Skip to content

GitLab

Commit 790e49e5 authored by stephenwu's avatar stephenwu
Browse files

Merge branch 'master' of https://github.com/tensorflow/models into run_superglue

parents 8ab018b0 5bb827c3
Hide whitespace changes
Inline Side-by-side
Showing with 276 additions and 194 deletions
official/vision/beta/modeling/factory_test.py
View file @ 790e49e5
# Lint as: python3
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -12,7 +11,8 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# Lint as: python3
"""Tests for factory.py."""
# Import libraries
......
official/vision/beta/modeling/heads/__init__.py
View file @ 790e49e5
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""Heads package definition."""
from official.vision.beta.modeling.heads.dense_prediction_heads import RetinaNetHead
from official.vision.beta.modeling.heads.dense_prediction_heads import RPNHead
from official.vision.beta.modeling.heads.instance_heads import DetectionHead
from official.vision.beta.modeling.heads.instance_heads import MaskHead
from official.vision.beta.modeling.heads.segmentation_heads import SegmentationHead
official/vision/beta/modeling/heads/dense_prediction_heads.py
View file @ 790e49e5
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -11,8 +11,8 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Dense prediction heads."""
"""Contains definitions of dense prediction heads."""
# Import libraries
import numpy as np
......@@ -23,7 +23,7 @@ from official.modeling import tf_utils
@tf.keras.utils.register_keras_serializable(package='Vision')
class RetinaNetHead(tf.keras.layers.Layer):
"""RetinaNet head."""
"""Creates a RetinaNet head."""
def __init__(self,
min_level,
......@@ -40,31 +40,30 @@ class RetinaNetHead(tf.keras.layers.Layer):
kernel_regularizer=None,
bias_regularizer=None,
**kwargs):
"""Initialize params to build RetinaNet head.
"""Initializes a RetinaNet head.
Args:
min_level: `int` number of minimum feature level.
max_level: `int` number of maximum feature level.
num_classes: `int` number of classes to predict.
num_anchors_per_location: `int` number of number of anchors per pixel
min_level: An `int` number of minimum feature level.
max_level: An `int` number of maximum feature level.
num_classes: An `int` number of classes to predict.
num_anchors_per_location: An `int` number of number of anchors per pixel
location.
num_convs: `int` number that represents the number of the intermediate
num_convs: An `int` number that represents the number of the intermediate
conv layers before the prediction.
num_filters: `int` number that represents the number of filters of the
num_filters: An `int` number that represents the number of filters of the
intermediate conv layers.
use_separable_conv: `bool`, indicating whether the separable conv layers
is used.
activation: `string`, indicating which activation is used, e.g. 'relu',
use_separable_conv: A `bool` that indicates whether the separable
convolution layers is used.
activation: A `str` that indicates which activation is used, e.g. 'relu',
'swish', etc.
use_sync_bn: `bool`, whether to use synchronized batch normalization
across different replicas.
norm_momentum: `float`, the momentum parameter of the normalization
layers.
norm_epsilon: `float`, the epsilon parameter of the normalization layers.
kernel_regularizer: `tf.keras.regularizers.Regularizer` object for layer
kernal.
bias_regularizer: `tf.keras.regularizers.Regularizer` object for bias.
**kwargs: other keyword arguments passed to Layer.
use_sync_bn: A `bool` that indicates whether to use synchronized batch
normalization across different replicas.
norm_momentum: A `float` of normalization momentum for the moving average.
norm_epsilon: A `float` added to variance to avoid dividing by zero.
kernel_regularizer: A `tf.keras.regularizers.Regularizer` object for
Conv2D. Default is None.
bias_regularizer: A `tf.keras.regularizers.Regularizer` object for Conv2D.
**kwargs: Additional keyword arguments to be passed.
"""
super(RetinaNetHead, self).__init__(**kwargs)
self._config_dict = {
......@@ -209,21 +208,22 @@ class RetinaNetHead(tf.keras.layers.Layer):
"""Forward pass of the RetinaNet head.
Args:
features: a dict of tensors
- key: `str`, the level of the multilevel features.
- values: `Tensor`, the feature map tensors, whose shape is
features: A `dict` of `tf.Tensor` where
- key: A `str` of the level of the multilevel features.
- values: A `tf.Tensor`, the feature map tensors, whose shape is
[batch, height_l, width_l, channels].
Returns:
scores: a dict of tensors which includes scores of the predictions.
- key: `str`, the level of the multilevel predictions.
- values: `Tensor`, the box scores predicted from a particular feature
level, whose shape is
scores: A `dict` of `tf.Tensor` which includes scores of the predictions.
- key: A `str` of the level of the multilevel predictions.
- values: A `tf.Tensor` of the box scores predicted from a particular
feature level, whose shape is
[batch, height_l, width_l, num_classes * num_anchors_per_location].
boxes: a dict of tensors which includes coordinates of the predictions.
- key: `str`, the level of the multilevel predictions.
- values: `Tensor`, the box scores predicted from a particular feature
level, whose shape is
boxes: A `dict` of `tf.Tensor` which includes coordinates of the
predictions.
- key: A `str` of the level of the multilevel predictions.
- values: A `tf.Tensor` of the box scores predicted from a particular
feature level, whose shape is
[batch, height_l, width_l, 4 * num_anchors_per_location].
"""
scores = {}
......@@ -260,7 +260,7 @@ class RetinaNetHead(tf.keras.layers.Layer):
@tf.keras.utils.register_keras_serializable(package='Vision')
class RPNHead(tf.keras.layers.Layer):
"""Region Proposal Network head."""
"""Creates a Region Proposal Network (RPN) head."""
def __init__(self,
min_level,
......@@ -276,29 +276,29 @@ class RPNHead(tf.keras.layers.Layer):
kernel_regularizer=None,
bias_regularizer=None,
**kwargs):
"""Initialize params to build Region Proposal Network head.
"""Initializes a Region Proposal Network head.
Args:
min_level: `int` number of minimum feature level.
max_level: `int` number of maximum feature level.
num_anchors_per_location: `int` number of number of anchors per pixel
min_level: An `int` number of minimum feature level.
max_level: An `int` number of maximum feature level.
num_anchors_per_location: An `int` number of number of anchors per pixel
location.
num_convs: `int` number that represents the number of the intermediate
conv layers before the prediction.
num_filters: `int` number that represents the number of filters of the
intermediate conv layers.
use_separable_conv: `bool`, indicating whether the separable conv layers
is used.
activation: `string`, indicating which activation is used, e.g. 'relu',
num_convs: An `int` number that represents the number of the intermediate
convolution layers before the prediction.
num_filters: An `int` number that represents the number of filters of the
intermediate convolution layers.
use_separable_conv: A `bool` that indicates whether the separable
convolution layers is used.
activation: A `str` that indicates which activation is used, e.g. 'relu',
'swish', etc.
use_sync_bn: `bool`, whether to use synchronized batch normalization
across different replicas.
norm_momentum: `float`, the momentum parameter of the normalizaton layers.
norm_epsilon: `float`, the epsilon parameter of the normalization layers.
kernel_regularizer: `tf.keras.regularizers.Regularizer` object for layer
kernel.
bias_regularizer: `tf.keras.regularizers.Regularizer` object for bias.
**kwargs: other keyword arguments passed to Layer.
use_sync_bn: A `bool` that indicates whether to use synchronized batch
normalization across different replicas.
norm_momentum: A `float` of normalization momentum for the moving average.
norm_epsilon: A `float` added to variance to avoid dividing by zero.
kernel_regularizer: A `tf.keras.regularizers.Regularizer` object for
Conv2D. Default is None.
bias_regularizer: A `tf.keras.regularizers.Regularizer` object for Conv2D.
**kwargs: Additional keyword arguments to be passed.
"""
super(RPNHead, self).__init__(**kwargs)
self._config_dict = {
......@@ -428,6 +428,27 @@ class RPNHead(tf.keras.layers.Layer):
super(RPNHead, self).build(input_shape)
def call(self, features):
"""Forward pass of the RPN head.
Args:
features: A `dict` of `tf.Tensor` where
- key: A `str` of the level of the multilevel features.
- values: A `tf.Tensor`, the feature map tensors, whose shape is [batch,
height_l, width_l, channels].
Returns:
scores: A `dict` of `tf.Tensor` which includes scores of the predictions.
- key: A `str` of the level of the multilevel predictions.
- values: A `tf.Tensor` of the box scores predicted from a particular
feature level, whose shape is
[batch, height_l, width_l, num_classes * num_anchors_per_location].
boxes: A `dict` of `tf.Tensor` which includes coordinates of the
predictions.
- key: A `str` of the level of the multilevel predictions.
- values: A `tf.Tensor` of the box scores predicted from a particular
feature level, whose shape is
[batch, height_l, width_l, 4 * num_anchors_per_location].
"""
scores = {}
boxes = {}
for i, level in enumerate(
......
official/vision/beta/modeling/heads/dense_prediction_heads_test.py
View file @ 790e49e5
# Lint as: python3
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -12,7 +11,8 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# Lint as: python3
"""Tests for dense_prediction_heads.py."""
# Import libraries
......
official/vision/beta/modeling/heads/instance_heads.py
View file @ 790e49e5
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -11,8 +11,8 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Instance prediction heads."""
"""Contains definitions of instance prediction heads."""
# Import libraries
import tensorflow as tf
......@@ -22,7 +22,7 @@ from official.modeling import tf_utils
@tf.keras.utils.register_keras_serializable(package='Vision')
class DetectionHead(tf.keras.layers.Layer):
"""Detection head."""
"""Creates a detection head."""
def __init__(self,
num_classes,
......@@ -38,31 +38,30 @@ class DetectionHead(tf.keras.layers.Layer):
kernel_regularizer=None,
bias_regularizer=None,
**kwargs):
"""Initialize params to build the detection head.
"""Initializes a detection head.
Args:
num_classes: a integer for the number of classes.
num_convs: `int` number that represents the number of the intermediate
conv layers before the FC layers.
num_filters: `int` number that represents the number of filters of the
intermediate conv layers.
use_separable_conv: `bool`, indicating whether the separable conv layers
is used.
num_fcs: `int` number that represents the number of FC layers before the
predictions.
fc_dims: `int` number that represents the number of dimension of the FC
num_classes: An `int` for the number of classes.
num_convs: An `int` number that represents the number of the intermediate
convolution layers before the FC layers.
num_filters: An `int` number that represents the number of filters of the
intermediate convolution layers.
use_separable_conv: A `bool` that indicates whether the separable
convolution layers is used.
num_fcs: An `int` number that represents the number of FC layers before
the predictions.
fc_dims: An `int` number that represents the number of dimension of the FC
layers.
activation: `string`, indicating which activation is used, e.g. 'relu',
activation: A `str` that indicates which activation is used, e.g. 'relu',
'swish', etc.
use_sync_bn: `bool`, whether to use synchronized batch normalization
across different replicas.
norm_momentum: `float`, the momentum parameter of the normalization
layers.
norm_epsilon: `float`, the epsilon parameter of the normalization layers.
kernel_regularizer: `tf.keras.regularizers.Regularizer` object for layer
kernel.
bias_regularizer: `tf.keras.regularizers.Regularizer` object for bias.
**kwargs: other keyword arguments passed to Layer.
use_sync_bn: A `bool` that indicates whether to use synchronized batch
normalization across different replicas.
norm_momentum: A `float` of normalization momentum for the moving average.
norm_epsilon: A `float` added to variance to avoid dividing by zero.
kernel_regularizer: A `tf.keras.regularizers.Regularizer` object for
Conv2D. Default is None.
bias_regularizer: A `tf.keras.regularizers.Regularizer` object for Conv2D.
**kwargs: Additional keyword arguments to be passed.
"""
super(DetectionHead, self).__init__(**kwargs)
self._config_dict = {
......@@ -165,18 +164,17 @@ class DetectionHead(tf.keras.layers.Layer):
super(DetectionHead, self).build(input_shape)
def call(self, inputs, training=None):
"""Box and class branches for the Mask-RCNN model.
"""Forward pass of box and class branches for the Mask-RCNN model.
Args:
inputs: ROI features, a tensor of shape
[batch_size, num_instances, roi_height, roi_width, roi_channels],
representing the ROI features.
training: a boolean indicating whether it is in `training` mode.
inputs: A `tf.Tensor` of the shape [batch_size, num_instances, roi_height,
roi_width, roi_channels], representing the ROI features.
training: a `bool` indicating whether it is in `training` mode.
Returns:
class_outputs: a tensor with a shape of
class_outputs: A `tf.Tensor` of the shape
[batch_size, num_rois, num_classes], representing the class predictions.
box_outputs: a tensor with a shape of
box_outputs: A `tf.Tensor` of the shape
[batch_size, num_rois, num_classes * 4], representing the box
predictions.
"""
......@@ -211,7 +209,7 @@ class DetectionHead(tf.keras.layers.Layer):
@tf.keras.utils.register_keras_serializable(package='Vision')
class MaskHead(tf.keras.layers.Layer):
"""Mask head."""
"""Creates a mask head."""
def __init__(self,
num_classes,
......@@ -227,31 +225,30 @@ class MaskHead(tf.keras.layers.Layer):
bias_regularizer=None,
class_agnostic=False,
**kwargs):
"""Initialize params to build the mask head.
"""Initializes a mask head.
Args:
num_classes: `int`, the number of classes.
upsample_factor: `int`, >= 1, the upsample factor to generate the
final predicted masks.
num_convs: `int` number that represents the number of the intermediate
conv layers before the mask prediction layers.
num_filters: `int` number that represents the number of filters of the
intermediate conv layers.
use_separable_conv: `bool`, indicating whether the separable conv layers
is used.
activation: `string`, indicating which activation is used, e.g. 'relu',
num_classes: An `int` of the number of classes.
upsample_factor: An `int` that indicates the upsample factor to generate
the final predicted masks. It should be >= 1.
num_convs: An `int` number that represents the number of the intermediate
convolution layers before the mask prediction layers.
num_filters: An `int` number that represents the number of filters of the
intermediate convolution layers.
use_separable_conv: A `bool` that indicates whether the separable
convolution layers is used.
activation: A `str` that indicates which activation is used, e.g. 'relu',
'swish', etc.
use_sync_bn: `bool`, whether to use synchronized batch normalization
across different replicas.
norm_momentum: `float`, the momentum parameter of the normalization
layers.
norm_epsilon: `float`, the epsilon parameter of the normalization layers.
kernel_regularizer: `tf.keras.regularizers.Regularizer` object for layer
kernel.
bias_regularizer: `tf.keras.regularizers.Regularizer` object for bias.
class_agnostic: `bool`, if set, we use a single channel mask head that
use_sync_bn: A `bool` that indicates whether to use synchronized batch
normalization across different replicas.
norm_momentum: A `float` of normalization momentum for the moving average.
norm_epsilon: A `float` added to variance to avoid dividing by zero.
kernel_regularizer: A `tf.keras.regularizers.Regularizer` object for
Conv2D. Default is None.
bias_regularizer: A `tf.keras.regularizers.Regularizer` object for Conv2D.
class_agnostic: A `bool`. If set, we use a single channel mask head that
is shared between all classes.
**kwargs: other keyword arguments passed to Layer.
**kwargs: Additional keyword arguments to be passed.
"""
super(MaskHead, self).__init__(**kwargs)
self._config_dict = {
......@@ -368,19 +365,18 @@ class MaskHead(tf.keras.layers.Layer):
super(MaskHead, self).build(input_shape)
def call(self, inputs, training=None):
"""Mask branch for the Mask-RCNN model.
"""Forward pass of mask branch for the Mask-RCNN model.
Args:
inputs: a list of two tensors
inputs[0]: ROI features, a tensor of shape
[batch_size, num_instances, roi_height, roi_width, roi_channels],
representing the ROI features.
inputs[1]: ROI classes, a tensor of shape
[batch_size, num_instances], representing the classes of the ROIs.
training: a boolean indicating whether it is in `training` mode.
inputs: A `list` of two tensors where
inputs[0]: A `tf.Tensor` of shape [batch_size, num_instances,
roi_height, roi_width, roi_channels], representing the ROI features.
inputs[1]: A `tf.Tensor` of shape [batch_size, num_instances],
representing the classes of the ROIs.
training: A `bool` indicating whether it is in `training` mode.
Returns:
mask_outputs: a tensor of shape
mask_outputs: A `tf.Tensor` of shape
[batch_size, num_instances, roi_height * upsample_factor,
roi_width * upsample_factor], representing the mask predictions.
"""
......
official/vision/beta/modeling/heads/instance_heads_test.py
View file @ 790e49e5
# Lint as: python3
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -12,7 +11,8 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# Lint as: python3
"""Tests for instance_heads.py."""
# Import libraries
......
official/vision/beta/modeling/heads/segmentation_heads.py
View file @ 790e49e5
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -11,8 +11,8 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Segmentation heads."""
"""Contains definitions of segmentation heads."""
import tensorflow as tf
......@@ -23,7 +23,7 @@ from official.vision.beta.ops import spatial_transform_ops
@tf.keras.utils.register_keras_serializable(package='Vision')
class SegmentationHead(tf.keras.layers.Layer):
"""Segmentation head."""
"""Creates a segmentation head."""
def __init__(self,
num_classes,
......@@ -41,38 +41,37 @@ class SegmentationHead(tf.keras.layers.Layer):
kernel_regularizer=None,
bias_regularizer=None,
**kwargs):
"""Initialize params to build segmentation head.
"""Initializes a segmentation head.
Args:
num_classes: `int` number of mask classification categories. The number of
classes does not include background class.
level: `int` or `str`, level to use to build segmentation head.
num_convs: `int` number of stacked convolution before the last prediction
layer.
num_filters: `int` number to specify the number of filters used.
num_classes: An `int` number of mask classification categories. The number
of classes does not include background class.
level: An `int` or `str`, level to use to build segmentation head.
num_convs: An `int` number of stacked convolution before the last
prediction layer.
num_filters: An `int` number to specify the number of filters used.
Default is 256.
upsample_factor: `int` number to specify the upsampling factor to generate
finer mask. Default 1 means no upsampling is applied.
upsample_factor: An `int` number to specify the upsampling factor to
generate finer mask. Default 1 means no upsampling is applied.
feature_fusion: One of `deeplabv3plus`, `pyramid_fusion`, or None. If
`deeplabv3plus`, features from decoder_features[level] will be fused
with low level feature maps from backbone. If `pyramid_fusion`,
multiscale features will be resized and fused at the target level.
low_level: `int`, backbone level to be used for feature fusion. This arg
is used when feature_fusion is set to deeplabv3plus.
low_level_num_filters: `int`, reduced number of filters for the low
level features before fusing it with higher level features. This args is
only used when feature_fusion is set to deeplabv3plus.
activation: `string`, indicating which activation is used, e.g. 'relu',
low_level: An `int` of backbone level to be used for feature fusion. It is
used when feature_fusion is set to `deeplabv3plus`.
low_level_num_filters: An `int` of reduced number of filters for the low
level features before fusing it with higher level features. It is only
used when feature_fusion is set to `deeplabv3plus`.
activation: A `str` that indicates which activation is used, e.g. 'relu',
'swish', etc.
use_sync_bn: `bool`, whether to use synchronized batch normalization
across different replicas.
norm_momentum: `float`, the momentum parameter of the normalization
layers.
norm_epsilon: `float`, the epsilon parameter of the normalization layers.
kernel_regularizer: `tf.keras.regularizers.Regularizer` object for layer
kernel.
bias_regularizer: `tf.keras.regularizers.Regularizer` object for bias.
**kwargs: other keyword arguments passed to Layer.
use_sync_bn: A `bool` that indicates whether to use synchronized batch
normalization across different replicas.
norm_momentum: A `float` of normalization momentum for the moving average.
norm_epsilon: A `float` added to variance to avoid dividing by zero.
kernel_regularizer: A `tf.keras.regularizers.Regularizer` object for
Conv2D. Default is None.
bias_regularizer: A `tf.keras.regularizers.Regularizer` object for Conv2D.
**kwargs: Additional keyword arguments to be passed.
"""
super(SegmentationHead, self).__init__(**kwargs)
......@@ -160,17 +159,17 @@ class SegmentationHead(tf.keras.layers.Layer):
"""Forward pass of the segmentation head.
Args:
backbone_output: a dict of tensors
- key: `str`, the level of the multilevel features.
- values: `Tensor`, the feature map tensors, whose shape is
backbone_output: A `dict` of tensors
- key: A `str` of the level of the multilevel features.
- values: A `tf.Tensor` of the feature map tensors, whose shape is
[batch, height_l, width_l, channels].
decoder_output: a dict of tensors
- key: `str`, the level of the multilevel features.
- values: `Tensor`, the feature map tensors, whose shape is
decoder_output: A `dict` of tensors
- key: A `str` of the level of the multilevel features.
- values: A `tf.Tensor` of the feature map tensors, whose shape is
[batch, height_l, width_l, channels].
Returns:
segmentation prediction mask: `Tensor`, the segmentation mask scores
predicted from input feature.
segmentation prediction mask: A `tf.Tensor` of the segmentation mask
scores predicted from input features.
"""
if self._config_dict['feature_fusion'] == 'deeplabv3plus':
# deeplabv3+ feature fusion
......
official/vision/beta/modeling/heads/segmentation_heads_test.py
View file @ 790e49e5
# Lint as: python3
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -12,7 +11,8 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# Lint as: python3
"""Tests for segmentation_heads.py."""
# Import libraries
......
official/vision/beta/modeling/layers/__init__.py
View file @ 790e49e5
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""Layers package definition."""
from official.vision.beta.modeling.layers.box_sampler import BoxSampler
from official.vision.beta.modeling.layers.detection_generator import DetectionGenerator
from official.vision.beta.modeling.layers.detection_generator import MultilevelDetectionGenerator
from official.vision.beta.modeling.layers.mask_sampler import MaskSampler
from official.vision.beta.modeling.layers.nn_blocks import BottleneckBlock
from official.vision.beta.modeling.layers.nn_blocks import BottleneckResidualInner
from official.vision.beta.modeling.layers.nn_blocks import DepthwiseSeparableConvBlock
from official.vision.beta.modeling.layers.nn_blocks import InvertedBottleneckBlock
from official.vision.beta.modeling.layers.nn_blocks import ResidualBlock
from official.vision.beta.modeling.layers.nn_blocks import ResidualInner
from official.vision.beta.modeling.layers.nn_blocks import ReversibleLayer
from official.vision.beta.modeling.layers.nn_blocks_3d import BottleneckBlock3D
from official.vision.beta.modeling.layers.nn_blocks_3d import SelfGating
from official.vision.beta.modeling.layers.nn_layers import CausalConvMixin
from official.vision.beta.modeling.layers.nn_layers import Conv2D
from official.vision.beta.modeling.layers.nn_layers import Conv3D
from official.vision.beta.modeling.layers.nn_layers import DepthwiseConv2D
from official.vision.beta.modeling.layers.nn_layers import GlobalAveragePool3D
from official.vision.beta.modeling.layers.nn_layers import PositionalEncoding
from official.vision.beta.modeling.layers.nn_layers import Scale
from official.vision.beta.modeling.layers.nn_layers import SpatialAveragePool3D
from official.vision.beta.modeling.layers.nn_layers import SqueezeExcitation
from official.vision.beta.modeling.layers.nn_layers import StochasticDepth
from official.vision.beta.modeling.layers.nn_layers import TemporalSoftmaxPool
from official.vision.beta.modeling.layers.roi_aligner import MultilevelROIAligner
from official.vision.beta.modeling.layers.roi_generator import MultilevelROIGenerator
from official.vision.beta.modeling.layers.roi_sampler import ROISampler
official/vision/beta/modeling/layers/box_sampler.py
View file @ 790e49e5
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -11,7 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Contains definitions of box sampler."""
# Import libraries
......
official/vision/beta/modeling/layers/detection_generator.py
View file @ 790e49e5
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -11,7 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Contains definitions of generators to generate the final detections."""
# Import libraries
......
official/vision/beta/modeling/layers/detection_generator_test.py
View file @ 790e49e5
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -11,7 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for detection_generator.py."""
# Import libraries
......
official/vision/beta/modeling/layers/mask_sampler.py
View file @ 790e49e5
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -11,7 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Contains definitions of mask sampler."""
# Import libraries
......
official/vision/beta/modeling/layers/nn_blocks.py
View file @ 790e49e5
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -11,7 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Contains common building blocks for neural networks."""
from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Text
......
official/vision/beta/modeling/layers/nn_blocks_3d.py
View file @ 790e49e5
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -11,7 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Contains common building blocks for 3D networks."""
# Import libraries
import tensorflow as tf
......@@ -210,7 +210,7 @@ class BottleneckBlock3D(tf.keras.layers.Layer):
'temporal_kernel_size': self._temporal_kernel_size,
'temporal_strides': self._temporal_strides,
'spatial_strides': self._spatial_strides,
'use_projection': self._use_projection,
'use_self_gating': self._use_self_gating,
'kernel_initializer': self._kernel_initializer,
'kernel_regularizer': self._kernel_regularizer,
'bias_regularizer': self._bias_regularizer,
......
official/vision/beta/modeling/layers/nn_blocks_3d_test.py
View file @ 790e49e5
# Lint as: python3
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -12,7 +11,8 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# Lint as: python3
"""Tests for resnet."""
# Import libraries
......
official/vision/beta/modeling/layers/nn_blocks_test.py
View file @ 790e49e5
# Lint as: python3
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -12,7 +11,8 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# Lint as: python3
"""Tests for nn_blocks."""
from typing import Any, Iterable, Tuple
......
official/vision/beta/modeling/layers/nn_layers.py
View file @ 790e49e5
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -11,7 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Contains common building blocks for neural networks."""
from typing import Callable, Dict, List, Optional, Tuple, Union
......@@ -232,7 +232,7 @@ class StochasticDepth(tf.keras.layers.Layer):
batch_size = tf.shape(inputs)[0]
random_tensor = keep_prob
random_tensor += tf.random.uniform(
[batch_size, 1, 1, 1], dtype=inputs.dtype)
[batch_size] + [1] * (inputs.shape.rank - 1), dtype=inputs.dtype)
binary_tensor = tf.floor(random_tensor)
output = tf.math.divide(inputs, keep_prob) * binary_tensor
return output
......@@ -590,7 +590,7 @@ class GlobalAveragePool3D(tf.keras.layers.Layer):
# regular global average pooling.
# Shape: [batch_size, 1, 1, 1, channels]
x = tf.reduce_sum(inputs, axis=(1, 2, 3), keepdims=True)
x = x / tf.cast(inputs.shape[2] * inputs.shape[3], x.dtype)
x = x / tf.cast(tf.shape(inputs)[2] * tf.shape(inputs)[3], x.dtype)
x = x + buffer
# Shape: [batch_size, 1, 1, 1, channels]
......@@ -713,7 +713,7 @@ class CausalConvMixin:
# When buffer padding, use 'valid' padding across time. The output shape
# across time should be the input shape minus any padding, assuming
# the stride across time is 1.
if self._use_buffered_input:
if self._use_buffered_input and spatial_output_shape[0] is not None:
padding = self._compute_buffered_causal_padding(use_buffered_input=False)
spatial_output_shape[0] -= sum(padding[1])
return spatial_output_shape
......
official/vision/beta/modeling/layers/nn_layers_test.py
View file @ 790e49e5
# Lint as: python3
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -12,7 +11,8 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# Lint as: python3
"""Tests for nn_layers."""
# Import libraries
......
official/vision/beta/modeling/layers/roi_aligner.py
View file @ 790e49e5
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -11,7 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Contains definitions of ROI aligner."""
import tensorflow as tf
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment