def get_swish(**kwargs):
backend, layers, models, keras_utils = get_submodules_from_kwargs(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
"""
backend, layers, models, keras_utils = get_submodules_from_kwargs(kwargs)
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 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,
freeze_bn=False,
**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.
"""
global backend, layers, models, keras_utils
backend, layers, models, keras_utils = get_submodules_from_kwargs(kwargs)
features = []
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 = BatchNormalization(freeze=freeze_bn, axis=bn_axis, name='stem_bn')(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),
freeze_bn=freeze_bn)
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,
freeze_bn=freeze_bn)
block_num += 1
if idx < len(blocks_args) - 1 and blocks_args[idx + 1].strides[0] == 2:
features.append(x)
elif idx == len(blocks_args) - 1:
features.append(x)
return features
