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efficientnet_v2.py
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efficientnet_v2.py
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# 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.
# ==============================================================================
# pylint: disable=invalid-name
# pylint: disable=missing-docstring
"""EfficientNet V2 models for Keras.
Reference:
- [EfficientNetV2: Smaller Models and Faster Training](
https://arxiv.org/abs/2104.00298) (ICML 2021)
"""
import copy
import math
from keras import backend
from keras import layers
from keras.applications import imagenet_utils
from keras.engine import training
from keras.utils import data_utils
from keras.utils import layer_utils
import tensorflow.compat.v2 as tf
# pylint: disable=g-direct-tensorflow-import
from tensorflow.python.util.tf_export import keras_export
BASE_WEIGHTS_PATH = "https://storage.googleapis.com/tensorflow/keras-applications/efficientnet_v2/"
WEIGHTS_HASHES = {
"b0": ("21ecbf6da12460d5c40bb2f29ceb2188",
"893217f2bb855e2983157299931e43ff"),
"b1": ("069f0534ff22adf035c89e2d9547a9dc",
"0e80663031ca32d657f9caa404b6ec37"),
"b2": ("424e49f28180edbde1e94797771950a7",
"1dfe2e7a5d45b6632553a8961ea609eb"),
"b3": ("1f1fc43bd98a6e4fd8fdfd551e02c7a0",
"f6abf7b5849ac99a89b50dd3fd532856"),
"-s": ("e1d88a8495beba45748fedd0cecbe016",
"af0682fb74e8c54910f2d4393339c070"),
"-m": ("a3bf6aa3276309f4fc6a34aa114c95cd",
"1b8dc055df72dde80d614482840fe342"),
"-l": ("27e6d408b53c7ebc868fefa357689935",
"b0b66b5c863aef5b46e8608fe1711615"),
}
DEFAULT_BLOCKS_ARGS = {
"efficientnetv2-s": [{
"kernel_size": 3,
"num_repeat": 2,
"input_filters": 24,
"output_filters": 24,
"expand_ratio": 1,
"se_ratio": 0.0,
"strides": 1,
"conv_type": 1,
}, {
"kernel_size": 3,
"num_repeat": 4,
"input_filters": 24,
"output_filters": 48,
"expand_ratio": 4,
"se_ratio": 0.0,
"strides": 2,
"conv_type": 1,
}, {
"conv_type": 1,
"expand_ratio": 4,
"input_filters": 48,
"kernel_size": 3,
"num_repeat": 4,
"output_filters": 64,
"se_ratio": 0,
"strides": 2,
}, {
"conv_type": 0,
"expand_ratio": 4,
"input_filters": 64,
"kernel_size": 3,
"num_repeat": 6,
"output_filters": 128,
"se_ratio": 0.25,
"strides": 2,
}, {
"conv_type": 0,
"expand_ratio": 6,
"input_filters": 128,
"kernel_size": 3,
"num_repeat": 9,
"output_filters": 160,
"se_ratio": 0.25,
"strides": 1,
}, {
"conv_type": 0,
"expand_ratio": 6,
"input_filters": 160,
"kernel_size": 3,
"num_repeat": 15,
"output_filters": 256,
"se_ratio": 0.25,
"strides": 2,
}],
"efficientnetv2-m": [
{
"kernel_size": 3,
"num_repeat": 3,
"input_filters": 24,
"output_filters": 24,
"expand_ratio": 1,
"se_ratio": 0,
"strides": 1,
"conv_type": 1,
},
{
"kernel_size": 3,
"num_repeat": 5,
"input_filters": 24,
"output_filters": 48,
"expand_ratio": 4,
"se_ratio": 0,
"strides": 2,
"conv_type": 1,
},
{
"kernel_size": 3,
"num_repeat": 5,
"input_filters": 48,
"output_filters": 80,
"expand_ratio": 4,
"se_ratio": 0,
"strides": 2,
"conv_type": 1,
},
{
"kernel_size": 3,
"num_repeat": 7,
"input_filters": 80,
"output_filters": 160,
"expand_ratio": 4,
"se_ratio": 0.25,
"strides": 2,
"conv_type": 0,
},
{
"kernel_size": 3,
"num_repeat": 14,
"input_filters": 160,
"output_filters": 176,
"expand_ratio": 6,
"se_ratio": 0.25,
"strides": 1,
"conv_type": 0,
},
{
"kernel_size": 3,
"num_repeat": 18,
"input_filters": 176,
"output_filters": 304,
"expand_ratio": 6,
"se_ratio": 0.25,
"strides": 2,
"conv_type": 0,
},
{
"kernel_size": 3,
"num_repeat": 5,
"input_filters": 304,
"output_filters": 512,
"expand_ratio": 6,
"se_ratio": 0.25,
"strides": 1,
"conv_type": 0,
},
],
"efficientnetv2-l": [
{
"kernel_size": 3,
"num_repeat": 4,
"input_filters": 32,
"output_filters": 32,
"expand_ratio": 1,
"se_ratio": 0,
"strides": 1,
"conv_type": 1,
},
{
"kernel_size": 3,
"num_repeat": 7,
"input_filters": 32,
"output_filters": 64,
"expand_ratio": 4,
"se_ratio": 0,
"strides": 2,
"conv_type": 1,
},
{
"kernel_size": 3,
"num_repeat": 7,
"input_filters": 64,
"output_filters": 96,
"expand_ratio": 4,
"se_ratio": 0,
"strides": 2,
"conv_type": 1,
},
{
"kernel_size": 3,
"num_repeat": 10,
"input_filters": 96,
"output_filters": 192,
"expand_ratio": 4,
"se_ratio": 0.25,
"strides": 2,
"conv_type": 0,
},
{
"kernel_size": 3,
"num_repeat": 19,
"input_filters": 192,
"output_filters": 224,
"expand_ratio": 6,
"se_ratio": 0.25,
"strides": 1,
"conv_type": 0,
},
{
"kernel_size": 3,
"num_repeat": 25,
"input_filters": 224,
"output_filters": 384,
"expand_ratio": 6,
"se_ratio": 0.25,
"strides": 2,
"conv_type": 0,
},
{
"kernel_size": 3,
"num_repeat": 7,
"input_filters": 384,
"output_filters": 640,
"expand_ratio": 6,
"se_ratio": 0.25,
"strides": 1,
"conv_type": 0,
},
],
"efficientnetv2-b0": [
{
"kernel_size": 3,
"num_repeat": 1,
"input_filters": 32,
"output_filters": 16,
"expand_ratio": 1,
"se_ratio": 0,
"strides": 1,
"conv_type": 1,
},
{
"kernel_size": 3,
"num_repeat": 2,
"input_filters": 16,
"output_filters": 32,
"expand_ratio": 4,
"se_ratio": 0,
"strides": 2,
"conv_type": 1,
},
{
"kernel_size": 3,
"num_repeat": 2,
"input_filters": 32,
"output_filters": 48,
"expand_ratio": 4,
"se_ratio": 0,
"strides": 2,
"conv_type": 1,
},
{
"kernel_size": 3,
"num_repeat": 3,
"input_filters": 48,
"output_filters": 96,
"expand_ratio": 4,
"se_ratio": 0.25,
"strides": 2,
"conv_type": 0,
},
{
"kernel_size": 3,
"num_repeat": 5,
"input_filters": 96,
"output_filters": 112,
"expand_ratio": 6,
"se_ratio": 0.25,
"strides": 1,
"conv_type": 0,
},
{
"kernel_size": 3,
"num_repeat": 8,
"input_filters": 112,
"output_filters": 192,
"expand_ratio": 6,
"se_ratio": 0.25,
"strides": 2,
"conv_type": 0,
},
],
"efficientnetv2-b1": [
{
"kernel_size": 3,
"num_repeat": 1,
"input_filters": 32,
"output_filters": 16,
"expand_ratio": 1,
"se_ratio": 0,
"strides": 1,
"conv_type": 1,
},
{
"kernel_size": 3,
"num_repeat": 2,
"input_filters": 16,
"output_filters": 32,
"expand_ratio": 4,
"se_ratio": 0,
"strides": 2,
"conv_type": 1,
},
{
"kernel_size": 3,
"num_repeat": 2,
"input_filters": 32,
"output_filters": 48,
"expand_ratio": 4,
"se_ratio": 0,
"strides": 2,
"conv_type": 1,
},
{
"kernel_size": 3,
"num_repeat": 3,
"input_filters": 48,
"output_filters": 96,
"expand_ratio": 4,
"se_ratio": 0.25,
"strides": 2,
"conv_type": 0,
},
{
"kernel_size": 3,
"num_repeat": 5,
"input_filters": 96,
"output_filters": 112,
"expand_ratio": 6,
"se_ratio": 0.25,
"strides": 1,
"conv_type": 0,
},
{
"kernel_size": 3,
"num_repeat": 8,
"input_filters": 112,
"output_filters": 192,
"expand_ratio": 6,
"se_ratio": 0.25,
"strides": 2,
"conv_type": 0,
},
],
"efficientnetv2-b2": [
{
"kernel_size": 3,
"num_repeat": 1,
"input_filters": 32,
"output_filters": 16,
"expand_ratio": 1,
"se_ratio": 0,
"strides": 1,
"conv_type": 1,
},
{
"kernel_size": 3,
"num_repeat": 2,
"input_filters": 16,
"output_filters": 32,
"expand_ratio": 4,
"se_ratio": 0,
"strides": 2,
"conv_type": 1,
},
{
"kernel_size": 3,
"num_repeat": 2,
"input_filters": 32,
"output_filters": 48,
"expand_ratio": 4,
"se_ratio": 0,
"strides": 2,
"conv_type": 1,
},
{
"kernel_size": 3,
"num_repeat": 3,
"input_filters": 48,
"output_filters": 96,
"expand_ratio": 4,
"se_ratio": 0.25,
"strides": 2,
"conv_type": 0,
},
{
"kernel_size": 3,
"num_repeat": 5,
"input_filters": 96,
"output_filters": 112,
"expand_ratio": 6,
"se_ratio": 0.25,
"strides": 1,
"conv_type": 0,
},
{
"kernel_size": 3,
"num_repeat": 8,
"input_filters": 112,
"output_filters": 192,
"expand_ratio": 6,
"se_ratio": 0.25,
"strides": 2,
"conv_type": 0,
},
],
"efficientnetv2-b3": [
{
"kernel_size": 3,
"num_repeat": 1,
"input_filters": 32,
"output_filters": 16,
"expand_ratio": 1,
"se_ratio": 0,
"strides": 1,
"conv_type": 1,
},
{
"kernel_size": 3,
"num_repeat": 2,
"input_filters": 16,
"output_filters": 32,
"expand_ratio": 4,
"se_ratio": 0,
"strides": 2,
"conv_type": 1,
},
{
"kernel_size": 3,
"num_repeat": 2,
"input_filters": 32,
"output_filters": 48,
"expand_ratio": 4,
"se_ratio": 0,
"strides": 2,
"conv_type": 1,
},
{
"kernel_size": 3,
"num_repeat": 3,
"input_filters": 48,
"output_filters": 96,
"expand_ratio": 4,
"se_ratio": 0.25,
"strides": 2,
"conv_type": 0,
},
{
"kernel_size": 3,
"num_repeat": 5,
"input_filters": 96,
"output_filters": 112,
"expand_ratio": 6,
"se_ratio": 0.25,
"strides": 1,
"conv_type": 0,
},
{
"kernel_size": 3,
"num_repeat": 8,
"input_filters": 112,
"output_filters": 192,
"expand_ratio": 6,
"se_ratio": 0.25,
"strides": 2,
"conv_type": 0,
},
],
}
CONV_KERNEL_INITIALIZER = {
"class_name": "VarianceScaling",
"config": {
"scale": 2.0,
"mode": "fan_out",
"distribution": "truncated_normal"
}
}
DENSE_KERNEL_INITIALIZER = {
"class_name": "VarianceScaling",
"config": {
"scale": 1. / 3.,
"mode": "fan_out",
"distribution": "uniform"
}
}
BASE_DOCSTRING = """Instantiates the {name} architecture.
Reference:
- [EfficientNetV2: Smaller Models and Faster Training](
https://arxiv.org/abs/2104.00298) (ICML 2021)
This function returns a Keras image classification model,
optionally loaded with weights pre-trained on ImageNet.
For image classification use cases, see
[this page for detailed examples](
https://keras.io/api/applications/#usage-examples-for-image-classification-models).
For transfer learning use cases, make sure to read the
[guide to transfer learning & fine-tuning](
https://keras.io/guides/transfer_learning/).
Note: each Keras Application expects a specific kind of input preprocessing.
For EfficientNetV2, by default input preprocessing is included as a part of the
model (as a `Rescaling` layer), and thus
`tf.keras.applications.efficientnet_v2.preprocess_input` is actually a
pass-through function. In this use case, EfficientNetV2 models expect their inputs
to be float tensors of pixels with values in the [0-255] range.
At the same time, preprocessing as a part of the model (i.e. `Rescaling`
layer) can be disabled by setting `include_preprocessing` argument to False.
With preprocessing disabled EfficientNetV2 models expect their inputs to be float
tensors of pixels with values in the [-1, 1] range.
Args:
include_top: Boolean, whether to include the fully-connected
layer at the top of the network. Defaults to True.
weights: One of `None` (random initialization),
`"imagenet"` (pre-training on ImageNet),
or the path to the weights file to be loaded. Defaults to `"imagenet"`.
input_tensor: Optional Keras tensor
(i.e. output of `layers.Input()`)
to use as image input for the model.
input_shape: Optional shape tuple, only to be specified
if `include_top` is False.
It should have exactly 3 inputs channels.
pooling: Optional pooling mode for feature extraction
when `include_top` is `False`. Defaults to None.
- `None` means that the output of the model will be
the 4D tensor output of the
last convolutional layer.
- `"avg"` means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a 2D tensor.
- `"max"` means that global max pooling will
be applied.
classes: Optional number of classes to classify images
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified. Defaults to 1000 (number of
ImageNet classes).
classifier_activation: A string or callable. The activation function to use
on the `"top"` layer. Ignored unless `include_top=True`. Set
`classifier_activation=None` to return the logits of the "top" layer.
Defaults to `"softmax"`.
When loading pretrained weights, `classifier_activation` can only
be `None` or `"softmax"`.
Returns:
A `keras.Model` instance.
"""
def round_filters(filters, width_coefficient, min_depth, depth_divisor):
"""Round number of filters based on depth multiplier."""
filters *= width_coefficient
minimum_depth = min_depth or depth_divisor
new_filters = max(
minimum_depth,
int(filters + depth_divisor / 2) // depth_divisor * depth_divisor,
)
return int(new_filters)
def round_repeats(repeats, depth_coefficient):
"""Round number of repeats based on depth multiplier."""
return int(math.ceil(depth_coefficient * repeats))
def MBConvBlock(
input_filters: int,
output_filters: int,
expand_ratio=1,
kernel_size=3,
strides=1,
se_ratio=0.0,
bn_momentum=0.9,
activation="swish",
survival_probability: float = 0.8,
name=None,
):
"""MBConv block: Mobile Inverted Residual Bottleneck."""
bn_axis = 3 if backend.image_data_format() == "channels_last" else 1
if name is None:
name = backend.get_uid("block0")
def apply(inputs):
# Expansion phase
filters = input_filters * expand_ratio
if expand_ratio != 1:
x = layers.Conv2D(
filters=filters,
kernel_size=1,
strides=1,
kernel_initializer=CONV_KERNEL_INITIALIZER,
padding="same",
data_format="channels_last",
use_bias=False,
name=name + "expand_conv",
)(inputs)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=bn_momentum,
name=name + "expand_bn",
)(x)
x = layers.Activation(activation, name=name + "expand_activation")(x)
else:
x = inputs
# Depthwise conv
x = layers.DepthwiseConv2D(
kernel_size=kernel_size,
strides=strides,
depthwise_initializer=CONV_KERNEL_INITIALIZER,
padding="same",
data_format="channels_last",
use_bias=False,
name=name + "dwconv2",
)(x)
x = layers.BatchNormalization(
axis=bn_axis, momentum=bn_momentum, name=name + "bn")(x)
x = layers.Activation(activation, name=name + "activation")(x)
# Squeeze and excite
if 0 < se_ratio <= 1:
filters_se = max(1, int(input_filters * se_ratio))
se = layers.GlobalAveragePooling2D(name=name + "se_squeeze")(x)
if bn_axis == 1:
se_shape = (filters, 1, 1)
else:
se_shape = (1, 1, filters)
se = layers.Reshape(se_shape, name=name + "se_reshape")(se)
se = layers.Conv2D(
filters_se,
1,
padding="same",
activation=activation,
kernel_initializer=CONV_KERNEL_INITIALIZER,
name=name + "se_reduce",
)(se)
se = layers.Conv2D(
filters,
1,
padding="same",
activation="sigmoid",
kernel_initializer=CONV_KERNEL_INITIALIZER,
name=name + "se_expand",
)(se)
x = layers.multiply([x, se], name=name + "se_excite")
# Output phase
x = layers.Conv2D(
filters=output_filters,
kernel_size=1,
strides=1,
kernel_initializer=CONV_KERNEL_INITIALIZER,
padding="same",
data_format="channels_last",
use_bias=False,
name=name + "project_conv",
)(x)
x = layers.BatchNormalization(
axis=bn_axis, momentum=bn_momentum, name=name + "project_bn")(x)
if strides == 1 and input_filters == output_filters:
if survival_probability:
x = layers.Dropout(
survival_probability,
noise_shape=(None, 1, 1, 1),
name=name + "drop",
)(x)
x = layers.add([x, inputs], name=name + "add")
return x
return apply
def FusedMBConvBlock(
input_filters: int,
output_filters: int,
expand_ratio=1,
kernel_size=3,
strides=1,
se_ratio=0.0,
bn_momentum=0.9,
activation="swish",
survival_probability: float = 0.8,
name=None,
):
"""Fused MBConv Block: Fusing the proj conv1x1 and depthwise_conv into a conv2d."""
bn_axis = 3 if backend.image_data_format() == "channels_last" else 1
if name is None:
name = backend.get_uid("block0")
def apply(inputs):
filters = input_filters * expand_ratio
if expand_ratio != 1:
x = layers.Conv2D(
filters,
kernel_size=kernel_size,
strides=strides,
kernel_initializer=CONV_KERNEL_INITIALIZER,
data_format="channels_last",
padding="same",
use_bias=False,
name=name + "expand_conv",
)(inputs)
x = layers.BatchNormalization(
axis=bn_axis, momentum=bn_momentum, name=name + "expand_bn")(x)
x = layers.Activation(
activation=activation, name=name + "expand_activation")(x)
else:
x = inputs
# Squeeze and excite
if 0 < se_ratio <= 1:
filters_se = max(1, int(input_filters * se_ratio))
se = layers.GlobalAveragePooling2D(name=name + "se_squeeze")(x)
if bn_axis == 1:
se_shape = (filters, 1, 1)
else:
se_shape = (1, 1, filters)
se = layers.Reshape(se_shape, name=name + "se_reshape")(se)
se = layers.Conv2D(
filters_se,
1,
padding="same",
activation=activation,
kernel_initializer=CONV_KERNEL_INITIALIZER,
name=name + "se_reduce",
)(se)
se = layers.Conv2D(
filters,
1,
padding="same",
activation="sigmoid",
kernel_initializer=CONV_KERNEL_INITIALIZER,
name=name + "se_expand",
)(se)
x = layers.multiply([x, se], name=name + "se_excite")
# Output phase:
x = layers.Conv2D(
output_filters,
kernel_size=1 if expand_ratio != 1 else kernel_size,
strides=1 if expand_ratio != 1 else strides,
kernel_initializer=CONV_KERNEL_INITIALIZER,
padding="same",
use_bias=False,
name=name + "project_conv",
)(x)
x = layers.BatchNormalization(
axis=bn_axis, momentum=bn_momentum, name=name + "project_bn")(x)
if expand_ratio == 1:
x = layers.Activation(
activation=activation, name=name + "project_activation")(x)
# Residual:
if strides == 1 and input_filters == output_filters:
if survival_probability:
x = layers.Dropout(
survival_probability,
noise_shape=(None, 1, 1, 1),
name=name + "drop",
)(x)
x = layers.add([x, inputs], name=name + "add")
return x
return apply
def EfficientNetV2(
width_coefficient,
depth_coefficient,
default_size,
dropout_rate=0.2,
drop_connect_rate=0.2,
depth_divisor=8,
min_depth=8,
bn_momentum=0.9,
activation="swish",
blocks_args="default",
model_name="efficientnetv2",
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
include_preprocessing=True,
):
"""Instantiates the EfficientNetV2 architecture using given scaling coefficients.
Args:
width_coefficient: float, scaling coefficient for network width.
depth_coefficient: float, scaling coefficient for network depth.
default_size: integer, default input image size.
dropout_rate: float, dropout rate before final classifier layer.
drop_connect_rate: float, dropout rate at skip connections.
depth_divisor: integer, a unit of network width.
min_depth: integer, minimum number of filters.
bn_momentum: float. Momentum parameter for Batch Normalization layers.
activation: activation function.
blocks_args: list of dicts, parameters to construct block modules.
model_name: string, model name.
include_top: whether to include the fully-connected layer at the top of the
network.
weights: one of `None` (random initialization), `"imagenet"` (pre-training
on ImageNet), or the path to the weights file to be loaded.
input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) or
numpy array to use as image input for the model.
input_shape: optional shape tuple, only to be specified if `include_top` is
False. It should have exactly 3 inputs channels.
pooling: optional pooling mode for feature extraction when `include_top` is
`False`. - `None` means that the output of the model will be the 4D tensor
output of the last convolutional layer. - "avg" means that global average
pooling will be applied to the output of the last convolutional layer, and
thus the output of the model will be a 2D tensor. - `"max"` means that
global max pooling will be applied.
classes: optional number of classes to classify images into, only to be
specified if `include_top` is True, and if no `weights` argument is
specified.
classifier_activation: A string or callable. The activation function to use
on the `"top"` layer. Ignored unless `include_top=True`. Set
`classifier_activation=None` to return the logits of the `"top"` layer.
include_preprocessing: Boolean, whether to include the preprocessing layer
(`Rescaling`) at the bottom of the network. Defaults to `True`.
Returns:
A `keras.Model` instance.
Raises:
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
ValueError: if `classifier_activation` is not `"softmax"` or `None` when
using a pretrained top layer.
"""
if blocks_args == "default":
blocks_args = DEFAULT_BLOCKS_ARGS[model_name]
if not (weights in {"imagenet", None} or tf.io.gfile.exists(weights)):
raise ValueError("The `weights` argument should be either "
"`None` (random initialization), `imagenet` "
"(pre-training on ImageNet), "
"or the path to the weights file to be loaded."
f"Received: weights={weights}")
if weights == "imagenet" and include_top and classes != 1000:
raise ValueError("If using `weights` as `'imagenet'` with `include_top`"
" as true, `classes` should be 1000"
f"Received: classes={classes}")
# Determine proper input shape
input_shape = imagenet_utils.obtain_input_shape(
input_shape,
default_size=default_size,
min_size=32,
data_format=backend.image_data_format(),
require_flatten=include_top,
weights=weights)
if input_tensor is None:
img_input = layers.Input(shape=input_shape)
else:
if not backend.is_keras_tensor(input_tensor):
img_input = layers.Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
bn_axis = 3 if backend.image_data_format() == "channels_last" else 1
x = img_input
if include_preprocessing:
# Apply original V1 preprocessing for Bx variants
# if number of channels allows it
num_channels = input_shape[bn_axis - 1]
if model_name.split("-")[-1].startswith("b") and num_channels == 3:
x = layers.Rescaling(scale=1. / 255)(x)
x = layers.Normalization(
mean=[0.485, 0.456, 0.406],
variance=[0.229**2, 0.224**2, 0.225**2],
axis=bn_axis,
)(x)
else:
x = layers.Rescaling(scale=1. / 128.0, offset=-1)(x)
# Build stem
stem_filters = round_filters(
filters=blocks_args[0]["input_filters"],
width_coefficient=width_coefficient,
min_depth=min_depth,
depth_divisor=depth_divisor,
)
x = layers.Conv2D(
filters=stem_filters,
kernel_size=3,
strides=2,
kernel_initializer=CONV_KERNEL_INITIALIZER,
padding="same",
use_bias=False,
name="stem_conv",
)(x)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=bn_momentum,
name="stem_bn",
)(x)
x = layers.Activation(activation, name="stem_activation")(x)
# Build blocks
blocks_args = copy.deepcopy(blocks_args)
b = 0
blocks = float(sum(args["num_repeat"] for args in blocks_args))
for (i, args) in enumerate(blocks_args):
assert args["num_repeat"] > 0
# Update block input and output filters based on depth multiplier.
args["input_filters"] = round_filters(
filters=args["input_filters"],
width_coefficient=width_coefficient,
min_depth=min_depth,
depth_divisor=depth_divisor)
args["output_filters"] = round_filters(
filters=args["output_filters"],
width_coefficient=width_coefficient,
min_depth=min_depth,
depth_divisor=depth_divisor)
# Determine which conv type to use:
block = {0: MBConvBlock, 1: FusedMBConvBlock}[args.pop("conv_type")]
repeats = round_repeats(
repeats=args.pop("num_repeat"), depth_coefficient=depth_coefficient)
for j in range(repeats):
# The first block needs to take care of stride and filter size increase.
if j > 0:
args["strides"] = 1
args["input_filters"] = args["output_filters"]
x = block(
activation=activation,
bn_momentum=bn_momentum,
survival_probability=drop_connect_rate * b / blocks,
name="block{}{}_".format(i + 1, chr(j + 97)),
**args,
)(x)
b += 1
# Build top
top_filters = round_filters(
filters=1280,
width_coefficient=width_coefficient,
min_depth=min_depth,