initial commit

examples/main.py

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+# Imports
+# =============================================================================
+import os
+from distutils.util import strtobool
+from pathlib import Path
+from argparse import ArgumentParser, Namespace
+
+import warnings
+
+import torch
+from pytorch_lightning import Trainer
+from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
+
+from ml_lib.modules.utils import LightningBaseModule
+from ml_lib.utils.config import Config
+from ml_lib.utils.logging import Logger
+from ml_lib.utils.model_io import SavedLightningModels
+
+warnings.filterwarnings('ignore', category=FutureWarning)
+warnings.filterwarnings('ignore', category=UserWarning)
+
+_ROOT = Path(__file__).parent
+
+# Parameter Configuration
+# =============================================================================
+# Argument Parser
+main_arg_parser = ArgumentParser(description="parser for fast-neural-style")
+
+# Main Parameters
+main_arg_parser.add_argument("--main_debug", type=strtobool, default=False, help="")
+main_arg_parser.add_argument("--main_eval", type=strtobool, default=True, help="")
+main_arg_parser.add_argument("--main_seed", type=int, default=69, help="")
+
+# Data Parameters
+main_arg_parser.add_argument("--data_worker", type=int, default=10, help="")
+main_arg_parser.add_argument("--data_dataset_length", type=int, default=10000, help="")
+main_arg_parser.add_argument("--data_root", type=str, default='data', help="")
+main_arg_parser.add_argument("--data_map_root", type=str, default='res/shapes', help="")
+main_arg_parser.add_argument("--data_normalized", type=strtobool, default=True, help="")
+main_arg_parser.add_argument("--data_use_preprocessed", type=strtobool, default=True, help="")
+
+main_arg_parser.add_argument("--data_mode", type=str, default='vae_no_label_in_map', help="")
+
+# Transformations
+main_arg_parser.add_argument("--transformations_to_tensor", type=strtobool, default=False, help="")
+
+# Transformations
+main_arg_parser.add_argument("--train_outpath", type=str, default="output", help="")
+main_arg_parser.add_argument("--train_version", type=strtobool, required=False, help="")
+main_arg_parser.add_argument("--train_epochs", type=int, default=500, help="")
+main_arg_parser.add_argument("--train_batch_size", type=int, default=200, help="")
+main_arg_parser.add_argument("--train_lr", type=float, default=1e-3, help="")
+main_arg_parser.add_argument("--train_num_sanity_val_steps", type=int, default=0, help="")
+
+# Model
+main_arg_parser.add_argument("--model_type", type=str, default="CNNRouteGenerator", help="")
+main_arg_parser.add_argument("--model_activation", type=str, default="leaky_relu", help="")
+main_arg_parser.add_argument("--model_filters", type=str, default="[16, 32, 64]", help="")
+main_arg_parser.add_argument("--model_classes", type=int, default=2, help="")
+main_arg_parser.add_argument("--model_lat_dim", type=int, default=16, help="")
+main_arg_parser.add_argument("--model_use_bias", type=strtobool, default=True, help="")
+main_arg_parser.add_argument("--model_use_norm", type=strtobool, default=False, help="")
+main_arg_parser.add_argument("--model_use_res_net", type=strtobool, default=False, help="")
+main_arg_parser.add_argument("--model_dropout", type=float, default=0.00, help="")
+
+# Project
+main_arg_parser.add_argument("--project_name", type=str, default='traj-gen', help="")
+main_arg_parser.add_argument("--project_owner", type=str, default='si11ium', help="")
+main_arg_parser.add_argument("--project_neptune_key", type=str, default=os.getenv('NEPTUNE_KEY'), help="")
+
+# Parse it
+args: Namespace = main_arg_parser.parse_args()
+
+
+def run_lightning_loop(config_obj):
+
+    # Logging
+    # ================================================================================
+    # Logger
+    with Logger(config_obj) as logger:
+        # Callbacks
+        # =============================================================================
+        # Checkpoint Saving
+        checkpoint_callback = ModelCheckpoint(
+            filepath=str(logger.log_dir / 'ckpt_weights'),
+            verbose=True, save_top_k=0,
+        )
+
+        # =============================================================================
+        # Early Stopping
+        # TODO: For This to work, one must set a validation step and End Eval and Score
+        early_stopping_callback = EarlyStopping(
+            monitor='val_loss',
+            min_delta=0.0,
+            patience=0,
+        )
+
+        # Model
+        # =============================================================================
+        # Init
+        model: LightningBaseModule = config_obj.model_class(config_obj.model_paramters)
+        model.init_weights(torch.nn.init.xavier_normal_)
+        if model.name == 'CNNRouteGeneratorDiscriminated':
+            # ToDo: Make this dependent on the used seed
+            path = logger.outpath / 'classifier_cnn' / 'version_0'
+            disc_model = SavedLightningModels.load_checkpoint(path).restore()
+            model.set_discriminator(disc_model)
+
+        # Trainer
+        # =============================================================================
+        trainer = Trainer(max_epochs=config_obj.train.epochs,
+                          show_progress_bar=True,
+                          weights_save_path=logger.log_dir,
+                          gpus=[0] if torch.cuda.is_available() else None,
+                          check_val_every_n_epoch=10,
+                          # num_sanity_val_steps=config_obj.train.num_sanity_val_steps,
+                          # row_log_interval=(model.n_train_batches * 0.1),  # TODO: Better Value / Setting
+                          # log_save_interval=(model.n_train_batches * 0.2),  # TODO: Better Value / Setting
+                          checkpoint_callback=checkpoint_callback,
+                          logger=logger,
+                          fast_dev_run=config_obj.main.debug,
+                          early_stop_callback=None
+                          )
+
+        # Train It
+        trainer.fit(model)
+
+        # Save the last state & all parameters
+        trainer.save_checkpoint(logger.log_dir / 'weights.ckpt')
+        model.save_to_disk(logger.log_dir)
+
+        # Evaluate It
+        if config_obj.main.eval:
+            trainer.test()
+
+    return model
+
+
+if __name__ == "__main__":
+
+    config = Config.read_namespace(args)
+    trained_model = run_lightning_loop(config)

examples/multi_run.py

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+import warnings
+
+from ml_lib.utils.config import Config
+
+warnings.filterwarnings('ignore', category=FutureWarning)
+warnings.filterwarnings('ignore', category=UserWarning)
+
+# Imports
+# =============================================================================
+
+from main import run_lightning_loop, args
+
+
+if __name__ == '__main__':
+
+    # Model Settings
+    config = Config().read_namespace(args)
+    # use_bias, activation, model, use_norm, max_epochs, filters
+    cnn_classifier = dict(train_epochs=10, model_use_bias=True, model_use_norm=True, model_activation='leaky_relu',
+                          model_type='classifier_cnn', model_filters=[16, 32, 64], data_batchsize=512)
+    # use_bias, activation, model, use_norm, max_epochs, sr, feature_mixed_dim, filters
+
+    for arg_dict in [cnn_classifier]:
+        for seed in range(5):
+            arg_dict.update(main_seed=seed)
+
+            config = config.update(arg_dict)
+
+            run_lightning_loop(config)

examples/variables.py

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+# Labels for classes
+HOMOTOPIC = 1
+ALTERNATIVE = 0
+ANY = -1
+
+# Colors for img files
+WHITE = 255
+BLACK = 0
+
+# Variables for plotting
+PADDING = 0.25
+DPI = 50