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Functions373 in github.com/buaacxf/VIPTR

⨍ Functions 373◇ Types & classes 124
↓ 1 callersFunctiontia_distort
(src, segment=4)
dataload/aug/augment.py:12
↓ 1 callersFunctiontia_perspective
(src)
dataload/aug/augment.py:88
↓ 1 callersFunctiontia_stretch
(src, segment=4)
dataload/aug/augment.py:54
↓ 1 callersFunctiontrain
dataset preparation
train_benchmark.py:27
↓ 1 callersFunctionwarp
warp
dataload/dataAug.py:317
↓ 1 callersFunctionwindows2img
img_splits_hw: B' H W C
modules/VIPTRv1-T_ch.py:742
↓ 1 callersFunctionwindows2img
img_splits_hw: B' H W C
modules/VIPTRv2T_ch.py:672
↓ 1 callersFunctionwindows2img
img_splits_hw: B' H W C
modules/VIPTRv1.py:742
↓ 1 callersFunctionwindows2img
img_splits_hw: B' H W C
modules/VIPTRv2.py:729
FunctionCreateDataset
(config,lmdb_type)
dataload/loader.py:141
FunctionImgCV2PIL
(img)
dataload/dataAug.py:20
FunctionImgPIL2CV
(img)
dataload/dataAug.py:10
FunctionImgPIL2CV_fast
(img)
dataload/dataAug.py:15
FunctionVIPTRv1L
(opt)
modules/VIPTRv1-T_ch.py:1006
Method__call__
(self, optimizer)
optimizer.py:38
Method__call__
(self, optimizer)
optimizer.py:61
Method__call__
(self, optimizer)
optimizer.py:79
Method__call__
(self, optimizer)
optimizer.py:100
Method__call__
(self, optimizer)
optimizer.py:121
Method__call__
(self, optimizer)
optimizer.py:147
Method__call__
(self, img)
dataset.py:263
Method__call__
(self, img)
dataset.py:278
Method__call__
(self, batch)
dataset.py:301
Method__call__
(self, img)
dataload/loader.py:82
Method__call__
(self, batch)
dataload/loader.py:110
Method__call__
(self, data)
dataload/dataAug.py:47
Method__getitem__
(self, index)
dataset.py:181
Method__getitem__
(self, index)
dataset.py:237
Method__getitem__
(self, index)
dataload/loader.py:49
Method__init__
(self, step_each_epoch, milestones, warmup_epoch=0,
optimizer.py:48
Method__init__
(self, step_each_epoch, warmup_epoch=0, last_epoch=-1,
optimizer.py:70
Method__init__
(self, epochs, step_each_epoch, warmup_epoch=0,
optimizer.py:89
Method__init__
(self, epochs, step_each_epoch, warmup_epoch=0,
optimizer.py:110
Method__init__
(self, step_each_epoch, epochs, lr_end=1e-7,
optimizer.py:132
Method__init__
(self, character)
utils.py:8
Method__init__
(self, character)
utils.py:59
Method__init__
(self, character)
utils.py:106
Method__init__
(self)
utils.py:153
Method__init__
Modulate the data ratio in the batch. For example, when select_data is "MJ-ST" and batch_ratio is "0.5-0.5", the 50% of the b
dataset.py:19
Method__init__
(self, root, opt)
dataset.py:131
Method__init__
(self, root, opt)
dataset.py:221
Method__init__
(self, size, interpolation=Image.BICUBIC)
dataset.py:258
Method__init__
(self, max_size, PAD_type='right')
dataset.py:272
Method__init__
(self, imgH=32, imgW=100, keep_ratio_with_pad=False, use_type='valid', use_aug=False, aug_prob=0.4)
dataset.py:292
Method__init__
(self, opt)
model.py:25
Method__init__
(self, in_channels, out_channels, kernel_size=3,
modules/SVTR.py:39
Method__init__
(self, local_rank, drop_prob=None)
modules/SVTR.py:71
Method__init__
(self)
modules/SVTR.py:81
Method__init__
( self, dim, num_heads=8, HW=[8, 25], local_k=[3,
modules/SVTR.py:113
Method__init__
(self, dim, local_rank=-1, num_heads=8, mi
modules/SVTR.py:140
Method__init__
(self, dim, num_heads, local_rank=-1, mixe
modules/SVTR.py:201
Method__init__
(self, img_size=[32, 100], in_channels=3, embed_dim=768,
modules/SVTR.py:268
Method__init__
(self, in_channels, out_channels, types='Pool',
modules/SVTR.py:342
Method__init__
( self, img_size=[32, 100], in_channels=3, embed_dim=[64, 128,
modules/SVTR.py:389
Method__init__
(self, dim, kernel_size, stride, padding)
modules/VIPTRv1-T_ch.py:25
Method__init__
(self, in_channels, out_channels, kernel_size=3,
modules/VIPTRv1-T_ch.py:40
Method__init__
recurrent_chunk_size: (clh clw) num_chunks: (nch ncw) clh * clw == cl nch * ncw == nc default: clh==clw, clh
modules/VIPTRv1-T_ch.py:71
Method__init__
(self, embed_dim, num_heads, value_factor=1)
modules/VIPTRv1-T_ch.py:139
Method__init__
( self, embed_dim, ffn_dim, activation_fn=F.gelu,
modules/VIPTRv1-T_ch.py:269
Method__init__
(self, in_dim, hidden_dim, out_chans=None, act_layer=nn.GELU, dropout=0.)
modules/VIPTRv1-T_ch.py:316
Method__init__
(self, dim, num_heads=8, qkv_bias=False, q
modules/VIPTRv1-T_ch.py:335
Method__init__
(self, dim, num_heads, mlp_ratio=4., qkv_b
modules/VIPTRv1-T_ch.py:368
Method__init__
(self, dim, num_heads=1, qk_scale=None, attn_drop=0,
modules/VIPTRv1-T_ch.py:413
Method__init__
(self, dim=64, sr_ratio=1, num_heads=1, ml
modules/VIPTRv1-T_ch.py:475
Method__init__
(self, retention: str, embed_dim: int, num_heads: int, ffn_dim: int, out_dim, drop_path=0., l
modules/VIPTRv1-T_ch.py:516
Method__init__
(self, dim, out_dim, norm_layer=nn.LayerNorm)
modules/VIPTRv1-T_ch.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-T_ch.py:588
Method__init__
(self, dim, reso, num_heads, split_size=7, mlp_ratio=4., qkv_bias=False, qk_scale=None,
modules/VIPTRv1-T_ch.py:674
Method__init__
(self, embed_dim, out_dim, depth, num_heads, init_value: float, heads_range: float, mlp_ratio
modules/VIPTRv1-T_ch.py:754
Method__init__
(self, in_chans=3, embed_dim=96, norm_layer=None)
modules/VIPTRv1-T_ch.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-T_ch.py:882
Method__init__
(self, nIn, nHidden, nOut)
modules/sequence_modeling.py:23
Method__init__
(self, input_size, hidden_size, output_size)
modules/sequence_modeling.py:41
Method__init__
(self, input_size, hidden_size, output_size)
modules/sequence_modeling.py:83
Method__init__
(self, dim, kernel_size, stride, padding)
modules/VIPTRv2T_ch.py:24
Method__init__
(self, in_channels, out_channels, kernel_size=3,
modules/VIPTRv2T_ch.py:38
Method__init__
recurrent_chunk_size: (clh clw) num_chunks: (nch ncw) clh * clw == cl nch * ncw == nc default: clh==cl
modules/VIPTRv2T_ch.py:68
Method__init__
(self, embed_dim, num_heads, value_factor=1)
modules/VIPTRv2T_ch.py:135
Method__init__
( self, embed_dim, ffn_dim, activation_fn=F.gelu,
modules/VIPTRv2T_ch.py:263
Method__init__
(self, in_dim, hidden_dim, out_chans=None, act_layer=nn.GELU, dropout=0.)
modules/VIPTRv2T_ch.py:309
Method__init__
(self, dim, num_heads=1, qk_scale=None, attn_drop=0,
modules/VIPTRv2T_ch.py:328
Method__init__
(self, dim, init_value=1e-5)
modules/VIPTRv2T_ch.py:388
Method__init__
(self, dim=64, sr_ratio=1, num_heads=1,
modules/VIPTRv2T_ch.py:400
Method__init__
(self, retention: str, embed_dim: int, num_heads: int, ffn_dim: int, out_dim, drop_path=0., layerscale=False,
modules/VIPTRv2T_ch.py:447
Method__init__
(self, dim, out_dim, norm_layer=nn.LayerNorm)
modules/VIPTRv2T_ch.py:497
Method__init__
(self, dim, resolution, idx, split_size=7, dim_out=None, num_heads=8, attn_drop=0., proj_drop=0.,
modules/VIPTRv2T_ch.py:516
Method__init__
(self, dim, reso, num_heads, split_size=7, mlp_ratio=4., qkv_bias=False, qk_scale=None,
modules/VIPTRv2T_ch.py:603
Method__init__
(self, embed_dim, out_dim, depth, num_heads, init_value: float, heads_range: float, mlp_rati
modules/VIPTRv2T_ch.py:683
Method__init__
(self, in_chans=3, embed_dim=96, norm_layer=None)
modules/VIPTRv2T_ch.py:740
Method__init__
(self, in_chans=3, out_dim=192, embed_dims=[96, 192, 384, 768], depths=[2, 2, 6, 2], num_hea
modules/VIPTRv2T_ch.py:770
Method__init__
(self, dim, kernel_size, stride, padding)
modules/VIPTRv1.py:25
Method__init__
(self, in_channels, out_channels, kernel_size=3,
modules/VIPTRv1.py:40
Method__init__
recurrent_chunk_size: (clh clw) num_chunks: (nch ncw) clh * clw == cl nch * ncw == nc default: clh==clw, clh
modules/VIPTRv1.py:71
Method__init__
(self, embed_dim, num_heads, value_factor=1)
modules/VIPTRv1.py:139
Method__init__
( self, embed_dim, ffn_dim, activation_fn=F.gelu,
modules/VIPTRv1.py:269
Method__init__
(self, in_dim, hidden_dim, out_chans=None, act_layer=nn.GELU, dropout=0.)
modules/VIPTRv1.py:316
Method__init__
(self, dim, num_heads=8, qkv_bias=False, q
modules/VIPTRv1.py:335
Method__init__
(self, dim, num_heads, mlp_ratio=4., qkv_b
modules/VIPTRv1.py:368
Method__init__
(self, dim, num_heads=1, qk_scale=None, attn_drop=0,
modules/VIPTRv1.py:413
Method__init__
(self, dim=64, sr_ratio=1, num_heads=1, ml
modules/VIPTRv1.py:475
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