def _decode_block_string(self, block_string):
"""Gets a block through a string notation of arguments."""
if six.PY2:
assert isinstance(block_string, (str, unicode))
else:
assert isinstance(block_string, str)
ops = block_string.split('_')
options = {}
for op in ops:
splits = re.split(r'(\d.*)', op)
if len(splits) >= 2:
key, value = splits[:2]
options[key] = value
if 's' not in options or len(options['s']) != 2:
raise ValueError('Strides options should be a pair of integers.')
return efficientnet_model.BlockArgs(
kernel_size=int(options['k']),
num_repeat=int(options['r']),
input_filters=int(options['i']),
output_filters=int(options['o']),
expand_ratio=int(options['e']),
id_skip=('noskip' not in block_string),
se_ratio=float(options['se']) if 'se' in options else None,
strides=[int(options['s'][0]),
int(options['s'][1])],
conv_type=int(options['c']) if 'c' in options else 0,
fused_conv=int(options['f']) if 'f' in options else 0,
super_pixel=int(options['p']) if 'p' in options else 0,
condconv=('cc' in block_string))
def test_reduction_endpoint_with_single_block_without_sp(self):
"""Test reduction point with single block/layer."""
images = tf.zeros((10, 128, 128, 3), dtype=tf.float32)
global_params = efficientnet_model.GlobalParams(
1.0,
1.0,
0,
'channels_last',
num_classes=10,
batch_norm=utils.TpuBatchNormalization)
blocks_args = [
efficientnet_model.BlockArgs(
kernel_size=3,
num_repeat=1,
input_filters=3,
output_filters=6,
expand_ratio=6,
id_skip=False,
strides=[2, 2],
se_ratio=0.8,
conv_type=0,
fused_conv=0,
super_pixel=0)
]
model = efficientnet_model.Model(blocks_args, global_params)
_ = model(images, training=True)
self.assertIn('reduction_1', model.endpoints)
# single block should have one and only one reduction endpoint
self.assertNotIn('reduction_2', model.endpoints)
def test_bottleneck_block_with_superpixel_layer(self):
"""Test for creating a model with fused bottleneck block arguments."""
images = tf.zeros((10, 128, 128, 3), dtype=tf.float32)
global_params = efficientnet_model.GlobalParams(
1.0,
1.0,
0,
'channels_last',
num_classes=10,
batch_norm=utils.TpuBatchNormalization)
blocks_args = [
efficientnet_model.BlockArgs(
kernel_size=3,
num_repeat=3,
input_filters=3,
output_filters=6,
expand_ratio=6,
id_skip=True,
strides=[2, 2],
conv_type=0,
fused_conv=0,
super_pixel=1)
]
model = efficientnet_model.Model(blocks_args, global_params)
outputs = model(images, training=True)
self.assertEqual((10, 10), outputs.shape)
def test_variables(self):
"""Test for variables in blocks to be included in `model.variables`."""
images = tf.zeros((10, 128, 128, 3), dtype=tf.float32)
global_params = efficientnet_model.GlobalParams(
1.0,
1.0,
0,
'channels_last',
num_classes=10,
batch_norm=utils.TpuBatchNormalization)
blocks_args = [
efficientnet_model.BlockArgs(
kernel_size=3,
num_repeat=3,
input_filters=3,
output_filters=6,
expand_ratio=6,
id_skip=False,
strides=[2, 2],
se_ratio=0.8,
conv_type=0,
fused_conv=0,
super_pixel=0)
]
model = efficientnet_model.Model(blocks_args, global_params)
_ = model(images, training=True)
var_names = {var.name for var in model.variables}
self.assertIn('model/blocks_0/conv2d/kernel:0', var_names)
