def train(config):
"""
Build network
Parameters
----------
config: dict
Model training configuration
"""
# load dataset:
training_data = load_dataset(config['training_data'], config['batch_size'])
validation_data = load_dataset(config['validation_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'])
# enable early stopping:
callbacks = [
keras.callbacks.EarlyStopping('val_sparse_categorical_accuracy',
min_delta=0.01,
patience=10),
keras.callbacks.TensorBoard('./logs/{}'.format(config['log_dir']),
update_freq=50),
keras.callbacks.ModelCheckpoint('./logs/{}/model/weights.ckpt'.format(
config['log_dir']),
'val_sparse_categorical_accuracy',
save_best_only=True)
]
model.build(input_shape=(config['batch_size'],
KITTIPCDClassificationDataset.N,
KITTIPCDClassificationDataset.d +
KITTIPCDClassificationDataset.C))
print(model.summary())
model.compile(optimizer=keras.optimizers.Adam(config['lr']),
loss=keras.losses.SparseCategoricalCrossentropy(),
metrics=[keras.metrics.SparseCategoricalAccuracy()])
model.fit(training_data,
validation_data=validation_data,
validation_steps=20,
validation_freq=1,
callbacks=callbacks,
epochs=100,
verbose=1)
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
def train():
if config['msg'] == True:
model = CLS_MSG_Model(config['batch_size'], config['num_classes'],
config['bn'])
else:
model = CLS_SSG_Model(config['batch_size'], config['num_classes'],
config['bn'])
train_ds = load_dataset(config['train_ds'], config['batch_size'])
val_ds = load_dataset(config['val_ds'], config['batch_size'])
callbacks = [
keras.callbacks.EarlyStopping('val_sparse_categorical_accuracy',
min_delta=0.01,
patience=10),
keras.callbacks.TensorBoard('./logs/{}'.format(config['log_dir']),
update_freq=50),
keras.callbacks.ModelCheckpoint('./logs/{}/model/weights.ckpt'.format(
config['log_dir']),
'val_sparse_categorical_accuracy',
save_best_only=True)
]
model.build(input_shape=(config['batch_size'], 8192, 3))
print(model.summary())
model.compile(optimizer=keras.optimizers.Adam(config['lr']),
loss=keras.losses.SparseCategoricalCrossentropy(),
metrics=[keras.metrics.SparseCategoricalAccuracy()])
model.fit(train_ds,
validation_data=val_ds,
validation_steps=20,
validation_freq=1,
callbacks=callbacks,
epochs=100,
verbose=1)
def train(config, params):
if params['msg'] == True:
model = CLS_MSG_Model(params['batch_size'], params['num_points'],
params['num_classes'], params['bn'])
else:
model = CLS_SSG_Model(params['batch_size'], params['num_points'],
params['num_classes'], params['bn'])
model.build(input_shape=(params['batch_size'], params['num_points'], 3))
print(model.summary())
print('[info] model training...')
optimizer = tf.keras.optimizers.Adam(lr=params['lr'])
loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
acc_object = tf.keras.metrics.SparseCategoricalAccuracy()
train_ds = TFDataset(os.path.join(config['dataset_dir'], 'train.tfrecord'),
params['batch_size'])
val_ds = TFDataset(os.path.join(config['dataset_dir'], 'val.tfrecord'),
params['batch_size'])
train_summary_writer = tf.summary.create_file_writer(
os.path.join(config['log_dir'], config['log_code']))
with train_summary_writer.as_default():
while True:
train_pts, train_labels = train_ds.get_batch()
loss, acc = train_step(optimizer, model, loss_object, acc_object,
train_pts, train_labels)
if optimizer.iterations % config['log_freq'] == 0:
tf.summary.scalar('train loss',
loss,
step=optimizer.iterations)
tf.summary.scalar('train accuracy',
acc,
step=optimizer.iterations)
if optimizer.iterations % config['test_freq'] == 0:
test_pts, test_labels = val_ds.get_batch()
test_loss, test_acc = test_step(optimizer, model, loss_object,
acc_object, test_pts,
test_labels)
tf.summary.scalar('test loss',
test_loss,
step=optimizer.iterations)
tf.summary.scalar('test accuracy',
test_acc,
step=optimizer.iterations)