def testBuildClassificationNetwork(self):
batch_size = 5
num_frames = 64
height, width = 224, 224
num_classes = 1000
inputs = tf.random_uniform((batch_size, num_frames, height, width, 3))
logits, end_points = i3d.i3d(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
self.assertTrue('Predictions' in end_points)
self.assertListEqual(end_points['Predictions'].get_shape().as_list(),
[batch_size, num_classes])
def testBuildClassificationNetwork(self):
batch_size = 5
num_frames = 64
height, width = 224, 224
num_classes = 1000
inputs = tf.random_uniform((batch_size, num_frames, height, width, 3))
logits, end_points = i3d.i3d(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
self.assertTrue('Predictions' in end_points)
self.assertListEqual(end_points['Predictions'].get_shape().as_list(),
[batch_size, num_classes])
def testEvaluation(self):
batch_size = 2
num_frames = 64
height, width = 224, 224
num_classes = 1000
eval_inputs = tf.random_uniform(
(batch_size, num_frames, height, width, 3))
logits, _ = i3d.i3d(eval_inputs, num_classes, is_training=False)
predictions = tf.argmax(logits, 1)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(predictions)
self.assertEquals(output.shape, (batch_size, ))
def testEvaluation(self):
batch_size = 2
num_frames = 64
height, width = 224, 224
num_classes = 1000
eval_inputs = tf.random_uniform((batch_size, num_frames, height, width, 3))
logits, _ = i3d.i3d(eval_inputs, num_classes,
is_training=False)
predictions = tf.argmax(logits, 1)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(predictions)
self.assertEquals(output.shape, (batch_size,))
# tensorboard directory where the summaries are saved during training
tb_dir = get_output_tb_dir(imdb, args.tag)
print('TensorFlow summaries will be saved to `{:s}`'.format(tb_dir))
# also add the validation set, but with no flipping images
orgflip = cfg.TRAIN.USE_FLIPPED
cfg.TRAIN.USE_FLIPPED = False
_, valroidb = combined_roidb(args.imdbval_name)
print('{:d} validation roidb entries'.format(len(valroidb)))
cfg.TRAIN.USE_FLIPPED = orgflip
# load network
if args.net == 'vgg16':
# net = vgg16()
net = i3d()
elif args.net == 'res50':
net = resnetv1(num_layers=50)
elif args.net == 'res101':
net = resnetv1(num_layers=101)
elif args.net == 'res152':
net = resnetv1(num_layers=152)
elif args.net == 'mobile':
net = mobilenetv1()
else:
raise NotImplementedError
print(output_dir)
train_net(net,
imdb,
roidb,
valroidb,
