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|The behavior differs depending on the optimization options of openvino and edgetpu.|
|20|RESIZE_NEAREST_NEIGHBOR|tf.image.resize Or tf.image.resize_nearest_neighbor|The behavior differs depending on the optimization options of openvino and edgetpu.|
|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, MediaPipe|
|39|LEAKY_RELU|tf.keras.layers.LeakyReLU||
|40|MAXIMUM|tf.math.maximum||
|41|MINIMUM|tf.math.minimum||
|42|MaxPoolingWithArgmax2D|tf.raw_ops.MaxPoolWithArgmax|CUSTOM, MediaPipe|
|43|MaxUnpooling2D|tf.cast, tf.shape, tf.math.floordiv, tf.math.floormod, tf.ones_like, tf.shape, tf.concat, tf.reshape, tf.transpose, tf.scatter_nd|CUSTOM, MediaPipe|
|44|GATHER|tf.gather||
|45|CAST|tf.cast||
|46|SLICE|tf.slice||
|47|PACK|tf.stack||
|48|UNPACK|tf.unstack||
|49|ARG_MAX|tf.math.argmax Or tf.math.reduce_max, tf.subtract, tf.math.minimum, tf.multiply|The behavior differs depending on the optimization options of edgetpu.|
|50|EXP|tf.exp||
|51|TOPK_V2|tf.math.top_k||
|52|LOG_SOFTMAX|tf.nn.log_softmax||
|53|L2_NORMALIZATION|tf.math.l2_normalize||
|54|LESS|tf.math.less||
|55|LESS_EQUAL|tf.math.less_equal||
|56|GREATER|tf.math.greater||
|57|GREATER_EQUAL|tf.math.greater_equal||
|58|NEG|tf.math.negative||
|59|WHERE|tf.where||
|60|SELECT|tf.where||
|61|SELECT_V2|tf.where||
|62|PADV2|tf.raw_ops.PadV2||
|63|SIN|tf.math.sin||
|64|TILE|tf.tile||
|65|EQUAL|tf.math.equal||
|66|NOT_EQUAL|tf.math.not_equal||
|67|LOG|tf.math.log||
|68|SQRT|tf.math.sqrt||
|69|ARG_MIN|tf.math.argmin or tf.math.negative,tf.math.argmax||
|70|REDUCE_PROD|tf.math.reduce_prod||
|71|LOGICAL_OR|tf.math.logical_or||
|72|LOGICAL_AND|tf.math.logical_and||
|73|LOGICAL_NOT|tf.math.logical_not||
|74|REDUCE_MIN|tf.math.reduce_min or tf.math.negative,tf.math.reduce_max||
|75|REDUCE_ANY|tf.math.reduce_any||
|76|SQUARE|tf.math.square||
|77|ZEROS_LIKE|tf.zeros_like||
|78|FILL|tf.fill||
|79|FLOOR_MOD|tf.math.floormod||
|80|RANGE|tf.range||
|81|ABS|tf.math.abs||
|82|UNIQUE|tf.unique||
|83|CEIL|tf.math.ceil||
|84|REVERSE_V2|tf.reverse||
|85|ADD_N|tf.math.add_n||
|86|GATHER_ND|tf.gather_nd||
|87|COS|tf.math.cos||
|88|RANK|tf.math.rank||
|89|ELU|tf.nn.elu||
|90|WHILE|tf.while_loop||
|91|REVERSE_SEQUENCE|tf.reverse_sequence||
|92|MATRIX_DIAG|tf.linalg.diag||
|93|ROUND|tf.math.round||
|94|NON_MAX_SUPPRESSION_V4|tf.raw_ops.NonMaxSuppressionV4||
|95|NON_MAX_SUPPRESSION_V5|tf.raw_ops.NonMaxSuppressionV5, tf.raw_ops.NonMaxSuppressionV4, tf.raw_ops.NonMaxSuppressionV3||
|96|SCATTER_ND|tf.scatter_nd||
|97|SEGMENT_SUM|tf.math.segment_sum||
|98|CUMSUM|tf.math.cumsum||
|99|BROADCAST_TO|tf.broadcast_to||
|100|RFFT2D|tf.signal.rfft2d||
|101|L2_POOL_2D|tf.square, tf.keras.layers.AveragePooling2D, tf.sqrt||
|102|LOCAL_RESPONSE_NORMALIZATION|tf.nn.local_response_normalization||
|103|RELU_N1_TO_1|tf.minimum, tf.maximum||
|104|SPLIT_V|tf.raw_ops.SplitV||
|105|MATRIX_SET_DIAG|tf.linalg.set_diag||
|106|SHAPE|tf.shape||
|107|EXPAND_DIMS|tf.expand_dims||
|108|SQUEEZE|tf.squeeze||
|109|FlexRFFT|tf.signal.rfft|Flex OP|
|110|FlexImag|tf.math.imag|Flex OP|
|111|FlexReal|tf.math.real|Flex OP|
|112|FlexRFFT2D|tf.signal.rfft2d|Flex OP|
|113|FlexComplexAbs|tf.raw_ops.ComplexAbs|Flex OP|
|114|IMAG|tf.math.imag||
|115|REAL|tf.math.real||
|116|COMPLEX_ABS|tf.raw_ops.ComplexAbs||
|117|TFLite_Detection_PostProcess|tf.divide, tf.strided_slice, tf.math.argmax, tf.math.reduce_max, tf.math.multiply, tf.math.add, tf.math.exp, tf.math.subtract, tf.expand_dims, tf.gather, tf.reshape, tf.identity, tf.raw_ops.NonMaxSuppressionV5|CUSTOM|
|118|ONE_HOT|tf.one_hot||
|119|FlexMultinomial|tf.random.categorical|Flex OP|
|120|FlexAll|tf.math.reduce_all|Flex OP|
|121|FlexErf|tf.math.erf|Flex OP|
|122|FlexRoll|tf.roll|Flex OP|
|123|CONV_3D|tf.keras.layers.Conv3D||
|124|CONV_3D_TRANSPOSE|tf.nn.conv3d_transpose||
|125|Densify|(const)||
|126|SPACE_TO_BATCH_ND|tf.space_to_batch_nd||
|127|BATCH_TO_SPACE_ND|tf.compat.v1.batch_to_space_nd||
|128|TransformLandmarks|tf.reshape, tf.linalg.matmul, tf.math.add|CUSTOM, MediaPipe|
|129|TransformTensorBilinear|tf.reshape, tf.linalg.matmul, tf.math.add, tf.tile, tf.math.floor, tf.math.subtract, tf.math.multiply, tf.math.reduce_prod, tf.cast, tf.math.maximum, tf.math.maximum, tf.concat, tf.gather_nd|CUSTOM, MediaPipe|
|130|Landmarks2TransformMatrix|tf.constant, tf.math.subtract, tf.math.norm, tf.math.divide, tf.linalg.matmul, tf.concat, tf.transpose, tf.gather, tf.math.reduce_min, tf.math.reduce_max, tf.math.multiply, tf.zeros, tf.math.add, tf.tile|CUSTOM, MediaPipe|
