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"""Model utility functions."""
import torch
import os.path
import numpy as np
import matplotlib.pyplot as plt
from src.utils.logging_util import LoggingUtils
logger = LoggingUtils.configure_logger(log_name=__name__)
def save_model(model, filepath="model.pth"):
"""Funtion to save model weights to a file."""
save_dir = os.path.dirname(filepath)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
torch.save(model.state_dict(), filepath)
logger.info(f"Model saved to {filepath}")
def load_model(model_class, filepath, device, model_args):
"""Function to load model weights and initialize the model."""
model = model_class(**model_args).to(device)
model.load_state_dict(torch.load(filepath, map_location=device))
model.eval()
logger.info(f"Model loaded from {filepath}")
return model
def display_num_param(net):
nb_param = 0
for param in net.parameters():
nb_param += param.numel()
print('Number of parameters: {} ({:.2f} million)'.format(nb_param, nb_param/1e6))
def get_error(scores, labels):
bs=scores.size(0)
predicted_labels = scores.argmax(dim=1)
indicator = (predicted_labels == labels)
num_matches=indicator.sum()
return 1-num_matches.float()/bs
def show(X):
if X.dim() == 3 and X.size(0) == 3:
plt.imshow( np.transpose( X.numpy() , (1, 2, 0)) )
plt.show()
elif X.dim() == 2:
plt.imshow( X.numpy() , cmap='gray' )
plt.show()
else:
logger.error("WRONG TENSOR SIZE") |