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Functions571 in github.com/DAMO-DI-ML/NeurIPS2022-FiLM

⨍ Functions 571◇ Types & classes 171
Method__init__
(self, root_path, flag='pred', size=None, features='S', data_path='ETTh1.csv',
data_provider/data_loader_mzq.py:289
Method__init__
(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False)
models/Logformer.py:123
Method__init__
(self, configs)
models/Transformer.py:14
Method__init__
N: the order of the HiPPO projection dt: discretization step size - should be roughly inverse to the length of the sequence
models/FiLM.py:28
Method__init__
(self, in_channels, out_channels,seq_len, modes1,compression=0,ratio=0.5,mode_type=0)
models/FiLM.py:75
Method__init__
(self, configs)
models/Autoformer_sin.py:21
Method__init__
(self, configs)
models/AE.py:15
Method__init__
(self, configs)
models/Informer.py:15
Method__init__
(self, configs, N=256, N2=32)
models/S4_model.py:25
Method__init__
(self, configs)
models/Transformer_sin.py:15
Method__init__
(self, configs)
models/Reformer.py:24
Method__init__
(self, configs)
models/Autoformer.py:24
Method__init__
(self, configs,size=25)
models/LSTM.py:23
Method__init__
(self, d_model, d_inner, n_head, d_k, d_v, dropout=0.1, normalize_before=True, use_tvm=False, q_k_mask=None, k
models/pyraformer/Layers.py:169
Method__init__
(self, d_model, d_inner, n_head, d_k, d_v, dropout=0.1, normalize_before=True)
models/pyraformer/Layers.py:196
Method__init__
(self, c_in, window_size)
models/pyraformer/Layers.py:213
Method__init__
(self, d_model, window_size, d_inner)
models/pyraformer/Layers.py:231
Method__init__
(self, d_model, window_size, d_inner)
models/pyraformer/Layers.py:264
Method__init__
(self, d_model, window_size, d_inner)
models/pyraformer/Layers.py:300
Method__init__
(self, d_model, window_size, d_inner)
models/pyraformer/Layers.py:333
Method__init__
(self, dim, num_types)
models/pyraformer/Layers.py:366
Method__init__
(self, opt)
models/pyraformer/Pyraformer_SS.py:12
Method__init__
(self, n_head, d_model, d_k, d_v, dropout, normalize_before, q_k_mask, k_q_mask)
models/pyraformer/PAM_TVM.py:8
Method__init__
(self, d_in, d_hid, dropout=0.1, normalize_before=True)
models/pyraformer/SubLayers.py:70
Method__init__
(self, temperature, attn_dropout=0.2)
models/pyraformer/Modules.py:9
Method__init__
(self, opt)
models/pyraformer/Pyraformer_LR.py:17
Method__init__
(self, d_model, max_len=5000)
models/pyraformer/embed.py:19
Method__init__
(self, c_in, d_model)
models/pyraformer/embed.py:38
Method__init__
(self, c_in, d_model)
models/pyraformer/embed.py:52
Method__init__
(self, d_model)
models/pyraformer/embed.py:71
Method__init__
(self, c_in, d_model, temporal_size, seq_num, dropout=0.1)
models/pyraformer/embed.py:98
Method__init__
(self, cov_size, num_seq, d_model, input_size, device)
models/pyraformer/embed.py:116
Method__init__
(self, opt)
models/pyraformer/graph_attention.py:250
Method__init__
(self, opt)
models/pyraformer/graph_attention.py:320
Method__init__
(self, net)
models/reformer_pytorch/recorder.py:6
Method__init__
(self, f, g, depth=None, send_signal = False)
models/reformer_pytorch/reversible.py:42
Method__init__
(self, f, g)
models/reformer_pytorch/reversible.py:107
Method__init__
(self, blocks, layer_dropout = 0., reverse_thres = 0, send_signal = False)
models/reformer_pytorch/reversible.py:137
Method__init__
(self, val)
models/reformer_pytorch/reformer_pytorch.py:112
Method__init__
(self, tensor, transpose = False, normalize = False)
models/reformer_pytorch/reformer_pytorch.py:120
Method__init__
(self, fn)
models/reformer_pytorch/reformer_pytorch.py:135
Method__init__
(self, dim, eps=1e-5)
models/reformer_pytorch/reformer_pytorch.py:144
Method__init__
(self, norm_class, dim, fn)
models/reformer_pytorch/reformer_pytorch.py:154
Method__init__
( self, dropout = 0., bucket_size = 16, n_hashes = 8,
models/reformer_pytorch/reformer_pytorch.py:181
Method__init__
(self, causal = False, dropout = 0.)
models/reformer_pytorch/reformer_pytorch.py:453
Method__init__
(self, dim, heads = 8, bucket_size = 64, n_hashes = 8, causal = False, dim_head = None, attn_chunks = 1, rando
models/reformer_pytorch/reformer_pytorch.py:498
Method__init__
(self, dim, mult = 4, dropout = 0., activation = None, glu = False)
models/reformer_pytorch/reformer_pytorch.py:612
Method__init__
(self, dim, max_seq_len)
models/reformer_pytorch/reformer_pytorch.py:637
Method__init__
(self, dim)
models/reformer_pytorch/reformer_pytorch.py:646
Method__init__
(self, dim, depth, heads = 8, dim_head = None, bucket_size = 32, n_hashes = 8, ff_chunks = 100, attn_chunks =
models/reformer_pytorch/reformer_pytorch.py:678
Method__init__
(self, num_tokens, dim, depth, max_seq_len, heads = 8, dim_head = 64, bucket_size = 64, n_hashes = 4, ff_chunk
models/reformer_pytorch/reformer_pytorch.py:726
Method__init__
(self, net, ignore_index = -100, pad_value = 0)
models/reformer_pytorch/generative_tools.py:28
Method__init__
(self, dim, ignore_index = 0, pad_value = 0, **kwargs)
models/reformer_pytorch/reformer_enc_dec.py:40
Method__init__
(self, net)
models/reformer_pytorch/autopadder.py:17
Method__len__
(self)
data_provider/data_loader.py:95
Method__len__
(self)
data_provider/data_loader.py:185
Method__len__
(self)
data_provider/data_loader.py:284
Method__len__
(self)
data_provider/data_loader.py:368
Method__len__
(self)
data_provider/data_loader_mzq.py:96
Method__len__
(self)
data_provider/data_loader_mzq.py:182
Method__len__
(self)
data_provider/data_loader_mzq.py:282
Method__len__
(self)
data_provider/data_loader_mzq.py:376
Method__repr__
(self)
utils/timefeatures.py:16
Method_build_model
(self)
exp/exp_AE.py:29
Method_build_model
(self)
exp/exp_main.py:34
Method_get_data
(self)
exp/exp_basic.py:27
Methodbackward_diff
Computes the 'forward diff' or Euler update rule: (I - d A)^-1 u + d (I - d A)^-1 B v d: (...) u: (..., n) v: (...)
utils/op.py:108
Functionbilinear
dt: (...) timescales A: (... N N) B: (... N)
layers/S4.py:427
Methodbilinear
Computes the bilinear (aka trapezoid or Tustin's) update rule. (I - d/2 A)^-1 (I + d/2 A) u + d B (I - d/2 A)^-1 B v
utils/op.py:121
Functionbitreversal_permutation
(n)
utils/unroll.py:24
Functioncache_fn
(f)
layers/LSHAttention_reformer.py:52
Functioncache_fn
(f)
models/reformer_pytorch/reformer_pytorch.py:60
Functioncache_method_decorator
(cache_attr, cache_namespace, reexecute = False)
layers/LSHAttention_reformer.py:63
Functioncache_method_decorator
(cache_attr, cache_namespace, reexecute = False)
models/reformer_pytorch/reformer_pytorch.py:71
Functioncached_fn
(*args, **kwargs)
layers/LSHAttention_reformer.py:55
Functioncached_fn
(*args, **kwargs)
models/reformer_pytorch/reformer_pytorch.py:63
Functioncast_tuple
(x)
layers/LSHAttention_reformer.py:46
Methodcompl_mul1d
(self, input, weights)
layers/FourierCorrelation.py:95
Methodcompl_mul1d
(self, input, weights)
layers/FourierCorrelation.py:203
Methodcpu
(self)
layers/utils.py:292
Methodcuda
(self)
layers/utils.py:288
Methodd_output
(self)
layers/S4.py:1109
Methodd_state
(self)
layers/S4.py:1105
Methoddecode
(self, x, sample_idx=None)
layers/utils.py:272
Methoddecode
(self, x, sample_idx=None)
layers/utils.py:309
Methoddecode
(self, x)
layers/utils.py:339
Functiondecor_time
(func)
layers/AutoCorrelation.py:14
Methoddefault_state
(self, *batch_shape)
layers/S4.py:830
Methoddefault_state
(self, *args, **kwargs)
layers/S4.py:960
Methodeject
(self)
models/reformer_pytorch/recorder.py:14
Methodencode
(self, x)
layers/utils.py:268
Methodencode
(self, x)
layers/utils.py:305
Methodencode
(self, x)
layers/utils.py:332
Functionexists
(val)
layers/mwt.py:746
Functionexpand_dim
(dim, k, t)
layers/LSHAttention_reformer.py:83
Methodforward
(self, queries, keys, values, attn_mask)
layers/mwt.py:32
Methodforward
(self, values)
layers/mwt.py:69
Methodforward
(self, q, k, v, attn_mask=None)
layers/mwt.py:107
Methodforward
(self, q, k, v, mask)
layers/mwt.py:205
Methodforward
(self, x)
layers/mwt.py:270
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