def testBuildNonExistingLayerLargeModel(self):
"""Tests that the model is built correctly without unnecessary layers."""
inputs = tf.random_uniform((5, 331, 331, 3))
tf.train.create_global_step()
with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
pnasnet.build_pnasnet_large(inputs, 1000)
vars_names = [x.op.name for x in tf.trainable_variables()]
self.assertIn('cell_stem_0/1x1/weights', vars_names)
self.assertNotIn('cell_stem_1/comb_iter_0/right/1x1/weights', vars_names)
def testBuildPreLogitsLargeModel(self):
batch_size = 5
height, width = 331, 331
num_classes = None
inputs = tf.random_uniform((batch_size, height, width, 3))
tf.train.create_global_step()
with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
net, end_points = pnasnet.build_pnasnet_large(inputs, num_classes)
self.assertFalse('AuxLogits' in end_points)
self.assertFalse('Predictions' in end_points)
self.assertTrue(net.op.name.startswith('final_layer/Mean'))
self.assertListEqual(net.get_shape().as_list(), [batch_size, 4320])
def testOverrideHParamsLargeModel(self):
batch_size = 5
height, width = 331, 331
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
tf.train.create_global_step()
config = pnasnet.large_imagenet_config()
config.set_hparam('data_format', 'NCHW')
with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
_, end_points = pnasnet.build_pnasnet_large(
inputs, num_classes, config=config)
self.assertListEqual(
end_points['Stem'].shape.as_list(), [batch_size, 540, 42, 42])
def _extract_proposal_features(self, preprocessed_inputs, scope):
"""Extracts first stage RPN features.
Extracts features using the first half of the PNASNet network.
We construct the network in `align_feature_maps=True` mode, which means
that all VALID paddings in the network are changed to SAME padding so that
the feature maps are aligned.
Args:
preprocessed_inputs: A [batch, height, width, channels] float32 tensor
representing a batch of images.
scope: A scope name.
Returns:
rpn_feature_map: A tensor with shape [batch, height, width, depth]
end_points: A dictionary mapping feature extractor tensor names to tensors
Raises:
ValueError: If the created network is missing the required activation.
"""
del scope
if len(preprocessed_inputs.get_shape().as_list()) != 4:
raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
'tensor of shape %s' % preprocessed_inputs.get_shape())
with slim.arg_scope(pnasnet_large_arg_scope_for_detection(
is_batch_norm_training=self._train_batch_norm)):
with arg_scope([slim.conv2d,
slim.batch_norm,
slim.separable_conv2d],
reuse=self._reuse_weights):
_, end_points = pnasnet.build_pnasnet_large(
preprocessed_inputs, num_classes=None,
is_training=self._is_training,
final_endpoint='Cell_7')
# Note that both 'Cell_6' and 'Cell_7' have equal depth = 2160.
# Cell_7 is the last cell before second reduction.
rpn_feature_map = tf.concat([end_points['Cell_6'],
end_points['Cell_7']], 3)
# pnasnet.py does not maintain the batch size in the first dimension.
# This work around permits us retaining the batch for below.
batch = preprocessed_inputs.get_shape().as_list()[0]
shape_without_batch = rpn_feature_map.get_shape().as_list()[1:]
rpn_feature_map_shape = [batch] + shape_without_batch
rpn_feature_map.set_shape(rpn_feature_map_shape)
return rpn_feature_map, end_points
def testNoAuxHeadLargeModel(self):
batch_size = 5
height, width = 331, 331
num_classes = 1000
for use_aux_head in (True, False):
tf.reset_default_graph()
inputs = tf.random_uniform((batch_size, height, width, 3))
tf.train.create_global_step()
config = pnasnet.large_imagenet_config()
config.set_hparam('use_aux_head', int(use_aux_head))
with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
_, end_points = pnasnet.build_pnasnet_large(inputs, num_classes,
config=config)
self.assertEqual('AuxLogits' in end_points, use_aux_head)
def testBuildLogitsLargeModel(self):
batch_size = 5
height, width = 331, 331
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
tf.train.create_global_step()
with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
logits, end_points = pnasnet.build_pnasnet_large(inputs, num_classes)
auxlogits = end_points['AuxLogits']
predictions = end_points['Predictions']
self.assertListEqual(auxlogits.get_shape().as_list(),
[batch_size, num_classes])
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
self.assertListEqual(predictions.get_shape().as_list(),
[batch_size, num_classes])
def extract_features(self, preprocessed_inputs):
"""Extract features from preprocessed inputs.
Args:
preprocessed_inputs: a [batch, height, width, channels] float tensor
representing a batch of images.
Returns:
feature_maps: a list of tensors where the ith tensor has shape
[batch, height_i, width_i, depth_i]
"""
feature_map_layout = {
'from_layer': ['Cell_7', 'Cell_11', '', '', '', ''],
'layer_depth': [-1, -1, 512, 256, 256, 128],
'use_explicit_padding': self._use_explicit_padding,
'use_depthwise': self._use_depthwise,
}
with slim.arg_scope(
pnasnet_large_arg_scope_for_detection(
is_batch_norm_training=self._is_training)):
with slim.arg_scope(
[slim.conv2d, slim.batch_norm, slim.separable_conv2d],
reuse=self._reuse_weights):
with (slim.arg_scope(self._conv_hyperparams_fn())
if self._override_base_feature_extractor_hyperparams else
context_manager.IdentityContextManager()):
_, image_features = pnasnet.build_pnasnet_large(
ops.pad_to_multiple(preprocessed_inputs,
self._pad_to_multiple),
num_classes=None,
is_training=self._is_training,
final_endpoint='Cell_11')
with tf.variable_scope('SSD_feature_maps', reuse=self._reuse_weights):
with slim.arg_scope(self._conv_hyperparams_fn()):
feature_maps = feature_map_generators.multi_resolution_feature_maps(
feature_map_layout=feature_map_layout,
depth_multiplier=self._depth_multiplier,
min_depth=self._min_depth,
insert_1x1_conv=True,
image_features=image_features)
return feature_maps.values()
def testAllEndPointsShapesLargeModel(self):
batch_size = 5
height, width = 331, 331
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
tf.train.create_global_step()
with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
_, end_points = pnasnet.build_pnasnet_large(inputs, num_classes)
endpoints_shapes = {'Stem': [batch_size, 42, 42, 540],
'Cell_0': [batch_size, 42, 42, 1080],
'Cell_1': [batch_size, 42, 42, 1080],
'Cell_2': [batch_size, 42, 42, 1080],
'Cell_3': [batch_size, 42, 42, 1080],
'Cell_4': [batch_size, 21, 21, 2160],
'Cell_5': [batch_size, 21, 21, 2160],
'Cell_6': [batch_size, 21, 21, 2160],
'Cell_7': [batch_size, 21, 21, 2160],
'Cell_8': [batch_size, 11, 11, 4320],
'Cell_9': [batch_size, 11, 11, 4320],
'Cell_10': [batch_size, 11, 11, 4320],
'Cell_11': [batch_size, 11, 11, 4320],
'global_pool': [batch_size, 4320],
# Logits and predictions
'AuxLogits': [batch_size, 1000],
'Predictions': [batch_size, 1000],
'Logits': [batch_size, 1000],
}
self.assertEqual(len(end_points), 17)
self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
for endpoint_name in endpoints_shapes:
tf.logging.info('Endpoint name: {}'.format(endpoint_name))
expected_shape = endpoints_shapes[endpoint_name]
self.assertIn(endpoint_name, end_points)
self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
expected_shape)