-
Notifications
You must be signed in to change notification settings - Fork 1.2k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
[Feature] Support Imgaug for augmentations in the data pipeline. (#492)
* imgaug first commit. * update changelog * add unittest & fix a few bugs * add imgaug in optional.txt * add docs & add iaa.Augmenter as input & add unittest * improve codecov * fix * fix __repr__ * fix changelog * fix docs/typo/class name, etc. * add modality assert for imgaug * remove iaa.Rotate sample * 1. fix multi-gpu bug 2. add tsn/i3d demo config 3. add assert for in&out dtype 4. update docs
- Loading branch information
1 parent
910d2fb
commit 3a3e10a
Showing
7 changed files
with
572 additions
and
3 deletions.
There are no files selected for viewing
132 changes: 132 additions & 0 deletions
132
configs/recognition/i3d/i3d_r50_video_imgaug_32x2x1_100e_kinetics400_rgb.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,132 @@ | ||
# model settings | ||
model = dict( | ||
type='Recognizer3D', | ||
backbone=dict( | ||
type='ResNet3d', | ||
pretrained2d=True, | ||
pretrained='torchvision://resnet50', | ||
depth=50, | ||
conv_cfg=dict(type='Conv3d'), | ||
norm_eval=False, | ||
inflate=((1, 1, 1), (1, 0, 1, 0), (1, 0, 1, 0, 1, 0), (0, 1, 0)), | ||
zero_init_residual=False), | ||
cls_head=dict( | ||
type='I3DHead', | ||
num_classes=400, | ||
in_channels=2048, | ||
spatial_type='avg', | ||
dropout_ratio=0.5, | ||
init_std=0.01)) | ||
# model training and testing settings | ||
train_cfg = None | ||
test_cfg = dict(average_clips='prob') | ||
# dataset settings | ||
dataset_type = 'VideoDataset' | ||
data_root = 'data/kinetics400/videos_train' | ||
data_root_val = 'data/kinetics400/videos_val' | ||
ann_file_train = 'data/kinetics400/kinetics400_train_list_videos.txt' | ||
ann_file_val = 'data/kinetics400/kinetics400_val_list_videos.txt' | ||
ann_file_test = 'data/kinetics400/kinetics400_val_list_videos.txt' | ||
img_norm_cfg = dict( | ||
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False) | ||
train_pipeline = [ | ||
dict(type='DecordInit'), | ||
dict(type='SampleFrames', clip_len=32, frame_interval=2, num_clips=1), | ||
dict(type='DecordDecode'), | ||
dict(type='Resize', scale=(-1, 256)), | ||
dict( | ||
type='MultiScaleCrop', | ||
input_size=224, | ||
scales=(1, 0.8), | ||
random_crop=False, | ||
max_wh_scale_gap=0), | ||
dict(type='Resize', scale=(224, 224), keep_ratio=False), | ||
dict( | ||
type='Imgaug', | ||
transforms=[ | ||
dict(type='Fliplr', p=0.5), | ||
dict(type='Rotate', rotate=(-20, 20)), | ||
dict(type='Dropout', p=(0, 0.05)) | ||
]), | ||
# dict(type='Imgaug', transforms='default'), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='FormatShape', input_format='NCTHW'), | ||
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), | ||
dict(type='ToTensor', keys=['imgs', 'label']) | ||
] | ||
val_pipeline = [ | ||
dict(type='DecordInit'), | ||
dict( | ||
type='SampleFrames', | ||
clip_len=32, | ||
frame_interval=2, | ||
num_clips=1, | ||
test_mode=True), | ||
dict(type='DecordDecode'), | ||
dict(type='Resize', scale=(-1, 256)), | ||
dict(type='CenterCrop', crop_size=224), | ||
dict(type='Flip', flip_ratio=0), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='FormatShape', input_format='NCTHW'), | ||
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), | ||
dict(type='ToTensor', keys=['imgs']) | ||
] | ||
test_pipeline = [ | ||
dict(type='DecordInit'), | ||
dict( | ||
type='SampleFrames', | ||
clip_len=32, | ||
frame_interval=2, | ||
num_clips=10, | ||
test_mode=True), | ||
dict(type='DecordDecode'), | ||
dict(type='Resize', scale=(-1, 256)), | ||
dict(type='ThreeCrop', crop_size=256), | ||
dict(type='Flip', flip_ratio=0), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='FormatShape', input_format='NCTHW'), | ||
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), | ||
dict(type='ToTensor', keys=['imgs']) | ||
] | ||
data = dict( | ||
videos_per_gpu=8, | ||
workers_per_gpu=4, | ||
train=dict( | ||
type=dataset_type, | ||
ann_file=ann_file_train, | ||
data_prefix=data_root, | ||
pipeline=train_pipeline), | ||
val=dict( | ||
type=dataset_type, | ||
ann_file=ann_file_val, | ||
data_prefix=data_root_val, | ||
pipeline=val_pipeline), | ||
test=dict( | ||
type=dataset_type, | ||
ann_file=ann_file_val, | ||
data_prefix=data_root_val, | ||
pipeline=test_pipeline)) | ||
# optimizer | ||
optimizer = dict( | ||
type='SGD', lr=0.01, momentum=0.9, | ||
weight_decay=0.0001) # 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=[40, 80]) | ||
total_epochs = 100 | ||
checkpoint_config = dict(interval=5) | ||
evaluation = dict( | ||
interval=5, metrics=['top_k_accuracy', 'mean_class_accuracy']) | ||
log_config = dict( | ||
interval=20, | ||
hooks=[ | ||
dict(type='TextLoggerHook'), | ||
# dict(type='TensorboardLoggerHook'), | ||
]) | ||
# runtime settings | ||
dist_params = dict(backend='nccl') | ||
log_level = 'INFO' | ||
work_dir = './work_dirs/i3d_r50_video_3d_32x2x1_100e_kinetics400_rgb/' | ||
load_from = None | ||
resume_from = None | ||
workflow = [('train', 1)] |
128 changes: 128 additions & 0 deletions
128
configs/recognition/tsn/tsn_r50_video_imgaug_1x1x8_100e_kinetics400_rgb.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,128 @@ | ||
# model settings | ||
model = dict( | ||
type='Recognizer2D', | ||
backbone=dict( | ||
type='ResNet', | ||
pretrained='torchvision://resnet50', | ||
depth=50, | ||
norm_eval=False), | ||
cls_head=dict( | ||
type='TSNHead', | ||
num_classes=400, | ||
in_channels=2048, | ||
spatial_type='avg', | ||
consensus=dict(type='AvgConsensus', dim=1), | ||
dropout_ratio=0.4, | ||
init_std=0.01)) | ||
# model training and testing settings | ||
train_cfg = None | ||
test_cfg = dict(average_clips=None) | ||
# dataset settings | ||
dataset_type = 'VideoDataset' | ||
data_root = 'data/kinetics400/videos_train' | ||
data_root_val = 'data/kinetics400/videos_val' | ||
ann_file_train = 'data/kinetics400/kinetics400_train_list_videos.txt' | ||
ann_file_val = 'data/kinetics400/kinetics400_val_list_videos.txt' | ||
ann_file_test = 'data/kinetics400/kinetics400_val_list_videos.txt' | ||
img_norm_cfg = dict( | ||
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False) | ||
train_pipeline = [ | ||
dict(type='DecordInit'), | ||
dict(type='SampleFrames', clip_len=1, frame_interval=1, num_clips=8), | ||
dict(type='DecordDecode'), | ||
dict( | ||
type='MultiScaleCrop', | ||
input_size=224, | ||
scales=(1, 0.875, 0.75, 0.66), | ||
random_crop=False, | ||
max_wh_scale_gap=1), | ||
dict(type='Resize', scale=(224, 224), keep_ratio=False), | ||
dict(type='Flip', flip_ratio=0.5), | ||
dict(type='Imgaug', transforms='default'), | ||
# dict( | ||
# type='Imgaug', | ||
# transforms=[ | ||
# dict(type='Rotate', rotate=(-20, 20)), | ||
# dict(type='Dropout', p=(0, 0.05)) | ||
# ]), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='FormatShape', input_format='NCHW'), | ||
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), | ||
dict(type='ToTensor', keys=['imgs', 'label']) | ||
] | ||
val_pipeline = [ | ||
dict(type='DecordInit'), | ||
dict( | ||
type='SampleFrames', | ||
clip_len=1, | ||
frame_interval=1, | ||
num_clips=8, | ||
test_mode=True), | ||
dict(type='DecordDecode'), | ||
dict(type='Resize', scale=(-1, 256)), | ||
dict(type='CenterCrop', crop_size=224), | ||
dict(type='Flip', flip_ratio=0), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='FormatShape', input_format='NCHW'), | ||
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), | ||
dict(type='ToTensor', keys=['imgs']) | ||
] | ||
test_pipeline = [ | ||
dict(type='DecordInit'), | ||
dict( | ||
type='SampleFrames', | ||
clip_len=1, | ||
frame_interval=1, | ||
num_clips=25, | ||
test_mode=True), | ||
dict(type='DecordDecode'), | ||
dict(type='Resize', scale=(-1, 256)), | ||
dict(type='ThreeCrop', crop_size=256), | ||
dict(type='Flip', flip_ratio=0), | ||
dict(type='Normalize', **img_norm_cfg), | ||
dict(type='FormatShape', input_format='NCHW'), | ||
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), | ||
dict(type='ToTensor', keys=['imgs']) | ||
] | ||
data = dict( | ||
videos_per_gpu=32, | ||
workers_per_gpu=4, | ||
train=dict( | ||
type=dataset_type, | ||
ann_file=ann_file_train, | ||
data_prefix=data_root, | ||
pipeline=train_pipeline), | ||
val=dict( | ||
type=dataset_type, | ||
ann_file=ann_file_val, | ||
data_prefix=data_root_val, | ||
pipeline=val_pipeline), | ||
test=dict( | ||
type=dataset_type, | ||
ann_file=ann_file_test, | ||
data_prefix=data_root_val, | ||
pipeline=test_pipeline)) | ||
# optimizer | ||
optimizer = dict( | ||
type='SGD', lr=0.01, momentum=0.9, | ||
weight_decay=0.0001) # 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=[40, 80]) | ||
total_epochs = 100 | ||
checkpoint_config = dict(interval=1) | ||
evaluation = dict( | ||
interval=5, metrics=['top_k_accuracy', 'mean_class_accuracy']) | ||
log_config = dict( | ||
interval=20, | ||
hooks=[ | ||
dict(type='TextLoggerHook'), | ||
# dict(type='TensorboardLoggerHook'), | ||
]) | ||
# runtime settings | ||
dist_params = dict(backend='nccl') | ||
log_level = 'INFO' | ||
work_dir = './work_dirs/tsn_r50_video_1x1x8_100e_kinetics400_rgb/' | ||
load_from = None | ||
resume_from = None | ||
workflow = [('train', 1)] |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.