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Functions595 in github.com/MediaBrain-SJTU/MedKLIP

⨍ Functions 595◇ Types & classes 91
Method__init__
(self, optimizer: torch.optim.Optimizer, param_group_field: str,
Sample_Finetuning_SIIMACR/I2_segmentation/scheduler/scheduler.py:25
Method__init__
(self, optimizer: torch.optim.Optimizer, t_initial: int, t_
Sample_Finetuning_SIIMACR/I2_segmentation/scheduler/cosine_lr.py:28
Method__init__
(self, optimizer: torch.optim.Optimizer, decay_t: float, de
Sample_Finetuning_SIIMACR/I2_segmentation/scheduler/step_lr.py:17
Method__init__
(self, N=2, M=10, isPIL=False, augs=[])
Sample_Finetuning_SIIMACR/I2_segmentation/dataset/randaugment.py:312
Method__init__
(self, csv_path,is_train=True)
Sample_Finetuning_SIIMACR/I2_segmentation/dataset/dataset_siim_acr.py:12
Method__init__
(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0)
Sample_Finetuning_SIIMACR/I2_segmentation/optim/radam.py:90
Method__init__
(self, base_optimizer, alpha=0.5, k=6)
Sample_Finetuning_SIIMACR/I2_segmentation/optim/lookahead.py:13
Method__init__
(self, params, lr=0.1, betas=(0.9, 0.999), eps=1e-8, weight_decay=0.0, hessian_power=1.0, upd
Sample_Finetuning_SIIMACR/I2_segmentation/optim/adahessian.py:26
Method__init__
(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, delta=0.1, wd_ratio=0.1
Sample_Finetuning_SIIMACR/I2_segmentation/optim/adamp.py:17
Method__init__
(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=1e-2, amsgrad=False)
Sample_Finetuning_SIIMACR/I2_segmentation/optim/adamw.py:36
Method__init__
(self, params, lr=required, momentum=0, dampening=0, weight_decay=0, nesterov=False, eps=1e-8
Sample_Finetuning_SIIMACR/I2_segmentation/optim/sgdp.py:17
Method__init__
(self, params, lr=None, eps=1e-30, eps_scale=1e-3, clip_threshold=1.0, decay_rate=-0.8, betas
Sample_Finetuning_SIIMACR/I2_segmentation/optim/adafactor.py:43
Method__init__
(self, params, lr=1e-2, alpha=0.9, eps=1e-10, weight_decay=0, momentum=0., centered=False, de
Sample_Finetuning_SIIMACR/I2_segmentation/optim/rmsprop_tf.py:48
Method__init__
(self, params, lr=2e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, schedule_decay=4e-3)
Sample_Finetuning_SIIMACR/I2_segmentation/optim/nadam.py:28
Method__init__
(self, params, lr=1e-3, betas=(0.95, 0.98), eps=1e-8, weight_decay=0, grad_averaging=False, a
Sample_Finetuning_SIIMACR/I2_segmentation/optim/nvnovograd.py:32
Method__init__
(self, params, grad_averaging=False, lr=0.1, betas=(0.95, 0.98), eps=1e-8, weight_decay=0)
Sample_Finetuning_SIIMACR/I2_segmentation/optim/novograd.py:13
Method__init__
(self, in_channels, out_channels, padding=1, kernel_size=3, stride=1, with_nonlinearity=True)
Sample_Finetuning_SIIMACR/I2_segmentation/models/resunet.py:13
Method__init__
(self, in_channels, out_channels, up_conv_in_channels=None, up_conv_out_channels=None, upsamp
Sample_Finetuning_SIIMACR/I2_segmentation/models/resunet.py:50
Method__init__
(self, res_base_model,out_size,imagenet_pretrain,linear_probe=False)
Sample_Finetuning_SIIMACR/I2_segmentation/models/resunet.py:82
Method__init__
(self, csv_path,is_train = True)
Sample_zero-shot_Classification_CXR14/dataset/dataset.py:21
Method__init__
(self, N=2, M=10, isPIL=False, augs=[])
Sample_zero-shot_Classification_CXR14/dataset/randaugment.py:312
Method__init__
(self, config, disease_book)
Sample_zero-shot_Classification_CXR14/models/model_MedKLIP.py:28
Method__init__
(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu", normalize_before
Sample_zero-shot_Classification_CXR14/models/transformer.py:61
Method__init__
( self, vocab_file, do_lower_case=True, do_basic_tokenize=True, never_
Sample_zero-shot_Classification_CXR14/models/tokenization_bert.py:161
Method__init__
(self, do_lower_case=True, never_split=None, tokenize_chinese_chars=True, strip_accents=None)
Sample_zero-shot_Classification_CXR14/models/tokenization_bert.py:360
Method__init__
(self, vocab, unk_token, max_input_chars_per_word=100)
Sample_zero-shot_Classification_CXR14/models/tokenization_bert.py:491
Method__len__
(self)
PreTrain_MedKLIP/dataset/dataset.py:95
Method__len__
(self)
PreTrain_MedKLIP/data_file/preprocessing/adj_matrix.py:182
Method__len__
(self)
Sample_Zero-Shot_Grounding_RSNA/dataset/dataset_RSNA.py:73
Method__len__
(self)
Sample_Finetuning_SIIMACR/I1_classification/dataset/dataset_siim_acr.py:76
Method__len__
(self)
Sample_Finetuning_SIIMACR/I2_segmentation/dataset/dataset_siim_acr.py:55
Method__len__
(self)
Sample_zero-shot_Classification_CXR14/dataset/dataset.py:58
Method__setstate__
(self, state)
PreTrain_MedKLIP/optim/radam.py:95
Method__setstate__
(self, state)
PreTrain_MedKLIP/optim/adamw.py:50
Method__setstate__
(self, state)
PreTrain_MedKLIP/optim/rmsprop_tf.py:65
Method__setstate__
(self, state)
PreTrain_MedKLIP/optim/nvnovograd.py:49
Method__setstate__
(self, state)
Sample_Finetuning_SIIMACR/I1_classification/optim/radam.py:95
Method__setstate__
(self, state)
Sample_Finetuning_SIIMACR/I1_classification/optim/adamw.py:50
Method__setstate__
(self, state)
Sample_Finetuning_SIIMACR/I1_classification/optim/rmsprop_tf.py:65
Method__setstate__
(self, state)
Sample_Finetuning_SIIMACR/I1_classification/optim/nvnovograd.py:49
Method__setstate__
(self, state)
Sample_Finetuning_SIIMACR/I2_segmentation/optim/radam.py:95
Method__setstate__
(self, state)
Sample_Finetuning_SIIMACR/I2_segmentation/optim/adamw.py:50
Method__setstate__
(self, state)
Sample_Finetuning_SIIMACR/I2_segmentation/optim/rmsprop_tf.py:65
Method__setstate__
(self, state)
Sample_Finetuning_SIIMACR/I2_segmentation/optim/nvnovograd.py:49
Method__str__
(self)
PreTrain_MedKLIP/utils.py:67
Method__str__
(self)
PreTrain_MedKLIP/utils.py:97
Method__str__
(self)
Sample_Finetuning_SIIMACR/I2_segmentation/utils.py:89
Method__str__
(self)
Sample_Finetuning_SIIMACR/I2_segmentation/utils.py:119
Method_channel_view
(self, x)
PreTrain_MedKLIP/optim/adamp.py:23
Method_channel_view
(self, x)
PreTrain_MedKLIP/optim/sgdp.py:23
Method_channel_view
(self, x)
Sample_Finetuning_SIIMACR/I1_classification/optim/adamp.py:23
Method_channel_view
(self, x)
Sample_Finetuning_SIIMACR/I1_classification/optim/sgdp.py:23
Method_channel_view
(self, x)
Sample_Finetuning_SIIMACR/I2_segmentation/optim/adamp.py:23
Method_channel_view
(self, x)
Sample_Finetuning_SIIMACR/I2_segmentation/optim/sgdp.py:23
Method_convert_id_to_token
Converts an index (integer) in a token (str) using the vocab.
PreTrain_MedKLIP/models/tokenization_bert.py:236
Method_convert_id_to_token
Converts an index (integer) in a token (str) using the vocab.
Sample_Zero-Shot_Grounding_RSNA/models/tokenization_bert.py:236
Method_convert_id_to_token
Converts an index (integer) in a token (str) using the vocab.
Sample_zero-shot_Classification_CXR14/models/tokenization_bert.py:236
Method_convert_token_to_id
Converts a token (str) in an id using the vocab.
PreTrain_MedKLIP/models/tokenization_bert.py:232
Method_convert_token_to_id
Converts a token (str) in an id using the vocab.
Sample_Zero-Shot_Grounding_RSNA/models/tokenization_bert.py:232
Method_convert_token_to_id
Converts a token (str) in an id using the vocab.
Sample_zero-shot_Classification_CXR14/models/tokenization_bert.py:232
Method_init_weights
r"""Initialize weights like BERT - N(0.0, 0.02), bias = 0.
PreTrain_MedKLIP/models/model_MedKLIP.py:216
Method_init_weights
r"""Initialize weights like BERT - N(0.0, 0.02), bias = 0.
Sample_Zero-Shot_Grounding_RSNA/models/model_MedKLIP.py:143
Method_init_weights
r"""Initialize weights like BERT - N(0.0, 0.02), bias = 0.
Sample_zero-shot_Classification_CXR14/models/model_MedKLIP.py:134
Method_layer_view
(self, x)
PreTrain_MedKLIP/optim/adamp.py:26
Method_layer_view
(self, x)
PreTrain_MedKLIP/optim/sgdp.py:26
Method_layer_view
(self, x)
Sample_Finetuning_SIIMACR/I1_classification/optim/adamp.py:26
Method_layer_view
(self, x)
Sample_Finetuning_SIIMACR/I1_classification/optim/sgdp.py:26
Method_layer_view
(self, x)
Sample_Finetuning_SIIMACR/I2_segmentation/optim/adamp.py:26
Method_layer_view
(self, x)
Sample_Finetuning_SIIMACR/I2_segmentation/optim/sgdp.py:26
Method_tokenize
(self, text)
PreTrain_MedKLIP/models/tokenization_bert.py:218
Method_tokenize
(self, text)
Sample_Zero-Shot_Grounding_RSNA/models/tokenization_bert.py:218
Method_tokenize
(self, text)
Sample_zero-shot_Classification_CXR14/models/tokenization_bert.py:218
Functionautocontrast_func
same output as PIL.ImageOps.autocontrast
PreTrain_MedKLIP/dataset/randaugment.py:10
Functionautocontrast_func
same output as PIL.ImageOps.autocontrast
Sample_Finetuning_SIIMACR/I1_classification/dataset/randaugment.py:10
Functionautocontrast_func
same output as PIL.ImageOps.autocontrast
Sample_Finetuning_SIIMACR/I2_segmentation/dataset/randaugment.py:10
Functionautocontrast_func
same output as PIL.ImageOps.autocontrast
Sample_zero-shot_Classification_CXR14/dataset/randaugment.py:10
Methodavg
(self)
PreTrain_MedKLIP/utils.py:48
Methodavg
(self)
Sample_Finetuning_SIIMACR/I2_segmentation/utils.py:70
Functionbrightness_func
same output as PIL.ImageEnhance.Contrast
PreTrain_MedKLIP/dataset/randaugment.py:122
Functionbrightness_func
same output as PIL.ImageEnhance.Contrast
Sample_Finetuning_SIIMACR/I1_classification/dataset/randaugment.py:122
Functionbrightness_func
same output as PIL.ImageEnhance.Contrast
Sample_Finetuning_SIIMACR/I2_segmentation/dataset/randaugment.py:122
Functionbrightness_func
same output as PIL.ImageEnhance.Contrast
Sample_zero-shot_Classification_CXR14/dataset/randaugment.py:122
Methodbuild_inputs_with_special_tokens
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens
PreTrain_MedKLIP/models/tokenization_bert.py:245
Methodbuild_inputs_with_special_tokens
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens
Sample_Zero-Shot_Grounding_RSNA/models/tokenization_bert.py:245
Methodbuild_inputs_with_special_tokens
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens
Sample_zero-shot_Classification_CXR14/models/tokenization_bert.py:245
Functioncolor_func
same output as PIL.ImageEnhance.Color
PreTrain_MedKLIP/dataset/randaugment.py:87
Functioncolor_func
same output as PIL.ImageEnhance.Color
Sample_Finetuning_SIIMACR/I1_classification/dataset/randaugment.py:87
Functioncolor_func
same output as PIL.ImageEnhance.Color
Sample_Finetuning_SIIMACR/I2_segmentation/dataset/randaugment.py:87
Functioncolor_func
same output as PIL.ImageEnhance.Color
Sample_zero-shot_Classification_CXR14/dataset/randaugment.py:87
Functioncompute_acc
(logits, label, reduction='mean')
PreTrain_MedKLIP/utils.py:174
Functioncompute_acc
(logits, label, reduction='mean')
Sample_Finetuning_SIIMACR/I2_segmentation/utils.py:196
Functioncompute_n_params
(model, return_str=True)
PreTrain_MedKLIP/utils.py:181
Functioncompute_n_params
(model, return_str=True)
Sample_Finetuning_SIIMACR/I2_segmentation/utils.py:203
Functioncontrast_func
same output as PIL.ImageEnhance.Contrast
PreTrain_MedKLIP/dataset/randaugment.py:109
Functioncontrast_func
same output as PIL.ImageEnhance.Contrast
Sample_Finetuning_SIIMACR/I1_classification/dataset/randaugment.py:109
Functioncontrast_func
same output as PIL.ImageEnhance.Contrast
Sample_Finetuning_SIIMACR/I2_segmentation/dataset/randaugment.py:109
Functioncontrast_func
same output as PIL.ImageEnhance.Contrast
Sample_zero-shot_Classification_CXR14/dataset/randaugment.py:109
Methodconvert_tokens_to_string
Converts a sequence of tokens (string) in a single string.
PreTrain_MedKLIP/models/tokenization_bert.py:240
Methodconvert_tokens_to_string
Converts a sequence of tokens (string) in a single string.
Sample_Zero-Shot_Grounding_RSNA/models/tokenization_bert.py:240
Methodconvert_tokens_to_string
Converts a sequence of tokens (string) in a single string.
Sample_zero-shot_Classification_CXR14/models/tokenization_bert.py:240
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