tflite2tensorflow 0.2.0

Generate saved_model and .pb from .tflite.

tflite2tensorflow

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

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
29	SUM	tf.math.reduce_sum
30	POW	tf.math.pow
31	SPLIT	tf.split
32	SOFTMAX	tf.nn.softmax
33	STRIDED_SLICE	tf.strided_slice
34	TRANSPOSE	ttf.transpose
35	SPACE_TO_DEPTH	tf.nn.space_to_depth
36	DEPTH_TO_SPACE	tf.nn.depth_to_space
37	REDUCE_MAX	tf.math.reduce_max
38	Convolution2DTransposeBias	tf.nn.conv2d_transpose, tf.math.add	CUSTOM, MeditPipe

2. Environment

• Python3.6+
• TensorFlow v2.4.0+ or tf-nightly
• Add a custom OP to the TFLite runtime to build the whl installer (for Python), MaxPoolingWithArgmax2D, MaxUnpooling2D, Convolution2DTransposeBias

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