def test_check_min_image_dim_static_shape(self):
input_tensor = tf.constant(np.zeros([1, 42, 42, 3]))
_ = shape_utils.check_min_image_dim(33, input_tensor)
with self.assertRaisesRegexp(
ValueError, 'image size must be >= 64 in both height and width.'):
_ = shape_utils.check_min_image_dim(64, input_tensor)
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]
Raises:
ValueError: depth multiplier is not supported.
"""
if self._depth_multiplier != 1.0:
raise ValueError('Depth multiplier not supported.')
preprocessed_inputs = shape_utils.check_min_image_dim(
129, preprocessed_inputs)
with tf.variable_scope(
self._resnet_scope_name, reuse=self._reuse_weights) as scope:
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
with (slim.arg_scope(self._conv_hyperparams_fn())
if self._override_base_feature_extractor_hyperparams else
context_manager.IdentityContextManager()):
_, image_features = self._resnet_base_fn(
inputs=ops.pad_to_multiple(preprocessed_inputs,
self._pad_to_multiple),
num_classes=None,
is_training=None,
global_pool=False,
output_stride=None,
store_non_strided_activations=True,
scope=scope)
image_features = self._filter_features(image_features)
with slim.arg_scope(self._conv_hyperparams_fn()):
with tf.variable_scope(self._fpn_scope_name,
reuse=self._reuse_weights):
fpn_features = feature_map_generators.fpn_top_down_feature_maps(
[(key, image_features[key])
for key in ['block2', 'block3', 'block4']],
depth=256)
last_feature_map = fpn_features['top_down_block4']
coarse_features = {}
for i in range(5, 7):
last_feature_map = slim.conv2d(
last_feature_map,
num_outputs=256,
kernel_size=[3, 3],
stride=2,
padding='SAME',
scope='bottom_up_block{}'.format(i))
coarse_features['bottom_up_block{}'.format(i)] = last_feature_map
return [fpn_features['top_down_block2'],
fpn_features['top_down_block3'],
fpn_features['top_down_block4'],
coarse_features['bottom_up_block5'],
coarse_features['bottom_up_block6']]
def test_check_min_image_dim_dynamic_shape(self):
input_placeholder = tf.placeholder(tf.float32, shape=[1, None, None, 3])
image_tensor = shape_utils.check_min_image_dim(33, input_placeholder)
with self.test_session() as sess:
sess.run(image_tensor,
feed_dict={input_placeholder: np.zeros([1, 42, 42, 3])})
with self.assertRaises(tf.errors.InvalidArgumentError):
sess.run(image_tensor,
feed_dict={input_placeholder: np.zeros([1, 32, 32, 3])})
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]
"""
preprocessed_inputs = shape_utils.check_min_image_dim(
33, preprocessed_inputs)
feature_map_layout = {
'from_layer': ['layer_15/expansion_output', 'layer_19', '', '', '', ''],
'layer_depth': [-1, -1, 512, 256, 256, 128],
'use_depthwise': self._use_depthwise,
'use_explicit_padding': self._use_explicit_padding,
}
with tf.variable_scope('MobilenetV2', reuse=self._reuse_weights) as scope:
with slim.arg_scope(
mobilenet_v2.training_scope(is_training=None, bn_decay=0.9997)), \
slim.arg_scope(
[mobilenet.depth_multiplier], min_depth=self._min_depth):
with (slim.arg_scope(self._conv_hyperparams_fn())
if self._override_base_feature_extractor_hyperparams else
context_manager.IdentityContextManager()):
# TODO(b/68150321): Enable fused batch norm once quantization
# supports it.
with slim.arg_scope([slim.batch_norm], fused=False):
_, image_features = mobilenet_v2.mobilenet_base(
ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
final_endpoint='layer_19',
depth_multiplier=self._depth_multiplier,
use_explicit_padding=self._use_explicit_padding,
scope=scope)
with slim.arg_scope(self._conv_hyperparams_fn()):
# TODO(b/68150321): Enable fused batch norm once quantization
# supports it.
with slim.arg_scope([slim.batch_norm], fused=False):
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 _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]
"""
preprocessed_inputs = shape_utils.check_min_image_dim(
33, preprocessed_inputs)
feature_map_layout = {
'from_layer': ['Conv2d_11_pointwise', 'Conv2d_13_pointwise', '', '',
'', ''],
'layer_depth': [-1, -1, 512, 256, 256, 128],
'use_explicit_padding': self._use_explicit_padding,
'use_depthwise': self._use_depthwise,
}
with tf.variable_scope('MobilenetV1',
reuse=self._reuse_weights) as scope:
with slim.arg_scope(
mobilenet_v1.mobilenet_v1_arg_scope(
is_training=(self._batch_norm_trainable and self._is_training))):
# TODO(skligys): Enable fused batch norm once quantization supports it.
with slim.arg_scope([slim.batch_norm], fused=False):
_, image_features = mobilenet_v1.mobilenet_v1_base(
ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
final_endpoint='Conv2d_13_pointwise',
min_depth=self._min_depth,
depth_multiplier=self._depth_multiplier,
use_explicit_padding=self._use_explicit_padding,
scope=scope)
with slim.arg_scope(self._conv_hyperparams):
# TODO(skligys): Enable fused batch norm once quantization supports it.
with slim.arg_scope([slim.batch_norm], fused=False):
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 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]
Raises:
ValueError: depth multiplier is not supported.
"""
if self._depth_multiplier != 1.0:
raise ValueError('Depth multiplier not supported.')
preprocessed_inputs = shape_utils.check_min_image_dim(
129, preprocessed_inputs)
with tf.variable_scope(
self._resnet_scope_name, reuse=self._reuse_weights) as scope:
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
with (slim.arg_scope(self._conv_hyperparams_fn())
if self._override_base_feature_extractor_hyperparams else
context_manager.IdentityContextManager()):
with slim.arg_scope(
[resnet_v1.bottleneck],
use_bounded_activations=self._use_bounded_activations):
_, activations = self._resnet_base_fn(
inputs=ops.pad_to_multiple(preprocessed_inputs,
self._pad_to_multiple),
num_classes=None,
is_training=None,
global_pool=False,
output_stride=None,
store_non_strided_activations=True,
scope=scope)
with slim.arg_scope(self._conv_hyperparams_fn()):
feature_maps = feature_map_generators.pooling_pyramid_feature_maps(
base_feature_map_depth=self._base_feature_map_depth,
num_layers=self._num_layers,
image_features={
'image_features': self._filter_features(activations)['block3']
})
return feature_maps.values()
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]
"""
preprocessed_inputs = shape_utils.check_min_image_dim(
33, preprocessed_inputs)
feature_map_layout = {
'from_layer': ['Conv2d_11_pointwise', 'Conv2d_13_pointwise', '', '',
'', ''],
'layer_depth': [-1, -1, 512, 256, 256, 128],
'use_explicit_padding': self._use_explicit_padding,
'use_depthwise': self._use_depthwise,
}
with tf.variable_scope('MobilenetV1',
reuse=self._reuse_weights) as scope:
with slim.arg_scope(
mobilenet_v1.mobilenet_v1_arg_scope(
is_training=None, regularize_depthwise=True)):
with (slim.arg_scope(self._conv_hyperparams_fn())
if self._override_base_feature_extractor_hyperparams
else context_manager.IdentityContextManager()):
_, image_features = mobilenet_v1.mobilenet_v1_base(
ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
final_endpoint='Conv2d_13_pointwise',
min_depth=self._min_depth,
depth_multiplier=self._depth_multiplier,
use_explicit_padding=self._use_explicit_padding,
scope=scope)
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 _extract_proposal_features(self, preprocessed_inputs, scope):
"""Extracts first stage RPN features.
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]
activations: A dictionary mapping feature extractor tensor names to
tensors
Raises:
InvalidArgumentError: If the spatial size of `preprocessed_inputs`
(height or width) is less than 33.
ValueError: If the created network is missing the required activation.
"""
preprocessed_inputs.get_shape().assert_has_rank(4)
preprocessed_inputs = shape_utils.check_min_image_dim(
min_dim=33, image_tensor=preprocessed_inputs)
with slim.arg_scope(
mobilenet_v1.mobilenet_v1_arg_scope(
is_training=self._train_batch_norm,
weight_decay=self._weight_decay)):
with tf.variable_scope('MobilenetV1',
reuse=self._reuse_weights) as scope:
params = {}
if self._skip_last_stride:
params['conv_defs'] = _get_mobilenet_conv_no_last_stride_defs(
conv_depth_ratio_in_percentage=self.
_conv_depth_ratio_in_percentage)
_, activations = mobilenet_v1.mobilenet_v1_base(
preprocessed_inputs,
final_endpoint='Conv2d_11_pointwise',
min_depth=self._min_depth,
depth_multiplier=self._depth_multiplier,
scope=scope,
**params)
return activations['Conv2d_11_pointwise'], activations
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]
"""
preprocessed_inputs = shape_utils.check_min_image_dim(
33, preprocessed_inputs)
image_features = self.mobilenet_v2(
ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple))
feature_maps = self.feature_map_generator({
'layer_15/expansion_output': image_features[0],
'layer_19': image_features[1]})
return feature_maps.values()
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]
"""
preprocessed_inputs = shape_utils.check_min_image_dim(
33, preprocessed_inputs)
feature_map_layout = {
'from_layer': ['Mixed_4c', 'Mixed_5c', '', '', '', ''],
'layer_depth': [-1, -1, 512, 256, 256, 128],
'use_explicit_padding': self._use_explicit_padding,
'use_depthwise': self._use_depthwise,
}
with slim.arg_scope(self._conv_hyperparams_fn()):
with tf.variable_scope('InceptionV2',
reuse=self._reuse_weights) as scope:
_, image_features = inception_v2.inception_v2_base(
ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
final_endpoint='Mixed_5c',
min_depth=self._min_depth,
depth_multiplier=self._depth_multiplier,
scope=scope)
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 _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]
"""
preprocessed_inputs = shape_utils.check_min_image_dim(
33, preprocessed_inputs)
feature_map_layout = {
'from_layer': ['Mixed_5d', 'Mixed_6e', 'Mixed_7c', '', '', ''],
'layer_depth': [-1, -1, -1, 512, 256, 128],
'use_explicit_padding': self._use_explicit_padding,
'use_depthwise': self._use_depthwise,
}
with slim.arg_scope(self._conv_hyperparams):
with tf.variable_scope('InceptionV3', reuse=self._reuse_weights) as scope:
_, image_features = inception_v3.inception_v3_base(
ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
final_endpoint='Mixed_7c',
min_depth=self._min_depth,
depth_multiplier=self._depth_multiplier,
scope=scope)
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 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]
"""
preprocessed_inputs = shape_utils.check_min_image_dim(
33, preprocessed_inputs)
with tf.variable_scope('MobilenetV1',
reuse=self._reuse_weights) as scope:
with slim.arg_scope(
mobilenet_v1.mobilenet_v1_arg_scope(
is_training=None, regularize_depthwise=True)):
with (slim.arg_scope(self._conv_hyperparams_fn())
if self._override_base_feature_extractor_hyperparams
else context_manager.IdentityContextManager()):
_, image_features = mobilenet_v1.mobilenet_v1_base(
ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
final_endpoint='Conv2d_13_pointwise',
min_depth=self._min_depth,
depth_multiplier=self._depth_multiplier,
use_explicit_padding=self._use_explicit_padding,
scope=scope)
with slim.arg_scope(self._conv_hyperparams_fn()):
feature_maps = feature_map_generators.pooling_pyramid_feature_maps(
base_feature_map_depth=0,
num_layers=6,
image_features={
'image_features': image_features['Conv2d_11_pointwise']
})
return feature_maps.values()
def _extract_proposal_features(self, preprocessed_inputs, scope):
"""Extracts first stage RPN features.
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]
activations: A dictionary mapping feature extractor tensor names to
tensors
Raises:
InvalidArgumentError: If the spatial size of `preprocessed_inputs`
(height or width) is less than 33.
ValueError: If the created network is missing the required activation.
"""
preprocessed_inputs = shape_utils.check_min_image_dim(
33, preprocessed_inputs)
with tf.variable_scope('MobilenetV3',
reuse=self._reuse_weights) as scope:
with slim.arg_scope(
mobilenet_v3.training_scope(is_training=None, bn_decay=0.9997)), \
slim.arg_scope(
[mobilenet.depth_multiplier], min_depth=self._min_depth):
_, activations = mobilenet_v3.mobilenet_base(
preprocessed_inputs,
conv_defs=mobilenet_v3.V3_LARGE_DETECTION,
final_endpoint='layer_17',
depth_multiplier=self._depth_multiplier,
scope=scope)
return activations['layer_17'], activations
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]
"""
preprocessed_inputs = shape_utils.check_min_image_dim(
33, preprocessed_inputs)
with tf.variable_scope('MobilenetV2', reuse=self._reuse_weights) as scope:
with slim.arg_scope(
mobilenet_v2.training_scope(is_training=None, bn_decay=0.99)), \
slim.arg_scope(
[mobilenet.depth_multiplier], min_depth=self._min_depth):
with slim.arg_scope(
training_scope(l2_weight_decay=4e-5,
is_training=self._is_training)):
_, image_features = mobilenet_v2.mobilenet_base(
ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
final_endpoint='layer_18',
depth_multiplier=self._depth_multiplier,
use_explicit_padding=self._use_explicit_padding,
scope=scope)
multiplier_func = functools.partial(
_apply_multiplier,
multiplier=self._depth_multiplier,
min_depth=self._min_depth)
with tf.variable_scope('MnasFPN', reuse=self._reuse_weights):
with slim.arg_scope(
training_scope(l2_weight_decay=1e-4, is_training=self._is_training)):
# Create C6 by downsampling C5.
c6 = slim.max_pool2d(
_maybe_pad(image_features['layer_18'], self._use_explicit_padding),
[3, 3],
stride=[2, 2],
padding='VALID' if self._use_explicit_padding else 'SAME',
scope='C6_downsample')
c6 = slim.conv2d(
c6,
multiplier_func(self._fpn_layer_depth),
[1, 1],
activation_fn=tf.identity,
normalizer_fn=slim.batch_norm,
weights_regularizer=None, # this 1x1 has no kernel regularizer.
padding='VALID',
scope='C6_Conv1x1')
image_features['C6'] = tf.identity(c6) # Needed for quantization.
for k in sorted(image_features.keys()):
tf.logging.error('{}: {}'.format(k, image_features[k]))
mnasfpn_inputs = [
image_features['layer_7'], # C3
image_features['layer_14'], # C4
image_features['layer_18'], # C5
image_features['C6'] # C6
]
self._verify_config(mnasfpn_inputs)
feature_maps = mnasfpn(
mnasfpn_inputs,
head_def=self._head_def,
output_channel=self._fpn_layer_depth,
use_explicit_padding=self._use_explicit_padding,
use_native_resize_op=self._use_native_resize_op,
multiplier_func=multiplier_func)
return feature_maps
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]
"""
preprocessed_inputs = shape_utils.check_min_image_dim(
33, preprocessed_inputs)
depth = lambda d: max(int(d * self._depth_multiplier), self._min_depth)
trunc_normal = lambda stddev: tf.truncated_normal_initializer(
0.0, stddev)
#add convolution autoencoder
encoder_1_conv = slim.conv2d(preprocessed_inputs,
depth(64), [3, 3],
weights_initializer=trunc_normal(0.09),
scope='encoder_c1_conv')
#encoder_1_pool = slim.max_pool2d(encoder_1_conv, [2, 2], stride=2,
# scope='encoder_c1_pool')
#encoder_1_dropout1 = slim.dropout(encoder_1_pool, 0.7, scope='encoder_c1_dropout1')
encoder_2_conv = slim.conv2d(encoder_1_conv,
depth(128), [5, 5],
weights_initializer=trunc_normal(0.09),
scope='encoder_c2_conv')
encoder_3_conv = slim.conv2d(encoder_2_conv,
depth(128), [5, 5],
weights_initializer=trunc_normal(0.09),
scope='encoder_c3_conv')
#decoder
decoder_3_deconv = slim.conv2d_transpose(
encoder_3_conv,
depth(128), [5, 5],
weights_initializer=trunc_normal(0.09),
scope='decoder_c3_deconv')
decoder_2_deconv = slim.conv2d_transpose(
decoder_3_deconv,
depth(128), [5, 5],
weights_initializer=trunc_normal(0.09),
scope='decoder_c2_deconv')
decoder_1_deconv = slim.conv2d_transpose(
decoder_2_deconv,
depth(1), [3, 3],
weights_initializer=trunc_normal(0.09),
scope='decoder_c1_deconv')
feature_map_layout = {
'from_layer': ['Mixed_4c', 'Mixed_5c', '', '', '', ''],
'layer_depth': [-1, -1, 512, 256, 256, 128],
'use_explicit_padding': self._use_explicit_padding,
'use_depthwise': self._use_depthwise,
}
with slim.arg_scope(self._conv_hyperparams_fn()):
with tf.variable_scope('InceptionV2',
reuse=self._reuse_weights) as scope:
_, image_features = inception_v2.inception_v2_base(
ops.pad_to_multiple(decoder_1_deconv,
self._pad_to_multiple),
final_endpoint='Mixed_5c',
min_depth=self._min_depth,
depth_multiplier=self._depth_multiplier,
scope=scope)
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 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]
"""
preprocessed_inputs = shape_utils.check_min_image_dim(
33, preprocessed_inputs)
with tf.variable_scope('MobilenetV1',
reuse=self._reuse_weights) as scope:
with slim.arg_scope(
mobilenet_v1.mobilenet_v1_arg_scope(
is_training=None, regularize_depthwise=True)):
with (slim.arg_scope(self._conv_hyperparams_fn())
if self._override_base_feature_extractor_hyperparams else
context_manager.IdentityContextManager()):
_, image_features = mobilenet_v1.mobilenet_v1_base(
ops.pad_to_multiple(preprocessed_inputs,
self._pad_to_multiple),
final_endpoint='Conv2d_13_pointwise',
min_depth=self._min_depth,
depth_multiplier=self._depth_multiplier,
use_explicit_padding=self._use_explicit_padding,
scope=scope)
depth_fn = lambda d: max(int(d * self._depth_multiplier), self.
_min_depth)
with slim.arg_scope(self._conv_hyperparams_fn()):
with tf.variable_scope('fpn', reuse=self._reuse_weights):
feature_blocks = [
'Conv2d_3_pointwise', 'Conv2d_5_pointwise',
'Conv2d_11_pointwise', 'Conv2d_13_pointwise'
]
base_fpn_max_level = min(self._fpn_max_level, 5)
feature_block_list = []
for level in range(self._fpn_min_level,
base_fpn_max_level + 1):
feature_block_list.append(feature_blocks[level - 2])
fpn_features = feature_map_generators.fpn_top_down_feature_maps(
[(key, image_features[key])
for key in feature_block_list],
depth=depth_fn(256))
feature_maps = []
for level in range(self._fpn_min_level,
base_fpn_max_level + 1):
feature_maps.append(fpn_features['top_down_{}'.format(
feature_blocks[level - 2])])
last_feature_map = fpn_features['top_down_{}'.format(
feature_blocks[base_fpn_max_level - 2])]
# Construct coarse features
for i in range(base_fpn_max_level + 1,
self._fpn_max_level + 1):
last_feature_map = slim.conv2d(
last_feature_map,
num_outputs=depth_fn(256),
kernel_size=[3, 3],
stride=2,
padding='SAME',
scope='bottom_up_Conv2d_{}'.format(
i - base_fpn_max_level + 13))
feature_maps.append(last_feature_map)
return feature_maps
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]
Raises:
ValueError: depth multiplier is not supported.
"""
if self._depth_multiplier != 1.0:
raise ValueError('Depth multiplier not supported.')
preprocessed_inputs = shape_utils.check_min_image_dim(
129, preprocessed_inputs)
with tf.variable_scope(self._resnet_scope_name,
reuse=self._reuse_weights) as scope:
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
with (slim.arg_scope(self._conv_hyperparams_fn())
if self._override_base_feature_extractor_hyperparams else
context_manager.IdentityContextManager()):
_, image_features = self._resnet_base_fn(
inputs=ops.pad_to_multiple(preprocessed_inputs,
self._pad_to_multiple),
num_classes=None,
is_training=None,
global_pool=False,
output_stride=None,
store_non_strided_activations=True,
scope=scope)
image_features = self._filter_features(image_features)
with slim.arg_scope(self._conv_hyperparams_fn()):
with tf.variable_scope(self._fpn_scope_name,
reuse=self._reuse_weights):
fpn_features = feature_map_generators.fpn_top_down_feature_maps(
[(key, image_features[key])
for key in ['block2', 'block3', 'block4']],
depth=256)
last_feature_map = fpn_features['top_down_block4']
coarse_features = {}
for i in range(5, 7):
last_feature_map = slim.conv2d(
last_feature_map,
num_outputs=256,
kernel_size=[3, 3],
stride=2,
padding='SAME',
scope='bottom_up_block{}'.format(i))
coarse_features['bottom_up_block{}'.format(
i)] = last_feature_map
return [
fpn_features['top_down_block2'], fpn_features['top_down_block3'],
fpn_features['top_down_block4'],
coarse_features['bottom_up_block5'],
coarse_features['bottom_up_block6']
]
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]
Raises:
ValueError if conv_defs is not provided or from_layer does not meet the
size requirement.
"""
if not self._conv_defs:
raise ValueError('Must provide backbone conv defs.')
if len(self._from_layer) != 2:
raise ValueError('SSD input feature names are not provided.')
preprocessed_inputs = shape_utils.check_min_image_dim(
33, preprocessed_inputs)
feature_map_layout = {
'from_layer':
[self._from_layer[0], self._from_layer[1], '', '', '', ''],
'layer_depth': [-1, -1, 128, 128, 128,
128], #[-1, -1, 512, 256, 256, 128]
'use_depthwise':
self._use_depthwise,
'use_explicit_padding':
self._use_explicit_padding,
}
with tf.variable_scope(self._scope_name,
reuse=self._reuse_weights) as scope:
with slim.arg_scope(
mobilenet_v3.training_scope(is_training=None, bn_decay=0.9997)), \
slim.arg_scope(
[mobilenet.depth_multiplier], min_depth=self._min_depth):
with (slim.arg_scope(self._conv_hyperparams_fn())
if self._override_base_feature_extractor_hyperparams else
context_manager.IdentityContextManager()):
_, image_features = mobilenet_v3.mobilenet_base(
ops.pad_to_multiple(preprocessed_inputs,
self._pad_to_multiple),
conv_defs=self._conv_defs,
final_endpoint=self._from_layer[1],
depth_multiplier=self._depth_multiplier,
use_explicit_padding=self._use_explicit_padding,
scope=scope)
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 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]
Raises:
ValueError: depth multiplier is not supported.
"""
if self._depth_multiplier != 1.0:
raise ValueError('Depth multiplier not supported.')
preprocessed_inputs = shape_utils.check_min_image_dim(
129, preprocessed_inputs)
with tf.variable_scope(self._resnet_scope_name,
reuse=self._reuse_weights) as scope:
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
with (slim.arg_scope(self._conv_hyperparams_fn())
if self._override_base_feature_extractor_hyperparams else
context_manager.IdentityContextManager()):
_, image_features = self._resnet_base_fn(
inputs=ops.pad_to_multiple(preprocessed_inputs,
self._pad_to_multiple),
num_classes=None,
is_training=None,
global_pool=False,
output_stride=None,
# store_non_strided_activations=True,
scope=scope)
image_features = self._filter_features(image_features)
with slim.arg_scope(self._conv_hyperparams_fn()):
with tf.variable_scope(self._fpn_scope_name,
reuse=self._reuse_weights):
base_fpn_max_level = min(self._fpn_max_level, 5)
feature_block_list = []
for level in range(self._fpn_min_level,
base_fpn_max_level + 1):
feature_block_list.append('block{}'.format(level - 1))
fpn_features = feature_map_generators.fpn_top_down_feature_maps(
[(key, image_features[key])
for key in feature_block_list],
depth=self._additional_layer_depth,
use_explicit_padding=True)
feature_maps = []
for level in range(self._fpn_min_level,
base_fpn_max_level + 1):
feature_maps.append(
fpn_features['top_down_block{}'.format(level - 1)])
last_feature_map = fpn_features['top_down_block{}'.format(
base_fpn_max_level - 1)]
# Construct coarse features
for i in range(base_fpn_max_level, self._fpn_max_level):
last_feature_map = slim.conv2d(
last_feature_map,
num_outputs=self._additional_layer_depth,
kernel_size=[3, 3],
stride=2,
padding='SAME',
scope='bottom_up_block{}'.format(i))
feature_maps.append(last_feature_map)
return feature_maps
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]
"""
preprocessed_inputs = shape_utils.check_min_image_dim(
33, preprocessed_inputs)
with tf.variable_scope('MobilenetV2', reuse=self._reuse_weights) as scope:
with slim.arg_scope(
mobilenet_v2.training_scope(is_training=None, bn_decay=0.9997)), \
slim.arg_scope(
[mobilenet.depth_multiplier], min_depth=self._min_depth):
with (slim.arg_scope(self._conv_hyperparams_fn())
if self._override_base_feature_extractor_hyperparams else
context_manager.IdentityContextManager()):
_, image_features = mobilenet_v2.mobilenet_base(
ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
final_endpoint='layer_19',
depth_multiplier=self._depth_multiplier,
conv_defs=_CONV_DEFS if self._use_depthwise else None,
use_explicit_padding=self._use_explicit_padding,
scope=scope)
depth_fn = lambda d: max(int(d * self._depth_multiplier), self._min_depth)
with slim.arg_scope(self._conv_hyperparams_fn()):
with tf.variable_scope('fpn', reuse=self._reuse_weights):
feature_blocks = [
'layer_4', 'layer_7', 'layer_14', 'layer_19'
]
base_fpn_max_level = min(self._fpn_max_level, 5)
feature_block_list = []
for level in range(self._fpn_min_level, base_fpn_max_level + 1):
feature_block_list.append(feature_blocks[level - 2])
fpn_features = feature_map_generators.fpn_top_down_feature_maps(
[(key, image_features[key]) for key in feature_block_list],
depth=depth_fn(self._additional_layer_depth),
use_depthwise=self._use_depthwise)
feature_maps = []
for level in range(self._fpn_min_level, base_fpn_max_level + 1):
feature_maps.append(fpn_features['top_down_{}'.format(
feature_blocks[level - 2])])
last_feature_map = fpn_features['top_down_{}'.format(
feature_blocks[base_fpn_max_level - 2])]
# Construct coarse features
for i in range(base_fpn_max_level + 1, self._fpn_max_level + 1):
if self._use_depthwise:
conv_op = functools.partial(
slim.separable_conv2d, depth_multiplier=1)
else:
conv_op = slim.conv2d
last_feature_map = conv_op(
last_feature_map,
num_outputs=depth_fn(self._additional_layer_depth),
kernel_size=[3, 3],
stride=2,
padding='SAME',
scope='bottom_up_Conv2d_{}'.format(i - base_fpn_max_level + 19))
feature_maps.append(last_feature_map)
return feature_maps
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]
"""
preprocessed_inputs = shape_utils.check_min_image_dim(
33, preprocessed_inputs)
with tf.variable_scope('MobilenetV1',
reuse=self._reuse_weights) as scope:
with slim.arg_scope(
mobilenet_v1.mobilenet_v1_arg_scope(
is_training=None, regularize_depthwise=True)):
with (slim.arg_scope(self._conv_hyperparams_fn())
if self._override_base_feature_extractor_hyperparams
else context_manager.IdentityContextManager()):
_, image_features = mobilenet_v1.mobilenet_v1_base(
ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
final_endpoint='Conv2d_13_pointwise',
min_depth=self._min_depth,
depth_multiplier=self._depth_multiplier,
use_explicit_padding=self._use_explicit_padding,
scope=scope)
depth_fn = lambda d: max(int(d * self._depth_multiplier), self._min_depth)
with slim.arg_scope(self._conv_hyperparams_fn()):
with tf.variable_scope('fpn', reuse=self._reuse_weights):
feature_blocks = [
'Conv2d_3_pointwise', 'Conv2d_5_pointwise', 'Conv2d_11_pointwise',
'Conv2d_13_pointwise'
]
base_fpn_max_level = min(self._fpn_max_level, 5)
feature_block_list = []
for level in range(self._fpn_min_level, base_fpn_max_level + 1):
feature_block_list.append(feature_blocks[level - 2])
fpn_features = feature_map_generators.fpn_top_down_feature_maps(
[(key, image_features[key]) for key in feature_block_list],
depth=depth_fn(256))
feature_maps = []
for level in range(self._fpn_min_level, base_fpn_max_level + 1):
feature_maps.append(fpn_features['top_down_{}'.format(
feature_blocks[level - 2])])
last_feature_map = fpn_features['top_down_{}'.format(
feature_blocks[base_fpn_max_level - 2])]
# Construct coarse features
for i in range(base_fpn_max_level + 1, self._fpn_max_level + 1):
last_feature_map = slim.conv2d(
last_feature_map,
num_outputs=depth_fn(256),
kernel_size=[3, 3],
stride=2,
padding='SAME',
scope='bottom_up_Conv2d_{}'.format(i - base_fpn_max_level + 13))
feature_maps.append(last_feature_map)
return feature_maps
def extract_features(self,
preprocessed_inputs,
state_saver=None,
state_name='lstm_state',
unroll_length=5,
scope=None):
"""Extracts features from preprocessed inputs.
The features include the base network features, lstm features and SSD
features, organized in the following name scope:
<parent scope>/MobilenetV1/...
<parent scope>/LSTM/...
<parent scope>/FeatureMaps/...
Args:
preprocessed_inputs: A [batch, height, width, channels] float tensor
representing a batch of consecutive frames from video clips.
state_saver: A state saver object with methods `state` and `save_state`.
state_name: A python string for the name to use with the state_saver.
unroll_length: The number of steps to unroll the lstm.
scope: The scope for the base network of the feature extractor.
Returns:
A list of tensors where the ith tensor has shape [batch, height_i,
width_i, depth_i]
"""
preprocessed_inputs = shape_utils.check_min_image_dim(
33, preprocessed_inputs)
with slim.arg_scope(
mobilenet_v1.mobilenet_v1_arg_scope(
is_training=self._is_training)):
with (slim.arg_scope(self._conv_hyperparams_fn())
if self._override_base_feature_extractor_hyperparams else
context_manager.IdentityContextManager()):
with slim.arg_scope([slim.batch_norm], fused=False):
# Base network.
with tf.variable_scope(scope,
self._base_network_scope,
reuse=self._reuse_weights) as scope:
net, image_features = mobilenet_v1.mobilenet_v1_base(
ops.pad_to_multiple(preprocessed_inputs,
self._pad_to_multiple),
final_endpoint='Conv2d_13_pointwise',
min_depth=self._min_depth,
depth_multiplier=self._depth_multiplier,
scope=scope)
with slim.arg_scope(self._conv_hyperparams_fn()):
with slim.arg_scope([slim.batch_norm],
fused=False,
is_training=self._is_training):
# ConvLSTM layers.
with tf.variable_scope(
'LSTM', reuse=self._reuse_weights) as lstm_scope:
lstm_cell = lstm_cells.BottleneckConvLSTMCell(
filter_size=(3, 3),
output_size=(net.shape[1].value, net.shape[2].value),
num_units=max(self._min_depth, self._lstm_state_depth),
activation=tf.nn.relu6,
visualize_gates=True)
net_seq = list(tf.split(net, unroll_length))
if state_saver is None:
init_state = lstm_cell.init_state(
state_name, net.shape[0].value / unroll_length,
tf.float32)
else:
c = state_saver.state('%s_c' % state_name)
h = state_saver.state('%s_h' % state_name)
init_state = (c, h)
# Identities added for inputing state tensors externally.
c_ident = tf.identity(init_state[0],
name='lstm_state_in_c')
h_ident = tf.identity(init_state[1],
name='lstm_state_in_h')
init_state = (c_ident, h_ident)
net_seq, states_out = rnn_decoder.rnn_decoder(
net_seq, init_state, lstm_cell, scope=lstm_scope)
batcher_ops = None
self._states_out = states_out
if state_saver is not None:
self._step = state_saver.state('%s_step' % state_name)
batcher_ops = [
state_saver.save_state('%s_c' % state_name,
states_out[-1][0]),
state_saver.save_state('%s_h' % state_name,
states_out[-1][1]),
state_saver.save_state('%s_step' % state_name,
self._step - 1)
]
with tf_ops.control_dependencies(batcher_ops):
image_features['Conv2d_13_pointwise_lstm'] = tf.concat(
net_seq, 0)
# Identities added for reading output states, to be reused externally.
tf.identity(states_out[-1][0], name='lstm_state_out_c')
tf.identity(states_out[-1][1], name='lstm_state_out_h')
# SSD layers.
with tf.variable_scope('FeatureMaps',
reuse=self._reuse_weights):
feature_maps = feature_map_generators.multi_resolution_feature_maps(
feature_map_layout=self._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 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]
"""
preprocessed_inputs = shape_utils.check_min_image_dim(
33, preprocessed_inputs)
feature_map_layout = {
'from_layer': ['vgg_16/conv4_3_norm', 'vgg_16/fc7', 'vgg_16/conv6_2', 'vgg_16/conv7_2', 'vgg_16/conv8_2', 'vgg_16/conv9_2'],
'layer_depth': [-1, -1, -1, -1, -1, -1],
'use_explicit_padding': self._use_explicit_padding,
'use_depthwise': self._use_depthwise,
}
net,image_features = vgg.vgg_16(ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
final_endpoint='pool5',spatial_squeeze=False)
#double check scale filler
image_features['vgg_16/conv4_3_norm']=custom_layers.l2_normalization(image_features['vgg_16/conv4/conv4_3'],scaling=True,scope='vgg_16/conv4_3_norm')
with slim.arg_scope(self._conv_hyperparams):
with tf.variable_scope('vgg_16', reuse=self._reuse_weights) as scope:
# In [5]: net
# Out[5]: <tf.Tensor 'vgg_16/pool5/MaxPool:0' shape=(32, 18, 18, 512) dtype=float32>
# In [6]: end_points
# Out[6]:
# OrderedDict([('vgg_16/conv1/conv1_1',
# <tf.Tensor 'vgg_16/conv1/conv1_1/Relu:0' shape=(32, 300, 300, 64) dtype=float32>),
# ('vgg_16/conv1/conv1_2',
# <tf.Tensor 'vgg_16/conv1/conv1_2/Relu:0' shape=(32, 300, 300, 64) dtype=float32>),
# ('vgg_16/pool1',
# <tf.Tensor 'vgg_16/pool1/MaxPool:0' shape=(32, 150, 150, 64) dtype=float32>),
# ('vgg_16/conv2/conv2_1',
# <tf.Tensor 'vgg_16/conv2/conv2_1/Relu:0' shape=(32, 150, 150, 128) dtype=float32>),
# ('vgg_16/conv2/conv2_2',
# <tf.Tensor 'vgg_16/conv2/conv2_2/Relu:0' shape=(32, 150, 150, 128) dtype=float32>),
# ('vgg_16/pool2',
# <tf.Tensor 'vgg_16/pool2/MaxPool:0' shape=(32, 75, 75, 128) dtype=float32>),
# ('vgg_16/conv3/conv3_1',
# <tf.Tensor 'vgg_16/conv3/conv3_1/Relu:0' shape=(32, 75, 75, 256) dtype=float32>),
# ('vgg_16/conv3/conv3_2',
# <tf.Tensor 'vgg_16/conv3/conv3_2/Relu:0' shape=(32, 75, 75, 256) dtype=float32>),
# ('vgg_16/conv3/conv3_3',
# <tf.Tensor 'vgg_16/conv3/conv3_3/Relu:0' shape=(32, 75, 75, 256) dtype=float32>),
# ('vgg_16/pool3',
# <tf.Tensor 'vgg_16/pool3/MaxPool:0' shape=(32, 37, 37, 256) dtype=float32>),
# ('vgg_16/conv4/conv4_1',
# <tf.Tensor 'vgg_16/conv4/conv4_1/Relu:0' shape=(32, 37, 37, 512) dtype=float32>),
# ('vgg_16/conv4/conv4_2',
# <tf.Tensor 'vgg_16/conv4/conv4_2/Relu:0' shape=(32, 37, 37, 512) dtype=float32>),
# ('vgg_16/conv4/conv4_3',
# <tf.Tensor 'vgg_16/conv4/conv4_3/Relu:0' shape=(32, 37, 37, 512) dtype=float32>),
# ('vgg_16/pool4',
# <tf.Tensor 'vgg_16/pool4/MaxPool:0' shape=(32, 18, 18, 512) dtype=float32>),
# ('vgg_16/conv5/conv5_1',
# <tf.Tensor 'vgg_16/conv5/conv5_1/Relu:0' shape=(32, 18, 18, 512) dtype=float32>),
# ('vgg_16/conv5/conv5_2',
# <tf.Tensor 'vgg_16/conv5/conv5_2/Relu:0' shape=(32, 18, 18, 512) dtype=float32>),
# ('vgg_16/conv5/conv5_3',
# <tf.Tensor 'vgg_16/conv5/conv5_3/Relu:0' shape=(32, 18, 18, 512) dtype=float32>),
# ('vgg_16/pool5',
# <tf.Tensor 'vgg_16/pool5/MaxPool:0' shape=(32, 18, 18, 512) dtype=float32>)])
end_points_collection = scope.original_name_scope + '_end_points'
with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
outputs_collections=end_points_collection):
net = slim.convolution(net, 1024, [3, 3], padding='SAME', rate=6, scope='fc6')
# def convolution(inputs,
# num_outputs,
# kernel_size,
# stride=1,
# padding='SAME',
# data_format=None,
# rate=1,
# activation_fn=nn.relu,
# normalizer_fn=None,
# normalizer_params=None,
# weights_initializer=initializers.xavier_initializer(),
# weights_regularizer=None,
# biases_initializer=init_ops.zeros_initializer(),
# biases_regularizer=None,
# reuse=None,
# variables_collections=None,
# outputs_collections=None,
# trainable=True,
# scope=None):
#fc6 is dilated conv
# layer {
# name: "fc6"
# type: "Convolution"
# bottom: "pool5"
# top: "fc6"
# param {
# lr_mult: 1.0
# decay_mult: 1.0
# }
# param {
# lr_mult: 2.0
# decay_mult: 0.0
# }
# convolution_param {
# num_output: 1024
# pad: 6
# kernel_size: 3
# weight_filler {
# type: "xavier"
# }
# bias_filler {
# type: "constant"
# value: 0.0
# }
# dilation: 6
# }
# }
# layer {
# name: "relu6"
# type: "ReLU"
# bottom: "fc6"
# top: "fc6"
# }
#fc7 is 1*1 conv
# layer {
# name: "fc7"
# type: "Convolution"
# bottom: "fc6"
# top: "fc7"
# param {
# lr_mult: 1.0
# decay_mult: 1.0
# }
# param {
# lr_mult: 2.0
# decay_mult: 0.0
# }
# convolution_param {
# num_output: 1024
# kernel_size: 1
# weight_filler {
# type: "xavier"
# }
# bias_filler {
# type: "constant"
# value: 0.0
# }
# }
# }
# layer {
# name: "relu7"
# type: "ReLU"
# bottom: "fc7"
# top: "fc7"
# }
net = slim.conv2d(net,1024,[1,1],padding='SAME',scope='fc7')
net = slim.conv2d(net,256, [1,1],padding='SAME',scope='conv6_1')
net = slim.conv2d(net,512, [3,3],padding='SAME',stride=2,scope='conv6_2')
net = slim.conv2d(net,128, [1,1],padding='SAME',scope='conv7_1')
net = slim.conv2d(net,256, [3,3],padding='SAME',stride=2,scope='conv7_2')
net = slim.conv2d(net,128, [1,1],padding='VALID',scope='conv8_1')
net = slim.conv2d(net,256, [3,3],padding='VALID',stride=1,scope='conv8_2')
net = slim.conv2d(net,128, [1,1],padding='VALID',scope='conv9_1')
net = slim.conv2d(net,256, [3,3],padding='VALID',stride=1,scope='conv9_2')
_image_features_new = slim.utils.convert_collection_to_dict(end_points_collection)
for k,v in _image_features_new.iteritems():
image_features[k] = v
# _, image_features = inception_v3.inception_v3_base(
# ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
# final_endpoint='Mixed_7c',
# min_depth=self._min_depth,
# depth_multiplier=self._depth_multiplier,
# scope=scope)
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 extract_features(self, preprocessed_inputs, state_saver=None,
state_name='lstm_state', unroll_length=10, scope=None):
"""Extract features from preprocessed inputs.
The features include the base network features, lstm features and SSD
features, organized in the following name scope:
<scope>/MobilenetV2_1/...
<scope>/MobilenetV2_2/...
<scope>/LSTM/...
<scope>/FeatureMap/...
Args:
preprocessed_inputs: a [batch, height, width, channels] float tensor
representing a batch of consecutive frames from video clips.
state_saver: A state saver object with methods `state` and `save_state`.
state_name: Python string, the name to use with the state_saver.
unroll_length: number of steps to unroll the lstm.
scope: Scope for the base network of the feature extractor.
Returns:
feature_maps: a list of tensors where the ith tensor has shape
[batch, height_i, width_i, depth_i]
Raises:
ValueError: if interleave_method not recognized or large and small base
network output feature maps of different sizes.
"""
preprocessed_inputs = shape_utils.check_min_image_dim(
33, preprocessed_inputs)
preprocessed_inputs = ops.pad_to_multiple(
preprocessed_inputs, self._pad_to_multiple)
batch_size = preprocessed_inputs.shape[0].value / unroll_length
batch_axis = 0
nets = []
# Batch processing of mobilenet features.
with slim.arg_scope(mobilenet_v2.training_scope(
is_training=self._is_training,
bn_decay=0.9997)), \
slim.arg_scope([mobilenet.depth_multiplier],
min_depth=self._min_depth, divisible_by=8):
# Big model.
net, _ = self.extract_base_features_large(preprocessed_inputs)
nets.append(net)
large_base_feature_shape = net.shape
# Small models
net, _ = self.extract_base_features_small(preprocessed_inputs)
nets.append(net)
small_base_feature_shape = net.shape
if not (large_base_feature_shape[1] == small_base_feature_shape[1] and
large_base_feature_shape[2] == small_base_feature_shape[2]):
raise ValueError('Large and Small base network feature map dimension '
'not equal!')
with slim.arg_scope(self._conv_hyperparams_fn()):
with tf.variable_scope('LSTM', reuse=self._reuse_weights):
output_size = (large_base_feature_shape[1], large_base_feature_shape[2])
lstm_cell, init_state, step = self.create_lstm_cell(
batch_size, output_size, state_saver, state_name)
nets_seq = [
tf.split(net, unroll_length, axis=batch_axis) for net in nets
]
net_seq, states_out = rnn_decoder.multi_input_rnn_decoder(
nets_seq,
init_state,
lstm_cell,
step,
selection_strategy=self._interleave_method,
is_training=self._is_training,
is_quantized=self._is_quantized,
pre_bottleneck=self._pre_bottleneck,
flatten_state=self._flatten_state,
scope=None)
self._states_out = states_out
batcher_ops = None
if state_saver is not None:
self._step = state_saver.state(state_name + '_step')
batcher_ops = [
state_saver.save_state(state_name + '_c', states_out[-1][0]),
state_saver.save_state(state_name + '_h', states_out[-1][1]),
state_saver.save_state(state_name + '_step', self._step + 1)]
image_features = {}
with tf_ops.control_dependencies(batcher_ops):
image_features['layer_19'] = tf.concat(net_seq, 0)
# SSD layers.
with tf.variable_scope('FeatureMap'):
feature_maps = feature_map_generators.multi_resolution_feature_maps(
feature_map_layout=self._feature_map_layout,
depth_multiplier=self._depth_multiplier,
min_depth=self._min_depth,
insert_1x1_conv=True,
image_features=image_features,
pool_residual=True)
return feature_maps.values()
def graph_fn(input_tensor): return shape_utils.check_min_image_dim(33, input_tensor)
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]
"""
preprocessed_inputs = shape_utils.check_min_image_dim(
33, preprocessed_inputs)
with tf.variable_scope('MobilenetV2',
reuse=self._reuse_weights) as scope:
with slim.arg_scope(
mobilenet_v2.training_scope(is_training=None, bn_decay=0.9997)), \
slim.arg_scope(
[mobilenet.depth_multiplier], min_depth=self._min_depth):
with (slim.arg_scope(self._conv_hyperparams_fn())
if self._override_base_feature_extractor_hyperparams else
context_manager.IdentityContextManager()):
_, image_features = mobilenet_v2.mobilenet_base(
ops.pad_to_multiple(preprocessed_inputs,
self._pad_to_multiple),
final_endpoint='layer_19',
depth_multiplier=self._depth_multiplier,
conv_defs=self._conv_defs,
use_explicit_padding=self._use_explicit_padding,
scope=scope)
depth_fn = lambda d: max(int(d * self._depth_multiplier), self.
_min_depth)
with slim.arg_scope(self._conv_hyperparams_fn()):
with tf.variable_scope('fpn', reuse=self._reuse_weights):
feature_blocks = [
'layer_4', 'layer_7', 'layer_14', 'layer_19'
]
base_fpn_max_level = min(self._fpn_max_level, 5)
feature_block_list = []
for level in range(self._fpn_min_level,
base_fpn_max_level + 1):
feature_block_list.append(feature_blocks[level - 2])
fpn_features = feature_map_generators.fpn_top_down_feature_maps(
[(key, image_features[key])
for key in feature_block_list],
depth=depth_fn(self._additional_layer_depth),
use_depthwise=self._use_depthwise,
use_explicit_padding=self._use_explicit_padding)
feature_maps = []
for level in range(self._fpn_min_level,
base_fpn_max_level + 1):
feature_maps.append(fpn_features['top_down_{}'.format(
feature_blocks[level - 2])])
last_feature_map = fpn_features['top_down_{}'.format(
feature_blocks[base_fpn_max_level - 2])]
# Construct coarse features
padding = 'VALID' if self._use_explicit_padding else 'SAME'
kernel_size = 3
for i in range(base_fpn_max_level + 1,
self._fpn_max_level + 1):
if self._use_depthwise:
conv_op = functools.partial(slim.separable_conv2d,
depth_multiplier=1)
else:
conv_op = slim.conv2d
if self._use_explicit_padding:
last_feature_map = ops.fixed_padding(
last_feature_map, kernel_size)
last_feature_map = conv_op(
last_feature_map,
num_outputs=depth_fn(self._additional_layer_depth),
kernel_size=[kernel_size, kernel_size],
stride=2,
padding=padding,
scope='bottom_up_Conv2d_{}'.format(
i - base_fpn_max_level + 19))
feature_maps.append(last_feature_map)
return feature_maps
def extract_features(self,
preprocessed_inputs,
state_saver=None,
state_name='lstm_state',
unroll_length=5,
scope=None):
"""Extracts features from preprocessed inputs.
The features include the base network features, lstm features and SSD
features, organized in the following name scope:
<parent scope>/MobilenetV1/...
<parent scope>/LSTM/...
<parent scope>/FeatureMaps/...
Args:
preprocessed_inputs: A [batch, height, width, channels] float tensor
representing a batch of consecutive frames from video clips.
state_saver: A state saver object with methods `state` and `save_state`.
state_name: A python string for the name to use with the state_saver.
unroll_length: The number of steps to unroll the lstm.
scope: The scope for the base network of the feature extractor.
Returns:
A list of tensors where the ith tensor has shape [batch, height_i,
width_i, depth_i]
"""
preprocessed_inputs = shape_utils.check_min_image_dim(
33, preprocessed_inputs)
with slim.arg_scope(
mobilenet_v1.mobilenet_v1_arg_scope(is_training=self._is_training)):
with (slim.arg_scope(self._conv_hyperparams_fn())
if self._override_base_feature_extractor_hyperparams else
context_manager.IdentityContextManager()):
with slim.arg_scope([slim.batch_norm], fused=False):
# Base network.
with tf.variable_scope(
scope, self._base_network_scope,
reuse=self._reuse_weights) as scope:
net, image_features = mobilenet_v1.mobilenet_v1_base(
ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
final_endpoint='Conv2d_13_pointwise',
min_depth=self._min_depth,
depth_multiplier=self._depth_multiplier,
scope=scope)
with slim.arg_scope(self._conv_hyperparams_fn()):
with slim.arg_scope(
[slim.batch_norm], fused=False, is_training=self._is_training):
# ConvLSTM layers.
with tf.variable_scope('LSTM', reuse=self._reuse_weights) as lstm_scope:
lstm_cell = lstm_cells.BottleneckConvLSTMCell(
filter_size=(3, 3),
output_size=(net.shape[1].value, net.shape[2].value),
num_units=max(self._min_depth, self._lstm_state_depth),
activation=tf.nn.relu6,
visualize_gates=True)
net_seq = list(tf.split(net, unroll_length))
if state_saver is None:
init_state = lstm_cell.init_state(
state_name, net.shape[0].value / unroll_length, tf.float32)
else:
c = state_saver.state('%s_c' % state_name)
h = state_saver.state('%s_h' % state_name)
init_state = (c, h)
# Identities added for inputing state tensors externally.
c_ident = tf.identity(init_state[0], name='lstm_state_in_c')
h_ident = tf.identity(init_state[1], name='lstm_state_in_h')
init_state = (c_ident, h_ident)
net_seq, states_out = rnn_decoder.rnn_decoder(
net_seq, init_state, lstm_cell, scope=lstm_scope)
batcher_ops = None
self._states_out = states_out
if state_saver is not None:
self._step = state_saver.state('%s_step' % state_name)
batcher_ops = [
state_saver.save_state('%s_c' % state_name, states_out[-1][0]),
state_saver.save_state('%s_h' % state_name, states_out[-1][1]),
state_saver.save_state('%s_step' % state_name, self._step - 1)
]
with tf_ops.control_dependencies(batcher_ops):
image_features['Conv2d_13_pointwise_lstm'] = tf.concat(net_seq, 0)
# Identities added for reading output states, to be reused externally.
tf.identity(states_out[-1][0], name='lstm_state_out_c')
tf.identity(states_out[-1][1], name='lstm_state_out_h')
# SSD layers.
with tf.variable_scope('FeatureMaps', reuse=self._reuse_weights):
feature_maps = feature_map_generators.multi_resolution_feature_maps(
feature_map_layout=self._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()
