Functions — github.com/AI4HealthUOL/ecg-fm-benchmarking

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Functions823 in github.com/AI4HealthUOL/ecg-fm-benchmarking

⨍ Functions 823◇ Types & classes 227

Method__init__

( self, d_input, # None to disable encoder d_output, # None to disable decoder

code/clinical_ts/models/s4_model.py:9

Method__init__

(self, ni, nf, ks=3, stride=1, padding=None, bias=None, norm_type=NormType.Batch, bn_1st=True,

code/clinical_ts/models/xresnet1d.py:38

Method__init__

(self, expansion, ni, nf, stride=1, kernel_size=3, groups=1, nh1=None, nh2=None, dw=False, g2=1,

code/clinical_ts/models/xresnet1d.py:88

Method__init__

(self, block, expansion, layers, input_channels=3, num_classes=1000, stem_szs=(32,32,64), input_size=1000, hea

code/clinical_ts/models/xresnet1d.py:129

Method__init__

(self, d_input, d_output, bias=True)

code/clinical_ts/models/s42.py:158

Method__init__

L: Maximum length; this module computes an SSM kernel of length L w: (N) p: (r, N) low-rank correction to A q: (r, N)

code/clinical_ts/models/s42.py:529

Method__init__

( self, H, N=64, L=1, measure="legs", rank=1, channels

code/clinical_ts/models/s42.py:962

Method__init__

(self, num_classes=1, num_output_tokens=1)

code/clinical_ts/models/fm_eeg.py:12

Method__init__

(self, num_classes, num_output_tokens, pretrained_path=None, eval_mode="finetuning_linear", lr=1e-3, discrimin

code/clinical_ts/models/fm_ecg.py:40

Method__init__

(self, num_classes, num_output_tokens, pretrained_path=None, eval_mode="finetuning_linear", lr=1e-3, discrimin

code/clinical_ts/models/fm_ecg.py:184

Method__init__

(self, num_classes)

code/clinical_ts/models/fm_ecg.py:311

Method__init__

(self, num_classes, num_output_tokens, pretrained_path=None, eval_mode="finetuning_linear", lr=1e-3, discrimin

code/clinical_ts/models/fm_ecg.py:345

Method__init__

(self, num_classes, num_output_tokens, backbone="resnet", pretrained_path=None, eval_mode="finetuning_linear",

code/clinical_ts/models/fm_ecg.py:490

Method__init__

(self, num_classes, num_output_tokens, pretrained_path=None, eval_mode="finetuning_linear", lr=1e-3, discrimin

code/clinical_ts/models/fm_ecg.py:640

Method__init__

(self, num_classes, num_output_tokens, pretrained_path=None, eval_mode="finetuning_linear", lr=1e-3, discrimin

code/clinical_ts/models/fm_ecg.py:845

Method__init__

(self, lambd)

code/clinical_ts/models/basic_conv1d.py:44

Method__init__

Output will be 2*sz or 2 if sz is None

code/clinical_ts/models/basic_conv1d.py:86

Method__init__

(self,channels,reduction=16)

code/clinical_ts/models/basic_conv1d.py:96

Method__init__

(self, filters=[128,128,128,128],kernel_size=3, stride=2, dilation=1, pool=0, pool_stride=1, squeeze_excite_re

code/clinical_ts/models/basic_conv1d.py:140

Method__init__

(self, ni, nb_filters, kss, stride=1, act='linear', bottleneck_size=32)

code/clinical_ts/models/inception1d.py:21

Method__init__

(self, input_channels, kss, depth, bottleneck_size, nb_filters, use_residual)

code/clinical_ts/models/inception1d.py:50

Method__init__

(self, num_classes=2, input_channels=8, kss=[39,19,9], depth=6, bottleneck_size=32, nb_filters=32, use_residua

code/clinical_ts/models/inception1d.py:74

Method__init__

Output will be 2*sz or 2 if sz is None

code/clinical_ts/models/ecg_foundation_models/ecgfm_ked.py:59

Method__init__

(self, ni, nf, ks=3, stride=1, padding=None, bias=None, ndim=2, norm_type=NormType.Batch, bn_1st=True,

code/clinical_ts/models/ecg_foundation_models/ecgfm_ked.py:105

Method__init__

(self, block, expansion, layers, p=0.0, input_channels=3, num_classes=1000, stem_szs=(32, 32, 64),

code/clinical_ts/models/ecg_foundation_models/ecgfm_ked.py:180

Method__init__

(self, in_channels, out_channels, kernel_size, stride, groups=1)

code/clinical_ts/models/ecg_foundation_models/ecg_founder.py:19

Method__init__

(self, kernel_size)

code/clinical_ts/models/ecg_foundation_models/ecg_founder.py:59

Method__init__

(self, in_channels, out_channels, ratio, kernel_size, stride, groups, downsample, is_first_block=False, use_bn

code/clinical_ts/models/ecg_foundation_models/ecg_founder.py:101

Method__init__

(self, in_channels, out_channels, ratio, kernel_size, stride, groups, i_stage, m_blocks, use_bn=True, use_do=T

code/clinical_ts/models/ecg_foundation_models/ecg_founder.py:219

Method__init__

( self, hubert_ecg : HuBERTECG, num_labels : int, classifier_hidden_size : int

code/clinical_ts/models/ecg_foundation_models/hubert_ecg/hubert_ecg_classification.py:28

Method__init__

(self, ensemble_length: int = 1, vocab_sizes: List[int] = [100], **kwargs)

code/clinical_ts/models/ecg_foundation_models/hubert_ecg/hubert_ecg.py:16

Method__init__

(self, seq_len: int = 2250, patch_size: int = 1, num_leads:

code/clinical_ts/models/ecg_foundation_models/st_mem/mlae.py:27

Method__init__

(self, seq_len: int = 2250, patch_size: int = 75, num_leads

code/clinical_ts/models/ecg_foundation_models/st_mem/mtae.py:47

Method__init__

(self, seq_len: int = 2250, patch_size: int = 75, num_leads

code/clinical_ts/models/ecg_foundation_models/st_mem/st_mem.py:48

Method__init__

(self, drop_prob: float, scale_by_keep: bool = True)

code/clinical_ts/models/ecg_foundation_models/st_mem/encoder/vit.py:26

Method__init__

(self, dim: int, fn: nn.Module)

code/clinical_ts/models/ecg_foundation_models/st_mem/encoder/vit.py:43

Method__init__

(self, input_dim: int, output_dim: int, hidden_dim: int,

code/clinical_ts/models/ecg_foundation_models/st_mem/encoder/vit.py:58

Method__init__

(self, input_dim: int, output_dim: int, heads: int = 8,

code/clinical_ts/models/ecg_foundation_models/st_mem/encoder/vit.py:75

Method__init__

(self, input_dim: int, output_dim: int, hidden_dim: int,

code/clinical_ts/models/ecg_foundation_models/st_mem/encoder/vit.py:115

Method__init__

(self, seq_len: int, patch_size: int, num_leads: int,

code/clinical_ts/models/ecg_foundation_models/st_mem/encoder/st_mem_vit.py:25

Method__init__

(self, seq_len: int, patch_size: int, num_leads: int,

code/clinical_ts/models/ecg_foundation_models/st_mem/encoder/mlae_vit.py:24

Method__init__

(self, in_channels, out_channels, stride=1)

code/clinical_ts/models/ecg_foundation_models/merl/resnet1d.py:7

Method__init__

(self, in_channels, out_channels, stride=1)

code/clinical_ts/models/ecg_foundation_models/merl/resnet1d.py:35

Method__init__

(self, drop_prob: float, scale_by_keep: bool = True)

code/clinical_ts/models/ecg_foundation_models/merl/vit1d.py:20

Method__init__

(self, dim: int, fn: nn.Module)

code/clinical_ts/models/ecg_foundation_models/merl/vit1d.py:37

Method__init__

(self, input_dim: int, output_dim: int, hidden_dim: int,

code/clinical_ts/models/ecg_foundation_models/merl/vit1d.py:52

Method__init__

(self, input_dim: int, output_dim: int, heads: int = 8,

code/clinical_ts/models/ecg_foundation_models/merl/vit1d.py:69

Method__init__

(self, input_dim: int, output_dim: int, hidden_dim: int,

code/clinical_ts/models/ecg_foundation_models/merl/vit1d.py:109

Method__init__

(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.)

code/clinical_ts/models/ecg_foundation_models/ecg_jepa/ecg_jepa.py:129

Method__init__

(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., dr

code/clinical_ts/models/ecg_foundation_models/ecg_jepa/ecg_jepa.py:158

Method__init__

(self, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4., qkv_bias=False, qk_scale=None, dro

code/clinical_ts/models/ecg_foundation_models/ecg_jepa/ecg_jepa.py:177

Method__init__

(self, predictor_embed_dim=192, depth=4, num_heads=6, mlp_ratio=4., qkv_bias=False, qk_scale=None,

code/clinical_ts/models/ecg_foundation_models/ecg_jepa/ecg_jepa.py:195

Method__init__

( self, embed_dim=384, depth=12, num_heads=6,

code/clinical_ts/models/ecg_foundation_models/ecg_jepa/ecg_jepa.py:214

Method__init__

( self, embed_dim=384, predictor_embed_dim=192,

code/clinical_ts/models/ecg_foundation_models/ecg_jepa/ecg_jepa.py:413

Method__init__

( self, encoder_embed_dim=384, encoder_depth=12,

code/clinical_ts/models/ecg_foundation_models/ecg_jepa/ecg_jepa.py:515

Method__init__

(self, hparams_loss)

code/clinical_ts/loss/supervised.py:24

Method__init__

(self, hparams_loss)

code/clinical_ts/loss/supervised.py:56

Method__init__

(self, hparams_loss)

code/clinical_ts/loss/supervised.py:75

Method__init__

(self, hparams_loss)

code/clinical_ts/loss/supervised.py:98

Method__init__

(self, hparams_loss)

code/clinical_ts/loss/supervised.py:143

Method__init__

(self, hparams_loss)

code/clinical_ts/loss/selfsupervised.py:13

Method__init__

(self, extension_dir, source_files, params_list, benchmark_script, benchmark_args, npool=8, v

code/extensions/cauchy/tuner.py:137

Method__len__

(self)

code/clinical_ts/data/time_series_dataset.py:254

Method__repr__

(self)

code/clinical_ts/models/ecg_foundation_models/st_mem/mtae.py:276

Method__repr__

(self)

code/clinical_ts/models/ecg_foundation_models/st_mem/st_mem.py:294

Method__repr__

(self)

code/clinical_ts/models/ecg_foundation_models/st_mem/encoder/vit.py:237

Method__repr__

(self)

code/clinical_ts/models/ecg_foundation_models/st_mem/encoder/st_mem_vit.py:127

Method__str__

(self)

code/clinical_ts/template_modules.py:201

Method__str__

(self)

code/clinical_ts/template_modules.py:243

Method__str__

(self)

code/clinical_ts/template_modules.py:266

Method__str__

(self)

code/clinical_ts/template_modules.py:287

Method__str__

(self)

code/clinical_ts/template_modules.py:309

Method__str__

(self)

code/clinical_ts/template_modules.py:332

Method__str__

(self)

code/clinical_ts/template_modules.py:354

Method__str__

(self)

code/clinical_ts/template_modules.py:574

Method__str__

(self)

code/clinical_ts/template_modules.py:672

Method__str__

(self)

code/clinical_ts/utils/heads.py:25

Method__str__

(self)

code/clinical_ts/ts/head.py:348

Function_age_to_categorical

(age)

code/clinical_ts/utils/ecg_utils.py:105

Method_asdict

Convert the TSData object to a dictionary, excluding None fields.

code/clinical_ts/data/time_series_dataset.py:32

Function_eval

(ids, input_tuple, score_fn, input_tuple2=None,score_fn_kwargs={})

code/clinical_ts/utils/bootstrap_utils.py:9

Method_eval

(self,targs,preds)

code/clinical_ts/metric/base.py:99

Method_eval

(self,targs,preds)

code/clinical_ts/metric/base.py:145

Method_eval

(self,targs,preds)

code/clinical_ts/metric/base.py:201

Method_eval

(self,targs,preds)

code/clinical_ts/metric/base.py:237

Method_eval

(self,targs,preds)

code/clinical_ts/metric/base.py:335

Function_f

(f)

code/clinical_ts/models/ecg_foundation_models/ecgfm_ked.py:23

Function_fc

(in_planes,out_planes, act="relu", bn=True)

code/clinical_ts/ts/basic_conv1d_modules/basic_conv1d.py:72

Function_fc

(in_planes,out_planes, act="relu", bn=True)

code/clinical_ts/models/basic_conv1d.py:72

Function_freeze_bn_stats

(model, freeze=True)

code/clinical_ts/utils/misc_utils.py:31

Method_init_weights

(self, m)

code/clinical_ts/models/ecg_foundation_models/st_mem/mtae.py:135

Method_init_weights

(self, m)

code/clinical_ts/models/ecg_foundation_models/st_mem/st_mem.py:139

Method_init_weights

(self, m)

code/clinical_ts/models/ecg_foundation_models/ecg_jepa/ecg_jepa.py:274

Method_init_weights

(self, m)

code/clinical_ts/models/ecg_foundation_models/ecg_jepa/ecg_jepa.py:473

Method_prefetch_dataset_stats

()

code/clinical_ts/template_model.py:131

Function_sex_to_categorical

(sex)

code/clinical_ts/utils/ecg_utils.py:124

Method_to_tensor

(data,transpose=False)

code/clinical_ts/data/time_series_dataset_transforms.py:253

Methodaggregate_predictions

(self, preds,targs,idmap=None,aggregate_fn = np.mean,verbose=False)

code/clinical_ts/data/time_series_dataset.py:109

Functionappend_to_df_memmap

(path_df_memmap1,path_df_memmap2,path_memmap1,path_memmap2,file_id1=0,file_id2=0,col_data="data")

code/clinical_ts/data/time_series_dataset_utils.py:289

Functionappend_to_memmap

appends the contents of memmap2(file_id2) to memmap1(file_id1 in case of split files)

code/clinical_ts/data/time_series_dataset_utils.py:259

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