s473580/Intelegentny_Pszczelarz
s473580/Intelegentny_Pszczelarz
2
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
36e4e4f8a5
Intelegentny_Pszczelarz/.venv/Lib/site-packages/keras/applications/mobilenet_v3.py

699 lines
24 KiB
Python
Raw Normal View History

feature: "ANN commit 2"
2023-06-19 00:49:18 +02:00
# Copyright 2020 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.
# ==============================================================================
"""MobileNet v3 models for Keras."""
import tensorflow.compat.v2 as tf
from keras import backend
from keras import models
from keras.applications import imagenet_utils
from keras.layers import VersionAwareLayers
from keras.utils import data_utils
from keras.utils import layer_utils
# isort: off
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util.tf_export import keras_export
# TODO(scottzhu): Change this to the GCS path.
BASE_WEIGHT_PATH = (
"https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v3/"
)
WEIGHTS_HASHES = {
"large_224_0.75_float": (
"765b44a33ad4005b3ac83185abf1d0eb",
"40af19a13ebea4e2ee0c676887f69a2e",
),
"large_224_1.0_float": (
"59e551e166be033d707958cf9e29a6a7",
"07fb09a5933dd0c8eaafa16978110389",
),
"large_minimalistic_224_1.0_float": (
"675e7b876c45c57e9e63e6d90a36599c",
"ec5221f64a2f6d1ef965a614bdae7973",
),
"small_224_0.75_float": (
"cb65d4e5be93758266aa0a7f2c6708b7",
"ebdb5cc8e0b497cd13a7c275d475c819",
),
"small_224_1.0_float": (
"8768d4c2e7dee89b9d02b2d03d65d862",
"d3e8ec802a04aa4fc771ee12a9a9b836",
),
"small_minimalistic_224_1.0_float": (
"99cd97fb2fcdad2bf028eb838de69e37",
"cde8136e733e811080d9fcd8a252f7e4",
),
}
layers = VersionAwareLayers()
BASE_DOCSTRING = """Instantiates the {name} architecture.
Reference:
- [Searching for MobileNetV3](
https://arxiv.org/pdf/1905.02244.pdf) (ICCV 2019)
The following table describes the performance of MobileNets v3:
------------------------------------------------------------------------
MACs stands for Multiply Adds
|Classification Checkpoint|MACs(M)|Parameters(M)|Top1 Accuracy|Pixel1 CPU(ms)|
|---|---|---|---|---|
| mobilenet_v3_large_1.0_224 | 217 | 5.4 | 75.6 | 51.2 |
| mobilenet_v3_large_0.75_224 | 155 | 4.0 | 73.3 | 39.8 |
| mobilenet_v3_large_minimalistic_1.0_224 | 209 | 3.9 | 72.3 | 44.1 |
| mobilenet_v3_small_1.0_224 | 66 | 2.9 | 68.1 | 15.8 |
| mobilenet_v3_small_0.75_224 | 44 | 2.4 | 65.4 | 12.8 |
| mobilenet_v3_small_minimalistic_1.0_224 | 65 | 2.0 | 61.9 | 12.2 |
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 MobileNetV3, by default input preprocessing is included as a part of the
model (as a `Rescaling` layer), and thus
`tf.keras.applications.mobilenet_v3.preprocess_input` is actually a
pass-through function. In this use case, MobileNetV3 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 MobileNetV3 models expect their inputs to be float
tensors of pixels with values in the [-1, 1] range.
Args:
input_shape: Optional shape tuple, to be specified if you would
like to use a model with an input image resolution that is not
(224, 224, 3).
It should have exactly 3 inputs channels (224, 224, 3).
You can also omit this option if you would like
to infer input_shape from an input_tensor.
If you choose to include both input_tensor and input_shape then
input_shape will be used if they match, if the shapes
do not match then we will throw an error.
E.g. `(160, 160, 3)` would be one valid value.
alpha: controls the width of the network. This is known as the
depth multiplier in the MobileNetV3 paper, but the name is kept for
consistency with MobileNetV1 in Keras.
- If `alpha` < 1.0, proportionally decreases the number
of filters in each layer.
- If `alpha` > 1.0, proportionally increases the number
of filters in each layer.
- If `alpha` = 1, default number of filters from the paper
are used at each layer.
minimalistic: In addition to large and small models this module also
contains so-called minimalistic models, these models have the same
per-layer dimensions characteristic as MobilenetV3 however, they don't
utilize any of the advanced blocks (squeeze-and-excite units, hard-swish,
and 5x5 convolutions). While these models are less efficient on CPU, they
are much more performant on GPU/DSP.
include_top: Boolean, whether to include the fully-connected
layer at the top of the network. Defaults to `True`.
weights: String, 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.
pooling: String, 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: Integer, optional number of classes to classify images
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified.
dropout_rate: fraction of the input units to drop on the last layer.
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"`.
include_preprocessing: Boolean, whether to include the preprocessing
layer (`Rescaling`) at the bottom of the network. Defaults to `True`.
Call arguments:
inputs: A floating point `numpy.array` or a `tf.Tensor`, 4D with 3 color
channels, with values in the range [0, 255] if `include_preprocessing`
is True and in the range [-1, 1] otherwise.
Returns:
A `keras.Model` instance.
"""
def MobileNetV3(
stack_fn,
last_point_ch,
input_shape=None,
alpha=1.0,
model_type="large",
minimalistic=False,
include_top=True,
weights="imagenet",
input_tensor=None,
classes=1000,
pooling=None,
dropout_rate=0.2,
classifier_activation="softmax",
include_preprocessing=True,
):
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 and default size.
# If both input_shape and input_tensor are used, they should match
if input_shape is not None and input_tensor is not None:
try:
is_input_t_tensor = backend.is_keras_tensor(input_tensor)
except ValueError:
try:
is_input_t_tensor = backend.is_keras_tensor(
layer_utils.get_source_inputs(input_tensor)
)
except ValueError:
raise ValueError(
"input_tensor: ",
input_tensor,
"is not type input_tensor. "
f"Received type(input_tensor)={type(input_tensor)}",
)
if is_input_t_tensor:
if backend.image_data_format() == "channels_first":
if backend.int_shape(input_tensor)[1] != input_shape[1]:
raise ValueError(
"When backend.image_data_format()=channels_first, "
"input_shape[1] must equal "
"backend.int_shape(input_tensor)[1]. Received "
f"input_shape={input_shape}, "
"backend.int_shape(input_tensor)="
f"{backend.int_shape(input_tensor)}"
)
else:
if backend.int_shape(input_tensor)[2] != input_shape[1]:
raise ValueError(
"input_shape[1] must equal "
"backend.int_shape(input_tensor)[2]. Received "
f"input_shape={input_shape}, "
"backend.int_shape(input_tensor)="
f"{backend.int_shape(input_tensor)}"
)
else:
raise ValueError(
"input_tensor specified: ",
input_tensor,
"is not a keras tensor",
)
# If input_shape is None, infer shape from input_tensor
if input_shape is None and input_tensor is not None:
try:
backend.is_keras_tensor(input_tensor)
except ValueError:
raise ValueError(
"input_tensor: ",
input_tensor,
"is type: ",
type(input_tensor),
"which is not a valid type",
)
if backend.is_keras_tensor(input_tensor):
if backend.image_data_format() == "channels_first":
rows = backend.int_shape(input_tensor)[2]
cols = backend.int_shape(input_tensor)[3]
input_shape = (3, cols, rows)
else:
rows = backend.int_shape(input_tensor)[1]
cols = backend.int_shape(input_tensor)[2]
input_shape = (cols, rows, 3)
# If input_shape is None and input_tensor is None using standard shape
if input_shape is None and input_tensor is None:
input_shape = (None, None, 3)
if backend.image_data_format() == "channels_last":
row_axis, col_axis = (0, 1)
else:
row_axis, col_axis = (1, 2)
rows = input_shape[row_axis]
cols = input_shape[col_axis]
if rows and cols and (rows < 32 or cols < 32):
raise ValueError(
"Input size must be at least 32x32; Received `input_shape="
f"{input_shape}`"
)
if weights == "imagenet":
if (
not minimalistic
and alpha not in [0.75, 1.0]
or minimalistic
and alpha != 1.0
):
raise ValueError(
"If imagenet weights are being loaded, "
"alpha can be one of `0.75`, `1.0` for non minimalistic "
"or `1.0` for minimalistic only."
)
if rows != cols or rows != 224:
logging.warning(
"`input_shape` is undefined or non-square, "
"or `rows` is not 224. "
"Weights for input shape (224, 224) will be "
"loaded as the default."
)
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
if minimalistic:
kernel = 3
activation = relu
se_ratio = None
else:
kernel = 5
activation = hard_swish
se_ratio = 0.25
x = img_input
if include_preprocessing:
x = layers.Rescaling(scale=1.0 / 127.5, offset=-1.0)(x)
x = layers.Conv2D(
16,
kernel_size=3,
strides=(2, 2),
padding="same",
use_bias=False,
name="Conv",
)(x)
x = layers.BatchNormalization(
axis=channel_axis, epsilon=1e-3, momentum=0.999, name="Conv/BatchNorm"
)(x)
x = activation(x)
x = stack_fn(x, kernel, activation, se_ratio)
last_conv_ch = _depth(backend.int_shape(x)[channel_axis] * 6)
# if the width multiplier is greater than 1 we
# increase the number of output channels
if alpha > 1.0:
last_point_ch = _depth(last_point_ch * alpha)
x = layers.Conv2D(
last_conv_ch,
kernel_size=1,
padding="same",
use_bias=False,
name="Conv_1",
)(x)
x = layers.BatchNormalization(
axis=channel_axis, epsilon=1e-3, momentum=0.999, name="Conv_1/BatchNorm"
)(x)
x = activation(x)
if include_top:
x = layers.GlobalAveragePooling2D(keepdims=True)(x)
x = layers.Conv2D(
last_point_ch,
kernel_size=1,
padding="same",
use_bias=True,
name="Conv_2",
)(x)
x = activation(x)
if dropout_rate > 0:
x = layers.Dropout(dropout_rate)(x)
x = layers.Conv2D(
classes, kernel_size=1, padding="same", name="Logits"
)(x)
x = layers.Flatten()(x)
imagenet_utils.validate_activation(classifier_activation, weights)
x = layers.Activation(
activation=classifier_activation, 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 = models.Model(inputs, x, name="MobilenetV3" + model_type)
# Load weights.
if weights == "imagenet":
model_name = "{}{}_224_{}_float".format(
model_type, "_minimalistic" if minimalistic else "", str(alpha)
)
if include_top:
file_name = "weights_mobilenet_v3_" + model_name + ".h5"
file_hash = WEIGHTS_HASHES[model_name][0]
else:
file_name = "weights_mobilenet_v3_" + model_name + "_no_top_v2.h5"
file_hash = WEIGHTS_HASHES[model_name][1]
weights_path = data_utils.get_file(
file_name,
BASE_WEIGHT_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
@keras_export("keras.applications.MobileNetV3Small")
def MobileNetV3Small(
input_shape=None,
alpha=1.0,
minimalistic=False,
include_top=True,
weights="imagenet",
input_tensor=None,
classes=1000,
pooling=None,
dropout_rate=0.2,
classifier_activation="softmax",
include_preprocessing=True,
):
def stack_fn(x, kernel, activation, se_ratio):
def depth(d):
return _depth(d * alpha)
x = _inverted_res_block(x, 1, depth(16), 3, 2, se_ratio, relu, 0)
x = _inverted_res_block(x, 72.0 / 16, depth(24), 3, 2, None, relu, 1)
x = _inverted_res_block(x, 88.0 / 24, depth(24), 3, 1, None, relu, 2)
x = _inverted_res_block(
x, 4, depth(40), kernel, 2, se_ratio, activation, 3
)
x = _inverted_res_block(
x, 6, depth(40), kernel, 1, se_ratio, activation, 4
)
x = _inverted_res_block(
x, 6, depth(40), kernel, 1, se_ratio, activation, 5
)
x = _inverted_res_block(
x, 3, depth(48), kernel, 1, se_ratio, activation, 6
)
x = _inverted_res_block(
x, 3, depth(48), kernel, 1, se_ratio, activation, 7
)
x = _inverted_res_block(
x, 6, depth(96), kernel, 2, se_ratio, activation, 8
)
x = _inverted_res_block(
x, 6, depth(96), kernel, 1, se_ratio, activation, 9
)
x = _inverted_res_block(
x, 6, depth(96), kernel, 1, se_ratio, activation, 10
)
return x
return MobileNetV3(
stack_fn,
1024,
input_shape,
alpha,
"small",
minimalistic,
include_top,
weights,
input_tensor,
classes,
pooling,
dropout_rate,
classifier_activation,
include_preprocessing,
)
@keras_export("keras.applications.MobileNetV3Large")
def MobileNetV3Large(
input_shape=None,
alpha=1.0,
minimalistic=False,
include_top=True,
weights="imagenet",
input_tensor=None,
classes=1000,
pooling=None,
dropout_rate=0.2,
classifier_activation="softmax",
include_preprocessing=True,
):
def stack_fn(x, kernel, activation, se_ratio):
def depth(d):
return _depth(d * alpha)
x = _inverted_res_block(x, 1, depth(16), 3, 1, None, relu, 0)
x = _inverted_res_block(x, 4, depth(24), 3, 2, None, relu, 1)
x = _inverted_res_block(x, 3, depth(24), 3, 1, None, relu, 2)
x = _inverted_res_block(x, 3, depth(40), kernel, 2, se_ratio, relu, 3)
x = _inverted_res_block(x, 3, depth(40), kernel, 1, se_ratio, relu, 4)
x = _inverted_res_block(x, 3, depth(40), kernel, 1, se_ratio, relu, 5)
x = _inverted_res_block(x, 6, depth(80), 3, 2, None, activation, 6)
x = _inverted_res_block(x, 2.5, depth(80), 3, 1, None, activation, 7)
x = _inverted_res_block(x, 2.3, depth(80), 3, 1, None, activation, 8)
x = _inverted_res_block(x, 2.3, depth(80), 3, 1, None, activation, 9)
x = _inverted_res_block(
x, 6, depth(112), 3, 1, se_ratio, activation, 10
)
x = _inverted_res_block(
x, 6, depth(112), 3, 1, se_ratio, activation, 11
)
x = _inverted_res_block(
x, 6, depth(160), kernel, 2, se_ratio, activation, 12
)
x = _inverted_res_block(
x, 6, depth(160), kernel, 1, se_ratio, activation, 13
)
x = _inverted_res_block(
x, 6, depth(160), kernel, 1, se_ratio, activation, 14
)
return x
return MobileNetV3(
stack_fn,
1280,
input_shape,
alpha,
"large",
minimalistic,
include_top,
weights,
input_tensor,
classes,
pooling,
dropout_rate,
classifier_activation,
include_preprocessing,
)
MobileNetV3Small.__doc__ = BASE_DOCSTRING.format(name="MobileNetV3Small")
MobileNetV3Large.__doc__ = BASE_DOCSTRING.format(name="MobileNetV3Large")
def relu(x):
return layers.ReLU()(x)
def hard_sigmoid(x):
return layers.ReLU(6.0)(x + 3.0) * (1.0 / 6.0)
def hard_swish(x):
return layers.Multiply()([x, hard_sigmoid(x)])
# This function is taken from the original tf repo.
# It ensures that all layers have a channel number that is divisible by 8
# It can be seen here:
# https://github.com/tensorflow/models/blob/master/research/
# slim/nets/mobilenet/mobilenet.py
def _depth(v, divisor=8, min_value=None):
if min_value is None:
min_value = divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_v < 0.9 * v:
new_v += divisor
return new_v
def _se_block(inputs, filters, se_ratio, prefix):
x = layers.GlobalAveragePooling2D(
keepdims=True, name=prefix + "squeeze_excite/AvgPool"
)(inputs)
x = layers.Conv2D(
_depth(filters * se_ratio),
kernel_size=1,
padding="same",
name=prefix + "squeeze_excite/Conv",
)(x)
x = layers.ReLU(name=prefix + "squeeze_excite/Relu")(x)
x = layers.Conv2D(
filters,
kernel_size=1,
padding="same",
name=prefix + "squeeze_excite/Conv_1",
)(x)
x = hard_sigmoid(x)
x = layers.Multiply(name=prefix + "squeeze_excite/Mul")([inputs, x])
return x
def _inverted_res_block(
x, expansion, filters, kernel_size, stride, se_ratio, activation, block_id
):
channel_axis = 1 if backend.image_data_format() == "channels_first" else -1
shortcut = x
prefix = "expanded_conv/"
infilters = backend.int_shape(x)[channel_axis]
if block_id:
# Expand
prefix = f"expanded_conv_{block_id}/"
x = layers.Conv2D(
_depth(infilters * expansion),
kernel_size=1,
padding="same",
use_bias=False,
name=prefix + "expand",
)(x)
x = layers.BatchNormalization(
axis=channel_axis,
epsilon=1e-3,
momentum=0.999,
name=prefix + "expand/BatchNorm",
)(x)
x = activation(x)
if stride == 2:
x = layers.ZeroPadding2D(
padding=imagenet_utils.correct_pad(x, kernel_size),
name=prefix + "depthwise/pad",
)(x)
x = layers.DepthwiseConv2D(
kernel_size,
strides=stride,
padding="same" if stride == 1 else "valid",
use_bias=False,
name=prefix + "depthwise",
)(x)
x = layers.BatchNormalization(
axis=channel_axis,
epsilon=1e-3,
momentum=0.999,
name=prefix + "depthwise/BatchNorm",
)(x)
x = activation(x)
if se_ratio:
x = _se_block(x, _depth(infilters * expansion), se_ratio, prefix)
x = layers.Conv2D(
filters,
kernel_size=1,
padding="same",
use_bias=False,
name=prefix + "project",
)(x)
x = layers.BatchNormalization(
axis=channel_axis,
epsilon=1e-3,
momentum=0.999,
name=prefix + "project/BatchNorm",
)(x)
if stride == 1 and infilters == filters:
x = layers.Add(name=prefix + "Add")([shortcut, x])
return x
@keras_export("keras.applications.mobilenet_v3.preprocess_input")
def preprocess_input(x, data_format=None):
"""A placeholder method for backward compatibility.
The preprocessing logic has been included in the mobilenet_v3 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.mobilenet_v3.decode_predictions")
def decode_predictions(preds, top=5):
return imagenet_utils.decode_predictions(preds, top=top)
decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
Reference in New Issue Copy Permalink