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official/vision/beta/projects/mosaic/modeling/mosaic_blocks.py deleted 100644 → 0
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# Copyright 2022 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.
"""Definitions of building blocks for MOSAIC model.
Reference:
[MOSAIC: Mobile Segmentation via decoding Aggregated Information and encoded
Context](https://arxiv.org/pdf/2112.11623.pdf)
"""
from typing import Any, Dict, List, Optional, Tuple
import tensorflow as tf
from official.modeling import tf_utils
@tf.keras.utils.register_keras_serializable(package='Vision')
class MultiKernelGroupConvBlock(tf.keras.layers.Layer):
"""A multi-kernel grouped convolution block.
This block is used in the segmentation neck introduced in MOSAIC.
Reference:
[MOSAIC: Mobile Segmentation via decoding Aggregated Information and encoded
Context](https://arxiv.org/pdf/2112.11623.pdf)
"""
def __init__(
self,
output_filter_depths: List[int],
kernel_sizes: List[int],
use_sync_bn: bool = False,
batchnorm_momentum: float = 0.99,
batchnorm_epsilon: float = 0.001,
activation: str = 'relu',
dropout: float = 0.5,
kernel_initializer: str = 'GlorotUniform',
kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
use_depthwise_convolution: bool = True,
**kwargs):
"""Initializes a Multi-kernel Grouped Convolution Block.
Args:
output_filter_depths: A list of integers representing the numbers of
output channels or filter depths of convolution groups.
kernel_sizes: A list of integers denoting the convolution kernel sizes in
each convolution group.
use_sync_bn: A bool, whether or not to use sync batch normalization.
batchnorm_momentum: A float for the momentum in BatchNorm. Defaults to
0.99.
batchnorm_epsilon: A float for the epsilon value in BatchNorm. Defaults to
0.001.
activation: A `str` for the activation fuction type. Defaults to 'relu'.
dropout: A float for the dropout rate before output. Defaults to 0.5.
kernel_initializer: Kernel initializer for conv layers. Defaults to
`glorot_uniform`.
kernel_regularizer: Kernel regularizer for conv layers. Defaults to None.
use_depthwise_convolution: Allows spatial pooling to be separable
depthwise convolusions.
**kwargs: Other keyword arguments for the layer.
"""
super(MultiKernelGroupConvBlock, self).__init__(**kwargs)
if len(output_filter_depths) != len(kernel_sizes):
raise ValueError('The number of output groups must match #kernels.')
self._output_filter_depths = output_filter_depths
self._kernel_sizes = kernel_sizes
self._num_groups = len(self._kernel_sizes)
self._use_sync_bn = use_sync_bn
self._batchnorm_momentum = batchnorm_momentum
self._batchnorm_epsilon = batchnorm_epsilon
self._activation = activation
self._dropout = dropout
self._kernel_initializer = kernel_initializer
self._kernel_regularizer = kernel_regularizer
self._use_depthwise_convolution = use_depthwise_convolution
# To apply BN before activation. Putting BN between conv and activation also
# helps quantization where conv+bn+activation are fused into a single op.
self._activation_fn = tf_utils.get_activation(activation)
if self._use_sync_bn:
self._bn_op = tf.keras.layers.experimental.SyncBatchNormalization
else:
self._bn_op = tf.keras.layers.BatchNormalization
if tf.keras.backend.image_data_format() == 'channels_last':
self._bn_axis = -1
self._group_split_axis = -1
else:
self._bn_axis = 1
self._group_split_axis = 1
def build(self, input_shape: List[int]) -> None:
"""Builds the block with the given input shape."""
input_channels = input_shape[self._group_split_axis]
if input_channels % self._num_groups != 0:
raise ValueError('The number of input channels must be divisible by '
'the number of groups for evenly group split.')
self._conv_branches = []
if self._use_depthwise_convolution:
for i, conv_kernel_size in enumerate(self._kernel_sizes):
depthwise_conv = tf.keras.layers.DepthwiseConv2D(
kernel_size=(conv_kernel_size, conv_kernel_size),
depth_multiplier=1,
padding='same',
depthwise_regularizer=self._kernel_regularizer,
depthwise_initializer=self._kernel_initializer,
use_bias=False)
feature_conv = tf.keras.layers.Conv2D(
filters=self._output_filter_depths[i],
kernel_size=(1, 1),
padding='same',
kernel_regularizer=self._kernel_regularizer,
kernel_initializer=self._kernel_initializer,
activation=None,
use_bias=False)
batchnorm_op = self._bn_op(
axis=self._bn_axis,
momentum=self._batchnorm_momentum,
epsilon=self._batchnorm_epsilon)
# Use list manually as current QAT API does not support sequential model
# within a tf.keras.Sequential block, e.g. conv_branch =
# tf.keras.Sequential([depthwise_conv, feature_conv, batchnorm_op,])
conv_branch = [depthwise_conv, feature_conv, batchnorm_op]
self._conv_branches.append(conv_branch)
else:
for i, conv_kernel_size in enumerate(self._kernel_sizes):
norm_conv = tf.keras.layers.Conv2D(
filters=self._output_filter_depths[i],
kernel_size=(conv_kernel_size, conv_kernel_size),
padding='same',
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
activation=None,
use_bias=False)
batchnorm_op = self._bn_op(
axis=self._bn_axis,
momentum=self._batchnorm_momentum,
epsilon=self._batchnorm_epsilon)
conv_branch = [norm_conv, batchnorm_op]
self._conv_branches.append(conv_branch)
self._concat_groups = tf.keras.layers.Concatenate(
axis=self._group_split_axis)
def call(self, inputs: tf.Tensor) -> tf.Tensor:
"""Calls this group convolution block with the given inputs."""
inputs_splits = tf.split(inputs,
num_or_size_splits=self._num_groups,
axis=self._group_split_axis)
output_branches = []
for i, x in enumerate(inputs_splits):
conv_branch = self._conv_branches[i]
# Apply layers sequentially and manually.
for layer in conv_branch:
x = layer(x)
# Apply activation function after BN, which also helps quantization
# where conv+bn+activation are fused into a single op.
x = self._activation_fn(x)
output_branches.append(x)
x = self._concat_groups(output_branches)
return x
def get_config(self) -> Dict[str, Any]:
"""Returns a config dictionary for initialization from serialization."""
config = {
'output_filter_depths': self._output_filter_depths,
'kernel_sizes': self._kernel_sizes,
'num_groups': self._num_groups,
'use_sync_bn': self._use_sync_bn,
'batchnorm_momentum': self._batchnorm_momentum,
'batchnorm_epsilon': self._batchnorm_epsilon,
'activation': self._activation,
'dropout': self._dropout,
'kernel_initializer': self._kernel_initializer,
'kernel_regularizer': self._kernel_regularizer,
'use_depthwise_convolution': self._use_depthwise_convolution,
}
base_config = super(MultiKernelGroupConvBlock, self).get_config()
base_config.update(config)
return base_config
@tf.keras.utils.register_keras_serializable(package='Vision')
class MosaicEncoderBlock(tf.keras.layers.Layer):
"""Implements the encoder module/block of MOSAIC model.
Spatial Pyramid Pooling and Multi-kernel Conv layer
SpatialPyramidPoolingMultiKernelConv
References:
[MOSAIC: Mobile Segmentation via decoding Aggregated Information and encoded
context](https://arxiv.org/pdf/2112.11623.pdf)
"""
def __init__(
self,
branch_filter_depths: List[int],
conv_kernel_sizes: List[int],
pyramid_pool_bin_nums: List[int],
use_sync_bn: bool = False,
batchnorm_momentum: float = 0.99,
batchnorm_epsilon: float = 0.001,
activation: str = 'relu',
dropout: float = 0.5,
kernel_initializer: str = 'glorot_uniform',
kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
interpolation: str = 'bilinear',
use_depthwise_convolution: bool = True,
**kwargs):
"""Initializes a MOSAIC encoder block which is deployed after a backbone.
Args:
branch_filter_depths: A list of integers for the number of convolution
channels in each branch at a pyramid level after SpatialPyramidPooling.
conv_kernel_sizes: A list of integers representing the convolution kernel
sizes in the Multi-kernel Convolution blocks in the encoder.
pyramid_pool_bin_nums: A list of integers for the number of bins at each
level of the Spatial Pyramid Pooling.
use_sync_bn: A bool, whether or not to use sync batch normalization.
batchnorm_momentum: A float for the momentum in BatchNorm. Defaults to
0.99.
batchnorm_epsilon: A float for the epsilon value in BatchNorm. Defaults to
0.001.
activation: A `str` for the activation function type. Defaults to 'relu'.
dropout: A float for the dropout rate before output. Defaults to 0.5.
kernel_initializer: Kernel initializer for conv layers. Defaults to
`glorot_uniform`.
kernel_regularizer: Kernel regularizer for conv layers. Defaults to None.
interpolation: The interpolation method for upsampling. Defaults to
`bilinear`.
use_depthwise_convolution: Use depthwise separable convolusions in the
Multi-kernel Convolution blocks in the encoder.
**kwargs: Other keyword arguments for the layer.
"""
super().__init__(**kwargs)
self._branch_filter_depths = branch_filter_depths
self._conv_kernel_sizes = conv_kernel_sizes
self._pyramid_pool_bin_nums = pyramid_pool_bin_nums
self._use_sync_bn = use_sync_bn
self._batchnorm_momentum = batchnorm_momentum
self._batchnorm_epsilon = batchnorm_epsilon
self._activation = activation
self._dropout = dropout
self._kernel_initializer = kernel_initializer
self._kernel_regularizer = kernel_regularizer
self._interpolation = interpolation
self._use_depthwise_convolution = use_depthwise_convolution
self._activation_fn = tf_utils.get_activation(activation)
if self._use_sync_bn:
self._bn_op = tf.keras.layers.experimental.SyncBatchNormalization
else:
self._bn_op = tf.keras.layers.BatchNormalization
if tf.keras.backend.image_data_format() == 'channels_last':
self._bn_axis = -1
self._channel_axis = -1
else:
self._bn_axis = 1
self._channel_axis = 1
def get_bin_pool_kernel_and_stride(
self,
input_size: int,
num_of_bin: int) -> Tuple[int, int]:
"""Calculates the kernel size and stride for spatial bin pooling.
Args:
input_size: Input dimension (a scalar).
num_of_bin: The number of bins used for spatial bin pooling.
Returns:
The Kernel and Stride for spatial bin pooling (a scalar).
"""
bin_overlap = int(input_size % num_of_bin)
pooling_stride = int(input_size // num_of_bin)
pooling_kernel = pooling_stride + bin_overlap
return pooling_kernel, pooling_stride
def build(self, input_shape: List[int]) -> None:
"""Builds this MOSAIC encoder block with the given input shape."""
self._data_format = tf.keras.backend.image_data_format()
if self._data_format == 'channels_last':
height = input_shape[1]
width = input_shape[2]
else:
height = input_shape[2]
width = input_shape[3]
self._global_pool_branch = None
self._spatial_pyramid = []
for pyramid_pool_bin_num in self._pyramid_pool_bin_nums:
if pyramid_pool_bin_num == 1:
global_pool = tf.keras.layers.GlobalAveragePooling2D(
data_format=self._data_format, keepdims=True)
global_projection = tf.keras.layers.Conv2D(
filters=max(self._branch_filter_depths),
kernel_size=(1, 1),
padding='same',
activation=None,
kernel_regularizer=self._kernel_regularizer,
kernel_initializer=self._kernel_initializer,
use_bias=False)
batch_norm_global_branch = self._bn_op(
axis=self._bn_axis,
momentum=self._batchnorm_momentum,
epsilon=self._batchnorm_epsilon)
# Use list manually instead of tf.keras.Sequential([])
self._global_pool_branch = [
global_pool,
global_projection,
batch_norm_global_branch,
]
else:
if height < pyramid_pool_bin_num or width < pyramid_pool_bin_num:
raise ValueError('The number of pooling bins must be smaller than '
'input sizes.')
assert pyramid_pool_bin_num >= 2, (
'Except for the gloabl pooling, the number of bins in pyramid '
'pooling must be at least two.')
pool_height, stride_height = self.get_bin_pool_kernel_and_stride(
height, pyramid_pool_bin_num)
pool_width, stride_width = self.get_bin_pool_kernel_and_stride(
width, pyramid_pool_bin_num)
bin_pool_level = tf.keras.layers.AveragePooling2D(
pool_size=(pool_height, pool_width),
strides=(stride_height, stride_width),
padding='valid',
data_format=self._data_format)
self._spatial_pyramid.append(bin_pool_level)
# Grouped multi-kernel Convolution.
self._multi_kernel_group_conv = MultiKernelGroupConvBlock(
output_filter_depths=self._branch_filter_depths,
kernel_sizes=self._conv_kernel_sizes,
use_sync_bn=self._use_sync_bn,
batchnorm_momentum=self._batchnorm_momentum,
batchnorm_epsilon=self._batchnorm_epsilon,
activation=self._activation,
dropout=self._dropout,
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
use_depthwise_convolution=self._use_depthwise_convolution)
# Encoder's final 1x1 feature projection.
# Considering the relatively large #channels merged before projection,
# enlarge the projection #channels to the sum of the filter depths of
# branches.
self._output_channels = sum(self._branch_filter_depths)
# Use list manually instead of tf.keras.Sequential([]).
self._encoder_projection = [
tf.keras.layers.Conv2D(
filters=self._output_channels,
kernel_size=(1, 1),
padding='same',
activation=None,
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
use_bias=False),
self._bn_op(
axis=self._bn_axis,
momentum=self._batchnorm_momentum,
epsilon=self._batchnorm_epsilon),
]
# Use the TF2 default feature alignment rule for bilinear resizing.
self._upsample = tf.keras.layers.Resizing(
height, width, interpolation='bilinear', crop_to_aspect_ratio=False)
self._dropout_layer = tf.keras.layers.Dropout(rate=self._dropout)
self._concat_layer = tf.keras.layers.Concatenate(axis=self._channel_axis)
def call(self,
inputs: tf.Tensor,
training: Optional[bool] = None) -> tf.Tensor:
"""Calls this MOSAIC encoder block with the given input."""
if training is None:
training = tf.keras.backend.learning_phase()
branches = []
for bin_pool_level in self._spatial_pyramid:
x = inputs
x = bin_pool_level(x)
x = self._multi_kernel_group_conv(x)
x = self._upsample(x)
branches.append(x)
if self._global_pool_branch is not None:
x = inputs
for layer in self._global_pool_branch:
x = layer(x)
x = self._activation_fn(x)
x = self._upsample(x)
branches.append(x)
x = self._concat_layer(branches)
for layer in self._encoder_projection:
x = layer(x)
x = self._activation_fn(x)
return x
def get_config(self) -> Dict[str, Any]:
"""Returns a config dictionary for initialization from serialization."""
config = {
'branch_filter_depths': self._branch_filter_depths,
'conv_kernel_sizes': self._conv_kernel_sizes,
'pyramid_pool_bin_nums': self._pyramid_pool_bin_nums,
'use_sync_bn': self._use_sync_bn,
'batchnorm_momentum': self._batchnorm_momentum,
'batchnorm_epsilon': self._batchnorm_epsilon,
'activation': self._activation,
'dropout': self._dropout,
'kernel_initializer': self._kernel_initializer,
'kernel_regularizer': self._kernel_regularizer,
'interpolation': self._interpolation,
'use_depthwise_convolution': self._use_depthwise_convolution,
}
base_config = super().get_config()
base_config.update(config)
return base_config
official/vision/beta/projects/mosaic/modeling/mosaic_blocks_test.py deleted 100644 → 0
View file @ c3d1001c
# Copyright 2022 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
"""Tests for mosaic_blocks."""
# Import libraries
from absl.testing import parameterized
import tensorflow as tf
from official.vision.beta.projects.mosaic.modeling import mosaic_blocks
class MosaicBlocksTest(parameterized.TestCase, tf.test.TestCase):
def test_multi_kernel_group_conv_block(self):
block = mosaic_blocks.MultiKernelGroupConvBlock([64, 64], [3, 5])
inputs = tf.ones([1, 4, 4, 448])
outputs = block(inputs)
self.assertAllEqual(outputs.shape, [1, 4, 4, 128])
def test_mosaic_encoder_block(self):
block = mosaic_blocks.MosaicEncoderBlock([64, 64], [3, 5], [1, 4, 8, 16])
inputs = tf.ones([1, 32, 32, 448])
outputs = block(inputs)
self.assertAllEqual(outputs.shape, [1, 32, 32, 128])
def test_mosaic_encoder_block_odd_input_overlap_pool(self):
block = mosaic_blocks.MosaicEncoderBlock([64, 64], [3, 5], [1, 4, 8, 16])
inputs = tf.ones([1, 31, 31, 448])
outputs = block(inputs)
self.assertAllEqual(outputs.shape, [1, 31, 31, 128])
def test_mosaic_encoder_non_separable_block(self):
block = mosaic_blocks.MosaicEncoderBlock([64, 64], [3, 5], [1, 4, 8, 16],
use_depthwise_convolution=False)
inputs = tf.ones([1, 32, 32, 448])
outputs = block(inputs)
self.assertAllEqual(outputs.shape, [1, 32, 32, 128])
if __name__ == '__main__':
tf.test.main()
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