wehub-resource-sync
45 lines
1.7 KiB
Python
45 lines
1.7 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 sys
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import tensorflow as tf
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tf_models_path = os.path.realpath("./models")
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sys.path.insert(1, tf_models_path)
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try:
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from official.legacy.image_classification.efficientnet import efficientnet_model
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except Exception:
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print("Error importing TF official models codebase.")
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def create_efficientnet_model(model_version="b0"):
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model_name = "efficientnet-" + model_version
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model_configs = dict(efficientnet_model.MODEL_CONFIGS)
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assert model_name in model_configs, "Model name is not valid!"
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config = model_configs[model_name]
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# Set the dataformat of the model to NCHW for training and inference
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tf.keras.backend.set_image_data_format("channels_first")
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# B0=(224, 224, 3); B3=(300, 300, 3)
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image_input = tf.keras.layers.Input(
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shape=(config.resolution, config.resolution, config.input_channels), name="image_input", dtype=tf.float32
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)
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outputs = efficientnet_model.efficientnet(image_input, config)
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model = tf.keras.Model(inputs=image_input, outputs=outputs)
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return model
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