Python nets.resnet_v1.bottleneck() Examples
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code examples of nets.resnet_v1.bottleneck().
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Example #1
| Source File: resnet_v1_test.py From tensorflow_yolo2 with MIT License | 6 votes |
|
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 slim.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)
Example #2
| Source File: resnet_v1_util.py From motion-rcnn with MIT License | 6 votes |
|
def resnet_v1_block(scope, base_depth, num_units, stride):
"""Helper function for creating a resnet_v1 bottleneck block.
Args:
scope: The scope of the block.
base_depth: The depth of the bottleneck layer for each unit.
num_units: The number of units in the block.
stride: The stride of the block, implemented as a stride in the first unit.
All other units have stride=1.
Note that the default slim implementation places the stride in the last unit,
which is less memory efficient and a deviation from the resnet paper.
Returns:
A resnet_v1 bottleneck block.
"""
return resnet_utils.Block(scope, resnet_v1.bottleneck, [{
'depth': base_depth * 4,
'depth_bottleneck': base_depth,
'stride': stride
}] + [{
'depth': base_depth * 4,
'depth_bottleneck': base_depth,
'stride': 1
}] * (num_units - 1))
Example #3
| Source File: resnet_v1_test.py From terngrad with Apache License 2.0 | 6 votes |
|
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 slim.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)
Example #4
| Source File: resnet_v1_test.py From Action_Recognition_Zoo with MIT License | 6 votes |
|
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 slim.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)
Example #5
| Source File: resnet_v1_test.py From MobileNet with Apache License 2.0 | 6 votes |
|
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 slim.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)
Example #6
| Source File: resnet_v1_test.py From HumanRecognition with MIT License | 6 votes |
|
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 slim.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)
Example #7
| Source File: resnet_v1_test.py From Optical-Flow-Guided-Feature with MIT License | 6 votes |
|
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 slim.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)
Example #8
| Source File: resnet_v1_test.py From ECO-pytorch with BSD 2-Clause "Simplified" License | 6 votes |
|
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 slim.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)
Example #9
| Source File: resnet_v1_test.py From hops-tensorflow with Apache License 2.0 | 6 votes |
|
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 slim.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)
Example #10
| Source File: faster_rcnn_resnet_v1_feature_extractor.py From MAX-Object-Detector with Apache License 2.0 | 5 votes |
|
def _extract_box_classifier_features(self, proposal_feature_maps, scope):
"""Extracts second stage box classifier features.
Args:
proposal_feature_maps: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, crop_height, crop_width, depth]
representing the feature map cropped to each proposal.
scope: A scope name (unused).
Returns:
proposal_classifier_features: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, height, width, depth]
representing box classifier features for each proposal.
"""
with tf.variable_scope(self._architecture, reuse=self._reuse_weights):
with slim.arg_scope(
resnet_utils.resnet_arg_scope(
batch_norm_epsilon=1e-5,
batch_norm_scale=True,
weight_decay=self._weight_decay)):
with slim.arg_scope([slim.batch_norm],
is_training=self._train_batch_norm):
blocks = [
resnet_utils.Block('block4', resnet_v1.bottleneck, [{
'depth': 2048,
'depth_bottleneck': 512,
'stride': 1
}] * 3)
]
proposal_classifier_features = resnet_utils.stack_blocks_dense(
proposal_feature_maps, blocks)
return proposal_classifier_features
Example #11
| Source File: faster_rcnn_resnet_v1_feature_extractor.py From mtl-ssl with Apache License 2.0 | 5 votes |
|
def _extract_box_classifier_features(self, proposal_feature_maps, scope):
"""Extracts second stage box classifier features.
Args:
proposal_feature_maps: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, crop_height, crop_width, depth]
representing the feature map cropped to each proposal.
scope: A scope name (unused).
Returns:
proposal_classifier_features: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, height, width, depth]
representing box classifier features for each proposal.
"""
with tf.variable_scope(self._architecture, reuse=self._reuse_weights):
with slim.arg_scope(
resnet_utils.resnet_arg_scope(
batch_norm_epsilon=1e-5,
batch_norm_scale=True,
weight_decay=self._weight_decay,
trainable=self._is_training,
batch_norm_trainable=self._is_training and self._batch_norm_trainable
)):
with slim.arg_scope([slim.batch_norm], is_training=False):
blocks = [
resnet_utils.Block('block4', resnet_v1.bottleneck, [{
'depth': 2048,
'depth_bottleneck': 512,
'stride': 1
}] * 3)
]
if self._is_training:
proposal_classifier_features = resnet_utils.stack_blocks_dense(
proposal_feature_maps, blocks)
else:
proposal_classifier_features = resnet_utils.stack_blocks_dense(
proposal_feature_maps, blocks, block_trainable=[False,False,False,False])
return proposal_classifier_features
Example #12
| Source File: faster_rcnn_resnet_v1_feature_extractor.py From models with Apache License 2.0 | 5 votes |
|
def _extract_box_classifier_features(self, proposal_feature_maps, scope):
"""Extracts second stage box classifier features.
Args:
proposal_feature_maps: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, crop_height, crop_width, depth]
representing the feature map cropped to each proposal.
scope: A scope name (unused).
Returns:
proposal_classifier_features: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, height, width, depth]
representing box classifier features for each proposal.
"""
with tf.variable_scope(self._architecture, reuse=self._reuse_weights):
with slim.arg_scope(
resnet_utils.resnet_arg_scope(
batch_norm_epsilon=1e-5,
batch_norm_scale=True,
activation_fn=self._activation_fn,
weight_decay=self._weight_decay)):
with slim.arg_scope([slim.batch_norm],
is_training=self._train_batch_norm):
blocks = [
resnet_utils.Block('block4', resnet_v1.bottleneck, [{
'depth': 2048,
'depth_bottleneck': 512,
'stride': 1
}] * 3)
]
proposal_classifier_features = resnet_utils.stack_blocks_dense(
proposal_feature_maps, blocks)
return proposal_classifier_features
Example #13
| Source File: faster_rcnn_resnet_v1_feature_extractor.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
|
def _extract_box_classifier_features(self, proposal_feature_maps, scope):
"""Extracts second stage box classifier features.
Args:
proposal_feature_maps: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, crop_height, crop_width, depth]
representing the feature map cropped to each proposal.
scope: A scope name (unused).
Returns:
proposal_classifier_features: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, height, width, depth]
representing box classifier features for each proposal.
"""
with tf.variable_scope(self._architecture, reuse=self._reuse_weights):
with slim.arg_scope(
resnet_utils.resnet_arg_scope(
batch_norm_epsilon=1e-5,
batch_norm_scale=True,
weight_decay=self._weight_decay)):
with slim.arg_scope([slim.batch_norm],
is_training=self._train_batch_norm):
blocks = [
resnet_utils.Block('block4', resnet_v1.bottleneck, [{
'depth': 2048,
'depth_bottleneck': 512,
'stride': 1
}] * 3)
]
proposal_classifier_features = resnet_utils.stack_blocks_dense(
proposal_feature_maps, blocks)
return proposal_classifier_features
Example #14
| Source File: resnet_v1_test.py From HumanRecognition with MIT License | 5 votes |
|
def _resnet_small(self,
inputs,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
reuse=None,
scope='resnet_v1_small'):
"""A shallow and thin ResNet v1 for faster tests."""
bottleneck = resnet_v1.bottleneck
blocks = [
resnet_utils.Block(
'block1', bottleneck, [(4, 1, 1)] * 2 + [(4, 1, 2)]),
resnet_utils.Block(
'block2', bottleneck, [(8, 2, 1)] * 2 + [(8, 2, 2)]),
resnet_utils.Block(
'block3', bottleneck, [(16, 4, 1)] * 2 + [(16, 4, 2)]),
resnet_utils.Block(
'block4', bottleneck, [(32, 8, 1)] * 2)]
return resnet_v1.resnet_v1(inputs, blocks, num_classes,
is_training=is_training,
global_pool=global_pool,
output_stride=output_stride,
include_root_block=include_root_block,
reuse=reuse,
scope=scope)
Example #15
| Source File: resnet_v1_test.py From HumanRecognition with MIT License | 5 votes |
|
def testAtrousValuesBottleneck(self):
self._atrousValues(resnet_v1.bottleneck)
Example #16
| Source File: faster_rcnn_resnet_v1_feature_extractor.py From object_detection_kitti with Apache License 2.0 | 5 votes |
|
def _extract_box_classifier_features(self, proposal_feature_maps, scope):
"""Extracts second stage box classifier features.
Args:
proposal_feature_maps: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, crop_height, crop_width, depth]
representing the feature map cropped to each proposal.
scope: A scope name (unused).
Returns:
proposal_classifier_features: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, height, width, depth]
representing box classifier features for each proposal.
"""
with tf.variable_scope(self._architecture, reuse=self._reuse_weights):
with slim.arg_scope(
resnet_utils.resnet_arg_scope(
batch_norm_epsilon=1e-5,
batch_norm_scale=True,
weight_decay=self._weight_decay)):
with slim.arg_scope([slim.batch_norm], is_training=False):
blocks = [
resnet_utils.Block('block4', resnet_v1.bottleneck, [{
'depth': 2048,
'depth_bottleneck': 512,
'stride': 1
}] * 3)
]
proposal_classifier_features = resnet_utils.stack_blocks_dense(
proposal_feature_maps, blocks)
return proposal_classifier_features
Example #17
| Source File: faster_rcnn_resnet_v1_feature_extractor.py From object_detector_app with MIT License | 5 votes |
|
def _extract_box_classifier_features(self, proposal_feature_maps, scope):
"""Extracts second stage box classifier features.
Args:
proposal_feature_maps: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, crop_height, crop_width, depth]
representing the feature map cropped to each proposal.
scope: A scope name (unused).
Returns:
proposal_classifier_features: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, height, width, depth]
representing box classifier features for each proposal.
"""
with tf.variable_scope(self._architecture, reuse=self._reuse_weights):
with slim.arg_scope(
resnet_utils.resnet_arg_scope(
batch_norm_epsilon=1e-5,
batch_norm_scale=True,
weight_decay=self._weight_decay)):
with slim.arg_scope([slim.batch_norm], is_training=False):
blocks = [
resnet_utils.Block('block4', resnet_v1.bottleneck, [{
'depth': 2048,
'depth_bottleneck': 512,
'stride': 1
}] * 3)
]
proposal_classifier_features = resnet_utils.stack_blocks_dense(
proposal_feature_maps, blocks)
return proposal_classifier_features
Example #18
| Source File: resnet_v1_test.py From Optical-Flow-Guided-Feature with MIT License | 5 votes |
|
def testAtrousValuesBottleneck(self):
self._atrousValues(resnet_v1.bottleneck)
Example #19
| Source File: resnet_v1_test.py From Optical-Flow-Guided-Feature with MIT License | 5 votes |
|
def _resnet_small(self,
inputs,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
reuse=None,
scope='resnet_v1_small'):
"""A shallow and thin ResNet v1 for faster tests."""
bottleneck = resnet_v1.bottleneck
blocks = [
resnet_utils.Block(
'block1', bottleneck, [(4, 1, 1)] * 2 + [(4, 1, 2)]),
resnet_utils.Block(
'block2', bottleneck, [(8, 2, 1)] * 2 + [(8, 2, 2)]),
resnet_utils.Block(
'block3', bottleneck, [(16, 4, 1)] * 2 + [(16, 4, 2)]),
resnet_utils.Block(
'block4', bottleneck, [(32, 8, 1)] * 2)]
return resnet_v1.resnet_v1(inputs, blocks, num_classes,
is_training=is_training,
global_pool=global_pool,
output_stride=output_stride,
include_root_block=include_root_block,
reuse=reuse,
scope=scope)
Example #20
| Source File: faster_rcnn_resnet_v1_feature_extractor.py From MBMD with MIT License | 5 votes |
|
def _extract_box_classifier_features(self, proposal_feature_maps, scope):
"""Extracts second stage box classifier features.
Args:
proposal_feature_maps: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, crop_height, crop_width, depth]
representing the feature map cropped to each proposal.
scope: A scope name (unused).
Returns:
proposal_classifier_features: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, height, width, depth]
representing box classifier features for each proposal.
"""
with tf.variable_scope(self._architecture, reuse=self._reuse_weights):
with slim.arg_scope(
resnet_utils.resnet_arg_scope(
batch_norm_epsilon=1e-5,
batch_norm_scale=True,
weight_decay=self._weight_decay)):
with slim.arg_scope([slim.batch_norm], is_training=False):
blocks = [
resnet_utils.Block('block4', resnet_v1.bottleneck, [{
'depth': 2048,
'depth_bottleneck': 512,
'stride': 1
}] * 3)
]
proposal_classifier_features = resnet_utils.stack_blocks_dense(
proposal_feature_maps, blocks)
return proposal_classifier_features
Example #21
| Source File: faster_rcnn_resnet_v1_feature_extractor.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
|
def _extract_box_classifier_features(self, proposal_feature_maps, scope):
"""Extracts second stage box classifier features.
Args:
proposal_feature_maps: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, crop_height, crop_width, depth]
representing the feature map cropped to each proposal.
scope: A scope name (unused).
Returns:
proposal_classifier_features: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, height, width, depth]
representing box classifier features for each proposal.
"""
with tf.variable_scope(self._architecture, reuse=self._reuse_weights):
with slim.arg_scope(
resnet_utils.resnet_arg_scope(
batch_norm_epsilon=1e-5,
batch_norm_scale=True,
weight_decay=self._weight_decay)):
with slim.arg_scope([slim.batch_norm],
is_training=self._train_batch_norm):
blocks = [
resnet_utils.Block('block4', resnet_v1.bottleneck, [{
'depth': 2048,
'depth_bottleneck': 512,
'stride': 1
}] * 3)
]
proposal_classifier_features = resnet_utils.stack_blocks_dense(
proposal_feature_maps, blocks)
return proposal_classifier_features
Example #22
| Source File: faster_rcnn_resnet_v1_feature_extractor.py From object_detection_with_tensorflow with MIT License | 5 votes |
|
def _extract_box_classifier_features(self, proposal_feature_maps, scope):
"""Extracts second stage box classifier features.
Args:
proposal_feature_maps: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, crop_height, crop_width, depth]
representing the feature map cropped to each proposal.
scope: A scope name (unused).
Returns:
proposal_classifier_features: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, height, width, depth]
representing box classifier features for each proposal.
"""
with tf.variable_scope(self._architecture, reuse=self._reuse_weights):
with slim.arg_scope(
resnet_utils.resnet_arg_scope(
batch_norm_epsilon=1e-5,
batch_norm_scale=True,
weight_decay=self._weight_decay)):
with slim.arg_scope([slim.batch_norm],
is_training=self._train_batch_norm):
blocks = [
resnet_utils.Block('block4', resnet_v1.bottleneck, [{
'depth': 2048,
'depth_bottleneck': 512,
'stride': 1
}] * 3)
]
proposal_classifier_features = resnet_utils.stack_blocks_dense(
proposal_feature_maps, blocks)
return proposal_classifier_features
Example #23
| Source File: faster_rcnn_resnet_v1_feature_extractor.py From hands-detection with MIT License | 5 votes |
|
def _extract_box_classifier_features(self, proposal_feature_maps, scope):
"""Extracts second stage box classifier features.
Args:
proposal_feature_maps: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, crop_height, crop_width, depth]
representing the feature map cropped to each proposal.
scope: A scope name (unused).
Returns:
proposal_classifier_features: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, height, width, depth]
representing box classifier features for each proposal.
"""
with tf.variable_scope(self._architecture, reuse=self._reuse_weights):
with slim.arg_scope(
resnet_utils.resnet_arg_scope(
batch_norm_epsilon=1e-5,
batch_norm_scale=True,
weight_decay=self._weight_decay)):
with slim.arg_scope([slim.batch_norm], is_training=False):
blocks = [
resnet_utils.Block('block4', resnet_v1.bottleneck, [{
'depth': 2048,
'depth_bottleneck': 512,
'stride': 1
}] * 3)
]
proposal_classifier_features = resnet_utils.stack_blocks_dense(
proposal_feature_maps, blocks)
return proposal_classifier_features
Example #24
| Source File: resnet_v1_test.py From ECO-pytorch with BSD 2-Clause "Simplified" License | 5 votes |
|
def _resnet_small(self,
inputs,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
reuse=None,
scope='resnet_v1_small'):
"""A shallow and thin ResNet v1 for faster tests."""
bottleneck = resnet_v1.bottleneck
blocks = [
resnet_utils.Block(
'block1', bottleneck, [(4, 1, 1)] * 2 + [(4, 1, 2)]),
resnet_utils.Block(
'block2', bottleneck, [(8, 2, 1)] * 2 + [(8, 2, 2)]),
resnet_utils.Block(
'block3', bottleneck, [(16, 4, 1)] * 2 + [(16, 4, 2)]),
resnet_utils.Block(
'block4', bottleneck, [(32, 8, 1)] * 2)]
return resnet_v1.resnet_v1(inputs, blocks, num_classes,
is_training=is_training,
global_pool=global_pool,
output_stride=output_stride,
include_root_block=include_root_block,
reuse=reuse,
scope=scope)
Example #25
| Source File: resnet_v1_test.py From ECO-pytorch with BSD 2-Clause "Simplified" License | 5 votes |
|
def testAtrousValuesBottleneck(self):
self._atrousValues(resnet_v1.bottleneck)
Example #26
| Source File: resnet_v1_test.py From Action_Recognition_Zoo with MIT License | 5 votes |
|
def _resnet_small(self,
inputs,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
reuse=None,
scope='resnet_v1_small'):
"""A shallow and thin ResNet v1 for faster tests."""
bottleneck = resnet_v1.bottleneck
blocks = [
resnet_utils.Block(
'block1', bottleneck, [(4, 1, 1)] * 2 + [(4, 1, 2)]),
resnet_utils.Block(
'block2', bottleneck, [(8, 2, 1)] * 2 + [(8, 2, 2)]),
resnet_utils.Block(
'block3', bottleneck, [(16, 4, 1)] * 2 + [(16, 4, 2)]),
resnet_utils.Block(
'block4', bottleneck, [(32, 8, 1)] * 2)]
return resnet_v1.resnet_v1(inputs, blocks, num_classes,
is_training=is_training,
global_pool=global_pool,
output_stride=output_stride,
include_root_block=include_root_block,
reuse=reuse,
scope=scope)
Example #27
| Source File: resnet_v1_test.py From Action_Recognition_Zoo with MIT License | 5 votes |
|
def testAtrousValuesBottleneck(self):
self._atrousValues(resnet_v1.bottleneck)
Example #28
| Source File: faster_rcnn_resnet_v1_feature_extractor.py From moveo_ros with MIT License | 5 votes |
|
def _extract_box_classifier_features(self, proposal_feature_maps, scope):
"""Extracts second stage box classifier features.
Args:
proposal_feature_maps: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, crop_height, crop_width, depth]
representing the feature map cropped to each proposal.
scope: A scope name (unused).
Returns:
proposal_classifier_features: A 4-D float tensor with shape
[batch_size * self.max_num_proposals, height, width, depth]
representing box classifier features for each proposal.
"""
with tf.variable_scope(self._architecture, reuse=self._reuse_weights):
with slim.arg_scope(
resnet_utils.resnet_arg_scope(
batch_norm_epsilon=1e-5,
batch_norm_scale=True,
weight_decay=self._weight_decay)):
with slim.arg_scope([slim.batch_norm], is_training=False):
blocks = [
resnet_utils.Block('block4', resnet_v1.bottleneck, [{
'depth': 2048,
'depth_bottleneck': 512,
'stride': 1
}] * 3)
]
proposal_classifier_features = resnet_utils.stack_blocks_dense(
proposal_feature_maps, blocks)
return proposal_classifier_features
Example #29
| Source File: resnet_v1_test.py From tensorflow_yolo2 with MIT License | 5 votes |
|
def _resnet_small(self,
inputs,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
reuse=None,
scope='resnet_v1_small'):
"""A shallow and thin ResNet v1 for faster tests."""
bottleneck = resnet_v1.bottleneck
blocks = [
resnet_utils.Block(
'block1', bottleneck, [(4, 1, 1)] * 2 + [(4, 1, 2)]),
resnet_utils.Block(
'block2', bottleneck, [(8, 2, 1)] * 2 + [(8, 2, 2)]),
resnet_utils.Block(
'block3', bottleneck, [(16, 4, 1)] * 2 + [(16, 4, 2)]),
resnet_utils.Block(
'block4', bottleneck, [(32, 8, 1)] * 2)]
return resnet_v1.resnet_v1(inputs, blocks, num_classes,
is_training=is_training,
global_pool=global_pool,
output_stride=output_stride,
include_root_block=include_root_block,
reuse=reuse,
scope=scope)
Example #30
| Source File: resnet_v1_test.py From tensorflow_yolo2 with MIT License | 5 votes |
|
def testAtrousValuesBottleneck(self):
self._atrousValues(resnet_v1.bottleneck)
