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import torch.nn.functional as F | |
# Seed | |
SEED = 1 | |
# Dataset | |
CLASSES = ( | |
"Airplane", | |
"Automobile", | |
"Bird", | |
"Cat", | |
"Deer", | |
"Dog", | |
"Frog", | |
"Horse", | |
"Ship", | |
"Truck", | |
) | |
SHUFFLE = True | |
DATA_DIR = "../data" | |
NUM_WORKERS = 4 | |
PIN_MEMORY = True | |
# Training Hyperparameters | |
CRITERION = F.cross_entropy | |
INPUT_SIZE = (3, 32, 32) | |
NUM_CLASSES = 10 | |
LEARNING_RATE = 0.001 | |
WEIGHT_DECAY = 1e-4 | |
BATCH_SIZE = 512 | |
NUM_EPOCHS = 24 | |
DROPOUT_PERCENTAGE = 0.05 | |
LAYER_NORM = "bn" # Batch Normalization | |
# OPTIMIZER & SCHEDULER | |
LRFINDER_END_LR = 0.1 | |
LRFINDER_NUM_ITERATIONS = 50 | |
LRFINDER_STEP_MODE = "exp" | |
OCLR_DIV_FACTOR = 100 | |
OCLR_FINAL_DIV_FACTOR = 100 | |
OCLR_THREE_PHASE = False | |
OCLR_ANNEAL_STRATEGY = "linear" | |
# Compute Related | |
ACCELERATOR = "cpu" | |
PRECISION = 32 | |
# Store | |
TRAINING_STAT_STORE = "Store/training_stats.csv" | |
MODEL_SAVE_PATH = "Store/model.pth" | |
PRED_STORE_PATH = "Store/pred_store.pth" | |
EXAMPLE_IMG_PATH = "Store/examples/" | |
# Visualization | |
NORM_CONF_MAT = True | |