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Functions211 in github.com/Wangyulin-user/TUMSyn

⨍ Functions 211◇ Types & classes 49
↓ 10 callersFunctionget_files
(text_path)
CLIP_training/dataloader/dataset.py:102
↓ 10 callersMethoditem
(self)
utils.py:78
↓ 10 callersFunctionread_img
(img_path)
CLIP_training/dataloader/dataset.py:118
↓ 10 callersMethodtokenize
(self, text)
CLIP_training/dataloader/dataset.py:44
↓ 8 callersMethod__init__
(self, in_features, hidden_features=None, bias=False)
models/cross_att.py:76
↓ 6 callersMethod__init__
(self, embed_dim: int, # vision image_resolution: int,
CLIP_training/model/model.py:248
↓ 6 callersMethod__init__
(self, embed_dim: int, # vision image_resolution: int,
CLIP/model.py:248
↓ 6 callersMethodadd
(self, v, n=1.0)
utils.py:74
↓ 6 callersMethodload_state_dict
(self, state_dict, strict=True)
models/resencoder.py:120
↓ 4 callersMethod_make_layer
(self, planes, blocks, stride=1)
CLIP_training/model/model.py:126
↓ 4 callersMethod_make_layer
(self, planes, blocks, stride=1)
CLIP/model.py:126
↓ 4 callersMethodencode_text
(self, text)
CLIP/model.py:347
↓ 4 callersMethodt
(self)
utils.py:90
↓ 3 callersMethod__init__
( self, conv, n_feats, kernel_size, bias=True, bn=True, act=nn.ReLU(True), res_scale=1
models/resencoder.py:21
↓ 3 callersMethodgen_feat
(self, inp)
models/lccd.py:24
↓ 3 callersFunctionload_config_file
(file_path)
CLIP_training/utils/util.py:25
↓ 3 callersFunctionlog
(obj, filename='log.txt')
utils.py:111
↓ 2 callersMethod__init__
(self)
utils.py:84
↓ 2 callersMethod_open
Open the current base file with the (original) mode and encoding. Return the resulting stream.
utils_clip/logger.py:57
↓ 2 callersMethod_open
Open the current base file with the (original) mode and encoding. Return the resulting stream.
CLIP_training/utils/logger.py:57
↓ 2 callersFunctionbytes_to_unicode
Returns list of utf-8 byte and a corresponding list of unicode strings. The reversible bpe codes work on unicode strings. This means you
utils_clip/simple_tokenizer.py:16
↓ 2 callersFunctionbytes_to_unicode
Returns list of utf-8 byte and a corresponding list of unicode strings. The reversible bpe codes work on unicode strings. This means you
CLIP_training/utils/simple_tokenizer.py:16
↓ 2 callersMethodencode
(self, text)
utils_clip/simple_tokenizer.py:121
↓ 2 callersFunctionget_pairs
Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings).
utils_clip/simple_tokenizer.py:38
↓ 2 callersFunctionget_pairs
Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings).
CLIP_training/utils/simple_tokenizer.py:38
↓ 2 callersFunctionimg_pad
(img, target_shape)
demo.py:34
↓ 2 callersFunctionload_config_file
(file_path)
utils_clip/util.py:25
↓ 2 callersFunctionmake_data_loader
(spec, tag='')
train.py:13
↓ 2 callersFunctionmkdir
(path)
CLIP_training/utils/util.py:15
↓ 2 callersMethodtokenize
(self, texts, tokenizer, context_length=90)
datasets/image_folder.py:45
↓ 1 callersMethod__init__
(self, in_dim=0, out_dim=0, hidden_list = [])
models/linear.py:6
↓ 1 callersMethod__init__
( self, conv, n_feats, kernel_size=3, bias=True, bn=False, act=nn.ReLU(True), res_scale=1)
models/fusionnet.py:10
↓ 1 callersFunction_get_pred
(crop_size, overlap_ratio, model, img_vol_0, coord_size, coord_hr, seq_tgt)
demo.py:73
↓ 1 callersMethodattention
(self, x: torch.Tensor)
CLIP_training/model/model.py:189
↓ 1 callersMethodattention
(self, x: torch.Tensor)
CLIP/model.py:189
↓ 1 callersFunctionbasic_clean
(text)
utils_clip/simple_tokenizer.py:50
↓ 1 callersFunctionbasic_clean
(text)
CLIP_training/utils/simple_tokenizer.py:50
↓ 1 callersMethodbpe
(self, token)
utils_clip/simple_tokenizer.py:80
↓ 1 callersMethodbpe
(self, token)
CLIP_training/utils/simple_tokenizer.py:80
↓ 1 callersMethodbuild_attention_mask
(self)
CLIP_training/model/model.py:332
↓ 1 callersMethodbuild_attention_mask
(self)
CLIP/model.py:332
↓ 1 callersFunctioncalculate_patch_index
(target_size, patch_size, overlap_ratio=0.25)
demo.py:49
↓ 1 callersMethodclose
Closes the stream.
utils_clip/logger.py:35
↓ 1 callersMethodclose
Closes the stream.
CLIP_training/utils/logger.py:35
↓ 1 callersMethoddecode
(self, tokens)
utils_clip/simple_tokenizer.py:129
↓ 1 callersMethoddecode
(self, tokens)
CLIP_training/utils/simple_tokenizer.py:129
↓ 1 callersFunctiondefault_bpe
()
utils_clip/simple_tokenizer.py:11
↓ 1 callersFunctiondefault_bpe
()
CLIP_training/utils/simple_tokenizer.py:11
↓ 1 callersMethodencode
(self, text)
CLIP_training/utils/simple_tokenizer.py:121
↓ 1 callersMethodencode_image
(self, image)
CLIP_training/model/model.py:344
↓ 1 callersMethodencode_image
(self, image)
CLIP/model.py:344
↓ 1 callersMethodencode_text
(self, text)
CLIP_training/model/model.py:347
↓ 1 callersFunctionensure_path
(path, remove=True)
utils.py:118
↓ 1 callersMethodforward
(self, image, text)
CLIP_training/model/model.py:362
↓ 1 callersMethodforward
(self, image, text)
CLIP/model.py:362
↓ 1 callersFunctionget_cosine_schedule_with_warmup
Create a schedule with a learning rate that decreases following the values of the cosine function between the initial lr set in the optimizer
CLIP_training/utils/custom_schedulers.py:11
↓ 1 callersFunctionget_dataloader
(config, dataset, is_train = True)
CLIP_training/dataloader/data_loaders.py:4
↓ 1 callersMethodget_target_tensor
(self, input, target_is_real)
utils.py:222
↓ 1 callersMethodinitialize_parameters
(self)
CLIP_training/model/model.py:303
↓ 1 callersMethodinitialize_parameters
(self)
CLIP/model.py:303
↓ 1 callersFunctionmain
(config_, save_path)
train.py:102
↓ 1 callersFunctionmain
()
CLIP_training/train.py:165
↓ 1 callersFunctionmake_data_loaders
()
train.py:25
↓ 1 callersFunctionpadding_or_crop
(x,length=128)
CLIP_training/dataloader/dataset.py:125
↓ 1 callersFunctionpercentile_clip
(input_tensor, reference_tensor=None, p_min=0.01, p_max=99.9, strictlyPositive=True)
CLIP_training/dataloader/dataset.py:92
↓ 1 callersFunctionprepare_training
()
train.py:31
↓ 1 callersFunctionsave_checkpoint
Checkpointing. Saves model and optimizer state_dict() and current epoch and global training steps.
CLIP_training/train.py:133
↓ 1 callersFunctionset_log_path
(path)
utils.py:106
↓ 1 callersFunctionset_new_spacing
(ori_spacing, coord_size, crop_size)
demo.py:13
↓ 1 callersFunctionset_seed
(seed, n_gpu)
CLIP_training/utils/util.py:29
↓ 1 callersFunctionsetup_logger
(name, save_dir, distributed_rank, filename="log.txt")
CLIP_training/utils/logger.py:80
↓ 1 callersMethodsingle_forward
(self, src_lr, coord_hr, prompt_tgt)
models/lccd.py:51
↓ 1 callersFunctionto_3d
(x)
models/cross_att.py:8
↓ 1 callersFunctionto_4d
(x, d, h, w)
models/cross_att.py:12
↓ 1 callersFunctiontokenize
(texts, tokenizer, context_length=90)
demo.py:19
↓ 1 callersFunctiontrain
(train_loader, model_G, optimizer_G)
train.py:65
↓ 1 callersFunctiontrain
Trains the model.
CLIP_training/train.py:24
↓ 1 callersFunctionwhitespace_clean
(text)
utils_clip/simple_tokenizer.py:56
↓ 1 callersFunctionwhitespace_clean
(text)
CLIP_training/utils/simple_tokenizer.py:56
Method__call__
(self, input, target_is_real)
utils.py:240
Method__getitem__
(self, idx)
CLIP_training/dataloader/dataset.py:55
Method__getitem__
(self, idx)
datasets/image_folder.py:62
Method__getitem__
(self, idx)
datasets/image_folder.py:97
Method__getitem__
(self, idx)
datasets/wrappers.py:23
Method__init__
(self)
utils.py:54
Method__init__
(self)
utils.py:70
Method__init__
(self, use_lsgan=True, target_real_label=1.0, target_fake_label=0.0, tensor=torch.FloatTensor
utils.py:209
Method__init__
(self, bpe_path: str = default_bpe())
utils_clip/simple_tokenizer.py:63
Method__init__
Open the specified file and use it as the stream for logging.
utils_clip/logger.py:15
Method__init__
(self, bpe_path: str = default_bpe())
CLIP_training/utils/simple_tokenizer.py:63
Method__init__
Open the specified file and use it as the stream for logging.
CLIP_training/utils/logger.py:15
Method__init__
(self, config, text_tokenizer)
CLIP_training/dataloader/dataset.py:17
Method__init__
(self, inplanes, planes, stride=1)
CLIP_training/model/model.py:13
Method__init__
(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None)
CLIP_training/model/model.py:57
Method__init__
(self, layers, output_dim, heads, input_resolution=224, width=64)
CLIP_training/model/model.py:101
Method__init__
(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None)
CLIP_training/model/model.py:176
Method__init__
(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None)
CLIP_training/model/model.py:200
Method__init__
(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int)
CLIP_training/model/model.py:211
Method__init__
(self, root_path_1, root_path_2, prompt_D1_M1, prompt_D1_M2, repeat=1, cache='none')
datasets/image_folder.py:26
Method__init__
(self, root_path_1, root_path_2, prompt_D1_M1, prompt_D1_M2, repeat, cache, **kwargs)
datasets/image_folder.py:91
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