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project Refactor, CNN Classifier Basics

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Steffen Illium
2020-03-08 23:46:02 +01:00
parent 75e8a61628
commit cd4fdf2de3
20 changed files with 441 additions and 239 deletions
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28
main.py
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@ -10,13 +10,11 @@ import warnings
import torch
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
from torch.utils.data import DataLoader
from lib.modules.utils import LightningBaseModule
from lib.utils.config import Config
from lib.utils.logging import Logger
from lib.evaluation.classification import ROCEvaluation
from lib.utils.model_io import SavedLightningModels
warnings.filterwarnings('ignore', category=FutureWarning)
warnings.filterwarnings('ignore', category=UserWarning)
@ -36,8 +34,8 @@ 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_batchsize", type=int, default=100, 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/maps', 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="")
# Transformations
main_arg_parser.add_argument("--transformations_to_tensor", type=strtobool, default=False, help="")
@ -50,10 +48,11 @@ main_arg_parser.add_argument("--train_batch_size", type=int, default=256, help="
main_arg_parser.add_argument("--train_lr", type=float, default=0.002, help="")
# Model
main_arg_parser.add_argument("--model_type", type=str, default="classifier_cnn", help="")
main_arg_parser.add_argument("--model_type", type=str, default="generator_cnn", help="")
main_arg_parser.add_argument("--model_activation", type=str, default="relu", help="")
main_arg_parser.add_argument("--model_filters", type=str, default="[32, 16, 4]", help="")
main_arg_parser.add_argument("--model_classes", type=int, default=2, help="")
main_arg_parser.add_argument("--model_lat_dim", type=int, default=2, 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=True, help="")
main_arg_parser.add_argument("--model_dropout", type=float, default=0.00, help="")
@ -77,9 +76,10 @@ def run_lightning_loop(config_obj):
# =============================================================================
# Checkpoint Saving
checkpoint_callback = ModelCheckpoint(
filepath=str(logger.log_dir / 'ckpt_weights'),
verbose=True, save_top_k=5,
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
@ -94,6 +94,11 @@ def run_lightning_loop(config_obj):
# Init
model: LightningBaseModule = config_obj.model_class(config_obj.model_paramters)
model.init_weights()
if model.name == 'CNNRouteGenerator':
# ToDo: Make this dependent on the used seed
path = Path(Path(config_obj.train.outpath) / 'classifier_cnn' / 'version_0')
disc_model = SavedLightningModels.load_checkpoint(path).restore()
model.set_discriminator(disc_model)
# Trainer
# =============================================================================
@ -101,8 +106,8 @@ def run_lightning_loop(config_obj):
show_progress_bar=True,
weights_save_path=logger.log_dir,
gpus=[0] if torch.cuda.is_available() else None,
row_log_interval=(model.data_len * 0.01), # TODO: Better Value / Setting
log_save_interval=(model.data_len * 0.04), # TODO: Better Value / Setting
# 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,
@ -110,7 +115,7 @@ def run_lightning_loop(config_obj):
)
# Train It
trainer.fit(model,)
trainer.fit(model)
# Save the last state & all parameters
trainer.save_checkpoint(logger.log_dir / 'weights.ckpt')
@ -118,6 +123,7 @@ def run_lightning_loop(config_obj):
# Evaluate It
trainer.test()
return model