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# * Copyright 2018 The TensorFlow Authors.
# * Copyright (c) 2022-2023 STMicroelectronics.
# * All rights reserved.
# *
# * This software is licensed under terms that can be found in the LICENSE file in
# * the root directory of this software component.
# * If no LICENSE file comes with this software, it is provided AS-IS.
# *--------------------------------------------------------------------------------------------*/
import keras
from keras import layers
from keras.src.applications import imagenet_utils
def get_resnet50v2(input_shape: tuple, num_classes: int = None, dropout: float = None,
pretrained: bool = True, **kwargs) -> keras.Model:
"""
Returns a ResNet50v2 model with a custom classifier.
Args:
input_shape (tuple): The shape of the input tensor.
dropout (float, optional): The dropout rate for the custom classifier. Defaults to 1e-6.
num_classes (int, optional): The number of output classes. Defaults to None.
pretrained (tool, optional): The pre-trained weights to use. Either "imagenet"
or None. Defaults to "imagenet".
Returns:
keras.Model: The ResNet50V2 model with a custom classifier.
"""
if dropout:
# Model loaded for training
base_model = keras.applications.resnet_v2.ResNet50V2(input_shape=input_shape,
weights="imagenet" if pretrained else None,
pooling="avg",
classes=num_classes,
classifier_activation="softmax",
include_top=False)
x = layers.Dropout(rate=dropout, name="dropout")(base_model.output)
if num_classes > 2:
outputs = layers.Dense(num_classes, activation="softmax")(x)
else:
outputs = layers.Dense(1, activation="sigmoid")(x)
else:
# Instantiate a base model
base_model = keras.applications.resnet_v2.ResNet50V2(input_shape=input_shape,
weights="imagenet" if pretrained else None,
pooling="avg",
classes=num_classes,
classifier_activation="softmax",
include_top=True)
outputs = base_model.output
# Create the Keras model
model = keras.Model(inputs=base_model.input, outputs=outputs, name="resnet50v2")
return model
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