map_location=lambda storage, loc: storage)
model.load_state_dict(state_dict)
print("model loaded from {}".format(os.path.abspath(save_model_path)))
## generate predictions on test data set
if run_test:
## first optize sigmoid thresholds
print("optimizing sigmoid cutoffs for each class")
sig_scores, y_array = get_scores(model, train_loader, dtype)
sigmoid_threshold = optimize_F2(sig_scores, y_array)
print("optimal thresholds: ", sigmoid_threshold)
print("running model on test set")
test_dataset = ResnetTestDataset(test_csv_path,
test_img_path,
dtype,
img_ext=img_ext,
channel_means=NPY_7CHANNEL_MEAN,
channel_stds=NPY_7CHANNEL_STD)
test_loader = DataLoader(test_dataset,
batch_size=batch_size,
num_workers=num_workers)
test_preds = test_model(model,
test_loader,
train_loader.dataset.mlb,
dtype,
sigmoid_threshold=sigmoid_threshold,
out_file_name=test_results_csv_path)
print("test set results saved as {}".format(
os.path.abspath(test_results_csv_path)))
val_acc_history.append(f2_acc)
loss_history += epoch_losses
print("END epoch {}/{}: validation F2 score = {:.02f}".format(
epoch + 1, num_epochs, f2_acc))
## serialize model data and save as .pkl file
torch.save(model.state_dict(), save_model_path)
print("model saved as {}".format(os.path.abspath(save_model_path)))
## save loss and accuracy as .mat file
save_accuracy_and_loss_mat(save_mat_path, train_acc_history,
val_acc_history, loss_history, num_epochs)
## load model params from file
else:
state_dict = torch.load(save_model_path,
map_location=lambda storage, loc: storage)
model.load_state_dict(state_dict)
print("model loaded from {}".format(os.path.abspath(save_model_path)))
## generate predictions on test data set
if run_test:
test_dataset = AmazonTestDataset(test_csv_path, test_img_path, img_ext,
dtype)
test_loader = DataLoader(test_dataset,
batch_size=batch_size,
num_workers=num_workers)
test_preds = test_model(model,
test_loader,
train_loader.dataset.mlb,
dtype,
out_file_name=test_results_csv_path)
print("test set results saved as {}".format(
os.path.abspath(test_results_csv_path)))