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Functions373 in github.com/buaacxf/VIPTR

⨍ Functions 373◇ Types & classes 124
Method__init__
(self, retention: str, embed_dim: int, num_heads: int, ffn_dim: int, out_dim, drop_path=0., l
modules/VIPTRv1.py:516
Method__init__
(self, dim, out_dim, norm_layer=nn.LayerNorm)
modules/VIPTRv1.py:568
Method__init__
(self, dim, resolution, idx, split_size=7, dim_out=None, num_heads=8, attn_drop=0., proj_drop=0.,
modules/VIPTRv1.py:588
Method__init__
(self, dim, reso, num_heads, split_size=7, mlp_ratio=4., qkv_bias=False, qk_scale=None,
modules/VIPTRv1.py:674
Method__init__
(self, embed_dim, out_dim, depth, num_heads, init_value: float, heads_range: float, mlp_ratio
modules/VIPTRv1.py:754
Method__init__
(self, in_chans=3, embed_dim=96, norm_layer=None)
modules/VIPTRv1.py:853
Method__init__
(self, in_chans=3, out_dim=192, embed_dims=[96, 192, 384, 768], depths=[2, 2, 6, 2], num_head
modules/VIPTRv1.py:882
Method__init__
(self, in_planes, num_ctrlpoints, activation='none')
modules/stn_head.py:24
Method__init__
(self, n_class)
modules/rec_sar_loss.py:4
Method__init__
(self, input_size, hidden_size, num_embeddings)
modules/prediction.py:63
Method__init__
(self, flatten: bool = True, blank: int = 0, reduction:
modules/dctc_loss.py:8
Method__init__
(self, input_channel, output_channel=512)
modules/feature_extraction.py:8
Method__init__
(self, input_channel, output_channel=512)
modules/feature_extraction.py:34
Method__init__
(self, input_channel, output_channel=512)
modules/feature_extraction.py:57
Method__init__
(self, output_channel)
modules/feature_extraction.py:97
Method__init__
(self, inplanes, planes, stride=1, downsample=None)
modules/feature_extraction.py:120
Method__init__
(self, input_channel, output_channel, block, layers)
modules/feature_extraction.py:154
Method__init__
(self, output_image_size=None, num_control_points=None, margins=None)
modules/tps_spatial_transformer.py:54
Method__init__
(self, dim, kernel_size, stride, padding)
modules/VIPTRv2.py:24
Method__init__
(self, in_channels, out_channels, kernel_size=3,
modules/VIPTRv2.py:38
Method__init__
recurrent_chunk_size: (clh clw) num_chunks: (nch ncw) clh * clw == cl nch * ncw == nc default: clh==clw, clh
modules/VIPTRv2.py:68
Method__init__
(self, embed_dim, num_heads, value_factor=1)
modules/VIPTRv2.py:135
Method__init__
( self, embed_dim, ffn_dim, activation_fn=F.gelu,
modules/VIPTRv2.py:263
Method__init__
(self, in_dim, hidden_dim, out_chans=None, act_layer=nn.GELU, dropout=0.)
modules/VIPTRv2.py:309
Method__init__
(self, dim, num_heads=8, qkv_bias=False, q
modules/VIPTRv2.py:327
Method__init__
(self, dim, num_heads, mlp_ratio=4., qkv_b
modules/VIPTRv2.py:359
Method__init__
(self, dim, num_heads=1, qk_scale=None, attn_drop=0,
modules/VIPTRv2.py:403
Method__init__
(self, dim=64, sr_ratio=1, num_heads=1, ml
modules/VIPTRv2.py:464
Method__init__
(self, retention: str, embed_dim: int, num_heads: int, ffn_dim: int, out_dim, drop_path=0., layerscale=False,
modules/VIPTRv2.py:507
Method__init__
(self, dim, out_dim, norm_layer=nn.LayerNorm)
modules/VIPTRv2.py:556
Method__init__
(self, dim, resolution, idx, split_size=7, dim_out=None, num_heads=8, attn_drop=0., proj_drop=0.,
modules/VIPTRv2.py:575
Method__init__
(self, dim, reso, num_heads, split_size=7, mlp_ratio=4., qkv_bias=False, qk_scale=None,
modules/VIPTRv2.py:662
Method__init__
(self, embed_dim, out_dim, depth, num_heads, init_value: float, heads_range: float, mlp_ratio
modules/VIPTRv2.py:740
Method__init__
(self, in_chans=3, embed_dim=96, norm_layer=None)
modules/VIPTRv2.py:917
Method__init__
(self, in_chans=3, out_dim=192, embed_dims=[96, 192, 384, 768], depths=[2, 2, 6, 2], num_head
modules/VIPTRv2.py:946
Method__init__
Based on RARE TPS input: batch_I: Batch Input Image [batch_size x I_channel_num x I_height x I_width] I_size : (heigh
modules/transformation.py:11
Method__init__
(self, F, I_channel_num)
modules/transformation.py:45
Method__init__
(self,config,lmdb_file)
dataload/loader.py:31
Method__init__
(self, height=32, max_width=280, types='train')
dataload/loader.py:76
Method__init__
(self, config,trans_type)
dataload/loader.py:100
Method__init__
(self, prob=0.5, image_shape=(32, 320, 3), max_text_length=
dataload/dataAug.py:26
Method__init__
(self, use_tia)
dataload/dataAug.py:192
Method__init__
(self, src, src_pts, dst_pts, dst_w, dst_h, trans_ratio=1.)
dataload/aug/warp_mls.py:7
Method__len__
(self)
dataset.py:178
Method__len__
(self)
dataset.py:234
Method__len__
(self)
dataload/loader.py:46
Method_init_weights
(self, m)
modules/SVTR.py:539
Method_init_weights
(self, m)
modules/VIPTRv1-T_ch.py:950
Method_init_weights
(self, m)
modules/VIPTRv2T_ch.py:829
Method_init_weights
(self, m)
modules/VIPTRv1.py:944
Method_init_weights
(self, m)
modules/VIPTRv2.py:1006
Functionadjust_learning_rate_cos
(opt, optimizer, epoch)
optimizer.py:231
Functionadjust_learning_rate_warm
(opt, optimizer, epoch)
optimizer.py:222
Methoddecode
convert text-index into text-label.
utils.py:85
Methoddecode
convert text-index into text-label.
utils.py:141
Methodencode
convert text-label into text-index. input: text: text labels of each image. [batch_size] output: text: concate
utils.py:70
Functionfix_param
(model, opt)
optimizer.py:188
Methodforward
Transformation stage
model.py:112
Methodforward
(self, inputs)
modules/SVTR.py:60
Methodforward
(self, x)
modules/SVTR.py:76
Methodforward
(self, input)
modules/SVTR.py:84
Methodforward
(self, x)
modules/SVTR.py:103
Methodforward
(self, x)
modules/SVTR.py:130
Methodforward
(self, x)
modules/SVTR.py:179
Methodforward
(self, x)
modules/SVTR.py:254
Methodforward
(self, x)
modules/SVTR.py:333
Methodforward
(self, x)
modules/SVTR.py:371
Methodforward
(self, x)
modules/SVTR.py:573
Methodforward
x: (b h w c)
modules/VIPTRv1-T_ch.py:29
Methodforward
(self, inputs)
modules/VIPTRv1-T_ch.py:63
Methodforward
slen: (h, w) h * w == l recurrent is not implemented
modules/VIPTRv1-T_ch.py:114
Methodforward
x: (b h w c) mask_h: (n h h) mask_w: (n w w)
modules/VIPTRv1-T_ch.py:162
Methodforward
x: (b h w c) rel_pos: mask: (n l l)
modules/VIPTRv1-T_ch.py:235
Methodforward
x: (b h w c)
modules/VIPTRv1-T_ch.py:297
Methodforward
(self, x)
modules/VIPTRv1-T_ch.py:330
Methodforward
(self, x)
modules/VIPTRv1-T_ch.py:352
Methodforward
(self, x, size=None)
modules/VIPTRv1-T_ch.py:406
Methodforward
(self, x, size, relative_pos_enc=None)
modules/VIPTRv1-T_ch.py:448
Methodforward
(self, x, relative_pos_enc=None)
modules/VIPTRv1-T_ch.py:506
Methodforward
( self, x: torch.Tensor, incremental_state=None, chunkwise_rec
modules/VIPTRv1-T_ch.py:538
Methodforward
x: B H W C
modules/VIPTRv1-T_ch.py:575
Methodforward
x: B L C
modules/VIPTRv1-T_ch.py:639
Methodforward
x: B, H*W, C
modules/VIPTRv1-T_ch.py:709
Methodforward
(self, x, size)
modules/VIPTRv1-T_ch.py:813
Methodforward
(self, x)
modules/VIPTRv1-T_ch.py:876
Methodforward
(self, x)
modules/VIPTRv1-T_ch.py:981
Methodforward
(self, input)
modules/sequence_modeling.py:29
Methodforward
input : visual feature [batch_size x T x input_size] output : contextual feature [batch_size x T x output_size]
modules/sequence_modeling.py:48
Methodforward
input : visual feature [batch_size x T x input_size] output : contextual feature [batch_size x T x output_size]
modules/sequence_modeling.py:71
Methodforward
input : visual feature [batch_size x T x input_size] output : contextual feature [batch_size x T x output_size]
modules/sequence_modeling.py:88
Methodforward
x: (b h w c)
modules/VIPTRv2T_ch.py:28
Methodforward
(self, inputs)
modules/VIPTRv2T_ch.py:61
Methodforward
slen: (h, w) h * w == l recurrent is not implemented
modules/VIPTRv2T_ch.py:111
Methodforward
x: (b h w c) mask_h: (n h h) mask_w: (n w w)
modules/VIPTRv2T_ch.py:158
Methodforward
x: (b h w c) rel_pos: mask: (n l l)
modules/VIPTRv2T_ch.py:230
Methodforward
x: (b h w c)
modules/VIPTRv2T_ch.py:291
Methodforward
(self, x)
modules/VIPTRv2T_ch.py:323
Methodforward
(self, x, size, relative_pos_enc=None)
modules/VIPTRv2T_ch.py:363
Methodforward
(self, x)
modules/VIPTRv2T_ch.py:394
Methodforward
(self, x, relative_pos_enc=None)
modules/VIPTRv2T_ch.py:438
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