MobilenetV3_small_cityscapes_trainfine Tensorflow Lite (.tflite) conversion

https://stackoverflow.com/questions/53228969/unable-to-test-and-deploy-a-deeplabv3-mobilenetv2-tensorflow-lite-segmentation-m

Android端末向けDeepLab用モデルのエクスポート

[deeplab] Key aspp1_depthwise/BatchNorm/beta not found in checkpoint

https://spamgodisreal.blogspot.com/2019/12/re-tensorflowtensorflow-using_3.html

When you want to fine-tune DeepLab on other datasets, there are a few cases

# See feature_extractor.network_map for supported model variants.
# models/research/deeplab/core/feature_extractor.py

networks_map = {
    'mobilenet_v2': _mobilenet_v2,
    'mobilenet_v3_large_seg': mobilenet_v3_large_seg,
    'mobilenet_v3_small_seg': mobilenet_v3_small_seg,
    'resnet_v1_18': resnet_v1_beta.resnet_v1_18,
    'resnet_v1_18_beta': resnet_v1_beta.resnet_v1_18_beta,
    'resnet_v1_50': resnet_v1_beta.resnet_v1_50,
    'resnet_v1_50_beta': resnet_v1_beta.resnet_v1_50_beta,
    'resnet_v1_101': resnet_v1_beta.resnet_v1_101,
    'resnet_v1_101_beta': resnet_v1_beta.resnet_v1_101_beta,
    'xception_41': xception.xception_41,
    'xception_65': xception.xception_65,
    'xception_71': xception.xception_71,
    'nas_pnasnet': nas_network.pnasnet,
    'nas_hnasnet': nas_network.hnasnet,
}

python deeplab\export_model.py --checkpoint_path=C:\work\git\models\research\deeplab_mnv3_small_cityscapes_trainfine\model.ckpt  --quantize_delay_step=0 --export_path=C:\work\git\models\research\deeplab_mnv3_small_cityscapes_trainfine\deeplab_mnv3_small_cityscapes_trainfine.pb --model_variant="mobilenet_v3_small_seg"

Not found: Key MobilenetV3/Conv/conv_quant/max not found in checkpoint

python deeplab/train.py \
    --logtostderr \
    --training_number_of_steps=30000 \
    --train_split="train" \
    --model_variant="mobilenet_v3_small_seg" \
    --train_crop_size="513,513" \
    --train_batch_size=8 \
    --base_learning_rate=3e-5 \
    --dataset="pascal_voc_seg" \
    --initialize_last_layer \
    --quantize_delay_step=0 \
    --save_interval_secs=300 \
    --save_summaries_secs=300 \
    --tf_initial_checkpoint=${PATH_TO_TRAINED_FLOAT_MODEL} \
    --train_logdir=${PATH_TO_TRAIN_DIR} \
    --dataset_dir=${PATH_TO_DATASET}

$ cd tensorflow/tensorflow/lite/python
$ sudo bazel run tflite_convert -- \
--graph_def_file=${HOME}/Downloads/deeplab_mnv3_small_cityscapes_trainfine_2019_11_15/deeplab_mnv3_small_cityscapes_trainfine/frozen_inference_graph.pb \
--output_file=${HOME}/Downloads/deeplab_mnv3_small_cityscapes_trainfine_2019_11_15/deeplab_mnv3_small_cityscapes_trainfine/deeplab_mnv3_small_cityscapes_trainfine.tflite \
--input_arrays=ImageTensor \
--input_shape=1,1024,2048,3 \
--inference_type=FLOAT \
--inference_input_type=QUANTIZED_UINT8 \
--std_dev_values=127.5 \
--mean_values=127.5 \
--output_arrays=SemanticPredictions

$ cd tensorflow/tensorflow/lite/python
$ sudo bazel run \
tflite_convert -- \
--graph_def_file=${HOME}/Downloads/deeplab_mnv3_small_cityscapes_trainfine_2019_11_15/deeplab_mnv3_small_cityscapes_trainfine/frozen_inference_graph.pb \
--output_file=${HOME}/Downloads/deeplab_mnv3_small_cityscapes_trainfine_2019_11_15/deeplab_mnv3_small_cityscapes_trainfine/deeplab_mnv3_small_cityscapes_trainfine.tflite \
--input_arrays=ImageTensor \
--input_shape=1,1024,2048,3 \
--inference_type=FLOAT \
--inference_input_type=QUANTIZED_UINT8 \
--std_dev_values=127.5 \
--mean_values=127.5 \
--change_concat_input_ranges=true \
--output_arrays=SemanticPredictions

$ sudo bazel run \
-c opt \
tensorflow/lite/tools/benchmark:benchmark_model -- \
--graph=${HOME}/Downloads/deeplab_mnv3_small_cityscapes_trainfine_2019_11_15/deeplab_mnv3_small_cityscapes_trainfine/deeplab_mnv3_small_cityscapes_trainfine.tflite