python_code stringlengths 0 992k | repo_name stringlengths 8 46 | file_path stringlengths 5 162 |
|---|---|---|
# model settings
evidence_loss = dict(type='EvidenceLoss',
num_classes=101,
evidence='exp',
loss_type='log',
with_kldiv=False,
with_avuloss=False,
annealing_method='exp')
model = dict(
... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tpn/tpn_slowonly_edlloss_nokl_r50_8x8x1_150e_kinetics_rgb.py |
# model settings
evidence_loss = dict(type='EvidenceLoss',
num_classes=101,
evidence='exp',
loss_type='log',
with_avuloss=True,
annealing_method='exp')
model = dict(
type='Recognizer3D',
backbone=dict(
... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tpn/tpn_slowonly_edlloss_avuc_r50_8x8x1_150e_kinetics_rgb.py |
# model settings
model = dict(
type='Recognizer3DRPL',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained='torchvision://resnet50',
lateral=False,
out_indices=(2, 3),
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inf... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tpn/finetune_ucf101_tpn_slowonly_rpl.py |
# model settings
model = dict(
type='Recognizer3DBNN',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained='torchvision://resnet50',
lateral=False,
out_indices=(2, 3),
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inf... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tpn/tpn_slowonly_bnn_r50_8x8x1_150e_kinetics_rgb.py |
# model settings
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained='torchvision://resnet50',
lateral=False,
out_indices=(2, 3),
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inflat... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tpn/inference_tpn_slowonly_dnn.py |
# model settings
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained='torchvision://resnet50',
lateral=False,
out_indices=(2, 3),
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inflat... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tpn/inference_tpn_slowonly_enn.py |
# model settings
model = dict(
type='Recognizer3D',
backbone=dict(type='X3D', gamma_w=1, gamma_b=2.25, gamma_d=2.2),
cls_head=dict(
type='X3DHead',
in_channels=432,
num_classes=400,
spatial_type='avg',
dropout_ratio=0.5,
fc1_bias=False))
# model training and t... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/x3d/x3d_m_16x5x1_facebook_kinetics400_rgb.py |
# model settings
model = dict(
type='Recognizer3D',
backbone=dict(type='X3D', gamma_w=1, gamma_b=2.25, gamma_d=2.2),
cls_head=dict(
type='X3DHead',
in_channels=432,
num_classes=400,
spatial_type='avg',
dropout_ratio=0.5,
fc1_bias=False))
# model training and t... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/x3d/x3d_s_13x6x1_facebook_kinetics400_rgb.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_inference_1x1x3_100e_kinetics400_rgb.py |
model = dict(
type='Recognizer2D',
backbone=dict(
type='ResNet',
pretrained='torchvision://resnet50',
depth=50,
norm_eval=False),
cls_head=dict(
type='TSNHead',
num_classes=51,
in_channels=2048,
spatial_type='avg',
consensus=dict(type='... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_1x1x8_50e_hmdb51_kinetics400_rgb.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_320p_1x1x3_110e_kinetics400_flow.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_video_inference_1x1x3_100e_kinetics400_rgb.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_video_1x1x8_100e_kinetics400_rgb.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_fp16_r50_1x1x3_100e_kinetics400_rgb.py |
# model settings
model = dict(
type='Recognizer2D',
backbone=dict(
type='ResNet',
pretrained='modelzoo/tsn_r50_320p_1x1x8_110e_kinetics400_flow.pth',
depth=50,
in_channels=10,
norm_eval=False),
cls_head=dict(
type='TSNHead',
num_classes=200,
in... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_320p_1x1x8_150e_activitynet_video_flow.py |
# model settings
model = dict(
type='Recognizer2D',
backbone=dict(
type='ResNet',
pretrained='torchvision://resnet50',
depth=50,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=True),
cls_head=dict(
type='TSNHead',
num_classes=174,
... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_1x1x16_50e_sthv1_rgb.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_video_320p_1x1x3_100e_kinetics400_rgb.py |
# 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=174,
in_channels=2048,
spatial_type='avg',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_1x1x8_50e_sthv2_rgb.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_video_dense_1x1x8_100e_kinetics400_rgb.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_dense_1x1x8_100e_kinetics400_rgb.py |
model = dict(
type='Recognizer2D',
backbone=dict(
type='ResNet',
pretrained='torchvision://resnet50',
depth=50,
norm_eval=False),
cls_head=dict(
type='TSNHead',
num_classes=51,
in_channels=2048,
spatial_type='avg',
consensus=dict(type='... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_1x1x8_50e_hmdb51_mit_rgb.py |
# model settings
model = dict(
type='Recognizer2D',
backbone=dict(
type='ResNet',
pretrained='modelzoo/tsn_r50_320p_1x1x8_100e_kinetics400_rgb.pth',
depth=50,
norm_eval=False),
cls_head=dict(
type='TSNHead',
num_classes=200,
in_channels=2048,
s... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_320p_1x1x8_50e_activitynet_clip_rgb.py |
# model settings
model = dict(
type='Recognizer2D',
backbone=dict(
type='ResNet',
pretrained='modelzoo/tsn_r50_320p_1x1x8_100e_kinetics400_rgb.pth',
depth=50,
norm_eval=False),
cls_head=dict(
type='TSNHead',
num_classes=200,
in_channels=2048,
s... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_320p_1x1x8_50e_activitynet_video_rgb.py |
model = dict(
type='Recognizer2D',
backbone=dict(
type='ResNet',
pretrained='torchvision://resnet50',
depth=50,
norm_eval=False),
cls_head=dict(
type='TSNHead',
num_classes=51,
in_channels=2048,
spatial_type='avg',
consensus=dict(type='... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_1x1x8_50e_hmdb51_imagenet_rgb.py |
# 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=339,
in_channels=2048,
spatial_type='avg',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_1x1x6_100e_mit_rgb.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_dense_1x1x5_100e_kinetics400_rgb.py |
# model settings
model = dict(
type='Recognizer2D',
backbone=dict(
type='ResNet',
pretrained='modelzoo/tsn_r50_320p_1x1x8_110e_kinetics400_flow.pth',
depth=50,
in_channels=10,
norm_eval=False),
cls_head=dict(
type='TSNHead',
num_classes=200,
in... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_320p_1x1x8_150e_activitynet_clip_flow.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_320p_1x1x8_110e_kinetics400_flow.py |
# model settings
model = dict(
type='Recognizer2D',
backbone=dict(
type='ResNet',
pretrained='torchvision://resnet50',
depth=50,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=True),
cls_head=dict(
type='TSNHead',
num_classes=174,
... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_1x1x8_50e_sthv1_rgb.py |
# 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=174,
in_channels=2048,
spatial_type='avg',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_1x1x16_50e_sthv2_rgb.py |
# 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=101,
in_channels=2048,
spatial_type='avg',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_1x1x3_75e_ucf101_rgb.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_320p_1x1x8_100e_kinetics400_rgb.py |
# 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=700,
in_channels=2048,
spatial_type='avg',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_video_1x1x8_100e_kinetics700_rgb.py |
# 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=600,
in_channels=2048,
spatial_type='avg',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_video_1x1x8_100e_kinetics600_rgb.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r50_320p_1x1x3_100e_kinetics400_rgb.py |
# model settings
model = dict(
type='Recognizer2D',
backbone=dict(
type='ResNet',
pretrained='torchvision://resnet101',
depth=101,
norm_eval=False),
cls_head=dict(
type='TSNHead',
num_classes=313,
in_channels=2048,
spatial_type='avg',
c... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/tsn_r101_1x1x5_50e_mmit_rgb.py |
# model settings
category_nums = dict(
action=739, attribute=117, concept=291, event=69, object=1678, scene=248)
target_cate = 'object'
model = dict(
type='Recognizer2D',
backbone=dict(
type='ResNet',
pretrained='torchvision://resnet18',
depth=18,
norm_eval=False),
cls_h... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/hvu/tsn_r18_1x1x8_100e_hvu_concept_rgb.py |
# model settings
category_nums = dict(
action=739, attribute=117, concept=291, event=69, object=1678, scene=248)
target_cate = 'action'
model = dict(
type='Recognizer2D',
backbone=dict(
type='ResNet',
pretrained='torchvision://resnet18',
depth=18,
norm_eval=False),
cls_h... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/hvu/tsn_r18_1x1x8_100e_hvu_action_rgb.py |
# model settings
category_nums = dict(
action=739, attribute=117, concept=291, event=69, object=1678, scene=248)
target_cate = 'scene'
model = dict(
type='Recognizer2D',
backbone=dict(
type='ResNet',
pretrained='torchvision://resnet18',
depth=18,
norm_eval=False),
cls_he... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/hvu/tsn_r18_1x1x8_100e_hvu_scene_rgb.py |
# model settings
category_nums = dict(
action=739, attribute=117, concept=291, event=69, object=1678, scene=248)
target_cate = 'attribute'
model = dict(
type='Recognizer2D',
backbone=dict(
type='ResNet',
pretrained='torchvision://resnet18',
depth=18,
norm_eval=False),
cl... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/hvu/tsn_r18_1x1x8_100e_hvu_attribute_rgb.py |
# model settings
category_nums = dict(
action=739, attribute=117, concept=291, event=69, object=1678, scene=248)
target_cate = 'event'
model = dict(
type='Recognizer2D',
backbone=dict(
type='ResNet',
pretrained='torchvision://resnet18',
depth=18,
norm_eval=False),
cls_he... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/hvu/tsn_r18_1x1x8_100e_hvu_event_rgb.py |
# model settings
category_nums = dict(
action=739, attribute=117, concept=291, event=69, object=1678, scene=248)
target_cate = 'object'
model = dict(
type='Recognizer2D',
backbone=dict(
type='ResNet',
pretrained='torchvision://resnet18',
depth=18,
norm_eval=False),
cls_h... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/hvu/tsn_r18_1x1x8_100e_hvu_object_rgb.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/data_benchmark/tsn_r50_multiscalecrop_320p_1x1x3_100e_kinetics400_rgb.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/data_benchmark/tsn_r50_multiscalecrop_340x256_1x1x3_100e_kinetics400_rgb.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/data_benchmark/tsn_r50_multiscalecrop_256p_1x1x3_100e_kinetics400_rgb.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/data_benchmark/tsn_r50_test_340x256_1x1x25_3crop_100e_kinetics400_rgb.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/data_benchmark/tsn_r50_test_340x256_1x1x25_10crop_100e_kinetics400_rgb.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/data_benchmark/tsn_r50_randomresizedcrop_340x256_1x1x3_100e_kinetics400_rgb.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/data_benchmark/tsn_r50_test_320p_1x1x25_10crop_100e_kinetics400_rgb.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/data_benchmark/tsn_r50_test_320p_1x1x25_3crop_100e_kinetics400_rgb.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/data_benchmark/tsn_r50_test_256p_1x1x25_10crop_100e_kinetics400_rgb.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/data_benchmark/tsn_r50_test_256p_1x1x25_3crop_100e_kinetics400_rgb.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/data_benchmark/tsn_r50_randomresizedcrop_320p_1x1x3_100e_kinetics400_rgb.py |
# 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',
con... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/tsn/data_benchmark/tsn_r50_randomresizedcrop_256p_1x1x3_100e_kinetics400_rgb.py |
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained='torchvision://resnet50',
lateral=False,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
norm_eval=False),
... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_8x8x1_150e_kinetics400_rgb.py |
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained='torchvision://resnet50',
lateral=False,
in_channels=2,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_flow.py |
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowOnly',
depth=101,
pretrained=None,
lateral=False,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
norm_eval=False),
cls_head=dict(
... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/slowonly/slowonly_r101_8x8x1_196e_kinetics400_rgb.py |
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained='torchvision://resnet50',
lateral=False,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
norm_eval=False),
... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_150e_kinetics400_rgb.py |
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained='torchvision://resnet50',
lateral=False,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
norm_eval=False),
... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/slowonly/slowonly_imagenet_pretrained_r50_4x16x1_120e_gym99_rgb.py |
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained=None,
lateral=False,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
norm_eval=False),
cls_head=dict(
... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/slowonly/slowonly_r50_8x8x1_256e_kinetics400_rgb.py |
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained=None,
lateral=False,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
norm_eval=False),
cls_head=dict(
... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics600_rgb.py |
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained=None,
lateral=False,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
norm_eval=False),
cls_head=dict(
... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/slowonly/slowonly_r50_video_8x8x1_256e_kinetics700_rgb.py |
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained=None,
lateral=False,
in_channels=2,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
with_pool2=False,
... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/slowonly/slowonly_kinetics_pretrained_r50_4x16x1_120e_gym99_flow.py |
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained=None,
lateral=False,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
norm_eval=False),
cls_head=dict(
... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/slowonly/slowonly_r50_video_4x16x1_256e_kinetics400_rgb.py |
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained='torchvision://resnet50',
lateral=False,
in_channels=2,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_flow.py |
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained=None,
lateral=False,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
norm_eval=False),
cls_head=dict(
... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/slowonly/slowonly_r50_4x16x1_256e_kinetics400_rgb.py |
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained=None,
lateral=False,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
norm_eval=False),
cls_head=dict(
... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/slowonly/slowonly_r50_video_inference_4x16x1_256e_kinetics400_rgb.py |
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained=None,
lateral=False,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
norm_eval=False),
cls_head=dict(
... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/slowonly/data_benchmark/slowonly_r50_randomresizedcrop_340x256_4x16x1_256e_kinetics400_rgb.py |
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained=None,
lateral=False,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
norm_eval=False),
cls_head=dict(
... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/slowonly/data_benchmark/slowonly_r50_randomresizedcrop_320p_4x16x1_256e_kinetics400_rgb.py |
model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowOnly',
depth=50,
pretrained=None,
lateral=False,
conv1_kernel=(1, 7, 7),
conv1_stride_t=1,
pool1_stride_t=1,
inflate=(0, 0, 1, 1),
norm_eval=False),
cls_head=dict(
... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition/slowonly/data_benchmark/slowonly_r50_randomresizedcrop_256p_4x16x1_256e_kinetics400_rgb.py |
# model settings
model = dict(
type='AudioRecognizer',
backbone=dict(
type='ResNetAudio',
depth=50,
pretrained=None,
in_channels=1,
norm_eval=False),
cls_head=dict(
type='AudioTSNHead',
num_classes=400,
in_channels=1024,
dropout_ratio=0... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition_audio/audioonly/audioonly_r50_64x1x1_100e_kinetics400_audio_feature.py |
# model settings
model = dict(
type='AudioRecognizer',
backbone=dict(type='ResNet', depth=50, in_channels=1, norm_eval=False),
cls_head=dict(
type='AudioTSNHead',
num_classes=400,
in_channels=2048,
dropout_ratio=0.5,
init_std=0.01))
# model training and testing settin... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition_audio/resnet/tsn_r50_64x1x1_100e_kinetics400_audio.py |
# model settings
model = dict(
type='AudioRecognizer',
backbone=dict(type='ResNet', depth=18, in_channels=1, norm_eval=False),
cls_head=dict(
type='AudioTSNHead',
num_classes=400,
in_channels=512,
dropout_ratio=0.5,
init_std=0.01))
# model training and testing setting... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/recognition_audio/resnet/tsn_r18_64x1x1_100e_kinetics400_audio_feature.py |
# model settings
model = dict(
type='BMN',
temporal_dim=100,
boundary_ratio=0.5,
num_samples=32,
num_samples_per_bin=3,
feat_dim=400,
soft_nms_alpha=0.4,
soft_nms_low_threshold=0.5,
soft_nms_high_threshold=0.9,
post_process_top_k=100)
# model training and testing settings
train_c... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/localization/bmn/bmn_400x100_2x8_9e_activitynet_feature.py |
# model training and testing settings
train_cfg = dict(
ssn=dict(
assigner=dict(
positive_iou_threshold=0.7,
background_iou_threshold=0.01,
incomplete_iou_threshold=0.3,
background_coverage_threshold=0.02,
incomplete_overlap_threshold=0.01),
... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/localization/ssn/ssn_r50_450e_thumos14_rgb_test.py |
# model training and testing settings
train_cfg = dict(
ssn=dict(
assigner=dict(
positive_iou_threshold=0.7,
background_iou_threshold=0.01,
incomplete_iou_threshold=0.3,
background_coverage_threshold=0.02,
incomplete_overlap_threshold=0.01),
... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/localization/ssn/ssn_r50_450e_thumos14_rgb_train.py |
# model settings
model = dict(
type='TEM',
temporal_dim=100,
boundary_ratio=0.1,
tem_feat_dim=400,
tem_hidden_dim=512,
tem_match_threshold=0.5)
# model training and testing settings
train_cfg = None
test_cfg = dict(average_clips='score')
# dataset settings
dataset_type = 'ActivityNetDataset'
dat... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/localization/bsn/bsn_tem_400x100_1x16_20e_activitynet_feature.py |
# dataset settings
dataset_type = 'ActivityNetDataset'
data_root = 'data/ActivityNet/activitynet_feature_cuhk/csv_mean_100/'
data_root_val = 'data/ActivityNet/activitynet_feature_cuhk/csv_mean_100/'
ann_file_train = 'data/ActivityNet/anet_anno_train.json'
ann_file_val = 'data/ActivityNet/anet_anno_val.json'
ann_file_te... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/localization/bsn/bsn_pgm_400x100_activitynet_feature.py |
# model settings
model = dict(
type='PEM',
pem_feat_dim=32,
pem_hidden_dim=256,
pem_u_ratio_m=1,
pem_u_ratio_l=2,
pem_high_temporal_iou_threshold=0.6,
pem_low_temporal_iou_threshold=2.2,
soft_nms_alpha=0.75,
soft_nms_low_threshold=0.65,
soft_nms_high_threshold=0.9,
post_proce... | InternVideo-main | Downstream/Open-Set-Action-Recognition/configs/localization/bsn/bsn_pem_400x100_1x16_20e_activitynet_feature.py |
from itertools import count
import os
import numpy as np
import math
import sys
import time
import datetime
import logging
from typing import Iterable, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import Mixup
from timm.utils import accuracy, ModelEma
import utils
from al... | InternVideo-main | Downstream/Spatial-Temporal-Action-Localization/engine_for_finetuning.py |
import numpy as np
class TubeMaskingGenerator:
def __init__(self, input_size, mask_ratio):
self.frames, self.height, self.width = input_size
self.num_patches_per_frame = self.height * self.width
self.total_patches = self.frames * self.num_patches_per_frame
self.num_masks_per_frame ... | InternVideo-main | Downstream/Spatial-Temporal-Action-Localization/masking_generator.py |
import torch
import torchvision.transforms.functional as F
import warnings
import random
import numpy as np
import torchvision
from PIL import Image, ImageOps
import numbers
class GroupRandomCrop(object):
def __init__(self, size):
if isinstance(size, numbers.Number):
self.size = (int(size), in... | InternVideo-main | Downstream/Spatial-Temporal-Action-Localization/transforms.py |
import math
import sys
from typing import Iterable
import torch
import torch.nn as nn
import utils
from einops import rearrange
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
def train_one_epoch(model: torch.nn.Module, data_loader: Iterable, optimizer: torch.optim.Optimizer,
... | InternVideo-main | Downstream/Spatial-Temporal-Action-Localization/engine_for_pretraining.py |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from modeling_finetune import Block, _cfg, PatchEmbed, get_sinusoid_encoding_table
from timm.models.registry import register_model
from timm.models.layers import trunc_normal_ as __call_trunc_normal_
def tru... | InternVideo-main | Downstream/Spatial-Temporal-Action-Localization/modeling_pretrain.py |
# -*- coding: utf-8 -*-
import argparse
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from PIL import Image
from pathlib import Path
from timm.models import create_model
from datasets import DataAugmentationForVideoMAE
from torchvision.transforms import ToPILImage
from einops import rearrange
fro... | InternVideo-main | Downstream/Spatial-Temporal-Action-Localization/run_videomae_vis.py |
import os
from torchvision import transforms
from transforms import *
from masking_generator import TubeMaskingGenerator
from kinetics import VideoClsDataset, VideoMAE
from data.ava import AVAVideoDataset,KineticsDataset,AKDataset
from data.transforms import TransformsCfg
import alphaction.config.paths_catalog as path... | InternVideo-main | Downstream/Spatial-Temporal-Action-Localization/datasets.py |
"""
This implementation is based on
https://github.com/rwightman/pytorch-image-models/blob/master/timm/data/auto_augment.py
pulished under an Apache License 2.0.
COMMENT FROM ORIGINAL:
AutoAugment, RandAugment, and AugMix for PyTorch
This code implements the searched ImageNet policies with various tweaks and
improveme... | InternVideo-main | Downstream/Spatial-Temporal-Action-Localization/rand_augment.py |
import numpy as np
from PIL import Image
import torch
def convert_img(img):
"""Converts (H, W, C) numpy.ndarray to (C, W, H) format
"""
if len(img.shape) == 3:
img = img.transpose(2, 0, 1)
if len(img.shape) == 2:
img = np.expand_dims(img, 0)
return img
class ClipToTensor(object):... | InternVideo-main | Downstream/Spatial-Temporal-Action-Localization/volume_transforms.py |
import argparse
import datetime
from operator import is_
import numpy as np
import time
import torch
import torch.backends.cudnn as cudnn
import json
import os
from functools import partial
from pathlib import Path
from collections import OrderedDict
from timm.data.mixup import Mixup
from timm.models import create_mo... | InternVideo-main | Downstream/Spatial-Temporal-Action-Localization/run_class_finetuning.py |
import argparse
import datetime
import numpy as np
import time
import torch
import torch.backends.cudnn as cudnn
import json
import os
from pathlib import Path
from timm.models import create_model
from optim_factory import create_optimizer
from datasets import build_pretraining_dataset
from engine_for_pretraining impor... | InternVideo-main | Downstream/Spatial-Temporal-Action-Localization/run_mae_pretraining.py |
import numbers
import cv2
import numpy as np
import PIL
import torch
def _is_tensor_clip(clip):
return torch.is_tensor(clip) and clip.ndimension() == 4
def crop_clip(clip, min_h, min_w, h, w):
if isinstance(clip[0], np.ndarray):
cropped = [img[min_h:min_h + h, min_w:min_w + w, :] for img in clip]
... | InternVideo-main | Downstream/Spatial-Temporal-Action-Localization/functional.py |
import io
import os
import math
import time
import json
from collections import defaultdict, deque
import datetime
import numpy as np
from timm.utils import get_state_dict
from torch.utils.data._utils.collate import default_collate
from pathlib import Path
import subprocess
import torch
import torch.distributed as dist... | InternVideo-main | Downstream/Spatial-Temporal-Action-Localization/utils.py |
"""
This implementation is based on
https://github.com/rwightman/pytorch-image-models/blob/master/timm/data/random_erasing.py
pulished under an Apache License 2.0.
"""
import math
import random
import torch
def _get_pixels(
per_pixel, rand_color, patch_size, dtype=torch.float32, device="cuda"
):
# NOTE I've s... | InternVideo-main | Downstream/Spatial-Temporal-Action-Localization/random_erasing.py |
#!/usr/bin/env python3
import math
import numpy as np
import random
import torch
import torchvision.transforms.functional as F
from PIL import Image
from torchvision import transforms
from rand_augment import rand_augment_transform
from random_erasing import RandomErasing
import numbers
import PIL
import torchvision... | InternVideo-main | Downstream/Spatial-Temporal-Action-Localization/video_transforms.py |
import os
import numpy as np
from numpy.lib.function_base import disp
import torch
import decord
from PIL import Image
from torchvision import transforms
from random_erasing import RandomErasing
import warnings
from decord import VideoReader, cpu
from torch.utils.data import Dataset
import video_transforms as video_tra... | InternVideo-main | Downstream/Spatial-Temporal-Action-Localization/kinetics.py |
from functools import partial
import numpy as np
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import drop_path, to_2tuple, trunc_normal_
from timm.models.registry import register_model
from dataclasses import dataclass
from alphaction.modeling.poole... | InternVideo-main | Downstream/Spatial-Temporal-Action-Localization/modeling_finetune.py |
import torch
from torch import optim as optim
from timm.optim.adafactor import Adafactor
from timm.optim.adahessian import Adahessian
from timm.optim.adamp import AdamP
from timm.optim.lookahead import Lookahead
from timm.optim.nadam import Nadam
from timm.optim.novograd import NovoGrad
from timm.optim.nvnovograd impo... | InternVideo-main | Downstream/Spatial-Temporal-Action-Localization/optim_factory.py |
InternVideo-main | Downstream/Spatial-Temporal-Action-Localization/alphaction/__init__.py | |
import torch
from torch import nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
# import alphaction._custom_cuda_ext as _C
class _SoftmaxFocalLoss(Function):
@staticmethod
def forward(ctx, logits, targets, gamma, alpha):
ctx.gamma = gamma
ctx.alph... | InternVideo-main | Downstream/Spatial-Temporal-Action-Localization/alphaction/layers/softmax_focal_loss.py |
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