# Copyright 2019 The TensorFlow Authors, Pavel Yakubovskiy, Björn Barz. 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 for EfficientNet model.
[1] Mingxing Tan, Quoc V. Le
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.
ICML'19, https://arxiv.org/abs/1905.11946
"""
# Code of this model implementation is mostly written by
# Björn Barz ([@Callidior](https://github.com/Callidior))
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import math
import string
import collections
from six.moves import xrange
from keras_applications.imagenet_utils import _obtain_input_shape
from keras_applications.imagenet_utils import decode_predictions
from tensorflow.keras import backend
from tensorflow.keras import layers
from tensorflow.keras import models
from tensorflow.keras import utils as keras_utils
BASE_WEIGHTS_PATH = (
'https://github.com/Callidior/keras-applications/'
'releases/download/efficientnet/')
WEIGHTS_HASHES = {
'efficientnet-b0': ('163292582f1c6eaca8e7dc7b51b01c61'
'5b0dbc0039699b4dcd0b975cc21533dc',
'c1421ad80a9fc67c2cc4000f666aa507'
'89ce39eedb4e06d531b0c593890ccff3'),
'efficientnet-b1': ('d0a71ddf51ef7a0ca425bab32b7fa7f1'
'6043ee598ecee73fc674d9560c8f09b0',
'75de265d03ac52fa74f2f510455ba64f'
'9c7c5fd96dc923cd4bfefa3d680c4b68'),
'efficientnet-b2': ('bb5451507a6418a574534aa76a91b106'
'f6b605f3b5dde0b21055694319853086',
'433b60584fafba1ea3de07443b74cfd3'
'2ce004a012020b07ef69e22ba8669333'),
'efficientnet-b3': ('03f1fba367f070bd2545f081cfa7f3e7'
'6f5e1aa3b6f4db700f00552901e75ab9',
'c5d42eb6cfae8567b418ad3845cfd63a'
'a48b87f1bd5df8658a49375a9f3135c7'),
'efficientnet-b4': ('98852de93f74d9833c8640474b2c698d'
'b45ec60690c75b3bacb1845e907bf94f',
'7942c1407ff1feb34113995864970cd4'
'd9d91ea64877e8d9c38b6c1e0767c411'),
'efficientnet-b5': ('30172f1d45f9b8a41352d4219bf930ee'
'3339025fd26ab314a817ba8918fefc7d',
'9d197bc2bfe29165c10a2af8c2ebc675'
'07f5d70456f09e584c71b822941b1952'),
'efficientnet-b6': ('f5270466747753485a082092ac9939ca'
'a546eb3f09edca6d6fff842cad938720',
'1d0923bb038f2f8060faaf0a0449db4b'
'96549a881747b7c7678724ac79f427ed'),
'efficientnet-b7': ('876a41319980638fa597acbbf956a82d'
'10819531ff2dcb1a52277f10c7aefa1a',
'60b56ff3a8daccc8d96edfd40b204c11'
'3e51748da657afd58034d54d3cec2bac')
}
BlockArgs = collections.namedtuple('BlockArgs', [
'kernel_size', 'num_repeat', 'input_filters', 'output_filters',
'expand_ratio', 'id_skip', 'strides', 'se_ratio'
])
# defaults will be a public argument for namedtuple in Python 3.7
# https://docs.python.org/3/library/collections.html#collections.namedtuple
BlockArgs.__new__.__defaults__ = (None,) * len(BlockArgs._fields)
DEFAULT_BLOCKS_ARGS = [
BlockArgs(kernel_size=3, num_repeat=1, input_filters=32, output_filters=16,
expand_ratio=1, id_skip=True, strides=[1, 1], se_ratio=0.25),
BlockArgs(kernel_size=3, num_repeat=2, input_filters=16, output_filters=24,
expand_ratio=6, id_skip=True, strides=[2, 2], se_ratio=0.25),
BlockArgs(kernel_size=5, num_repeat=2, input_filters=24, output_filters=40,
expand_ratio=6, id_skip=True, strides=[2, 2], se_ratio=0.25),
BlockArgs(kernel_size=3, num_repeat=3, input_filters=40, output_filters=80,
expand_ratio=6, id_skip=True, strides=[2, 2], se_ratio=0.25),
BlockArgs(kernel_size=5, num_repeat=3, input_filters=80, output_filters=112,
expand_ratio=6, id_skip=True, strides=[1, 1], se_ratio=0.25),
BlockArgs(kernel_size=5, num_repeat=4, input_filters=112, output_filters=192,
expand_ratio=6, id_skip=True, strides=[2, 2], se_ratio=0.25),
BlockArgs(kernel_size=3, num_repeat=1, input_filters=192, output_filters=320,
expand_ratio=6, id_skip=True, strides=[1, 1], se_ratio=0.25)
]
CONV_KERNEL_INITIALIZER = {
'class_name': 'VarianceScaling',
'config': {
'scale': 2.0,
'mode': 'fan_out',
# EfficientNet actually uses an untruncated normal distribution for
# initializing conv layers, but keras.initializers.VarianceScaling use
# a truncated distribution.
# We decided against a custom initializer for better serializability.
'distribution': 'normal'
}
}
DENSE_KERNEL_INITIALIZER = {
'class_name': 'VarianceScaling',
'config': {
'scale': 1. / 3.,
'mode': 'fan_out',
'distribution': 'uniform'
}
}
def get_swish(**kwargs):
def swish(x):
"""Swish activation function: x * sigmoid(x).
Reference: [Searching for Activation Functions](https://arxiv.org/abs/1710.05941)
"""
if backend.backend() == 'tensorflow':
try:
# The native TF implementation has a more
# memory-efficient gradient implementation
return backend.tf.nn.swish(x)
except AttributeError:
pass
return x * backend.sigmoid(x)
return swish
def get_dropout(**kwargs):
"""Wrapper over custom dropout. Fix problem of ``None`` shape for tf.keras.
It is not possible to define FixedDropout class as global object,
because we do not have modules for inheritance at first time.
Issue:
https://github.com/tensorflow/tensorflow/issues/30946
"""
class FixedDropout(layers.Dropout):
def _get_noise_shape(self, inputs):
if self.noise_shape is None:
return self.noise_shape
symbolic_shape = backend.shape(inputs)
noise_shape = [symbolic_shape[axis] if shape is None else shape
for axis, shape in enumerate(self.noise_shape)]
return tuple(noise_shape)
return FixedDropout
def round_filters(filters, width_coefficient, depth_divisor):
"""Round number of filters based on width multiplier."""
filters *= width_coefficient
new_filters = int(filters + depth_divisor / 2) // depth_divisor * depth_divisor
new_filters = max(depth_divisor, new_filters)
# Make sure that round down does not go down by more than 10%.
if new_filters < 0.9 * filters:
new_filters += 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 mb_conv_block(inputs, block_args, activation, drop_rate=None, prefix='', ):
"""Mobile Inverted Residual Bottleneck."""
has_se = (block_args.se_ratio is not None) and (0 < block_args.se_ratio <= 1)
bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1
# workaround over non working dropout with None in noise_shape in tf.keras
Dropout = get_dropout(
backend=backend,
layers=layers,
models=models,
utils=keras_utils
)
# Expansion phase
filters = block_args.input_filters * block_args.expand_ratio
if block_args.expand_ratio != 1:
x = layers.Conv2D(filters, 1,
padding='same',
use_bias=False,
kernel_initializer=CONV_KERNEL_INITIALIZER,
name=prefix + 'expand_conv')(inputs)
x = layers.BatchNormalization(axis=bn_axis, name=prefix + 'expand_bn')(x)
x = layers.Activation(activation, name=prefix + 'expand_activation')(x)
else:
x = inputs
# Depthwise Convolution
x = layers.DepthwiseConv2D(block_args.kernel_size,
strides=block_args.strides,
padding='same',
use_bias=False,
depthwise_initializer=CONV_KERNEL_INITIALIZER,
name=prefix + 'dwconv')(x)
x = layers.BatchNormalization(axis=bn_axis, name=prefix + 'bn')(x)
x = layers.Activation(activation, name=prefix + 'activation')(x)
# Squeeze and Excitation phase
if has_se:
num_reduced_filters = max(1, int(
block_args.input_filters * block_args.se_ratio
))
se_tensor = layers.GlobalAveragePooling2D(name=prefix + 'se_squeeze')(x)
target_shape = (1, 1, filters) if backend.image_data_format() == 'channels_last' else (filters, 1, 1)
se_tensor = layers.Reshape(target_shape, name=prefix + 'se_reshape')(se_tensor)
se_tensor = layers.Conv2D(num_reduced_filters, 1,
activation=activation,
padding='same',
use_bias=True,
kernel_initializer=CONV_KERNEL_INITIALIZER,
name=prefix + 'se_reduce')(se_tensor)
se_tensor = layers.Conv2D(filters, 1,
activation='sigmoid',
padding='same',
use_bias=True,
kernel_initializer=CONV_KERNEL_INITIALIZER,
name=prefix + 'se_expand')(se_tensor)
if backend.backend() == 'theano':
# For the Theano backend, we have to explicitly make
# the excitation weights broadcastable.
pattern = ([True, True, True, False] if backend.image_data_format() == 'channels_last'
else [True, False, True, True])
se_tensor = layers.Lambda(
lambda x: backend.pattern_broadcast(x, pattern),
name=prefix + 'se_broadcast')(se_tensor)
x = layers.multiply([x, se_tensor], name=prefix + 'se_excite')
# Output phase
x = layers.Conv2D(block_args.output_filters, 1,
padding='same',
use_bias=False,
kernel_initializer=CONV_KERNEL_INITIALIZER,
name=prefix + 'project_conv')(x)
x = layers.BatchNormalization(axis=bn_axis, name=prefix + 'project_bn')(x)
if block_args.id_skip and all(
s == 1 for s in block_args.strides
) and block_args.input_filters == block_args.output_filters:
if drop_rate and (drop_rate > 0):
x = Dropout(drop_rate,
noise_shape=(None, 1, 1, 1),
name=prefix + 'drop')(x)
x = layers.add([x, inputs], name=prefix + 'add')
return x
def EfficientNet(width_coefficient,
depth_coefficient,
default_resolution,
dropout_rate=0.2,
drop_connect_rate=0.2,
depth_divisor=8,
blocks_args=DEFAULT_BLOCKS_ARGS,
model_name='efficientnet',
include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs):
"""Instantiates the EfficientNet architecture using given scaling coefficients.
Optionally loads weights pre-trained on ImageNet.
Note that the data format convention used by the model is
the one specified in your Keras config at `~/.keras/keras.json`.
# Arguments
width_coefficient: float, scaling coefficient for network width.
depth_coefficient: float, scaling coefficient for network depth.
default_resolution: int, default input image size.
dropout_rate: float, dropout rate before final classifier layer.
drop_connect_rate: float, dropout rate at skip connections.
depth_divisor: int.
blocks_args: A list of BlockArgs 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()`)
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.
# Returns
A Keras model instance.
# Raises
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
"""
if not (weights in {'imagenet', None} or os.path.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.')
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')
# Determine proper input shape
input_shape = _obtain_input_shape(input_shape,
default_size=default_resolution,
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 backend.backend() == 'tensorflow':
from tensorflow.python.keras.backend import is_keras_tensor
else:
is_keras_tensor = backend.is_keras_tensor
if not 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
activation = get_swish(**kwargs)
# Build stem
x = img_input
x = layers.Conv2D(round_filters(32, width_coefficient, depth_divisor), 3,
strides=(2, 2),
padding='same',
use_bias=False,
kernel_initializer=CONV_KERNEL_INITIALIZER,
name='stem_conv')(x)
x = layers.BatchNormalization(axis=bn_axis, name='stem_bn')(x)
x = layers.Activation(activation, name='stem_activation')(x)
# Build blocks
num_blocks_total = sum(block_args.num_repeat for block_args in blocks_args)
block_num = 0
for idx, block_args in enumerate(blocks_args):
assert block_args.num_repeat > 0
# Update block input and output filters based on depth multiplier.
block_args = block_args._replace(
input_filters=round_filters(block_args.input_filters,
width_coefficient, depth_divisor),
output_filters=round_filters(block_args.output_filters,
width_coefficient, depth_divisor),
num_repeat=round_repeats(block_args.num_repeat, depth_coefficient))
# The first block needs to take care of stride and filter size increase.
drop_rate = drop_connect_rate * float(block_num) / num_blocks_total
x = mb_conv_block(x, block_args,
activation=activation,
drop_rate=drop_rate,
prefix='block{}a_'.format(idx + 1))
block_num += 1
if block_args.num_repeat > 1:
# pylint: disable=protected-access
block_args = block_args._replace(
input_filters=block_args.output_filters, strides=[1, 1])
# pylint: enable=protected-access
for bidx in xrange(block_args.num_repeat - 1):
drop_rate = drop_connect_rate * float(block_num) / num_blocks_total
block_prefix = 'block{}{}_'.format(
idx + 1,
string.ascii_lowercase[bidx + 1]
)
x = mb_conv_block(x, block_args,
activation=activation,
drop_rate=drop_rate,
prefix=block_prefix)
block_num += 1
# Build top
x = layers.Conv2D(round_filters(1280, width_coefficient, depth_divisor), 1,
padding='same',
use_bias=False,
kernel_initializer=CONV_KERNEL_INITIALIZER,
name='top_conv')(x)
x = layers.BatchNormalization(axis=bn_axis, name='top_bn')(x)
x = layers.Activation(activation, name='top_activation')(x)
if include_top:
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
if dropout_rate and dropout_rate > 0:
x = layers.Dropout(dropout_rate, name='top_dropout')(x)
x = layers.Dense(classes,
activation='softmax',
kernel_initializer=DENSE_KERNEL_INITIALIZER,
name='probs')(x)
else:
if pooling == 'avg':
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
elif pooling == 'max':
x = layers.GlobalMaxPooling2D(name='max_pool')(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = keras_utils.get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = models.Model(inputs, x, name=model_name)
# Load weights.
if weights == 'imagenet':
if include_top:
file_name = model_name + '_weights_tf_dim_ordering_tf_kernels_autoaugment.h5'
file_hash = WEIGHTS_HASHES[model_name][0]
else:
file_name = model_name + '_weights_tf_dim_ordering_tf_kernels_autoaugment_notop.h5'
file_hash = WEIGHTS_HASHES[model_name][1]
weights_path = keras_utils.get_file(file_name,
BASE_WEIGHTS_PATH + file_name,
cache_subdir='models',
file_hash=file_hash)
model.load_weights(weights_path)
elif weights is not None:
model.load_weights(weights)
return model
def EfficientNetB0(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs):
return EfficientNet(1.0, 1.0, 224, 0.2,
model_name='efficientnet-b0',
include_top=include_top, weights=weights,
input_tensor=input_tensor, input_shape=input_shape,
pooling=pooling, classes=classes,
**kwargs)
def EfficientNetB1(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs):
return EfficientNet(1.0, 1.1, 240, 0.2,
model_name='efficientnet-b1',
include_top=include_top, weights=weights,
input_tensor=input_tensor, input_shape=input_shape,
pooling=pooling, classes=classes,
**kwargs)
def EfficientNetB2(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs):
return EfficientNet(1.1, 1.2, 260, 0.3,
model_name='efficientnet-b2',
include_top=include_top, weights=weights,
input_tensor=input_tensor, input_shape=input_shape,
pooling=pooling, classes=classes,
**kwargs)
def EfficientNetB3(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs):
return EfficientNet(1.2, 1.4, 300, 0.3,
model_name='efficientnet-b3',
include_top=include_top, weights=weights,
input_tensor=input_tensor, input_shape=input_shape,
pooling=pooling, classes=classes,
**kwargs)
def EfficientNetB4(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs):
return EfficientNet(1.4, 1.8, 380, 0.4,
model_name='efficientnet-b4',
include_top=include_top, weights=weights,
input_tensor=input_tensor, input_shape=input_shape,
pooling=pooling, classes=classes,
**kwargs)
def EfficientNetB5(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs):
return EfficientNet(1.6, 2.2, 456, 0.4,
model_name='efficientnet-b5',
include_top=include_top, weights=weights,
input_tensor=input_tensor, input_shape=input_shape,
pooling=pooling, classes=classes,
**kwargs)
def EfficientNetB6(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs):
return EfficientNet(1.8, 2.6, 528, 0.5,
model_name='efficientnet-b6',
include_top=include_top, weights=weights,
input_tensor=input_tensor, input_shape=input_shape,
pooling=pooling, classes=classes,
**kwargs)
def EfficientNetB7(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs):
return EfficientNet(2.0, 3.1, 600, 0.5,
model_name='efficientnet-b7',
include_top=include_top, weights=weights,
input_tensor=input_tensor, input_shape=input_shape,
pooling=pooling, classes=classes,
**kwargs)
def EfficientNetB0_224x224(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs):
return EfficientNet(1.0, 1.0, 224, 0.2,
model_name='efficientnet-b0',
include_top=include_top, weights=weights,
input_tensor=input_tensor, input_shape=input_shape,
pooling=pooling, classes=classes,
**kwargs)
def EfficientNetB1_224x224(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs):
return EfficientNet(1.0, 1.1, 224, 0.2,
model_name='efficientnet-b1',
include_top=include_top, weights=weights,
input_tensor=input_tensor, input_shape=input_shape,
pooling=pooling, classes=classes,
**kwargs)
def EfficientNetB2_224x224(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs):
return EfficientNet(1.1, 1.2, 224, 0.3,
model_name='efficientnet-b2',
include_top=include_top, weights=weights,
input_tensor=input_tensor, input_shape=input_shape,
pooling=pooling, classes=classes,
**kwargs)
def EfficientNetB3_224x224(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs):
return EfficientNet(1.2, 1.4, 224, 0.3,
model_name='efficientnet-b3',
include_top=include_top, weights=weights,
input_tensor=input_tensor, input_shape=input_shape,
pooling=pooling, classes=classes,
**kwargs)
def EfficientNetB4_224x224(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs):
return EfficientNet(1.4, 1.8, 224, 0.4,
model_name='efficientnet-b4',
include_top=include_top, weights=weights,
input_tensor=input_tensor, input_shape=input_shape,
pooling=pooling, classes=classes,
**kwargs)
def EfficientNetB5_224x224(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs):
return EfficientNet(1.6, 2.2, 224, 0.4,
model_name='efficientnet-b5',
include_top=include_top, weights=weights,
input_tensor=input_tensor, input_shape=input_shape,
pooling=pooling, classes=classes,
**kwargs)
def EfficientNetB6_224x224(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs):
return EfficientNet(1.8, 2.6, 224, 0.5,
model_name='efficientnet-b6',
include_top=include_top, weights=weights,
input_tensor=input_tensor, input_shape=input_shape,
pooling=pooling, classes=classes,
**kwargs)
def EfficientNetB7_224x224(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs):
return EfficientNet(2.0, 3.1, 224, 0.5,
model_name='efficientnet-b7',
include_top=include_top, weights=weights,
input_tensor=input_tensor, input_shape=input_shape,
pooling=pooling, classes=classes,
**kwargs)
setattr(EfficientNetB0, '__doc__', EfficientNet.__doc__)
setattr(EfficientNetB1, '__doc__', EfficientNet.__doc__)
setattr(EfficientNetB2, '__doc__', EfficientNet.__doc__)
setattr(EfficientNetB3, '__doc__', EfficientNet.__doc__)
setattr(EfficientNetB4, '__doc__', EfficientNet.__doc__)
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setattr(EfficientNetB6, '__doc__', EfficientNet.__doc__)
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setattr(EfficientNetB0_224x224, '__doc__', EfficientNet.__doc__)
setattr(EfficientNetB1_224x224, '__doc__', EfficientNet.__doc__)
setattr(EfficientNetB2_224x224, '__doc__', EfficientNet.__doc__)
setattr(EfficientNetB3_224x224, '__doc__', EfficientNet.__doc__)
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setattr(EfficientNetB5_224x224, '__doc__', EfficientNet.__doc__)
setattr(EfficientNetB6_224x224, '__doc__', EfficientNet.__doc__)
setattr(EfficientNetB7_224x224, '__doc__', EfficientNet.__doc__)
