81bbf66aab
* Add project code * Logger improvements * Improvements to web demo code * added create_wlasl_landmarks_dataset.py and xtract_mediapipe_landmarks.py * Fix rotation augmentation * fixed error in docstring, and removed unnecessary replace -1 -> 0 * Readme updates * Share base notebooks * Add notebooks and unify for different datasets * requirements update * fixes * Make evaluate more deterministic * Allow training with clearml * refactor preprocessing and apply linter * Minor fixes * Minor notebook tweaks * Readme updates * Fix PR comments * Remove unneeded code * Add banner to Readme --------- Co-authored-by: Gabriel Lema <gabriel.lema@xmartlabs.com>
85 lines
5.1 KiB
Python
85 lines
5.1 KiB
Python
import argparse
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def get_default_args():
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parser = argparse.ArgumentParser(add_help=False)
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parser.add_argument("--experiment_name", type=str, default="lsa_64_spoter",
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help="Name of the experiment after which the logs and plots will be named")
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parser.add_argument("--num_classes", type=int, default=100, help="Number of classes to be recognized by the model")
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parser.add_argument("--hidden_dim", type=int, default=108,
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help="Hidden dimension of the underlying Transformer model")
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parser.add_argument("--seed", type=int, default=379,
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help="Seed with which to initialize all the random components of the training")
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# Embeddings
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parser.add_argument("--classification_model", action='store_true', default=False,
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help="Select SPOTER model to train, pass only for original classification model")
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parser.add_argument("--vector_length", type=int, default=32,
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help="Number of features used in the embedding vector")
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parser.add_argument("--epoch_iters", type=int, default=-1,
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help="Iterations per epoch while training embeddings. Will loop through dataset once if -1")
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parser.add_argument("--batch_size", type=int, default=32, help="Batch Size during training and validation")
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parser.add_argument("--hard_triplet_mining", type=str, default=None,
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help="Strategy to select hard triplets, options [None, in_batch]")
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parser.add_argument("--triplet_loss_margin", type=float, default=1,
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help="Margin used in triplet loss margin (See documentation)")
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parser.add_argument("--normalize_embeddings", action='store_true', default=False,
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help="Normalize model output to keep vector length to one")
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parser.add_argument("--filter_easy_triplets", action='store_true', default=False,
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help="Filter easy triplets in online in batch triplets")
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# Data
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parser.add_argument("--dataset_name", type=str, default="", help="Dataset name")
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parser.add_argument("--dataset_project", type=str, default="Sign Language Recognition", help="Dataset project name")
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parser.add_argument("--training_set_path", type=str, default="",
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help="Path to the training dataset CSV file (relative to root dataset)")
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parser.add_argument("--validation_set_path", type=str, default="", help="Path to the validation dataset CSV file")
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parser.add_argument("--dataset_loader", type=str, default="local",
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help="Dataset loader to use, options: [clearml, local]")
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# Training hyperparameters
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parser.add_argument("--epochs", type=int, default=1300, help="Number of epochs to train the model for")
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parser.add_argument("--lr", type=float, default=0.001, help="Learning rate for the model training")
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parser.add_argument("--dropout", type=float, default=0.1,
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help="Dropout used in transformer layer")
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parser.add_argument("--augmentations_prob", type=float, default=0.5, help="How often to use data augmentation")
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# Checkpointing
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parser.add_argument("--save_checkpoints_every", type=int, default=-1,
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help="Determines every how many epochs the weight checkpoints are saved. If -1 only best model \
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after final epoch")
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# Optimizer
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parser.add_argument("--optimizer", type=str, default="SGD",
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help="Optimizer used during training, options: [SGD, ADAM]")
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# Tracker
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parser.add_argument("--tracker", type=str, default="none",
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help="Experiment tracker to use, options: [clearml, none]")
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# Scheduler
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parser.add_argument("--scheduler_factor", type=float, default=0,
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help="Factor for the ReduceLROnPlateau scheduler")
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parser.add_argument("--scheduler_patience", type=int, default=10,
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help="Patience for the ReduceLROnPlateau scheduler")
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parser.add_argument("--scheduler_warmup", type=int, default=400,
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help="Warmup epochs before scheduler starts")
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# Gaussian noise normalization
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parser.add_argument("--gaussian_mean", type=float, default=0, help="Mean parameter for Gaussian noise layer")
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parser.add_argument("--gaussian_std", type=float, default=0.001,
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help="Standard deviation parameter for Gaussian noise layer")
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# Batch Sorting
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parser.add_argument("--start_mining_hard", type=int, default=None, help="On which epoch to start hard mining")
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parser.add_argument("--hard_mining_pre_batch_multipler", type=int, default=16,
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help="How many batches should be computed at once")
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parser.add_argument("--hard_mining_pre_batch_mining_count", type=int, default=5,
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help="How many times to loop through a list of computed batches")
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parser.add_argument("--hard_mining_scheduler_triplets_threshold", type=float, default=0,
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help="Enables batching grouping scheduler if > 0. Defines threshold for when to decay the \
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distance threshold of the batch sorter")
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return parser
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