SYMBOL INDEX (577 symbols across 31 files)

FILE: cupy_module/dsepconv.py
  class Stream (line 7) | class Stream:
  function cupy_kernel (line 451) | def cupy_kernel(strFunction, objectVariables):
  function cupy_launch (line 511) | def cupy_launch(strFunction, strKernel):
  class _FunctionDSepconv (line 518) | class _FunctionDSepconv(torch.autograd.Function):
    method forward (line 520) | def forward(self, input, vertical, horizontal, offset_x, offset_y, mask):
    method backward (line 571) | def backward(self, gradOutput):
  function FunctionDSepconv (line 701) | def FunctionDSepconv(tensorInput, tensorVertical, tensorHorizontal, tens...
  class ModuleDSepconv (line 707) | class ModuleDSepconv(torch.nn.Module):
    method __init__ (line 708) | def __init__(self):
    method forward (line 713) | def forward(self, tensorInput, tensorVertical, tensorHorizontal, tenso...

FILE: evaluate.py
  function main (line 26) | def main():

FILE: evaluate_vqm.py
  function main (line 16) | def main():

FILE: interpolate_yuv.py
  function main (line 31) | def main():

FILE: ldm/data/bvi_vimeo.py
  class Vimeo90k_triplet (line 11) | class Vimeo90k_triplet(Dataset):
    method __init__ (line 12) | def __init__(self, db_dir, train=True,  crop_sz=(256,256), augment_s=T...
    method __getitem__ (line 29) | def __getitem__(self, index):
    method __len__ (line 52) | def __len__(self):
  class Vimeo90k_quintuplet (line 56) | class Vimeo90k_quintuplet(Dataset):
    method __init__ (line 57) | def __init__(self, db_dir, train=True,  crop_sz=(256,256), augment_s=T...
    method __getitem__ (line 74) | def __getitem__(self, index):
    method __len__ (line 94) | def __len__(self):
  class BVIDVC_triplet (line 98) | class BVIDVC_triplet(Dataset):
    method __init__ (line 99) | def __init__(self, db_dir, res=None, crop_sz=(256,256), augment_s=True...
    method __getitem__ (line 107) | def __getitem__(self, index):
    method __len__ (line 133) | def __len__(self):
  class BVIDVC_quintuplet (line 137) | class BVIDVC_quintuplet(Dataset):
    method __init__ (line 138) | def __init__(self, db_dir, res=None, crop_sz=(256,256), augment_s=True...
    method __getitem__ (line 146) | def __getitem__(self, index):
    method __len__ (line 166) | def __len__(self):
  class Sampler (line 170) | class Sampler(Dataset):
    method __init__ (line 171) | def __init__(self, datasets, p_datasets=None, iter=False, samples_per_...
    method __getitem__ (line 186) | def __getitem__(self, index):
    method __len__ (line 199) | def __len__(self):
  class BVI_Vimeo_triplet (line 206) | class BVI_Vimeo_triplet(Dataset):
    method __init__ (line 207) | def __init__(self, db_dir, crop_sz=[256,256], p_datasets=None, iter=Fa...
    method __getitem__ (line 225) | def __getitem__(self, index):
    method __len__ (line 238) | def __len__(self):

FILE: ldm/data/testsets.py
  class TripletTestSet (line 17) | class TripletTestSet:
    method __init__ (line 18) | def __init__(self):
    method eval (line 21) | def eval(self, model, sample_func, metrics=['PSNR', 'SSIM'], output_di...
  class Middlebury_others (line 80) | class Middlebury_others(TripletTestSet):
    method __init__ (line 81) | def __init__(self, db_dir):
  class Davis (line 93) | class Davis(TripletTestSet):
    method __init__ (line 94) | def __init__(self, db_dir):
  class Ucf (line 107) | class Ucf(TripletTestSet):
    method __init__ (line 108) | def __init__(self, db_dir):
  class Snufilm (line 121) | class Snufilm(TripletTestSet):
    method __init__ (line 122) | def __init__(self, db_dir, mode):
  class Snufilm_easy (line 139) | class Snufilm_easy(Snufilm):
    method __init__ (line 140) | def __init__(self, db_dir):
  class Snufilm_medium (line 144) | class Snufilm_medium(Snufilm):
    method __init__ (line 145) | def __init__(self, db_dir):
  class Snufilm_hard (line 149) | class Snufilm_hard(Snufilm):
    method __init__ (line 150) | def __init__(self, db_dir):
  class Snufilm_extreme (line 154) | class Snufilm_extreme(Snufilm):
    method __init__ (line 155) | def __init__(self, db_dir):
  class VFITex_triplet (line 159) | class VFITex_triplet:
    method __init__ (line 160) | def __init__(self, db_dir):
    method eval (line 166) | def eval(self, model, sample_func, metrics=['PSNR', 'SSIM'], output_di...
  class Davis90_triplet (line 253) | class Davis90_triplet:
    method __init__ (line 254) | def __init__(self, db_dir):
    method eval (line 260) | def eval(self, model, sample_func, metrics=['PSNR', 'SSIM'], output_di...
  class Ucf101_triplet (line 341) | class Ucf101_triplet:
    method __init__ (line 342) | def __init__(self, db_dir):
    method eval (line 356) | def eval(self, model, sample_func, metrics=['PSNR', 'SSIM'], output_di...

FILE: ldm/data/testsets_vqm.py
  class TripletTestSet (line 16) | class TripletTestSet:
    method __init__ (line 17) | def __init__(self):
    method eval (line 20) | def eval(self, metrics=['FloLPIPS'], output_dir=None, output_name='out...
  class Middlebury_others (line 65) | class Middlebury_others(TripletTestSet):
    method __init__ (line 66) | def __init__(self, db_dir):
  class Davis (line 78) | class Davis(TripletTestSet):
    method __init__ (line 79) | def __init__(self, db_dir):
  class Ucf (line 92) | class Ucf(TripletTestSet):
    method __init__ (line 93) | def __init__(self, db_dir):
  class Snufilm (line 106) | class Snufilm(TripletTestSet):
    method __init__ (line 107) | def __init__(self, db_dir, mode):
  class Snufilm_easy (line 124) | class Snufilm_easy(Snufilm):
    method __init__ (line 125) | def __init__(self, db_dir):
  class Snufilm_medium (line 129) | class Snufilm_medium(Snufilm):
    method __init__ (line 130) | def __init__(self, db_dir):
  class Snufilm_hard (line 134) | class Snufilm_hard(Snufilm):
    method __init__ (line 135) | def __init__(self, db_dir):
  class Snufilm_extreme (line 139) | class Snufilm_extreme(Snufilm):
    method __init__ (line 140) | def __init__(self, db_dir):
  class VFITex_triplet (line 144) | class VFITex_triplet:
    method __init__ (line 145) | def __init__(self, db_dir):
    method eval (line 151) | def eval(self, metrics=['FloLPIPS'], output_dir=None, output_name=None...
  class Davis90_triplet (line 223) | class Davis90_triplet:
    method __init__ (line 224) | def __init__(self, db_dir):
    method eval (line 230) | def eval(self, metrics=['FloLPIPS'], output_dir=None, output_name=None...
  class Ucf101_triplet (line 296) | class Ucf101_triplet:
    method __init__ (line 297) | def __init__(self, db_dir):
    method eval (line 311) | def eval(self, metrics=['FloLPIPS'], output_dir=None, output_name='out...

FILE: ldm/data/vfitransforms.py
  function rand_crop (line 7) | def rand_crop(*args, sz):
  function rand_flip (line 15) | def rand_flip(*args, p):
  function rand_reverse (line 26) | def rand_reverse(*args, p):

FILE: ldm/lr_scheduler.py
  class LambdaWarmUpCosineScheduler (line 4) | class LambdaWarmUpCosineScheduler:
    method __init__ (line 8) | def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_...
    method schedule (line 17) | def schedule(self, n, **kwargs):
    method __call__ (line 32) | def __call__(self, n, **kwargs):
  class LambdaWarmUpCosineScheduler2 (line 36) | class LambdaWarmUpCosineScheduler2:
    method __init__ (line 41) | def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths...
    method find_in_interval (line 52) | def find_in_interval(self, n):
    method schedule (line 59) | def schedule(self, n, **kwargs):
    method __call__ (line 77) | def __call__(self, n, **kwargs):
  class LambdaLinearScheduler (line 81) | class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2):
    method schedule (line 83) | def schedule(self, n, **kwargs):

FILE: ldm/models/autoencoder.py
  class VQFlowNet (line 18) | class VQFlowNet(pl.LightningModule):
    method __init__ (line 19) | def __init__(self,
    method ema_scope (line 74) | def ema_scope(self, context=None):
    method init_from_ckpt (line 88) | def init_from_ckpt(self, path, ignore_keys=list()):
    method on_train_batch_end (line 102) | def on_train_batch_end(self, *args, **kwargs):
    method encode (line 106) | def encode(self, x, ret_feature=False):
    method encode_to_prequant (line 145) | def encode_to_prequant(self, x):
    method decode (line 150) | def decode(self, quant, x_prev, x_next):
    method decode_code (line 167) | def decode_code(self, code_b):
    method forward (line 172) | def forward(self, input, x_prev, x_next, return_pred_indices=False):
    method get_input (line 180) | def get_input(self, batch, k):
    method training_step (line 198) | def training_step(self, batch, batch_idx, optimizer_idx):
    method validation_step (line 221) | def validation_step(self, batch, batch_idx):
    method _validation_step (line 227) | def _validation_step(self, batch, batch_idx, suffix=""):
    method configure_optimizers (line 254) | def configure_optimizers(self):
    method get_last_layer (line 287) | def get_last_layer(self):
    method log_images (line 290) | def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
    method to_rgb (line 314) | def to_rgb(self, x):
  class VQFlowNetInterface (line 323) | class VQFlowNetInterface(VQFlowNet):
    method __init__ (line 324) | def __init__(self, **kwargs):
    method encode (line 327) | def encode(self, x, ret_feature=False):
    method decode (line 366) | def decode(self, h, x_prev, x_next, phi_prev_list, phi_next_list, forc...

FILE: ldm/models/diffusion/ddim.py
  class DDIMSampler (line 11) | class DDIMSampler(object):
    method __init__ (line 12) | def __init__(self, model, schedule="linear", **kwargs):
    method register_buffer (line 18) | def register_buffer(self, name, attr):
    method make_schedule (line 24) | def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddi...
    method sample (line 56) | def sample(self,
    method ddim_sampling (line 115) | def ddim_sampling(self, cond, shape,
    method p_sample_ddim (line 168) | def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_origin...

FILE: ldm/models/diffusion/ddpm.py
  function disabled_train (line 33) | def disabled_train(self, mode=True):
  class DDPM (line 40) | class DDPM(pl.LightningModule):
    method __init__ (line 42) | def __init__(self,
    method register_schedule (line 113) | def register_schedule(self, given_betas=None, beta_schedule="linear", ...
    method ema_scope (line 168) | def ema_scope(self, context=None):
    method init_from_ckpt (line 182) | def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
    method q_mean_variance (line 200) | def q_mean_variance(self, x_start, t):
    method predict_start_from_noise (line 212) | def predict_start_from_noise(self, x_t, t, noise):
    method q_posterior (line 218) | def q_posterior(self, x_start, x_t, t):
    method p_mean_variance (line 227) | def p_mean_variance(self, x, t, clip_denoised: bool):
    method p_sample (line 240) | def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
    method p_sample_loop (line 249) | def p_sample_loop(self, shape, return_intermediates=False):
    method sample (line 264) | def sample(self, batch_size=16, return_intermediates=False):
    method q_sample (line 270) | def q_sample(self, x_start, t, noise=None):
    method get_loss (line 275) | def get_loss(self, pred, target, mean=True):
    method p_losses (line 290) | def p_losses(self, x_start, t, noise=None):
    method forward (line 319) | def forward(self, x, *args, **kwargs):
    method get_input (line 325) | def get_input(self, batch, k):
    method shared_step (line 333) | def shared_step(self, batch):
    method training_step (line 338) | def training_step(self, batch, batch_idx):
    method validation_step (line 354) | def validation_step(self, batch, batch_idx):
    method on_train_batch_end (line 362) | def on_train_batch_end(self, *args, **kwargs):
    method _get_rows_from_list (line 366) | def _get_rows_from_list(self, samples):
    method log_images (line 374) | def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=Non...
    method configure_optimizers (line 411) | def configure_optimizers(self):
  class LatentDiffusion (line 420) | class LatentDiffusion(DDPM):
    method __init__ (line 422) | def __init__(self,
    method make_cond_schedule (line 462) | def make_cond_schedule(self, ):
    method register_schedule (line 468) | def register_schedule(self,
    method instantiate_first_stage (line 477) | def instantiate_first_stage(self, config):
    method instantiate_cond_stage (line 484) | def instantiate_cond_stage(self, config):
    method _get_denoise_row_from_list (line 505) | def _get_denoise_row_from_list(self, samples, desc='', force_no_decode...
    method get_first_stage_encoding (line 517) | def get_first_stage_encoding(self, encoder_posterior):
    method get_learned_conditioning (line 524) | def get_learned_conditioning(self, c):
    method get_input (line 537) | def get_input(self, batch, k, return_first_stage_outputs=False, force_...
    method decode_first_stage (line 589) | def decode_first_stage(self, z, predict_cids=False, force_not_quantize...
    method encode_first_stage (line 602) | def encode_first_stage(self, x):
    method shared_step (line 605) | def shared_step(self, batch, **kwargs):
    method forward (line 610) | def forward(self, x, c, *args, **kwargs):
    method apply_model (line 622) | def apply_model(self, x_noisy, t, cond, return_ids=False):
    method p_losses (line 641) | def p_losses(self, x_start, cond, t, noise=None):
    method p_mean_variance (line 676) | def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codeboo...
    method p_sample (line 708) | def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
    method progressive_denoising (line 739) | def progressive_denoising(self, cond, shape, verbose=True, callback=No...
    method p_sample_loop (line 795) | def p_sample_loop(self, cond, shape, return_intermediates=False,
    method sample (line 846) | def sample(self, cond, batch_size=16, return_intermediates=False, x_T=...
    method sample_log (line 864) | def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
    method log_images (line 880) | def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200,...
    method configure_optimizers (line 989) | def configure_optimizers(self):
    method to_rgb (line 1014) | def to_rgb(self, x):
  class DiffusionWrapper (line 1023) | class DiffusionWrapper(pl.LightningModule):
    method __init__ (line 1024) | def __init__(self, diff_model_config, conditioning_key):
    method forward (line 1030) | def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
  class LatentDiffusionVFI (line 1063) | class LatentDiffusionVFI(DDPM):
    method __init__ (line 1065) | def __init__(self,
    method make_cond_schedule (line 1105) | def make_cond_schedule(self, ):
    method register_schedule (line 1111) | def register_schedule(self,
    method instantiate_first_stage (line 1120) | def instantiate_first_stage(self, config):
    method instantiate_cond_stage (line 1127) | def instantiate_cond_stage(self, config):
    method _get_denoise_row_from_list (line 1148) | def _get_denoise_row_from_list(self, samples, xc=None, phi_prev_list=N...
    method get_first_stage_encoding (line 1161) | def get_first_stage_encoding(self, encoder_posterior):
    method get_learned_conditioning (line 1168) | def get_learned_conditioning(self, c):
    method get_input (line 1180) | def get_input(self, batch, k, return_first_stage_outputs=False, force_...
    method decode_first_stage (line 1228) | def decode_first_stage(self, z, xc=None, phi_prev_list=None, phi_next_...
    method encode_first_stage (line 1241) | def encode_first_stage(self, x):
    method shared_step (line 1244) | def shared_step(self, batch, **kwargs):
    method forward (line 1249) | def forward(self, x, c, *args, **kwargs):
    method apply_model (line 1261) | def apply_model(self, x_noisy, t, cond, return_ids=False):
    method p_losses (line 1280) | def p_losses(self, x_start, cond, t, noise=None):
    method p_mean_variance (line 1315) | def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codeboo...
    method p_sample (line 1347) | def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
    method progressive_denoising (line 1378) | def progressive_denoising(self, cond, shape, verbose=True, callback=No...
    method p_sample_loop (line 1434) | def p_sample_loop(self, cond, shape, return_intermediates=False,
    method sample (line 1485) | def sample(self, cond, batch_size=16, return_intermediates=False, x_T=...
    method sample_ddpm (line 1502) | def sample_ddpm(self, conditioning, batch_size=16, return_intermediate...
    method sample_log (line 1522) | def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
    method log_images (line 1538) | def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200,...
    method configure_optimizers (line 1620) | def configure_optimizers(self):
    method to_rgb (line 1645) | def to_rgb(self, x):

FILE: ldm/modules/attention.py
  function exists (line 11) | def exists(val):
  function uniq (line 15) | def uniq(arr):
  function default (line 19) | def default(val, d):
  function max_neg_value (line 25) | def max_neg_value(t):
  function init_ (line 29) | def init_(tensor):
  class GEGLU (line 37) | class GEGLU(nn.Module):
    method __init__ (line 38) | def __init__(self, dim_in, dim_out):
    method forward (line 42) | def forward(self, x):
  class FeedForward (line 47) | class FeedForward(nn.Module):
    method __init__ (line 48) | def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
    method forward (line 63) | def forward(self, x):
  function zero_module (line 67) | def zero_module(module):
  function Normalize (line 76) | def Normalize(in_channels):
  class LinearAttention (line 80) | class LinearAttention(nn.Module):
    method __init__ (line 81) | def __init__(self, dim, heads=4, dim_head=32):
    method forward (line 88) | def forward(self, x):
  class SpatialSelfAttention (line 99) | class SpatialSelfAttention(nn.Module):
    method __init__ (line 100) | def __init__(self, in_channels):
    method forward (line 126) | def forward(self, x):
  class CrossAttention (line 152) | class CrossAttention(nn.Module):
    method __init__ (line 157) | def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, ...
    method forward (line 174) | def forward(self, x, context=None, mask=None):
  class SpatialCrossAttention (line 200) | class SpatialCrossAttention(nn.Module):
    method __init__ (line 207) | def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, ...
    method forward (line 226) | def forward(self, x, context=None):
  function posemb_sincos_2d (line 259) | def posemb_sincos_2d(patches, temperature = 10000, dtype = torch.float32):
  class SpatialCrossAttentionWithPosEmb (line 276) | class SpatialCrossAttentionWithPosEmb(nn.Module):
    method __init__ (line 283) | def __init__(self, in_channels=None, heads=8, dim_head=64, dropout=0.):
    method forward (line 312) | def forward(self, x, context=None):
  class BasicTransformerBlock (line 361) | class BasicTransformerBlock(nn.Module):
    method __init__ (line 369) | def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None,...
    method forward (line 380) | def forward(self, x, context=None):
    method _forward (line 383) | def _forward(self, x, context=None):
  class SpatialTransformer (line 390) | class SpatialTransformer(nn.Module):
    method __init__ (line 399) | def __init__(self, in_channels, n_heads, d_head,
    method forward (line 423) | def forward(self, x, context=None):

FILE: ldm/modules/diffusionmodules/model.py
  function get_timestep_embedding (line 13) | def get_timestep_embedding(timesteps, embedding_dim):
  function nonlinearity (line 34) | def nonlinearity(x):
  function Normalize (line 39) | def Normalize(in_channels, num_groups=32):
  class IdentityWrapper (line 43) | class IdentityWrapper(nn.Module):
    method __init__ (line 47) | def __init__(self) -> None:
    method forward (line 51) | def forward(self, x, context=None):
  class Upsample (line 56) | class Upsample(nn.Module):
    method __init__ (line 57) | def __init__(self, in_channels, with_conv):
    method forward (line 67) | def forward(self, x):
  class Downsample (line 74) | class Downsample(nn.Module):
    method __init__ (line 75) | def __init__(self, in_channels, with_conv):
    method forward (line 86) | def forward(self, x):
  class ResnetBlock (line 96) | class ResnetBlock(nn.Module):
    method __init__ (line 97) | def __init__(self, *, in_channels, out_channels=None, conv_shortcut=Fa...
    method forward (line 135) | def forward(self, x, temb):
  class LinAttnBlock (line 158) | class LinAttnBlock(LinearAttention):
    method __init__ (line 160) | def __init__(self, in_channels):
  class AttnBlock (line 164) | class AttnBlock(nn.Module):
    method __init__ (line 165) | def __init__(self, in_channels):
    method forward (line 192) | def forward(self, x):
  function make_attn (line 219) | def make_attn(in_channels, attn_type="vanilla"):
  class FIEncoder (line 234) | class FIEncoder(nn.Module):
    method __init__ (line 235) | def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
    method forward (line 300) | def forward(self, x, ret_feature=False):
  class FlowEncoder (line 333) | class FlowEncoder(FIEncoder):
    method __init__ (line 334) | def __init__(self, *, ch, out_ch, ch_mult=(1, 2, 4, 8), num_res_blocks...
  class FlowDecoderWithResidual (line 354) | class FlowDecoderWithResidual(nn.Module):
    method __init__ (line 355) | def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
    method forward (line 520) | def forward(self, z, cond_dict):

FILE: ldm/modules/diffusionmodules/openaimodel.py
  function convert_module_to_f16 (line 26) | def convert_module_to_f16(x):
  function convert_module_to_f32 (line 29) | def convert_module_to_f32(x):
  class AttentionPool2d (line 34) | class AttentionPool2d(nn.Module):
    method __init__ (line 39) | def __init__(
    method forward (line 53) | def forward(self, x):
  class TimestepBlock (line 64) | class TimestepBlock(nn.Module):
    method forward (line 70) | def forward(self, x, emb):
  class TimestepEmbedSequential (line 76) | class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
    method forward (line 82) | def forward(self, x, emb, context=None):
  class Upsample (line 93) | class Upsample(nn.Module):
    method __init__ (line 102) | def __init__(self, channels, use_conv, dims=2, out_channels=None, padd...
    method forward (line 111) | def forward(self, x):
  class TransposedUpsample (line 123) | class TransposedUpsample(nn.Module):
    method __init__ (line 125) | def __init__(self, channels, out_channels=None, ks=5):
    method forward (line 132) | def forward(self,x):
  class Downsample (line 136) | class Downsample(nn.Module):
    method __init__ (line 145) | def __init__(self, channels, use_conv, dims=2, out_channels=None,paddi...
    method forward (line 160) | def forward(self, x):
  class ResBlock (line 165) | class ResBlock(TimestepBlock):
    method __init__ (line 181) | def __init__(
    method forward (line 245) | def forward(self, x, emb):
    method _forward (line 257) | def _forward(self, x, emb):
  class AttentionBlock (line 280) | class AttentionBlock(nn.Module):
    method __init__ (line 287) | def __init__(
    method forward (line 316) | def forward(self, x):
    method _forward (line 320) | def _forward(self, x):
  function count_flops_attn (line 329) | def count_flops_attn(model, _x, y):
  class QKVAttentionLegacy (line 349) | class QKVAttentionLegacy(nn.Module):
    method __init__ (line 354) | def __init__(self, n_heads):
    method forward (line 358) | def forward(self, qkv):
    method count_flops (line 377) | def count_flops(model, _x, y):
  class QKVAttention (line 381) | class QKVAttention(nn.Module):
    method __init__ (line 386) | def __init__(self, n_heads):
    method forward (line 390) | def forward(self, qkv):
    method count_flops (line 411) | def count_flops(model, _x, y):
  class UNetModel (line 415) | class UNetModel(nn.Module):
    method __init__ (line 445) | def __init__(
    method convert_to_fp16 (line 711) | def convert_to_fp16(self):
    method convert_to_fp32 (line 719) | def convert_to_fp32(self):
    method forward (line 727) | def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
  class EncoderUNetModel (line 762) | class EncoderUNetModel(nn.Module):
    method __init__ (line 768) | def __init__(
    method convert_to_fp16 (line 941) | def convert_to_fp16(self):
    method convert_to_fp32 (line 948) | def convert_to_fp32(self):
    method forward (line 955) | def forward(self, x, timesteps):

FILE: ldm/modules/diffusionmodules/util.py
  function make_beta_schedule (line 21) | def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_e...
  function make_ddim_timesteps (line 46) | def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_...
  function make_ddim_sampling_parameters (line 63) | def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbos...
  function betas_for_alpha_bar (line 77) | def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.9...
  function extract_into_tensor (line 96) | def extract_into_tensor(a, t, x_shape):
  function checkpoint (line 102) | def checkpoint(func, inputs, params, flag):
  class CheckpointFunction (line 119) | class CheckpointFunction(torch.autograd.Function):
    method forward (line 121) | def forward(ctx, run_function, length, *args):
    method backward (line 131) | def backward(ctx, *output_grads):
  function timestep_embedding (line 151) | def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=Fal...
  function zero_module (line 174) | def zero_module(module):
  function scale_module (line 183) | def scale_module(module, scale):
  function mean_flat (line 192) | def mean_flat(tensor):
  function normalization (line 199) | def normalization(channels):
  class SiLU (line 209) | class SiLU(nn.Module):
    method forward (line 210) | def forward(self, x):
  class GroupNorm32 (line 214) | class GroupNorm32(nn.GroupNorm):
    method forward (line 215) | def forward(self, x):
  function conv_nd (line 218) | def conv_nd(dims, *args, **kwargs):
  function linear (line 231) | def linear(*args, **kwargs):
  function avg_pool_nd (line 238) | def avg_pool_nd(dims, *args, **kwargs):
  class HybridConditioner (line 251) | class HybridConditioner(nn.Module):
    method __init__ (line 253) | def __init__(self, c_concat_config, c_crossattn_config):
    method forward (line 258) | def forward(self, c_concat, c_crossattn):
  function noise_like (line 264) | def noise_like(shape, device, repeat=False):

FILE: ldm/modules/ema.py
  class LitEma (line 5) | class LitEma(nn.Module):
    method __init__ (line 6) | def __init__(self, model, decay=0.9999, use_num_upates=True):
    method forward (line 25) | def forward(self,model):
    method copy_to (line 46) | def copy_to(self, model):
    method store (line 55) | def store(self, parameters):
    method restore (line 64) | def restore(self, parameters):

FILE: ldm/modules/losses/vqperceptual.py
  function hinge_d_loss_with_exemplar_weights (line 11) | def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights):
  function adopt_weight (line 20) | def adopt_weight(weight, global_step, threshold=0, value=0.):
  function measure_perplexity (line 26) | def measure_perplexity(predicted_indices, n_embed):
  function l1 (line 35) | def l1(x, y):
  function l2 (line 39) | def l2(x, y):
  class VQLPIPSWithDiscriminator (line 43) | class VQLPIPSWithDiscriminator(nn.Module):
    method __init__ (line 44) | def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0,
    method calculate_adaptive_weight (line 85) | def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
    method forward (line 98) | def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx,

FILE: ldm/modules/maxvit.py
  function exists (line 12) | def exists(val):
  function default (line 16) | def default(val, d):
  class PreNormResidual (line 22) | class PreNormResidual(nn.Module):
    method __init__ (line 23) | def __init__(self, dim, fn):
    method forward (line 28) | def forward(self, x, c=None):
  class SqueezeExcitation (line 34) | class SqueezeExcitation(nn.Module):
    method __init__ (line 35) | def __init__(self, dim, shrinkage_rate = 0.25):
    method forward (line 48) | def forward(self, x):
  class FeedForward (line 52) | class FeedForward(nn.Module):
    method __init__ (line 53) | def __init__(self, dim, mult = 4, dropout = 0.):
    method forward (line 63) | def forward(self, x):
  class Attention (line 67) | class Attention(nn.Module):
    method __init__ (line 68) | def __init__(
    method forward (line 108) | def forward(self, x, c=None):
  class Dropsample (line 158) | class Dropsample(nn.Module):
    method __init__ (line 159) | def __init__(self, prob = 0):
    method forward (line 163) | def forward(self, x):
  class MBConvResidual (line 173) | class MBConvResidual(nn.Module):
    method __init__ (line 174) | def __init__(self, fn, dropout = 0.):
    method forward (line 179) | def forward(self, x):
  function MBConv (line 185) | def MBConv(
  class MaxAttentionBlock (line 215) | class MaxAttentionBlock(nn.Module):
    method __init__ (line 216) | def __init__(self, in_channels, heads=8, dim_head=64, dropout=0., wind...
    method forward (line 232) | def forward(self, x):
  class SpatialCrossAttentionWithMax (line 249) | class SpatialCrossAttentionWithMax(nn.Module):
    method __init__ (line 250) | def __init__(self, in_channels, heads=8, dim_head=64, ctx_dim=None, dr...
    method forward (line 274) | def forward(self, x, context=None):
  class SpatialTransformerWithMax (line 298) | class SpatialTransformerWithMax(nn.Module):
    method __init__ (line 307) | def __init__(self, in_channels, n_heads, d_head, dropout=0., context_d...
    method forward (line 325) | def forward(self, x, context=None):

FILE: ldm/util.py
  function log_txt_as_img (line 17) | def log_txt_as_img(wh, xc, size=10):
  function ismap (line 41) | def ismap(x):
  function isimage (line 47) | def isimage(x):
  function exists (line 53) | def exists(x):
  function default (line 57) | def default(val, d):
  function mean_flat (line 63) | def mean_flat(tensor):
  function count_params (line 71) | def count_params(model, verbose=False):
  function instantiate_from_config (line 78) | def instantiate_from_config(config):
  function get_obj_from_str (line 88) | def get_obj_from_str(string, reload=False):
  function _do_parallel_data_prefetch (line 96) | def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False):
  function parallel_data_prefetch (line 108) | def parallel_data_prefetch(

FILE: main.py
  function get_parser (line 23) | def get_parser(**parser_kwargs):
  function nondefault_trainer_args (line 125) | def nondefault_trainer_args(opt):
  class WrappedDataset (line 132) | class WrappedDataset(Dataset):
    method __init__ (line 135) | def __init__(self, dataset):
    method __len__ (line 138) | def __len__(self):
    method __getitem__ (line 141) | def __getitem__(self, idx):
  function worker_init_fn (line 145) | def worker_init_fn(_):
  class DataModuleFromConfig (line 154) | class DataModuleFromConfig(pl.LightningDataModule):
    method __init__ (line 155) | def __init__(self, batch_size, train=None, validation=None, test=None,...
    method prepare_data (line 177) | def prepare_data(self):
    method setup (line 181) | def setup(self, stage=None):
    method _train_dataloader (line 189) | def _train_dataloader(self):
    method _val_dataloader (line 198) | def _val_dataloader(self, shuffle=False):
    method _test_dataloader (line 209) | def _test_dataloader(self, shuffle=False):
    method _predict_dataloader (line 218) | def _predict_dataloader(self, shuffle=False):
  class SetupCallback (line 227) | class SetupCallback(Callback):
    method __init__ (line 228) | def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, light...
    method on_keyboard_interrupt (line 238) | def on_keyboard_interrupt(self, trainer, pl_module):
    method on_fit_start (line 244) | def on_fit_start(self, trainer, pl_module):
  class ImageLogger (line 276) | class ImageLogger(Callback):
    method __init__ (line 277) | def __init__(self, batch_frequency, val_batch_frequency, max_images, c...
    method _testtube (line 299) | def _testtube(self, pl_module, images, batch_idx, split):
    method log_local (line 310) | def log_local(self, save_dir, split, images,
    method log_img (line 329) | def log_img(self, pl_module, batch, batch_idx, split="train"):
    method check_frequency (line 382) | def check_frequency(self, check_idx):
    method on_train_batch_end (line 393) | def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch...
    method on_validation_batch_end (line 397) | def on_validation_batch_end(self, trainer, pl_module, outputs, batch, ...
    method on_validation_epoch_end (line 404) | def on_validation_epoch_end(self, trainer, pl_module):
  class CUDACallback (line 410) | class CUDACallback(Callback):
    method on_train_epoch_start (line 412) | def on_train_epoch_start(self, trainer, pl_module):
    method on_train_epoch_end (line 418) | def on_train_epoch_end(self, trainer, pl_module, outputs=None):
  function melk (line 681) | def melk(*args, **kwargs):
  function divein (line 689) | def divein(*args, **kwargs):

FILE: metrics/flolpips/correlation/correlation.py
  function cupy_kernel (line 235) | def cupy_kernel(strFunction, objVariables):
  function cupy_launch (line 274) | def cupy_launch(strFunction, strKernel):
  class _FunctionCorrelation (line 278) | class _FunctionCorrelation(torch.autograd.Function):
    method forward (line 280) | def forward(self, first, second):
    method backward (line 333) | def backward(self, gradOutput):
  function FunctionCorrelation (line 385) | def FunctionCorrelation(tenFirst, tenSecond):
  class ModuleCorrelation (line 389) | class ModuleCorrelation(torch.nn.Module):
    method __init__ (line 390) | def __init__(self):
    method forward (line 394) | def forward(self, tenFirst, tenSecond):

FILE: metrics/flolpips/flolpips.py
  function spatial_average (line 17) | def spatial_average(in_tens, keepdim=True):
  function mw_spatial_average (line 20) | def mw_spatial_average(in_tens, flow, keepdim=True):
  function mtw_spatial_average (line 28) | def mtw_spatial_average(in_tens, flow, texture, keepdim=True):
  function m2w_spatial_average (line 41) | def m2w_spatial_average(in_tens, flow, keepdim=True):
  function upsample (line 48) | def upsample(in_tens, out_HW=(64,64)): # assumes scale factor is same fo...
  class LPIPS (line 53) | class LPIPS(nn.Module):
    method __init__ (line 54) | def __init__(self, pretrained=True, net='alex', version='0.1', lpips=T...
    method forward (line 111) | def forward(self, in0, in1, retPerLayer=False, normalize=False):
  class ScalingLayer (line 157) | class ScalingLayer(nn.Module):
    method __init__ (line 158) | def __init__(self):
    method forward (line 163) | def forward(self, inp):
  class NetLinLayer (line 167) | class NetLinLayer(nn.Module):
    method __init__ (line 169) | def __init__(self, chn_in, chn_out=1, use_dropout=False):
    method forward (line 176) | def forward(self, x):
  class Dist2LogitLayer (line 179) | class Dist2LogitLayer(nn.Module):
    method __init__ (line 181) | def __init__(self, chn_mid=32, use_sigmoid=True):
    method forward (line 193) | def forward(self,d0,d1,eps=0.1):
  class BCERankingLoss (line 196) | class BCERankingLoss(nn.Module):
    method __init__ (line 197) | def __init__(self, chn_mid=32):
    method forward (line 203) | def forward(self, d0, d1, judge):
  class FakeNet (line 209) | class FakeNet(nn.Module):
    method __init__ (line 210) | def __init__(self, use_gpu=True, colorspace='Lab'):
  class L2 (line 215) | class L2(FakeNet):
    method forward (line 216) | def forward(self, in0, in1, retPerLayer=None):
  class DSSIM (line 231) | class DSSIM(FakeNet):
    method forward (line 233) | def forward(self, in0, in1, retPerLayer=None):
  function print_network (line 246) | def print_network(net):
  class FloLPIPS (line 254) | class FloLPIPS(LPIPS):
    method __init__ (line 255) | def __init__(self, pretrained=True, net='alex', version='0.1', lpips=T...
    method forward (line 258) | def forward(self, in0, in1, flow, retPerLayer=False, normalize=False):
  function calc_flolpips (line 278) | def calc_flolpips(dis_path, ref_path):

FILE: metrics/flolpips/pretrained_networks.py
  class squeezenet (line 5) | class squeezenet(torch.nn.Module):
    method __init__ (line 6) | def __init__(self, requires_grad=False, pretrained=True):
    method forward (line 35) | def forward(self, X):
  class alexnet (line 56) | class alexnet(torch.nn.Module):
    method __init__ (line 57) | def __init__(self, requires_grad=False, pretrained=True):
    method forward (line 80) | def forward(self, X):
  class vgg16 (line 96) | class vgg16(torch.nn.Module):
    method __init__ (line 97) | def __init__(self, requires_grad=False, pretrained=True):
    method forward (line 120) | def forward(self, X):
  class resnet (line 138) | class resnet(torch.nn.Module):
    method __init__ (line 139) | def __init__(self, requires_grad=False, pretrained=True, num=18):
    method forward (line 162) | def forward(self, X):

FILE: metrics/flolpips/pwcnet.py
  function backwarp (line 45) | def backwarp(tenInput, tenFlow):
  class Network (line 71) | class Network(torch.nn.Module):
    method __init__ (line 72) | def __init__(self):
    method forward (line 263) | def forward(self, tenFirst, tenSecond, *args):
    method extract_pyramid_single (line 295) | def extract_pyramid_single(self, tenFirst):
  function estimate (line 310) | def estimate(tenFirst, tenSecond):

FILE: metrics/flolpips/utils.py
  function normalize_tensor (line 6) | def normalize_tensor(in_feat,eps=1e-10):
  function l2 (line 10) | def l2(p0, p1, range=255.):
  function dssim (line 13) | def dssim(p0, p1, range=255.):
  function tensor2im (line 17) | def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.):
  function tensor2np (line 22) | def tensor2np(tensor_obj):
  function np2tensor (line 26) | def np2tensor(np_obj):
  function tensor2tensorlab (line 30) | def tensor2tensorlab(image_tensor,to_norm=True,mc_only=False):
  function read_frame_yuv2rgb (line 44) | def read_frame_yuv2rgb(stream, width, height, iFrame, bit_depth, pix_fmt...

FILE: metrics/lpips/__init__.py
  function normalize_tensor (line 42) | def normalize_tensor(in_feat,eps=1e-10):
  function l2 (line 46) | def l2(p0, p1, range=255.):
  function psnr (line 49) | def psnr(p0, p1, peak=255.):
  function dssim (line 52) | def dssim(p0, p1, range=255.):
  function rgb2lab (line 56) | def rgb2lab(in_img,mean_cent=False):
  function tensor2np (line 63) | def tensor2np(tensor_obj):
  function np2tensor (line 67) | def np2tensor(np_obj):
  function tensor2tensorlab (line 71) | def tensor2tensorlab(image_tensor,to_norm=True,mc_only=False):
  function tensorlab2tensor (line 85) | def tensorlab2tensor(lab_tensor,return_inbnd=False):
  function load_image (line 103) | def load_image(path):
  function rgb2lab (line 116) | def rgb2lab(input):
  function tensor2im (line 120) | def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.):
  function im2tensor (line 125) | def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.):
  function tensor2vec (line 129) | def tensor2vec(vector_tensor):
  function tensor2im (line 133) | def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.):
  function im2tensor (line 139) | def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.):
  function voc_ap (line 146) | def voc_ap(rec, prec, use_07_metric=False):

FILE: metrics/lpips/lpips.py
  function spatial_average (line 14) | def spatial_average(in_tens, keepdim=True):
  function upsample (line 22) | def upsample(in_tens, out_HW=(64,64)): # assumes scale factor is same fo...
  class LPIPS (line 27) | class LPIPS(nn.Module):
    method __init__ (line 28) | def __init__(self, pretrained=True, net='alex', version='0.1', lpips=T...
    method forward (line 85) | def forward(self, in0, in1, retPerLayer=False, normalize=False):
  class ScalingLayer (line 132) | class ScalingLayer(nn.Module):
    method __init__ (line 133) | def __init__(self):
    method forward (line 138) | def forward(self, inp):
  class NetLinLayer (line 142) | class NetLinLayer(nn.Module):
    method __init__ (line 144) | def __init__(self, chn_in, chn_out=1, use_dropout=False):
    method forward (line 151) | def forward(self, x):
  class Dist2LogitLayer (line 154) | class Dist2LogitLayer(nn.Module):
    method __init__ (line 156) | def __init__(self, chn_mid=32, use_sigmoid=True):
    method forward (line 168) | def forward(self,d0,d1,eps=0.1):
  class BCERankingLoss (line 171) | class BCERankingLoss(nn.Module):
    method __init__ (line 172) | def __init__(self, chn_mid=32):
    method forward (line 178) | def forward(self, d0, d1, judge):
  class FakeNet (line 184) | class FakeNet(nn.Module):
    method __init__ (line 185) | def __init__(self, use_gpu=True, colorspace='Lab'):
  class L2 (line 190) | class L2(FakeNet):
    method forward (line 191) | def forward(self, in0, in1, retPerLayer=None):
  class DSSIM (line 206) | class DSSIM(FakeNet):
    method forward (line 208) | def forward(self, in0, in1, retPerLayer=None):
  function print_network (line 221) | def print_network(net):

FILE: metrics/lpips/pretrained_networks.py
  class squeezenet (line 5) | class squeezenet(torch.nn.Module):
    method __init__ (line 6) | def __init__(self, requires_grad=False, pretrained=True):
    method forward (line 35) | def forward(self, X):
  class alexnet (line 56) | class alexnet(torch.nn.Module):
    method __init__ (line 57) | def __init__(self, requires_grad=False, pretrained=True):
    method forward (line 80) | def forward(self, X):
  class vgg16 (line 96) | class vgg16(torch.nn.Module):
    method __init__ (line 97) | def __init__(self, requires_grad=False, pretrained=True):
    method forward (line 120) | def forward(self, X):
  class resnet (line 138) | class resnet(torch.nn.Module):
    method __init__ (line 139) | def __init__(self, requires_grad=False, pretrained=True, num=18):
    method forward (line 162) | def forward(self, X):

FILE: metrics/pytorch_ssim/__init__.py
  function gaussian (line 7) | def gaussian(window_size, sigma):
  function create_window (line 11) | def create_window(window_size, channel):
  function create_window_3d (line 18) | def create_window_3d(window_size, channel=1):
  function _ssim (line 26) | def _ssim(img1, img2, window, window_size, channel, size_average = True):
  function ssim_matlab (line 49) | def ssim_matlab(img1, img2, window_size=11, window=None, size_average=Tr...
  class SSIM (line 105) | class SSIM(torch.nn.Module):
    method __init__ (line 106) | def __init__(self, window_size = 11, size_average = True):
    method forward (line 113) | def forward(self, img1, img2):
  function ssim (line 131) | def ssim(img1, img2, window_size = 11, size_average = True):

FILE: utility.py
  function read_frame_yuv2rgb (line 9) | def read_frame_yuv2rgb(stream, width, height, iFrame, bit_depth, pix_fmt...
  function CharbonnierFunc (line 65) | def CharbonnierFunc(data, epsilon=0.001):
  function moduleNormalize (line 69) | def moduleNormalize(frame):
  function gaussian_kernel (line 73) | def gaussian_kernel(sz, sigma):
  function quantize (line 81) | def quantize(imTensor):
  function tensor2rgb (line 85) | def tensor2rgb(tensor):
  function calc_psnr (line 95) | def calc_psnr(gt, out, *args):
  function calc_ssim (line 104) | def calc_ssim(gt, out, *args):
  function calc_lpips (line 108) | def calc_lpips(gt, out, *args):
  function calc_flolpips (line 114) | def calc_flolpips(gt_list, out_list, inputs_list):