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models/official/vision/modeling/backbones/resnet_unet.py

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# Copyright 2025 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.
"""Contains definitions of ResNet UNet style."""
from typing import Callable, Optional, List
import tensorflow as tf, tf_keras
from official.modeling import hyperparams
from official.modeling import tf_utils
from official.vision.modeling.backbones import factory
from official.vision.modeling.layers import nn_blocks
from official.vision.modeling.layers import nn_layers
from official.vision.ops import spatial_transform_ops
layers = tf_keras.layers
# Specifications for different ResNet variants.
# Each entry specifies block configurations of the particular ResNet variant.
# Each element in the block configuration is in the following format:
# (block_fn, num_filters, block_repeats)
RESNET_SPECS = {
10: [
('residual', 64, 1),
('residual', 128, 1),
('residual', 256, 1),
('residual', 512, 1),
],
18: [
('residual', 64, 2),
('residual', 128, 2),
('residual', 256, 2),
('residual', 512, 2),
],
34: [
('residual', 64, 3),
('residual', 128, 4),
('residual', 256, 6),
('residual', 512, 3),
],
50: [
('bottleneck', 64, 3),
('bottleneck', 128, 4),
('bottleneck', 256, 6),
('bottleneck', 512, 3),
],
101: [
('bottleneck', 64, 3),
('bottleneck', 128, 4),
('bottleneck', 256, 23),
('bottleneck', 512, 3),
],
152: [
('bottleneck', 64, 3),
('bottleneck', 128, 8),
('bottleneck', 256, 36),
('bottleneck', 512, 3),
],
200: [
('bottleneck', 64, 3),
('bottleneck', 128, 24),
('bottleneck', 256, 36),
('bottleneck', 512, 3),
],
270: [
('bottleneck', 64, 4),
('bottleneck', 128, 29),
('bottleneck', 256, 53),
('bottleneck', 512, 4),
],
350: [
('bottleneck', 64, 4),
('bottleneck', 128, 36),
('bottleneck', 256, 72),
('bottleneck', 512, 4),
],
420: [
('bottleneck', 64, 4),
('bottleneck', 128, 44),
('bottleneck', 256, 87),
('bottleneck', 512, 4),
],
}
def conv_2d(*args, **kwargs):
return tf_keras.layers.Conv2D(
kernel_initializer=tf_keras.initializers.truncated_normal(stddev=0.02),
bias_initializer='zeros',
*args,
**kwargs,
)
def dense(*args, **kwargs):
return tf_keras.layers.Dense(
kernel_initializer=tf_keras.initializers.truncated_normal(stddev=0.02),
bias_initializer='zeros',
*args,
**kwargs,
)
class ConvNeXtBlock(tf_keras.Model):
"""ConvNeXt block."""
def __init__(
self,
dim,
drop_rate=0.0,
layer_scale_init_value=1e-6,
norm_fn=None,
kernel_size=7,
se_ratio=0.0625,
**kwargs,
):
super().__init__(**kwargs)
self.depthwise_conv = tf_keras.layers.DepthwiseConv2D(
kernel_size=kernel_size,
padding='same',
depthwise_initializer=tf_keras.initializers.truncated_normal(
stddev=0.02
),
bias_initializer='zeros',
)
if norm_fn:
self.norm = norm_fn()
else:
self.norm = tf_keras.layers.LayerNormalization(epsilon=1e-6)
self.pointwise_conv1 = dense(4 * dim)
self.act = tf_keras.layers.Activation('gelu')
self.pointwise_conv2 = dense(dim)
if layer_scale_init_value > 0:
self.gamma = self.add_weight(
name='layer_scale',
shape=(1, 1, 1, dim),
initializer=tf_keras.initializers.Constant(layer_scale_init_value))
else:
self.gamma = None
self.drop_path = nn_layers.StochasticDepth(
drop_rate
) if drop_rate > 0 else tf_keras.layers.Activation('linear')
if se_ratio and se_ratio > 0 and se_ratio <= 1:
self._squeeze_excitation = nn_layers.SqueezeExcitation(
activation='gelu',
in_filters=4 * dim,
out_filters=4 * dim,
se_ratio=se_ratio,)
else:
self._squeeze_excitation = None
def call(self, x, training=None):
inputs = x
x = self.depthwise_conv(x)
x = self.norm(x)
x = self.pointwise_conv1(x)
x = self.act(x)
if self._squeeze_excitation:
x = self._squeeze_excitation(x)
x = self.pointwise_conv2(x)
if self.gamma is not None:
x = self.gamma * x
x = inputs + self.drop_path(x, training=training)
return x
@tf_keras.utils.register_keras_serializable(package='Vision')
class ResNetUNet(tf_keras.Model):
"""Creates ResNet and ResNet-RS family models.
This implements the Deep Residual Network from:
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
Deep Residual Learning for Image Recognition.
(https://arxiv.org/pdf/1512.03385) and
Irwan Bello, William Fedus, Xianzhi Du, Ekin D. Cubuk, Aravind Srinivas,
Tsung-Yi Lin, Jonathon Shlens, Barret Zoph.
Revisiting ResNets: Improved Training and Scaling Strategies.
(https://arxiv.org/abs/2103.07579).
"""
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,
upsample_repeats: Optional[List[int]] = None,
upsample_filters: Optional[List[int]] = None,
upsample_kernel_sizes: Optional[List[int]] = None,
scale_stem: bool = True,
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,
bn_trainable: bool = True,
classification_output: bool = False,
**kwargs):
"""Initializes a ResNet model.
Args:
model_id: An `int` of the depth of ResNet backbone model.
input_specs: A `tf_keras.layers.InputSpec` of the input tensor.
depth_multiplier: A `float` of the depth multiplier to uniformaly scale up
all layers in channel size. This argument is also referred to as
`width_multiplier` in (https://arxiv.org/abs/2103.07579).
stem_type: A `str` of stem type of ResNet. Default to `v0`. If set to
`v1`, use ResNet-D type stem (https://arxiv.org/abs/1812.01187).
resnetd_shortcut: A `bool` of whether to use ResNet-D shortcut in
downsampling blocks.
replace_stem_max_pool: A `bool` of whether to replace the max pool in stem
with a stride-2 conv,
se_ratio: A `float` or None. Ratio of the Squeeze-and-Excitation layer.
init_stochastic_depth_rate: A `float` of initial stochastic depth rate.
upsample_repeats: A `list` for upsample repeats of the ConvNext blocks for
each level starting from L5, then L4, and so on.
upsample_filters: A `list` for the upsample filter sizes for the ConvNext
blocks for each level.
upsample_kernel_sizes: A `list` for upsample kernel sizes for the ConvNext
blocks for each level.
scale_stem: A `bool` of whether to scale stem layers.
activation: A `str` name of the activation function.
use_sync_bn: If True, use synchronized batch normalization.
norm_momentum: A `float` of normalization momentum for the moving average.
norm_epsilon: A small `float` added to variance to avoid dividing by zero.
kernel_initializer: A str for kernel initializer of convolutional layers.
kernel_regularizer: A `tf_keras.regularizers.Regularizer` object for
Conv2D. Default to None.
bias_regularizer: A `tf_keras.regularizers.Regularizer` object for Conv2D.
Default to None.
bn_trainable: A `bool` that indicates whether batch norm layers should be
trainable. Default to True.
classification_output: A `bool` to output the correct level needed for
classification (L3), only set to True for pretraining.
**kwargs: Additional keyword arguments to be passed.
"""
self._model_id = model_id
self._input_specs = input_specs
self._depth_multiplier = depth_multiplier
self._stem_type = stem_type
self._resnetd_shortcut = resnetd_shortcut
self._replace_stem_max_pool = replace_stem_max_pool
self._se_ratio = se_ratio
self._init_stochastic_depth_rate = init_stochastic_depth_rate
self._upsample_repeats = upsample_repeats
self._upsample_filters = upsample_filters
self._upsample_kernel_sizes = upsample_kernel_sizes
self._scale_stem = scale_stem
self._use_sync_bn = use_sync_bn
self._activation = activation
self._norm_momentum = norm_momentum
self._norm_epsilon = norm_epsilon
if use_sync_bn:
self._norm = layers.experimental.SyncBatchNormalization
else:
self._norm = layers.BatchNormalization
self._kernel_initializer = kernel_initializer
self._kernel_regularizer = kernel_regularizer
self._bias_regularizer = bias_regularizer
self._bn_trainable = bn_trainable
self._classification_output = classification_output
if tf_keras.backend.image_data_format() == 'channels_last':
bn_axis = -1
else:
bn_axis = 1
# Build ResNet.
inputs = tf_keras.Input(shape=input_specs.shape[1:])
stem_depth_multiplier = self._depth_multiplier if scale_stem else 1.0
if stem_type == 'v0':
x = layers.Conv2D(
filters=int(64 * stem_depth_multiplier),
kernel_size=7,
strides=2,
use_bias=False,
padding='same',
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)(
inputs)
x = self._norm(
axis=bn_axis,
momentum=norm_momentum,
epsilon=norm_epsilon,
trainable=bn_trainable)(
x)
x = tf_utils.get_activation(activation, use_keras_layer=True)(x)
elif stem_type == 'v1':
x = layers.Conv2D(
filters=int(32 * stem_depth_multiplier),
kernel_size=3,
strides=2,
use_bias=False,
padding='same',
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)(
inputs)
x = self._norm(
axis=bn_axis,
momentum=norm_momentum,
epsilon=norm_epsilon,
trainable=bn_trainable)(
x)
x = tf_utils.get_activation(activation, use_keras_layer=True)(x)
x = layers.Conv2D(
filters=int(32 * stem_depth_multiplier),
kernel_size=3,
strides=1,
use_bias=False,
padding='same',
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)(
x)
x = self._norm(
axis=bn_axis,
momentum=norm_momentum,
epsilon=norm_epsilon,
trainable=bn_trainable)(
x)
x = tf_utils.get_activation(activation, use_keras_layer=True)(x)
x = layers.Conv2D(
filters=int(64 * stem_depth_multiplier),
kernel_size=3,
strides=1,
use_bias=False,
padding='same',
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)(
x)
x = self._norm(
axis=bn_axis,
momentum=norm_momentum,
epsilon=norm_epsilon,
trainable=bn_trainable)(
x)
x = tf_utils.get_activation(activation, use_keras_layer=True)(x)
else:
raise ValueError('Stem type {} not supported.'.format(stem_type))
if replace_stem_max_pool:
x = layers.Conv2D(
filters=int(64 * self._depth_multiplier),
kernel_size=3,
strides=2,
use_bias=False,
padding='same',
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)(
x)
x = self._norm(
axis=bn_axis,
momentum=norm_momentum,
epsilon=norm_epsilon,
trainable=bn_trainable)(
x)
x = tf_utils.get_activation(activation, use_keras_layer=True)(x)
else:
x = layers.MaxPool2D(pool_size=3, strides=2, padding='same')(x)
endpoints = {}
for i, spec in enumerate(RESNET_SPECS[model_id]):
if spec[0] == 'residual':
block_fn = nn_blocks.ResidualBlock
elif spec[0] == 'bottleneck':
block_fn = nn_blocks.BottleneckBlock
else:
raise ValueError('Block fn `{}` is not supported.'.format(spec[0]))
x = self._block_group(
inputs=x,
filters=int(spec[1] * self._depth_multiplier),
strides=(1 if i == 0 else 2),
block_fn=block_fn,
block_repeats=spec[2],
stochastic_depth_drop_rate=nn_layers.get_stochastic_depth_rate(
self._init_stochastic_depth_rate, i + 2, 8),
name='block_group_l{}'.format(i + 2))
endpoints[str(i + 2)] = x
norm_layer = lambda: tf_keras.layers.LayerNormalization(epsilon=1e-6)
for i in range(len(upsample_filters)):
backbone_feature = layers.Conv2D(
filters=int(upsample_filters[i] * stem_depth_multiplier),
kernel_size=1,
strides=1,
use_bias=False,
padding='same',
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)(
endpoints['{}'.format(5 - i)])
backbone_feature = norm_layer()(backbone_feature)
if i == 0:
x = backbone_feature
else:
x = layers.Conv2D(
filters=int(upsample_filters[i] * stem_depth_multiplier),
kernel_size=1,
strides=1,
use_bias=False,
padding='same',
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)(
x)
x = norm_layer()(x)
x = spatial_transform_ops.nearest_upsampling(
x, scale=2,
use_keras_layer=True) + backbone_feature
for _ in range(upsample_repeats[i]):
x = ConvNeXtBlock(
int(upsample_filters[i] * self._depth_multiplier),
drop_rate=nn_layers.get_stochastic_depth_rate(
self._init_stochastic_depth_rate, i + 6, 8),
kernel_size=upsample_kernel_sizes[i])(x)
x = tf_utils.get_activation(activation, use_keras_layer=True)(x)
endpoints[str(5 - i)] = x
if classification_output:
endpoints['6'] = endpoints[str(5 - len(upsample_repeats) + 1)]
self._output_specs = {l: endpoints[l].get_shape() for l in endpoints}
super().__init__(inputs=inputs, outputs=endpoints, **kwargs)
def _block_group(self,
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:
inputs: A `tf.Tensor` of size `[batch, channels, height, width]`.
filters: An `int` number of filters for the first convolution of the
layer.
strides: An `int` stride to use for the first convolution of the layer.
If greater than 1, this layer will downsample the input.
block_fn: The type of block group. Either `nn_blocks.ResidualBlock` or
`nn_blocks.BottleneckBlock`.
block_repeats: An `int` number of blocks contained in the layer.
stochastic_depth_drop_rate: A `float` of drop rate of the current block
group.
name: A `str` name for the block.
Returns:
The output `tf.Tensor` of the block layer.
"""
x = block_fn(
filters=filters,
strides=strides,
use_projection=True,
stochastic_depth_drop_rate=stochastic_depth_drop_rate,
se_ratio=self._se_ratio,
resnetd_shortcut=self._resnetd_shortcut,
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer,
activation=self._activation,
use_sync_bn=self._use_sync_bn,
norm_momentum=self._norm_momentum,
norm_epsilon=self._norm_epsilon,
bn_trainable=self._bn_trainable)(
inputs)
for _ in range(1, block_repeats):
x = block_fn(
filters=filters,
strides=1,
use_projection=False,
stochastic_depth_drop_rate=stochastic_depth_drop_rate,
se_ratio=self._se_ratio,
resnetd_shortcut=self._resnetd_shortcut,
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer,
activation=self._activation,
use_sync_bn=self._use_sync_bn,
norm_momentum=self._norm_momentum,
norm_epsilon=self._norm_epsilon,
bn_trainable=self._bn_trainable)(
x)
return tf_keras.layers.Activation('linear', name=name)(x)
def get_config(self):
config_dict = {
'model_id': self._model_id,
'depth_multiplier': self._depth_multiplier,
'stem_type': self._stem_type,
'resnetd_shortcut': self._resnetd_shortcut,
'replace_stem_max_pool': self._replace_stem_max_pool,
'activation': self._activation,
'se_ratio': self._se_ratio,
'init_stochastic_depth_rate': self._init_stochastic_depth_rate,
'upsample_repeats': self._upsample_repeats,
'upsample_filters': self._upsample_filters,
'upsample_kernel_sizes': self._upsample_kernel_sizes,
'scale_stem': self._scale_stem,
'use_sync_bn': self._use_sync_bn,
'norm_momentum': self._norm_momentum,
'norm_epsilon': self._norm_epsilon,
'kernel_initializer': self._kernel_initializer,
'kernel_regularizer': self._kernel_regularizer,
'bias_regularizer': self._bias_regularizer,
'bn_trainable': self._bn_trainable,
'classification_output': self._classification_output,
}
return config_dict
@classmethod
def from_config(cls, config, custom_objects=None):
return cls(**config)
@property
def output_specs(self):
"""A dict of {level: TensorShape} pairs for the model output."""
return self._output_specs
@factory.register_backbone_builder('resnet_unet')
def build_resnet(
input_specs: tf_keras.layers.InputSpec,
backbone_config: hyperparams.Config,
norm_activation_config: hyperparams.Config,
l2_regularizer: tf_keras.regularizers.Regularizer = None) -> tf_keras.Model: # pytype: disable=annotation-type-mismatch # typed-keras
"""Builds ResNet ConvNext Unet backbone from a config."""
backbone_type = backbone_config.type
backbone_cfg = backbone_config.get()
assert backbone_type == 'resnet_unet', (
f'Inconsistent backbone type {backbone_type}')
return ResNetUNet(
model_id=backbone_cfg.model_id,
input_specs=input_specs,
depth_multiplier=backbone_cfg.depth_multiplier,
upsample_repeats=backbone_cfg.upsample_repeats,
upsample_filters=backbone_cfg.upsample_filters,
upsample_kernel_sizes=backbone_cfg.upsample_kernel_sizes,
stem_type=backbone_cfg.stem_type,
resnetd_shortcut=backbone_cfg.resnetd_shortcut,
replace_stem_max_pool=backbone_cfg.replace_stem_max_pool,
se_ratio=backbone_cfg.se_ratio,
init_stochastic_depth_rate=backbone_cfg.stochastic_depth_drop_rate,
scale_stem=backbone_cfg.scale_stem,
activation=norm_activation_config.activation,
use_sync_bn=norm_activation_config.use_sync_bn,
norm_momentum=norm_activation_config.norm_momentum,
norm_epsilon=norm_activation_config.norm_epsilon,
kernel_regularizer=l2_regularizer,
bn_trainable=backbone_cfg.bn_trainable,
classification_output=backbone_cfg.classification_output)
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