Intelegentny_Pszczelarz/.venv/Lib/site-packages/keras/applications/xception.py
2023-06-19 00:49:18 +02:00

381 lines
13 KiB
Python

# Copyright 2016 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.
# ==============================================================================
"""Xception V1 model for Keras.
On ImageNet, this model gets to a top-1 validation accuracy of 0.790
and a top-5 validation accuracy of 0.945.
Reference:
- [Xception: Deep Learning with Depthwise Separable Convolutions](
https://arxiv.org/abs/1610.02357) (CVPR 2017)
"""
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
TF_WEIGHTS_PATH = (
"https://storage.googleapis.com/tensorflow/keras-applications/"
"xception/xception_weights_tf_dim_ordering_tf_kernels.h5"
)
TF_WEIGHTS_PATH_NO_TOP = (
"https://storage.googleapis.com/tensorflow/keras-applications/"
"xception/xception_weights_tf_dim_ordering_tf_kernels_notop.h5"
)
layers = VersionAwareLayers()
@keras_export(
"keras.applications.xception.Xception", "keras.applications.Xception"
)
def Xception(
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
):
"""Instantiates the Xception architecture.
Reference:
- [Xception: Deep Learning with Depthwise Separable Convolutions](
https://arxiv.org/abs/1610.02357) (CVPR 2017)
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/).
The default input image size for this model is 299x299.
Note: each Keras Application expects a specific kind of input preprocessing.
For Xception, call `tf.keras.applications.xception.preprocess_input` on your
inputs before passing them to the model.
`xception.preprocess_input` will scale input pixels between -1 and 1.
Args:
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 (otherwise the input shape
has to be `(299, 299, 3)`.
It should have exactly 3 inputs channels,
and width and height should be no smaller than 71.
E.g. `(150, 150, 3)` would be one valid value.
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 block.
- `avg` means that global average pooling
will be applied to the output of the
last convolutional block, 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.
When loading pretrained weights, `classifier_activation` can only
be `None` or `"softmax"`.
Returns:
A `keras.Model` instance.
"""
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=299,
min_size=71,
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
channel_axis = 1 if backend.image_data_format() == "channels_first" else -1
x = layers.Conv2D(
32, (3, 3), strides=(2, 2), use_bias=False, name="block1_conv1"
)(img_input)
x = layers.BatchNormalization(axis=channel_axis, name="block1_conv1_bn")(x)
x = layers.Activation("relu", name="block1_conv1_act")(x)
x = layers.Conv2D(64, (3, 3), use_bias=False, name="block1_conv2")(x)
x = layers.BatchNormalization(axis=channel_axis, name="block1_conv2_bn")(x)
x = layers.Activation("relu", name="block1_conv2_act")(x)
residual = layers.Conv2D(
128, (1, 1), strides=(2, 2), padding="same", use_bias=False
)(x)
residual = layers.BatchNormalization(axis=channel_axis)(residual)
x = layers.SeparableConv2D(
128, (3, 3), padding="same", use_bias=False, name="block2_sepconv1"
)(x)
x = layers.BatchNormalization(axis=channel_axis, name="block2_sepconv1_bn")(
x
)
x = layers.Activation("relu", name="block2_sepconv2_act")(x)
x = layers.SeparableConv2D(
128, (3, 3), padding="same", use_bias=False, name="block2_sepconv2"
)(x)
x = layers.BatchNormalization(axis=channel_axis, name="block2_sepconv2_bn")(
x
)
x = layers.MaxPooling2D(
(3, 3), strides=(2, 2), padding="same", name="block2_pool"
)(x)
x = layers.add([x, residual])
residual = layers.Conv2D(
256, (1, 1), strides=(2, 2), padding="same", use_bias=False
)(x)
residual = layers.BatchNormalization(axis=channel_axis)(residual)
x = layers.Activation("relu", name="block3_sepconv1_act")(x)
x = layers.SeparableConv2D(
256, (3, 3), padding="same", use_bias=False, name="block3_sepconv1"
)(x)
x = layers.BatchNormalization(axis=channel_axis, name="block3_sepconv1_bn")(
x
)
x = layers.Activation("relu", name="block3_sepconv2_act")(x)
x = layers.SeparableConv2D(
256, (3, 3), padding="same", use_bias=False, name="block3_sepconv2"
)(x)
x = layers.BatchNormalization(axis=channel_axis, name="block3_sepconv2_bn")(
x
)
x = layers.MaxPooling2D(
(3, 3), strides=(2, 2), padding="same", name="block3_pool"
)(x)
x = layers.add([x, residual])
residual = layers.Conv2D(
728, (1, 1), strides=(2, 2), padding="same", use_bias=False
)(x)
residual = layers.BatchNormalization(axis=channel_axis)(residual)
x = layers.Activation("relu", name="block4_sepconv1_act")(x)
x = layers.SeparableConv2D(
728, (3, 3), padding="same", use_bias=False, name="block4_sepconv1"
)(x)
x = layers.BatchNormalization(axis=channel_axis, name="block4_sepconv1_bn")(
x
)
x = layers.Activation("relu", name="block4_sepconv2_act")(x)
x = layers.SeparableConv2D(
728, (3, 3), padding="same", use_bias=False, name="block4_sepconv2"
)(x)
x = layers.BatchNormalization(axis=channel_axis, name="block4_sepconv2_bn")(
x
)
x = layers.MaxPooling2D(
(3, 3), strides=(2, 2), padding="same", name="block4_pool"
)(x)
x = layers.add([x, residual])
for i in range(8):
residual = x
prefix = "block" + str(i + 5)
x = layers.Activation("relu", name=prefix + "_sepconv1_act")(x)
x = layers.SeparableConv2D(
728,
(3, 3),
padding="same",
use_bias=False,
name=prefix + "_sepconv1",
)(x)
x = layers.BatchNormalization(
axis=channel_axis, name=prefix + "_sepconv1_bn"
)(x)
x = layers.Activation("relu", name=prefix + "_sepconv2_act")(x)
x = layers.SeparableConv2D(
728,
(3, 3),
padding="same",
use_bias=False,
name=prefix + "_sepconv2",
)(x)
x = layers.BatchNormalization(
axis=channel_axis, name=prefix + "_sepconv2_bn"
)(x)
x = layers.Activation("relu", name=prefix + "_sepconv3_act")(x)
x = layers.SeparableConv2D(
728,
(3, 3),
padding="same",
use_bias=False,
name=prefix + "_sepconv3",
)(x)
x = layers.BatchNormalization(
axis=channel_axis, name=prefix + "_sepconv3_bn"
)(x)
x = layers.add([x, residual])
residual = layers.Conv2D(
1024, (1, 1), strides=(2, 2), padding="same", use_bias=False
)(x)
residual = layers.BatchNormalization(axis=channel_axis)(residual)
x = layers.Activation("relu", name="block13_sepconv1_act")(x)
x = layers.SeparableConv2D(
728, (3, 3), padding="same", use_bias=False, name="block13_sepconv1"
)(x)
x = layers.BatchNormalization(
axis=channel_axis, name="block13_sepconv1_bn"
)(x)
x = layers.Activation("relu", name="block13_sepconv2_act")(x)
x = layers.SeparableConv2D(
1024, (3, 3), padding="same", use_bias=False, name="block13_sepconv2"
)(x)
x = layers.BatchNormalization(
axis=channel_axis, name="block13_sepconv2_bn"
)(x)
x = layers.MaxPooling2D(
(3, 3), strides=(2, 2), padding="same", name="block13_pool"
)(x)
x = layers.add([x, residual])
x = layers.SeparableConv2D(
1536, (3, 3), padding="same", use_bias=False, name="block14_sepconv1"
)(x)
x = layers.BatchNormalization(
axis=channel_axis, name="block14_sepconv1_bn"
)(x)
x = layers.Activation("relu", name="block14_sepconv1_act")(x)
x = layers.SeparableConv2D(
2048, (3, 3), padding="same", use_bias=False, name="block14_sepconv2"
)(x)
x = layers.BatchNormalization(
axis=channel_axis, name="block14_sepconv2_bn"
)(x)
x = layers.Activation("relu", name="block14_sepconv2_act")(x)
if include_top:
x = layers.GlobalAveragePooling2D(name="avg_pool")(x)
imagenet_utils.validate_activation(classifier_activation, weights)
x = layers.Dense(
classes, activation=classifier_activation, name="predictions"
)(x)
else:
if pooling == "avg":
x = layers.GlobalAveragePooling2D()(x)
elif pooling == "max":
x = layers.GlobalMaxPooling2D()(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="xception")
# Load weights.
if weights == "imagenet":
if include_top:
weights_path = data_utils.get_file(
"xception_weights_tf_dim_ordering_tf_kernels.h5",
TF_WEIGHTS_PATH,
cache_subdir="models",
file_hash="0a58e3b7378bc2990ea3b43d5981f1f6",
)
else:
weights_path = data_utils.get_file(
"xception_weights_tf_dim_ordering_tf_kernels_notop.h5",
TF_WEIGHTS_PATH_NO_TOP,
cache_subdir="models",
file_hash="b0042744bf5b25fce3cb969f33bebb97",
)
model.load_weights(weights_path)
elif weights is not None:
model.load_weights(weights)
return model
@keras_export("keras.applications.xception.preprocess_input")
def preprocess_input(x, data_format=None):
return imagenet_utils.preprocess_input(
x, data_format=data_format, mode="tf"
)
@keras_export("keras.applications.xception.decode_predictions")
def decode_predictions(preds, top=5):
return imagenet_utils.decode_predictions(preds, top=top)
preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
mode="",
ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF,
error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC,
)
decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__