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Intelegentny_Pszczelarz/.venv/Lib/site-packages/keras/applications/efficientnet.py

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feature: "ANN commit 2"
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# Copyright 2019 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.
# ==============================================================================
"""EfficientNet models for Keras.
Reference:
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](
https://arxiv.org/abs/1905.11946) (ICML 2019)
"""
import copy
import math
import tensorflow.compat.v2 as tf
from keras import backend
from keras.applications import imagenet_utils
from keras.engine import training
from keras.layers import VersionAwareLayers
from keras.utils import data_utils
from keras.utils import layer_utils
# isort: off
from tensorflow.python.util.tf_export import keras_export
BASE_WEIGHTS_PATH = "https://storage.googleapis.com/keras-applications/"
WEIGHTS_HASHES = {
"b0": (
"902e53a9f72be733fc0bcb005b3ebbac",
"50bc09e76180e00e4465e1a485ddc09d",
),
"b1": (
"1d254153d4ab51201f1646940f018540",
"74c4e6b3e1f6a1eea24c589628592432",
),
"b2": (
"b15cce36ff4dcbd00b6dd88e7857a6ad",
"111f8e2ac8aa800a7a99e3239f7bfb39",
),
"b3": (
"ffd1fdc53d0ce67064dc6a9c7960ede0",
"af6d107764bb5b1abb91932881670226",
),
"b4": (
"18c95ad55216b8f92d7e70b3a046e2fc",
"ebc24e6d6c33eaebbd558eafbeedf1ba",
),
"b5": (
"ace28f2a6363774853a83a0b21b9421a",
"38879255a25d3c92d5e44e04ae6cec6f",
),
"b6": (
"165f6e37dce68623721b423839de8be5",
"9ecce42647a20130c1f39a5d4cb75743",
),
"b7": (
"8c03f828fec3ef71311cd463b6759d99",
"cbcfe4450ddf6f3ad90b1b398090fe4a",
),
}
DEFAULT_BLOCKS_ARGS = [
{
"kernel_size": 3,
"repeats": 1,
"filters_in": 32,
"filters_out": 16,
"expand_ratio": 1,
"id_skip": True,
"strides": 1,
"se_ratio": 0.25,
},
{
"kernel_size": 3,
"repeats": 2,
"filters_in": 16,
"filters_out": 24,
"expand_ratio": 6,
"id_skip": True,
"strides": 2,
"se_ratio": 0.25,
},
{
"kernel_size": 5,
"repeats": 2,
"filters_in": 24,
"filters_out": 40,
"expand_ratio": 6,
"id_skip": True,
"strides": 2,
"se_ratio": 0.25,
},
{
"kernel_size": 3,
"repeats": 3,
"filters_in": 40,
"filters_out": 80,
"expand_ratio": 6,
"id_skip": True,
"strides": 2,
"se_ratio": 0.25,
},
{
"kernel_size": 5,
"repeats": 3,
"filters_in": 80,
"filters_out": 112,
"expand_ratio": 6,
"id_skip": True,
"strides": 1,
"se_ratio": 0.25,
},
{
"kernel_size": 5,
"repeats": 4,
"filters_in": 112,
"filters_out": 192,
"expand_ratio": 6,
"id_skip": True,
"strides": 2,
"se_ratio": 0.25,
},
{
"kernel_size": 3,
"repeats": 1,
"filters_in": 192,
"filters_out": 320,
"expand_ratio": 6,
"id_skip": True,
"strides": 1,
"se_ratio": 0.25,
},
]
CONV_KERNEL_INITIALIZER = {
"class_name": "VarianceScaling",
"config": {
"scale": 2.0,
"mode": "fan_out",
"distribution": "truncated_normal",
},
}
DENSE_KERNEL_INITIALIZER = {
"class_name": "VarianceScaling",
"config": {
"scale": 1.0 / 3.0,
"mode": "fan_out",
"distribution": "uniform",
},
}
layers = VersionAwareLayers()
BASE_DOCSTRING = """Instantiates the {name} architecture.
Reference:
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](
https://arxiv.org/abs/1905.11946) (ICML 2019)
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 EfficientNet, input preprocessing is included as part of the model
(as a `Rescaling` layer), and thus
`tf.keras.applications.efficientnet.preprocess_input` is actually a
pass-through function. EfficientNet models expect their inputs to be float
tensors of pixels with values in the [0-255] range.
Args:
include_top: 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 `str` 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.
"""
IMAGENET_STDDEV_RGB = [0.229, 0.224, 0.225]
def EfficientNet(
width_coefficient,
depth_coefficient,
default_size,
dropout_rate=0.2,
drop_connect_rate=0.2,
depth_divisor=8,
activation="swish",
blocks_args="default",
model_name="efficientnet",
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
):
"""Instantiates the EfficientNet architecture.
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.
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()`)
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 `str` 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.
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
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."
)
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 = 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
def round_filters(filters, divisor=depth_divisor):
"""Round number of filters based on depth multiplier."""
filters *= width_coefficient
new_filters = max(
divisor, int(filters + divisor / 2) // divisor * divisor
)
# Make sure that round down does not go down by more than 10%.
if new_filters < 0.9 * filters:
new_filters += divisor
return int(new_filters)
def round_repeats(repeats):
"""Round number of repeats based on depth multiplier."""
return int(math.ceil(depth_coefficient * repeats))
# Build stem
x = img_input
x = layers.Rescaling(1.0 / 255.0)(x)
x = layers.Normalization(axis=bn_axis)(x)
if weights == "imagenet":
# Note that the normaliztion layer uses square value of STDDEV as the
# variance for the layer: result = (input - mean) / sqrt(var)
# However, the original implemenetation uses (input - mean) / var to
# normalize the input, we need to divide another sqrt(var) to match the
# original implementation.
# See https://github.com/tensorflow/tensorflow/issues/49930 for more
# details
x = layers.Rescaling(1.0 / tf.math.sqrt(IMAGENET_STDDEV_RGB))(x)
x = layers.ZeroPadding2D(
padding=imagenet_utils.correct_pad(x, 3), name="stem_conv_pad"
)(x)
x = layers.Conv2D(
round_filters(32),
3,
strides=2,
padding="valid",
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
blocks_args = copy.deepcopy(blocks_args)
b = 0
blocks = float(sum(round_repeats(args["repeats"]) for args in blocks_args))
for i, args in enumerate(blocks_args):
assert args["repeats"] > 0
# Update block input and output filters based on depth multiplier.
args["filters_in"] = round_filters(args["filters_in"])
args["filters_out"] = round_filters(args["filters_out"])
for j in range(round_repeats(args.pop("repeats"))):
# The first block needs to take care of stride and filter size
# increase.
if j > 0:
args["strides"] = 1
args["filters_in"] = args["filters_out"]
x = block(
x,
activation,
drop_connect_rate * b / blocks,
name=f"block{i + 1}{chr(j + 97)}_",
**args,
)
b += 1
# Build top
x = layers.Conv2D(
round_filters(1280),
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 > 0:
x = layers.Dropout(dropout_rate, name="top_dropout")(x)
imagenet_utils.validate_activation(classifier_activation, weights)
x = layers.Dense(
classes,
activation=classifier_activation,
kernel_initializer=DENSE_KERNEL_INITIALIZER,
name="predictions",
)(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 = layer_utils.get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = training.Model(inputs, x, name=model_name)
# Load weights.
if weights == "imagenet":
if include_top:
file_suffix = ".h5"
file_hash = WEIGHTS_HASHES[model_name[-2:]][0]
else:
file_suffix = "_notop.h5"
file_hash = WEIGHTS_HASHES[model_name[-2:]][1]
file_name = model_name + file_suffix
weights_path = data_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 block(
inputs,
activation="swish",
drop_rate=0.0,
name="",
filters_in=32,
filters_out=16,
kernel_size=3,
strides=1,
expand_ratio=1,
se_ratio=0.0,
id_skip=True,
):
"""An inverted residual block.
Args:
inputs: input tensor.
activation: activation function.
drop_rate: float between 0 and 1, fraction of the input units to drop.
name: string, block label.
filters_in: integer, the number of input filters.
filters_out: integer, the number of output filters.
kernel_size: integer, the dimension of the convolution window.
strides: integer, the stride of the convolution.
expand_ratio: integer, scaling coefficient for the input filters.
se_ratio: float between 0 and 1, fraction to squeeze the input filters.
id_skip: boolean.
Returns:
output tensor for the block.
"""
bn_axis = 3 if backend.image_data_format() == "channels_last" else 1
# Expansion phase
filters = filters_in * expand_ratio
if expand_ratio != 1:
x = layers.Conv2D(
filters,
1,
padding="same",
use_bias=False,
kernel_initializer=CONV_KERNEL_INITIALIZER,
name=name + "expand_conv",
)(inputs)
x = layers.BatchNormalization(axis=bn_axis, name=name + "expand_bn")(x)
x = layers.Activation(activation, name=name + "expand_activation")(x)
else:
x = inputs
# Depthwise Convolution
if strides == 2:
x = layers.ZeroPadding2D(
padding=imagenet_utils.correct_pad(x, kernel_size),
name=name + "dwconv_pad",
)(x)
conv_pad = "valid"
else:
conv_pad = "same"
x = layers.DepthwiseConv2D(
kernel_size,
strides=strides,
padding=conv_pad,
use_bias=False,
depthwise_initializer=CONV_KERNEL_INITIALIZER,
name=name + "dwconv",
)(x)
x = layers.BatchNormalization(axis=bn_axis, name=name + "bn")(x)
x = layers.Activation(activation, name=name + "activation")(x)
# Squeeze and Excitation phase
if 0 < se_ratio <= 1:
filters_se = max(1, int(filters_in * 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_out,
1,
padding="same",
use_bias=False,
kernel_initializer=CONV_KERNEL_INITIALIZER,
name=name + "project_conv",
)(x)
x = layers.BatchNormalization(axis=bn_axis, name=name + "project_bn")(x)
if id_skip and strides == 1 and filters_in == filters_out:
if drop_rate > 0:
x = layers.Dropout(
drop_rate, noise_shape=(None, 1, 1, 1), name=name + "drop"
)(x)
x = layers.add([x, inputs], name=name + "add")
return x
@keras_export(
"keras.applications.efficientnet.EfficientNetB0",
"keras.applications.EfficientNetB0",
)
def EfficientNetB0(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
**kwargs,
):
return EfficientNet(
1.0,
1.0,
224,
0.2,
model_name="efficientnetb0",
include_top=include_top,
weights=weights,
input_tensor=input_tensor,
input_shape=input_shape,
pooling=pooling,
classes=classes,
classifier_activation=classifier_activation,
**kwargs,
)
@keras_export(
"keras.applications.efficientnet.EfficientNetB1",
"keras.applications.EfficientNetB1",
)
def EfficientNetB1(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
**kwargs,
):
return EfficientNet(
1.0,
1.1,
240,
0.2,
model_name="efficientnetb1",
include_top=include_top,
weights=weights,
input_tensor=input_tensor,
input_shape=input_shape,
pooling=pooling,
classes=classes,
classifier_activation=classifier_activation,
**kwargs,
)
@keras_export(
"keras.applications.efficientnet.EfficientNetB2",
"keras.applications.EfficientNetB2",
)
def EfficientNetB2(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
**kwargs,
):
return EfficientNet(
1.1,
1.2,
260,
0.3,
model_name="efficientnetb2",
include_top=include_top,
weights=weights,
input_tensor=input_tensor,
input_shape=input_shape,
pooling=pooling,
classes=classes,
classifier_activation=classifier_activation,
**kwargs,
)
@keras_export(
"keras.applications.efficientnet.EfficientNetB3",
"keras.applications.EfficientNetB3",
)
def EfficientNetB3(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
**kwargs,
):
return EfficientNet(
1.2,
1.4,
300,
0.3,
model_name="efficientnetb3",
include_top=include_top,
weights=weights,
input_tensor=input_tensor,
input_shape=input_shape,
pooling=pooling,
classes=classes,
classifier_activation=classifier_activation,
**kwargs,
)
@keras_export(
"keras.applications.efficientnet.EfficientNetB4",
"keras.applications.EfficientNetB4",
)
def EfficientNetB4(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
**kwargs,
):
return EfficientNet(
1.4,
1.8,
380,
0.4,
model_name="efficientnetb4",
include_top=include_top,
weights=weights,
input_tensor=input_tensor,
input_shape=input_shape,
pooling=pooling,
classes=classes,
classifier_activation=classifier_activation,
**kwargs,
)
@keras_export(
"keras.applications.efficientnet.EfficientNetB5",
"keras.applications.EfficientNetB5",
)
def EfficientNetB5(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
**kwargs,
):
return EfficientNet(
1.6,
2.2,
456,
0.4,
model_name="efficientnetb5",
include_top=include_top,
weights=weights,
input_tensor=input_tensor,
input_shape=input_shape,
pooling=pooling,
classes=classes,
classifier_activation=classifier_activation,
**kwargs,
)
@keras_export(
"keras.applications.efficientnet.EfficientNetB6",
"keras.applications.EfficientNetB6",
)
def EfficientNetB6(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
**kwargs,
):
return EfficientNet(
1.8,
2.6,
528,
0.5,
model_name="efficientnetb6",
include_top=include_top,
weights=weights,
input_tensor=input_tensor,
input_shape=input_shape,
pooling=pooling,
classes=classes,
classifier_activation=classifier_activation,
**kwargs,
)
@keras_export(
"keras.applications.efficientnet.EfficientNetB7",
"keras.applications.EfficientNetB7",
)
def EfficientNetB7(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
**kwargs,
):
return EfficientNet(
2.0,
3.1,
600,
0.5,
model_name="efficientnetb7",
include_top=include_top,
weights=weights,
input_tensor=input_tensor,
input_shape=input_shape,
pooling=pooling,
classes=classes,
classifier_activation=classifier_activation,
**kwargs,
)
EfficientNetB0.__doc__ = BASE_DOCSTRING.format(name="EfficientNetB0")
EfficientNetB1.__doc__ = BASE_DOCSTRING.format(name="EfficientNetB1")
EfficientNetB2.__doc__ = BASE_DOCSTRING.format(name="EfficientNetB2")
EfficientNetB3.__doc__ = BASE_DOCSTRING.format(name="EfficientNetB3")
EfficientNetB4.__doc__ = BASE_DOCSTRING.format(name="EfficientNetB4")
EfficientNetB5.__doc__ = BASE_DOCSTRING.format(name="EfficientNetB5")
EfficientNetB6.__doc__ = BASE_DOCSTRING.format(name="EfficientNetB6")
EfficientNetB7.__doc__ = BASE_DOCSTRING.format(name="EfficientNetB7")
@keras_export("keras.applications.efficientnet.preprocess_input")
def preprocess_input(x, data_format=None):
"""A placeholder method for backward compatibility.
The preprocessing logic has been included in the efficientnet model
implementation. Users are no longer required to call this method to
normalize the input data. This method does nothing and only kept as a
placeholder to align the API surface between old and new version of model.
Args:
x: A floating point `numpy.array` or a `tf.Tensor`.
data_format: Optional data format of the image tensor/array. Defaults to
None, in which case the global setting
`tf.keras.backend.image_data_format()` is used (unless you changed it,
it defaults to "channels_last").{mode}
Returns:
Unchanged `numpy.array` or `tf.Tensor`.
"""
return x
@keras_export("keras.applications.efficientnet.decode_predictions")
def decode_predictions(preds, top=5):
return imagenet_utils.decode_predictions(preds, top=top)
decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
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