def testAtrousFullyConvolutionalValues(self):
"""Verify dense feature extraction with atrous convolution."""
nominal_stride = 32
for output_stride in [4, 8, 16, 32, None]:
with arg_scope(resnet_utils.resnet_arg_scope()):
with ops.Graph().as_default():
with self.cached_session() as sess:
random_seed.set_random_seed(0)
inputs = create_test_input(2, 81, 81, 3)
# Dense feature extraction followed by subsampling.
output, _ = self._resnet_small(
inputs,
None,
is_training=False,
global_pool=False,
output_stride=output_stride)
if output_stride is None:
factor = 1
else:
factor = nominal_stride // output_stride
output = resnet_utils.subsample(output, factor)
# Make the two networks use the same weights.
variable_scope.get_variable_scope().reuse_variables()
# Feature extraction at the nominal network rate.
expected, _ = self._resnet_small(inputs,
None,
is_training=False,
global_pool=False)
sess.run(variables.global_variables_initializer())
self.assertAllClose(output.eval(),
expected.eval(),
atol=2e-4,
rtol=1e-4)
def testEndPointsV1(self):
"""Test the end points of a tiny v1 bottleneck network."""
blocks = [
resnet_v1.resnet_v1_block('block1',
base_depth=1,
num_units=2,
stride=2),
resnet_v1.resnet_v1_block('block2',
base_depth=2,
num_units=2,
stride=1),
]
inputs = create_test_input(2, 32, 16, 3)
with arg_scope(resnet_utils.resnet_arg_scope()):
_, end_points = self._resnet_plain(inputs, blocks, scope='tiny')
expected = [
'tiny/block1/unit_1/bottleneck_v1/shortcut',
'tiny/block1/unit_1/bottleneck_v1/conv1',
'tiny/block1/unit_1/bottleneck_v1/conv2',
'tiny/block1/unit_1/bottleneck_v1/conv3',
'tiny/block1/unit_2/bottleneck_v1/conv1',
'tiny/block1/unit_2/bottleneck_v1/conv2',
'tiny/block1/unit_2/bottleneck_v1/conv3',
'tiny/block2/unit_1/bottleneck_v1/shortcut',
'tiny/block2/unit_1/bottleneck_v1/conv1',
'tiny/block2/unit_1/bottleneck_v1/conv2',
'tiny/block2/unit_1/bottleneck_v1/conv3',
'tiny/block2/unit_2/bottleneck_v1/conv1',
'tiny/block2/unit_2/bottleneck_v1/conv2',
'tiny/block2/unit_2/bottleneck_v1/conv3'
]
self.assertItemsEqual(expected, end_points)
def resnet_inference(inputs, is_train=True):
x = tf.reshape(inputs, [FLAGS.batch_size, 64, 64, 1])
with slim.arg_scope(resnet_arg_scope(is_training=(MODE == "train"))):
net, enpoints = resnet_v2.resnet_v2_50(
x, num_classes=FLAGS.label_size)
net = tf.reshape(net, [FLAGS.batch_size, FLAGS.label_size])
return net
def PSPNet(inputs , num_classes, is_training):
'''A TensorFlow implementation of PSPNet model based on
https://github.com/hszhao/PSPNet/tree/master/evaluation/prototxt
Args:
inputs: A 4-D tensor with dimensions [batch_size, height, width, channels]
num_classes: Integer, the total number of categories in the dataset
is_training : Bool, whether to updates the running means and variances during the training.
Returns:
A score map with dimensions [batch_size, 1/8*height, 1/8*width, num_classes]
'''
with slim.arg_scope(resnet_utils.resnet_arg_scope()):
net, end_points = resnet_v2.resnet_v2_101(inputs,
num_classes=None,
is_training=is_training,
global_pool=False,
output_stride=8,
reuse=None,
scope='resnet_v2_101')
with tf.variable_scope("PSPNet") as sc:
shape = tf.shape(net)[1:3]
net = PyramidPoolingModule(net, shape = shape)
net = slim.conv2d(net, 512, [3, 3], activation_fn=None, scope='conv5')
net = slim.batch_norm(net, fused=True, scope='conv5_bn')
net = tf.nn.relu(net, name='conv5_bn_relu')
logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, scope='logits')
return logits
def testEndPointsV1(self):
"""Test the end points of a tiny v1 bottleneck network."""
bottleneck = resnet_v1.bottleneck
blocks = [
resnet_utils.Block('block1', bottleneck, [(4, 1, 1), (4, 1, 2)]),
resnet_utils.Block('block2', bottleneck, [(8, 2, 1), (8, 2, 1)])
]
inputs = create_test_input(2, 32, 16, 3)
with arg_scope(resnet_utils.resnet_arg_scope()):
_, end_points = self._resnet_plain(inputs, blocks, scope='tiny')
expected = [
'tiny/block1/unit_1/bottleneck_v1/shortcut',
'tiny/block1/unit_1/bottleneck_v1/shortcut/BatchNorm',
'tiny/block1/unit_1/bottleneck_v1/conv1',
'tiny/block1/unit_1/bottleneck_v1/conv2',
'tiny/block1/unit_1/bottleneck_v1/conv3',
'tiny/block1/unit_1/bottleneck_v1/conv3/BatchNorm',
'tiny/block1/unit_2/bottleneck_v1/conv1',
'tiny/block1/unit_2/bottleneck_v1/conv2',
'tiny/block1/unit_2/bottleneck_v1/conv3',
'tiny/block1/unit_2/bottleneck_v1/conv3/BatchNorm',
'tiny/block2/unit_1/bottleneck_v1/shortcut',
'tiny/block2/unit_1/bottleneck_v1/shortcut/BatchNorm',
'tiny/block2/unit_1/bottleneck_v1/conv1',
'tiny/block2/unit_1/bottleneck_v1/conv2',
'tiny/block2/unit_1/bottleneck_v1/conv3',
'tiny/block2/unit_1/bottleneck_v1/conv3/BatchNorm',
'tiny/block2/unit_2/bottleneck_v1/conv1',
'tiny/block2/unit_2/bottleneck_v1/conv2',
'tiny/block2/unit_2/bottleneck_v1/conv3',
'tiny/block2/unit_2/bottleneck_v1/conv3/BatchNorm'
]
self.assertItemsEqual(expected, end_points)
def extract_features(inputs, contexts, is_training):
# TODO - Add skip connections between conv-deconv layers
with slim.arg_scope(resnet_utils.resnet_arg_scope(is_training=is_training)):
conv1 = tf.layers.conv2d(inputs, filters=128, kernel_size=3, strides=1, padding='same')
context_layer = expand_context(contexts, height, width)
contexted_batch = tf.concat([conv1, context_layer], axis=3)
net, end_points = resnet_v2.resnet_v2_101(contexted_batch,
None,
global_pool=False,
output_stride=16)
deconv1 = deconv_block(net,num_inputs=2048, num_outputs=1024,
is_training=is_training, scope='deconv1')
deconv2 = deconv_block(deconv1, num_inputs=1024, num_outputs=512,
stride=2, is_training=is_training, scope='deconv2')
deconv3 = deconv_block(deconv2, num_inputs=512, num_outputs=256,
stride=2, is_training=is_training, scope='deconv3')
deconv4 = deconv_block(deconv3, num_inputs=256,num_outputs=128,
stride=2, is_training=is_training, scope='deconv4')
deconv5 = deconv_block(deconv4, num_inputs=128,num_outputs=64,
stride=2, is_training=is_training, scope='deconv5')
return deconv5, net
def testEndPointsV2(self):
"""Test the end points of a tiny v2 bottleneck network."""
blocks = [
resnet_v2.resnet_v2_block(
'block1', base_depth=1, num_units=2, stride=2),
resnet_v2.resnet_v2_block(
'block2', base_depth=2, num_units=2, stride=1),
]
inputs = create_test_input(2, 32, 16, 3)
with arg_scope(resnet_utils.resnet_arg_scope()):
_, end_points = self._resnet_plain(inputs, blocks, scope='tiny')
expected = [
'tiny/block1/unit_1/bottleneck_v2/shortcut',
'tiny/block1/unit_1/bottleneck_v2/conv1',
'tiny/block1/unit_1/bottleneck_v2/conv2',
'tiny/block1/unit_1/bottleneck_v2/conv3',
'tiny/block1/unit_2/bottleneck_v2/conv1',
'tiny/block1/unit_2/bottleneck_v2/conv2',
'tiny/block1/unit_2/bottleneck_v2/conv3',
'tiny/block2/unit_1/bottleneck_v2/shortcut',
'tiny/block2/unit_1/bottleneck_v2/conv1',
'tiny/block2/unit_1/bottleneck_v2/conv2',
'tiny/block2/unit_1/bottleneck_v2/conv3',
'tiny/block2/unit_2/bottleneck_v2/conv1',
'tiny/block2/unit_2/bottleneck_v2/conv2',
'tiny/block2/unit_2/bottleneck_v2/conv3'
]
self.assertItemsEqual(expected, end_points)
def _encoder(self, input_images, scope_name = "encoder", trainable = True, scope_reuse = False):
with arg_scope(resnet_utils.resnet_arg_scope()):
output, end_points = resnet_v2.resnet_v2_50(input_images, output_stride=8, global_pool=False,reuse=scope_reuse)#(256, 256, 2048)==>(32, 32, 2048)
hidden_state = decoder_layer(output, out_channels = self.lstm_channel, stride = 1, scope_name = 'encoder_layer1', trainable = trainable)#(32, 32, 2048)==>(32, 32, 512)
print hidden_state.get_shape()
tf.summary.histogram(hidden_state.op.name + "/activation", hidden_state)
return hidden_state
def testAtrousFullyConvolutionalValues(self):
"""Verify dense feature extraction with atrous convolution."""
nominal_stride = 32
for output_stride in [4, 8, 16, 32, None]:
with arg_scope(resnet_utils.resnet_arg_scope()):
with ops.Graph().as_default():
with self.test_session() as sess:
random_seed.set_random_seed(0)
inputs = create_test_input(2, 81, 81, 3)
# Dense feature extraction followed by subsampling.
output, _ = self._resnet_small(
inputs,
None,
is_training=False,
global_pool=False,
output_stride=output_stride)
if output_stride is None:
factor = 1
else:
factor = nominal_stride // output_stride
output = resnet_utils.subsample(output, factor)
# Make the two networks use the same weights.
variable_scope.get_variable_scope().reuse_variables()
# Feature extraction at the nominal network rate.
expected, _ = self._resnet_small(
inputs, None, is_training=False, global_pool=False)
sess.run(variables.global_variables_initializer())
self.assertAllClose(
output.eval(), expected.eval(), atol=1e-4, rtol=1e-4)
def Deeplab_v2(inputs , num_classes, is_training):
'''A TensorFlow implementation of Deeplab_v2 model based on
http://liangchiehchen.com/projects/DeepLabv2_resnet.html
Args:
inputs: A 4-D tensor with dimensions [batch_size, height, width, channels]
num_classes: Integer, the total number of categories in the dataset
is_training : Bool, whether to updates the running means and variances during the training.
Returns:
A score map with dimensions [batch_size, 1/8*height, 1/8*width, num_classes]
'''
with slim.arg_scope(resnet_utils.resnet_arg_scope()) as sc:
net, _ = resnet_v2.resnet_v2_101(inputs,
num_classes=None,
is_training=is_training,
global_pool=False,
output_stride=8,
reuse=None,
scope='resnet_v2_101')
# ASPP module without BN layers
with tf.variable_scope('Deeplab_v2'):
pool6 = slim.conv2d(net, num_classes, [3,3], rate=6, activation_fn=None, scope='pool6')
pool12 = slim.conv2d(net, num_classes, [3,3], rate=12, activation_fn=None, scope='pool12')
pool18 = slim.conv2d(net, num_classes, [3,3], rate=18, activation_fn=None, scope='pool18')
pool24 = slim.conv2d(net, num_classes, [3,3], rate=24, activation_fn=None, scope='pool24')
logits = tf.add_n([pool6, pool12, pool18, pool24], name='logits')
return logits
def testEndPointsV2(self):
"""Test the end points of a tiny v2 bottleneck network."""
bottleneck = resnet_v2.bottleneck
blocks = [
resnet_utils.Block('block1', bottleneck, [(4, 1, 1), (4, 1, 2)]),
resnet_utils.Block('block2', bottleneck, [(8, 2, 1), (8, 2, 1)])
]
inputs = create_test_input(2, 32, 16, 3)
with arg_scope(resnet_utils.resnet_arg_scope()):
_, end_points = self._resnet_plain(inputs, blocks, scope='tiny')
expected = [
'tiny/block1/unit_1/bottleneck_v2/shortcut',
'tiny/block1/unit_1/bottleneck_v2/conv1',
'tiny/block1/unit_1/bottleneck_v2/conv2',
'tiny/block1/unit_1/bottleneck_v2/conv3',
'tiny/block1/unit_2/bottleneck_v2/conv1',
'tiny/block1/unit_2/bottleneck_v2/conv2',
'tiny/block1/unit_2/bottleneck_v2/conv3',
'tiny/block2/unit_1/bottleneck_v2/shortcut',
'tiny/block2/unit_1/bottleneck_v2/conv1',
'tiny/block2/unit_1/bottleneck_v2/conv2',
'tiny/block2/unit_1/bottleneck_v2/conv3',
'tiny/block2/unit_2/bottleneck_v2/conv1',
'tiny/block2/unit_2/bottleneck_v2/conv2',
'tiny/block2/unit_2/bottleneck_v2/conv3'
]
self.assertItemsEqual(expected, end_points)
def testClassificationEndPoints(self):
global_pool = True
num_classes = 10
inputs = create_test_input(2, 224, 224, 3)
with arg_scope(resnet_utils.resnet_arg_scope()):
logits, end_points = self._resnet_small(
inputs, num_classes, global_pool, scope='resnet')
self.assertTrue(logits.op.name.startswith('resnet/logits'))
self.assertListEqual(logits.get_shape().as_list(), [2, 1, 1, num_classes])
self.assertTrue('predictions' in end_points)
self.assertListEqual(end_points['predictions'].get_shape().as_list(),
[2, 1, 1, num_classes])
def testClassificationEndPoints(self):
global_pool = True
num_classes = 10
inputs = create_test_input(2, 224, 224, 3)
with arg_scope(resnet_utils.resnet_arg_scope()):
logits, end_points = self._resnet_small(
inputs, num_classes, global_pool=global_pool, scope='resnet')
self.assertTrue(logits.op.name.startswith('resnet/logits'))
self.assertListEqual(logits.get_shape().as_list(), [2, 1, 1, num_classes])
self.assertTrue('predictions' in end_points)
self.assertListEqual(end_points['predictions'].get_shape().as_list(),
[2, 1, 1, num_classes])
def testFullyConvolutionalUnknownHeightWidth(self):
batch = 2
height, width = 65, 65
global_pool = False
inputs = create_test_input(batch, None, None, 3)
with arg_scope(resnet_utils.resnet_arg_scope()):
output, _ = self._resnet_small(inputs, None, global_pool)
self.assertListEqual(output.get_shape().as_list(), [batch, None, None, 32])
images = create_test_input(batch, height, width, 3)
with self.test_session() as sess:
sess.run(variables.global_variables_initializer())
output = sess.run(output, {inputs: images.eval()})
self.assertEqual(output.shape, (batch, 3, 3, 32))
def testFullyConvolutionalUnknownHeightWidth(self):
batch = 2
height, width = 65, 65
global_pool = False
inputs = create_test_input(batch, None, None, 3)
with arg_scope(resnet_utils.resnet_arg_scope()):
output, _ = self._resnet_small(inputs, None, global_pool=global_pool)
self.assertListEqual(output.get_shape().as_list(), [batch, None, None, 32])
images = create_test_input(batch, height, width, 3)
with self.test_session() as sess:
sess.run(variables.global_variables_initializer())
output = sess.run(output, {inputs: images.eval()})
self.assertEqual(output.shape, (batch, 3, 3, 32))
def create_resnet(image_input,
is_training,
scope="",
resnet_out="block4",
resnet_version=50,
cbn=None):
"""
Create a resnet by overidding the classic batchnorm with conditional batchnorm
:param image_input: placeholder with image
:param is_training: are you using the resnet at training_time or test_time
:param scope: tensorflow scope
:param resnet_version: 50/101/152
:param cbn: the cbn factory
:return: the resnet output
"""
if cbn is None:
# assert False, "\n" \
# "There is a bug with classic batchnorm with slim networks (https://github.com/tensorflow/tensorflow/issues/4887). \n" \
# "Please use the following config -> 'cbn': {'use_cbn':true, 'excluded_scope_names': ['*']}"
arg_sc = slim_utils.resnet_arg_scope(is_training=is_training)
else:
arg_sc = get_resnet_arg_scope(cbn.apply)
# Pick the correct version of the resnet
if resnet_version == 50:
current_resnet = resnet_v1.resnet_v1_50
elif resnet_version == 101:
current_resnet = resnet_v1.resnet_v1_101
elif resnet_version == 152:
current_resnet = resnet_v1.resnet_v1_152
else:
raise ValueError("Unsupported resnet version")
resnet_scope = os.path.join('resnet_v1_{}/'.format(resnet_version),
resnet_out)
with slim.arg_scope(arg_sc):
net, end_points = current_resnet(
image_input, 1000) # 1000 is the number of softmax class
if len(scope) > 0 and not scope.endswith("/"):
scope += "/"
print("Use: {}".format(resnet_scope))
out = end_points[scope + resnet_scope]
return out
def testFullyConvolutionalEndpointShapes(self):
global_pool = False
num_classes = 10
inputs = create_test_input(2, 321, 321, 3)
with arg_scope(resnet_utils.resnet_arg_scope()):
_, end_points = self._resnet_small(
inputs, num_classes, global_pool=global_pool, scope='resnet')
endpoint_to_shape = {
'resnet/block1': [2, 41, 41, 4],
'resnet/block2': [2, 21, 21, 8],
'resnet/block3': [2, 11, 11, 16],
'resnet/block4': [2, 11, 11, 32]
}
for endpoint in endpoint_to_shape:
shape = endpoint_to_shape[endpoint]
self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)
def testClassificationShapes(self):
global_pool = True
num_classes = 10
inputs = create_test_input(2, 224, 224, 3)
with arg_scope(resnet_utils.resnet_arg_scope()):
_, end_points = self._resnet_small(
inputs, num_classes, global_pool=global_pool, scope='resnet')
endpoint_to_shape = {
'resnet/block1': [2, 28, 28, 4],
'resnet/block2': [2, 14, 14, 8],
'resnet/block3': [2, 7, 7, 16],
'resnet/block4': [2, 7, 7, 32]
}
for endpoint in endpoint_to_shape:
shape = endpoint_to_shape[endpoint]
self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)
def testFullyConvolutionalEndpointShapes(self):
global_pool = False
num_classes = 10
inputs = create_test_input(2, 321, 321, 3)
with arg_scope(resnet_utils.resnet_arg_scope()):
_, end_points = self._resnet_small(
inputs, num_classes, global_pool, scope='resnet')
endpoint_to_shape = {
'resnet/block1': [2, 41, 41, 4],
'resnet/block2': [2, 21, 21, 8],
'resnet/block3': [2, 11, 11, 16],
'resnet/block4': [2, 11, 11, 32]
}
for endpoint in endpoint_to_shape:
shape = endpoint_to_shape[endpoint]
self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)
def testClassificationShapes(self):
global_pool = True
num_classes = 10
inputs = create_test_input(2, 224, 224, 3)
with arg_scope(resnet_utils.resnet_arg_scope()):
_, end_points = self._resnet_small(
inputs, num_classes, global_pool, scope='resnet')
endpoint_to_shape = {
'resnet/block1': [2, 28, 28, 4],
'resnet/block2': [2, 14, 14, 8],
'resnet/block3': [2, 7, 7, 16],
'resnet/block4': [2, 7, 7, 32]
}
for endpoint in endpoint_to_shape:
shape = endpoint_to_shape[endpoint]
self.assertListEqual(end_points[endpoint].get_shape().as_list(), shape)
def testAtrousValuesBottleneck(self):
"""Verify the values of dense feature extraction by atrous convolution.
Make sure that dense feature extraction by stack_blocks_dense() followed by
subsampling gives identical results to feature extraction at the nominal
network output stride using the simple self._stack_blocks_nondense() above.
"""
block = resnet_v1.resnet_v1_block
blocks = [
block('block1', base_depth=1, num_units=2, stride=2),
block('block2', base_depth=2, num_units=2, stride=2),
block('block3', base_depth=4, num_units=2, stride=2),
block('block4', base_depth=8, num_units=2, stride=1),
]
nominal_stride = 8
# Test both odd and even input dimensions.
height = 30
width = 31
with arg_scope(resnet_utils.resnet_arg_scope()):
with arg_scope([layers.batch_norm], is_training=False):
for output_stride in [1, 2, 4, 8, None]:
with ops.Graph().as_default():
with self.cached_session() as sess:
random_seed.set_random_seed(0)
inputs = create_test_input(1, height, width, 3)
# Dense feature extraction followed by subsampling.
output = resnet_utils.stack_blocks_dense(
inputs, blocks, output_stride)
if output_stride is None:
factor = 1
else:
factor = nominal_stride // output_stride
output = resnet_utils.subsample(output, factor)
# Make the two networks use the same weights.
variable_scope.get_variable_scope(
).reuse_variables()
# Feature extraction at the nominal network rate.
expected = self._stack_blocks_nondense(
inputs, blocks)
sess.run(variables.global_variables_initializer())
output, expected = sess.run([output, expected])
self.assertAllClose(output,
expected,
atol=1e-4,
rtol=1e-4)
def create_resnet(image_input,
is_training,
scope="",
resnet_out="block4",
resnet_version=50,
cbn=None):
"""
Create a resnet by overidding the classic batchnorm with conditional batchnorm
:param image_input: placeholder with image
:param is_training: are you using the resnet at training_time or test_time
:param scope: tensorflow scope
:param resnet_version: 50/101/152
:param cbn: the cbn factory
:return: the resnet output
"""
if cbn is None:
arg_sc = slim_utils.resnet_arg_scope()
else:
arg_sc = get_resnet_arg_scope(cbn.apply)
# Pick the correct version of the resnet
if resnet_version == 50:
current_resnet = resnet_v1.resnet_v1_50
elif resnet_version == 101:
current_resnet = resnet_v1.resnet_v1_101
elif resnet_version == 152:
current_resnet = resnet_v1.resnet_v1_152
else:
raise ValueError("Unsupported resnet version")
resnet_scope = os.path.join('resnet_v1_{}/'.format(resnet_version),
resnet_out)
with slim.arg_scope(arg_sc):
net, end_points = current_resnet(
image_input, 1000,
is_training=is_training) # 1000 is the number of softmax class
if len(scope) > 0 and not scope.endswith("/"):
scope += "/"
print("Use: {}".format(resnet_scope))
out = end_points[scope + resnet_scope]
return out
def _atrousValues(self, bottleneck):
"""Verify the values of dense feature extraction by atrous convolution.
Make sure that dense feature extraction by stack_blocks_dense() followed by
subsampling gives identical results to feature extraction at the nominal
network output stride using the simple self._stack_blocks_nondense() above.
Args:
bottleneck: The bottleneck function.
"""
blocks = [
resnet_utils.Block('block1', bottleneck, [(4, 1, 1), (4, 1, 2)]),
resnet_utils.Block('block2', bottleneck, [(8, 2, 1), (8, 2, 2)]),
resnet_utils.Block('block3', bottleneck, [(16, 4, 1), (16, 4, 2)]),
resnet_utils.Block('block4', bottleneck, [(32, 8, 1), (32, 8, 1)])
]
nominal_stride = 8
# Test both odd and even input dimensions.
height = 30
width = 31
with arg_scope(resnet_utils.resnet_arg_scope(is_training=False)):
for output_stride in [1, 2, 4, 8, None]:
with ops.Graph().as_default():
with self.test_session() as sess:
random_seed.set_random_seed(0)
inputs = create_test_input(1, height, width, 3)
# Dense feature extraction followed by subsampling.
output = resnet_utils.stack_blocks_dense(
inputs, blocks, output_stride)
if output_stride is None:
factor = 1
else:
factor = nominal_stride // output_stride
output = resnet_utils.subsample(output, factor)
# Make the two networks use the same weights.
variable_scope.get_variable_scope().reuse_variables()
# Feature extraction at the nominal network rate.
expected = self._stack_blocks_nondense(inputs, blocks)
sess.run(variables.global_variables_initializer())
output, expected = sess.run([output, expected])
self.assertAllClose(output,
expected,
atol=1e-4,
rtol=1e-4)
def testUnknownBatchSize(self):
batch = 2
height, width = 65, 65
global_pool = True
num_classes = 10
inputs = create_test_input(None, height, width, 3)
with arg_scope(resnet_utils.resnet_arg_scope()):
logits, _ = self._resnet_small(
inputs, num_classes, global_pool, scope='resnet')
self.assertTrue(logits.op.name.startswith('resnet/logits'))
self.assertListEqual(logits.get_shape().as_list(),
[None, 1, 1, num_classes])
images = create_test_input(batch, height, width, 3)
with self.test_session() as sess:
sess.run(variables.global_variables_initializer())
output = sess.run(logits, {inputs: images.eval()})
self.assertEqual(output.shape, (batch, 1, 1, num_classes))
def slim_resnet_v2_50(net_in,
num_classes=1000,
weight_decay=0.0001,
is_training=False,
reuse=None):
net_in = tl.layers.InputLayer(net_in, name='input_layer')
with slim.arg_scope(
resnet_arg_scope(is_training=is_training,
weight_decay=weight_decay)):
network = tl.layers.SlimNetsLayer(
layer=net_in,
slim_layer=resnet_v2_50,
slim_args={
'num_classes': num_classes,
},
name='resnet_v2_50') # same with the ckpt mode
return network
def testUnknownBatchSize(self):
batch = 2
height, width = 65, 65
global_pool = True
num_classes = 10
inputs = create_test_input(None, height, width, 3)
with arg_scope(resnet_utils.resnet_arg_scope()):
logits, _ = self._resnet_small(
inputs, num_classes, global_pool=global_pool, scope='resnet')
self.assertTrue(logits.op.name.startswith('resnet/logits'))
self.assertListEqual(logits.get_shape().as_list(),
[None, 1, 1, num_classes])
images = create_test_input(batch, height, width, 3)
with self.test_session() as sess:
sess.run(variables.global_variables_initializer())
output = sess.run(logits, {inputs: images.eval()})
self.assertEqual(output.shape, (batch, 1, 1, num_classes))
def _atrousValues(self, bottleneck):
"""Verify the values of dense feature extraction by atrous convolution.
Make sure that dense feature extraction by stack_blocks_dense() followed by
subsampling gives identical results to feature extraction at the nominal
network output stride using the simple self._stack_blocks_nondense() above.
Args:
bottleneck: The bottleneck function.
"""
blocks = [
resnet_utils.Block('block1', bottleneck, [(4, 1, 1), (4, 1, 2)]),
resnet_utils.Block('block2', bottleneck, [(8, 2, 1), (8, 2, 2)]),
resnet_utils.Block('block3', bottleneck, [(16, 4, 1), (16, 4, 2)]),
resnet_utils.Block('block4', bottleneck, [(32, 8, 1), (32, 8, 1)])
]
nominal_stride = 8
# Test both odd and even input dimensions.
height = 30
width = 31
with arg_scope(resnet_utils.resnet_arg_scope(is_training=False)):
for output_stride in [1, 2, 4, 8, None]:
with ops.Graph().as_default():
with self.test_session() as sess:
random_seed.set_random_seed(0)
inputs = create_test_input(1, height, width, 3)
# Dense feature extraction followed by subsampling.
output = resnet_utils.stack_blocks_dense(inputs, blocks,
output_stride)
if output_stride is None:
factor = 1
else:
factor = nominal_stride // output_stride
output = resnet_utils.subsample(output, factor)
# Make the two networks use the same weights.
variable_scope.get_variable_scope().reuse_variables()
# Feature extraction at the nominal network rate.
expected = self._stack_blocks_nondense(inputs, blocks)
sess.run(variables.global_variables_initializer())
output, expected = sess.run([output, expected])
self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4)
def testAtrousValuesBottleneck(self):
"""Verify the values of dense feature extraction by atrous convolution.
Make sure that dense feature extraction by stack_blocks_dense() followed by
subsampling gives identical results to feature extraction at the nominal
network output stride using the simple self._stack_blocks_nondense() above.
"""
block = resnet_v2.resnet_v2_block
blocks = [
block('block1', base_depth=1, num_units=2, stride=2),
block('block2', base_depth=2, num_units=2, stride=2),
block('block3', base_depth=4, num_units=2, stride=2),
block('block4', base_depth=8, num_units=2, stride=1),
]
nominal_stride = 8
# Test both odd and even input dimensions.
height = 30
width = 31
with arg_scope(resnet_utils.resnet_arg_scope()):
with arg_scope([layers.batch_norm], is_training=False):
for output_stride in [1, 2, 4, 8, None]:
with ops.Graph().as_default():
with self.test_session() as sess:
random_seed.set_random_seed(0)
inputs = create_test_input(1, height, width, 3)
# Dense feature extraction followed by subsampling.
output = resnet_utils.stack_blocks_dense(inputs, blocks,
output_stride)
if output_stride is None:
factor = 1
else:
factor = nominal_stride // output_stride
output = resnet_utils.subsample(output, factor)
# Make the two networks use the same weights.
variable_scope.get_variable_scope().reuse_variables()
# Feature extraction at the nominal network rate.
expected = self._stack_blocks_nondense(inputs, blocks)
sess.run(variables.global_variables_initializer())
output, expected = sess.run([output, expected])
self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4)
assert False, "Invalid mode: {}".format(args.mode)
# Define the output folder
out_file = "gw_{mode}_{network}_{feature_name}_{size}".format(
mode=args.mode, network=args.network, feature_name=args.feature_name, size=args.img_size)
print("Create networks...")
if args.network == "resnet":
ft_output = resnet.create_resnet(images,
resnet_out=args.feature_name,
resnet_version=args.resnet_version,
is_training=False)
# create network
with slim.arg_scope(slim_utils.resnet_arg_scope(is_training=False)):
_, end_points = resnet_v1.resnet_v1_152(images, 1000) # 1000 is the number of softmax class
elif args.network == "vgg":
_, end_points = vgg.vgg_16(images, is_training=False, dropout_keep_prob=1.0)
ft_name = os.path.join("vgg_16", args.feature_name)
ft_output = end_points[ft_name]
else:
assert False, "Incorrect Network"
extract_features(
img_input = images,
ft_output = ft_output,
dataset_cstor = dataset_cstor,
def GCN(inputs, num_classes, is_training):
'''A TensorFlow implementation of GCN model based on
"Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network"
Args:
inputs: A 4-D tensor with dimensions [batch_size, height, width, channels]
num_classes: Integer, the total number of categories in the dataset
is_training : Bool, whether to updates the running means and variances during the training.
Returns:
A score map with dimensions [batch_size, height, width, num_classes]
'''
with slim.arg_scope(resnet_utils.resnet_arg_scope()):
net, end_points = resnet_v2.resnet_v2_101(inputs,
num_classes=None,
is_training=is_training,
global_pool=False,
output_stride=None,
reuse=None,
scope='resnet_v2_101')
block1 = end_points[
"resnet_v2_101/block1/unit_2/bottleneck_v2"] # (56, 56, 256)
block2 = end_points[
"resnet_v2_101/block2/unit_3/bottleneck_v2"] # (28, 28, 512)
block3 = end_points[
"resnet_v2_101/block3/unit_22/bottleneck_v2"] # (14, 14, 1024)
block4 = net # (7, 7, 2048)
with tf.variable_scope("gcn") as sc:
down5 = GlobalConvBlock(block4, num_classes=num_classes)
down5 = BoundaryRefinementBlock(down5,
num_classes=num_classes,
kernel_size=[3, 3])
down5 = slim.conv2d_transpose(down5,
num_classes,
kernel_size=[4, 4],
stride=2,
activation_fn=None) # (14, 14, 21)
down4 = GlobalConvBlock(block3, num_classes=num_classes)
down4 = BoundaryRefinementBlock(down4,
num_classes=num_classes,
kernel_size=[3, 3])
down4 = tf.add(down4, down5)
down4 = BoundaryRefinementBlock(down4,
num_classes=num_classes,
kernel_size=[3, 3])
down4 = slim.conv2d_transpose(down4,
num_classes,
kernel_size=[4, 4],
stride=2,
activation_fn=None) # (28, 28, 21)
down3 = GlobalConvBlock(block2, num_classes=num_classes)
down3 = BoundaryRefinementBlock(down3,
num_classes=num_classes,
kernel_size=[3, 3])
down3 = tf.add(down3, down4)
down3 = BoundaryRefinementBlock(down3,
num_classes=num_classes,
kernel_size=[3, 3])
down3 = slim.conv2d_transpose(down3,
num_classes,
kernel_size=[4, 4],
stride=2,
activation_fn=None) # (56, 56, 21)
down2 = GlobalConvBlock(block1, num_classes=num_classes)
down2 = BoundaryRefinementBlock(down2,
num_classes=num_classes,
kernel_size=[3, 3])
down2 = tf.add(down2, down3)
down2 = BoundaryRefinementBlock(down2,
num_classes=num_classes,
kernel_size=[3, 3])
down2 = slim.conv2d_transpose(down2,
num_classes,
kernel_size=[4, 4],
stride=2,
activation_fn=None) # (112, 112, 21)
output = BoundaryRefinementBlock(down2,
num_classes=num_classes,
kernel_size=[3, 3])
output = slim.conv2d_transpose(output,
num_classes,
kernel_size=[4, 4],
stride=2,
activation_fn=None) # (224, 224, 21)
output = BoundaryRefinementBlock(output,
num_classes=num_classes,
kernel_size=[3, 3])
return output
def create_resnet(image_input,
is_training,
scope="",
scope_feature="",
resnet_out="logits",
resnet_version=50,
cbn=None):
"""
Create a resnet by overidding the classic batchnorm with conditional batchnorm
:param image_input: placeholder with image
:param is_training: are you using the resnet at training_time or test_time
:param scope: tensorflow scope
:param resnet_version: 50/101/152
:param cbn: the cbn factory
:return: the resnet output
"""
# print("resnet_out = {}".format(resnet_out))
if cbn is None:
# assert False, "\n" \
# "There is a bug with classic batchnorm with slim networks (https://github.com/tensorflow/tensorflow/issues/4887). \n" \
# "Please use the following config -> 'cbn': {'use_cbn':true, 'excluded_scope_names': ['*']}"
# arg_sc = slim_utils.resnet_arg_scope(is_training=is_training)
arg_sc = slim_utils.resnet_arg_scope()
# print("arg_sc = {}".format(arg_sc))
else:
arg_sc = get_resnet_arg_scope(cbn.apply)
# Pick the correct version of the resnet
if resnet_version == 50:
print("------ 50")
current_resnet = resnet_v1.resnet_v1_50
elif resnet_version == 101:
print("------ 101")
current_resnet = resnet_v1.resnet_v1_101
elif resnet_version == 152:
print("------ 152")
current_resnet = resnet_v1.resnet_v1_152
else:
raise ValueError("Unsupported resnet version")
resnet_scope = os.path.join('resnet_v1_{}/'.format(resnet_version),
resnet_out)
# print(" resnet_out = {} , resnet_scope = {}".format(resnet_out,resnet_scope))
# exit()
# print("current_resnet = {}".format(current_resnet))
# exit()
with slim.arg_scope(arg_sc):
net, end_points = current_resnet(
image_input, 1000,
scope="resnet_v1_50") # 1000 is the number of softmax class
print("net = {}, end_points = {}".format(net, end_points))
# print(" resnet | endpoint=",end_points)
if len(scope) > 0 and not scope.endswith("/"):
scope += "/"
out = end_points[
scope +
resnet_scope] # Tensor("oracle/resnet_v1_50/block4/unit_3/bottleneck_v1/Relu:0", shape=(32, 7, 7, 2048), dtype=float32)
# print("------------------------- out Use: {},output = {}".format(resnet_scope,out))
# out = tf.reshape(
# out,
# [-1,out.shape[3]],
# )
# print("-- net = {}, end_points={},out={} ".format(net,end_points,out))
# exit()
return out, end_points
def arg_scope(self, weight_decay=0.0005, is_training=None):
return resnet_utils.resnet_arg_scope(weight_decay=weight_decay,
is_training=None)
def _testDeprecatingIsTraining(self, network_fn):
batch_norm_fn = layers.batch_norm
@add_arg_scope
def batch_norm_expect_is_training(*args, **kwargs):
assert kwargs['is_training']
return batch_norm_fn(*args, **kwargs)
@add_arg_scope
def batch_norm_expect_is_not_training(*args, **kwargs):
assert not kwargs['is_training']
return batch_norm_fn(*args, **kwargs)
global_pool = True
num_classes = 10
inputs = create_test_input(2, 224, 224, 3)
# Default argument for resnet_arg_scope
layers.batch_norm = batch_norm_expect_is_training
with arg_scope(resnet_utils.resnet_arg_scope()):
network_fn(inputs,
num_classes,
global_pool=global_pool,
scope='resnet1')
layers.batch_norm = batch_norm_expect_is_training
with arg_scope(resnet_utils.resnet_arg_scope()):
network_fn(inputs,
num_classes,
is_training=True,
global_pool=global_pool,
scope='resnet2')
layers.batch_norm = batch_norm_expect_is_not_training
with arg_scope(resnet_utils.resnet_arg_scope()):
network_fn(inputs,
num_classes,
is_training=False,
global_pool=global_pool,
scope='resnet3')
# resnet_arg_scope with is_training set to True (deprecated)
layers.batch_norm = batch_norm_expect_is_training
with arg_scope(resnet_utils.resnet_arg_scope(is_training=True)):
network_fn(inputs,
num_classes,
global_pool=global_pool,
scope='resnet4')
layers.batch_norm = batch_norm_expect_is_training
with arg_scope(resnet_utils.resnet_arg_scope(is_training=True)):
network_fn(inputs,
num_classes,
is_training=True,
global_pool=global_pool,
scope='resnet5')
layers.batch_norm = batch_norm_expect_is_not_training
with arg_scope(resnet_utils.resnet_arg_scope(is_training=True)):
network_fn(inputs,
num_classes,
is_training=False,
global_pool=global_pool,
scope='resnet6')
# resnet_arg_scope with is_training set to False (deprecated)
layers.batch_norm = batch_norm_expect_is_not_training
with arg_scope(resnet_utils.resnet_arg_scope(is_training=False)):
network_fn(inputs,
num_classes,
global_pool=global_pool,
scope='resnet7')
layers.batch_norm = batch_norm_expect_is_training
with arg_scope(resnet_utils.resnet_arg_scope(is_training=False)):
network_fn(inputs,
num_classes,
is_training=True,
global_pool=global_pool,
scope='resnet8')
layers.batch_norm = batch_norm_expect_is_not_training
with arg_scope(resnet_utils.resnet_arg_scope(is_training=False)):
network_fn(inputs,
num_classes,
is_training=False,
global_pool=global_pool,
scope='resnet9')
layers.batch_norm = batch_norm_fn
def _testDeprecatingIsTraining(self, network_fn):
batch_norm_fn = layers.batch_norm
@add_arg_scope
def batch_norm_expect_is_training(*args, **kwargs):
assert kwargs['is_training']
return batch_norm_fn(*args, **kwargs)
@add_arg_scope
def batch_norm_expect_is_not_training(*args, **kwargs):
assert not kwargs['is_training']
return batch_norm_fn(*args, **kwargs)
global_pool = True
num_classes = 10
inputs = create_test_input(2, 224, 224, 3)
# Default argument for resnet_arg_scope
layers.batch_norm = batch_norm_expect_is_training
with arg_scope(resnet_utils.resnet_arg_scope()):
network_fn(inputs, num_classes, global_pool=global_pool, scope='resnet1')
layers.batch_norm = batch_norm_expect_is_training
with arg_scope(resnet_utils.resnet_arg_scope()):
network_fn(
inputs,
num_classes,
is_training=True,
global_pool=global_pool,
scope='resnet2')
layers.batch_norm = batch_norm_expect_is_not_training
with arg_scope(resnet_utils.resnet_arg_scope()):
network_fn(
inputs,
num_classes,
is_training=False,
global_pool=global_pool,
scope='resnet3')
# resnet_arg_scope with is_training set to True (deprecated)
layers.batch_norm = batch_norm_expect_is_training
with arg_scope(resnet_utils.resnet_arg_scope(is_training=True)):
network_fn(inputs, num_classes, global_pool=global_pool, scope='resnet4')
layers.batch_norm = batch_norm_expect_is_training
with arg_scope(resnet_utils.resnet_arg_scope(is_training=True)):
network_fn(
inputs,
num_classes,
is_training=True,
global_pool=global_pool,
scope='resnet5')
layers.batch_norm = batch_norm_expect_is_not_training
with arg_scope(resnet_utils.resnet_arg_scope(is_training=True)):
network_fn(
inputs,
num_classes,
is_training=False,
global_pool=global_pool,
scope='resnet6')
# resnet_arg_scope with is_training set to False (deprecated)
layers.batch_norm = batch_norm_expect_is_not_training
with arg_scope(resnet_utils.resnet_arg_scope(is_training=False)):
network_fn(inputs, num_classes, global_pool=global_pool, scope='resnet7')
layers.batch_norm = batch_norm_expect_is_training
with arg_scope(resnet_utils.resnet_arg_scope(is_training=False)):
network_fn(
inputs,
num_classes,
is_training=True,
global_pool=global_pool,
scope='resnet8')
layers.batch_norm = batch_norm_expect_is_not_training
with arg_scope(resnet_utils.resnet_arg_scope(is_training=False)):
network_fn(
inputs,
num_classes,
is_training=False,
global_pool=global_pool,
scope='resnet9')
layers.batch_norm = batch_norm_fn
x = tf.placeholder(tf.float32, shape=[batch_size, height, width, 6])
y_ = tf.placeholder(tf.float32, shape=[batch_size, height, width, 1])
network = tl.layers.InputLayer(x, name='input')
network = tl.layers.Conv2d(network, 16, (3, 3), (1, 1), act=tf.nn.relu, padding='VALID', name='conv1')
network = tl.layers.Conv2d(network, 16, (3, 3), (1, 1), act=tf.nn.relu, padding='VALID', name='conv2')
network = tl.layers.Conv2d(network, 32, (3, 3), (1, 1), act=tf.nn.relu, padding='VALID', name='conv3')
network = tl.layers.MaxPool2d(network, (2, 2), (2, 2), padding='VALID', name='pool1')
network = tl.layers.Conv2d(network, 32, (3, 3), (1, 1), act=tf.nn.relu, padding='VALID', name='conv4')
network = tl.layers.Conv2d(network, 32, (3, 3), (1, 1), act=tf.nn.relu, padding='VALID', name='conv5')
network = tl.layers.Conv2dLayer(network, shape = [1, 1, 32, 3], act=tf.nn.relu, name='conv6', padding='VALID')
network = tl.layers.MaxPool2d(network, (2, 2), (2, 2), padding='VALID', name='pool2')
with slim.arg_scope(resnet_arg_scope()):
network = tl.layers.SlimNetsLayer(layer=network,
slim_layer=resnet_v2_50,
slim_args={'num_classes':None,
'is_training':True,
'global_pool':False,
'output_stride':16,
# 'inputs' : [batch_size, 299, 299, 3],
},
name='resnet_v2_50' # <-- the name should be the same with the ckpt model
)
# network = tl.layers.MaxPool2d(network, (1, 1), (1, 1), padding='SAME', name='pool3')
network.print_layers()
network = tf.layers.