_temporal_pool=True),
bbox_head=dict(
type='BBoxHeadAVA',
in_channels=2304,
num_classes=81,
multilabel=True,
dropout_ratio=0.5)),
train_cfg=dict(
rcnn=dict(
assigner=dict(
type='MaxIoUAssignerAVA',
pos_iou_thr=0.9,
neg_iou_thr=0.9,
min_pos_iou=0.9),
sampler=dict(
type='RandomSampler',
num=32,
pos_fraction=1,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=1.0,
debug=False)),
test_cfg=dict(rcnn=dict(action_thr=0.002)))
dataset_type = 'AVADataset'
data_root = 'data/ava/rawframes'
anno_root = 'data/ava/annotations'
ann_file_train = f'{anno_root}/ava_train_v2.1.csv'
ann_file_val = f'{anno_root}/ava_val_v2.1.csv'
exclude_file_train = f'{anno_root}/ava_train_excluded_timestamps_v2.1.csv'
exclude_file_val = f'{anno_root}/ava_val_excluded_timestamps_v2.1.csv'
label_file = f'{anno_root}/ava_action_list_v2.1_for_activitynet_2018.pbtxt'
proposal_file_train = (f'{anno_root}/ava_dense_proposals_train.FAIR.'
'recall_93.9.pkl')
proposal_file_val = f'{anno_root}/ava_dense_proposals_val.FAIR.recall_93.9.pkl'
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='SampleAVAFrames', clip_len=32, frame_interval=2),
dict(type='RawFrameDecode'),
dict(type='RandomRescale', scale_range=(256, 320)),
dict(type='RandomCrop', size=256),
dict(type='Flip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
# Rename is needed to use mmdet detectors
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
dict(
type='ToDataContainer',
fields=[
dict(key=['proposals', 'gt_bboxes', 'gt_labels'], stack=False)
]),
dict(
type='Collect',
keys=['img', 'proposals', 'gt_bboxes', 'gt_labels'],
meta_keys=['scores', 'entity_ids'])
]
# The testing is w/o. any cropping / flipping
val_pipeline = [
dict(
type='SampleAVAFrames', clip_len=32, frame_interval=2, test_mode=True),
dict(type='RawFrameDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
# Rename is needed to use mmdet detectors
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals']),
dict(type='ToDataContainer', fields=[dict(key='proposals', stack=False)]),
dict(
type='Collect',
keys=['img', 'proposals'],
meta_keys=['scores', 'img_shape'],
nested=True)
]
data = dict(
videos_per_gpu=9,
workers_per_gpu=2,
val_dataloader=dict(videos_per_gpu=1),
test_dataloader=dict(videos_per_gpu=1),
train=dict(
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(
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']
optimizer = dict(type='SGD', lr=0.1125, momentum=0.9, weight_decay=0.00001)
# this lr is used for 8 gpus
optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
step=[10, 15],
warmup='linear',
warmup_by_epoch=True,
warmup_iters=5,
warmup_ratio=0.1)
total_epochs = 20
checkpoint_config = dict(interval=1)
workflow = [('train', 1)]
evaluation = dict(interval=1, save_best='mAP@0.5IOU')
log_config = dict(
interval=20, hooks=[
dict(type='TextLoggerHook'),
])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = ('./work_dirs/ava/'
'slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb')
load_from = ('https://download.openmmlab.com/mmaction/recognition/slowfast/'
'slowfast_r50_4x16x1_256e_kinetics400_rgb/'
'slowfast_r50_4x16x1_256e_kinetics400_rgb_20200704-bcde7ed7.pth')
resume_from = None
find_unused_parameters = False
configs/detection/ava/slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb_custom_classes.py 0 → 100644
View file @ 5b3e36dc
# custom classes of ava dataset
# Here we choose classes with AP in range [0.1, 0.3)
# AP is calculated by **slowonly** ckpt, which is trained by all 80 classes
custom_classes = [3, 6, 10, 27, 29, 38, 41, 48, 51, 53, 54, 59, 61, 64, 70, 72]
num_classes = len(custom_classes) + 1
# model setting
model = dict(
type='FastRCNN',
backbone=dict(
type='ResNet3dSlowFast',
pretrained=None,
resample_rate=8,
speed_ratio=8,
channel_ratio=8,
slow_pathway=dict(
type='resnet3d',
depth=50,
pretrained=None,
lateral=True,
conv1_kernel=(1, 7, 7),
dilations=(1, 1, 1, 1),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
spatial_strides=(1, 2, 2, 1)),
fast_pathway=dict(
type='resnet3d',
depth=50,
pretrained=None,
lateral=False,
base_channels=8,
conv1_kernel=(5, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
spatial_strides=(1, 2, 2, 1))),
roi_head=dict(
type='AVARoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor3D',
roi_layer_type='RoIAlign',
output_size=8,
with_temporal_pool=True),
bbox_head=dict(
type='BBoxHeadAVA',
in_channels=2304,
num_classes=num_classes,
multilabel=True,
dropout_ratio=0.5)),
train_cfg=dict(
rcnn=dict(
assigner=dict(
type='MaxIoUAssignerAVA',
pos_iou_thr=0.9,
neg_iou_thr=0.9,
min_pos_iou=0.9),
sampler=dict(
type='RandomSampler',
num=32,
pos_fraction=1,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=1.0,
debug=False)),
test_cfg=dict(rcnn=dict(action_thr=0.002)))
dataset_type = 'AVADataset'
data_root = 'data/ava/rawframes'
anno_root = 'data/ava/annotations'
ann_file_train = f'{anno_root}/ava_train_v2.1.csv'
ann_file_val = f'{anno_root}/ava_val_v2.1.csv'
exclude_file_train = f'{anno_root}/ava_train_excluded_timestamps_v2.1.csv'
exclude_file_val = f'{anno_root}/ava_val_excluded_timestamps_v2.1.csv'
label_file = f'{anno_root}/ava_action_list_v2.1.pbtxt'
proposal_file_train = (f'{anno_root}/ava_dense_proposals_train.FAIR.'
'recall_93.9.pkl')
proposal_file_val = f'{anno_root}/ava_dense_proposals_val.FAIR.recall_93.9.pkl'
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='SampleAVAFrames', clip_len=32, frame_interval=2),
dict(type='RawFrameDecode'),
dict(type='RandomRescale', scale_range=(256, 320)),
dict(type='RandomCrop', size=256),
dict(type='Flip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
# Rename is needed to use mmdet detectors
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
dict(
type='ToDataContainer',
fields=[
dict(key=['proposals', 'gt_bboxes', 'gt_labels'], stack=False)
]),
dict(
type='Collect',
keys=['img', 'proposals', 'gt_bboxes', 'gt_labels'],
meta_keys=['scores', 'entity_ids'])
]
# The testing is w/o. any cropping / flipping
val_pipeline = [
dict(
type='SampleAVAFrames', clip_len=32, frame_interval=2, test_mode=True),
dict(type='RawFrameDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
# Rename is needed to use mmdet detectors
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals']),
dict(type='ToDataContainer', fields=[dict(key='proposals', stack=False)]),
dict(
type='Collect',
keys=['img', 'proposals'],
meta_keys=['scores', 'img_shape'],
nested=True)
]
data = dict(
videos_per_gpu=9,
workers_per_gpu=2,
val_dataloader=dict(videos_per_gpu=1),
test_dataloader=dict(videos_per_gpu=1),
train=dict(
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,
num_classes=num_classes,
custom_classes=custom_classes,
data_prefix=data_root),
val=dict(
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,
num_classes=num_classes,
custom_classes=custom_classes,
data_prefix=data_root))
data['test'] = data['val']
optimizer = dict(type='SGD', lr=0.1125, momentum=0.9, weight_decay=0.00001)
# this lr is used for 8 gpus
optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
step=[10, 15],
warmup='linear',
warmup_by_epoch=True,
warmup_iters=5,
warmup_ratio=0.05)
total_epochs = 20
checkpoint_config = dict(interval=1)
workflow = [('train', 1)]
evaluation = dict(interval=1, save_best='mAP@0.5IOU')
log_config = dict(
interval=20, hooks=[
dict(type='TextLoggerHook'),
])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = ('./work_dirs/ava/'
'slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb_custom')
load_from = ('https://download.openmmlab.com/mmaction/recognition/slowfast/'
'slowfast_r50_4x16x1_256e_kinetics400_rgb/'
'slowfast_r50_4x16x1_256e_kinetics400_rgb_20200704-bcde7ed7.pth')
resume_from = None
find_unused_parameters = False
configs/detection/ava/slowfast_kinetics_pretrained_r50_8x8x1_20e_ava_rgb.py 0 → 100644
View file @ 5b3e36dc
# model setting
model = dict(
type='FastRCNN',
backbone=dict(
type='ResNet3dSlowFast',
pretrained=None,
resample_rate=4,
speed_ratio=4,
channel_ratio=8,
slow_pathway=dict(
type='resnet3d',
depth=50,
pretrained=None,
lateral=True,
fusion_kernel=7,
conv1_kernel=(1, 7, 7),
dilations=(1, 1, 1, 1),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
spatial_strides=(1, 2, 2, 1)),
fast_pathway=dict(
type='resnet3d',
depth=50,
pretrained=None,
lateral=False,
base_channels=8,
conv1_kernel=(5, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
spatial_strides=(1, 2, 2, 1))),
roi_head=dict(
type='AVARoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor3D',
roi_layer_type='RoIAlign',
output_size=8,
with_temporal_pool=True),
bbox_head=dict(
type='BBoxHeadAVA',
in_channels=2304,
num_classes=81,
multilabel=True,
dropout_ratio=0.5)),
train_cfg=dict(
rcnn=dict(
assigner=dict(
type='MaxIoUAssignerAVA',
pos_iou_thr=0.9,
neg_iou_thr=0.9,
min_pos_iou=0.9),
sampler=dict(
type='RandomSampler',
num=32,
pos_fraction=1,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=1.0,
debug=False)),
test_cfg=dict(rcnn=dict(action_thr=0.002)))
dataset_type = 'AVADataset'
data_root = 'data/ava/rawframes'
anno_root = 'data/ava/annotations'
ann_file_train = f'{anno_root}/ava_train_v2.1.csv'
ann_file_val = f'{anno_root}/ava_val_v2.1.csv'
exclude_file_train = f'{anno_root}/ava_train_excluded_timestamps_v2.1.csv'
exclude_file_val = f'{anno_root}/ava_val_excluded_timestamps_v2.1.csv'
label_file = f'{anno_root}/ava_action_list_v2.1_for_activitynet_2018.pbtxt'
proposal_file_train = (f'{anno_root}/ava_dense_proposals_train.FAIR.'
'recall_93.9.pkl')
proposal_file_val = f'{anno_root}/ava_dense_proposals_val.FAIR.recall_93.9.pkl'
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='SampleAVAFrames', clip_len=32, frame_interval=2),
dict(type='RawFrameDecode'),
dict(type='RandomRescale', scale_range=(256, 320)),
dict(type='RandomCrop', size=256),
dict(type='Flip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
# Rename is needed to use mmdet detectors
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
dict(
type='ToDataContainer',
fields=[
dict(key=['proposals', 'gt_bboxes', 'gt_labels'], stack=False)
]),
dict(
type='Collect',
keys=['img', 'proposals', 'gt_bboxes', 'gt_labels'],
meta_keys=['scores', 'entity_ids'])
]
# The testing is w/o. any cropping / flipping
val_pipeline = [
dict(
type='SampleAVAFrames', clip_len=32, frame_interval=2, test_mode=True),
dict(type='RawFrameDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
# Rename is needed to use mmdet detectors
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals']),
dict(type='ToDataContainer', fields=[dict(key='proposals', stack=False)]),
dict(
type='Collect',
keys=['img', 'proposals'],
meta_keys=['scores', 'img_shape'],
nested=True)
]
data = dict(
videos_per_gpu=5,
workers_per_gpu=2,
val_dataloader=dict(videos_per_gpu=1),
test_dataloader=dict(videos_per_gpu=1),
train=dict(
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(
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']
optimizer = dict(type='SGD', lr=0.075, momentum=0.9, weight_decay=0.00001)
# this lr is used for 8 gpus
optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
step=[10, 15],
warmup='linear',
warmup_by_epoch=True,
warmup_iters=5,
warmup_ratio=0.1)
total_epochs = 20
checkpoint_config = dict(interval=1)
workflow = [('train', 1)]
evaluation = dict(interval=1, save_best='mAP@0.5IOU')
log_config = dict(
interval=20, hooks=[
dict(type='TextLoggerHook'),
])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = ('./work_dirs/ava/'
'slowfast_kinetics_pretrained_r50_8x8x1_20e_ava_rgb')
load_from = ('https://download.openmmlab.com/mmaction/recognition/slowfast/'
'slowfast_r50_8x8x1_256e_kinetics400_rgb/'
'slowfast_r50_8x8x1_256e_kinetics400_rgb_20200716-73547d2b.pth')
resume_from = None
find_unused_parameters = False
configs/detection/ava/slowfast_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb.py 0 → 100644
View file @ 5b3e36dc
model = dict(
type='FastRCNN',
backbone=dict(
type='ResNet3dSlowFast',
pretrained=None,
resample_rate=4,
speed_ratio=4,
channel_ratio=8,
slow_pathway=dict(
type='resnet3d',
depth=50,
pretrained=None,
lateral=True,
fusion_kernel=7,
conv1_kernel=(1, 7, 7),
dilations=(1, 1, 1, 1),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
spatial_strides=(1, 2, 2, 1)),
fast_pathway=dict(
type='resnet3d',
depth=50,
pretrained=None,
lateral=False,
base_channels=8,
conv1_kernel=(5, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
spatial_strides=(1, 2, 2, 1))),
roi_head=dict(
type='AVARoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor3D',
roi_layer_type='RoIAlign',
output_size=8,
with_temporal_pool=True),
bbox_head=dict(
type='BBoxHeadAVA',
dropout_ratio=0.5,
in_channels=2304,
num_classes=81,
multilabel=True)),
train_cfg=dict(
rcnn=dict(
assigner=dict(
type='MaxIoUAssignerAVA',
pos_iou_thr=0.9,
neg_iou_thr=0.9,
min_pos_iou=0.9),
sampler=dict(
type='RandomSampler',
num=32,
pos_fraction=1,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=1.0,
debug=False)),
test_cfg=dict(rcnn=dict(action_thr=0.002)))
dataset_type = 'AVADataset'
data_root = 'data/ava/rawframes'
anno_root = 'data/ava/annotations'
ann_file_train = f'{anno_root}/ava_train_v2.2.csv'
ann_file_val = f'{anno_root}/ava_val_v2.2.csv'
exclude_file_train = f'{anno_root}/ava_train_excluded_timestamps_v2.2.csv'
exclude_file_val = f'{anno_root}/ava_val_excluded_timestamps_v2.2.csv'
label_file = f'{anno_root}/ava_action_list_v2.2_for_activitynet_2019.pbtxt'
proposal_file_train = (f'{anno_root}/ava_dense_proposals_train.FAIR.'
'recall_93.9.pkl')
proposal_file_val = f'{anno_root}/ava_dense_proposals_val.FAIR.recall_93.9.pkl'
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='SampleAVAFrames', clip_len=32, frame_interval=2),
dict(type='RawFrameDecode'),
dict(type='RandomRescale', scale_range=(256, 320)),
dict(type='RandomCrop', size=256),
dict(type='Flip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
dict(
type='ToDataContainer',
fields=[
dict(key=['proposals', 'gt_bboxes', 'gt_labels'], stack=False)
]),
dict(
type='Collect',
keys=['img', 'proposals', 'gt_bboxes', 'gt_labels'],
meta_keys=['scores', 'entity_ids'])
]
# The testing is w/o. any cropping / flipping
val_pipeline = [
dict(
type='SampleAVAFrames', clip_len=32, frame_interval=2, test_mode=True),
dict(type='RawFrameDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals']),
dict(type='ToDataContainer', fields=[dict(key='proposals', stack=False)]),
dict(
type='Collect',
keys=['img', 'proposals'],
meta_keys=['scores', 'img_shape'],
nested=True)
]
data = dict(
videos_per_gpu=6,
workers_per_gpu=2,
val_dataloader=dict(videos_per_gpu=1),
test_dataloader=dict(videos_per_gpu=1),
train=dict(
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(
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']
# optimizer
optimizer = dict(type='SGD', lr=0.075, momentum=0.9, weight_decay=0.00001)
# this lr is used for 8 gpus
optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
# learning policy
lr_config = dict(
policy='CosineAnnealing',
by_epoch=False,
min_lr=0,
warmup='linear',
warmup_by_epoch=True,
warmup_iters=2,
warmup_ratio=0.1)
total_epochs = 10
checkpoint_config = dict(interval=1)
workflow = [('train', 1)]
evaluation = dict(interval=1)
log_config = dict(
interval=20, hooks=[
dict(type='TextLoggerHook'),
])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/slowfast_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb' # noqa: E501
load_from = 'https://download.openmmlab.com/mmaction/recognition/slowfast/slowfast_r50_8x8x1_256e_kinetics400_rgb/slowfast_r50_8x8x1_256e_kinetics400_rgb_20200716-73547d2b.pth' # noqa: E501
resume_from = None
find_unused_parameters = False
configs/detection/ava/slowfast_temporal_max_focal_alpha3_gamma1_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb.py 0 → 100644
View file @ 5b3e36dc
model = dict(
type='FastRCNN',
backbone=dict(
type='ResNet3dSlowFast',
pretrained=None,
resample_rate=4,
speed_ratio=4,
channel_ratio=8,
slow_pathway=dict(
type='resnet3d',
depth=50,
pretrained=None,
lateral=True,
fusion_kernel=7,
conv1_kernel=(1, 7, 7),
dilations=(1, 1, 1, 1),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
spatial_strides=(1, 2, 2, 1)),
fast_pathway=dict(
type='resnet3d',
depth=50,
pretrained=None,
lateral=False,
base_channels=8,
conv1_kernel=(5, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
spatial_strides=(1, 2, 2, 1))),
roi_head=dict(
type='AVARoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor3D',
roi_layer_type='RoIAlign',
output_size=8,
with_temporal_pool=True,
temporal_pool_mode='max'),
bbox_head=dict(
type='BBoxHeadAVA',
dropout_ratio=0.5,
in_channels=2304,
focal_alpha=3.0,
focal_gamma=1.0,
num_classes=81,
multilabel=True)),
train_cfg=dict(
rcnn=dict(
assigner=dict(
type='MaxIoUAssignerAVA',
pos_iou_thr=0.9,
neg_iou_thr=0.9,
min_pos_iou=0.9),
sampler=dict(
type='RandomSampler',
num=32,
pos_fraction=1,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=1.0,
debug=False)),
test_cfg=dict(rcnn=dict(action_thr=0.002)))
dataset_type = 'AVADataset'
data_root = 'data/ava/rawframes'
anno_root = 'data/ava/annotations'
ann_file_train = f'{anno_root}/ava_train_v2.2.csv'
ann_file_val = f'{anno_root}/ava_val_v2.2.csv'
exclude_file_train = f'{anno_root}/ava_train_excluded_timestamps_v2.2.csv'
exclude_file_val = f'{anno_root}/ava_val_excluded_timestamps_v2.2.csv'
label_file = f'{anno_root}/ava_action_list_v2.2_for_activitynet_2019.pbtxt'
proposal_file_train = (f'{anno_root}/ava_dense_proposals_train.FAIR.'
'recall_93.9.pkl')
proposal_file_val = f'{anno_root}/ava_dense_proposals_val.FAIR.recall_93.9.pkl'
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='SampleAVAFrames', clip_len=32, frame_interval=2),
dict(type='RawFrameDecode'),
dict(type='RandomRescale', scale_range=(256, 320)),
dict(type='RandomCrop', size=256),
dict(type='Flip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
dict(
type='ToDataContainer',
fields=[
dict(key=['proposals', 'gt_bboxes', 'gt_labels'], stack=False)
]),
dict(
type='Collect',
keys=['img', 'proposals', 'gt_bboxes', 'gt_labels'],
meta_keys=['scores', 'entity_ids'])
]
# The testing is w/o. any cropping / flipping
val_pipeline = [
dict(
type='SampleAVAFrames', clip_len=32, frame_interval=2, test_mode=True),
dict(type='RawFrameDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals']),
dict(type='ToDataContainer', fields=[dict(key='proposals', stack=False)]),
dict(
type='Collect',
keys=['img', 'proposals'],
meta_keys=['scores', 'img_shape'],
nested=True)
]
data = dict(
videos_per_gpu=6,
workers_per_gpu=2,
val_dataloader=dict(videos_per_gpu=1),
test_dataloader=dict(videos_per_gpu=1),
train=dict(
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(
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']
# optimizer
optimizer = dict(type='SGD', lr=0.075, momentum=0.9, weight_decay=0.00001)
# this lr is used for 8 gpus
optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
# learning policy
lr_config = dict(
policy='CosineAnnealing',
by_epoch=False,
min_lr=0,
warmup='linear',
warmup_by_epoch=True,
warmup_iters=2,
warmup_ratio=0.1)
total_epochs = 10
checkpoint_config = dict(interval=1)
workflow = [('train', 1)]
evaluation = dict(interval=1)
log_config = dict(
interval=20, hooks=[
dict(type='TextLoggerHook'),
])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/slowfast_temporal_max_focal_alpha3_gamma1_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb' # noqa: E501
load_from = 'https://download.openmmlab.com/mmaction/recognition/slowfast/slowfast_r50_8x8x1_256e_kinetics400_rgb/slowfast_r50_8x8x1_256e_kinetics400_rgb_20200716-73547d2b.pth' # noqa: E501
resume_from = None
find_unused_parameters = False
configs/detection/ava/slowfast_temporal_max_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb.py 0 → 100644
View file @ 5b3e36dc
model = dict(
type='FastRCNN',
backbone=dict(
type='ResNet3dSlowFast',
pretrained=None,
resample_rate=4,
speed_ratio=4,
channel_ratio=8,
slow_pathway=dict(
type='resnet3d',
depth=50,
pretrained=None,
lateral=True,
fusion_kernel=7,
conv1_kernel=(1, 7, 7),
dilations=(1, 1, 1, 1),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
spatial_strides=(1, 2, 2, 1)),
fast_pathway=dict(
type='resnet3d',
depth=50,
pretrained=None,
lateral=False,
base_channels=8,
conv1_kernel=(5, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
spatial_strides=(1, 2, 2, 1))),
roi_head=dict(
type='AVARoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor3D',
roi_layer_type='RoIAlign',
output_size=8,
with_temporal_pool=True,
temporal_pool_mode='max'),
bbox_head=dict(
type='BBoxHeadAVA',
dropout_ratio=0.5,
in_channels=2304,
num_classes=81,
multilabel=True)),
train_cfg=dict(
rcnn=dict(
assigner=dict(
type='MaxIoUAssignerAVA',
pos_iou_thr=0.9,
neg_iou_thr=0.9,
min_pos_iou=0.9),
sampler=dict(
type='RandomSampler',
num=32,
pos_fraction=1,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=1.0,
debug=False)),
test_cfg=dict(rcnn=dict(action_thr=0.002)))
dataset_type = 'AVADataset'
data_root = 'data/ava/rawframes'
anno_root = 'data/ava/annotations'
ann_file_train = f'{anno_root}/ava_train_v2.2.csv'
ann_file_val = f'{anno_root}/ava_val_v2.2.csv'
exclude_file_train = f'{anno_root}/ava_train_excluded_timestamps_v2.2.csv'
exclude_file_val = f'{anno_root}/ava_val_excluded_timestamps_v2.2.csv'
label_file = f'{anno_root}/ava_action_list_v2.2_for_activitynet_2019.pbtxt'
proposal_file_train = (f'{anno_root}/ava_dense_proposals_train.FAIR.'
'recall_93.9.pkl')
proposal_file_val = f'{anno_root}/ava_dense_proposals_val.FAIR.recall_93.9.pkl'
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='SampleAVAFrames', clip_len=32, frame_interval=2),
dict(type='RawFrameDecode'),
dict(type='RandomRescale', scale_range=(256, 320)),
dict(type='RandomCrop', size=256),
dict(type='Flip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
dict(
type='ToDataContainer',
fields=[
dict(key=['proposals', 'gt_bboxes', 'gt_labels'], stack=False)
]),
dict(
type='Collect',
keys=['img', 'proposals', 'gt_bboxes', 'gt_labels'],
meta_keys=['scores', 'entity_ids'])
]
# The testing is w/o. any cropping / flipping
val_pipeline = [
dict(
type='SampleAVAFrames', clip_len=32, frame_interval=2, test_mode=True),
dict(type='RawFrameDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals']),
dict(type='ToDataContainer', fields=[dict(key='proposals', stack=False)]),
dict(
type='Collect',
keys=['img', 'proposals'],
meta_keys=['scores', 'img_shape'],
nested=True)
]
data = dict(
videos_per_gpu=6,
workers_per_gpu=2,
val_dataloader=dict(videos_per_gpu=1),
test_dataloader=dict(videos_per_gpu=1),
train=dict(
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(
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']
# optimizer
optimizer = dict(type='SGD', lr=0.075, momentum=0.9, weight_decay=0.00001)
# this lr is used for 8 gpus
optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
# learning policy
lr_config = dict(
policy='CosineAnnealing',
by_epoch=False,
min_lr=0,
warmup='linear',
warmup_by_epoch=True,
warmup_iters=2,
warmup_ratio=0.1)
total_epochs = 10
checkpoint_config = dict(interval=1)
workflow = [('train', 1)]
evaluation = dict(interval=1)
log_config = dict(
interval=20, hooks=[
dict(type='TextLoggerHook'),
])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/slowfast_temporal_max_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb' # noqa: E501
load_from = 'https://download.openmmlab.com/mmaction/recognition/slowfast/slowfast_r50_8x8x1_256e_kinetics400_rgb/slowfast_r50_8x8x1_256e_kinetics400_rgb_20200716-73547d2b.pth' # noqa: E501
resume_from = None
find_unused_parameters = False
configs/detection/ava/slowonly_kinetics_pretrained_r101_8x8x1_20e_ava_rgb.py 0 → 100644
View file @ 5b3e36dc
# model setting
model = dict(
type='FastRCNN',
backbone=dict(
type='ResNet3dSlowOnly',
depth=101,
pretrained=None,
pretrained2d=False,
lateral=False,
num_stages=4,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
spatial_strides=(1, 2, 2, 1)),
roi_head=dict(
type='AVARoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor3D',
roi_layer_type='RoIAlign',
output_size=8,
with_temporal_pool=True),
bbox_head=dict(
type='BBoxHeadAVA',
in_channels=2048,
num_classes=81,
multilabel=True,
dropout_ratio=0.5)),
train_cfg=dict(
rcnn=dict(
assigner=dict(
type='MaxIoUAssignerAVA',
pos_iou_thr=0.9,
neg_iou_thr=0.9,
min_pos_iou=0.9),
sampler=dict(
type='RandomSampler',
num=32,
pos_fraction=1,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=1.0,
debug=False)),
test_cfg=dict(rcnn=dict(action_thr=0.002)))
dataset_type = 'AVADataset'
data_root = 'data/ava/rawframes'
anno_root = 'data/ava/annotations'
ann_file_train = f'{anno_root}/ava_train_v2.1.csv'
ann_file_val = f'{anno_root}/ava_val_v2.1.csv'
exclude_file_train = f'{anno_root}/ava_train_excluded_timestamps_v2.1.csv'
exclude_file_val = f'{anno_root}/ava_val_excluded_timestamps_v2.1.csv'
label_file = f'{anno_root}/ava_action_list_v2.1_for_activitynet_2018.pbtxt'
proposal_file_train = (f'{anno_root}/ava_dense_proposals_train.FAIR.'
'recall_93.9.pkl')
proposal_file_val = f'{anno_root}/ava_dense_proposals_val.FAIR.recall_93.9.pkl'
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='SampleAVAFrames', clip_len=8, frame_interval=8),
dict(type='RawFrameDecode'),
dict(type='RandomRescale', scale_range=(256, 320)),
dict(type='RandomCrop', size=256),
dict(type='Flip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
# Rename is needed to use mmdet detectors
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
dict(
type='ToDataContainer',
fields=[
dict(key=['proposals', 'gt_bboxes', 'gt_labels'], stack=False)
]),
dict(
type='Collect',
keys=['img', 'proposals', 'gt_bboxes', 'gt_labels'],
meta_keys=['scores', 'entity_ids'])
]
# The testing is w/o. any cropping / flipping
val_pipeline = [
dict(type='SampleAVAFrames', clip_len=8, frame_interval=8, test_mode=True),
dict(type='RawFrameDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
# Rename is needed to use mmdet detectors
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals']),
dict(type='ToDataContainer', fields=[dict(key='proposals', stack=False)]),
dict(
type='Collect',
keys=['img', 'proposals'],
meta_keys=['scores', 'img_shape'],
nested=True)
]
data = dict(
videos_per_gpu=6,
workers_per_gpu=2,
# During testing, each video may have different shape
val_dataloader=dict(videos_per_gpu=1),
test_dataloader=dict(videos_per_gpu=1),
train=dict(
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(
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']
optimizer = dict(type='SGD', lr=0.075, momentum=0.9, weight_decay=0.00001)
# this lr is used for 8 gpus
optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
step=[10, 15],
warmup='linear',
warmup_by_epoch=True,
warmup_iters=5,
warmup_ratio=0.1)
total_epochs = 20
checkpoint_config = dict(interval=1)
workflow = [('train', 1)]
evaluation = dict(interval=1, save_best='mAP@0.5IOU')
log_config = dict(
interval=20, hooks=[
dict(type='TextLoggerHook'),
])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = ('./work_dirs/ava/'
'slowonly_kinetics_pretrained_r101_8x8x1_20e_ava_rgb')
load_from = ('https://download.openmmlab.com/mmaction/recognition/slowonly/'
'omni/slowonly_r101_without_omni_8x8x1_'
'kinetics400_rgb_20200926-0c730aef.pth')
resume_from = None
find_unused_parameters = False
configs/detection/ava/slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb.py 0 → 100644
View file @ 5b3e36dc
# model setting
model = dict(
type='FastRCNN',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained=None,
pretrained2d=False,
lateral=False,
num_stages=4,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
spatial_strides=(1, 2, 2, 1)),
roi_head=dict(
type='AVARoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor3D',
roi_layer_type='RoIAlign',
output_size=8,
with_temporal_pool=True),
bbox_head=dict(
type='BBoxHeadAVA',
in_channels=2048,
num_classes=81,
multilabel=True,
dropout_ratio=0.5)),
train_cfg=dict(
rcnn=dict(
assigner=dict(
type='MaxIoUAssignerAVA',
pos_iou_thr=0.9,
neg_iou_thr=0.9,
min_pos_iou=0.9),
sampler=dict(
type='RandomSampler',
num=32,
pos_fraction=1,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=1.0,
debug=False)),
test_cfg=dict(rcnn=dict(action_thr=0.002)))
dataset_type = 'AVADataset'
data_root = 'data/ava/rawframes'
anno_root = 'data/ava/annotations'
ann_file_train = f'{anno_root}/ava_train_v2.1.csv'
ann_file_val = f'{anno_root}/ava_val_v2.1.csv'
exclude_file_train = f'{anno_root}/ava_train_excluded_timestamps_v2.1.csv'
exclude_file_val = f'{anno_root}/ava_val_excluded_timestamps_v2.1.csv'
label_file = f'{anno_root}/ava_action_list_v2.1_for_activitynet_2018.pbtxt'
proposal_file_train = (f'{anno_root}/ava_dense_proposals_train.FAIR.'
'recall_93.9.pkl')
proposal_file_val = f'{anno_root}/ava_dense_proposals_val.FAIR.recall_93.9.pkl'
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='SampleAVAFrames', clip_len=4, frame_interval=16),
dict(type='RawFrameDecode'),
dict(type='RandomRescale', scale_range=(256, 320)),
dict(type='RandomCrop', size=256),
dict(type='Flip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
# Rename is needed to use mmdet detectors
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
dict(
type='ToDataContainer',
fields=[
dict(key=['proposals', 'gt_bboxes', 'gt_labels'], stack=False)
]),
dict(
type='Collect',
keys=['img', 'proposals', 'gt_bboxes', 'gt_labels'],
meta_keys=['scores', 'entity_ids'])
]
# The testing is w/o. any cropping / flipping
val_pipeline = [
dict(
type='SampleAVAFrames', clip_len=4, frame_interval=16, test_mode=True),
dict(type='RawFrameDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
# Rename is needed to use mmdet detectors
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals']),
dict(type='ToDataContainer', fields=[dict(key='proposals', stack=False)]),
dict(
type='Collect',
keys=['img', 'proposals'],
meta_keys=['scores', 'img_shape'],
nested=True)
]
data = dict(
videos_per_gpu=16,
workers_per_gpu=2,
val_dataloader=dict(videos_per_gpu=1),
test_dataloader=dict(videos_per_gpu=1),
train=dict(
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(
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']
optimizer = dict(type='SGD', lr=0.2, momentum=0.9, weight_decay=0.00001)
# this lr is used for 8 gpus
optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
step=[10, 15],
warmup='linear',
warmup_by_epoch=True,
warmup_iters=5,
warmup_ratio=0.1)
total_epochs = 20
checkpoint_config = dict(interval=1)
workflow = [('train', 1)]
evaluation = dict(interval=1, save_best='mAP@0.5IOU')
log_config = dict(
interval=20, hooks=[
dict(type='TextLoggerHook'),
])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = ('./work_dirs/ava/'
'slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb')
load_from = ('https://download.openmmlab.com/mmaction/recognition/slowonly/'
'slowonly_r50_4x16x1_256e_kinetics400_rgb/'
'slowonly_r50_4x16x1_256e_kinetics400_rgb_20200704-a69556c6.pth')
resume_from = None
find_unused_parameters = False
configs/detection/ava/slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb_custom_classes.py 0 → 100644
View file @ 5b3e36dc
# custom classes of ava dataset
# Here we choose classes with AP in range [0.1, 0.3)
# AP is calculated by original ckpt, which is trained by all 80 classes
custom_classes = [3, 6, 10, 27, 29, 38, 41, 48, 51, 53, 54, 59, 61, 64, 70, 72]
num_classes = len(custom_classes) + 1
# model setting
model = dict(
type='FastRCNN',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained=None,
pretrained2d=False,
lateral=False,
num_stages=4,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
spatial_strides=(1, 2, 2, 1)),
roi_head=dict(
type='AVARoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor3D',
roi_layer_type='RoIAlign',
output_size=8,
with_temporal_pool=True),
bbox_head=dict(
type='BBoxHeadAVA',
in_channels=2048,
num_classes=num_classes,
multilabel=True,
topk=(3, 5),
dropout_ratio=0.5)),
train_cfg=dict(
rcnn=dict(
assigner=dict(
type='MaxIoUAssignerAVA',
pos_iou_thr=0.9,
neg_iou_thr=0.9,
min_pos_iou=0.9),
sampler=dict(
type='RandomSampler',
num=32,
pos_fraction=1,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=1.0,
debug=False)),
test_cfg=dict(rcnn=dict(action_thr=0.002)))
dataset_type = 'AVADataset'
data_root = 'data/ava/rawframes'
anno_root = 'data/ava/annotations'
ann_file_train = f'{anno_root}/ava_train_v2.1.csv'
ann_file_val = f'{anno_root}/ava_val_v2.1.csv'
exclude_file_train = f'{anno_root}/ava_train_excluded_timestamps_v2.1.csv'
exclude_file_val = f'{anno_root}/ava_val_excluded_timestamps_v2.1.csv'
label_file = f'{anno_root}/ava_action_list_v2.1.pbtxt'
proposal_file_train = (f'{anno_root}/ava_dense_proposals_train.FAIR.'
'recall_93.9.pkl')
proposal_file_val = f'{anno_root}/ava_dense_proposals_val.FAIR.recall_93.9.pkl'
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='SampleAVAFrames', clip_len=4, frame_interval=16),
dict(type='RawFrameDecode'),
dict(type='RandomRescale', scale_range=(256, 320)),
dict(type='RandomCrop', size=256),
dict(type='Flip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
# Rename is needed to use mmdet detectors
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
dict(
type='ToDataContainer',
fields=[
dict(key=['proposals', 'gt_bboxes', 'gt_labels'], stack=False)
]),
dict(
type='Collect',
keys=['img', 'proposals', 'gt_bboxes', 'gt_labels'],
meta_keys=['scores', 'entity_ids'])
]
# The testing is w/o. any cropping / flipping
val_pipeline = [
dict(
type='SampleAVAFrames', clip_len=4, frame_interval=16, test_mode=True),
dict(type='RawFrameDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
# Rename is needed to use mmdet detectors
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals']),
dict(type='ToDataContainer', fields=[dict(key='proposals', stack=False)]),
dict(
type='Collect',
keys=['img', 'proposals'],
meta_keys=['scores', 'img_shape'],
nested=True)
]
data = dict(
videos_per_gpu=16,
workers_per_gpu=2,
val_dataloader=dict(videos_per_gpu=1),
test_dataloader=dict(videos_per_gpu=1),
train=dict(
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,
num_classes=num_classes,
custom_classes=custom_classes,
data_prefix=data_root),
val=dict(
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,
num_classes=num_classes,
custom_classes=custom_classes,
data_prefix=data_root))
data['test'] = data['val']
optimizer = dict(type='SGD', lr=0.2, momentum=0.9, weight_decay=0.00001)
# this lr is used for 8 gpus
optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
step=[10, 15],
warmup='linear',
warmup_by_epoch=True,
warmup_iters=5,
warmup_ratio=0.1)
total_epochs = 20
checkpoint_config = dict(interval=1)
workflow = [('train', 1)]
evaluation = dict(interval=1, save_best='mAP@0.5IOU')
log_config = dict(
interval=20, hooks=[
dict(type='TextLoggerHook'),
])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = ('./work_dirs/ava/'
'slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb_custom')
load_from = ('https://download.openmmlab.com/mmaction/recognition/slowonly/'
'slowonly_r50_4x16x1_256e_kinetics400_rgb/'
'slowonly_r50_4x16x1_256e_kinetics400_rgb_20200704-a69556c6.pth')
resume_from = None
find_unused_parameters = False
configs/detection/ava/slowonly_nl_kinetics_pretrained_r50_4x16x1_10e_ava_rgb.py 0 → 100644
View file @ 5b3e36dc
_base_ = ['../_base_/models/slowonly_r50_nl.py']
dataset_type = 'AVADataset'
data_root = 'data/ava/rawframes'
anno_root = 'data/ava/annotations'
ann_file_train = f'{anno_root}/ava_train_v2.1.csv'
ann_file_val = f'{anno_root}/ava_val_v2.1.csv'
exclude_file_train = f'{anno_root}/ava_train_excluded_timestamps_v2.1.csv'
exclude_file_val = f'{anno_root}/ava_val_excluded_timestamps_v2.1.csv'
label_file = f'{anno_root}/ava_action_list_v2.1_for_activitynet_2018.pbtxt'
proposal_file_train = (f'{anno_root}/ava_dense_proposals_train.FAIR.'
'recall_93.9.pkl')
proposal_file_val = f'{anno_root}/ava_dense_proposals_val.FAIR.recall_93.9.pkl'
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='SampleAVAFrames', clip_len=4, frame_interval=16),
dict(type='RawFrameDecode'),
dict(type='RandomRescale', scale_range=(256, 320)),
dict(type='RandomCrop', size=256),
dict(type='Flip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
# Rename is needed to use mmdet detectors
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
dict(
type='ToDataContainer',
fields=[
dict(key=['proposals', 'gt_bboxes', 'gt_labels'], stack=False)
]),
dict(
type='Collect',
keys=['img', 'proposals', 'gt_bboxes', 'gt_labels'],
meta_keys=['scores', 'entity_ids'])
]
# The testing is w/o. any cropping / flipping
val_pipeline = [
dict(
type='SampleAVAFrames', clip_len=4, frame_interval=16, test_mode=True),
dict(type='RawFrameDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
# Rename is needed to use mmdet detectors
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals']),
dict(type='ToDataContainer', fields=[dict(key='proposals', stack=False)]),
dict(
type='Collect',
keys=['img', 'proposals'],
meta_keys=['scores', 'img_shape'],
nested=True)
]
data = dict(
videos_per_gpu=12,
workers_per_gpu=2,
# During testing, each video may have different shape
val_dataloader=dict(videos_per_gpu=1),
test_dataloader=dict(videos_per_gpu=1),
train=dict(
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(
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']
optimizer = dict(
type='SGD', lr=0.3, momentum=0.9, weight_decay=1e-06, nesterov=True)
# this lr is used for 8 gpus
optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
step=[4, 6, 8],
warmup='linear',
warmup_iters=800,
warmup_ratio=0.01)
total_epochs = 10
checkpoint_config = dict(interval=1)
workflow = [('train', 1)]
evaluation = dict(interval=1, save_best='mAP@0.5IOU')
log_config = dict(
interval=20, hooks=[
dict(type='TextLoggerHook'),
])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = ('./work_dirs/ava/'
'slowonly_nl_kinetics_pretrained_r50_4x16x1_10e_ava_rgb')
load_from = (
'https://download.openmmlab.com/mmaction/recognition/slowonly/'
'slowonly_nl_embedded_gaussian_r50_4x16x1_150e_kinetics400_rgb/'
'slowonly_nl_embedded_gaussian_r50_4x16x1_150e_kinetics400_rgb_20210308-0d6e5a69.pth' # noqa: E501
)
resume_from = None
find_unused_parameters = False
configs/detection/ava/slowonly_nl_kinetics_pretrained_r50_8x8x1_10e_ava_rgb.py 0 → 100644
View file @ 5b3e36dc
_base_ = ['../_base_/models/slowonly_r50_nl.py']
dataset_type = 'AVADataset'
data_root = 'data/ava/rawframes'
anno_root = 'data/ava/annotations'
ann_file_train = f'{anno_root}/ava_train_v2.1.csv'
ann_file_val = f'{anno_root}/ava_val_v2.1.csv'
exclude_file_train = f'{anno_root}/ava_train_excluded_timestamps_v2.1.csv'
exclude_file_val = f'{anno_root}/ava_val_excluded_timestamps_v2.1.csv'
label_file = f'{anno_root}/ava_action_list_v2.1_for_activitynet_2018.pbtxt'
proposal_file_train = (f'{anno_root}/ava_dense_proposals_train.FAIR.'
'recall_93.9.pkl')
proposal_file_val = f'{anno_root}/ava_dense_proposals_val.FAIR.recall_93.9.pkl'
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='SampleAVAFrames', clip_len=8, frame_interval=8),
dict(type='RawFrameDecode'),
dict(type='RandomRescale', scale_range=(256, 320)),
dict(type='RandomCrop', size=256),
dict(type='Flip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
# Rename is needed to use mmdet detectors
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
dict(
type='ToDataContainer',
fields=[
dict(key=['proposals', 'gt_bboxes', 'gt_labels'], stack=False)
]),
dict(
type='Collect',
keys=['img', 'proposals', 'gt_bboxes', 'gt_labels'],
meta_keys=['scores', 'entity_ids'])
]
# The testing is w/o. any cropping / flipping
val_pipeline = [
dict(type='SampleAVAFrames', clip_len=8, frame_interval=8, test_mode=True),
dict(type='RawFrameDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
# Rename is needed to use mmdet detectors
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals']),
dict(type='ToDataContainer', fields=[dict(key='proposals', stack=False)]),
dict(
type='Collect',
keys=['img', 'proposals'],
meta_keys=['scores', 'img_shape'],
nested=True)
]
data = dict(
videos_per_gpu=6,
workers_per_gpu=2,
# During testing, each video may have different shape
val_dataloader=dict(videos_per_gpu=1),
test_dataloader=dict(videos_per_gpu=1),
train=dict(
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(
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']
optimizer = dict(
type='SGD', lr=0.15, momentum=0.9, weight_decay=1e-06, nesterov=True)
# this lr is used for 8x2 gpus
optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
step=[4, 6, 8],
warmup='linear',
warmup_iters=1600,
warmup_ratio=0.01)
total_epochs = 10
checkpoint_config = dict(interval=1)
workflow = [('train', 1)]
evaluation = dict(interval=1, save_best='mAP@0.5IOU')
log_config = dict(
interval=20, hooks=[
dict(type='TextLoggerHook'),
])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = ('./work_dirs/ava/'
'slowonly_nl_kinetics_pretrained_r50_8x8x1_10e_ava_rgb')
load_from = (
'https://download.openmmlab.com/mmaction/recognition/slowonly/'
'slowonly_nl_embedded_gaussian_r50_8x8x1_150e_kinetics400_rgb/'
'slowonly_nl_embedded_gaussian_r50_8x8x1_150e_kinetics400_rgb_20210308-e8dd9e82.pth' # noqa: E501
)
resume_from = None
find_unused_parameters = False
configs/detection/ava/slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb.py 0 → 100644
View file @ 5b3e36dc
# model setting
model = dict(
type='FastRCNN',
backbone=dict(
type='ResNet3dSlowOnly',
depth=101,
pretrained=None,
pretrained2d=False,
lateral=False,
num_stages=4,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
spatial_strides=(1, 2, 2, 1)),
roi_head=dict(
type='AVARoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor3D',
roi_layer_type='RoIAlign',
output_size=8,
with_temporal_pool=True),
bbox_head=dict(
type='BBoxHeadAVA',
in_channels=2048,
num_classes=81,
multilabel=True,
dropout_ratio=0.5)),
train_cfg=dict(
rcnn=dict(
assigner=dict(
type='MaxIoUAssignerAVA',
pos_iou_thr=0.9,
neg_iou_thr=0.9,
min_pos_iou=0.9),
sampler=dict(
type='RandomSampler',
num=32,
pos_fraction=1,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=1.0,
debug=False)),
test_cfg=dict(rcnn=dict(action_thr=0.002)))
dataset_type = 'AVADataset'
data_root = 'data/ava/rawframes'
anno_root = 'data/ava/annotations'
ann_file_train = f'{anno_root}/ava_train_v2.1.csv'
ann_file_val = f'{anno_root}/ava_val_v2.1.csv'
exclude_file_train = f'{anno_root}/ava_train_excluded_timestamps_v2.1.csv'
exclude_file_val = f'{anno_root}/ava_val_excluded_timestamps_v2.1.csv'
label_file = f'{anno_root}/ava_action_list_v2.1_for_activitynet_2018.pbtxt'
proposal_file_train = (f'{anno_root}/ava_dense_proposals_train.FAIR.'
'recall_93.9.pkl')
proposal_file_val = f'{anno_root}/ava_dense_proposals_val.FAIR.recall_93.9.pkl'
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='SampleAVAFrames', clip_len=8, frame_interval=8),
dict(type='RawFrameDecode'),
dict(type='RandomRescale', scale_range=(256, 320)),
dict(type='RandomCrop', size=256),
dict(type='Flip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
# Rename is needed to use mmdet detectors
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
dict(
type='ToDataContainer',
fields=[
dict(key=['proposals', 'gt_bboxes', 'gt_labels'], stack=False)
]),
dict(
type='Collect',
keys=['img', 'proposals', 'gt_bboxes', 'gt_labels'],
meta_keys=['scores', 'entity_ids'])
]
# The testing is w/o. any cropping / flipping
val_pipeline = [
dict(type='SampleAVAFrames', clip_len=8, frame_interval=8, test_mode=True),
dict(type='RawFrameDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
# Rename is needed to use mmdet detectors
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals']),
dict(type='ToDataContainer', fields=[dict(key='proposals', stack=False)]),
dict(
type='Collect',
keys=['img', 'proposals'],
meta_keys=['scores', 'img_shape'],
nested=True)
]
data = dict(
videos_per_gpu=6,
workers_per_gpu=2,
# During testing, each video may have different shape
val_dataloader=dict(videos_per_gpu=1),
test_dataloader=dict(videos_per_gpu=1),
train=dict(
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(
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']
optimizer = dict(type='SGD', lr=0.075, momentum=0.9, weight_decay=0.00001)
# this lr is used for 8 gpus
optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
step=[10, 15],
warmup='linear',
warmup_by_epoch=True,
warmup_iters=5,
warmup_ratio=0.1)
total_epochs = 20
checkpoint_config = dict(interval=1)
workflow = [('train', 1)]
evaluation = dict(interval=1, save_best='mAP@0.5IOU')
log_config = dict(
interval=20, hooks=[
dict(type='TextLoggerHook'),
])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = ('./work_dirs/ava/'
'slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb')
load_from = ('https://download.openmmlab.com/mmaction/recognition/slowonly/'
'omni/'
'slowonly_r101_omni_8x8x1_kinetics400_rgb_20200926-b5dbb701.pth')
resume_from = None
find_unused_parameters = False
configs/detection/ava/slowonly_omnisource_pretrained_r50_4x16x1_20e_ava_rgb.py 0 → 100644
View file @ 5b3e36dc
# model setting
model = dict(
type='FastRCNN',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained=None,
pretrained2d=False,
lateral=False,
num_stages=4,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
spatial_strides=(1, 2, 2, 1)),
roi_head=dict(
type='AVARoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor3D',
roi_layer_type='RoIAlign',
output_size=8,
with_temporal_pool=True),
bbox_head=dict(
type='BBoxHeadAVA',
in_channels=2048,
num_classes=81,
multilabel=True,
dropout_ratio=0.5)),
train_cfg=dict(
rcnn=dict(
assigner=dict(
type='MaxIoUAssignerAVA',
pos_iou_thr=0.9,
neg_iou_thr=0.9,
min_pos_iou=0.9),
sampler=dict(
type='RandomSampler',
num=32,
pos_fraction=1,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=1.0,
debug=False)),
test_cfg=dict(rcnn=dict(action_thr=0.002)))
dataset_type = 'AVADataset'
data_root = 'data/ava/rawframes'
anno_root = 'data/ava/annotations'
ann_file_train = f'{anno_root}/ava_train_v2.1.csv'
ann_file_val = f'{anno_root}/ava_val_v2.1.csv'
exclude_file_train = f'{anno_root}/ava_train_excluded_timestamps_v2.1.csv'
exclude_file_val = f'{anno_root}/ava_val_excluded_timestamps_v2.1.csv'
label_file = f'{anno_root}/ava_action_list_v2.1_for_activitynet_2018.pbtxt'
proposal_file_train = (f'{anno_root}/ava_dense_proposals_train.FAIR.'
'recall_93.9.pkl')
proposal_file_val = f'{anno_root}/ava_dense_proposals_val.FAIR.recall_93.9.pkl'
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='SampleAVAFrames', clip_len=4, frame_interval=16),
dict(type='RawFrameDecode'),
dict(type='RandomRescale', scale_range=(256, 320)),
dict(type='RandomCrop', size=256),
dict(type='Flip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
# Rename is needed to use mmdet detectors
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
dict(
type='ToDataContainer',
fields=[
dict(key=['proposals', 'gt_bboxes', 'gt_labels'], stack=False)
]),
dict(
type='Collect',
keys=['img', 'proposals', 'gt_bboxes', 'gt_labels'],
meta_keys=['scores', 'entity_ids'])
]
# The testing is w/o. any cropping / flipping
val_pipeline = [
dict(
type='SampleAVAFrames', clip_len=4, frame_interval=16, test_mode=True),
dict(type='RawFrameDecode'),
dict(type='Resize', scale=(-1, 256)),
dict(type='Normalize', **img_norm_cfg),
dict(type='FormatShape', input_format='NCTHW', collapse=True),
# Rename is needed to use mmdet detectors
dict(type='Rename', mapping=dict(imgs='img')),
dict(type='ToTensor', keys=['img', 'proposals']),
dict(type='ToDataContainer', fields=[dict(key='proposals', stack=False)]),
dict(
type='Collect',
keys=['img', 'proposals'],
meta_keys=['scores', 'img_shape'],
nested=True)
]
data = dict(
videos_per_gpu=16,
workers_per_gpu=2,
# During testing, each video may have different shape
val_dataloader=dict(videos_per_gpu=1),
test_dataloader=dict(videos_per_gpu=1),
train=dict(
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(
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']
optimizer = dict(type='SGD', lr=0.2, momentum=0.9, weight_decay=0.00001)
# this lr is used for 8 gpus
optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
step=[10, 15],
warmup='linear',
warmup_by_epoch=True,
warmup_iters=5,
warmup_ratio=0.1)
total_epochs = 20
checkpoint_config = dict(interval=1)
workflow = [('train', 1)]
evaluation = dict(interval=1, save_best='mAP@0.5IOU')
log_config = dict(
interval=20, hooks=[
dict(type='TextLoggerHook'),
])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = ('./work_dirs/ava/'
'slowonly_omnisource_pretrained_r50_4x16x1_20e_ava_rgb')
load_from = ('https://download.openmmlab.com/mmaction/recognition/slowonly/'
'omni/'
'slowonly_r50_omni_4x16x1_kinetics400_rgb_20200926-51b1f7ea.pth')
resume_from = None
find_unused_parameters = False
configs/detection/lfb/README.md 0 → 100644
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configs/detection/lfb/README_zh-CN.md 0 → 100644
View file @ 5b3e36dc
# LFB
## 简介
<!-- [ALGORITHM] -->
```BibTeX
@inproceedings{wu2019long,
title={Long-term feature banks for detailed video understanding},
author={Wu, Chao-Yuan and Feichtenhofer, Christoph and Fan, Haoqi and He, Kaiming and Krahenbuhl, Philipp and Girshick, Ross},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={284--293},
year={2019}
}
```
## 模型库
### AVA2.1
| 配置文件 | 模态 | 预训练 | 主干网络 | 输入 | GPU 数量 | 分辨率 | 平均精度 | log | json | ckpt |
| :-----------------------------------------------------------------------------------------------------------------------------------------------------: | :--: | :----------: | :--------------------------------------------------------------------------------------------------: | :--: | :------: | :------: | :------: | :------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [lfb_nl_kinetics_pretrained_slowonly_r50_4x16x1_20e_ava_rgb.py](/configs/detection/lfb/lfb_nl_kinetics_pretrained_slowonly_r50_4x16x1_20e_ava_rgb.py) | RGB | Kinetics-400 | [slowonly_r50_4x16x1](/configs/detection/ava/slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb.py) | 4x16 | 8 | 短边 256 | 24.11 | [log](https://download.openmmlab.com/mmaction/detection/lfb/lfb_nl_kinetics_pretrained_slowonly_r50_4x16x1_20e_ava_rgb/20210224_125052.log) | [json](https://download.openmmlab.com/mmaction/detection/lfb/lfb_nl_kinetics_pretrained_slowonly_r50_4x16x1_20e_ava_rgb/20210224_125052.log.json) | [ckpt](https://download.openmmlab.com/mmaction/detection/lfb/lfb_nl_kinetics_pretrained_slowonly_r50_4x16x1_20e_ava_rgb/lfb_nl_kinetics_pretrained_slowonly_r50_4x16x1_20e_ava_rgb_20210224-2ae136d9.pth) |
| [lfb_avg_kinetics_pretrained_slowonly_r50_4x16x1_20e_ava_rgb.py](/configs/detection/lfb/lfb_avg_kinetics_pretrained_slowonly_r50_4x16x1_20e_ava_rgb.py) | RGB | Kinetics-400 | [slowonly_r50_4x16x1](/configs/detection/ava/slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb.py) | 4x16 | 8 | 短边 256 | 20.17 | [log](https://download.openmmlab.com/mmaction/detection/lfb/lfb_avg_kinetics_pretrained_slowonly_r50_4x16x1_20e_ava_rgb/20210301_124812.log) | [json](https://download.openmmlab.com/mmaction/detection/lfb/lfb_avg_kinetics_pretrained_slowonly_r50_4x16x1_20e_ava_rgb/20210301_124812.log.json) | [ckpt](https://download.openmmlab.com/mmaction/detection/lfb/lfb_avg_kinetics_pretrained_slowonly_r50_4x16x1_20e_ava_rgb/lfb_avg_kinetics_pretrained_slowonly_r50_4x16x1_20e_ava_rgb_20210301-19c330b7.pth) |
| [lfb_max_kinetics_pretrained_slowonly_r50_4x16x1_20e_ava_rgb.py](/configs/detection/lfb/lfb_max_kinetics_pretrained_slowonly_r50_4x16x1_20e_ava_rgb.py) | RGB | Kinetics-400 | [slowonly_r50_4x16x1](/configs/detection/ava/slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb.py) | 4x16 | 8 | 短边 256 | 22.15 | [log](https://download.openmmlab.com/mmaction/detection/lfb/lfb_max_kinetics_pretrained_slowonly_r50_4x16x1_20e_ava_rgb/20210301_124812.log) | [json](https://download.openmmlab.com/mmaction/detection/lfb/lfb_max_kinetics_pretrained_slowonly_r50_4x16x1_20e_ava_rgb/20210301_124812.log.json) | [ckpt](https://download.openmmlab.com/mmaction/detection/lfb/lfb_max_kinetics_pretrained_slowonly_r50_4x16x1_20e_ava_rgb/lfb_max_kinetics_pretrained_slowonly_r50_4x16x1_20e_ava_rgb_20210301-37efcd15.pth) |
- 注:
1. 这里的 **GPU 数量** 指的是得到模型权重文件对应的 GPU 个数。默认地，MMAction2 所提供的配置文件对应使用 8 块 GPU 进行训练的情况。
依据 [线性缩放规则](https://arxiv.org/abs/1706.02677)，当用户使用不同数量的 GPU 或者每块 GPU 处理不同视频个数时，需要根据批大小等比例地调节学习率。
如，lr=0.01 对应 4 GPUs x 2 video/gpu，以及 lr=0.08 对应 16 GPUs x 4 video/gpu。
2. 本 LFB 模型暂没有使用原论文中的 `I3D-R50-NL` 作为主干网络，而是用 `slowonly_r50_4x16x1` 替代，但取得了同样的提升效果：（本模型：20.1 -> 24.11 而原论文模型：22.1 -> 25.8）。
3. 因为测试时，长时特征是被随机采样的，所以测试精度可能有一些偏差。
4. 在训练或测试 LFB 之前，用户需要使用配置文件特征库 [lfb_slowonly_r50_ava_infer.py](/configs/detection/lfb/lfb_slowonly_r50_ava_infer.py) 来推导长时特征库。有关推导长时特征库的更多细节，请参照[训练部分](#%E8%AE%AD%E7%BB%83)。
5. 用户也可以直接从 [AVA_train_val_float32_lfb](https://download.openmmlab.com/mmaction/detection/lfb/AVA_train_val_float32_lfb.rar) 或者 [AVA_train_val_float16_lfb](https://download.openmmlab.com/mmaction/detection/lfb/AVA_train_val_float16_lfb.rar) 下载 float32 或 float16 的长时特征库，并把它们放在 `lfb_prefix_path` 上。
## 训练
### a. 为训练 LFB 推导长时特征库
在训练或测试 LFB 之前，用户首先需要推导长时特征库。
具体来说，使用配置文件 [lfb_slowonly_r50_ava_infer](/configs/detection/lfb/lfb_slowonly_r50_ava_infer.py)，在训练集、验证集、测试集上都运行一次模型测试。
配置文件的默认设置是推导训练集的长时特征库，用户需要将 `dataset_mode` 设置成 `'val'` 来推导验证集的长时特征库，在推导过程中。共享头 [LFBInferHead](/mmaction/models/heads/lfb_infer_head.py) 会生成长时特征库。
AVA 训练集和验证集的 float32 精度的长时特征库文件大约占 3.3 GB。如果以半精度来存储长时特征，文件大约占 1.65 GB。
用户可以使用以下命令来推导 AVA 训练集和验证集的长时特征库，而特征库会被存储为 `lfb_prefix_path/lfb_train.pkl` 和 `lfb_prefix_path/lfb_val.pkl`。
```shell
# 在 lfb_slowonly_r50_ava_infer.py 中 设置 `dataset_mode = 'train'`
python tools/test.py configs/detection/lfb/lfb_slowonly_r50_ava_infer.py \
checkpoints/YOUR_BASELINE_CHECKPOINT.pth --eval mAP
# 在 lfb_slowonly_r50_ava_infer.py 中 设置 `dataset_mode = 'val'`
python tools/test.py configs/detection/lfb/lfb_slowonly_r50_ava_infer.py \
checkpoints/YOUR_BASELINE_CHECKPOINT.pth --eval mAP
```
MMAction2 使用来自配置文件 [slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb](/configs/detection/ava/slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb.py) 的模型权重文件 [slowonly_r50_4x16x1 checkpoint](https://download.openmmlab.com/mmaction/detection/ava/slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb/slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb_20201217-40061d5f.pth)作为推导长时特征库的 LFB 模型的主干网络的预训练模型。
### b. 训练 LFB
用户可以使用以下指令进行模型训练。
```shell
python tools/train.py ${CONFIG_FILE} [optional arguments]
```
例如：使用半精度的长时特征库在 AVA 数据集上训练 LFB 模型。
```shell
python tools/train.py configs/detection/lfb/lfb_nl_kinetics_pretrained_slowonly_r50_4x16x1_20e_ava_rgb.py \
--validate --seed 0 --deterministic
```
更多训练细节，可参考 [基础教程](/docs/zh_cn/getting_started.md#训练配置) 中的 **训练配置** 部分。
## 测试
### a. 为测试 LFB 推导长时特征库
在训练或测试 LFB 之前，用户首先需要推导长时特征库。如果用户之前已经生成了特征库文件，可以跳过这一步。
这一步做法与[训练部分](#Train)中的 **为训练 LFB 推导长时特征库** 相同。
### b. 测试 LFB
用户可以使用以下指令进行模型测试。
```shell
python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]
```
例如：使用半精度的长时特征库在 AVA 数据集上测试 LFB 模型，并将结果导出为一个 json 文件。
```shell
python tools/test.py configs/detection/lfb/lfb_nl_kinetics_pretrained_slowonly_r50_4x16x1_20e_ava_rgb.py \
checkpoints/SOME_CHECKPOINT.pth --eval mAP --out results.csv
```
更多测试细节，可参考 [基础教程](/docs/zh_cn/getting_started.md#测试某个数据集) 中的 **测试某个数据集** 部分。
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