tflite2tensorflow

【WIP】 Generate saved_model, tfjs, tf-trt, EdgeTPU, CoreML, quantized tflite and .pb from .tflite.

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1. Supported Layers

No. TFLite Layer TF Layer Remarks
1 CONV_2D tf.nn.conv2d
2 DEPTHWISE_CONV_2D tf.nn.depthwise_conv2d
3 MAX_POOL_2D tf.nn.max_pool
4 PAD tf.pad
5 MIRROR_PAD tf.raw_ops.MirrorPad
6 RELU tf.nn.relu
7 PRELU tf.keras.layers.PReLU
8 RELU6 tf.nn.relu6
9 RESHAPE tf.reshape
10 ADD tf.add
11 SUB tf.math.subtract
12 CONCATENATION tf.concat
13 LOGISTIC tf.math.sigmoid
14 TRANSPOSE_CONV tf.nn.conv2d_transpose
15 MUL tf.multiply
16 HARD_SWISH x*tf.nn.relu6(x+3)*0.16666667 Or x*tf.nn.relu6(x+3)*0.16666666
17 AVERAGE_POOL_2D tf.keras.layers.AveragePooling2D
18 FULLY_CONNECTED tf.keras.layers.Dense
19 RESIZE_BILINEAR tf.image.resize Or tf.image.resize_bilinear
20 RESIZE_NEAREST_NEIGHBOR tf.image.resize Or tf.image.resize_nearest_neighbor
21 MEAN tf.math.reduce_mean
22 SQUARED_DIFFERENCE tf.math.squared_difference
23 RSQRT tf.math.rsqrt
24 DEQUANTIZE (const)
25 FLOOR tf.math.floor
26 TANH tf.math.tanh
27 DIV tf.math.divide
28 FLOOR_DIV tf.math.floordiv

2. Environment

  • Python3.6+
  • TensorFlow v2.4.0+ or tf-nightly

3. Setup

To install using the Python Package Index (PyPI), use the following command.

$ pip3 install tflite2tensorflow --upgrade

To install with the latest source code of the main branch, use the following command.

$ pip3 install git+https://github.com/PINTO0309/tflite2tensorflow --upgrade

4. Usage / Execution sample

4-1. Step 1 : Generating saved_model and FreezeGraph (.pb)

$ tflite2tensorflow \
  --model_path magenta_arbitrary-image-stylization-v1-256_fp16_prediction_1.tflite \
  --flatc_path ./flatc \
  --schema_path schema.fbs \
  --output_pb True

or

$ tflite2tensorflow \
  --model_path magenta_arbitrary-image-stylization-v1-256_fp16_prediction_1.tflite \
  --flatc_path ./flatc \
  --schema_path schema.fbs \
  --output_pb True \
  --optimizing_hardswish_for_edgetpu True

4-2. Step 2 : Generation of quantized tflite, TFJS, TF-TRT, EdgeTPU, and CoreML

$ tflite2tensorflow \
  --model_path magenta_arbitrary-image-stylization-v1-256_fp16_prediction_1.tflite \
  --flatc_path ./flatc \
  --schema_path schema.fbs \
  --output_no_quant_float32_tflite True \
  --output_weight_quant_tflite True \
  --output_float16_quant_tflite True \
  --output_integer_quant_tflite True \
  --string_formulas_for_normalization 'data / 255.0' \
  --output_tfjs True \
  --output_coreml True \
  --output_tftrt True

or

$ tflite2tensorflow \
  --model_path magenta_arbitrary-image-stylization-v1-256_fp16_prediction_1.tflite \
  --flatc_path ./flatc \
  --schema_path schema.fbs \
  --output_no_quant_float32_tflite True \
  --output_weight_quant_tflite True \
  --output_float16_quant_tflite True \
  --output_integer_quant_tflite True \
  --output_edgetpu True \
  --string_formulas_for_normalization 'data / 255.0' \
  --output_tfjs True \
  --output_coreml True \
  --output_tftrt True

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (1.22.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for pinto0309 from gravatar.com pinto0309

Credits

Author: Katsuya Hyodo

License

MIT License

Requires

Python >3.6

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