parser.add_argument("checkpoint_file",
help='Path to single model checkpoint (.pkl) file.')
args = parser.parse_args()
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)
parser.add_argument("checkpoint_dir",
help='Folder containing all .pkl checkpoint files.')
args = parser.parse_args()
checkpoint_dir = args.checkpoint_dir
fold = int(args.split)
dataset_path = os.path.join(data_paths.tfd_data_path, 'npy_files/TFD_96/split_'+str(fold))
print 'Checkpoint directory: %s' % checkpoint_dir
print 'Testing on split %d\n' % fold
# 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 = []
f.write(str(pid)+'\n')
f.close()
# Load model
model = SupervisedModel('experiment', './', learning_rate=1e-2)
monitor = util.Monitor(model,
checkpoint_directory='checkpoints_'+str(train_split),
save_steps=1000)
# Add dropout flag to fully-connected layer
model.fc4.dropout = 0.5
model._compile()
# Loading TFD dataset
print('Loading Data')
supervised_data_loader = SupervisedDataLoader(
os.path.join(data_paths.tfd_data_path, 'npy_files/TFD_96/split_'+str(train_split)))
train_data_container = supervised_data_loader.load(0)
val_data_container = supervised_data_loader.load(1)
test_data_container = supervised_data_loader.load(2)
X_train = train_data_container.X
y_train = train_data_container.y
X_val = val_data_container.X
y_val = val_data_container.y
X_test = test_data_container.X
y_test = test_data_container.y
X_train = numpy.float32(X_train)
X_train /= 255.0
X_train *= 2.0
if args.which_set == 'train':
set_num = 0
elif args.which_set == 'val':
set_num = 1
else:
set_num = 2
print 'Checkpoint: %s' % checkpoint_file
print 'Evaluating on split %d' % fold
print 'Using %s set\n' % args.which_set
# Load model
model = SupervisedModel('evaluation', './')
# Load dataset
supervised_data_loader = SupervisedDataLoader(dataset_path)
data_container = supervised_data_loader.load(set_num)
data_container.X = numpy.float32(data_container.X)
data_container.X /= 255.0
data_container.X *= 2.0
print data_container.X.shape
# Construct evaluator
preprocessor = [util.Normer3(filter_size=5, num_channels=1)]
evaluator = util.Evaluator(model, data_container,
checkpoint_file, preprocessor)
# For the inputted checkpoint, compute the overall accuracy
accuracies = []
print 'Checkpoint: %s' % os.path.split(checkpoint_file)[1]
