def testDivisibleBy(self):
tf.reset_default_graph()
mobilenet_v2.mobilenet(tf.placeholder(tf.float32, (10, 224, 224, 16)),
conv_defs=mobilenet_v2.V2_DEF,
divisible_by=16,
min_depth=32)
s = [
op.outputs[0].get_shape().as_list()[-1]
for op in find_ops('Conv2D')
]
s = set(s)
self.assertSameElements(
[32, 64, 96, 160, 192, 320, 384, 576, 960, 1280, 1001], s)
def testDivisibleByWithArgScope(self):
tf.reset_default_graph()
# Verifies that depth_multiplier arg scope actually works
# if no default min_depth is provided.
with slim.arg_scope((mobilenet.depth_multiplier, ), min_depth=32):
mobilenet_v2.mobilenet(tf.placeholder(tf.float32,
(10, 224, 224, 2)),
conv_defs=mobilenet_v2.V2_DEF,
depth_multiplier=0.1)
s = [
op.outputs[0].get_shape().as_list()[-1]
for op in find_ops('Conv2D')
]
s = set(s)
self.assertSameElements(s, [32, 192, 128, 1001])
def testFineGrained(self):
tf.reset_default_graph()
# Verifies that depth_multiplier arg scope actually works
# if no default min_depth is provided.
mobilenet_v2.mobilenet(tf.placeholder(tf.float32, (10, 224, 224, 2)),
conv_defs=mobilenet_v2.V2_DEF,
depth_multiplier=0.01,
finegrain_classification_mode=True)
s = [
op.outputs[0].get_shape().as_list()[-1]
for op in find_ops('Conv2D')
]
s = set(s)
# All convolutions will be 8->48, except for the last one.
self.assertSameElements(s, [8, 48, 1001, 1280])
def testImageSizes(self):
for input_size, output_size in [(224, 7), (192, 6), (160, 5), (128, 4),
(96, 3)]:
tf.reset_default_graph()
_, ep = mobilenet_v2.mobilenet(
tf.placeholder(tf.float32, (10, input_size, input_size, 3)))
self.assertEqual(ep['layer_18/output'].get_shape().as_list()[1:3],
[output_size] * 2)
