wehub-resource-sync
96 lines
2.9 KiB
Python
96 lines
2.9 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os
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import tensorflow as tf
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from examples.data.data_loader import _NUM_CLASSES, _DEFAULT_IMAGE_SIZE, _NUM_CHANNELS
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from typing import Tuple
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MODELS_CLASSES_DICT = {
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"resnet_50v1": tf.keras.applications.ResNet50,
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"resnet_101v1": tf.keras.applications.ResNet101,
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"resnet_152v1": tf.keras.applications.ResNet152,
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"resnet_50v2": tf.keras.applications.ResNet50V2,
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"resnet_101v2": tf.keras.applications.ResNet101V2,
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"resnet_152v2": tf.keras.applications.ResNet152V2,
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"mobilenet_v1": tf.keras.applications.MobileNet,
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"mobilenet_v2": tf.keras.applications.MobileNetV2,
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"inception_v3": tf.keras.applications.InceptionV3,
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}
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def get_tfkeras_model(model_name: str = "mobilenet_v1", shape: Tuple = None) -> tf.keras.Model:
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"""
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Creates a native tf.keras.applications model.
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Args:
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model_name (str): Options={model_name_options}.
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Returns:
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model (tf.keras.Model): model corresponding to 'model_name'.
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Raises:
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ValueError: raised when 'model_name' is not supported.
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""".format(
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model_name_options=list(MODELS_CLASSES_DICT.keys())
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)
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try:
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model_class = MODELS_CLASSES_DICT[model_name]
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except ValueError:
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raise ValueError("Model {} was not found!".format(model_name))
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print("Loading model as {}".format(model_class))
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if shape is None:
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shape = (
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_DEFAULT_IMAGE_SIZE[model_name],
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_DEFAULT_IMAGE_SIZE[model_name],
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_NUM_CHANNELS,
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)
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input_img = tf.keras.layers.Input(shape=shape, name="input_1")
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model = model_class(
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include_top=True,
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weights="imagenet",
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input_tensor=input_img,
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input_shape=None,
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pooling=None,
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classes=_NUM_CLASSES,
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classifier_activation="softmax",
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)
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return model
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def print_model_weights_shapes(model):
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"""
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Print shape of each layer weight.
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Args:
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model: Keras model
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"""
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print([model.get_weights()[i].shape for i in range(len(model.get_weights()))])
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def ensure_dir(dirname):
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"""
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Create directory is doesn't exist already.
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Args:
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dirname: Name of the directory to create.
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"""
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if not os.path.exists(dirname):
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os.makedirs(dirname)
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