Python nets.mobilenet_v1.DepthSepConv() Examples
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Example #1
| Source File: mobilenet_v1_test.py From MAX-Object-Detector with Apache License 2.0 | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #2
| Source File: mobilenet_v1_test.py From Targeted-Adversarial-Attack with Apache License 2.0 | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #3
| Source File: mobilenet_v1_test.py From style_swap_tensorflow with Apache License 2.0 | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #4
| Source File: mobilenet_v1_test.py From TwinGAN with Apache License 2.0 | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #5
| Source File: mobilenet_v1_test.py From tf_classification with MIT License | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #6
| Source File: mobilenet_v1_test.py From Translation-Invariant-Attacks with Apache License 2.0 | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #7
| Source File: mobilenet_v1_test.py From Machine-Learning-with-TensorFlow-1.x with MIT License | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #8
| Source File: mobilenet_v1_test.py From hands-detection with MIT License | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #9
| Source File: faster_rcnn_mobilenet_v1_feature_extractor.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 6 votes |
|
def _get_mobilenet_conv_no_last_stride_defs(conv_depth_ratio_in_percentage):
if conv_depth_ratio_in_percentage not in [25, 50, 75, 100]:
raise ValueError(
'Only the following ratio percentages are supported: 25, 50, 75, 100')
conv_depth_ratio_in_percentage = float(conv_depth_ratio_in_percentage) / 100.0
channels = np.array([
32, 64, 128, 128, 256, 256, 512, 512, 512, 512, 512, 512, 1024, 1024
], dtype=np.float32)
channels = (channels * conv_depth_ratio_in_percentage).astype(np.int32)
return [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=channels[0]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[1]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=channels[2]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[3]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=channels[4]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[5]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=channels[6]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[7]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[8]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[9]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[10]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[11]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[12]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[13])
]
Example #10
| Source File: mobilenet_v1_test.py From object_detection_kitti with Apache License 2.0 | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #11
| Source File: mobilenet_v1_test.py From MBMD with MIT License | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #12
| Source File: mobilenet_v1_test.py From object_detection_with_tensorflow with MIT License | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #13
| Source File: mobilenet_v1_test.py From SENet-tensorflow-slim with MIT License | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #14
| Source File: mobilenet_v1_test.py From DeepLab_v3 with MIT License | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #15
| Source File: faster_rcnn_mobilenet_v1_feature_extractor.py From MAX-Object-Detector with Apache License 2.0 | 6 votes |
|
def _get_mobilenet_conv_no_last_stride_defs(conv_depth_ratio_in_percentage):
if conv_depth_ratio_in_percentage not in [25, 50, 75, 100]:
raise ValueError(
'Only the following ratio percentages are supported: 25, 50, 75, 100')
conv_depth_ratio_in_percentage = float(conv_depth_ratio_in_percentage) / 100.0
channels = np.array([
32, 64, 128, 128, 256, 256, 512, 512, 512, 512, 512, 512, 1024, 1024
], dtype=np.float32)
channels = (channels * conv_depth_ratio_in_percentage).astype(np.int32)
return [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=channels[0]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[1]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=channels[2]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[3]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=channels[4]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[5]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=channels[6]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[7]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[8]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[9]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[10]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[11]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[12]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[13])
]
Example #16
| Source File: mobilenet_v1_test.py From nasnet-tensorflow with Apache License 2.0 | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #17
| Source File: mobilenet_v1_test.py From g-tensorflow-models with Apache License 2.0 | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #18
| Source File: faster_rcnn_mobilenet_v1_feature_extractor.py From g-tensorflow-models with Apache License 2.0 | 6 votes |
|
def _get_mobilenet_conv_no_last_stride_defs(conv_depth_ratio_in_percentage):
if conv_depth_ratio_in_percentage not in [25, 50, 75, 100]:
raise ValueError(
'Only the following ratio percentages are supported: 25, 50, 75, 100')
conv_depth_ratio_in_percentage = float(conv_depth_ratio_in_percentage) / 100.0
channels = np.array([
32, 64, 128, 128, 256, 256, 512, 512, 512, 512, 512, 512, 1024, 1024
], dtype=np.float32)
channels = (channels * conv_depth_ratio_in_percentage).astype(np.int32)
return [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=channels[0]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[1]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=channels[2]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[3]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=channels[4]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[5]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=channels[6]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[7]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[8]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[9]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[10]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[11]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[12]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[13])
]
Example #19
| Source File: mobilenet_v1_test.py From models with Apache License 2.0 | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random.uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #20
| Source File: faster_rcnn_mobilenet_v1_feature_extractor.py From models with Apache License 2.0 | 6 votes |
|
def _get_mobilenet_conv_no_last_stride_defs(conv_depth_ratio_in_percentage):
if conv_depth_ratio_in_percentage not in [25, 50, 75, 100]:
raise ValueError(
'Only the following ratio percentages are supported: 25, 50, 75, 100')
conv_depth_ratio_in_percentage = float(conv_depth_ratio_in_percentage) / 100.0
channels = np.array([
32, 64, 128, 128, 256, 256, 512, 512, 512, 512, 512, 512, 1024, 1024
], dtype=np.float32)
channels = (channels * conv_depth_ratio_in_percentage).astype(np.int32)
return [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=channels[0]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[1]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=channels[2]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[3]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=channels[4]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[5]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=channels[6]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[7]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[8]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[9]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[10]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[11]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[12]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[13])
]
Example #21
| Source File: mobilenet_v1_test.py From Non-Targeted-Adversarial-Attacks with Apache License 2.0 | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #22
| Source File: mobilenet_v1_test.py From motion-rcnn with MIT License | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #23
| Source File: mobilenet_v1_test.py From mtl-ssl with Apache License 2.0 | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #24
| Source File: faster_rcnn_mobilenet_v1_feature_extractor.py From mtl-ssl with Apache License 2.0 | 6 votes |
|
def _get_mobilenet_conv_no_last_stride_defs(conv_depth_ratio_in_percentage):
if conv_depth_ratio_in_percentage not in [25, 50, 75, 100]:
raise ValueError(
'Only the following ratio percentages are supported: 25, 50, 75, 100')
conv_depth_ratio_in_percentage = float(conv_depth_ratio_in_percentage) / 100.0
channels = np.array([
32, 64, 128, 128, 256, 256, 512, 512, 512, 512, 512, 512, 1024, 1024
], dtype=np.float32)
channels = (channels * conv_depth_ratio_in_percentage).astype(np.int32)
return [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=channels[0]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[1]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=channels[2]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[3]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=channels[4]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[5]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=channels[6]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[7]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[8]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[9]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[10]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[11]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[12]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[13])
]
Example #25
| Source File: mobilenet_v1_test.py From multilabel-image-classification-tensorflow with MIT License | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #26
| Source File: faster_rcnn_mobilenet_v1_feature_extractor.py From multilabel-image-classification-tensorflow with MIT License | 6 votes |
|
def _get_mobilenet_conv_no_last_stride_defs(conv_depth_ratio_in_percentage):
if conv_depth_ratio_in_percentage not in [25, 50, 75, 100]:
raise ValueError(
'Only the following ratio percentages are supported: 25, 50, 75, 100')
conv_depth_ratio_in_percentage = float(conv_depth_ratio_in_percentage) / 100.0
channels = np.array([
32, 64, 128, 128, 256, 256, 512, 512, 512, 512, 512, 512, 1024, 1024
], dtype=np.float32)
channels = (channels * conv_depth_ratio_in_percentage).astype(np.int32)
return [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=channels[0]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[1]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=channels[2]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[3]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=channels[4]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[5]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=channels[6]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[7]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[8]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[9]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[10]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[11]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[12]),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=channels[13])
]
Example #27
| Source File: mobilenet_v1_test.py From yolo_v2 with Apache License 2.0 | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #28
| Source File: mobilenet_v1_test.py From R2CNN_Faster-RCNN_Tensorflow with MIT License | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #29
| Source File: mobilenet_v1_test.py From tf-pose with Apache License 2.0 | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
Example #30
| Source File: mobilenet_v1_test.py From ctw-baseline with MIT License | 6 votes |
|
def testBuildCustomNetworkUsingConvDefs(self):
batch_size = 5
height, width = 224, 224
conv_defs = [
mobilenet_v1.Conv(kernel=[3, 3], stride=2, depth=32),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=64),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=2, depth=128),
mobilenet_v1.DepthSepConv(kernel=[3, 3], stride=1, depth=512)
]
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1_base(
inputs, final_endpoint='Conv2d_3_pointwise', conv_defs=conv_defs)
self.assertTrue(net.op.name.startswith('MobilenetV1/Conv2d_3'))
self.assertListEqual(net.get_shape().as_list(),
[batch_size, 56, 56, 512])
expected_endpoints = ['Conv2d_0',
'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
'Conv2d_3_depthwise', 'Conv2d_3_pointwise']
self.assertItemsEqual(end_points.keys(), expected_endpoints)
