SYMBOL INDEX (1652 symbols across 212 files) FILE: DM_3/datasets_hdtf_wpose_lmk_block_lmk.py function resize (line 29) | def resize(im, desired_size, interpolation): class HDTF (line 45) | class HDTF(data.Dataset): method __init__ (line 46) | def __init__(self, data_dir, pose_dir, eye_blink_dir, max_num_frames=8... method check_head (line 105) | def check_head(self, frame_list, video_name, start, end): method get_block_data_for_two (line 118) | def get_block_data_for_two(self, path, start, end): method get_block_data (line 149) | def get_block_data(self, path, start, end): method check_len (line 184) | def check_len(self, name): method __len__ (line 189) | def __len__(self): method __getitem__ (line 192) | def __getitem__(self, idx): FILE: DM_3/datasets_hdtf_wpose_lmk_block_lmk_rand.py function resize (line 29) | def resize(im, desired_size, interpolation): class HDTF (line 45) | class HDTF(data.Dataset): method __init__ (line 46) | def __init__(self, data_dir, pose_dir, eye_blink_dir, max_num_frames=8... method check_head (line 110) | def check_head(self, frame_list, video_name, start, end): method get_block_data_for_two (line 123) | def get_block_data_for_two(self, path, start, end): method get_block_data (line 154) | def get_block_data(self, path, start, end): method check_len (line 189) | def check_len(self, name): method __len__ (line 194) | def __len__(self): method __getitem__ (line 197) | def __getitem__(self, idx): FILE: DM_3/modules/local_attention.py function exists (line 24) | def exists(x): function to_mask (line 27) | def to_mask(x, mask, mode='mul'): function extract_seq_patches (line 50) | def extract_seq_patches(x, kernel_size, rate): function window_attn (line 71) | def window_attn(x, y, z, kernel_size, mask, rate): function window_attn_2 (line 102) | def window_attn_2(x, y, z, kernel_size, mask, rate): # bad optimization function window_attn_stream (line 167) | def window_attn_stream(x, y, z, kernel_size, mask, rate): # bad optimiz... function create_sliding_window_mask (line 212) | def create_sliding_window_mask(x, win_size, rate): class OurLayer (line 228) | class OurLayer(nn.Module): method reuse (line 230) | def reuse(self, layer, *args, **kwargs): function heavy_computation (line 235) | def heavy_computation(x, y, attn, k_size, i): function heavy_computation2 (line 238) | def heavy_computation2(res, z, attn, k_size, i): function window_attn_mp (line 242) | def window_attn_mp(x, y, z, kernel_size, mask, rate): class LocalSelfAttention_opt (line 275) | class LocalSelfAttention_opt(OurLayer): method __init__ (line 277) | def __init__(self, d_model, heads, size_per_head, neighbors=3, rate=1,... method forward (line 300) | def forward(self, inputs, pos_bias, focus_present_mask=None,): class MultiHeadLocalAttention (line 345) | class MultiHeadLocalAttention(nn.Module): method __init__ (line 346) | def __init__(self, d_model, num_heads, window_size): method split_heads (line 369) | def split_heads(self, x, batch_size): method forward (line 374) | def forward(self, x): class Attention (line 404) | class Attention(nn.Module): method __init__ (line 405) | def __init__( method forward (line 424) | def forward( class RelativePositionBias (line 490) | class RelativePositionBias(nn.Module): method __init__ (line 491) | def __init__( method _relative_position_bucket (line 503) | def _relative_position_bucket(relative_position, num_buckets=32, max_d... method forward (line 522) | def forward(self, n, device): FILE: DM_3/modules/text.py function exists (line 6) | def exists(val): function get_tokenizer (line 18) | def get_tokenizer(): function get_bert (line 25) | def get_bert(): function tokenize (line 37) | def tokenize(texts, add_special_tokens=True): function bert_embed (line 57) | def bert_embed( FILE: DM_3/modules/video_flow_diffusion_model_multiGPU_v0_crema_vgg_floss_plus_faceemb_flow_fast_init_cond_mouth_mask_6D.py class Attention (line 26) | class Attention(nn.Module): method __init__ (line 27) | def __init__(self, params): method forward (line 32) | def forward(self, ctx_val, ctx_key, ctx_mask, ht_query): class Face_loc_Encoder (line 47) | class Face_loc_Encoder(nn.Module): method __init__ (line 48) | def __init__(self, dim = 1): method forward (line 53) | def forward(self, x): class Vgg19 (line 60) | class Vgg19(torch.nn.Module): method __init__ (line 65) | def __init__(self, requires_grad=False): method forward (line 93) | def forward(self, x): class ImagePyramide (line 103) | class ImagePyramide(torch.nn.Module): method __init__ (line 108) | def __init__(self, scales, num_channels): method forward (line 115) | def forward(self, x): class FlowDiffusion (line 121) | class FlowDiffusion(nn.Module): method __init__ (line 122) | def __init__(self, img_size=32, num_frames=40, sampling_timesteps=250, method update_num_frames (line 209) | def update_num_frames(self, new_num_frames): method generate_bbox_mask (line 214) | def generate_bbox_mask(self, bbox, size = 32): method generate_mouth_mask (line 238) | def generate_mouth_mask(self, mouth_lmk, origin_size, size = 32): method forward (line 257) | def forward(self, real_vid, ref_img, ref_text, ref_pose, ref_eye_blink... method sample_one_video (line 399) | def sample_one_video(self, real_vid, sample_img, sample_audio_hubert, ... method get_grid (line 466) | def get_grid(self, b, nf, H, W, normalize=True): method set_requires_grad (line 476) | def set_requires_grad(self, nets, requires_grad=False): FILE: DM_3/modules/video_flow_diffusion_model_multiGPU_v0_crema_vgg_floss_plus_faceemb_flow_fast_init_cond_mouth_mask_rand_6D.py class Attention (line 25) | class Attention(nn.Module): method __init__ (line 26) | def __init__(self, params): method forward (line 31) | def forward(self, ctx_val, ctx_key, ctx_mask, ht_query): class Face_loc_Encoder (line 46) | class Face_loc_Encoder(nn.Module): method __init__ (line 47) | def __init__(self, dim = 1): method forward (line 52) | def forward(self, x): class Vgg19 (line 59) | class Vgg19(torch.nn.Module): method __init__ (line 64) | def __init__(self, requires_grad=False): method forward (line 92) | def forward(self, x): class FlowDiffusion (line 103) | class FlowDiffusion(nn.Module): method __init__ (line 104) | def __init__(self, img_size=32, num_frames=40, sampling_timesteps=250, method update_num_frames (line 182) | def update_num_frames(self, new_num_frames): method generate_bbox_mask (line 187) | def generate_bbox_mask(self, bbox, size = 32): method generate_mouth_mask (line 208) | def generate_mouth_mask(self, mouth_lmk, origin_size, size = 32): method forward (line 227) | def forward(self, real_vid, ref_img, ref_text, ref_pose, ref_eye_blink... method sample_one_video (line 404) | def sample_one_video(self, real_vid, sample_img, sample_audio_hubert, ... method get_grid (line 454) | def get_grid(self, b, nf, H, W, normalize=True): method set_requires_grad (line 464) | def set_requires_grad(self, nets, requires_grad=False): FILE: DM_3/modules/video_flow_diffusion_model_multiGPU_v0_crema_vgg_floss_plus_faceemb_flow_fast_init_cond_test.py class Attention (line 18) | class Attention(nn.Module): method __init__ (line 19) | def __init__(self, params): method forward (line 24) | def forward(self, ctx_val, ctx_key, ctx_mask, ht_query): class Face_loc_Encoder (line 39) | class Face_loc_Encoder(nn.Module): method __init__ (line 40) | def __init__(self, dim = 1): method forward (line 45) | def forward(self, x): class Vgg19 (line 52) | class Vgg19(torch.nn.Module): method __init__ (line 57) | def __init__(self, requires_grad=False): method forward (line 85) | def forward(self, x): class FlowDiffusion (line 96) | class FlowDiffusion(nn.Module): method __init__ (line 97) | def __init__(self, img_size=32, num_frames=40, sampling_timesteps=250,... method update_num_frames (line 177) | def update_num_frames(self, new_num_frames): method generate_bbox_mask (line 182) | def generate_bbox_mask(self, bbox, size = 32): method forward (line 203) | def forward(self, real_vid, ref_img, ref_text, ref_pose, ref_eye_blink... method sample_one_video (line 325) | def sample_one_video(self, sample_img, sample_audio_hubert, sample_pos... method get_grid (line 408) | def get_grid(self, b, nf, H, W, normalize=True): method set_requires_grad (line 418) | def set_requires_grad(self, nets, requires_grad=False): FILE: DM_3/modules/video_flow_diffusion_multiGPU_v0_crema_plus_faceemb_ca_multi.py function exists (line 27) | def exists(x): function noop (line 31) | def noop(*args, **kwargs): function is_odd (line 35) | def is_odd(n): function default (line 39) | def default(val, d): function cycle (line 45) | def cycle(dl): function num_to_groups (line 51) | def num_to_groups(num, divisor): function prob_mask_like (line 60) | def prob_mask_like(shape, prob, device): function is_list_str (line 69) | def is_list_str(x): class RelativePositionBias (line 77) | class RelativePositionBias(nn.Module): method __init__ (line 78) | def __init__( method _relative_position_bucket (line 90) | def _relative_position_bucket(relative_position, num_buckets=32, max_d... method forward (line 109) | def forward(self, n, device): class EMA (line 123) | class EMA(): method __init__ (line 124) | def __init__(self, beta): method update_model_average (line 128) | def update_model_average(self, ma_model, current_model): method update_average (line 133) | def update_average(self, old, new): class Residual (line 139) | class Residual(nn.Module): method __init__ (line 140) | def __init__(self, fn): method forward (line 144) | def forward(self, x, *args, **kwargs): class SinusoidalPosEmb (line 148) | class SinusoidalPosEmb(nn.Module): method __init__ (line 149) | def __init__(self, dim): method forward (line 153) | def forward(self, x): function Upsample (line 163) | def Upsample(dim, use_deconv=True, padding_mode="reflect"): function Downsample (line 173) | def Downsample(dim): class LayerNorm (line 177) | class LayerNorm(nn.Module): method __init__ (line 178) | def __init__(self, dim, eps=1e-5): method forward (line 183) | def forward(self, x): class LayerNorm_img (line 188) | class LayerNorm_img(nn.Module): method __init__ (line 189) | def __init__(self, dim, stable = False): method forward (line 194) | def forward(self, x): class PreNorm (line 203) | class PreNorm(nn.Module): method __init__ (line 204) | def __init__(self, dim, fn): method forward (line 209) | def forward(self, x, **kwargs): class Identity (line 213) | class Identity(nn.Module): method __init__ (line 214) | def __init__(self, *args, **kwargs): method forward (line 217) | def forward(self, x, *args, **kwargs): function l2norm (line 220) | def l2norm(t): class Block (line 224) | class Block(nn.Module): method __init__ (line 225) | def __init__(self, dim, dim_out, groups=8): method forward (line 231) | def forward(self, x, time_scale_shift=None, audio_scale_shift=None): class ResnetBlock (line 249) | class ResnetBlock(nn.Module): method __init__ (line 250) | def __init__(self, dim, dim_out, *, time_emb_dim=None, audio_emb_dim=N... method forward (line 266) | def forward(self, x, time_emb=None, audio_emb=None): class ResnetBlock_ca (line 287) | class ResnetBlock_ca(nn.Module): method __init__ (line 288) | def __init__(self, dim, dim_out, *, time_emb_dim=None, audio_emb_dim=N... method forward (line 317) | def forward(self, x, time_emb=None, audio_emb=None): class ResnetBlock_ca_mul (line 361) | class ResnetBlock_ca_mul(nn.Module): method __init__ (line 362) | def __init__(self, dim, dim_out, *, time_emb_dim=None, audio_emb_dim=N... method forward (line 417) | def forward(self, x, time_emb=None, audio_emb=None): class CrossAttention (line 479) | class CrossAttention(nn.Module): method __init__ (line 480) | def __init__( method forward (line 514) | def forward(self, x, context, mask = None): class LinearCrossAttention (line 559) | class LinearCrossAttention(CrossAttention): method forward (line 560) | def forward(self, x, context, mask = None): class SpatialLinearAttention (line 599) | class SpatialLinearAttention(nn.Module): method __init__ (line 600) | def __init__(self, dim, heads=4, dim_head=32): method forward (line 608) | def forward(self, x): class EinopsToAndFrom (line 629) | class EinopsToAndFrom(nn.Module): method __init__ (line 630) | def __init__(self, from_einops, to_einops, fn): method forward (line 636) | def forward(self, x, **kwargs): class Attention (line 645) | class Attention(nn.Module): method __init__ (line 646) | def __init__( method forward (line 662) | def forward( class Unet3D (line 725) | class Unet3D(nn.Module): method __init__ (line 726) | def __init__( method forward_with_cond_scale (line 875) | def forward_with_cond_scale( method forward (line 888) | def forward( class DynamicNfUnet3D (line 955) | class DynamicNfUnet3D(Unet3D): method __init__ (line 956) | def __init__(self, default_num_frames=20, *args, **kwargs): method update_num_frames (line 960) | def update_num_frames(self, new_num_frames): function extract (line 965) | def extract(a, t, x_shape): function cosine_beta_schedule (line 971) | def cosine_beta_schedule(timesteps, s=0.008): class GaussianDiffusion (line 984) | class GaussianDiffusion(nn.Module): method __init__ (line 985) | def __init__( method q_mean_variance (line 1062) | def q_mean_variance(self, x_start, t): method predict_start_from_noise (line 1068) | def predict_start_from_noise(self, x_t, t, noise): method q_posterior (line 1074) | def q_posterior(self, x_start, x_t, t): method p_mean_variance (line 1083) | def p_mean_variance(self, x, t, fea, clip_denoised: bool, cond=None, c... method p_sample (line 1109) | def p_sample(self, x, t, fea, cond=None, cond_scale=1., clip_denoised=... method p_sample_loop (line 1120) | def p_sample_loop(self, fea, shape, cond=None, cond_scale=1.): method sample (line 1134) | def sample(self, fea, bbox_mask, cond=None, cond_scale=1., batch_size=... method ddim_sample (line 1153) | def ddim_sample(self, fea, shape, cond=None, cond_scale=1., clip_denoi... method interpolate (line 1207) | def interpolate(self, x1, x2, t=None, lam=0.5): method q_sample (line 1222) | def q_sample(self, x_start, t, noise=None): method p_losses (line 1230) | def p_losses(self, x_start, t, fea, bbox_mask, cond=None, noise=None, ... method forward (line 1270) | def forward(self, x, fea, bbox_mask, cond, *args, **kwargs): function seek_all_images (line 1289) | def seek_all_images(img, channels=3): class DynamicNfGaussianDiffusion (line 1303) | class DynamicNfGaussianDiffusion(GaussianDiffusion): method __init__ (line 1304) | def __init__(self, default_num_frames=20, *args, **kwargs): method update_num_frames (line 1308) | def update_num_frames(self, new_num_frames): function video_tensor_to_gif (line 1313) | def video_tensor_to_gif(tensor, path, duration=120, loop=0, optimize=True): function gif_to_tensor (line 1322) | def gif_to_tensor(path, channels=3, transform=T.ToTensor()): function identity (line 1328) | def identity(t, *args, **kwargs): function normalize_img (line 1332) | def normalize_img(t): function cast_num_frames (line 1340) | def cast_num_frames(t, *, frames): FILE: DM_3/modules/video_flow_diffusion_multiGPU_v0_crema_plus_faceemb_ca_multi_test.py function exists (line 27) | def exists(x): function noop (line 31) | def noop(*args, **kwargs): function is_odd (line 35) | def is_odd(n): function default (line 39) | def default(val, d): function cycle (line 45) | def cycle(dl): function num_to_groups (line 51) | def num_to_groups(num, divisor): function prob_mask_like (line 60) | def prob_mask_like(shape, prob, device): function is_list_str (line 69) | def is_list_str(x): class RelativePositionBias (line 77) | class RelativePositionBias(nn.Module): method __init__ (line 78) | def __init__( method _relative_position_bucket (line 92) | def _relative_position_bucket(relative_position, num_buckets=32, max_d... method forward (line 111) | def forward(self, n, device): class EMA (line 125) | class EMA(): method __init__ (line 126) | def __init__(self, beta): method update_model_average (line 130) | def update_model_average(self, ma_model, current_model): method update_average (line 135) | def update_average(self, old, new): class Residual (line 141) | class Residual(nn.Module): method __init__ (line 142) | def __init__(self, fn): method forward (line 146) | def forward(self, x, *args, **kwargs): class SinusoidalPosEmb (line 150) | class SinusoidalPosEmb(nn.Module): method __init__ (line 151) | def __init__(self, dim): method forward (line 155) | def forward(self, x): function Upsample (line 165) | def Upsample(dim, use_deconv=True, padding_mode="reflect"): function Downsample (line 175) | def Downsample(dim): class LayerNorm (line 179) | class LayerNorm(nn.Module): method __init__ (line 180) | def __init__(self, dim, eps=1e-5): method forward (line 185) | def forward(self, x): class LayerNorm_img (line 190) | class LayerNorm_img(nn.Module): method __init__ (line 191) | def __init__(self, dim, stable = False): method forward (line 196) | def forward(self, x): class PreNorm (line 205) | class PreNorm(nn.Module): method __init__ (line 206) | def __init__(self, dim, fn): method forward (line 211) | def forward(self, x, **kwargs): class Identity (line 215) | class Identity(nn.Module): method __init__ (line 216) | def __init__(self, *args, **kwargs): method forward (line 219) | def forward(self, x, *args, **kwargs): function l2norm (line 222) | def l2norm(t): class Block (line 226) | class Block(nn.Module): method __init__ (line 227) | def __init__(self, dim, dim_out, groups=8): method forward (line 233) | def forward(self, x, time_scale_shift=None, audio_scale_shift=None): class ResnetBlock (line 251) | class ResnetBlock(nn.Module): method __init__ (line 252) | def __init__(self, dim, dim_out, *, time_emb_dim=None, audio_emb_dim=N... method forward (line 268) | def forward(self, x, time_emb=None, audio_emb=None): class ResnetBlock_ca (line 289) | class ResnetBlock_ca(nn.Module): method __init__ (line 290) | def __init__(self, dim, dim_out, *, time_emb_dim=None, audio_emb_dim=N... method forward (line 319) | def forward(self, x, time_emb=None, audio_emb=None): class ResnetBlock_ca_mul (line 363) | class ResnetBlock_ca_mul(nn.Module): method __init__ (line 364) | def __init__(self, dim, dim_out, *, time_emb_dim=None, audio_emb_dim=N... method forward (line 419) | def forward(self, x, time_emb=None, audio_emb=None): class CrossAttention (line 481) | class CrossAttention(nn.Module): method __init__ (line 482) | def __init__( method forward (line 516) | def forward(self, x, context, mask = None): class LinearCrossAttention (line 561) | class LinearCrossAttention(CrossAttention): method forward (line 562) | def forward(self, x, context, mask = None): class SpatialLinearAttention (line 602) | class SpatialLinearAttention(nn.Module): method __init__ (line 603) | def __init__(self, dim, heads=4, dim_head=32): method forward (line 611) | def forward(self, x): class EinopsToAndFrom (line 632) | class EinopsToAndFrom(nn.Module): method __init__ (line 633) | def __init__(self, from_einops, to_einops, fn): method forward (line 639) | def forward(self, x, **kwargs): class Attention (line 648) | class Attention(nn.Module): method __init__ (line 649) | def __init__( method forward (line 665) | def forward( class Unet3D (line 728) | class Unet3D(nn.Module): method __init__ (line 729) | def __init__( method forward_with_cond_scale (line 879) | def forward_with_cond_scale( method forward (line 892) | def forward( class DynamicNfUnet3D (line 959) | class DynamicNfUnet3D(Unet3D): method __init__ (line 960) | def __init__(self, default_num_frames=20, *args, **kwargs): method update_num_frames (line 964) | def update_num_frames(self, new_num_frames): function extract (line 969) | def extract(a, t, x_shape): function cosine_beta_schedule (line 975) | def cosine_beta_schedule(timesteps, s=0.008): class GaussianDiffusion (line 988) | class GaussianDiffusion(nn.Module): method __init__ (line 989) | def __init__( method q_mean_variance (line 1066) | def q_mean_variance(self, x_start, t): method predict_start_from_noise (line 1072) | def predict_start_from_noise(self, x_t, t, noise): method q_posterior (line 1078) | def q_posterior(self, x_start, x_t, t): method p_mean_variance (line 1087) | def p_mean_variance(self, x, t, fea, clip_denoised: bool, cond=None, c... method p_sample (line 1113) | def p_sample(self, x, t, fea, cond=None, cond_scale=1., clip_denoised=... method p_sample_loop (line 1124) | def p_sample_loop(self, fea, shape, cond=None, cond_scale=1.): method sample (line 1138) | def sample(self, fea, bbox_mask, cond=None, cond_scale=1., batch_size=... method ddim_sample (line 1157) | def ddim_sample(self, fea, shape, cond=None, cond_scale=1., clip_denoi... method interpolate (line 1211) | def interpolate(self, x1, x2, t=None, lam=0.5): method q_sample (line 1226) | def q_sample(self, x_start, t, noise=None): method p_losses (line 1234) | def p_losses(self, x_start, t, fea, bbox_mask, cond=None, noise=None, ... method forward (line 1274) | def forward(self, x, fea, bbox_mask, cond, *args, **kwargs): function seek_all_images (line 1293) | def seek_all_images(img, channels=3): class DynamicNfGaussianDiffusion (line 1307) | class DynamicNfGaussianDiffusion(GaussianDiffusion): method __init__ (line 1308) | def __init__(self, default_num_frames=20, *args, **kwargs): method update_num_frames (line 1312) | def update_num_frames(self, new_num_frames): function video_tensor_to_gif (line 1317) | def video_tensor_to_gif(tensor, path, duration=120, loop=0, optimize=True): function gif_to_tensor (line 1326) | def gif_to_tensor(path, channels=3, transform=T.ToTensor()): function identity (line 1332) | def identity(t, *args, **kwargs): function normalize_img (line 1336) | def normalize_img(t): function cast_num_frames (line 1344) | def cast_num_frames(t, *, frames): FILE: DM_3/modules/video_flow_diffusion_multiGPU_v0_crema_plus_faceemb_ca_multi_test_local_opt.py function exists (line 28) | def exists(x): function noop (line 32) | def noop(*args, **kwargs): function is_odd (line 36) | def is_odd(n): function default (line 40) | def default(val, d): function cycle (line 46) | def cycle(dl): function num_to_groups (line 52) | def num_to_groups(num, divisor): function prob_mask_like (line 61) | def prob_mask_like(shape, prob, device): function is_list_str (line 70) | def is_list_str(x): class RelativePositionBias (line 78) | class RelativePositionBias(nn.Module): method __init__ (line 79) | def __init__( method _relative_position_bucket (line 93) | def _relative_position_bucket(relative_position, num_buckets=32, max_d... method forward (line 112) | def forward(self, n, device): class EMA (line 126) | class EMA(): method __init__ (line 127) | def __init__(self, beta): method update_model_average (line 131) | def update_model_average(self, ma_model, current_model): method update_average (line 136) | def update_average(self, old, new): class Residual (line 142) | class Residual(nn.Module): method __init__ (line 143) | def __init__(self, fn): method forward (line 147) | def forward(self, x, *args, **kwargs): class SinusoidalPosEmb (line 151) | class SinusoidalPosEmb(nn.Module): method __init__ (line 152) | def __init__(self, dim): method forward (line 156) | def forward(self, x): function Upsample (line 166) | def Upsample(dim, use_deconv=True, padding_mode="reflect"): function Downsample (line 176) | def Downsample(dim): class LayerNorm (line 180) | class LayerNorm(nn.Module): method __init__ (line 181) | def __init__(self, dim, eps=1e-5): method forward (line 186) | def forward(self, x): class LayerNorm_img (line 191) | class LayerNorm_img(nn.Module): method __init__ (line 192) | def __init__(self, dim, stable = False): method forward (line 197) | def forward(self, x): class PreNorm (line 206) | class PreNorm(nn.Module): method __init__ (line 207) | def __init__(self, dim, fn): method forward (line 212) | def forward(self, x, **kwargs): class Identity (line 216) | class Identity(nn.Module): method __init__ (line 217) | def __init__(self, *args, **kwargs): method forward (line 220) | def forward(self, x, *args, **kwargs): function l2norm (line 223) | def l2norm(t): class Block (line 227) | class Block(nn.Module): method __init__ (line 228) | def __init__(self, dim, dim_out, groups=8): method forward (line 234) | def forward(self, x, time_scale_shift=None, audio_scale_shift=None): class ResnetBlock (line 252) | class ResnetBlock(nn.Module): method __init__ (line 253) | def __init__(self, dim, dim_out, *, time_emb_dim=None, audio_emb_dim=N... method forward (line 269) | def forward(self, x, time_emb=None, audio_emb=None): class ResnetBlock_ca (line 290) | class ResnetBlock_ca(nn.Module): method __init__ (line 291) | def __init__(self, dim, dim_out, *, time_emb_dim=None, audio_emb_dim=N... method forward (line 320) | def forward(self, x, time_emb=None, audio_emb=None): class ResnetBlock_ca_mul (line 364) | class ResnetBlock_ca_mul(nn.Module): method __init__ (line 365) | def __init__(self, dim, dim_out, *, time_emb_dim=None, audio_emb_dim=N... method forward (line 420) | def forward(self, x, time_emb=None, audio_emb=None): class CrossAttention (line 482) | class CrossAttention(nn.Module): method __init__ (line 483) | def __init__( method forward (line 517) | def forward(self, x, context, mask = None): class LinearCrossAttention (line 562) | class LinearCrossAttention(CrossAttention): method forward (line 563) | def forward(self, x, context, mask = None): class SpatialLinearAttention (line 603) | class SpatialLinearAttention(nn.Module): method __init__ (line 604) | def __init__(self, dim, heads=4, dim_head=32): method forward (line 612) | def forward(self, x): class EinopsToAndFrom (line 633) | class EinopsToAndFrom(nn.Module): method __init__ (line 634) | def __init__(self, from_einops, to_einops, fn): method forward (line 640) | def forward(self, x, **kwargs): class Attention (line 649) | class Attention(nn.Module): method __init__ (line 650) | def __init__( method forward (line 666) | def forward( class Unet3D (line 729) | class Unet3D(nn.Module): method __init__ (line 730) | def __init__( method forward_with_cond_scale (line 881) | def forward_with_cond_scale( method forward (line 894) | def forward( class DynamicNfUnet3D (line 961) | class DynamicNfUnet3D(Unet3D): method __init__ (line 962) | def __init__(self, default_num_frames=20, *args, **kwargs): method update_num_frames (line 966) | def update_num_frames(self, new_num_frames): function extract (line 971) | def extract(a, t, x_shape): function cosine_beta_schedule (line 977) | def cosine_beta_schedule(timesteps, s=0.008): class GaussianDiffusion (line 990) | class GaussianDiffusion(nn.Module): method __init__ (line 991) | def __init__( method q_mean_variance (line 1068) | def q_mean_variance(self, x_start, t): method predict_start_from_noise (line 1074) | def predict_start_from_noise(self, x_t, t, noise): method q_posterior (line 1080) | def q_posterior(self, x_start, x_t, t): method p_mean_variance (line 1089) | def p_mean_variance(self, x, t, fea, clip_denoised: bool, cond=None, c... method p_sample (line 1115) | def p_sample(self, x, t, fea, cond=None, cond_scale=1., clip_denoised=... method p_sample_loop (line 1126) | def p_sample_loop(self, fea, shape, cond=None, cond_scale=1.): method sample (line 1140) | def sample(self, fea, bbox_mask, cond=None, cond_scale=1., batch_size=... method ddim_sample (line 1159) | def ddim_sample(self, fea, shape, cond=None, cond_scale=1., clip_denoi... method interpolate (line 1213) | def interpolate(self, x1, x2, t=None, lam=0.5): method q_sample (line 1228) | def q_sample(self, x_start, t, noise=None): method p_losses (line 1236) | def p_losses(self, x_start, t, fea, bbox_mask, cond=None, noise=None, ... method forward (line 1276) | def forward(self, x, fea, bbox_mask, cond, *args, **kwargs): function seek_all_images (line 1295) | def seek_all_images(img, channels=3): class DynamicNfGaussianDiffusion (line 1309) | class DynamicNfGaussianDiffusion(GaussianDiffusion): method __init__ (line 1310) | def __init__(self, default_num_frames=20, *args, **kwargs): method update_num_frames (line 1314) | def update_num_frames(self, new_num_frames): function video_tensor_to_gif (line 1319) | def video_tensor_to_gif(tensor, path, duration=120, loop=0, optimize=True): function gif_to_tensor (line 1328) | def gif_to_tensor(path, channels=3, transform=T.ToTensor()): function identity (line 1334) | def identity(t, *args, **kwargs): function normalize_img (line 1338) | def normalize_img(t): function cast_num_frames (line 1346) | def cast_num_frames(t, *, frames): FILE: DM_3/train_vdm_hdtf_wpose_plus_faceemb_init_cond_liploss_6D.py function get_arguments (line 112) | def get_arguments(): function sample_img (line 157) | def sample_img(rec_img_batch, idx=0): function main (line 166) | def main(): class AverageMeter (line 504) | class AverageMeter(object): method __init__ (line 507) | def __init__(self): method reset (line 510) | def reset(self): method update (line 516) | def update(self, val, n=1): function setup_seed (line 523) | def setup_seed(seed): FILE: DM_3/train_vdm_hdtf_wpose_plus_faceemb_init_cond_liploss_6D_s2.py function get_arguments (line 118) | def get_arguments(): function sample_img (line 163) | def sample_img(rec_img_batch, idx=0): function main (line 172) | def main(): class AverageMeter (line 531) | class AverageMeter(object): method __init__ (line 534) | def __init__(self): method reset (line 537) | def reset(self): method update (line 543) | def update(self, val, n=1): function setup_seed (line 550) | def setup_seed(seed): FILE: DM_3/utils.py class MultiEpochsDataLoader (line 5) | class MultiEpochsDataLoader(torch.utils.data.DataLoader): method __init__ (line 7) | def __init__(self, *args, **kwargs): method __len__ (line 14) | def __len__(self): method __iter__ (line 17) | def __iter__(self): class _RepeatSampler (line 22) | class _RepeatSampler(object): method __init__ (line 28) | def __init__(self, sampler): method __iter__ (line 31) | def __iter__(self): FILE: LFG/augmentation.py function crop_clip (line 20) | def crop_clip(clip, min_h, min_w, h, w): function pad_clip (line 34) | def pad_clip(clip, h, w): function resize_clip (line 42) | def resize_clip(clip, size, interpolation='bilinear'): function get_resize_sizes (line 81) | def get_resize_sizes(im_h, im_w, size): class RandomFlip (line 91) | class RandomFlip(object): method __init__ (line 92) | def __init__(self, time_flip=False, horizontal_flip=False): method __call__ (line 96) | def __call__(self, clip): class RandomResize (line 105) | class RandomResize(object): method __init__ (line 115) | def __init__(self, ratio=(3. / 4., 4. / 3.), interpolation='nearest'): method __call__ (line 119) | def __call__(self, clip): class RandomCrop (line 136) | class RandomCrop(object): method __init__ (line 143) | def __init__(self, size): method __call__ (line 149) | def __call__(self, clip): class RandomRotation (line 175) | class RandomRotation(object): method __init__ (line 184) | def __init__(self, degrees): method __call__ (line 197) | def __call__(self, clip): class ColorJitter (line 217) | class ColorJitter(object): method __init__ (line 230) | def __init__(self, brightness=0, contrast=0, saturation=0, hue=0): method get_params (line 236) | def get_params(self, brightness, contrast, saturation, hue): method __call__ (line 261) | def __call__(self, clip): class AllAugmentationTransform (line 323) | class AllAugmentationTransform: method __init__ (line 324) | def __init__(self, resize_param=None, rotation_param=None, flip_param=... method __call__ (line 342) | def __call__(self, clip): FILE: LFG/frames_dataset.py function read_video (line 26) | def read_video(name, frame_shape): class FramesDataset (line 76) | class FramesDataset(Dataset): method __init__ (line 84) | def __init__(self, root_dir, frame_shape=(256, 256, 3), id_sampling=Fa... method __len__ (line 118) | def __len__(self): method __getitem__ (line 121) | def __getitem__(self, idx): class DatasetRepeater (line 178) | class DatasetRepeater(Dataset): method __init__ (line 183) | def __init__(self, dataset, num_repeats=100): method __len__ (line 187) | def __len__(self): method __getitem__ (line 190) | def __getitem__(self, idx): class PairedDataset (line 194) | class PairedDataset(Dataset): method __init__ (line 199) | def __init__(self, initial_dataset, number_of_pairs, seed=0): method __len__ (line 224) | def __len__(self): method __getitem__ (line 227) | def __getitem__(self, idx): FILE: LFG/hdtf_dataset.py function resize (line 18) | def resize(im, desired_size, interpolation): class FramesDataset (line 36) | class FramesDataset(Dataset): method __init__ (line 44) | def __init__(self, root_dir, frame_shape=256, id_sampling=False, method __len__ (line 67) | def __len__(self): method __getitem__ (line 70) | def __getitem__(self, idx): class DatasetRepeater (line 109) | class DatasetRepeater(Dataset): method __init__ (line 114) | def __init__(self, dataset, num_repeats=100): method __len__ (line 118) | def __len__(self): method __getitem__ (line 121) | def __getitem__(self, idx): FILE: LFG/modules/avd_network.py class AVDNetwork (line 13) | class AVDNetwork(nn.Module): method __init__ (line 18) | def __init__(self, num_regions, id_bottle_size=64, pose_bottle_size=64... method region_params_to_emb (line 64) | def region_params_to_emb(x): method emb_to_region_params (line 71) | def emb_to_region_params(self, emb): method forward (line 77) | def forward(self, x_id, x_pose, alpha=0.2): FILE: LFG/modules/bg_motion_predictor.py class BGMotionPredictor (line 15) | class BGMotionPredictor(nn.Module): method __init__ (line 20) | def __init__(self, block_expansion, num_channels, max_features, num_bl... method forward (line 42) | def forward(self, source_image, driving_image): FILE: LFG/modules/flow_autoenc.py class FlowAE (line 14) | class FlowAE(nn.Module): method __init__ (line 15) | def __init__(self, is_train=False, method forward (line 39) | def forward(self): method set_train_input (line 49) | def set_train_input(self, ref_img, dri_img): FILE: LFG/modules/generator.py class Generator (line 19) | class Generator(nn.Module): method __init__ (line 25) | def __init__(self, num_channels, num_regions, block_expansion, max_fea... method deform_input (line 62) | def deform_input(inp, optical_flow): method apply_optical (line 71) | def apply_optical(self, input_previous=None, input_skip=None, motion_p... method forward (line 92) | def forward(self, source_image, driving_region_params, source_region_p... method compute_fea (line 132) | def compute_fea(self, source_image): method forward_with_flow (line 138) | def forward_with_flow(self, source_image, optical_flow, occlusion_map): FILE: LFG/modules/model.py class Vgg19 (line 19) | class Vgg19(torch.nn.Module): method __init__ (line 24) | def __init__(self, requires_grad=False): method forward (line 52) | def forward(self, x): class ImagePyramide (line 63) | class ImagePyramide(torch.nn.Module): method __init__ (line 68) | def __init__(self, scales, num_channels): method forward (line 75) | def forward(self, x): class Transform (line 82) | class Transform: method __init__ (line 87) | def __init__(self, bs, **kwargs): method transform_frame (line 102) | def transform_frame(self, frame): method warp_coordinates (line 108) | def warp_coordinates(self, coordinates): method jacobian (line 129) | def jacobian(self, coordinates): function detach_kp (line 137) | def detach_kp(kp): class ReconstructionModel (line 141) | class ReconstructionModel(torch.nn.Module): method __init__ (line 146) | def __init__(self, region_predictor, bg_predictor, generator, train_pa... method forward (line 164) | def forward(self, x): FILE: LFG/modules/pixelwise_flow_predictor.py class PixelwiseFlowPredictor (line 17) | class PixelwiseFlowPredictor(nn.Module): method __init__ (line 23) | def __init__(self, block_expansion, num_blocks, max_features, num_regi... method create_heatmap_representations (line 48) | def create_heatmap_representations(self, source_image, driving_region_... method create_sparse_motions (line 66) | def create_sparse_motions(self, source_image, driving_region_params, s... method create_deformed_source_image (line 95) | def create_deformed_source_image(self, source_image, sparse_motions): method forward (line 104) | def forward(self, source_image, driving_region_params, source_region_p... FILE: LFG/modules/region_predictor.py function svd (line 16) | def svd(covar, fast=False): class RegionPredictor (line 28) | class RegionPredictor(nn.Module): method __init__ (line 33) | def __init__(self, block_expansion, num_regions, num_channels, max_fea... method region2affine (line 60) | def region2affine(self, region): method forward (line 77) | def forward(self, x): FILE: LFG/modules/util.py function region2gaussian (line 22) | def region2gaussian(center, covar, spatial_size): function make_coordinate_grid (line 51) | def make_coordinate_grid(spatial_size, type): class ResBlock2d (line 70) | class ResBlock2d(nn.Module): method __init__ (line 75) | def __init__(self, in_features, kernel_size, padding): method forward (line 84) | def forward(self, x): class UpBlock2d (line 95) | class UpBlock2d(nn.Module): method __init__ (line 100) | def __init__(self, in_features, out_features, kernel_size=3, padding=1... method forward (line 107) | def forward(self, x): class DownBlock2d (line 115) | class DownBlock2d(nn.Module): method __init__ (line 120) | def __init__(self, in_features, out_features, kernel_size=3, padding=1... method forward (line 127) | def forward(self, x): class SameBlock2d (line 135) | class SameBlock2d(nn.Module): method __init__ (line 140) | def __init__(self, in_features, out_features, groups=1, kernel_size=3,... method forward (line 146) | def forward(self, x): class Encoder (line 153) | class Encoder(nn.Module): method __init__ (line 158) | def __init__(self, block_expansion, in_features, num_blocks=3, max_fea... method forward (line 168) | def forward(self, x): class Decoder (line 175) | class Decoder(nn.Module): method __init__ (line 180) | def __init__(self, block_expansion, in_features, num_blocks=3, max_fea... method forward (line 193) | def forward(self, x): class Hourglass (line 202) | class Hourglass(nn.Module): method __init__ (line 207) | def __init__(self, block_expansion, in_features, num_blocks=3, max_fea... method forward (line 213) | def forward(self, x): class AntiAliasInterpolation2d (line 217) | class AntiAliasInterpolation2d(nn.Module): method __init__ (line 222) | def __init__(self, channels, scale): method forward (line 256) | def forward(self, input): function to_homogeneous (line 267) | def to_homogeneous(coordinates): function from_homogeneous (line 275) | def from_homogeneous(coordinates): function draw_colored_heatmap (line 279) | def draw_colored_heatmap(heatmap, colormap, bg_color): class Visualizer (line 301) | class Visualizer: method __init__ (line 302) | def __init__(self, kp_size=5, draw_border=False, colormap='gist_rainbo... method draw_image_with_kp (line 308) | def draw_image_with_kp(self, image, kp_array): method create_image_column_with_kp (line 318) | def create_image_column_with_kp(self, images, kp): method create_image_column (line 322) | def create_image_column(self, images): method create_image_grid (line 329) | def create_image_grid(self, *args): method sample (line 339) | def sample(x, index): method visualize (line 342) | def visualize(self, driving, source, out, index=0): FILE: LFG/run_hdtf.py class Logger (line 29) | class Logger(object): method __init__ (line 30) | def __init__(self, filename='default.log', stream=sys.stdout): method write (line 34) | def write(self, message): method flush (line 38) | def flush(self): function setup_seed (line 42) | def setup_seed(seed): FILE: LFG/run_hdtf_crema.py class Logger (line 29) | class Logger(object): method __init__ (line 30) | def __init__(self, filename='default.log', stream=sys.stdout): method write (line 34) | def write(self, message): method flush (line 38) | def flush(self): function setup_seed (line 42) | def setup_seed(seed): FILE: LFG/sync_batchnorm/batchnorm.py function _sum_ft (line 24) | def _sum_ft(tensor): function _unsqueeze_ft (line 29) | def _unsqueeze_ft(tensor): class _SynchronizedBatchNorm (line 38) | class _SynchronizedBatchNorm(_BatchNorm): method __init__ (line 39) | def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True): method forward (line 48) | def forward(self, input): method __data_parallel_replicate__ (line 80) | def __data_parallel_replicate__(self, ctx, copy_id): method _data_parallel_master (line 90) | def _data_parallel_master(self, intermediates): method _compute_mean_std (line 113) | def _compute_mean_std(self, sum_, ssum, size): class SynchronizedBatchNorm1d (line 128) | class SynchronizedBatchNorm1d(_SynchronizedBatchNorm): method _check_input_dim (line 184) | def _check_input_dim(self, input): class SynchronizedBatchNorm2d (line 191) | class SynchronizedBatchNorm2d(_SynchronizedBatchNorm): method _check_input_dim (line 247) | def _check_input_dim(self, input): class SynchronizedBatchNorm3d (line 254) | class SynchronizedBatchNorm3d(_SynchronizedBatchNorm): method _check_input_dim (line 311) | def _check_input_dim(self, input): FILE: LFG/sync_batchnorm/comm.py class FutureResult (line 18) | class FutureResult(object): method __init__ (line 21) | def __init__(self): method put (line 26) | def put(self, result): method get (line 32) | def get(self): class SlavePipe (line 46) | class SlavePipe(_SlavePipeBase): method run_slave (line 49) | def run_slave(self, msg): class SyncMaster (line 56) | class SyncMaster(object): method __init__ (line 67) | def __init__(self, master_callback): method __getstate__ (line 78) | def __getstate__(self): method __setstate__ (line 81) | def __setstate__(self, state): method register_slave (line 84) | def register_slave(self, identifier): method run_master (line 102) | def run_master(self, master_msg): method nr_slaves (line 136) | def nr_slaves(self): FILE: LFG/sync_batchnorm/replicate.py class CallbackContext (line 23) | class CallbackContext(object): function execute_replication_callbacks (line 27) | def execute_replication_callbacks(modules): class DataParallelWithCallback (line 50) | class DataParallelWithCallback(DataParallel): method replicate (line 64) | def replicate(self, module, device_ids): function patch_replication_callback (line 70) | def patch_replication_callback(data_parallel): FILE: LFG/sync_batchnorm/unittest.py function as_numpy (line 17) | def as_numpy(v): class TorchTestCase (line 23) | class TorchTestCase(unittest.TestCase): method assertTensorClose (line 24) | def assertTensorClose(self, a, b, atol=1e-3, rtol=1e-3): FILE: LFG/test_flowautoenc_crema_video.py function get_arguments (line 67) | def get_arguments(): function extract_audio_by_frames (line 92) | def extract_audio_by_frames(input_wav_path, start_frame_index, num_frame... function sample_img (line 111) | def sample_img(rec_img_batch): function main (line 120) | def main(): class AverageMeter (line 312) | class AverageMeter(object): method __init__ (line 315) | def __init__(self): method reset (line 318) | def reset(self): method update (line 324) | def update(self, val, n=1): function setup_seed (line 331) | def setup_seed(seed): FILE: LFG/test_flowautoenc_hdtf_video.py function get_arguments (line 67) | def get_arguments(): function extract_audio_by_frames (line 92) | def extract_audio_by_frames(input_wav_path, start_frame_index, num_frame... function sample_img (line 111) | def sample_img(rec_img_batch): function main (line 120) | def main(): class AverageMeter (line 312) | class AverageMeter(object): method __init__ (line 315) | def __init__(self): method reset (line 318) | def reset(self): method update (line 324) | def update(self, val, n=1): function setup_seed (line 331) | def setup_seed(seed): FILE: LFG/test_flowautoenc_hdtf_video_256.py function get_arguments (line 67) | def get_arguments(): function extract_audio_by_frames (line 92) | def extract_audio_by_frames(input_wav_path, start_frame_index, num_frame... function sample_img (line 111) | def sample_img(rec_img_batch): function main (line 120) | def main(): class AverageMeter (line 292) | class AverageMeter(object): method __init__ (line 295) | def __init__(self): method reset (line 298) | def reset(self): method update (line 304) | def update(self, val, n=1): function setup_seed (line 311) | def setup_seed(seed): FILE: LFG/train.py class AverageMeter (line 16) | class AverageMeter(object): method __init__ (line 19) | def __init__(self): method reset (line 22) | def reset(self): method update (line 28) | def update(self, val, n=1): function train (line 35) | def train(config, generator, region_predictor, bg_predictor, checkpoint,... FILE: LFG/vis_flow.py function visualize_dense_optical_flow (line 5) | def visualize_dense_optical_flow(flow_tensor, save_path): function grid2flow (line 27) | def grid2flow(warped_grid, grid_size=64, img_size=256): FILE: PBnet/src/datasets/datasets_hdtf_pos_chunk_norm_2_fast.py function resize (line 28) | def resize(im, desired_size, interpolation): class HDTF (line 44) | class HDTF(data.Dataset): method __init__ (line 45) | def __init__(self, data_dir, max_num_frames=80, mode='train'): method check_head (line 96) | def check_head(self, frame_list, video_name, start, end): method get_block_data_for_two (line 107) | def get_block_data_for_two(self, path, start, end): method get_block_data (line 138) | def get_block_data(self, path, start, end): method check_len (line 173) | def check_len(self, name): method __len__ (line 177) | def __len__(self): method __getitem__ (line 180) | def __getitem__(self, idx): method update_parameters (line 211) | def update_parameters(self, parameters): FILE: PBnet/src/datasets/datasets_hdtf_pos_chunk_norm_eye_fast.py function resize (line 28) | def resize(im, desired_size, interpolation): class HDTF (line 44) | class HDTF(data.Dataset): method __init__ (line 45) | def __init__(self, data_dir, max_num_frames=80, mode='train'): method check_head (line 117) | def check_head(self, frame_list, video_name, start, end): method get_block_data_for_two (line 127) | def get_block_data_for_two(self, path, start, end): method get_block_data (line 158) | def get_block_data(self, path, start, end): method check_len (line 193) | def check_len(self, name): method __len__ (line 197) | def __len__(self): method __getitem__ (line 200) | def __getitem__(self, idx): method update_parameters (line 265) | def update_parameters(self, parameters): FILE: PBnet/src/datasets/datasets_hdtf_pos_df.py function resize (line 22) | def resize(im, desired_size, interpolation): class HDTF (line 38) | class HDTF(data.Dataset): method __init__ (line 39) | def __init__(self, data_dir, max_num_frames=80, min_num_frames=40, mod... method __len__ (line 92) | def __len__(self): method __getitem__ (line 100) | def __getitem__(self, idx): method update_parameters (line 159) | def update_parameters(self, parameters): FILE: PBnet/src/datasets/datasets_hdtf_pos_dict_norm_2.py function resize (line 28) | def resize(im, desired_size, interpolation): class HDTF (line 44) | class HDTF(data.Dataset): method __init__ (line 45) | def __init__(self, data_dir, max_num_frames=80, mode='train'): method check_head (line 110) | def check_head(self, frame_list, video_name, start, end): method get_block_data_for_two (line 120) | def get_block_data_for_two(self, path, start, end): method get_block_data (line 151) | def get_block_data(self, path, start, end): method check_len (line 186) | def check_len(self, name): method __len__ (line 190) | def __len__(self): method __getitem__ (line 193) | def __getitem__(self, idx): method update_parameters (line 240) | def update_parameters(self, parameters): FILE: PBnet/src/datasets/datasets_hdtf_wpose_lmk_block.py function resize (line 28) | def resize(im, desired_size, interpolation): class HDTF (line 44) | class HDTF(data.Dataset): method __init__ (line 45) | def __init__(self, data_dir, pose_dir, eye_blink_dir, max_num_frames=8... method check_head (line 102) | def check_head(self, frame_list, video_name, start, end): method get_block_data_for_two (line 112) | def get_block_data_for_two(self, path, start, end): method get_block_data (line 143) | def get_block_data(self, path, start, end): method check_len (line 178) | def check_len(self, name): method __len__ (line 183) | def __len__(self): method __getitem__ (line 186) | def __getitem__(self, idx): FILE: PBnet/src/datasets/get_dataset.py function get_dataset (line 1) | def get_dataset(name="ntu13"): function get_datasets (line 13) | def get_datasets(parameters): FILE: PBnet/src/datasets/tools.py function parse_info_name (line 5) | def parse_info_name(path): FILE: PBnet/src/evaluate/action2motion/accuracy.py function calculate_accuracy (line 4) | def calculate_accuracy(model, motion_loader, num_labels, classifier, dev... FILE: PBnet/src/evaluate/action2motion/diversity.py function calculate_diversity_multimodality (line 6) | def calculate_diversity_multimodality(activations, labels, num_labels): FILE: PBnet/src/evaluate/action2motion/evaluate.py class A2MEvaluation (line 9) | class A2MEvaluation: method __init__ (line 10) | def __init__(self, dataname, device): method compute_features (line 31) | def compute_features(self, model, motionloader): method calculate_activation_statistics (line 44) | def calculate_activation_statistics(activations): method evaluate (line 50) | def evaluate(self, model, loaders): FILE: PBnet/src/evaluate/action2motion/fid.py function calculate_fid (line 6) | def calculate_fid(statistics_1, statistics_2): function calculate_frechet_distance (line 11) | def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6): FILE: PBnet/src/evaluate/action2motion/models.py class MotionDiscriminator (line 6) | class MotionDiscriminator(nn.Module): method __init__ (line 7) | def __init__(self, input_size, hidden_size, hidden_layer, device, outp... method forward (line 20) | def forward(self, motion_sequence, lengths=None, hidden_unit=None): method initHidden (line 40) | def initHidden(self, num_samples, layer): class MotionDiscriminatorForFID (line 44) | class MotionDiscriminatorForFID(MotionDiscriminator): method forward (line 45) | def forward(self, motion_sequence, lengths=None, hidden_unit=None): function load_classifier (line 70) | def load_classifier(dataset_type, input_size_raw, num_classes, device): function load_classifier_for_fid (line 78) | def load_classifier_for_fid(dataset_type, input_size_raw, num_classes, d... function test (line 86) | def test(): FILE: PBnet/src/evaluate/evaluate_cvae.py function main (line 11) | def main(): FILE: PBnet/src/evaluate/evaluate_cvae_debug.py function main (line 10) | def main(): FILE: PBnet/src/evaluate/evaluate_cvae_f3.py function main (line 10) | def main(): FILE: PBnet/src/evaluate/evaluate_cvae_f3_debug.py function main (line 10) | def main(): FILE: PBnet/src/evaluate/evaluate_cvae_f3_mel.py function main (line 10) | def main(): FILE: PBnet/src/evaluate/evaluate_cvae_norm.py function main (line 10) | def main(): FILE: PBnet/src/evaluate/evaluate_cvae_norm_all.py function main (line 10) | def main(): FILE: PBnet/src/evaluate/evaluate_cvae_norm_all_seg.py function main (line 10) | def main(): FILE: PBnet/src/evaluate/evaluate_cvae_norm_all_seg_weye.py function main (line 11) | def main(): FILE: PBnet/src/evaluate/evaluate_cvae_norm_all_seg_weye2.py function main (line 10) | def main(): FILE: PBnet/src/evaluate/evaluate_cvae_norm_eye_pose.py function main (line 10) | def main(): FILE: PBnet/src/evaluate/evaluate_cvae_norm_eye_pose_test.py function main (line 10) | def main(): FILE: PBnet/src/evaluate/evaluate_cvae_onlyeye_all_seg.py function main (line 10) | def main(): FILE: PBnet/src/evaluate/othermetrics/acceleration.py function calculate_acceletation (line 7) | def calculate_acceletation(motionloader, device, xyz): FILE: PBnet/src/evaluate/othermetrics/evaluation.py class OtherMetricsEvaluation (line 8) | class OtherMetricsEvaluation: method __init__ (line 16) | def __init__(self, device): method compute_features (line 19) | def compute_features(self, model, motionloader, xyz=True): method reconstructionloss (line 33) | def reconstructionloss(self, motionloader, xyz=True): method evaluate (line 52) | def evaluate(self, model, num_classes, loaders, xyz=True): FILE: PBnet/src/evaluate/stgcn/accuracy.py function calculate_accuracy (line 4) | def calculate_accuracy(model, motion_loader, num_labels, classifier, dev... FILE: PBnet/src/evaluate/stgcn/diversity.py function calculate_diversity_multimodality (line 6) | def calculate_diversity_multimodality(activations, labels, num_labels, s... FILE: PBnet/src/evaluate/stgcn/evaluate.py class Evaluation (line 10) | class Evaluation: method __init__ (line 11) | def __init__(self, dataname, parameters, device, seed=None): method compute_features (line 35) | def compute_features(self, model, motionloader): method calculate_activation_statistics (line 48) | def calculate_activation_statistics(activations): method evaluate (line 54) | def evaluate(self, model, loaders): FILE: PBnet/src/evaluate/stgcn/fid.py function calculate_fid (line 6) | def calculate_fid(statistics_1, statistics_2): function calculate_frechet_distance (line 11) | def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6): FILE: PBnet/src/evaluate/tables/archtable.py function valformat (line 10) | def valformat(val, power=3): function format_values (line 16) | def format_values(values, key): function construct_table (line 33) | def construct_table(folder): function parse_opts (line 176) | def parse_opts(): FILE: PBnet/src/evaluate/tables/bstable.py function valformat (line 10) | def valformat(val, power=3): function format_values (line 16) | def format_values(values, key): function construct_table (line 33) | def construct_table(folder): function parse_opts (line 150) | def parse_opts(): FILE: PBnet/src/evaluate/tables/easy_table.py function get_gtname (line 9) | def get_gtname(mname): function get_genname (line 13) | def get_genname(mname): function get_reconsname (line 17) | def get_reconsname(mname): function valformat (line 21) | def valformat(val, power=3): function format_values (line 27) | def format_values(values, key, latex=True): function print_results (line 46) | def print_results(folder, evaluation): function parse_opts (line 94) | def parse_opts(): FILE: PBnet/src/evaluate/tables/easy_table_A2M.py function valformat (line 9) | def valformat(val, power=3): function construct_table (line 15) | def construct_table(folder, evaluation): function parse_opts (line 77) | def parse_opts(): FILE: PBnet/src/evaluate/tables/kltable.py function valformat (line 10) | def valformat(val, power=3): function format_values (line 16) | def format_values(values, key): function construct_table (line 33) | def construct_table(folder): function parse_opts (line 149) | def parse_opts(): FILE: PBnet/src/evaluate/tables/latexmodela2m.py function get_gtname (line 9) | def get_gtname(mname): function get_genname (line 13) | def get_genname(mname): function get_reconsname (line 17) | def get_reconsname(mname): function construct_table (line 21) | def construct_table(folder, evaluation): function parse_opts (line 78) | def parse_opts(): FILE: PBnet/src/evaluate/tables/latexmodelsa2m.py function valformat (line 10) | def valformat(val, power=3): function construct_table (line 16) | def construct_table(folder): function parse_opts (line 121) | def parse_opts(): FILE: PBnet/src/evaluate/tables/latexmodelsstgcn.py function get_gtname (line 10) | def get_gtname(mname): function get_genname (line 14) | def get_genname(mname): function get_reconsname (line 18) | def get_reconsname(mname): function valformat (line 22) | def valformat(val, power=3): function construct_table (line 28) | def construct_table(folder): function parse_opts (line 137) | def parse_opts(): FILE: PBnet/src/evaluate/tables/losstable.py function valformat (line 10) | def valformat(val, power=3): function format_values (line 16) | def format_values(values, key): function construct_table (line 33) | def construct_table(folder): function parse_opts (line 171) | def parse_opts(): FILE: PBnet/src/evaluate/tables/maketable.py function bold (line 31) | def bold(string): function colorize_template (line 35) | def colorize_template(string, color): function colorize_bold_template (line 39) | def colorize_bold_template(string, color): function format_table (line 43) | def format_table(val, gtval, mname): function get_gtname (line 94) | def get_gtname(mname): function get_genname (line 98) | def get_genname(mname): function get_reconsname (line 102) | def get_reconsname(mname): function collect_tables (line 106) | def collect_tables(folder, expname, lastepoch=False, norecons=False): function parse_opts (line 256) | def parse_opts(): FILE: PBnet/src/evaluate/tables/numlayertable.py function valformat (line 10) | def valformat(val, power=3): function format_values (line 16) | def format_values(values, key): function construct_table (line 33) | def construct_table(folder): function parse_opts (line 150) | def parse_opts(): FILE: PBnet/src/evaluate/tables/posereptable.py function valformat (line 10) | def valformat(val, power=3): function format_values (line 16) | def format_values(values, key): function construct_table (line 33) | def construct_table(folder): function parse_opts (line 157) | def parse_opts(): FILE: PBnet/src/evaluate/tools.py function format_metrics (line 4) | def format_metrics(metrics, formatter="{:.6}"): function save_metrics (line 11) | def save_metrics(path, metrics): function load_metrics (line 16) | def load_metrics(path): FILE: PBnet/src/evaluate/tvae_eval.py function evaluate (line 17) | def evaluate(parameters, dataset, folder, checkpointname, epoch, niter): FILE: PBnet/src/evaluate/tvae_eval_norm.py function transform (line 16) | def transform(x, min_val, max_val): function evaluate (line 20) | def evaluate(parameters, dataset, folder, checkpointname, epoch, niter): FILE: PBnet/src/evaluate/tvae_eval_norm_all.py function save_images_as_npy (line 16) | def save_images_as_npy(input_data, output_file): function save_as_chunk (line 24) | def save_as_chunk(dir, data): function transform (line 36) | def transform(x, min_val, max_val): function evaluate (line 40) | def evaluate(parameters, dataset, folder, checkpointname, epoch, niter): FILE: PBnet/src/evaluate/tvae_eval_norm_eye_pose.py function transform (line 17) | def transform(x, min_val, max_val): function evaluate (line 21) | def evaluate(parameters, dataset, folder, checkpointname, epoch, niter): FILE: PBnet/src/evaluate/tvae_eval_norm_eye_pose_seg.py function transform (line 18) | def transform(x, min_val, max_val): function save_images_as_npy (line 22) | def save_images_as_npy(input_data, output_file): function save_as_chunk (line 29) | def save_as_chunk(dir, data): function evaluate (line 39) | def evaluate(parameters, dataset, folder, checkpointname, epoch, niter): FILE: PBnet/src/evaluate/tvae_eval_norm_seg.py function save_images_as_npy (line 17) | def save_images_as_npy(input_data, output_file): function save_as_chunk (line 25) | def save_as_chunk(dir, data): function transform (line 37) | def transform(x, min_val, max_val): function evaluate (line 41) | def evaluate(parameters, dataset, folder, checkpointname, epoch, niter): FILE: PBnet/src/evaluate/tvae_eval_onlyeye_all_seg.py function save_images_as_npy (line 16) | def save_images_as_npy(input_data, output_file): function save_as_chunk (line 24) | def save_as_chunk(dir, data): function evaluate (line 40) | def evaluate(parameters, dataset, folder, checkpointname, epoch, niter): FILE: PBnet/src/evaluate/tvae_eval_single.py function inv_transform (line 25) | def inv_transform(x, min_val, max_val): function save_images_as_npy (line 29) | def save_images_as_npy(input_data, output_file): function evaluate (line 43) | def evaluate(parameters_pose, parameters_blink, audio_path, init_pose_pa... function get_arguments (line 114) | def get_arguments(): FILE: PBnet/src/evaluate/tvae_eval_single_both_eye_pose.py function inv_transform (line 25) | def inv_transform(x, min_val, max_val): function save_images_as_npy (line 29) | def save_images_as_npy(input_data, output_file): function evaluate (line 43) | def evaluate(parameters, audio_path, init_pose_path, init_blink_path, ch... function get_arguments (line 112) | def get_arguments(): FILE: PBnet/src/evaluate/tvae_eval_std.py function evaluate (line 17) | def evaluate(parameters, dataset, folder, checkpointname, epoch, niter): FILE: PBnet/src/evaluate/tvae_eval_train.py function evaluate (line 17) | def evaluate(parameters, dataset, folder, checkpointname, epoch, niter): FILE: PBnet/src/evaluate/tvae_eval_train_norm.py function transform (line 16) | def transform(x, min_val, max_val): function evaluate (line 20) | def evaluate(parameters, dataset, folder, checkpointname, epoch, niter): FILE: PBnet/src/evaluate/tvae_eval_train_std.py function evaluate (line 17) | def evaluate(parameters, dataset, folder, checkpointname, epoch, niter): FILE: PBnet/src/generate/generate_sequences.py function generate_actions (line 16) | def generate_actions(beta, model, dataset, epoch, params, folder, num_fr... function main (line 119) | def main(): FILE: PBnet/src/models/architectures/autotrans.py function subsequent_mask (line 13) | def subsequent_mask(size: int): function augment_x (line 25) | def augment_x(x, y, mask, lengths, num_classes, concatenate_time): function augment_z (line 44) | def augment_z(z, y, mask, lengths, num_classes, concatenate_time): class Decoder_AUTOTRANS (line 60) | class Decoder_AUTOTRANS(nn.Module): method __init__ (line 61) | def __init__(self, modeltype, njoints, nfeats, num_frames, num_classes... method forward (line 112) | def forward(self, batch): FILE: PBnet/src/models/architectures/fc.py class Encoder_FC (line 6) | class Encoder_FC(nn.Module): method __init__ (line 7) | def __init__(self, modeltype, njoints, nfeats, num_frames, num_classes... method forward (line 37) | def forward(self, batch): class Decoder_FC (line 57) | class Decoder_FC(nn.Module): method __init__ (line 58) | def __init__(self, modeltype, njoints, nfeats, num_frames, num_classes... method forward (line 84) | def forward(self, batch): FILE: PBnet/src/models/architectures/gru.py function augment_x (line 6) | def augment_x(x, y, mask, lengths, num_classes, concatenate_time): function augment_z (line 25) | def augment_z(z, y, mask, lengths, num_classes, concatenate_time): class Encoder_GRU (line 41) | class Encoder_GRU(nn.Module): method __init__ (line 42) | def __init__(self, modeltype, njoints, nfeats, num_frames, method forward (line 76) | def forward(self, batch): class Decoder_GRU (line 95) | class Decoder_GRU(nn.Module): method __init__ (line 96) | def __init__(self, modeltype, njoints, nfeats, num_frames, method forward (line 127) | def forward(self, batch): FILE: PBnet/src/models/architectures/mlp.py class Upsample (line 6) | class Upsample(nn.Module): method __init__ (line 7) | def __init__(self, input_dim, output_dim, kernel, stride): method forward (line 14) | def forward(self, x): class ResidualConv (line 17) | class ResidualConv(nn.Module): method __init__ (line 18) | def __init__(self, input_dim, output_dim, stride, padding): method forward (line 36) | def forward(self, x): class PositionalEncoding (line 40) | class PositionalEncoding(nn.Module): method __init__ (line 41) | def __init__(self, d_model, dropout=0.1, max_len=5000): method forward (line 54) | def forward(self, x): class RelativePositionBias (line 60) | class RelativePositionBias(nn.Module): method __init__ (line 61) | def __init__( method _relative_position_bucket (line 73) | def _relative_position_bucket(relative_position, num_buckets=32, max_d... method forward (line 92) | def forward(self, n, device): class TimeEncoding (line 102) | class TimeEncoding(nn.Module): method __init__ (line 103) | def __init__(self, d_model, dropout=0.1, max_len=5000): method forward (line 107) | def forward(self, x, mask, lengths): class ResUnet (line 115) | class ResUnet(nn.Module): method __init__ (line 116) | def __init__(self, channel=1, filters=[32, 64, 128, 256]): method forward (line 148) | def forward(self, x): class Encoder_MLP (line 176) | class Encoder_MLP(nn.Module): method __init__ (line 177) | def __init__(self, modeltype, num_frames, audio_dim=1024, pos_dim=7, p... method forward (line 203) | def forward(self, batch): class Decoder_MLP (line 232) | class Decoder_MLP(nn.Module): method __init__ (line 233) | def __init__(self, modeltype, num_frames, audio_dim=1024, pos_dim=7, p... method forward (line 259) | def forward(self, batch): FILE: PBnet/src/models/architectures/resnet34.py function conv3x3 (line 6) | def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): function conv1x1 (line 12) | def conv1x1(in_planes, out_planes, stride=1): class BasicBlock (line 16) | class BasicBlock(nn.Module): method __init__ (line 19) | def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, method forward (line 37) | def forward(self, x): class Bottleneck (line 56) | class Bottleneck(nn.Module): method __init__ (line 59) | def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, method forward (line 76) | def forward(self, x): class ResNet (line 98) | class ResNet(nn.Module): method __init__ (line 100) | def __init__(self, block, layers, num_classes=1000, zero_init_residual... method _make_layer (line 151) | def _make_layer(self, block, planes, blocks, stride=1, dilate=False): method forward (line 175) | def forward(self, x): function _resnet (line 192) | def _resnet(arch, block, layers, pretrained, progress, **kwargs): function resnet34 (line 196) | def resnet34(pretrained=False, progress=True, **kwargs): class MyResNet34 (line 208) | class MyResNet34(nn.Module): method __init__ (line 209) | def __init__(self,embedding_dim,input_channel = 3): method forward (line 212) | def forward(self, x): FILE: PBnet/src/models/architectures/tools/embeddings.py function get_activation (line 9) | def get_activation(activation_type): class MaskedNorm (line 38) | class MaskedNorm(nn.Module): method __init__ (line 44) | def __init__(self, norm_type, num_groups, num_features): method forward (line 58) | def forward(self, x: Tensor, mask: Tensor): class Embeddings (line 77) | class Embeddings(nn.Module): method __init__ (line 84) | def __init__( method forward (line 134) | def forward(self, x: Tensor, mask: Tensor = None) -> Tensor: method __repr__ (line 156) | def __repr__(self): class SpatialEmbeddings (line 164) | class SpatialEmbeddings(nn.Module): method __init__ (line 172) | def __init__( method forward (line 219) | def forward(self, x: Tensor, mask: Tensor) -> Tensor: method __repr__ (line 239) | def __repr__(self): FILE: PBnet/src/models/architectures/tools/resnet.py function conv3x3 (line 4) | def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): function conv1x1 (line 10) | def conv1x1(in_planes, out_planes, stride=1): class BasicBlock (line 14) | class BasicBlock(nn.Module): method __init__ (line 17) | def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, method forward (line 35) | def forward(self, x): class Bottleneck (line 54) | class Bottleneck(nn.Module): method __init__ (line 57) | def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, method forward (line 74) | def forward(self, x): class ResNet (line 96) | class ResNet(nn.Module): method __init__ (line 98) | def __init__(self, block, layers, num_classes=1000, zero_init_residual... method _make_layer (line 149) | def _make_layer(self, block, planes, blocks, stride=1, dilate=False): method forward (line 173) | def forward(self, x): function _resnet (line 190) | def _resnet(arch, block, layers, pretrained, progress, **kwargs): function resnet34 (line 194) | def resnet34(pretrained=False, progress=True, **kwargs): FILE: PBnet/src/models/architectures/tools/transformer_layers.py class MultiHeadedAttention (line 11) | class MultiHeadedAttention(nn.Module): method __init__ (line 19) | def __init__(self, num_heads: int, size: int, dropout: float = 0.1): method forward (line 42) | def forward(self, k: Tensor, v: Tensor, q: Tensor, mask: Tensor = None): class PositionwiseFeedForward (line 97) | class PositionwiseFeedForward(nn.Module): method __init__ (line 103) | def __init__(self, input_size, ff_size, dropout=0.1): method forward (line 120) | def forward(self, x): class PositionalEncoding (line 126) | class PositionalEncoding(nn.Module): method __init__ (line 136) | def __init__(self, method forward (line 159) | def forward(self, emb): class TransformerEncoderLayer (line 169) | class TransformerEncoderLayer(nn.Module): method __init__ (line 175) | def __init__(self, method forward (line 198) | def forward(self, x: Tensor, mask: Tensor) -> Tensor: class TransformerDecoderLayer (line 216) | class TransformerDecoderLayer(nn.Module): method __init__ (line 223) | def __init__(self, method forward (line 255) | def forward(self, FILE: PBnet/src/models/architectures/tools/util.py class MyResNet34 (line 10) | class MyResNet34(nn.Module): method __init__ (line 11) | def __init__(self,embedding_dim,input_channel = 3): method forward (line 14) | def forward(self, x): FILE: PBnet/src/models/architectures/transformer.py class PositionalEncoding (line 7) | class PositionalEncoding(nn.Module): method __init__ (line 8) | def __init__(self, d_model, dropout=0.1, max_len=5000): method forward (line 21) | def forward(self, x): class RelativePositionBias (line 27) | class RelativePositionBias(nn.Module): method __init__ (line 28) | def __init__( method _relative_position_bucket (line 40) | def _relative_position_bucket(relative_position, num_buckets=32, max_d... method forward (line 59) | def forward(self, n, device): class TimeEncoding (line 69) | class TimeEncoding(nn.Module): method __init__ (line 70) | def __init__(self, d_model, dropout=0.1, max_len=5000): method forward (line 74) | def forward(self, x, mask, lengths): class Encoder_TRANSFORMER (line 83) | class Encoder_TRANSFORMER(nn.Module): method __init__ (line 84) | def __init__(self, modeltype, num_frames, audio_dim=1024, pos_dim=7, p... method forward (line 134) | def forward(self, batch): class Decoder_TRANSFORMER (line 170) | class Decoder_TRANSFORMER(nn.Module): method __init__ (line 171) | def __init__(self, modeltype, num_frames, audio_dim=1024, pos_dim=7, p... method forward (line 226) | def forward(self, batch): FILE: PBnet/src/models/architectures/transformerdecoder.py class MultiheadAttention (line 17) | class MultiheadAttention(nn.Module): method __init__ (line 18) | def __init__(self, embed_size, heads, dropout = None, batch_first = No... method sinusoidal_position_embedding (line 35) | def sinusoidal_position_embedding(self, batch_size, nums_head, max_len... method RoPE (line 50) | def RoPE(self, q, k): method forward (line 73) | def forward(self, q, k, v, attn_mask = None, key_padding_mask=None, ne... class Transformer (line 108) | class Transformer(Module): method __init__ (line 143) | def __init__(self, d_model: int = 512, nhead: int = 8, num_encoder_lay... method forward (line 177) | def forward(self, src: Tensor, tgt: Tensor, src_mask: Optional[Tensor]... method generate_square_subsequent_mask (line 244) | def generate_square_subsequent_mask(sz: int) -> Tensor: method _reset_parameters (line 250) | def _reset_parameters(self): class TransformerEncoder (line 258) | class TransformerEncoder(Module): method __init__ (line 274) | def __init__(self, encoder_layer, num_layers, norm=None): method forward (line 280) | def forward(self, src: Tensor, mask: Optional[Tensor] = None, src_key_... class TransformerDecoder (line 302) | class TransformerDecoder(Module): method __init__ (line 319) | def __init__(self, decoder_layer, num_layers, norm=None): method forward (line 325) | def forward(self, tgt: Tensor, memory: Tensor, tgt_mask: Optional[Tens... class TransformerEncoderLayer (line 354) | class TransformerEncoderLayer(Module): method __init__ (line 387) | def __init__(self, d_model: int, nhead: int, dim_feedforward: int = 20... method __setstate__ (line 412) | def __setstate__(self, state): method forward (line 417) | def forward(self, src: Tensor, src_mask: Optional[Tensor] = None, src_... method _sa_block (line 442) | def _sa_block(self, x: Tensor, method _ff_block (line 451) | def _ff_block(self, x: Tensor) -> Tensor: class TransformerDecoderLayer (line 456) | class TransformerDecoderLayer(Module): method __init__ (line 492) | def __init__(self, d_model: int, nhead: int, dim_feedforward: int = 20... method __setstate__ (line 521) | def __setstate__(self, state): method forward (line 526) | def forward(self, tgt: Tensor, memory: Tensor, tgt_mask: Optional[Tens... method _sa_block (line 556) | def _sa_block(self, x: Tensor, method _mha_block (line 565) | def _mha_block(self, x: Tensor, mem: Tensor, method _ff_block (line 574) | def _ff_block(self, x: Tensor) -> Tensor: function _get_clones (line 579) | def _get_clones(module, N): function _get_activation_fn (line 583) | def _get_activation_fn(activation): FILE: PBnet/src/models/architectures/transformerdecoder4.py function exists (line 21) | def exists(x): class Attention (line 24) | class Attention(nn.Module): method __init__ (line 25) | def __init__( method forward (line 41) | def forward( class Attention_2 (line 102) | class Attention_2(nn.Module): method __init__ (line 103) | def __init__( method forward (line 121) | def forward( class PositionwiseFeedforwardLayer (line 170) | class PositionwiseFeedforwardLayer(nn.Module): method __init__ (line 171) | def __init__(self, d_model, d_ff, dropout): method forward (line 179) | def forward(self, x): class DecoderLayer (line 186) | class DecoderLayer(nn.Module): method __init__ (line 187) | def __init__(self, d_model, num_heads, d_ff, dropout, rotary_emb): method forward (line 201) | def forward(self, tgt, memory, tgt_mask=None, memory_mask=None): class TransformerDecoder (line 208) | class TransformerDecoder(nn.Module): method __init__ (line 209) | def __init__(self, num_layers, d_model, num_heads, dim_feedforward, dr... method forward (line 215) | def forward(self, tgt, memory, tgt_mask=None, memory_mask=None): FILE: PBnet/src/models/architectures/transformerdecoder5.py function exists (line 20) | def exists(x): class Attention (line 23) | class Attention(nn.Module): method __init__ (line 24) | def __init__( method forward (line 40) | def forward( class Attention_2 (line 101) | class Attention_2(nn.Module): method __init__ (line 102) | def __init__( method forward (line 120) | def forward( class PositionwiseFeedforwardLayer (line 169) | class PositionwiseFeedforwardLayer(nn.Module): method __init__ (line 170) | def __init__(self, d_model, d_ff, dropout): method forward (line 178) | def forward(self, x): class DecoderLayer (line 185) | class DecoderLayer(nn.Module): method __init__ (line 186) | def __init__(self, d_model, num_heads, d_ff, dropout, rotary_emb): method forward (line 202) | def forward(self, tgt, memory, tgt_mask=None, memory_mask=None): class TransformerDecoder (line 209) | class TransformerDecoder(nn.Module): method __init__ (line 210) | def __init__(self, num_layers, d_model, num_heads, dim_feedforward, dr... method forward (line 216) | def forward(self, tgt, memory, tgt_mask=None, memory_mask=None): FILE: PBnet/src/models/architectures/transformerreemb.py function exists (line 12) | def exists(x): class LayerNorm (line 15) | class LayerNorm(nn.Module): method __init__ (line 16) | def __init__(self, dim, eps=1e-5): method forward (line 21) | def forward(self, x): class PreNorm (line 26) | class PreNorm(nn.Module): method __init__ (line 27) | def __init__(self, dim, fn): method forward (line 32) | def forward(self, x, **kwargs): class Residual (line 36) | class Residual(nn.Module): method __init__ (line 37) | def __init__(self, fn): method forward (line 41) | def forward(self, x, *args, **kwargs): class EinopsToAndFrom (line 44) | class EinopsToAndFrom(nn.Module): method __init__ (line 45) | def __init__(self, from_einops, to_einops, fn): method forward (line 51) | def forward(self, x, **kwargs): class Attention (line 59) | class Attention(nn.Module): method __init__ (line 60) | def __init__( method forward (line 76) | def forward( class PositionalEncoding (line 138) | class PositionalEncoding(nn.Module): method __init__ (line 139) | def __init__(self, d_model, dropout=0.1, max_len=20000): method forward (line 152) | def forward(self, x): class RelativePositionBias (line 158) | class RelativePositionBias(nn.Module): method __init__ (line 159) | def __init__( method _relative_position_bucket (line 171) | def _relative_position_bucket(relative_position, num_buckets=32, max_d... method forward (line 190) | def forward(self, n, device, eval = False): class TimeEncoding (line 205) | class TimeEncoding(nn.Module): method __init__ (line 206) | def __init__(self, d_model, dropout=0.1, max_len=5000): method forward (line 210) | def forward(self, x, mask, lengths): class Encoder_TRANSFORMERREEMB (line 219) | class Encoder_TRANSFORMERREEMB(nn.Module): method __init__ (line 220) | def __init__(self, modeltype, num_frames, audio_dim=1024, pos_dim=7, p... method forward (line 270) | def forward(self, batch): class Decoder_TRANSFORMERREEMB (line 306) | class Decoder_TRANSFORMERREEMB(nn.Module): method __init__ (line 307) | def __init__(self, modeltype, num_frames, audio_dim=1024, pos_dim=7, p... method forward (line 375) | def forward(self, batch): FILE: PBnet/src/models/architectures/transformerreemb5.py function exists (line 13) | def exists(x): class LayerNorm (line 16) | class LayerNorm(nn.Module): method __init__ (line 17) | def __init__(self, dim, eps=1e-5): method forward (line 22) | def forward(self, x): class PreNorm (line 27) | class PreNorm(nn.Module): method __init__ (line 28) | def __init__(self, dim, fn): method forward (line 33) | def forward(self, x, **kwargs): class Residual (line 37) | class Residual(nn.Module): method __init__ (line 38) | def __init__(self, fn): method forward (line 42) | def forward(self, x, *args, **kwargs): class EinopsToAndFrom (line 45) | class EinopsToAndFrom(nn.Module): method __init__ (line 46) | def __init__(self, from_einops, to_einops, fn): method forward (line 52) | def forward(self, x, **kwargs): class PositionalEncoding (line 61) | class PositionalEncoding(nn.Module): method __init__ (line 62) | def __init__(self, d_model, dropout=0.1, max_len=20000): method forward (line 75) | def forward(self, x): class RelativePositionBias (line 81) | class RelativePositionBias(nn.Module): method __init__ (line 82) | def __init__( method _relative_position_bucket (line 94) | def _relative_position_bucket(relative_position, num_buckets=32, max_d... method forward (line 113) | def forward(self, n, device, eval = False): class TimeEncoding (line 132) | class TimeEncoding(nn.Module): method __init__ (line 133) | def __init__(self, d_model, dropout=0.1, max_len=5000): method forward (line 137) | def forward(self, x, mask, lengths): class Encoder_TRANSFORMERREEMB5 (line 146) | class Encoder_TRANSFORMERREEMB5(nn.Module): method __init__ (line 147) | def __init__(self, modeltype, num_frames, audio_dim=1024, pos_dim=6, e... method forward (line 198) | def forward(self, batch): class Decoder_TRANSFORMERREEMB5 (line 234) | class Decoder_TRANSFORMERREEMB5(nn.Module): method __init__ (line 235) | def __init__(self, modeltype, num_frames, audio_dim=1024, pos_dim=6, e... method forward (line 311) | def forward(self, batch): FILE: PBnet/src/models/architectures/transformerreemb6.py function exists (line 13) | def exists(x): class LayerNorm (line 16) | class LayerNorm(nn.Module): method __init__ (line 17) | def __init__(self, dim, eps=1e-5): method forward (line 22) | def forward(self, x): class PreNorm (line 27) | class PreNorm(nn.Module): method __init__ (line 28) | def __init__(self, dim, fn): method forward (line 33) | def forward(self, x, **kwargs): class Residual (line 37) | class Residual(nn.Module): method __init__ (line 38) | def __init__(self, fn): method forward (line 42) | def forward(self, x, *args, **kwargs): class EinopsToAndFrom (line 45) | class EinopsToAndFrom(nn.Module): method __init__ (line 46) | def __init__(self, from_einops, to_einops, fn): method forward (line 52) | def forward(self, x, **kwargs): class PositionalEncoding (line 61) | class PositionalEncoding(nn.Module): method __init__ (line 62) | def __init__(self, d_model, dropout=0.1, max_len=20000): method forward (line 75) | def forward(self, x): class RelativePositionBias (line 81) | class RelativePositionBias(nn.Module): method __init__ (line 82) | def __init__( method _relative_position_bucket (line 94) | def _relative_position_bucket(relative_position, num_buckets=32, max_d... method forward (line 113) | def forward(self, n, device, eval = False): class TimeEncoding (line 132) | class TimeEncoding(nn.Module): method __init__ (line 133) | def __init__(self, d_model, dropout=0.1, max_len=5000): method forward (line 137) | def forward(self, x, mask, lengths): class Encoder_TRANSFORMERREEMB6 (line 146) | class Encoder_TRANSFORMERREEMB6(nn.Module): method __init__ (line 147) | def __init__(self, modeltype, num_frames, audio_dim=1024, pos_dim=6, e... method forward (line 198) | def forward(self, batch): class Decoder_TRANSFORMERREEMB6 (line 234) | class Decoder_TRANSFORMERREEMB6(nn.Module): method __init__ (line 235) | def __init__(self, modeltype, num_frames, audio_dim=1024, pos_dim=6, e... method forward (line 310) | def forward(self, batch): FILE: PBnet/src/models/get_model.py function get_model (line 19) | def get_model(parameters): FILE: PBnet/src/models/modeltype/cae.py class CAE (line 10) | class CAE(nn.Module): method __init__ (line 11) | def __init__(self, encoder, decoder, device, lambdas, latent_dim, **kw... method forward (line 46) | def forward(self, batch): method compute_loss (line 66) | def compute_loss(self, batch, epoch = 0): method lengths_to_mask (line 88) | def lengths_to_mask(lengths): method generate_one (line 96) | def generate_one(self, cls, duration, fact=1, xyz=False): method generate (line 112) | def generate(self, pose, audio, durations, method return_latent (line 174) | def return_latent(self, batch, seed=None): FILE: PBnet/src/models/modeltype/cae_0.py class CAE (line 8) | class CAE(nn.Module): method __init__ (line 9) | def __init__(self, encoder, decoder, device, lambdas, latent_dim, **kw... method forward (line 43) | def forward(self, batch): method compute_loss (line 61) | def compute_loss(self, batch): method lengths_to_mask (line 73) | def lengths_to_mask(lengths): method generate_one (line 81) | def generate_one(self, cls, duration, fact=1, xyz=False): method generate (line 97) | def generate(self, pose, audio, durations, method return_latent (line 159) | def return_latent(self, batch, seed=None): FILE: PBnet/src/models/modeltype/cvae.py class CVAE (line 5) | class CVAE(CAE): method reparameterize (line 6) | def reparameterize(self, batch, seed=None): method forward (line 20) | def forward(self, batch): method return_latent (line 40) | def return_latent(self, batch, seed=None): FILE: PBnet/src/models/modeltype/lstm.py class MyResNet34 (line 11) | class MyResNet34(nn.Module): method __init__ (line 12) | def __init__(self,embedding_dim,input_channel = 3): method forward (line 15) | def forward(self, x): class LSTM (line 19) | class LSTM(nn.Module): method __init__ (line 20) | def __init__(self, encoder, decoder, device, lambdas, latent_dim, **kw... method compute_loss (line 33) | def compute_loss(self, batch): method forward (line 49) | def forward(self,batch): method lengths_to_mask (line 72) | def lengths_to_mask(lengths): method generate (line 80) | def generate(self, pose, audio, durations, FILE: PBnet/src/models/rotation2xyz.py class Rotation2xyz (line 8) | class Rotation2xyz: method __init__ (line 9) | def __init__(self, device): method __call__ (line 13) | def __call__(self, x, mask, pose_rep, translation, glob, FILE: PBnet/src/models/smpl.py class SMPL (line 60) | class SMPL(_SMPLLayer): method __init__ (line 63) | def __init__(self, model_path=SMPL_MODEL_PATH, **kwargs): method forward (line 82) | def forward(self, *args, **kwargs): FILE: PBnet/src/models/tools/graphconv.py class GraphConvolution (line 9) | class GraphConvolution(Module): method __init__ (line 14) | def __init__(self, in_features, out_features, bias=True): method reset_parameters (line 25) | def reset_parameters(self): method forward (line 31) | def forward(self, input, adj): method __repr__ (line 39) | def __repr__(self): FILE: PBnet/src/models/tools/hessian_penalty.py function hessian_penalty (line 29) | def hessian_penalty(G, batch, k=2, epsilon=0.1, reduction=torch.max, ret... function rademacher (line 67) | def rademacher(shape, device='cpu'): function multi_layer_second_directional_derivative (line 75) | def multi_layer_second_directional_derivative(G, batch, dz, G_z, epsilon... function stack_var_and_reduce (line 91) | def stack_var_and_reduce(list_of_activations, reduction=torch.max): function multi_stack_var_and_reduce (line 99) | def multi_stack_var_and_reduce(sdds, reduction=torch.max, return_separat... function listify (line 108) | def listify(x): function _test_hessian_penalty (line 116) | def _test_hessian_penalty(): FILE: PBnet/src/models/tools/losses.py function compute_rc_loss (line 9) | def compute_rc_loss(model, batch): function compute_reg_loss (line 23) | def compute_reg_loss(model, batch): function compute_rc_weight_loss (line 37) | def compute_rc_weight_loss(model, batch): function compute_hp_loss (line 62) | def compute_hp_loss(model, batch): function compute_kl_loss (line 67) | def compute_kl_loss(model, batch): function compute_ssim_loss (line 73) | def compute_ssim_loss(model, batch): function ssimnorm_loss (line 86) | def ssimnorm_loss(x, output, mask, bs): function ssimnorm_self_loss (line 100) | def ssimnorm_self_loss(x, output, mask, bs): function ssim255_loss (line 112) | def ssim255_loss(x, output, mask, bs): function comput_var_loss (line 126) | def comput_var_loss(model, batch): function compute_mmd_loss (line 147) | def compute_mmd_loss(model, batch): function get_loss_function (line 164) | def get_loss_function(ltype): function get_loss_names (line 168) | def get_loss_names(): FILE: PBnet/src/models/tools/mmd.py function compute_kernel (line 5) | def compute_kernel(x, y): function compute_mmd (line 17) | def compute_mmd(x, y): FILE: PBnet/src/models/tools/msssim_loss.py function gaussian (line 7) | def gaussian(window_size, sigma): function create_window (line 12) | def create_window(window_size, channel=1): function ssim (line 19) | def ssim(img1, img2, window_size=11, window=None, size_average=True, ful... function msssim (line 73) | def msssim(img1, img2, window_size=11, size_average=True, val_range=None... class SSIM (line 111) | class SSIM(torch.nn.Module): method __init__ (line 112) | def __init__(self, window_size=11, size_average=True, val_range=None): method forward (line 122) | def forward(self, img1, img2): class MSSSIM (line 134) | class MSSSIM(torch.nn.Module): method __init__ (line 135) | def __init__(self, window_size=11, size_average=True, channel=3): method forward (line 141) | def forward(self, img1, img2): FILE: PBnet/src/models/tools/normalize_data.py function normalize_data (line 3) | def normalize_data(data, min_vals, max_vals): FILE: PBnet/src/models/tools/ssim_loss.py function gaussian (line 7) | def gaussian(window_size, sigma): function create_window (line 11) | def create_window(window_size, channel): function _ssim (line 17) | def _ssim(img1, img2, window, window_size, channel, val_range = 1, size_... class SSIM (line 39) | class SSIM(torch.nn.Module): method __init__ (line 40) | def __init__(self, window_size = 11, size_average = True): method forward (line 47) | def forward(self, img1, img2): function ssim (line 65) | def ssim(img1, img2, window_size = 11, val_range=1, size_average = True): function read_pose_from_txt (line 75) | def read_pose_from_txt(file_path): FILE: PBnet/src/models/tools/tools.py class AutoParams (line 5) | class AutoParams(nn.Module): method __init__ (line 6) | def __init__(self, **kargs): function freeze_params (line 28) | def freeze_params(module: nn.Module) -> None: FILE: PBnet/src/parser/base.py function add_misc_options (line 4) | def add_misc_options(parser): function add_cuda_options (line 10) | def add_cuda_options(parser): function adding_cuda (line 19) | def adding_cuda(parameters): FILE: PBnet/src/parser/checkpoint.py function parser (line 6) | def parser(): function construct_checkpointname (line 23) | def construct_checkpointname(parameters, folder): FILE: PBnet/src/parser/dataset.py function add_dataset_options (line 4) | def add_dataset_options(parser): FILE: PBnet/src/parser/evaluation.py function parser (line 10) | def parser(): FILE: PBnet/src/parser/finetunning.py function parser (line 5) | def parser(): FILE: PBnet/src/parser/generate.py function add_generation_options (line 8) | def add_generation_options(parser): function parser (line 25) | def parser(): FILE: PBnet/src/parser/model.py function add_model_options (line 4) | def add_model_options(parser): function parse_modelname (line 30) | def parse_modelname(modelname): FILE: PBnet/src/parser/recognition.py function training_parser (line 10) | def training_parser(): FILE: PBnet/src/parser/tools.py function save_args (line 5) | def save_args(opt, folder): function load_args (line 14) | def load_args(filename): FILE: PBnet/src/parser/training.py function add_training_options (line 10) | def add_training_options(parser): function parser (line 19) | def parser(): FILE: PBnet/src/parser/visualize.py function construct_figname (line 9) | def construct_figname(parameters): function add_visualize_options (line 14) | def add_visualize_options(parser): function parser (line 56) | def parser(checkpoint=True): FILE: PBnet/src/preprocess/humanact12_process.py function splitname (line 7) | def splitname(name): function create_phpsd_name (line 17) | def create_phpsd_name(name): function get_frames (line 23) | def get_frames(name): function get_action (line 28) | def get_action(name, coarse=True): function process_datata (line 55) | def process_datata(savepath, posesfolder="data/PHPSDposes", datapath="da... FILE: PBnet/src/preprocess/phspdtools.py class Transform (line 7) | class Transform: method __init__ (line 8) | def __init__(self, R=np.eye(3, dtype='float'), t=np.zeros(3, 'float'),... method __mul__ (line 13) | def __mul__(self, other): method inv (line 22) | def inv(self): method transform (line 28) | def transform(self, xyz): method getmat4 (line 36) | def getmat4(self): function quat2R (line 44) | def quat2R(quat): function convert_param2tranform (line 83) | def convert_param2tranform(param, scale=1): class CameraParams (line 90) | class CameraParams: method __init__ (line 91) | def __init__(self, cam_folder="data/phspdCameras"): method get_intrinsic (line 117) | def get_intrinsic(self, cam_name, subject_no): method get_extrinsic (line 133) | def get_extrinsic(self, cams_name, subject_no): method get_gender (line 155) | def get_gender(self, subject_no): FILE: PBnet/src/preprocess/uestc_vibe_postprocessing.py function get_kinect_motion (line 14) | def get_kinect_motion(tar, videos, index): function motionto2d (line 25) | def motionto2d(motion, W=960, H=540): function motionto2dvibe (line 36) | def motionto2dvibe(motion, cam): function get_kcenter (line 41) | def get_kcenter(tar, videos, index): function get_concat_goodtracks (line 49) | def get_concat_goodtracks(allvibe, tar, videos, index): function interpolate_track (line 101) | def interpolate_track(gvibe): FILE: PBnet/src/recognition/compute_accuracy.py function compute_accuracy (line 16) | def compute_accuracy(model, datasets, parameters): function main (line 43) | def main(): FILE: PBnet/src/recognition/get_model.py function get_model (line 4) | def get_model(parameters): FILE: PBnet/src/recognition/models/stgcn.py class STGCN (line 11) | class STGCN(nn.Module): method __init__ (line 29) | def __init__(self, in_channels, num_class, graph_args, method forward (line 75) | def forward(self, batch): method compute_accuracy (line 114) | def compute_accuracy(self, batch): method compute_loss (line 123) | def compute_loss(self, batch): class st_gcn (line 134) | class st_gcn(nn.Module): method __init__ (line 155) | def __init__(self, method forward (line 203) | def forward(self, x, A): FILE: PBnet/src/recognition/models/stgcnutils/graph.py class Graph (line 7) | class Graph: method __init__ (line 26) | def __init__(self, method __str__ (line 42) | def __str__(self): method get_edge (line 45) | def get_edge(self, layout): method get_adjacency (line 99) | def get_adjacency(self, strategy): function get_hop_distance (line 144) | def get_hop_distance(num_node, edge, max_hop=1): function normalize_digraph (line 159) | def normalize_digraph(A): function normalize_undigraph (line 170) | def normalize_undigraph(A): FILE: PBnet/src/recognition/models/stgcnutils/tgcn.py class ConvTemporalGraphical (line 7) | class ConvTemporalGraphical(nn.Module): method __init__ (line 34) | def __init__(self, method forward (line 55) | def forward(self, x, A): FILE: PBnet/src/render/renderer.py function get_smpl_faces (line 19) | def get_smpl_faces(): class WeakPerspectiveCamera (line 23) | class WeakPerspectiveCamera(pyrender.Camera): method __init__ (line 24) | def __init__(self, method get_projection_matrix (line 38) | def get_projection_matrix(self, width=None, height=None): class Renderer (line 48) | class Renderer: method __init__ (line 49) | def __init__(self, background=None, resolution=(224, 224), bg_color=[0... method render (line 104) | def render(self, img, verts, cam, angle=None, axis=None, mesh_filename... function get_renderer (line 154) | def get_renderer(width, height): FILE: PBnet/src/render/rendermotion.py function get_rotation (line 9) | def get_rotation(theta=np.pi/3): function render_video (line 18) | def render_video(meshes, key, action, renderer, savepath, background, ca... function main (line 43) | def main(): FILE: PBnet/src/train/train_cvae_ganloss_ann_eye.py class ConvNormRelu (line 33) | class ConvNormRelu(nn.Module): method __init__ (line 34) | def __init__(self, in_channels, out_channels, kernel_size, stride, pad... method forward (line 46) | def forward(self, x): class D_patchgan (line 51) | class D_patchgan(nn.Module): method __init__ (line 52) | def __init__(self, n_downsampling=2, pos_dim=6, eye_dim=0, norm='batch'): method forward (line 71) | def forward(self, x): method calculate_GAN_loss (line 77) | def calculate_GAN_loss(self, batch): function get_model (line 91) | def get_model(parameters): function do_epochs (line 108) | def do_epochs(model, model_d, dataset, parameters, optimizer_g, optimize... FILE: PBnet/src/train/train_cvae_ganloss_ann_fast.py class ConvNormRelu (line 30) | class ConvNormRelu(nn.Module): method __init__ (line 31) | def __init__(self, in_channels, out_channels, kernel_size, stride, pad... method forward (line 43) | def forward(self, x): class D_patchgan (line 48) | class D_patchgan(nn.Module): method __init__ (line 49) | def __init__(self, n_downsampling=2, norm='batch'): method forward (line 66) | def forward(self, x): method calculate_GAN_loss (line 72) | def calculate_GAN_loss(self, batch): function get_model (line 86) | def get_model(parameters): function do_epochs (line 103) | def do_epochs(model, model_d, dataset, parameters, optimizer_g, optimize... FILE: PBnet/src/train/trainer.py function train_or_test (line 5) | def train_or_test(model, optimizer, iterator, device, mode="train"): function train (line 45) | def train(model, optimizer, iterator, device): function test (line 49) | def test(model, optimizer, iterator, device): FILE: PBnet/src/train/trainer_gan.py function train_or_test (line 6) | def train_or_test(model, model_d, optimizer_g, optimizer_d, iterator, de... function train (line 73) | def train(model, model_d, optimizer_g, optimizer_d, iterator, device): function test (line 77) | def test(model, model_d, optimizer_g, optimizer_d, iterator, device): FILE: PBnet/src/train/trainer_gan_ann.py function train_or_test (line 6) | def train_or_test(model, model_d, optimizer_g, optimizer_d, iterator, de... function train (line 79) | def train(model, model_d, optimizer_g, optimizer_d, iterator, device, ep... function test (line 83) | def test(model, model_d, optimizer_g, optimizer_d, iterator, device): FILE: PBnet/src/utils/fixseed.py function fixseed (line 6) | def fixseed(seed): FILE: PBnet/src/utils/get_model_and_data.py function get_model_and_data (line 6) | def get_model_and_data(parameters): FILE: PBnet/src/utils/misc.py function to_numpy (line 4) | def to_numpy(tensor): function to_torch (line 13) | def to_torch(ndarray): function cleanexit (line 22) | def cleanexit(): FILE: PBnet/src/utils/rotation_conversions.py function quaternion_to_matrix (line 37) | def quaternion_to_matrix(quaternions): function _copysign (line 68) | def _copysign(a, b): function _sqrt_positive_part (line 86) | def _sqrt_positive_part(x): function matrix_to_quaternion (line 97) | def matrix_to_quaternion(matrix): function _axis_angle_rotation (line 122) | def _axis_angle_rotation(axis: str, angle): function euler_angles_to_matrix (line 150) | def euler_angles_to_matrix(euler_angles, convention: str): function _angle_from_tan (line 175) | def _angle_from_tan( function _index_from_letter (line 208) | def _index_from_letter(letter: str): function matrix_to_euler_angles (line 217) | def matrix_to_euler_angles(matrix, convention: str): function random_quaternions (line 259) | def random_quaternions( function random_rotations (line 283) | def random_rotations( function random_rotation (line 306) | def random_rotation( function standardize_quaternion (line 325) | def standardize_quaternion(quaternions): function quaternion_raw_multiply (line 340) | def quaternion_raw_multiply(a, b): function quaternion_multiply (line 361) | def quaternion_multiply(a, b): function quaternion_invert (line 378) | def quaternion_invert(quaternion): function quaternion_apply (line 394) | def quaternion_apply(quaternion, point): function axis_angle_to_matrix (line 417) | def axis_angle_to_matrix(axis_angle): function matrix_to_axis_angle (line 433) | def matrix_to_axis_angle(matrix): function axis_angle_to_quaternion (line 449) | def axis_angle_to_quaternion(axis_angle): function quaternion_to_axis_angle (line 481) | def quaternion_to_axis_angle(quaternions): function rotation_6d_to_matrix (line 512) | def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor: function matrix_to_rotation_6d (line 536) | def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor: FILE: PBnet/src/utils/tensors.py function lengths_to_mask (line 4) | def lengths_to_mask(lengths): function collate_tensors (line 10) | def collate_tensors(batch): function collate (line 23) | def collate(batch): FILE: PBnet/src/utils/tensors_eye.py function lengths_to_mask (line 4) | def lengths_to_mask(lengths): function collate_tensors (line 10) | def collate_tensors(batch): function collate (line 23) | def collate(batch): FILE: PBnet/src/utils/tensors_eye_eval.py function lengths_to_mask (line 4) | def lengths_to_mask(lengths): function collate_tensors (line 10) | def collate_tensors(batch): function collate (line 23) | def collate(batch): FILE: PBnet/src/utils/tensors_hdtf.py function lengths_to_mask (line 4) | def lengths_to_mask(lengths): function collate_tensors (line 10) | def collate_tensors(batch): function collate_old (line 23) | def collate_old(batch): function collate (line 43) | def collate(batch): FILE: PBnet/src/utils/tensors_onlyeye.py function lengths_to_mask (line 4) | def lengths_to_mask(lengths): function collate_tensors (line 10) | def collate_tensors(batch): function collate (line 23) | def collate(batch): function collate_eval (line 46) | def collate_eval(batch): FILE: PBnet/src/utils/utils.py class _RepeatSampler (line 7) | class _RepeatSampler(object): method __init__ (line 9) | def __init__(self, sampler): method __iter__ (line 12) | def __iter__(self): class MultiEpochsDataLoader (line 16) | class MultiEpochsDataLoader(torch.utils.data.DataLoader): method __init__ (line 20) | def __init__(self, *args, **kwargs): method __len__ (line 25) | def __len__(self): method __iter__ (line 28) | def __iter__(self): class CudaDataLoader (line 32) | class CudaDataLoader: method __init__ (line 34) | def __init__(self, loader, device, queue_size=2): method load_loop (line 47) | def load_loop(self): method load_instance (line 53) | def load_instance(self, sample): method __iter__ (line 64) | def __iter__(self): method __next__ (line 68) | def __next__(self): method next (line 83) | def next(self): method __len__ (line 86) | def __len__(self): method sampler (line 90) | def sampler(self): method dataset (line 94) | def dataset(self): FILE: PBnet/src/utils/video.py function load_video (line 5) | def load_video(filename): class SaveVideo (line 12) | class SaveVideo: method __init__ (line 13) | def __init__(self, outname, fps): method __enter__ (line 17) | def __enter__(self): method __exit__ (line 23) | def __exit__(self, exc_type, exc_value, exc_traceback): method __iadd__ (line 26) | def __iadd__(self, data): FILE: PBnet/src/visualize/anim.py function add_shadow (line 30) | def add_shadow(img, shadow=15): function load_anim (line 38) | def load_anim(path, timesize=None): function plot_3d_motion (line 52) | def plot_3d_motion(motion, length, save_path, params, title="", interval... function plot_3d_motion_dico (line 134) | def plot_3d_motion_dico(x): FILE: PBnet/src/visualize/visualize.py function stack_images (line 11) | def stack_images(real, real_gens, gen): function generate_by_video (line 25) | def generate_by_video(visualization, reconstructions, generation, function viz_epoch (line 110) | def viz_epoch(model, dataset, epoch, params, folder, writer=None): function viz_dataset (line 257) | def viz_dataset(dataset, params, folder): function generate_by_video_sequences (line 319) | def generate_by_video_sequences(visualization, label_to_action_name, par... function stack_images_sequence (line 362) | def stack_images_sequence(visu): FILE: PBnet/src/visualize/visualize_checkpoint.py function main (line 14) | def main(): FILE: PBnet/src/visualize/visualize_nturefined.py function viz_ntu13 (line 11) | def viz_ntu13(dataset, device): FILE: extract_init_states/FaceBoxes/FaceBoxes.py function viz_bbox (line 33) | def viz_bbox(img, dets, wfp='out.jpg'): class FaceBoxes (line 48) | class FaceBoxes: method __init__ (line 49) | def __init__(self, timer_flag=False): method __call__ (line 61) | def __call__(self, img_): function main (line 144) | def main(): FILE: extract_init_states/FaceBoxes/FaceBoxes_ONNX.py function viz_bbox (line 33) | def viz_bbox(img, dets, wfp='out.jpg'): class FaceBoxes_ONNX (line 48) | class FaceBoxes_ONNX(object): method __init__ (line 49) | def __init__(self, timer_flag=False): method __call__ (line 56) | def __call__(self, img_): function main (line 147) | def main(): FILE: extract_init_states/FaceBoxes/models/faceboxes.py class BasicConv2d (line 8) | class BasicConv2d(nn.Module): method __init__ (line 10) | def __init__(self, in_channels, out_channels, **kwargs): method forward (line 15) | def forward(self, x): class Inception (line 21) | class Inception(nn.Module): method __init__ (line 22) | def __init__(self): method forward (line 32) | def forward(self, x): class CRelu (line 49) | class CRelu(nn.Module): method __init__ (line 51) | def __init__(self, in_channels, out_channels, **kwargs): method forward (line 56) | def forward(self, x): class FaceBoxesNet (line 64) | class FaceBoxesNet(nn.Module): method __init__ (line 66) | def __init__(self, phase, size, num_classes): method multibox (line 102) | def multibox(self, num_classes): method forward (line 113) | def forward(self, x): FILE: extract_init_states/FaceBoxes/onnx.py function convert_to_onnx (line 11) | def convert_to_onnx(onnx_path): FILE: extract_init_states/FaceBoxes/utils/box_utils.py function point_form (line 7) | def point_form(boxes): function center_size (line 19) | def center_size(boxes): function intersect (line 31) | def intersect(box_a, box_b): function jaccard (line 52) | def jaccard(box_a, box_b): function matrix_iou (line 73) | def matrix_iou(a, b): function matrix_iof (line 86) | def matrix_iof(a, b): function match (line 98) | def match(threshold, truths, priors, variances, labels, loc_t, conf_t, i... function encode (line 152) | def encode(matched, priors, variances): function decode (line 177) | def decode(loc, priors, variances): function log_sum_exp (line 198) | def log_sum_exp(x): function nms (line 212) | def nms(boxes, scores, overlap=0.5, top_k=200): FILE: extract_init_states/FaceBoxes/utils/build.py function find_in_path (line 18) | def find_in_path(name, path): class custom_build_ext (line 36) | class custom_build_ext(build_ext): method build_extensions (line 37) | def build_extensions(self): FILE: extract_init_states/FaceBoxes/utils/functions.py function check_keys (line 7) | def check_keys(model, pretrained_state_dict): function remove_prefix (line 20) | def remove_prefix(state_dict, prefix): function load_model (line 27) | def load_model(model, pretrained_path, load_to_cpu): FILE: extract_init_states/FaceBoxes/utils/nms/py_cpu_nms.py function py_cpu_nms (line 10) | def py_cpu_nms(dets, thresh): FILE: extract_init_states/FaceBoxes/utils/nms_wrapper.py function nms (line 13) | def nms(dets, thresh): FILE: extract_init_states/FaceBoxes/utils/prior_box.py class PriorBox (line 10) | class PriorBox(object): method __init__ (line 11) | def __init__(self, image_size=None): method forward (line 20) | def forward(self): FILE: extract_init_states/FaceBoxes/utils/timer.py class Timer (line 13) | class Timer(object): method __init__ (line 16) | def __init__(self): method tic (line 23) | def tic(self): method toc (line 28) | def toc(self, average=True): method clear (line 38) | def clear(self): FILE: extract_init_states/TDDFA_ONNX.py class TDDFA_ONNX (line 29) | class TDDFA_ONNX(object): method __init__ (line 32) | def __init__(self, **kvs): method __call__ (line 74) | def __call__(self, img_ori, objs, **kvs): method recon_vers (line 105) | def recon_vers(self, param_lst, roi_box_lst, **kvs): FILE: extract_init_states/bfm/bfm.py function _to_ctype (line 16) | def _to_ctype(arr): class BFMModel (line 22) | class BFMModel(object): method __init__ (line 23) | def __init__(self, bfm_fp, shape_dim=40, exp_dim=10): FILE: extract_init_states/bfm/bfm_onnx.py function _to_ctype (line 19) | def _to_ctype(arr): function _load_tri (line 25) | def _load_tri(bfm_fp): class BFMModel_ONNX (line 35) | class BFMModel_ONNX(nn.Module): method __init__ (line 38) | def __init__(self, bfm_fp, shape_dim=40, exp_dim=10): method forward (line 63) | def forward(self, *inps): function convert_bfm_to_onnx (line 73) | def convert_bfm_to_onnx(bfm_onnx_fp, shape_dim=40, exp_dim=10): FILE: extract_init_states/demo_pose_extract_2d_lmk_img.py function main (line 30) | def main(args,img, save_path, pose_path): FILE: extract_init_states/functions.py function get_suffix (line 15) | def get_suffix(filename): function crop_img (line 23) | def crop_img(img, roi_box): function calc_hypotenuse (line 56) | def calc_hypotenuse(pts): function parse_roi_box_from_landmark (line 65) | def parse_roi_box_from_landmark(pts): function parse_roi_box_from_bbox (line 85) | def parse_roi_box_from_bbox(bbox): function plot_image (line 101) | def plot_image(img): function draw_landmarks (line 112) | def draw_landmarks(img, pts, style='fancy', wfp=None, show_flag=False, *... function cv_draw_landmark (line 159) | def cv_draw_landmark(img_ori, pts, box=None, color=GREEN, size=1): function calculate_bbox (line 183) | def calculate_bbox(img, lmk): function calculate_eye (line 204) | def calculate_eye(lmk): FILE: extract_init_states/models/mobilenet_v1.py class DepthWiseBlock (line 22) | class DepthWiseBlock(nn.Module): method __init__ (line 23) | def __init__(self, inplanes, planes, stride=1, prelu=False): method forward (line 36) | def forward(self, x): class MobileNet (line 48) | class MobileNet(nn.Module): method __init__ (line 49) | def __init__(self, widen_factor=1.0, num_classes=1000, prelu=False, in... method forward (line 96) | def forward(self, x): function mobilenet (line 122) | def mobilenet(**kwargs): function mobilenet_2 (line 141) | def mobilenet_2(num_classes=62, input_channel=3): function mobilenet_1 (line 146) | def mobilenet_1(num_classes=62, input_channel=3): function mobilenet_075 (line 151) | def mobilenet_075(num_classes=62, input_channel=3): function mobilenet_05 (line 156) | def mobilenet_05(num_classes=62, input_channel=3): function mobilenet_025 (line 161) | def mobilenet_025(num_classes=62, input_channel=3): FILE: extract_init_states/models/mobilenet_v3.py function conv_bn (line 10) | def conv_bn(inp, oup, stride, conv_layer=nn.Conv2d, norm_layer=nn.BatchN... function conv_1x1_bn (line 18) | def conv_1x1_bn(inp, oup, conv_layer=nn.Conv2d, norm_layer=nn.BatchNorm2... class Hswish (line 26) | class Hswish(nn.Module): method __init__ (line 27) | def __init__(self, inplace=True): method forward (line 31) | def forward(self, x): class Hsigmoid (line 35) | class Hsigmoid(nn.Module): method __init__ (line 36) | def __init__(self, inplace=True): method forward (line 40) | def forward(self, x): class SEModule (line 44) | class SEModule(nn.Module): method __init__ (line 45) | def __init__(self, channel, reduction=4): method forward (line 56) | def forward(self, x): class Identity (line 63) | class Identity(nn.Module): method __init__ (line 64) | def __init__(self, channel): method forward (line 67) | def forward(self, x): function make_divisible (line 71) | def make_divisible(x, divisible_by=8): class MobileBottleneck (line 76) | class MobileBottleneck(nn.Module): method __init__ (line 77) | def __init__(self, inp, oup, kernel, stride, exp, se=False, nl='RE'): method forward (line 112) | def forward(self, x): class MobileNetV3 (line 119) | class MobileNetV3(nn.Module): method __init__ (line 120) | def __init__(self, widen_factor=1.0, num_classes=141, num_landmarks=13... method forward (line 208) | def forward(self, x): method _initialize_weights (line 221) | def _initialize_weights(self): function mobilenet_v3 (line 237) | def mobilenet_v3(**kwargs): FILE: extract_init_states/models/resnet.py function conv3x3 (line 9) | def conv3x3(in_planes, out_planes, stride=1): class BasicBlock (line 15) | class BasicBlock(nn.Module): method __init__ (line 18) | def __init__(self, inplanes, planes, stride=1, downsample=None): method forward (line 28) | def forward(self, x): class ResNet (line 47) | class ResNet(nn.Module): method __init__ (line 50) | def __init__(self, block, layers, num_classes=62, num_landmarks=136, i... method _make_layer (line 86) | def _make_layer(self, block, planes, blocks, stride=1): method forward (line 103) | def forward(self, x): function resnet22 (line 134) | def resnet22(**kwargs): function main (line 145) | def main(): FILE: extract_init_states/pose.py function P2sRt (line 18) | def P2sRt(P): function matrix2angle (line 39) | def matrix2angle(R): function angle2matrix (line 65) | def angle2matrix(theta): function angle2matrix_3ddfa (line 112) | def angle2matrix_3ddfa(angles): function calc_pose (line 140) | def calc_pose(param): function build_camera_box (line 150) | def build_camera_box(rear_size=90): function plot_pose_box (line 171) | def plot_pose_box(img, P, ver, color=(40, 255, 0), line_width=2): function viz_pose (line 201) | def viz_pose(img, param_lst, ver_lst, show_flag=False, wfp=None): function pose_6 (line 217) | def pose_6(param): function smooth_pose (line 231) | def smooth_pose(img, param_lst, ver_lst, pose_new, show_flag=False, wfp=... function get_pose (line 263) | def get_pose(img, param_lst, ver_lst, show_flag=False, wfp=None, wnp = N... FILE: extract_init_states/utils/asset/render.c type Tuple3D (line 9) | struct Tuple3D function _render (line 16) | void _render(const int *triangles, FILE: extract_init_states/utils/depth.py function depth (line 17) | def depth(img, ver_lst, tri, show_flag=False, wfp=None, with_bg_flag=True): FILE: extract_init_states/utils/functions.py function get_suffix (line 15) | def get_suffix(filename): function crop_img (line 23) | def crop_img(img, roi_box): function calc_hypotenuse (line 56) | def calc_hypotenuse(pts): function parse_roi_box_from_landmark (line 65) | def parse_roi_box_from_landmark(pts): function parse_roi_box_from_bbox (line 85) | def parse_roi_box_from_bbox(bbox): function plot_image (line 101) | def plot_image(img): function draw_landmarks (line 112) | def draw_landmarks(img, pts, style='fancy', wfp=None, show_flag=False, *... function cv_draw_landmark (line 159) | def cv_draw_landmark(img_ori, pts, box=None, color=GREEN, size=1): function calculate_bbox (line 183) | def calculate_bbox(img, lmk): function calculate_eye (line 204) | def calculate_eye(lmk): FILE: extract_init_states/utils/io.py function mkdir (line 11) | def mkdir(d): function _get_suffix (line 15) | def _get_suffix(filename): function _load (line 23) | def _load(fp): function _dump (line 31) | def _dump(wfp, obj): function _load_tensor (line 41) | def _load_tensor(fp, mode='cpu'): function _tensor_to_cuda (line 48) | def _tensor_to_cuda(x): function _load_gpu (line 55) | def _load_gpu(fp): FILE: extract_init_states/utils/onnx.py function convert_to_onnx (line 14) | def convert_to_onnx(**kvs): FILE: extract_init_states/utils/pncc.py function calc_ncc_code (line 21) | def calc_ncc_code(): function pncc (line 34) | def pncc(img, ver_lst, tri, show_flag=False, wfp=None, with_bg_flag=True): function main (line 57) | def main(): FILE: extract_init_states/utils/pose.py function P2sRt (line 18) | def P2sRt(P): function matrix2angle (line 39) | def matrix2angle(R): function angle2matrix (line 65) | def angle2matrix(theta): function angle2matrix_3ddfa (line 112) | def angle2matrix_3ddfa(angles): function calc_pose (line 140) | def calc_pose(param): function build_camera_box (line 150) | def build_camera_box(rear_size=90): function plot_pose_box (line 171) | def plot_pose_box(img, P, ver, color=(40, 255, 0), line_width=2): function viz_pose (line 201) | def viz_pose(img, param_lst, ver_lst, show_flag=False, wfp=None): function pose_6 (line 217) | def pose_6(param): function smooth_pose (line 231) | def smooth_pose(img, param_lst, ver_lst, pose_new, show_flag=False, wfp=... function get_pose (line 263) | def get_pose(img, param_lst, ver_lst, show_flag=False, wfp=None, wnp = N... FILE: extract_init_states/utils/render.py function render (line 30) | def render(img, ver_lst, tri, alpha=0.6, show_flag=False, wfp=None, with... FILE: extract_init_states/utils/render_ctypes.py class TrianglesMeshRender (line 27) | class TrianglesMeshRender(object): method __init__ (line 28) | def __init__( method __call__ (line 50) | def __call__(self, vertices, triangles, bg): function render (line 67) | def render(img, ver_lst, tri, alpha=0.6, show_flag=False, wfp=None, with... FILE: extract_init_states/utils/serialization.py function ser_to_ply_single (line 22) | def ser_to_ply_single(ver_lst, tri, height, wfp, reverse=True): function ser_to_ply_multiple (line 50) | def ser_to_ply_multiple(ver_lst, tri, height, wfp, reverse=True): function get_colors (line 84) | def get_colors(img, ver): function ser_to_obj_single (line 94) | def ser_to_obj_single(img, ver_lst, tri, height, wfp): function ser_to_obj_multiple (line 117) | def ser_to_obj_multiple(img, ver_lst, tri, height, wfp): FILE: extract_init_states/utils/tddfa_util.py function _to_ctype (line 14) | def _to_ctype(arr): function str2bool (line 20) | def str2bool(v): function load_model (line 29) | def load_model(model, checkpoint_fp): class ToTensorGjz (line 44) | class ToTensorGjz(object): method __call__ (line 45) | def __call__(self, pic): method __repr__ (line 50) | def __repr__(self): class NormalizeGjz (line 54) | class NormalizeGjz(object): method __init__ (line 55) | def __init__(self, mean, std): method __call__ (line 59) | def __call__(self, tensor): function similar_transform (line 64) | def similar_transform(pts3d, roi_box, size): function _parse_param (line 80) | def _parse_param(param): FILE: extract_init_states/utils/uv.py function load_uv_coords (line 22) | def load_uv_coords(fp): function process_uv (line 28) | def process_uv(uv_coords, uv_h=256, uv_w=256): function get_colors (line 41) | def get_colors(img, ver): function bilinear_interpolate (line 52) | def bilinear_interpolate(img, x, y): function uv_tex (line 79) | def uv_tex(img, ver_lst, tri, uv_h=256, uv_w=256, uv_c=3, show_flag=Fals... FILE: filter_fourier.py function gaussian_pdf (line 11) | def gaussian_pdf(x, mean, std): function gaussian_density (line 15) | def gaussian_density(length = 20, amplitude = 2, mean = 19, sigma = 3): function fourier_filter (line 21) | def fourier_filter(fea): function fourier_filter_1D (line 50) | def fourier_filter_1D(fea, dim): function hf_loss (line 69) | def hf_loss(fea, mask, dim): function hf_loss_2 (line 77) | def hf_loss_2(fea_x, fea_y, dim): class KalmanFilter1D (line 90) | class KalmanFilter1D: method __init__ (line 91) | def __init__(self, A, H, Q, R, x_init, P_init): method update (line 99) | def update(self, z): function kalman_filter (line 111) | def kalman_filter(observations, dim): function naive_filter (line 123) | def naive_filter(fea): FILE: hubert_extract/data_gen/process_lrs3/binarizer.py function load_video_npy (line 13) | def load_video_npy(fn): function cal_lm3d_in_video_dict (line 23) | def cal_lm3d_in_video_dict(video_dict, face3d_helper): function load_audio_npy (line 30) | def load_audio_npy(fn): FILE: hubert_extract/data_gen/process_lrs3/process_audio_hubert.py function get_hubert_from_16k_wav (line 14) | def get_hubert_from_16k_wav(wav_16k_name): function get_hubert_from_16k_speech (line 20) | def get_hubert_from_16k_speech(speech, device="cuda:1"): FILE: hubert_extract/data_gen/process_lrs3/process_audio_hubert_interpolate.py function get_hubert_from_16k_wav (line 18) | def get_hubert_from_16k_wav(wav_16k_name): function get_hubert_from_16k_speech (line 24) | def get_hubert_from_16k_speech(speech, device="cuda:1"): FILE: hubert_extract/data_gen/process_lrs3/process_audio_hubert_interpolate_batch.py function get_hubert_from_16k_wav (line 18) | def get_hubert_from_16k_wav(wav_16k_name): function get_hubert_from_16k_speech (line 24) | def get_hubert_from_16k_speech(speech, device="cuda:3"): FILE: hubert_extract/data_gen/process_lrs3/process_audio_hubert_interpolate_demo.py function get_hubert_from_16k_wav (line 28) | def get_hubert_from_16k_wav(wav_16k_name): function get_hubert_from_16k_speech (line 34) | def get_hubert_from_16k_speech(speech, device="cuda:0"): function get_arguments (line 97) | def get_arguments(): function convert_wav_to_16k (line 112) | def convert_wav_to_16k(input_file, output_file): function delete_file (line 121) | def delete_file(file_path): FILE: hubert_extract/data_gen/process_lrs3/process_audio_hubert_interpolate_single.py function get_hubert_from_16k_wav (line 18) | def get_hubert_from_16k_wav(wav_16k_name): function get_hubert_from_16k_speech (line 24) | def get_hubert_from_16k_speech(speech, device="cuda:1"): FILE: hubert_extract/data_gen/process_lrs3/process_audio_mel_f0.py function librosa_pad_lr (line 12) | def librosa_pad_lr(x, fsize, fshift, pad_sides=1): function extract_mel_from_fname (line 23) | def extract_mel_from_fname(wav_path, function extract_f0_from_wav_and_mel (line 58) | def extract_f0_from_wav_and_mel(wav, mel, function extract_mel_f0_from_fname (line 77) | def extract_mel_f0_from_fname(fname, out_name=None): FILE: misc.py function fig2data (line 16) | def fig2data(fig): function plot_grid (line 35) | def plot_grid(x, y, ax=None, **kwargs): function grid2fig (line 44) | def grid2fig(warped_grid, grid_size=32, img_size=256): function flow2fig (line 68) | def flow2fig(warped_grid, id_grid, grid_size=32, img_size=128): function conf2fig (line 79) | def conf2fig(conf, img_size=128): class Logger (line 86) | class Logger(object): method __init__ (line 87) | def __init__(self, filename='default.log', stream=sys.stdout): method write (line 91) | def write(self, message): method flush (line 95) | def flush(self): function resize (line 99) | def resize(im, desired_size, interpolation): function resample (line 116) | def resample(image, flow): function get_grid (line 140) | def get_grid(batchsize, size, minval=-1.0, maxval=1.0): function get_checkpoint (line 179) | def get_checkpoint(checkpoint_path, url=''): function download_file_from_google_drive (line 204) | def download_file_from_google_drive(file_id, destination): function get_confirm_token (line 224) | def get_confirm_token(response): function save_response_content (line 239) | def save_response_content(response, destination): function get_rank (line 256) | def get_rank(): function is_master (line 265) | def is_master(): FILE: sync_batchnorm/batchnorm.py function _sum_ft (line 24) | def _sum_ft(tensor): function _unsqueeze_ft (line 29) | def _unsqueeze_ft(tensor): class _SynchronizedBatchNorm (line 38) | class _SynchronizedBatchNorm(_BatchNorm): method __init__ (line 39) | def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True): method forward (line 48) | def forward(self, input): method __data_parallel_replicate__ (line 80) | def __data_parallel_replicate__(self, ctx, copy_id): method _data_parallel_master (line 90) | def _data_parallel_master(self, intermediates): method _compute_mean_std (line 113) | def _compute_mean_std(self, sum_, ssum, size): class SynchronizedBatchNorm1d (line 128) | class SynchronizedBatchNorm1d(_SynchronizedBatchNorm): method _check_input_dim (line 184) | def _check_input_dim(self, input): class SynchronizedBatchNorm2d (line 191) | class SynchronizedBatchNorm2d(_SynchronizedBatchNorm): method _check_input_dim (line 247) | def _check_input_dim(self, input): class SynchronizedBatchNorm3d (line 254) | class SynchronizedBatchNorm3d(_SynchronizedBatchNorm): method _check_input_dim (line 311) | def _check_input_dim(self, input): FILE: sync_batchnorm/comm.py class FutureResult (line 18) | class FutureResult(object): method __init__ (line 21) | def __init__(self): method put (line 26) | def put(self, result): method get (line 32) | def get(self): class SlavePipe (line 46) | class SlavePipe(_SlavePipeBase): method run_slave (line 49) | def run_slave(self, msg): class SyncMaster (line 56) | class SyncMaster(object): method __init__ (line 67) | def __init__(self, master_callback): method __getstate__ (line 78) | def __getstate__(self): method __setstate__ (line 81) | def __setstate__(self, state): method register_slave (line 84) | def register_slave(self, identifier): method run_master (line 102) | def run_master(self, master_msg): method nr_slaves (line 136) | def nr_slaves(self): FILE: sync_batchnorm/replicate.py class CallbackContext (line 23) | class CallbackContext(object): function execute_replication_callbacks (line 27) | def execute_replication_callbacks(modules): class DataParallelWithCallback (line 50) | class DataParallelWithCallback(DataParallel): method replicate (line 64) | def replicate(self, module, device_ids): method update_num_frames (line 69) | def update_num_frames(self, new_num_frames): function patch_replication_callback (line 75) | def patch_replication_callback(data_parallel): FILE: sync_batchnorm/replicate_ddp.py class CallbackContext (line 24) | class CallbackContext(object): function execute_replication_callbacks (line 28) | def execute_replication_callbacks(modules): class DataParallelWithCallback_ddp (line 51) | class DataParallelWithCallback_ddp(DistributedDataParallel): method replicate (line 65) | def replicate(self, module, device_ids): method update_num_frames (line 70) | def update_num_frames(self, new_num_frames): function patch_replication_callback_ddp (line 76) | def patch_replication_callback_ddp(data_parallel): FILE: sync_batchnorm/unittest.py function as_numpy (line 17) | def as_numpy(v): class TorchTestCase (line 23) | class TorchTestCase(unittest.TestCase): method assertTensorClose (line 24) | def assertTensorClose(self, a, b, atol=1e-3, rtol=1e-3): FILE: unified_video_generator.py function inv_transform (line 31) | def inv_transform(x, min_vals, max_vals): function load_args (line 34) | def load_args(filename): class VideoGenerator (line 39) | class VideoGenerator: method __init__ (line 40) | def __init__(self, args): method extract_pose (line 131) | def extract_pose(self): method process_audio (line 202) | def process_audio(self): method generate_pose_blink (line 252) | def generate_pose_blink(self): method generate_final_video (line 304) | def generate_final_video(self): method run (line 402) | def run(self): method _convert_wav_to_16k (line 417) | def _convert_wav_to_16k(input_file, output_file): method _get_hubert_from_16k_speech (line 434) | def _get_hubert_from_16k_speech(self, speech, device="cuda:0"): method _init_video_model (line 504) | def _init_video_model(self, model_config): method _process_output_frame (line 533) | def _process_output_frame(self, frame_batch, mean=(0.0, 0.0, 0.0), ind... method _extract_audio_segment (line 550) | def _extract_audio_segment(self, input_wav, start_frame, num_frames, f... method _combine_video_audio (line 567) | def _combine_video_audio(self, audio_path, video_path, output_path): function parse_args (line 588) | def parse_args(): function main (line 597) | def main():