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f8f4845c
编写于
4月 12, 2021
作者:
F
Fan Yang
提交者:
A. Unique TensorFlower
4月 12, 2021
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add more pytype checking.
PiperOrigin-RevId: 368129317
上级
4334a892
变更
20
隐藏空白更改
内联
并排
Showing
20 changed file
with
424 addition
and
352 deletion
+424
-352
official/vision/beta/modeling/backbones/efficientnet.py
official/vision/beta/modeling/backbones/efficientnet.py
+42
-31
official/vision/beta/modeling/backbones/factory.py
official/vision/beta/modeling/backbones/factory.py
+6
-3
official/vision/beta/modeling/backbones/mobilenet.py
official/vision/beta/modeling/backbones/mobilenet.py
+18
-18
official/vision/beta/modeling/backbones/resnet.py
official/vision/beta/modeling/backbones/resnet.py
+31
-25
official/vision/beta/modeling/backbones/resnet_3d.py
official/vision/beta/modeling/backbones/resnet_3d.py
+38
-32
official/vision/beta/modeling/backbones/resnet_deeplab.py
official/vision/beta/modeling/backbones/resnet_deeplab.py
+30
-26
official/vision/beta/modeling/backbones/revnet.py
official/vision/beta/modeling/backbones/revnet.py
+12
-11
official/vision/beta/modeling/backbones/spinenet.py
official/vision/beta/modeling/backbones/spinenet.py
+37
-30
official/vision/beta/modeling/backbones/spinenet_mobile.py
official/vision/beta/modeling/backbones/spinenet_mobile.py
+37
-30
official/vision/beta/modeling/classification_model.py
official/vision/beta/modeling/classification_model.py
+20
-17
official/vision/beta/modeling/decoders/aspp.py
official/vision/beta/modeling/decoders/aspp.py
+18
-16
official/vision/beta/modeling/decoders/factory.py
official/vision/beta/modeling/decoders/factory.py
+9
-3
official/vision/beta/modeling/decoders/fpn.py
official/vision/beta/modeling/decoders/fpn.py
+20
-17
official/vision/beta/modeling/decoders/nasfpn.py
official/vision/beta/modeling/decoders/nasfpn.py
+27
-22
official/vision/beta/modeling/factory.py
official/vision/beta/modeling/factory.py
+10
-8
official/vision/beta/modeling/factory_3d.py
official/vision/beta/modeling/factory_3d.py
+7
-6
official/vision/beta/modeling/maskrcnn_model.py
official/vision/beta/modeling/maskrcnn_model.py
+23
-20
official/vision/beta/modeling/retinanet_model.py
official/vision/beta/modeling/retinanet_model.py
+16
-15
official/vision/beta/modeling/segmentation_model.py
official/vision/beta/modeling/segmentation_model.py
+7
-8
official/vision/beta/modeling/video_classification_model.py
official/vision/beta/modeling/video_classification_model.py
+16
-14
未找到文件。
official/vision/beta/modeling/backbones/efficientnet.py
浏览文件 @
f8f4845c
...
...
@@ -15,8 +15,13 @@
"""Contains definitions of EfficientNet Networks."""
import
math
from
typing
import
Any
,
List
,
Tuple
# Import libraries
import
tensorflow
as
tf
from
official.modeling
import
hyperparams
from
official.modeling
import
tf_utils
from
official.vision.beta.modeling.backbones
import
factory
from
official.vision.beta.modeling.layers
import
nn_blocks
...
...
@@ -50,14 +55,32 @@ SCALING_MAP = {
}
def
round_repeats
(
repeats
,
multiplier
,
skip
=
False
):
class
BlockSpec
():
"""A container class that specifies the block configuration for MnasNet."""
def
__init__
(
self
,
block_fn
:
str
,
block_repeats
:
int
,
kernel_size
:
int
,
strides
:
int
,
expand_ratio
:
float
,
in_filters
:
int
,
out_filters
:
int
,
is_output
:
bool
,
width_scale
:
float
,
depth_scale
:
float
):
self
.
block_fn
=
block_fn
self
.
block_repeats
=
round_repeats
(
block_repeats
,
depth_scale
)
self
.
kernel_size
=
kernel_size
self
.
strides
=
strides
self
.
expand_ratio
=
expand_ratio
self
.
in_filters
=
nn_layers
.
round_filters
(
in_filters
,
width_scale
)
self
.
out_filters
=
nn_layers
.
round_filters
(
out_filters
,
width_scale
)
self
.
is_output
=
is_output
def
round_repeats
(
repeats
:
int
,
multiplier
:
float
,
skip
:
bool
=
False
)
->
int
:
"""Returns rounded number of filters based on depth multiplier."""
if
skip
or
not
multiplier
:
return
repeats
return
int
(
math
.
ceil
(
multiplier
*
repeats
))
def
block_spec_decoder
(
specs
,
width_scale
,
depth_scale
):
def
block_spec_decoder
(
specs
:
List
[
Tuple
[
Any
,
...]],
width_scale
:
float
,
depth_scale
:
float
)
->
List
[
BlockSpec
]:
"""Decodes and returns specs for a block."""
decoded_specs
=
[]
for
s
in
specs
:
...
...
@@ -69,22 +92,6 @@ def block_spec_decoder(specs, width_scale, depth_scale):
return
decoded_specs
class
BlockSpec
(
object
):
"""A container class that specifies the block configuration for MnasNet."""
def
__init__
(
self
,
block_fn
,
block_repeats
,
kernel_size
,
strides
,
expand_ratio
,
in_filters
,
out_filters
,
is_output
,
width_scale
,
depth_scale
):
self
.
block_fn
=
block_fn
self
.
block_repeats
=
round_repeats
(
block_repeats
,
depth_scale
)
self
.
kernel_size
=
kernel_size
self
.
strides
=
strides
self
.
expand_ratio
=
expand_ratio
self
.
in_filters
=
nn_layers
.
round_filters
(
in_filters
,
width_scale
)
self
.
out_filters
=
nn_layers
.
round_filters
(
out_filters
,
width_scale
)
self
.
is_output
=
is_output
@
tf
.
keras
.
utils
.
register_keras_serializable
(
package
=
'Vision'
)
class
EfficientNet
(
tf
.
keras
.
Model
):
"""Creates an EfficientNet family model.
...
...
@@ -96,17 +103,18 @@ class EfficientNet(tf.keras.Model):
"""
def
__init__
(
self
,
model_id
,
input_specs
=
layers
.
InputSpec
(
shape
=
[
None
,
None
,
None
,
3
]),
se_ratio
=
0.0
,
stochastic_depth_drop_rate
=
0.0
,
kernel_initializer
=
'VarianceScaling'
,
kernel_regularizer
=
None
,
bias_regularizer
=
None
,
activation
=
'relu'
,
use_sync_bn
=
False
,
norm_momentum
=
0.99
,
norm_epsilon
=
0.001
,
model_id
:
str
,
input_specs
:
tf
.
keras
.
layers
.
InputSpec
=
layers
.
InputSpec
(
shape
=
[
None
,
None
,
None
,
3
]),
se_ratio
:
float
=
0.0
,
stochastic_depth_drop_rate
:
float
=
0.0
,
kernel_initializer
:
str
=
'VarianceScaling'
,
kernel_regularizer
:
tf
.
keras
.
regularizers
.
Regularizer
=
None
,
bias_regularizer
:
tf
.
keras
.
regularizers
.
Regularizer
=
None
,
activation
:
str
=
'relu'
,
use_sync_bn
:
bool
=
False
,
norm_momentum
:
float
=
0.99
,
norm_epsilon
:
float
=
0.001
,
**
kwargs
):
"""Initializes an EfficientNet model.
...
...
@@ -205,7 +213,10 @@ class EfficientNet(tf.keras.Model):
super
(
EfficientNet
,
self
).
__init__
(
inputs
=
inputs
,
outputs
=
endpoints
,
**
kwargs
)
def
_block_group
(
self
,
inputs
,
specs
,
name
=
'block_group'
):
def
_block_group
(
self
,
inputs
:
tf
.
Tensor
,
specs
:
BlockSpec
,
name
:
str
=
'block_group'
):
"""Creates one group of blocks for the EfficientNet model.
Args:
...
...
@@ -286,7 +297,7 @@ class EfficientNet(tf.keras.Model):
@
factory
.
register_backbone_builder
(
'efficientnet'
)
def
build_efficientnet
(
input_specs
:
tf
.
keras
.
layers
.
InputSpec
,
model_config
,
model_config
:
hyperparams
.
Config
,
l2_regularizer
:
tf
.
keras
.
regularizers
.
Regularizer
=
None
)
->
tf
.
keras
.
Model
:
"""Builds EfficientNet backbone from a config."""
backbone_type
=
model_config
.
backbone
.
type
...
...
official/vision/beta/modeling/backbones/factory.py
浏览文件 @
f8f4845c
...
...
@@ -43,9 +43,11 @@ in place that uses it.
"""
# Import libraries
import
tensorflow
as
tf
from
official.core
import
registry
from
official.modeling
import
hyperparams
_REGISTERED_BACKBONE_CLS
=
{}
...
...
@@ -79,9 +81,10 @@ def register_backbone_builder(key: str):
return
registry
.
register
(
_REGISTERED_BACKBONE_CLS
,
key
)
def
build_backbone
(
input_specs
:
tf
.
keras
.
layers
.
InputSpec
,
model_config
,
l2_regularizer
:
tf
.
keras
.
regularizers
.
Regularizer
=
None
):
def
build_backbone
(
input_specs
:
tf
.
keras
.
layers
.
InputSpec
,
model_config
:
hyperparams
.
Config
,
l2_regularizer
:
tf
.
keras
.
regularizers
.
Regularizer
=
None
)
->
tf
.
keras
.
Model
:
"""Builds backbone from a config.
Args:
...
...
official/vision/beta/modeling/backbones/mobilenet.py
浏览文件 @
f8f4845c
...
...
@@ -26,7 +26,6 @@ from official.vision.beta.modeling.layers import nn_blocks
from
official.vision.beta.modeling.layers
import
nn_layers
layers
=
tf
.
keras
.
layers
regularizers
=
tf
.
keras
.
regularizers
# pylint: disable=pointless-string-statement
...
...
@@ -417,20 +416,21 @@ class BlockSpec(hyperparams.Config):
use_bias
:
bool
=
False
use_normalization
:
bool
=
True
activation
:
str
=
'relu6'
#
used for block type InvertedResConv
#
Used for block type InvertedResConv.
expand_ratio
:
Optional
[
float
]
=
6.
#
used for block type InvertedResConv with SE
#
Used for block type InvertedResConv with SE.
se_ratio
:
Optional
[
float
]
=
None
use_depthwise
:
bool
=
True
use_residual
:
bool
=
True
is_output
:
bool
=
True
def
block_spec_decoder
(
specs
:
Dict
[
Any
,
Any
],
filter_size_scale
:
float
,
# set to 1 for mobilenetv1
divisible_by
:
int
=
8
,
finegrain_classification_mode
:
bool
=
True
):
def
block_spec_decoder
(
specs
:
Dict
[
Any
,
Any
],
filter_size_scale
:
float
,
# Set to 1 for mobilenetv1.
divisible_by
:
int
=
8
,
finegrain_classification_mode
:
bool
=
True
):
"""Decodes specs for a block.
Args:
...
...
@@ -491,23 +491,23 @@ class MobileNet(tf.keras.Model):
self
,
model_id
:
str
=
'MobileNetV2'
,
filter_size_scale
:
float
=
1.0
,
input_specs
:
layers
.
InputSpec
=
layers
.
InputSpec
(
input_specs
:
tf
.
keras
.
layers
.
InputSpec
=
layers
.
InputSpec
(
shape
=
[
None
,
None
,
None
,
3
]),
# The followings are for hyper-parameter tuning
# The followings are for hyper-parameter tuning
.
norm_momentum
:
float
=
0.99
,
norm_epsilon
:
float
=
0.001
,
kernel_initializer
:
str
=
'VarianceScaling'
,
kernel_regularizer
:
Optional
[
regularizers
.
Regularizer
]
=
None
,
bias_regularizer
:
Optional
[
regularizers
.
Regularizer
]
=
None
,
# The followings should be kept the same most of the times
kernel_regularizer
:
Optional
[
tf
.
keras
.
regularizers
.
Regularizer
]
=
None
,
bias_regularizer
:
Optional
[
tf
.
keras
.
regularizers
.
Regularizer
]
=
None
,
# The followings should be kept the same most of the times
.
output_stride
:
int
=
None
,
min_depth
:
int
=
8
,
# divisible is not used in MobileNetV1
# divisible is not used in MobileNetV1
.
divisible_by
:
int
=
8
,
stochastic_depth_drop_rate
:
float
=
0.0
,
regularize_depthwise
:
bool
=
False
,
use_sync_bn
:
bool
=
False
,
# finegrain is not used in MobileNetV1
# finegrain is not used in MobileNetV1
.
finegrain_classification_mode
:
bool
=
True
,
**
kwargs
):
"""Initializes a MobileNet model.
...
...
@@ -636,8 +636,8 @@ class MobileNet(tf.keras.Model):
# A small catch for gpooling block with None strides
if
not
block_def
.
strides
:
block_def
.
strides
=
1
if
self
.
_output_stride
is
not
None
\
and
current_stride
==
self
.
_output_stride
:
if
(
self
.
_output_stride
is
not
None
and
current_stride
==
self
.
_output_stride
)
:
# If we have reached the target output_stride, then we need to employ
# atrous convolution with stride=1 and multiply the atrous rate by the
# current unit's stride for use in subsequent layers.
...
...
@@ -764,7 +764,7 @@ class MobileNet(tf.keras.Model):
@
factory
.
register_backbone_builder
(
'mobilenet'
)
def
build_mobilenet
(
input_specs
:
tf
.
keras
.
layers
.
InputSpec
,
model_config
,
model_config
:
hyperparams
.
Config
,
l2_regularizer
:
tf
.
keras
.
regularizers
.
Regularizer
=
None
)
->
tf
.
keras
.
Model
:
"""Builds MobileNet backbone from a config."""
backbone_type
=
model_config
.
backbone
.
type
...
...
official/vision/beta/modeling/backbones/resnet.py
浏览文件 @
f8f4845c
...
...
@@ -14,8 +14,12 @@
"""Contains definitions of ResNet and ResNet-RS models."""
from
typing
import
Callable
,
Optional
# Import libraries
import
tensorflow
as
tf
from
official.modeling
import
hyperparams
from
official.modeling
import
tf_utils
from
official.vision.beta.modeling.backbones
import
factory
from
official.vision.beta.modeling.layers
import
nn_blocks
...
...
@@ -99,23 +103,25 @@ class ResNet(tf.keras.Model):
(https://arxiv.org/abs/2103.07579).
"""
def
__init__
(
self
,
model_id
,
input_specs
=
layers
.
InputSpec
(
shape
=
[
None
,
None
,
None
,
3
]),
depth_multiplier
=
1.0
,
stem_type
=
'v0'
,
resnetd_shortcut
=
False
,
replace_stem_max_pool
=
False
,
se_ratio
=
None
,
init_stochastic_depth_rate
=
0.0
,
activation
=
'relu'
,
use_sync_bn
=
False
,
norm_momentum
=
0.99
,
norm_epsilon
=
0.001
,
kernel_initializer
=
'VarianceScaling'
,
kernel_regularizer
=
None
,
bias_regularizer
=
None
,
**
kwargs
):
def
__init__
(
self
,
model_id
:
int
,
input_specs
:
tf
.
keras
.
layers
.
InputSpec
=
layers
.
InputSpec
(
shape
=
[
None
,
None
,
None
,
3
]),
depth_multiplier
:
float
=
1.0
,
stem_type
:
str
=
'v0'
,
resnetd_shortcut
:
bool
=
False
,
replace_stem_max_pool
:
bool
=
False
,
se_ratio
:
Optional
[
float
]
=
None
,
init_stochastic_depth_rate
:
float
=
0.0
,
activation
:
str
=
'relu'
,
use_sync_bn
:
bool
=
False
,
norm_momentum
:
float
=
0.99
,
norm_epsilon
:
float
=
0.001
,
kernel_initializer
:
str
=
'VarianceScaling'
,
kernel_regularizer
:
Optional
[
tf
.
keras
.
regularizers
.
Regularizer
]
=
None
,
bias_regularizer
:
Optional
[
tf
.
keras
.
regularizers
.
Regularizer
]
=
None
,
**
kwargs
):
"""Initializes a ResNet model.
Args:
...
...
@@ -274,13 +280,13 @@ class ResNet(tf.keras.Model):
super
(
ResNet
,
self
).
__init__
(
inputs
=
inputs
,
outputs
=
endpoints
,
**
kwargs
)
def
_block_group
(
self
,
inputs
,
filters
,
strides
,
block_fn
,
block_repeats
=
1
,
stochastic_depth_drop_rate
=
0.0
,
name
=
'block_group'
):
inputs
:
tf
.
Tensor
,
filters
:
int
,
strides
:
int
,
block_fn
:
Callable
[...,
tf
.
keras
.
layers
.
Layer
]
,
block_repeats
:
int
=
1
,
stochastic_depth_drop_rate
:
float
=
0.0
,
name
:
str
=
'block_group'
):
"""Creates one group of blocks for the ResNet model.
Args:
...
...
@@ -366,7 +372,7 @@ class ResNet(tf.keras.Model):
@
factory
.
register_backbone_builder
(
'resnet'
)
def
build_resnet
(
input_specs
:
tf
.
keras
.
layers
.
InputSpec
,
model_config
,
model_config
:
hyperparams
.
Config
,
l2_regularizer
:
tf
.
keras
.
regularizers
.
Regularizer
=
None
)
->
tf
.
keras
.
Model
:
"""Builds ResNet backbone from a config."""
backbone_type
=
model_config
.
backbone
.
type
...
...
official/vision/beta/modeling/backbones/resnet_3d.py
浏览文件 @
f8f4845c
...
...
@@ -13,10 +13,12 @@
# limitations under the License.
"""Contains definitions of 3D Residual Networks."""
from
typing
import
List
,
Tuple
from
typing
import
Callable
,
List
,
Tuple
,
Optional
# Import libraries
import
tensorflow
as
tf
from
official.modeling
import
hyperparams
from
official.modeling
import
tf_utils
from
official.vision.beta.modeling.backbones
import
factory
from
official.vision.beta.modeling.layers
import
nn_blocks_3d
...
...
@@ -74,26 +76,28 @@ RESNET_SPECS = {
class
ResNet3D
(
tf
.
keras
.
Model
):
"""Creates a 3D ResNet family model."""
def
__init__
(
self
,
model_id
:
int
,
temporal_strides
:
List
[
int
],
temporal_kernel_sizes
:
List
[
Tuple
[
int
]],
use_self_gating
:
List
[
int
]
=
None
,
input_specs
=
layers
.
InputSpec
(
shape
=
[
None
,
None
,
None
,
None
,
3
]),
stem_type
=
'v0'
,
stem_conv_temporal_kernel_size
=
5
,
stem_conv_temporal_stride
=
2
,
stem_pool_temporal_stride
=
2
,
init_stochastic_depth_rate
=
0.0
,
activation
=
'relu'
,
se_ratio
=
None
,
use_sync_bn
=
False
,
norm_momentum
=
0.99
,
norm_epsilon
=
0.001
,
kernel_initializer
=
'VarianceScaling'
,
kernel_regularizer
=
None
,
bias_regularizer
=
None
,
**
kwargs
):
def
__init__
(
self
,
model_id
:
int
,
temporal_strides
:
List
[
int
],
temporal_kernel_sizes
:
List
[
Tuple
[
int
]],
use_self_gating
:
List
[
int
]
=
None
,
input_specs
:
tf
.
keras
.
layers
.
InputSpec
=
layers
.
InputSpec
(
shape
=
[
None
,
None
,
None
,
None
,
3
]),
stem_type
:
str
=
'v0'
,
stem_conv_temporal_kernel_size
:
int
=
5
,
stem_conv_temporal_stride
:
int
=
2
,
stem_pool_temporal_stride
:
int
=
2
,
init_stochastic_depth_rate
:
float
=
0.0
,
activation
:
str
=
'relu'
,
se_ratio
:
Optional
[
float
]
=
None
,
use_sync_bn
:
bool
=
False
,
norm_momentum
:
float
=
0.99
,
norm_epsilon
:
float
=
0.001
,
kernel_initializer
:
str
=
'VarianceScaling'
,
kernel_regularizer
:
Optional
[
tf
.
keras
.
regularizers
.
Regularizer
]
=
None
,
bias_regularizer
:
Optional
[
tf
.
keras
.
regularizers
.
Regularizer
]
=
None
,
**
kwargs
):
"""Initializes a 3D ResNet model.
Args:
...
...
@@ -259,16 +263,18 @@ class ResNet3D(tf.keras.Model):
super
(
ResNet3D
,
self
).
__init__
(
inputs
=
inputs
,
outputs
=
endpoints
,
**
kwargs
)
def
_block_group
(
self
,
inputs
,
filters
,
temporal_kernel_sizes
,
temporal_strides
,
spatial_strides
,
block_fn
=
nn_blocks_3d
.
BottleneckBlock3D
,
block_repeats
=
1
,
stochastic_depth_drop_rate
=
0.0
,
use_self_gating
=
False
,
name
=
'block_group'
):
inputs
:
tf
.
Tensor
,
filters
:
int
,
temporal_kernel_sizes
:
Tuple
[
int
],
temporal_strides
:
int
,
spatial_strides
:
int
,
block_fn
:
Callable
[
...,
tf
.
keras
.
layers
.
Layer
]
=
nn_blocks_3d
.
BottleneckBlock3D
,
block_repeats
:
int
=
1
,
stochastic_depth_drop_rate
:
float
=
0.0
,
use_self_gating
:
bool
=
False
,
name
:
str
=
'block_group'
):
"""Creates one group of blocks for the ResNet3D model.
Args:
...
...
@@ -410,7 +416,7 @@ def build_resnet3d(
@
factory
.
register_backbone_builder
(
'resnet_3d_rs'
)
def
build_resnet3d_rs
(
input_specs
:
tf
.
keras
.
layers
.
InputSpec
,
model_config
,
model_config
:
hyperparams
.
Config
,
l2_regularizer
:
tf
.
keras
.
regularizers
.
Regularizer
=
None
)
->
tf
.
keras
.
Model
:
"""Builds ResNet-3D-RS backbone from a config."""
backbone_cfg
=
model_config
.
backbone
.
get
()
...
...
official/vision/beta/modeling/backbones/resnet_deeplab.py
浏览文件 @
f8f4845c
...
...
@@ -14,6 +14,8 @@
"""Contains definitions of Residual Networks with Deeplab modifications."""
from
typing
import
Callable
,
Optional
,
Tuple
,
List
import
numpy
as
np
import
tensorflow
as
tf
from
official.modeling
import
tf_utils
...
...
@@ -53,23 +55,25 @@ class DilatedResNet(tf.keras.Model):
(https://arxiv.org/pdf/1706.05587)
"""
def
__init__
(
self
,
model_id
,
output_stride
,
input_specs
=
layers
.
InputSpec
(
shape
=
[
None
,
None
,
None
,
3
]),
stem_type
=
'v0'
,
se_ratio
=
None
,
init_stochastic_depth_rate
=
0.0
,
multigrid
=
None
,
last_stage_repeats
=
1
,
activation
=
'relu'
,
use_sync_bn
=
False
,
norm_momentum
=
0.99
,
norm_epsilon
=
0.001
,
kernel_initializer
=
'VarianceScaling'
,
kernel_regularizer
=
None
,
bias_regularizer
=
None
,
**
kwargs
):
def
__init__
(
self
,
model_id
:
int
,
output_stride
:
int
,
input_specs
:
tf
.
keras
.
layers
.
InputSpec
=
layers
.
InputSpec
(
shape
=
[
None
,
None
,
None
,
3
]),
stem_type
:
str
=
'v0'
,
se_ratio
:
Optional
[
float
]
=
None
,
init_stochastic_depth_rate
:
float
=
0.0
,
multigrid
:
Optional
[
Tuple
[
int
]]
=
None
,
last_stage_repeats
:
int
=
1
,
activation
:
str
=
'relu'
,
use_sync_bn
:
bool
=
False
,
norm_momentum
:
float
=
0.99
,
norm_epsilon
:
float
=
0.001
,
kernel_initializer
:
str
=
'VarianceScaling'
,
kernel_regularizer
:
Optional
[
tf
.
keras
.
regularizers
.
Regularizer
]
=
None
,
bias_regularizer
:
Optional
[
tf
.
keras
.
regularizers
.
Regularizer
]
=
None
,
**
kwargs
):
"""Initializes a ResNet model with DeepLab modification.
Args:
...
...
@@ -234,15 +238,15 @@ class DilatedResNet(tf.keras.Model):
inputs
=
inputs
,
outputs
=
endpoints
,
**
kwargs
)
def
_block_group
(
self
,
inputs
,
filters
,
strides
,
dilation_rate
,
block_fn
,
block_repeats
=
1
,
stochastic_depth_drop_rate
=
0.0
,
multigrid
=
None
,
name
=
'block_group'
):
inputs
:
tf
.
Tensor
,
filters
:
int
,
strides
:
int
,
dilation_rate
:
int
,
block_fn
:
Callable
[...,
tf
.
keras
.
layers
.
Layer
]
,
block_repeats
:
int
=
1
,
stochastic_depth_drop_rate
:
float
=
0.0
,
multigrid
:
Optional
[
List
[
int
]]
=
None
,
name
:
str
=
'block_group'
):
"""Creates one group of blocks for the ResNet model.
Deeplab applies strides at the last block.
...
...
official/vision/beta/modeling/backbones/revnet.py
浏览文件 @
f8f4845c
...
...
@@ -59,17 +59,18 @@ class RevNet(tf.keras.Model):
(https://arxiv.org/pdf/1707.04585.pdf)
"""
def
__init__
(
self
,
model_id
:
int
,
input_specs
:
tf
.
keras
.
layers
.
InputSpec
=
tf
.
keras
.
layers
.
InputSpec
(
shape
=
[
None
,
None
,
None
,
3
]),
activation
:
str
=
'relu'
,
use_sync_bn
:
bool
=
False
,
norm_momentum
:
float
=
0.99
,
norm_epsilon
:
float
=
0.001
,
kernel_initializer
:
str
=
'VarianceScaling'
,
kernel_regularizer
:
tf
.
keras
.
regularizers
.
Regularizer
=
None
,
**
kwargs
):
def
__init__
(
self
,
model_id
:
int
,
input_specs
:
tf
.
keras
.
layers
.
InputSpec
=
tf
.
keras
.
layers
.
InputSpec
(
shape
=
[
None
,
None
,
None
,
3
]),
activation
:
str
=
'relu'
,
use_sync_bn
:
bool
=
False
,
norm_momentum
:
float
=
0.99
,
norm_epsilon
:
float
=
0.001
,
kernel_initializer
:
str
=
'VarianceScaling'
,
kernel_regularizer
:
Optional
[
tf
.
keras
.
regularizers
.
Regularizer
]
=
None
,
**
kwargs
):
"""Initializes a RevNet model.
Args:
...
...
official/vision/beta/modeling/backbones/spinenet.py
浏览文件 @
f8f4845c
...
...
@@ -12,13 +12,16 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""Contains definitions of SpineNet Networks."""
import
math
from
typing
import
Any
,
List
,
Optional
,
Tuple
# Import libraries
from
absl
import
logging
import
tensorflow
as
tf
from
official.modeling
import
tf_utils
from
official.vision.beta.modeling.backbones
import
factory
from
official.vision.beta.modeling.layers
import
nn_blocks
...
...
@@ -95,14 +98,16 @@ SCALING_MAP = {
class
BlockSpec
(
object
):
"""A container class that specifies the block configuration for SpineNet."""
def
__init__
(
self
,
level
,
block_fn
,
input_offsets
,
is_output
):
def
__init__
(
self
,
level
:
int
,
block_fn
:
str
,
input_offsets
:
Tuple
[
int
,
int
],
is_output
:
bool
):
self
.
level
=
level
self
.
block_fn
=
block_fn
self
.
input_offsets
=
input_offsets
self
.
is_output
=
is_output
def
build_block_specs
(
block_specs
=
None
):
def
build_block_specs
(
block_specs
:
Optional
[
List
[
Tuple
[
Any
,
...]]]
=
None
)
->
List
[
BlockSpec
]:
"""Builds the list of BlockSpec objects for SpineNet."""
if
not
block_specs
:
block_specs
=
SPINENET_BLOCK_SPECS
...
...
@@ -121,32 +126,34 @@ class SpineNet(tf.keras.Model):
(https://arxiv.org/abs/1912.05027)
"""
def
__init__
(
self
,
input_specs
=
tf
.
keras
.
layers
.
InputSpec
(
shape
=
[
None
,
640
,
640
,
3
]),
min_level
=
3
,
max_level
=
7
,
block_specs
=
build_block_specs
(),
endpoints_num_filters
=
256
,
resample_alpha
=
0.5
,
block_repeats
=
1
,
filter_size_scale
=
1.0
,
init_stochastic_depth_rate
=
0.0
,
kernel_initializer
=
'VarianceScaling'
,
kernel_regularizer
=
None
,
bias_regularizer
=
None
,
activation
=
'relu'
,
use_sync_bn
=
False
,
norm_momentum
=
0.99
,
norm_epsilon
=
0.001
,
**
kwargs
):
def
__init__
(
self
,
input_specs
:
tf
.
keras
.
layers
.
InputSpec
=
tf
.
keras
.
layers
.
InputSpec
(
shape
=
[
None
,
640
,
640
,
3
]),
min_level
:
int
=
3
,
max_level
:
int
=
7
,
block_specs
:
List
[
BlockSpec
]
=
build_block_specs
(),
endpoints_num_filters
:
int
=
256
,
resample_alpha
:
float
=
0.5
,
block_repeats
:
int
=
1
,
filter_size_scale
:
float
=
1.0
,
init_stochastic_depth_rate
:
float
=
0.0
,
kernel_initializer
:
str
=
'VarianceScaling'
,
kernel_regularizer
:
Optional
[
tf
.
keras
.
regularizers
.
Regularizer
]
=
None
,
bias_regularizer
:
Optional
[
tf
.
keras
.
regularizers
.
Regularizer
]
=
None
,
activation
:
str
=
'relu'
,
use_sync_bn
:
bool
=
False
,
norm_momentum
:
float
=
0.99
,
norm_epsilon
:
float
=
0.001
,
**
kwargs
):
"""Initializes a SpineNet model.
Args:
input_specs: A `tf.keras.layers.InputSpec` of the input tensor.
min_level: An `int` of min level for output mutiscale features.
max_level: An `int` of max level for output mutiscale features.
block_specs:
The block specifications for the SpineNet model discovered by
NAS.
block_specs:
A list of block specifications for the SpineNet model
discovered by
NAS.
endpoints_num_filters: An `int` of feature dimension for the output
endpoints.
resample_alpha: A `float` of resampling factor in cross-scale connections.
...
...
@@ -214,13 +221,13 @@ class SpineNet(tf.keras.Model):
super
(
SpineNet
,
self
).
__init__
(
inputs
=
inputs
,
outputs
=
endpoints
)
def
_block_group
(
self
,
inputs
,
filters
,
strides
,
block_fn_cand
,
block_repeats
=
1
,
stochastic_depth_drop_rate
=
None
,
name
=
'block_group'
):
inputs
:
tf
.
Tensor
,
filters
:
int
,
strides
:
int
,
block_fn_cand
:
str
,
block_repeats
:
int
=
1
,
stochastic_depth_drop_rate
:
Optional
[
float
]
=
None
,
name
:
str
=
'block_group'
):
"""Creates one group of blocks for the SpineNet model."""
block_fn_candidates
=
{
'bottleneck'
:
nn_blocks
.
BottleneckBlock
,
...
...
official/vision/beta/modeling/backbones/spinenet_mobile.py
浏览文件 @
f8f4845c
...
...
@@ -29,10 +29,13 @@
# ==============================================================================
"""Contains definitions of Mobile SpineNet Networks."""
import
math
from
typing
import
Any
,
List
,
Optional
,
Tuple
# Import libraries
from
absl
import
logging
import
tensorflow
as
tf
from
official.modeling
import
tf_utils
from
official.vision.beta.modeling.backbones
import
factory
from
official.vision.beta.modeling.layers
import
nn_blocks
...
...
@@ -96,14 +99,16 @@ SCALING_MAP = {
class
BlockSpec
(
object
):
"""A container class that specifies the block configuration for SpineNet."""
def
__init__
(
self
,
level
,
block_fn
,
input_offsets
,
is_output
):
def
__init__
(
self
,
level
:
int
,
block_fn
:
str
,
input_offsets
:
Tuple
[
int
,
int
],
is_output
:
bool
):
self
.
level
=
level
self
.
block_fn
=
block_fn
self
.
input_offsets
=
input_offsets
self
.
is_output
=
is_output
def
build_block_specs
(
block_specs
=
None
):
def
build_block_specs
(
block_specs
:
Optional
[
List
[
Tuple
[
Any
,
...]]]
=
None
)
->
List
[
BlockSpec
]:
"""Builds the list of BlockSpec objects for SpineNet."""
if
not
block_specs
:
block_specs
=
SPINENET_BLOCK_SPECS
...
...
@@ -126,25 +131,27 @@ class SpineNetMobile(tf.keras.Model):
(https://arxiv.org/abs/2010.11426).
"""
def
__init__
(
self
,
input_specs
=
tf
.
keras
.
layers
.
InputSpec
(
shape
=
[
None
,
512
,
512
,
3
]),
min_level
=
3
,
max_level
=
7
,
block_specs
=
build_block_specs
(),
endpoints_num_filters
=
48
,
se_ratio
=
0.2
,
block_repeats
=
1
,
filter_size_scale
=
1.0
,
expand_ratio
=
6
,
init_stochastic_depth_rate
=
0.0
,
kernel_initializer
=
'VarianceScaling'
,
kernel_regularizer
=
None
,
bias_regularizer
=
None
,
activation
=
'relu'
,
use_sync_bn
=
False
,
norm_momentum
=
0.99
,
norm_epsilon
=
0.001
,
**
kwargs
):
def
__init__
(
self
,
input_specs
:
tf
.
keras
.
layers
.
InputSpec
=
tf
.
keras
.
layers
.
InputSpec
(
shape
=
[
None
,
512
,
512
,
3
]),
min_level
:
int
=
3
,
max_level
:
int
=
7
,
block_specs
:
List
[
BlockSpec
]
=
build_block_specs
(),
endpoints_num_filters
:
int
=
256
,
se_ratio
:
float
=
0.2
,
block_repeats
:
int
=
1
,
filter_size_scale
:
float
=
1.0
,
expand_ratio
:
int
=
6
,
init_stochastic_depth_rate
=
0.0
,
kernel_initializer
:
str
=
'VarianceScaling'
,
kernel_regularizer
:
Optional
[
tf
.
keras
.
regularizers
.
Regularizer
]
=
None
,
bias_regularizer
:
Optional
[
tf
.
keras
.
regularizers
.
Regularizer
]
=
None
,
activation
:
str
=
'relu'
,
use_sync_bn
:
bool
=
False
,
norm_momentum
:
float
=
0.99
,
norm_epsilon
:
float
=
0.001
,
**
kwargs
):
"""Initializes a Mobile SpineNet model.
Args:
...
...
@@ -222,15 +229,15 @@ class SpineNetMobile(tf.keras.Model):
super
().
__init__
(
inputs
=
inputs
,
outputs
=
endpoints
)
def
_block_group
(
self
,
inputs
,
in_filters
,
out_filters
,
strides
,
expand_ratio
=
6
,
block_repeats
=
1
,
se_ratio
=
0.2
,
stochastic_depth_drop_rate
=
None
,
name
=
'block_group'
):
inputs
:
tf
.
Tensor
,
in_filters
:
int
,
out_filters
:
int
,
strides
:
int
,
expand_ratio
:
int
=
6
,
block_repeats
:
int
=
1
,
se_ratio
:
float
=
0.2
,
stochastic_depth_drop_rate
:
Optional
[
float
]
=
None
,
name
:
str
=
'block_group'
):
"""Creates one group of blocks for the SpineNet model."""
x
=
nn_blocks
.
InvertedBottleneckBlock
(
in_filters
=
in_filters
,
...
...
official/vision/beta/modeling/classification_model.py
浏览文件 @
f8f4845c
...
...
@@ -14,6 +14,7 @@
"""Build classification models."""
from
typing
import
Any
,
Mapping
,
Optional
# Import libraries
import
tensorflow
as
tf
...
...
@@ -24,20 +25,22 @@ layers = tf.keras.layers
class
ClassificationModel
(
tf
.
keras
.
Model
):
"""A classification class builder."""
def
__init__
(
self
,
backbone
,
num_classes
,
input_specs
=
layers
.
InputSpec
(
shape
=
[
None
,
None
,
None
,
3
]),
dropout_rate
=
0.0
,
kernel_initializer
=
'random_uniform'
,
kernel_regularizer
=
None
,
bias_regularizer
=
None
,
add_head_batch_norm
=
False
,
use_sync_bn
:
bool
=
False
,
norm_momentum
:
float
=
0.99
,
norm_epsilon
:
float
=
0.001
,
skip_logits_layer
:
bool
=
False
,
**
kwargs
):
def
__init__
(
self
,
backbone
:
tf
.
keras
.
Model
,
num_classes
:
int
,
input_specs
:
tf
.
keras
.
layers
.
InputSpec
=
layers
.
InputSpec
(
shape
=
[
None
,
None
,
None
,
3
]),
dropout_rate
:
float
=
0.0
,
kernel_initializer
:
str
=
'random_uniform'
,
kernel_regularizer
:
Optional
[
tf
.
keras
.
regularizers
.
Regularizer
]
=
None
,
bias_regularizer
:
Optional
[
tf
.
keras
.
regularizers
.
Regularizer
]
=
None
,
add_head_batch_norm
:
bool
=
False
,
use_sync_bn
:
bool
=
False
,
norm_momentum
:
float
=
0.99
,
norm_epsilon
:
float
=
0.001
,
skip_logits_layer
:
bool
=
False
,
**
kwargs
):
"""Classification initialization function.
Args:
...
...
@@ -103,15 +106,15 @@ class ClassificationModel(tf.keras.Model):
self
.
_norm
=
norm
@
property
def
checkpoint_items
(
self
):
def
checkpoint_items
(
self
)
->
Mapping
[
str
,
tf
.
keras
.
Model
]
:
"""Returns a dictionary of items to be additionally checkpointed."""
return
dict
(
backbone
=
self
.
backbone
)
@
property
def
backbone
(
self
):
def
backbone
(
self
)
->
tf
.
keras
.
Model
:
return
self
.
_backbone
def
get_config
(
self
):
def
get_config
(
self
)
->
Mapping
[
str
,
Any
]
:
return
self
.
_config_dict
@
classmethod
...
...
official/vision/beta/modeling/decoders/aspp.py
浏览文件 @
f8f4845c
...
...
@@ -13,6 +13,7 @@
# limitations under the License.
"""Contains definitions of Atrous Spatial Pyramid Pooling (ASPP) decoder."""
from
typing
import
Any
,
List
,
Optional
,
Mapping
# Import libraries
import
tensorflow
as
tf
...
...
@@ -24,20 +25,21 @@ from official.vision import keras_cv
class
ASPP
(
tf
.
keras
.
layers
.
Layer
):
"""Creates an Atrous Spatial Pyramid Pooling (ASPP) layer."""
def
__init__
(
self
,
level
,
dilation_rates
,
num_filters
=
256
,
pool_kernel_size
=
None
,
use_sync_bn
=
False
,
norm_momentum
=
0.99
,
norm_epsilon
=
0.001
,
activation
=
'relu'
,
dropout_rate
=
0.0
,
kernel_initializer
=
'VarianceScaling'
,
kernel_regularizer
=
None
,
interpolation
=
'bilinear'
,
**
kwargs
):
def
__init__
(
self
,
level
:
int
,
dilation_rates
:
List
[
int
],
num_filters
:
int
=
256
,
pool_kernel_size
:
Optional
[
int
]
=
None
,
use_sync_bn
:
bool
=
False
,
norm_momentum
:
float
=
0.99
,
norm_epsilon
:
float
=
0.001
,
activation
:
str
=
'relu'
,
dropout_rate
:
float
=
0.0
,
kernel_initializer
:
str
=
'VarianceScaling'
,
kernel_regularizer
:
Optional
[
tf
.
keras
.
regularizers
.
Regularizer
]
=
None
,
interpolation
:
str
=
'bilinear'
,
**
kwargs
):
"""Initializes an Atrous Spatial Pyramid Pooling (ASPP) layer.
Args:
...
...
@@ -97,7 +99,7 @@ class ASPP(tf.keras.layers.Layer):
kernel_regularizer
=
self
.
_config_dict
[
'kernel_regularizer'
],
interpolation
=
self
.
_config_dict
[
'interpolation'
])
def
call
(
self
,
inputs
)
:
def
call
(
self
,
inputs
:
Mapping
[
str
,
tf
.
Tensor
])
->
Mapping
[
str
,
tf
.
Tensor
]
:
"""Calls the Atrous Spatial Pyramid Pooling (ASPP) layer on an input.
The output of ASPP will be a dict of {`level`, `tf.Tensor`} even if only one
...
...
@@ -120,7 +122,7 @@ class ASPP(tf.keras.layers.Layer):
outputs
[
level
]
=
self
.
aspp
(
inputs
[
level
])
return
outputs
def
get_config
(
self
):
def
get_config
(
self
)
->
Mapping
[
str
,
Any
]
:
return
self
.
_config_dict
@
classmethod
...
...
official/vision/beta/modeling/decoders/factory.py
浏览文件 @
f8f4845c
...
...
@@ -15,15 +15,21 @@
# Lint as: python3
"""Contains the factory method to create decoders."""
from
typing
import
Mapping
,
Optional
# Import libraries
import
tensorflow
as
tf
from
official.modeling
import
hyperparams
from
official.vision.beta.modeling
import
decoders
def
build_decoder
(
input_specs
,
model_config
,
l2_regularizer
:
tf
.
keras
.
regularizers
.
Regularizer
=
None
):
def
build_decoder
(
input_specs
:
Mapping
[
str
,
tf
.
TensorShape
],
model_config
:
hyperparams
.
Config
,
l2_regularizer
:
Optional
[
tf
.
keras
.
regularizers
.
Regularizer
]
=
None
)
->
tf
.
keras
.
Model
:
"""Builds decoder from a config.
Args:
...
...
official/vision/beta/modeling/decoders/fpn.py
浏览文件 @
f8f4845c
...
...
@@ -13,6 +13,7 @@
# limitations under the License.
"""Contains the definitions of Feature Pyramid Networks (FPN)."""
from
typing
import
Any
,
Mapping
,
Optional
# Import libraries
import
tensorflow
as
tf
...
...
@@ -32,20 +33,21 @@ class FPN(tf.keras.Model):
(https://arxiv.org/pdf/1612.03144)
"""
def
__init__
(
self
,
input_specs
,
min_level
=
3
,
max_level
=
7
,
num_filters
=
256
,
use_separable_conv
=
False
,
activation
=
'relu'
,
use_sync_bn
=
False
,
norm_momentum
=
0.99
,
norm_epsilon
=
0.001
,
kernel_initializer
=
'VarianceScaling'
,
kernel_regularizer
=
None
,
bias_regularizer
=
None
,
**
kwargs
):
def
__init__
(
self
,
input_specs
:
Mapping
[
str
,
tf
.
TensorShape
],
min_level
:
int
=
3
,
max_level
:
int
=
7
,
num_filters
:
int
=
256
,
use_separable_conv
:
bool
=
False
,
activation
:
str
=
'relu'
,
use_sync_bn
:
bool
=
False
,
norm_momentum
:
float
=
0.99
,
norm_epsilon
:
float
=
0.001
,
kernel_initializer
:
str
=
'VarianceScaling'
,
kernel_regularizer
:
Optional
[
tf
.
keras
.
regularizers
.
Regularizer
]
=
None
,
bias_regularizer
:
Optional
[
tf
.
keras
.
regularizers
.
Regularizer
]
=
None
,
**
kwargs
):
"""Initializes a Feature Pyramid Network (FPN).
Args:
...
...
@@ -162,7 +164,8 @@ class FPN(tf.keras.Model):
super
(
FPN
,
self
).
__init__
(
inputs
=
inputs
,
outputs
=
feats
,
**
kwargs
)
def
_build_input_pyramid
(
self
,
input_specs
,
min_level
):
def
_build_input_pyramid
(
self
,
input_specs
:
Mapping
[
str
,
tf
.
TensorShape
],
min_level
:
int
):
assert
isinstance
(
input_specs
,
dict
)
if
min
(
input_specs
.
keys
())
>
str
(
min_level
):
raise
ValueError
(
...
...
@@ -173,7 +176,7 @@ class FPN(tf.keras.Model):
inputs
[
level
]
=
tf
.
keras
.
Input
(
shape
=
spec
[
1
:])
return
inputs
def
get_config
(
self
):
def
get_config
(
self
)
->
Mapping
[
str
,
Any
]
:
return
self
.
_config_dict
@
classmethod
...
...
@@ -181,6 +184,6 @@ class FPN(tf.keras.Model):
return
cls
(
**
config
)
@
property
def
output_specs
(
self
):
def
output_specs
(
self
)
->
Mapping
[
str
,
tf
.
TensorShape
]
:
"""A dict of {level: TensorShape} pairs for the model output."""
return
self
.
_output_specs
official/vision/beta/modeling/decoders/nasfpn.py
浏览文件 @
f8f4845c
...
...
@@ -13,6 +13,7 @@
# limitations under the License.
"""Contains definitions of NAS-FPN."""
from
typing
import
Any
,
Mapping
,
List
,
Tuple
,
Optional
# Import libraries
from
absl
import
logging
...
...
@@ -35,17 +36,19 @@ NASFPN_BLOCK_SPECS = [
]
class
BlockSpec
(
object
):
class
BlockSpec
():
"""A container class that specifies the block configuration for NAS-FPN."""
def
__init__
(
self
,
level
,
combine_fn
,
input_offsets
,
is_output
):
def
__init__
(
self
,
level
:
int
,
combine_fn
:
str
,
input_offsets
:
Tuple
[
int
,
int
],
is_output
:
bool
):
self
.
level
=
level
self
.
combine_fn
=
combine_fn
self
.
input_offsets
=
input_offsets
self
.
is_output
=
is_output
def
build_block_specs
(
block_specs
=
None
):
def
build_block_specs
(
block_specs
:
Optional
[
List
[
Tuple
[
Any
,
...]]]
=
None
)
->
List
[
BlockSpec
]:
"""Builds the list of BlockSpec objects for NAS-FPN."""
if
not
block_specs
:
block_specs
=
NASFPN_BLOCK_SPECS
...
...
@@ -63,22 +66,23 @@ class NASFPN(tf.keras.Model):
(https://arxiv.org/abs/1904.07392)
"""
def
__init__
(
self
,
input_specs
,
min_level
=
3
,
max_level
=
7
,
block_specs
=
build_block_specs
(),
num_filters
=
256
,
num_repeats
=
5
,
use_separable_conv
=
False
,
activation
=
'relu'
,
use_sync_bn
=
False
,
norm_momentum
=
0.99
,
norm_epsilon
=
0.001
,
kernel_initializer
=
'VarianceScaling'
,
kernel_regularizer
=
None
,
bias_regularizer
=
None
,
**
kwargs
):
def
__init__
(
self
,
input_specs
:
Mapping
[
str
,
tf
.
TensorShape
],
min_level
:
int
=
3
,
max_level
:
int
=
7
,
block_specs
:
List
[
BlockSpec
]
=
build_block_specs
(),
num_filters
:
int
=
256
,
num_repeats
:
int
=
5
,
use_separable_conv
:
bool
=
False
,
activation
:
str
=
'relu'
,
use_sync_bn
:
bool
=
False
,
norm_momentum
:
float
=
0.99
,
norm_epsilon
:
float
=
0.001
,
kernel_initializer
:
str
=
'VarianceScaling'
,
kernel_regularizer
:
Optional
[
tf
.
keras
.
regularizers
.
Regularizer
]
=
None
,
bias_regularizer
:
Optional
[
tf
.
keras
.
regularizers
.
Regularizer
]
=
None
,
**
kwargs
):
"""Initializes a NAS-FPN model.
Args:
...
...
@@ -191,7 +195,8 @@ class NASFPN(tf.keras.Model):
for
level
in
output_feats
.
keys
()}
super
(
NASFPN
,
self
).
__init__
(
inputs
=
inputs
,
outputs
=
output_feats
,
**
kwargs
)
def
_build_input_pyramid
(
self
,
input_specs
,
min_level
):
def
_build_input_pyramid
(
self
,
input_specs
:
Mapping
[
str
,
tf
.
TensorShape
],
min_level
:
int
):
assert
isinstance
(
input_specs
,
dict
)
if
min
(
input_specs
.
keys
())
>
str
(
min_level
):
raise
ValueError
(
...
...
@@ -300,7 +305,7 @@ class NASFPN(tf.keras.Model):
logging
.
info
(
'Output feature pyramid: %s'
,
output_feats
)
return
output_feats
def
get_config
(
self
):
def
get_config
(
self
)
->
Mapping
[
str
,
Any
]
:
return
self
.
_config_dict
@
classmethod
...
...
@@ -308,6 +313,6 @@ class NASFPN(tf.keras.Model):
return
cls
(
**
config
)
@
property
def
output_specs
(
self
):
def
output_specs
(
self
)
->
Mapping
[
str
,
tf
.
TensorShape
]
:
"""A dict of {level: TensorShape} pairs for the model output."""
return
self
.
_output_specs
official/vision/beta/modeling/factory.py
浏览文件 @
f8f4845c
...
...
@@ -42,7 +42,7 @@ def build_classification_model(
input_specs
:
tf
.
keras
.
layers
.
InputSpec
,
model_config
:
classification_cfg
.
ImageClassificationModel
,
l2_regularizer
:
tf
.
keras
.
regularizers
.
Regularizer
=
None
,
skip_logits_layer
:
bool
=
False
):
skip_logits_layer
:
bool
=
False
)
->
tf
.
keras
.
Model
:
"""Builds the classification model."""
backbone
=
backbones
.
factory
.
build_backbone
(
input_specs
=
input_specs
,
...
...
@@ -64,9 +64,10 @@ def build_classification_model(
return
model
def
build_maskrcnn
(
input_specs
:
tf
.
keras
.
layers
.
InputSpec
,
model_config
:
maskrcnn_cfg
.
MaskRCNN
,
l2_regularizer
:
tf
.
keras
.
regularizers
.
Regularizer
=
None
):
def
build_maskrcnn
(
input_specs
:
tf
.
keras
.
layers
.
InputSpec
,
model_config
:
maskrcnn_cfg
.
MaskRCNN
,
l2_regularizer
:
tf
.
keras
.
regularizers
.
Regularizer
=
None
)
->
tf
.
keras
.
Model
:
"""Builds Mask R-CNN model."""
backbone
=
backbones
.
factory
.
build_backbone
(
input_specs
=
input_specs
,
...
...
@@ -194,9 +195,10 @@ def build_maskrcnn(input_specs: tf.keras.layers.InputSpec,
return
model
def
build_retinanet
(
input_specs
:
tf
.
keras
.
layers
.
InputSpec
,
model_config
:
retinanet_cfg
.
RetinaNet
,
l2_regularizer
:
tf
.
keras
.
regularizers
.
Regularizer
=
None
):
def
build_retinanet
(
input_specs
:
tf
.
keras
.
layers
.
InputSpec
,
model_config
:
retinanet_cfg
.
RetinaNet
,
l2_regularizer
:
tf
.
keras
.
regularizers
.
Regularizer
=
None
)
->
tf
.
keras
.
Model
:
"""Builds RetinaNet model."""
backbone
=
backbones
.
factory
.
build_backbone
(
input_specs
=
input_specs
,
...
...
@@ -253,7 +255,7 @@ def build_retinanet(input_specs: tf.keras.layers.InputSpec,
def
build_segmentation_model
(
input_specs
:
tf
.
keras
.
layers
.
InputSpec
,
model_config
:
segmentation_cfg
.
SemanticSegmentationModel
,
l2_regularizer
:
tf
.
keras
.
regularizers
.
Regularizer
=
None
):
l2_regularizer
:
tf
.
keras
.
regularizers
.
Regularizer
=
None
)
->
tf
.
keras
.
Model
:
"""Builds Segmentation model."""
backbone
=
backbones
.
factory
.
build_backbone
(
input_specs
=
input_specs
,
...
...
official/vision/beta/modeling/factory_3d.py
浏览文件 @
f8f4845c
...
...
@@ -53,11 +53,12 @@ def register_model_builder(key: str):
return
registry
.
register
(
_REGISTERED_MODEL_CLS
,
key
)
def
build_model
(
model_type
:
str
,
input_specs
:
tf
.
keras
.
layers
.
InputSpec
,
model_config
:
video_classification_cfg
.
hyperparams
.
Config
,
num_classes
:
int
,
l2_regularizer
:
tf
.
keras
.
regularizers
.
Regularizer
=
None
):
def
build_model
(
model_type
:
str
,
input_specs
:
tf
.
keras
.
layers
.
InputSpec
,
model_config
:
video_classification_cfg
.
hyperparams
.
Config
,
num_classes
:
int
,
l2_regularizer
:
tf
.
keras
.
regularizers
.
Regularizer
=
None
)
->
tf
.
keras
.
Model
:
"""Builds backbone from a config.
Args:
...
...
@@ -81,7 +82,7 @@ def build_video_classification_model(
input_specs
:
tf
.
keras
.
layers
.
InputSpec
,
model_config
:
video_classification_cfg
.
VideoClassificationModel
,
num_classes
:
int
,
l2_regularizer
:
tf
.
keras
.
regularizers
.
Regularizer
=
None
):
l2_regularizer
:
tf
.
keras
.
regularizers
.
Regularizer
=
None
)
->
tf
.
keras
.
Model
:
"""Builds the video classification model."""
input_specs_dict
=
{
'image'
:
input_specs
}
backbone
=
backbones
.
factory
.
build_backbone
(
...
...
official/vision/beta/modeling/maskrcnn_model.py
浏览文件 @
f8f4845c
...
...
@@ -14,6 +14,8 @@
"""Mask R-CNN model."""
from
typing
import
Any
,
Mapping
,
Optional
,
Union
# Import libraries
import
tensorflow
as
tf
...
...
@@ -25,17 +27,17 @@ class MaskRCNNModel(tf.keras.Model):
"""The Mask R-CNN model."""
def
__init__
(
self
,
backbone
,
decoder
,
rpn_head
,
detection_head
,
roi_generator
,
roi_sampler
,
roi_aligner
,
detection_generator
,
mask_head
=
None
,
mask_sampler
=
None
,
mask_roi_aligner
=
None
,
backbone
:
tf
.
keras
.
Model
,
decoder
:
tf
.
keras
.
Model
,
rpn_head
:
tf
.
keras
.
layers
.
Layer
,
detection_head
:
tf
.
keras
.
layers
.
Layer
,
roi_generator
:
tf
.
keras
.
layers
.
Layer
,
roi_sampler
:
tf
.
keras
.
layers
.
Layer
,
roi_aligner
:
tf
.
keras
.
layers
.
Layer
,
detection_generator
:
tf
.
keras
.
layers
.
Layer
,
mask_head
:
Optional
[
tf
.
keras
.
layers
.
Layer
]
=
None
,
mask_sampler
:
Optional
[
tf
.
keras
.
layers
.
Layer
]
=
None
,
mask_roi_aligner
:
Optional
[
tf
.
keras
.
layers
.
Layer
]
=
None
,
**
kwargs
):
"""Initializes the Mask R-CNN model.
...
...
@@ -85,13 +87,13 @@ class MaskRCNNModel(tf.keras.Model):
self
.
mask_roi_aligner
=
mask_roi_aligner
def
call
(
self
,
images
,
image_shape
,
anchor_boxes
=
None
,
gt_boxes
=
None
,
gt_classes
=
None
,
gt_masks
=
None
,
training
=
None
)
:
images
:
tf
.
Tensor
,
image_shape
:
tf
.
Tensor
,
anchor_boxes
:
Optional
[
Mapping
[
str
,
tf
.
Tensor
]]
=
None
,
gt_boxes
:
tf
.
Tensor
=
None
,
gt_classes
:
tf
.
Tensor
=
None
,
gt_masks
:
tf
.
Tensor
=
None
,
training
:
bool
=
None
)
->
Mapping
[
str
,
tf
.
Tensor
]
:
model_outputs
=
{}
# Feature extraction.
...
...
@@ -190,7 +192,8 @@ class MaskRCNNModel(tf.keras.Model):
return
model_outputs
@
property
def
checkpoint_items
(
self
):
def
checkpoint_items
(
self
)
->
Mapping
[
str
,
Union
[
tf
.
keras
.
Model
,
tf
.
keras
.
layers
.
Layer
]]:
"""Returns a dictionary of items to be additionally checkpointed."""
items
=
dict
(
backbone
=
self
.
backbone
,
...
...
@@ -203,7 +206,7 @@ class MaskRCNNModel(tf.keras.Model):
return
items
def
get_config
(
self
):
def
get_config
(
self
)
->
Mapping
[
str
,
Any
]
:
return
self
.
_config_dict
@
classmethod
...
...
official/vision/beta/modeling/retinanet_model.py
浏览文件 @
f8f4845c
...
...
@@ -13,7 +13,7 @@
# limitations under the License.
"""RetinaNet."""
from
typing
import
List
,
Optional
from
typing
import
Any
,
Mapping
,
List
,
Optional
,
Union
# Import libraries
import
tensorflow
as
tf
...
...
@@ -26,10 +26,10 @@ class RetinaNetModel(tf.keras.Model):
"""The RetinaNet model class."""
def
__init__
(
self
,
backbone
,
decoder
,
head
,
detection_generator
,
backbone
:
tf
.
keras
.
Model
,
decoder
:
tf
.
keras
.
Model
,
head
:
tf
.
keras
.
layers
.
Layer
,
detection_generator
:
tf
.
keras
.
layers
.
Layer
,
min_level
:
Optional
[
int
]
=
None
,
max_level
:
Optional
[
int
]
=
None
,
num_scales
:
Optional
[
int
]
=
None
,
...
...
@@ -74,10 +74,10 @@ class RetinaNetModel(tf.keras.Model):
self
.
_detection_generator
=
detection_generator
def
call
(
self
,
images
,
image_shape
=
None
,
anchor_boxes
=
None
,
training
=
None
)
:
images
:
tf
.
Tensor
,
image_shape
:
Optional
[
tf
.
Tensor
]
=
None
,
anchor_boxes
:
Optional
[
Mapping
[
str
,
tf
.
Tensor
]]
=
None
,
training
:
bool
=
None
)
->
Mapping
[
str
,
tf
.
Tensor
]
:
"""Forward pass of the RetinaNet model.
Args:
...
...
@@ -163,7 +163,8 @@ class RetinaNetModel(tf.keras.Model):
return
outputs
@
property
def
checkpoint_items
(
self
):
def
checkpoint_items
(
self
)
->
Mapping
[
str
,
Union
[
tf
.
keras
.
Model
,
tf
.
keras
.
layers
.
Layer
]]:
"""Returns a dictionary of items to be additionally checkpointed."""
items
=
dict
(
backbone
=
self
.
backbone
,
head
=
self
.
head
)
if
self
.
decoder
is
not
None
:
...
...
@@ -172,22 +173,22 @@ class RetinaNetModel(tf.keras.Model):
return
items
@
property
def
backbone
(
self
):
def
backbone
(
self
)
->
tf
.
keras
.
Model
:
return
self
.
_backbone
@
property
def
decoder
(
self
):
def
decoder
(
self
)
->
tf
.
keras
.
Model
:
return
self
.
_decoder
@
property
def
head
(
self
):
def
head
(
self
)
->
tf
.
keras
.
layers
.
Layer
:
return
self
.
_head
@
property
def
detection_generator
(
self
):
def
detection_generator
(
self
)
->
tf
.
keras
.
layers
.
Layer
:
return
self
.
_detection_generator
def
get_config
(
self
):
def
get_config
(
self
)
->
Mapping
[
str
,
Any
]
:
return
self
.
_config_dict
@
classmethod
...
...
official/vision/beta/modeling/segmentation_model.py
浏览文件 @
f8f4845c
...
...
@@ -13,6 +13,7 @@
# limitations under the License.
"""Build segmentation models."""
from
typing
import
Any
,
Mapping
,
Union
# Import libraries
import
tensorflow
as
tf
...
...
@@ -33,11 +34,8 @@ class SegmentationModel(tf.keras.Model):
different backbones, and decoders.
"""
def
__init__
(
self
,
backbone
,
decoder
,
head
,
**
kwargs
):
def
__init__
(
self
,
backbone
:
tf
.
keras
.
Model
,
decoder
:
tf
.
keras
.
Model
,
head
:
tf
.
keras
.
layers
.
Layer
,
**
kwargs
):
"""Segmentation initialization function.
Args:
...
...
@@ -56,7 +54,7 @@ class SegmentationModel(tf.keras.Model):
self
.
decoder
=
decoder
self
.
head
=
head
def
call
(
self
,
inputs
,
training
=
None
)
:
def
call
(
self
,
inputs
:
tf
.
Tensor
,
training
:
bool
=
None
)
->
tf
.
Tensor
:
backbone_features
=
self
.
backbone
(
inputs
)
if
self
.
decoder
:
...
...
@@ -67,14 +65,15 @@ class SegmentationModel(tf.keras.Model):
return
self
.
head
(
backbone_features
,
decoder_features
)
@
property
def
checkpoint_items
(
self
):
def
checkpoint_items
(
self
)
->
Mapping
[
str
,
Union
[
tf
.
keras
.
Model
,
tf
.
keras
.
layers
.
Layer
]]:
"""Returns a dictionary of items to be additionally checkpointed."""
items
=
dict
(
backbone
=
self
.
backbone
,
head
=
self
.
head
)
if
self
.
decoder
is
not
None
:
items
.
update
(
decoder
=
self
.
decoder
)
return
items
def
get_config
(
self
):
def
get_config
(
self
)
->
Mapping
[
str
,
Any
]
:
return
self
.
_config_dict
@
classmethod
...
...
official/vision/beta/modeling/video_classification_model.py
浏览文件 @
f8f4845c
...
...
@@ -13,7 +13,7 @@
# limitations under the License.
"""Build video classification models."""
from
typing
import
Mapping
from
typing
import
Any
,
Mapping
,
Optional
,
Union
import
tensorflow
as
tf
layers
=
tf
.
keras
.
layers
...
...
@@ -23,16 +23,17 @@ layers = tf.keras.layers
class
VideoClassificationModel
(
tf
.
keras
.
Model
):
"""A video classification class builder."""
def
__init__
(
self
,
backbone
:
tf
.
keras
.
Model
,
num_classes
:
int
,
input_specs
:
Mapping
[
str
,
tf
.
keras
.
layers
.
InputSpec
]
=
None
,
dropout_rate
:
float
=
0.0
,
aggregate_endpoints
:
bool
=
False
,
kernel_initializer
=
'random_uniform'
,
kernel_regularizer
=
None
,
bias_regularizer
=
None
,
**
kwargs
):
def
__init__
(
self
,
backbone
:
tf
.
keras
.
Model
,
num_classes
:
int
,
input_specs
:
Mapping
[
str
,
tf
.
keras
.
layers
.
InputSpec
]
=
None
,
dropout_rate
:
float
=
0.0
,
aggregate_endpoints
:
bool
=
False
,
kernel_initializer
:
str
=
'random_uniform'
,
kernel_regularizer
:
Optional
[
tf
.
keras
.
regularizers
.
Regularizer
]
=
None
,
bias_regularizer
:
Optional
[
tf
.
keras
.
regularizers
.
Regularizer
]
=
None
,
**
kwargs
):
"""Video Classification initialization function.
Args:
...
...
@@ -95,15 +96,16 @@ class VideoClassificationModel(tf.keras.Model):
inputs
=
inputs
,
outputs
=
x
,
**
kwargs
)
@
property
def
checkpoint_items
(
self
):
def
checkpoint_items
(
self
)
->
Mapping
[
str
,
Union
[
tf
.
keras
.
Model
,
tf
.
keras
.
layers
.
Layer
]]:
"""Returns a dictionary of items to be additionally checkpointed."""
return
dict
(
backbone
=
self
.
backbone
)
@
property
def
backbone
(
self
):
def
backbone
(
self
)
->
tf
.
keras
.
Model
:
return
self
.
_backbone
def
get_config
(
self
):
def
get_config
(
self
)
->
Mapping
[
str
,
Any
]
:
return
self
.
_config_dict
@
classmethod
...
...
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