checkpoint_file = args.checkpoint_file
fold = int(args.split)
dataset_path = os.path.join(data_paths.tfd_data_path, 'npy_files/TFD_96/split_'+str(fold))
print 'Checkpoint: %s' % checkpoint_file
print 'Testing on split %d\n' % fold
# Load model
model = SupervisedModel('evaluation', './')
# Load dataset
supervised_data_loader = SupervisedDataLoader(dataset_path)
test_data_container = supervised_data_loader.load(2)
test_data_container.X = numpy.float32(test_data_container.X)
test_data_container.X /= 255.0
test_data_container.X *= 2.0
# Construct evaluator
preprocessor = [util.Normer3(filter_size=5, num_channels=1)]
evaluator = util.Evaluator(model, test_data_container,
checkpoint_file, preprocessor)
# For the inputted checkpoint, compute the overall test accuracy
accuracies = []
print 'Checkpoint: %s' % os.path.split(checkpoint_file)[1]
evaluator.set_checkpoint(checkpoint_file)
accuracy = evaluator.run()
print 'Accuracy: %f\n' % accuracy
accuracies.append(accuracy)
# Load model
model = SupervisedModel('evaluation', './')
# Load data
supervised_data_loader = SupervisedDataLoader(dataset_path)
val_data_container = supervised_data_loader.load(1)
val_data_container.X = numpy.float32(val_data_container.X)
val_data_container.X /= 255.0
val_data_container.X *= 2.0
# Construct evaluator
preprocessor = [util.Normer3(filter_size=5, num_channels=1)]
checkpoint_file_list = sorted(
glob.glob(os.path.join(checkpoint_dir, '*.pkl')))
evaluator = util.Evaluator(model, val_data_container,
checkpoint_file_list[0], preprocessor)
# For each checkpoint, compute the overall val accuracy
accuracies = []
for checkpoint in checkpoint_file_list:
print 'Checkpoint: %s' % os.path.split(checkpoint)[1]
evaluator.set_checkpoint(checkpoint)
accuracy = evaluator.run()
print 'Accuracy: %f\n' % accuracy
accuracies.append(accuracy)
# Find checkpoint that produced the highest accuracy
max_accuracy = numpy.max(accuracies)
max_index = numpy.argmax(accuracies)
max_checkpoint = checkpoint_file_list[max_index]
print 'Max Checkpoint: %s' % max_checkpoint
