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Functions1,346 in github.com/NX-AI/vision-lstm

⨍ Functions 1,346◇ Types & classes 277

(self, iterable)

src/ksuit/utils/noop_tqdm.py:2

(self, forward=None, backward=None)

src/ksuit/utils/bidict.py:2

src/ksuit/utils/logging_utils.py:56

( self, global_dataset_paths, local_dataset_path=None, data_so

src/ksuit/providers/dataset_config_provider.py:5

(self, pattern_matcher: PatternMatcher = None)

src/ksuit/providers/metric_property_provider.py:8

(self, output_path: Path, model_path: Path, stage_name: str, stage_id: str)

src/ksuit/providers/path_provider.py:5

(self, path_provider: PathProvider, metric_property_provider: MetricPropertyProvider = None)

src/ksuit/providers/summary_providers/primitive_summary_provider.py:10

src/ksuit/providers/summary_providers/noop_summary_provider.py:5

(self, path_provider: PathProvider, metric_property_provider: MetricPropertyProvider = None)

src/ksuit/providers/summary_providers/wandb_summary_provider.py:10

src/ksuit/providers/summary_providers/base/summary_provider_base.py:5

(self, path_provider: PathProvider)

src/ksuit/providers/config_providers/primitive_config_provider.py:8

(self, path_provider: PathProvider)

src/ksuit/providers/config_providers/wandb_config_provider.py:9

( self, train_dataset_key, test_dataset_key, extractors,

src/ksuit/callbacks/offline_callbacks/offline_multiclass_knn_callback.py:13

(self, dataset_key, mode, ignore_index=-1, mode_kwargs=None, interpolation="bicubic", **kwargs)

src/ksuit/callbacks/offline_callbacks/offline_segmentation_callback.py:12

(self, dataset_key, to_cpu=False, topk=None, log_loss=True, **kwargs)

src/ksuit/callbacks/offline_callbacks/offline_accuracy_callback.py:10

(self, dataset_key, extractors, forward_kwargs=None, items_to_store=None, **kwargs)

src/ksuit/callbacks/offline_callbacks/offline_features_callback.py:12

(self, dataset_key, output_patterns_to_log=None, forward_kwargs=None, **kwargs)

src/ksuit/callbacks/offline_callbacks/offline_loss_callback.py:7

(self, dataset_key, mode, mode_kwargs=None, save_input=False, **kwargs)

src/ksuit/callbacks/visualization_callbacks/visualize_segmentation_callback.py:16

( self, metric_key, save_frozen_weights=True, tolerances=None,

src/ksuit/callbacks/checkpoint_callbacks/best_checkpoint_callback.py:6

( self, save_weights=True, save_optim=False, save_latest_weigh

src/ksuit/callbacks/checkpoint_callbacks/checkpoint_callback.py:7

( self, target_factors, model_paths=None, save_weights=True,

src/ksuit/callbacks/checkpoint_callbacks/ema_callback.py:12

(self, **kwargs)

src/ksuit/callbacks/default_callbacks/train_time_callback.py:9

(self, verbose, **kwargs)

src/ksuit/callbacks/default_callbacks/online_loss_callback.py:10

src/ksuit/callbacks/default_callbacks/eta_callback.py:13

(self, verbose=False, **kwargs)

src/ksuit/callbacks/default_callbacks/param_count_callback.py:10

(self, *args, **kwargs)

src/ksuit/callbacks/default_callbacks/progress_callback.py:8

( self, keys=None, patterns=None, verbose=False, r

src/ksuit/callbacks/online_callbacks/update_output_callback.py:11

(self, verbose=True, topk=None, **kwargs)

src/ksuit/callbacks/online_callbacks/online_accuracy_callback.py:11

(self, verbose=True, ignore_index=-1, **kwargs)

src/ksuit/callbacks/online_callbacks/online_segmentation_callback.py:11

( self, data_container: DataContainer = None, config_provider: ConfigProvi

src/ksuit/callbacks/base/callback_base.py:43

( self, every_n_epochs: int = None, every_n_updates: int = None,

src/ksuit/callbacks/base/periodic_callback.py:18

(self, path_provider: PathProvider, update_counter: UpdateCounter)

src/ksuit/callbacks/base/writers/log_writer.py:15

(self, path_provider: PathProvider, update_counter: UpdateCounter)

src/ksuit/callbacks/base/writers/checkpoint_writer.py:15

(self, verbose=False, **kwargs)

src/ksuit/callbacks/monitor_callbacks/nan_hook_monitor_callback.py:7

(self, name: str, logger)

src/ksuit/callbacks/monitor_callbacks/nan_hook_monitor_callback.py:16

(self, verbose=False, **kwargs)

src/ksuit/callbacks/monitor_callbacks/gradient_spike_monitor_callback.py:9

(self, name: str, logger)

src/ksuit/callbacks/monitor_callbacks/gradient_spike_monitor_callback.py:18

(self, verbose=False, **kwargs)

src/ksuit/callbacks/monitor_callbacks/nan_loss_monitor_callback.py:10

src/ksuit/callbacks/monitor_callbacks/nan_loss_monitor_callback.py:55

(self, name: str, logger)

src/ksuit/callbacks/monitor_callbacks/nan_loss_monitor_callback.py:66

(self, verbose=False, **kwargs)

src/ksuit/callbacks/monitor_callbacks/debug_monitor_callback.py:5

(self, max_skipped_updates_in_a_row=100, queue_size=50, tolerance_factor=0.2, **kwargs)

src/ksuit/callbacks/monitor_callbacks/skip_loss_spikes_callback.py:9

(self, max_skipped_updates_in_a_row=50, **kwargs)

src/ksuit/callbacks/monitor_callbacks/skip_nan_loss_callback.py:7

(self, tolerance_factor=10, recovery_tolerance=5, **kwargs)

src/ksuit/callbacks/monitor_callbacks/kill_on_loss_spike_callback.py:7

(self, model_paths, **kwargs)

src/ksuit/callbacks/monitor_callbacks/activation_monitor_callback.py:7

( self, model, torch_optim_ctor, schedule=None, we

src/ksuit/optim/optim_wrapper.py:21

(self, divisor=256)

src/ksuit/optim/lr_scalers/linear_lr_scaler.py:5

(self, divisor=256)

src/ksuit/optim/lr_scalers/sqrt_lr_scaler.py:7

(self, scale, name)

src/ksuit/optim/param_group_modifiers/lr_scale_by_name_modifier.py:5

(self, name, value=0.)

src/ksuit/optim/param_group_modifiers/weight_decay_by_name_modifier.py:13

src/ksuit/data/errors.py:2

(self, dataset: Dataset, mode: str, return_type=tuple, **kwargs)

src/ksuit/data/wrappers/mode_wrapper.py:8

(self, dataset, smoothing)

src/ksuit/data/wrappers/sample_wrappers/label_smoothing_wrapper.py:7

(self, dataset, transform, seed=None)

src/ksuit/data/wrappers/sample_wrappers/base/transform_wrapper_base.py:10

(self, dataset, num_classes, seed=0)

src/ksuit/data/wrappers/dataset_wrappers/class_subset_wrapper.py:7

(self, dataset, repetitions=None, min_size=None)

src/ksuit/data/wrappers/dataset_wrappers/repeat_wrapper.py:9

(self, dataset, indices=None, start_index=None, end_index=None, start_percent=None, end_percent=None)

src/ksuit/data/wrappers/dataset_wrappers/subset_wrapper.py:7

(self, dataset, seed=None)

src/ksuit/data/wrappers/dataset_wrappers/shuffle_wrapper.py:7

(self, collators)

src/ksuit/data/collators/compose_collator.py:5

(self, *args, **kwargs)

src/ksuit/data/collators/stochastic_collator.py:9

(self, collators=None)

src/ksuit/data/base/dataset.py:13

(self, dataset, indices, **kwargs)

src/ksuit/data/base/subset.py:8

(self, dataset: Dataset)

src/ksuit/data/base/wrapper.py:8

(self, transforms)

src/ksuit/data/transforms/base/compose_transform.py:7

(self, **kwargs)

src/ksuit/data/transforms/base/stochastic_transform.py:8

(self, mean, std, **kwargs)

src/ksuit/data/transforms/norm/image_moment_norm.py:8

(self, inverse=False, inplace=True, **kwargs)

src/ksuit/data/transforms/norm/base/norm_base.py:5

(self, model, trainer)

src/ksuit/trainers/classification_trainer.py:47

(self, model, trainer)

src/ksuit/trainers/segmentation_trainer.py:50

(self, metric_key, tolerance, metric_property_provider: MetricPropertyProvider = None, **kwargs)

src/ksuit/trainers/early_stoppers/metric_early_stopper.py:6

( self, every_n_epochs=None, every_n_updates=None, every_n_sam

src/ksuit/trainers/early_stoppers/base/early_stopper_base.py:7

( self, data_container: DataContainer, device: str, precision,

src/ksuit/trainers/base/sgd_trainer.py:54

( self, global_root, local_root=None, split=None,

src/ksuit/datasets/multiclass_dataset.py:11

(self, length=10, x_shape=(7,), num_classes=9, **kwargs)

src/ksuit/datasets/random_dataset.py:7

(self, repo, model, load_kwargs=None, source="github", **kwargs)

src/ksuit/models/torchhub_model.py:7

(self, **kwargs)

src/ksuit/models/linear_model.py:8

(self, model_name, model_kwargs=None, input_shape=None, output_shape=None, **kwargs)

src/ksuit/models/torchvision_model.py:8

(self, concat_cls=False, **kwargs)

src/ksuit/models/poolings/to_image.py:9

(self, index, **kwargs)

src/ksuit/models/poolings/index_pooling.py:5

(self, static_ctx=None)

src/ksuit/models/poolings/base/pooling_base.py:6

( self, input_shape=None, output_shape=None, ctor_kwargs=None,

src/ksuit/models/base/model_base.py:13

( self, optim_ctor=None, freezers=None, is_frozen=False,

src/ksuit/models/base/single_model.py:10

(self, *args, **kwargs)

src/ksuit/models/base/composite_model.py:8

(self, block_index=None, block_indices=None, num_last_blocks=None, **kwargs)

src/ksuit/models/extractors/block_extractor.py:6

src/ksuit/models/extractors/finalizers/concat_finalizer.py:5

src/ksuit/models/extractors/finalizers/index_finalizer.py:2

(self, dim: int = 1)

src/ksuit/models/extractors/finalizers/stack_finalizer.py:5

(self, outputs: dict, raise_exception: bool = False)

src/ksuit/models/extractors/base/forward_hook.py:7

( self, pooling=None, finalizer=None, raise_exception=False,

src/ksuit/models/extractors/base/extractor_base.py:6

(self, loss_function)

src/ksuit/losses/elementwise_loss.py:8

(self, weight=None, is_frozen=True, **kwargs)

src/ksuit/losses/base/loss_base.py:6

(self, beta=1.0)

src/ksuit/losses/basic/smooth_l1_loss.py:6

(self, dim, num_heads, bias=False)

vision_lstm/vision_lstm.py:140

(self, dim, kernel_size=4, bias=True)

vision_lstm/vision_lstm.py:194

( self, ndim: int = -1, weight: bool = True, bias: bool = Fals

vision_lstm/vision_lstm.py:228

(self, dim, num_heads)

vision_lstm/vision_lstm.py:292

(self, dim, direction, drop_path=0.0, norm_bias=False)

vision_lstm/vision_lstm.py:480

( self, dim=192, input_shape=(3, 224, 224), patch_size=16,

vision_lstm/vision_lstm.py:508

(self, *args, seqlens=None, **kwargs)

vision_lstm/vision_lstm_util.py:41

(self, dim, num_channels, resolution, patch_size, stride=None, init_weights="xavier_uniform")

vision_lstm/vision_lstm_util.py:63

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