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Functions327 in github.com/NationalGAILab/HoT

⨍ Functions 327◇ Types & classes 77

↓ 1 callersFunctionindex_points

model/motionbert/hot_motionbert.py:12

↓ 1 callersFunctionindex_points

model/mixste/hot_mixste.py:10

↓ 1 callersFunctionindex_points

model/mixste/tpc_mixste.py:10

↓ 1 callersMethodinit_weights

(self, pretrained='')

demo/lib/hrnet/lib/models/pose_hrnet.py:462

↓ 1 callersFunctioniou

Computes IUO between two bboxes in the form [x1,y1,x2,y2]

demo/lib/sort/sort.py:16

↓ 1 callersMethodjoints_left

common/skeleton.py:62

↓ 1 callersMethodjoints_right

common/skeleton.py:65

↓ 1 callersFunctionletterbox_image

resize image with unchanged aspect ratio using padding

demo/lib/yolov3/preprocess.py:9

↓ 1 callersFunctionload_model

(args=None, CUDA=None, inp_dim=416)

demo/lib/yolov3/human_detector.py:80

↓ 1 callersMethodload_weights

(self, weightfile)

demo/lib/yolov3/darknet.py:348

↓ 1 callersFunctionmodel_load

demo/lib/hrnet/gen_kpts.py:69

↓ 1 callersFunctionmpjpe_by_action_p1

(predicted, target, action, action_error_sum)

common/utils.py:19

↓ 1 callersFunctionmpjpe_by_action_p2

(predicted, target, action, action_error_sum)

common/utils.py:44

↓ 1 callersMethodnormalize_digraph

model/motionagformer/modules/graph.py:75

↓ 1 callersFunctionp_mpjpe

(predicted, target)

common/utils.py:70

↓ 1 callersFunctionparse_args

demo/lib/hrnet/gen_kpts.py:33

↓ 1 callersFunctionparse_cfg

Takes a configuration file Returns a list of blocks. Each blocks describes a block in the neural network to be built. Block is represent

demo/lib/yolov3/darknet.py:38

↓ 1 callersMethodpredict

Advances the state vector and returns the predicted bounding box estimate.

demo/lib/sort/sort.py:104

↓ 1 callersFunctionprint_error_action

(action_error_sum, is_train, data_type)

common/utils.py:125

↓ 1 callersMethodremove_joints

(self, joints_to_remove)

common/skeleton.py:26

↓ 1 callersFunctionreset_config

demo/lib/hrnet/gen_kpts.py:59

↓ 1 callersFunctionrevise_kpts

(h36m_kpts, h36m_scores, valid_frames)

demo/lib/preprocess.py:72

↓ 1 callersFunctionshow2Dpose

↓ 1 callersFunctionshow3Dpose

(vals, ax, fix_z)

↓ 1 callersFunctiontest

(actions, dataloader, model)

main_mixste.py:59

↓ 1 callersFunctiontest

(actions, dataloader, model)

main_mixste_tpc.py:62

↓ 1 callersFunctiontest

(actions, dataloader, model)

main_mhformer.py:53

↓ 1 callersFunctiontest

(actions, dataloader, model)

main_mhformer_tpc.py:56

↓ 1 callersFunctiontrain

(dataloader, model, optimizer, epoch)

main_mixste.py:28

↓ 1 callersFunctiontrain

(dataloader, model, optimizer, epoch)

main_mixste_tpc.py:28

↓ 1 callersFunctiontrain

(dataloader, model, optimizer, epoch)

main_mhformer.py:28

↓ 1 callersFunctiontrain

(dataloader, model, optimizer, epoch)

main_mhformer_tpc.py:28

↓ 1 callersFunctiontransform_preds

(coords, center, scale, output_size)

demo/lib/hrnet/lib/utils/transforms.py:50

↓ 1 callersMethodupdate

Updates the state vector with observed bbox.

demo/lib/sort/sort.py:94

↓ 1 callersFunctionupdate_config

demo/lib/hrnet/lib/config/default.py:126

↓ 1 callersFunctionworld_to_camera

common/camera.py:11

↓ 1 callersFunctionwrite_results

https://blog.paperspace.com/how-to-implement-a-yolo-v3-object-detector-from-scratch-in-pytorch-part-4/ prediction: (B x 10647 x 85)

demo/lib/yolov3/util.py:107

Method__getitem__

common/mocap_dataset.py:17

Method__getitem__

common/load_data_hm36.py:186

(self, dim, mlp_ratio=4., act_layer=nn.GELU, attn_drop=0., drop=0., drop_path=0., num_heads=8

model/motionagformer/motionagformer.py:14

(self, dim, mlp_ratio=4., act_layer=nn.GELU, attn_drop=0., drop=0., drop_path=0., num_heads=8

model/motionagformer/motionagformer.py:61

(self, dim, mlp_ratio=4., act_layer=nn.GELU, attn_drop=0., drop=0., drop_path=0., num_heads=8

model/motionagformer/hot_motionagformer.py:67

(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., length=27)

model/motionagformer/hot_motionagformer.py:113

(self, dim, mlp_ratio=4., act_layer=nn.GELU, attn_drop=0., drop=0., drop_path=0., num_heads=8

model/motionagformer/hot_motionagformer.py:145

(self, in_channels, out_channels, kernel_size=5,

model/motionagformer/modules/tcn.py:26

(self, dim_in, dim_out, num_nodes, neighbour_num=4, mode='spatial', use_temporal_similarity=True,

model/motionagformer/modules/graph.py:11

(self, dim_in, dim_out, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.,

model/motionagformer/modules/attention.py:10

:param channel_first: if True, during forward the tensor shape is [B, C, T, J] and fc layers are performed with

model/motionagformer/modules/mlp.py:5

(self, dim, mlp_ratio=4., act_layer=nn.GELU, attn_drop=0., drop=0., drop_path=0., num_heads=8

model/motionagformer/modules/metaformer.py:17

(self, in_channels, out_channels, adaptive=True)

model/motionagformer/modules/ctrgc.py:76

(self, num_channels, **kwargs)

model/motionagformer/modules/normalization.py:10

(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., downsampl

model/motionagformer/modules/ctr_attention.py:10

model/mhformer/tpc_mhformer.py:8

model/mhformer/mhformer.py:8

(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.)

model/mhformer/module/trans_hypothesis_cluster.py:82

(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.)

model/mhformer/module/trans_hypothesis_cluster.py:110

(self, dim, num_heads, mlp_hidden_dim, qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,

model/mhformer/module/trans_hypothesis_cluster.py:141

(self, dim, num_heads, mlp_hidden_dim, qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,

model/mhformer/module/trans_hypothesis_cluster.py:176

(self, depth=3, embed_dim=512, mlp_hidden_dim=1024, token_num=117, layer_index=1, h=8, drop_rate=0.1, length=2

model/mhformer/module/trans_hypothesis_cluster.py:219

(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.)

model/mhformer/module/trans_hypothesis.py:28

(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.)

model/mhformer/module/trans_hypothesis.py:56

(self, dim, num_heads, mlp_hidden_dim, qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,

model/mhformer/module/trans_hypothesis.py:87

(self, dim, num_heads, mlp_hidden_dim, qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,

model/mhformer/module/trans_hypothesis.py:122

(self, depth=3, embed_dim=512, mlp_hidden_dim=1024, h=8, drop_rate=0.1, length=27)

model/mhformer/module/trans_hypothesis.py:165

(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.)

model/mhformer/module/trans.py:28

(self, dim, num_heads, mlp_hidden_dim, qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,

model/mhformer/module/trans.py:57

(self, depth=3, embed_dim=512, mlp_hidden_dim=1024, h=8, drop_rate=0.1, length=27)

model/mhformer/module/trans.py:74

(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., st_mode='vanilla')

model/motionbert/hot_motionbert.py:81

(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., length=27)

model/motionbert/hot_motionbert.py:135

(self, dim, num_heads, mlp_hidden_dim, qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., d

model/motionbert/hot_motionbert.py:167

model/motionbert/hot_motionbert.py:197

(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., st_mode='vanilla')

model/motionbert/motionbert.py:30

(self, dim, num_heads, mlp_hidden_dim, qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., d

model/motionbert/motionbert.py:84

model/motionbert/motionbert.py:114

(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.)

model/mixste/mixste.py:30

(self, dim, num_heads, mlp_hidden_dim, qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,

model/mixste/mixste.py:58

model/mixste/mixste.py:78

(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.)

model/mixste/hot_mixste.py:81

(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., length=27)

model/mixste/hot_mixste.py:108

(self, dim, num_heads, mlp_hidden_dim, qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,

model/mixste/hot_mixste.py:141

model/mixste/hot_mixste.py:161

(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.)

model/mixste/tpc_mixste.py:84

(self, dim, num_heads, mlp_hidden_dim, qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,

model/mixste/tpc_mixste.py:112

model/mixste/tpc_mixste.py:132

(self, batch_size, cameras, poses_3d, poses_2d, poses_2d_GT, chunk_length=1, pad=0, causal_sh

common/generator_stride.py:5

common/utils.py:106

(self, fps, skeleton)

common/mocap_dataset.py:4

(self, batch_size, cameras, poses_3d, poses_2d, poses_2d_GT, chunk_length=1, pad=0, causal_sh

common/generator.py:5

(self, parents, joints_left, joints_right)

common/skeleton.py:6

(self, opt, dataset, root_path, train=True)

common/load_data_hm36.py:9

(self, path, opt, remove_static_joints=True)

common/h36m_dataset.py:204

(self, num_layers, input_size)

demo/lib/yolov3/darknet.py:16

(self, kernel_size)

demo/lib/yolov3/darknet.py:71

demo/lib/yolov3/darknet.py:83

(self, anchors)

demo/lib/yolov3/darknet.py:88

(self, stride=2)

demo/lib/yolov3/darknet.py:101

(self, stride=2)

demo/lib/yolov3/darknet.py:119

Initialises a tracker using initial bounding box.

demo/lib/sort/sort.py:67

Sets key parameters for SORT

demo/lib/sort/sort.py:168

(self, inplanes, planes, stride=1, downsample=None)

demo/lib/hrnet/lib/models/pose_hrnet.py:63

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