Python nets.resnet_v1.resnet_v1_block() Examples
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code examples of nets.resnet_v1.resnet_v1_block().
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Example #1
| Source File: resnet_v1_test.py From R2CNN-Plus-Plus_Tensorflow with MIT License | 6 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."""
block = resnet_v1.resnet_v1_block
blocks = [
block('block1', base_depth=1, num_units=3, stride=2),
block('block2', base_depth=2, num_units=3, stride=2),
block('block3', base_depth=4, num_units=3, stride=2),
block('block4', base_depth=8, num_units=2, stride=1),
]
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 #2
| Source File: resnet_v1_test.py From R3Det_Tensorflow with MIT License | 6 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."""
block = resnet_v1.resnet_v1_block
blocks = [
block('block1', base_depth=1, num_units=3, stride=2),
block('block2', base_depth=2, num_units=3, stride=2),
block('block3', base_depth=4, num_units=3, stride=2),
block('block4', base_depth=8, num_units=2, stride=1),
]
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 #3
| Source File: resnet_v1_test.py From tumblr-emotions with Apache License 2.0 | 6 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."""
block = resnet_v1.resnet_v1_block
blocks = [
block('block1', base_depth=1, num_units=3, stride=2),
block('block2', base_depth=2, num_units=3, stride=2),
block('block3', base_depth=4, num_units=3, stride=2),
block('block4', base_depth=8, num_units=2, stride=1),
]
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 #4
| Source File: resnet_v1_test.py From RetinaNet_Tensorflow_Rotation with MIT License | 6 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."""
block = resnet_v1.resnet_v1_block
blocks = [
block('block1', base_depth=1, num_units=3, stride=2),
block('block2', base_depth=2, num_units=3, stride=2),
block('block3', base_depth=4, num_units=3, stride=2),
block('block4', base_depth=8, num_units=2, stride=1),
]
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 #5
| Source File: resnet_v1_test.py From R2CNN_Faster-RCNN_Tensorflow with MIT License | 6 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."""
block = resnet_v1.resnet_v1_block
blocks = [
block('block1', base_depth=1, num_units=3, stride=2),
block('block2', base_depth=2, num_units=3, stride=2),
block('block3', base_depth=4, num_units=3, stride=2),
block('block4', base_depth=8, num_units=2, stride=1),
]
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 #6
| Source File: resnet_v1_test.py From Translation-Invariant-Attacks with Apache License 2.0 | 5 votes |
|
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 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 style_swap_tensorflow with Apache License 2.0 | 5 votes |
|
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 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 Machine-Learning-with-TensorFlow-1.x 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,
spatial_squeeze=True,
reuse=None,
scope='resnet_v1_small'):
"""A shallow and thin ResNet v1 for faster tests."""
block = resnet_v1.resnet_v1_block
blocks = [
block('block1', base_depth=1, num_units=3, stride=2),
block('block2', base_depth=2, num_units=3, stride=2),
block('block3', base_depth=4, num_units=3, stride=2),
block('block4', base_depth=8, num_units=2, stride=1),
]
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,
spatial_squeeze=spatial_squeeze,
reuse=reuse,
scope=scope)
Example #9
| Source File: resnet_v1_test.py From Machine-Learning-with-TensorFlow-1.x with MIT License | 5 votes |
|
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 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: resnet_v1_test.py From Translation-Invariant-Attacks with Apache License 2.0 | 5 votes |
|
def _resnet_small(self,
inputs,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
spatial_squeeze=True,
reuse=None,
scope='resnet_v1_small'):
"""A shallow and thin ResNet v1 for faster tests."""
block = resnet_v1.resnet_v1_block
blocks = [
block('block1', base_depth=1, num_units=3, stride=2),
block('block2', base_depth=2, num_units=3, stride=2),
block('block3', base_depth=4, num_units=3, stride=2),
block('block4', base_depth=8, num_units=2, stride=1),
]
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,
spatial_squeeze=spatial_squeeze,
reuse=reuse,
scope=scope)
Example #11
| Source File: resnet_v1_test.py From Creative-Adversarial-Networks 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,
spatial_squeeze=True,
reuse=None,
scope='resnet_v1_small'):
"""A shallow and thin ResNet v1 for faster tests."""
block = resnet_v1.resnet_v1_block
blocks = [
block('block1', base_depth=1, num_units=3, stride=2),
block('block2', base_depth=2, num_units=3, stride=2),
block('block3', base_depth=4, num_units=3, stride=2),
block('block4', base_depth=8, num_units=2, stride=1),
]
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,
spatial_squeeze=spatial_squeeze,
reuse=reuse,
scope=scope)
Example #12
| Source File: resnet_v1_test.py From TwinGAN with Apache License 2.0 | 5 votes |
|
def _resnet_small(self,
inputs,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
spatial_squeeze=True,
reuse=None,
scope='resnet_v1_small'):
"""A shallow and thin ResNet v1 for faster tests."""
block = resnet_v1.resnet_v1_block
blocks = [
block('block1', base_depth=1, num_units=3, stride=2),
block('block2', base_depth=2, num_units=3, stride=2),
block('block3', base_depth=4, num_units=3, stride=2),
block('block4', base_depth=8, num_units=2, stride=1),
]
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,
spatial_squeeze=spatial_squeeze,
reuse=reuse,
scope=scope)
Example #13
| Source File: resnet_v1_test.py From TwinGAN with Apache License 2.0 | 5 votes |
|
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 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 #14
| Source File: resnet_v1_test.py From style_swap_tensorflow with Apache License 2.0 | 5 votes |
|
def _resnet_small(self,
inputs,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
spatial_squeeze=True,
reuse=None,
scope='resnet_v1_small'):
"""A shallow and thin ResNet v1 for faster tests."""
block = resnet_v1.resnet_v1_block
blocks = [
block('block1', base_depth=1, num_units=3, stride=2),
block('block2', base_depth=2, num_units=3, stride=2),
block('block3', base_depth=4, num_units=3, stride=2),
block('block4', base_depth=8, num_units=2, stride=1),
]
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,
spatial_squeeze=spatial_squeeze,
reuse=reuse,
scope=scope)
Example #15
| Source File: resnet_v1_test.py From ICPR_TextDection with GNU General Public License v3.0 | 5 votes |
|
def _resnet_small(self,
inputs,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
spatial_squeeze=True,
reuse=None,
scope='resnet_v1_small'):
"""A shallow and thin ResNet v1 for faster tests."""
block = resnet_v1.resnet_v1_block
blocks = [
block('block1', base_depth=1, num_units=3, stride=2),
block('block2', base_depth=2, num_units=3, stride=2),
block('block3', base_depth=4, num_units=3, stride=2),
block('block4', base_depth=8, num_units=2, stride=1),
]
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,
spatial_squeeze=spatial_squeeze,
reuse=reuse,
scope=scope)
Example #16
| Source File: resnet_v1_test.py From MAX-Image-Segmenter with Apache License 2.0 | 5 votes |
|
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 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.keys())
Example #17
| Source File: resnet_v1_test.py From MAX-Image-Segmenter with Apache License 2.0 | 5 votes |
|
def _resnet_small(self,
inputs,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
spatial_squeeze=True,
reuse=None,
scope='resnet_v1_small'):
"""A shallow and thin ResNet v1 for faster tests."""
block = resnet_v1.resnet_v1_block
blocks = [
block('block1', base_depth=1, num_units=3, stride=2),
block('block2', base_depth=2, num_units=3, stride=2),
block('block3', base_depth=4, num_units=3, stride=2),
block('block4', base_depth=8, num_units=2, stride=1),
]
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,
spatial_squeeze=spatial_squeeze,
reuse=reuse,
scope=scope)
Example #18
| Source File: resnet_v1_test.py From BMW-TensorFlow-Training-GUI with Apache License 2.0 | 5 votes |
|
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 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.keys())
Example #19
| Source File: resnet_v1_test.py From yolo_v2 with Apache License 2.0 | 5 votes |
|
def _resnet_small(self,
inputs,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
spatial_squeeze=True,
reuse=None,
scope='resnet_v1_small'):
"""A shallow and thin ResNet v1 for faster tests."""
block = resnet_v1.resnet_v1_block
blocks = [
block('block1', base_depth=1, num_units=3, stride=2),
block('block2', base_depth=2, num_units=3, stride=2),
block('block3', base_depth=4, num_units=3, stride=2),
block('block4', base_depth=8, num_units=2, stride=1),
]
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,
spatial_squeeze=spatial_squeeze,
reuse=reuse,
scope=scope)
Example #20
| Source File: resnet_v1_test.py From Creative-Adversarial-Networks with MIT License | 5 votes |
|
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 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 #21
| Source File: delf_v1.py From Gun-Detector with Apache License 2.0 | 5 votes |
|
def GetResnet50Subnetwork(self,
images,
is_training=False,
global_pool=False,
reuse=None):
"""Constructs resnet_v1_50 part of the DELF model.
Args:
images: A tensor of size [batch, height, width, channels].
is_training: Whether or not the model is in training mode.
global_pool: If True, perform global average pooling after feature
extraction. This may be useful for DELF's descriptor fine-tuning stage.
reuse: Whether or not the layer and its variables should be reused.
Returns:
net: A rank-4 tensor of size [batch, height_out, width_out, channels_out].
If global_pool is True, height_out = width_out = 1.
end_points: A set of activations for external use.
"""
block = resnet_v1.resnet_v1_block
blocks = [
block('block1', base_depth=64, num_units=3, stride=2),
block('block2', base_depth=128, num_units=4, stride=2),
block('block3', base_depth=256, num_units=6, stride=2),
]
if self._target_layer_type == 'resnet_v1_50/block4':
blocks.append(block('block4', base_depth=512, num_units=3, stride=1))
net, end_points = resnet_v1.resnet_v1(
images,
blocks,
is_training=is_training,
global_pool=global_pool,
reuse=reuse,
scope='resnet_v1_50')
return net, end_points
Example #22
| Source File: resnet_v1_test.py From Gun-Detector with Apache License 2.0 | 5 votes |
|
def _resnet_small(self,
inputs,
num_classes=None,
is_training=True,
global_pool=True,
output_stride=None,
include_root_block=True,
spatial_squeeze=True,
reuse=None,
scope='resnet_v1_small'):
"""A shallow and thin ResNet v1 for faster tests."""
block = resnet_v1.resnet_v1_block
blocks = [
block('block1', base_depth=1, num_units=3, stride=2),
block('block2', base_depth=2, num_units=3, stride=2),
block('block3', base_depth=4, num_units=3, stride=2),
block('block4', base_depth=8, num_units=2, stride=1),
]
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,
spatial_squeeze=spatial_squeeze,
reuse=reuse,
scope=scope)
Example #23
| Source File: resnet_v1_test.py From Gun-Detector with Apache License 2.0 | 5 votes |
|
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 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.keys())
Example #24
| Source File: resnet_v1_test.py From CBAM-tensorflow-slim 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,
spatial_squeeze=True,
reuse=None,
scope='resnet_v1_small'):
"""A shallow and thin ResNet v1 for faster tests."""
block = resnet_v1.resnet_v1_block
blocks = [
block('block1', base_depth=1, num_units=3, stride=2),
block('block2', base_depth=2, num_units=3, stride=2),
block('block3', base_depth=4, num_units=3, stride=2),
block('block4', base_depth=8, num_units=2, stride=1),
]
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,
spatial_squeeze=spatial_squeeze,
reuse=reuse,
scope=scope)
Example #25
| Source File: resnet_v1_test.py From CBAM-tensorflow-slim with MIT License | 5 votes |
|
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 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.keys())
Example #26
| Source File: resnet_v1_test.py From DOTA_models with Apache License 2.0 | 5 votes |
|
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 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 #27
| Source File: resnet_v1_test.py From Hands-On-Machine-Learning-with-OpenCV-4 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,
spatial_squeeze=True,
reuse=None,
scope='resnet_v1_small'):
"""A shallow and thin ResNet v1 for faster tests."""
block = resnet_v1.resnet_v1_block
blocks = [
block('block1', base_depth=1, num_units=3, stride=2),
block('block2', base_depth=2, num_units=3, stride=2),
block('block3', base_depth=4, num_units=3, stride=2),
block('block4', base_depth=8, num_units=2, stride=1),
]
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,
spatial_squeeze=spatial_squeeze,
reuse=reuse,
scope=scope)
Example #28
| Source File: resnet_v1_test.py From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License | 5 votes |
|
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 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 #29
| Source File: delf_v1.py From yolo_v2 with Apache License 2.0 | 5 votes |
|
def GetResnet50Subnetwork(self,
images,
is_training=False,
global_pool=False,
reuse=None):
"""Constructs resnet_v1_50 part of the DELF model.
Args:
images: A tensor of size [batch, height, width, channels].
is_training: Whether or not the model is in training mode.
global_pool: If True, perform global average pooling after feature
extraction. This may be useful for DELF's descriptor fine-tuning stage.
reuse: Whether or not the layer and its variables should be reused.
Returns:
net: A rank-4 tensor of size [batch, height_out, width_out, channels_out].
If global_pool is True, height_out = width_out = 1.
end_points: A set of activations for external use.
"""
block = resnet_v1.resnet_v1_block
blocks = [
block('block1', base_depth=64, num_units=3, stride=2),
block('block2', base_depth=128, num_units=4, stride=2),
block('block3', base_depth=256, num_units=6, stride=2),
]
if self._target_layer_type == 'resnet_v1_50/block4':
blocks.append(block('block4', base_depth=512, num_units=3, stride=1))
net, end_points = resnet_v1.resnet_v1(
images,
blocks,
is_training=is_training,
global_pool=global_pool,
reuse=reuse,
scope='resnet_v1_50')
return net, end_points
Example #30
| Source File: resnet_v1_test.py From RetinaNet_Tensorflow_Rotation with MIT License | 5 votes |
|
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 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)
