def predict(config):
"""
Load trained network and make predictions
Parameters
----------
config: dict
Model training configuration
"""
# load dataset:
data = load_dataset(config['test_data'], config['batch_size'])
# init model:
if config['msg'] == True:
model = CLS_MSG_Model(config['batch_size'], config['num_classes'],
config['batch_normalization'])
else:
model = CLS_SSG_Model(config['batch_size'], config['num_classes'],
config['batch_normalization'])
# load params:
model.load_weights(config['checkpoint_path'])
y_truths = []
y_preds = []
for X, y in data:
y_truths.append(y.numpy().flatten())
y_preds.append(np.argmax(model(X), axis=1))
y_truth = np.hstack(y_truths)
y_pred = np.hstack(y_preds)
# get decoder
decoder = KITTIPCDClassificationDataset(
input_dir=
'/workspace/data/kitti_3d_object_classification_normal_resampled'
).get_decoder()
# get confusion matrix:
conf_mat = confusion_matrix(y_truth, y_pred)
# change to percentage:
conf_mat = np.dot(np.diag(1.0 / conf_mat.sum(axis=1)), conf_mat)
conf_mat = 100.0 * conf_mat
labels = [decoder[i] for i in range(config['num_classes'])]
plt.figure(figsize=(10, 10))
sn.heatmap(conf_mat, annot=True, xticklabels=labels, yticklabels=labels)
plt.title('KITTI 3D Object Classification -- Confusion Matrix')
plt.show()
print(classification_report(y_truth, y_pred, target_names=labels))
def predict(config): """ Load trained network and make predictions Parameters ---------- config: dict Model training configuration """ # load dataset: data = load_dataset(config['test_data'], config['batch_size']) # init model: if config['msg'] == True: model = CLS_MSG_Model(config['batch_size'], config['num_classes'], config['batch_normalization']) else: model = CLS_SSG_Model(config['batch_size'], config['num_classes'], config['batch_normalization']) # load params: model.load_weights(config['checkpoint_path']) y_truths = [] y_preds = [] for X, y in data: y_truths.append(y.numpy().flatten()) y_preds.append(np.argmax(model(X), axis=1)) y_truth = np.hstack(y_truths) y_pred = np.hstack(y_preds) # get decoder decoder = ModelNet40Dataset(input_dir='/workspace/data/modelnet40_normal_resampled').get_decoder() plt.figure(figsize = (10,10)) sn.heatmap(confusion_matrix(y_truth, y_pred), annot=True) plt.show() print( classification_report( y_truth, y_pred, target_names=[ decoder[i] for i in range(40) ] ) )
def load_model(config):
"""
Load pre-trained object classification network
"""
# init model:
if config['msg'] == True:
model = CLS_MSG_Model(config['batch_size'], config['num_classes'],
config['batch_normalization'])
else:
model = CLS_SSG_Model(config['batch_size'], config['num_classes'],
config['batch_normalization'])
# load params:
model.load_weights(config['checkpoint_path'])
return model