From 3e3ccce1135c25dd1d99dc7c2b8ff589c93ee7ea Mon Sep 17 00:00:00 2001 From: Ramana Radhakrishnan Date: Wed, 22 Apr 2020 07:09:11 +0100 Subject: [PATCH] Factor out import of common tflite.Operator in tflite frontend. (#5355) * Restructure imports in tflite frontend. These python modules are needed for every tflite file parsed. Factorize out imports of the common most ones. Now that the import of operator is common, asserts can be commonized. Loses 473 lines of duplication. * Only restrict to tflite.Operator --- python/tvm/relay/frontend/tflite.py | 156 +--------------------------- 1 file changed, 5 insertions(+), 151 deletions(-) diff --git a/python/tvm/relay/frontend/tflite.py b/python/tvm/relay/frontend/tflite.py index d489bd34f7ac..a2e090408e92 100644 --- a/python/tvm/relay/frontend/tflite.py +++ b/python/tvm/relay/frontend/tflite.py @@ -159,7 +159,12 @@ def convert_op_to_relay(self): op = self.subgraph.Operators(op_idx) op_code_str = self.get_op_code_str(op) output_tensors = self.get_output_tensors(op) + try: + from tflite.Operator import Operator + except ImportError: + raise ImportError("The tflite package must be installed") + assert isinstance(op, Operator) ret = self.convert_map[op_code_str](op) if len(output_tensors) == 1: @@ -288,12 +293,6 @@ def has_same_qnn_params(self, lhs_tensor, rhs_tensor): def is_quantized(self, op): """Check if an input tensor is quantized.""" - try: - from tflite.Operator import Operator - except ImportError: - raise ImportError("The tflite package must be installed") - - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) first_tensor = input_tensors[0] return first_tensor.qnn_params is not None @@ -335,12 +334,10 @@ def convert_reshape(self, op): """Convert TFLite reshape""" try: from tflite.BuiltinOptions import BuiltinOptions - from tflite.Operator import Operator from tflite.ReshapeOptions import ReshapeOptions except ImportError: raise ImportError("The tflite package must be installed") - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert input_tensors, "input tensors should not be empty" input_tensor = input_tensors[0] @@ -368,7 +365,6 @@ def _convert_resize(self, method, op): """Generic method to Convert TFLite RESIZE operators""" try: from tflite.BuiltinOptions import BuiltinOptions - from tflite.Operator import Operator from tflite.ResizeBilinearOptions import ResizeBilinearOptions # ResizeNearestNeighborOptions was added in tflite v1.13 tflite_ver = 1120 @@ -378,7 +374,6 @@ def _convert_resize(self, method, op): except ImportError: raise ImportError("The tflite package must be installed") - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 2, "input tensors length should be 2" @@ -421,14 +416,12 @@ def convert_resize_nearest_neighbor(self, op): def convert_l2_normalization(self, op): """Convert TFLite L2_NORMALIZATION """ try: - from tflite.Operator import Operator from tflite.BuiltinOptions import BuiltinOptions from tflite.L2NormOptions import L2NormOptions from tflite.ActivationFunctionType import ActivationFunctionType except ImportError: raise ImportError("The tflite package must be installed") - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 1, "input tensors length should be 1" input_tensor = input_tensors[0] @@ -467,13 +460,11 @@ def convert_l2_normalization(self, op): def convert_lrn(self, op): """Convert TFLite LOCAL_RESPONSE_NORMALIZATION """ try: - from tflite.Operator import Operator from tflite.BuiltinOptions import BuiltinOptions from tflite.LocalResponseNormalizationOptions import LocalResponseNormalizationOptions except ImportError: raise ImportError("The tflite package must be installed") - assert isinstance(op, Operator) if self.is_quantized(op): raise tvm.error.OpNotImplemented( 'TFlite quantized LRN operator is not supported yet.') @@ -503,12 +494,6 @@ def convert_lrn(self, op): def convert_logistic(self, op): """Convert TFLite LOGISTIC""" - try: - from tflite.Operator import Operator - except ImportError: - raise ImportError("The tflite package must be installed") - - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 1, "input tensors length should be 1" @@ -529,12 +514,6 @@ def convert_logistic(self, op): def convert_softmax(self, op): """Convert TFLite softmax""" - try: - from tflite.Operator import Operator - except ImportError: - raise ImportError("The tflite package must be installed") - - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 1, "input tensors length should be 1" @@ -564,12 +543,6 @@ def convert_softmax(self, op): def convert_tanh(self, op): """Convert TFLite TANH""" - try: - from tflite.Operator import Operator - except ImportError: - raise ImportError("The tflite package must be installed") - - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 1, "input tensors length should be 1" @@ -581,12 +554,6 @@ def convert_tanh(self, op): def convert_relu(self, op): """Convert TFLite ReLU""" - try: - from tflite.Operator import Operator - except ImportError: - raise ImportError("The tflite package must be installed") - - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 1, "input tensors length should be 1" @@ -598,12 +565,6 @@ def convert_relu(self, op): def convert_hard_swish(self, op): """Convert TFLite Hard swish""" - try: - from tflite.Operator import Operator - except ImportError: - raise ImportError("The tflite package must be installed") - assert isinstance(op, Operator) - input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 1, "input tensors length should be 1" input_tensor = input_tensors[0] @@ -635,14 +596,12 @@ def _hard_swish(data): def convert_concatenation(self, op): """Convert TFLite concatenation""" try: - from tflite.Operator import Operator from tflite.ConcatenationOptions import ConcatenationOptions from tflite.BuiltinOptions import BuiltinOptions from tflite.ActivationFunctionType import ActivationFunctionType except ImportError: raise ImportError("The tflite package must be installed") - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) >= 1, "input tensors should greater than 1" in_exprs = [self.get_expr(input_tensor.tensor_idx) for input_tensor in input_tensors] @@ -683,12 +642,6 @@ def convert_concatenation(self, op): def _convert_unary_elemwise(self, relay_op, op): """Generic method to convert TFLite unary elemwise functions""" - try: - from tflite.Operator import Operator - except ImportError: - raise ImportError("The tflite package must be installed") - - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 1, "input tensors length should be 1" @@ -784,12 +737,6 @@ def convert_neg(self, op): def convert_elu(self, op): """Convert TFLite ELU""" - try: - from tflite.Operator import Operator - except ImportError: - raise ImportError("The tflite package must be installed") - assert isinstance(op, Operator) - if self.is_quantized(op): raise tvm.error.OpNotImplemented( 'TFlite quantized ELU operator is not supported yet.') @@ -807,12 +754,6 @@ def convert_elu(self, op): def convert_square(self, op): """Convert TFLite SQUARE""" - try: - from tflite.Operator import Operator - except ImportError: - raise ImportError("The tflite package must be installed") - - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 1, "input tensors length should be 1" input_tensor = input_tensors[0] @@ -834,7 +775,6 @@ def convert_square(self, op): def _convert_elemwise(self, relay_op, op): """Generic method to Convert TFLite elemwise""" try: - from tflite.Operator import Operator from tflite.AddOptions import AddOptions from tflite.SubOptions import SubOptions from tflite.MulOptions import MulOptions @@ -844,7 +784,6 @@ def _convert_elemwise(self, relay_op, op): except ImportError: raise ImportError("The tflite package must be installed") - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 2, "input tensors length should be 2" @@ -1025,12 +964,6 @@ def convert_not_equal(self, op): def _convert_logical_binary(self, relay_op, op): """Generic method to convert logical binary ops""" - try: - from tflite.Operator import Operator - except ImportError: - raise ImportError("The tflite package must be installed") - - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 2, "input tensors length should be 2" @@ -1052,12 +985,6 @@ def convert_logical_or(self, op): def convert_zeros_like(self, op): """Convert TFLite ZEROS LIKE""" - try: - from tflite.Operator import Operator - except ImportError: - raise ImportError("The tflite package must be installed") - - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 1, "input tensors length should be 1" @@ -1071,12 +998,10 @@ def _convert_reduce(self, relay_op, op): """Generic method to Convert TFLite MEAN operators""" try: from tflite.BuiltinOptions import BuiltinOptions - from tflite.Operator import Operator from tflite.ReducerOptions import ReducerOptions except ImportError: raise ImportError("The tflite package must be installed") - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 2, "input tensors length should be 2" @@ -1135,7 +1060,6 @@ def _convert_reduce_any(self, op): def convert_fully_connected(self, op): """Convert TFLite fully connected""" try: - from tflite.Operator import Operator from tflite.FullyConnectedOptions import FullyConnectedOptions from tflite.BuiltinOptions import BuiltinOptions from tflite.TensorType import TensorType @@ -1143,7 +1067,6 @@ def convert_fully_connected(self, op): except ImportError: raise ImportError("The tflite package must be installed") - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) >= 2, "input tensors length should be >= 2" @@ -1238,12 +1161,10 @@ def convert_squeeze(self, op): """Convert TFLite squeeze""" try: from tflite.BuiltinOptions import BuiltinOptions - from tflite.Operator import Operator from tflite.SqueezeOptions import SqueezeOptions except ImportError: raise ImportError("The tflite package must be installed") - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) output_tensors = self.get_output_tensors(op) assert len(input_tensors) == 1, "input tensors length should be 1" @@ -1287,14 +1208,12 @@ def convert_conv(self, op, conv_type): from tflite.BuiltinOptions import BuiltinOptions from tflite.ActivationFunctionType import ActivationFunctionType from tflite.TensorType import TensorType - from tflite.Operator import Operator from tflite.Conv2DOptions import Conv2DOptions from tflite.DepthwiseConv2DOptions import DepthwiseConv2DOptions from tflite.Padding import Padding except ImportError: raise ImportError("The tflite package must be installed") - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) >= 2, "input tensors length should be >= 2" @@ -1455,12 +1374,10 @@ def convert_split(self, op): """split implementation.""" try: from tflite.BuiltinOptions import BuiltinOptions - from tflite.Operator import Operator from tflite.SplitOptions import SplitOptions except ImportError: raise ImportError("The tflite package must be installed") - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 2, "input tensors length should be == 2" @@ -1490,12 +1407,6 @@ def convert_split(self, op): def convert_slice(self, op): """Convert TFLite SLICE""" - try: - from tflite.Operator import Operator - except ImportError: - raise ImportError("The tflite package must be installed") - - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 3, "input tensors length should be == 3" input_tensor = input_tensors[0] @@ -1519,12 +1430,6 @@ def convert_slice(self, op): def convert_transpose(self, op): """transpose implementation.""" - try: - from tflite.Operator import Operator - except ImportError: - raise ImportError("The tflite package must be installed") - - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 2, "input tensors length should be 2" input_tensor = input_tensors[0] @@ -1545,13 +1450,11 @@ def convert_transpose(self, op): def convert_cast(self, op): """Convert TFLite CAST""" try: - from tflite.Operator import Operator from tflite.BuiltinOptions import BuiltinOptions from tflite.CastOptions import CastOptions except ImportError: raise ImportError("The tflite package must be installed") - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 1, "input tensors length should be 1" input_tensor = input_tensors[0] @@ -1569,12 +1472,6 @@ def convert_cast(self, op): def convert_tile(self, op): """tile implementation.""" - try: - from tflite.Operator import Operator - except ImportError: - raise ImportError("The tflite package must be installed") - - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 2, "input tensors length should be 2" input_tensor = input_tensors[0] @@ -1591,12 +1488,6 @@ def convert_tile(self, op): def convert_topk_v2(self, op): """ Convert TFLite TOPK_v2 """ - try: - from tflite.Operator import Operator - except ImportError: - raise ImportError("The tflite package must be installed") - - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 2, "input tensors length should be 2" input_tensor = input_tensors[0] @@ -1612,13 +1503,11 @@ def convert_pool2d(self, op, pool_type): try: from tflite.BuiltinOptions import BuiltinOptions from tflite.ActivationFunctionType import ActivationFunctionType - from tflite.Operator import Operator from tflite.Pool2DOptions import Pool2DOptions from tflite.Padding import Padding except ImportError: raise ImportError("The tflite package must be installed") - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 1, "input tensors length should be 1" input_tensor = input_tensors[0] @@ -1689,12 +1578,6 @@ def convert_pool2d(self, op, pool_type): def convert_pad(self, op): """Convert TFLite PAD""" - try: - from tflite.Operator import Operator - except ImportError: - raise ImportError("The tflite package must be installed") - - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 2, "input tensors length should be 2" @@ -1740,7 +1623,6 @@ def convert_floor_mod(self, op): def convert_mirror_pad(self, op): """Convert TFLite MIRROR_PAD""" try: - from tflite.Operator import Operator from tflite.BuiltinOptions import BuiltinOptions from tflite.MirrorPadOptions import MirrorPadOptions except ImportError: @@ -1751,7 +1633,6 @@ def convert_mirror_pad(self, op): raise tvm.error.OpNotImplemented( 'TFlite quantized MIRROR_PAD operator is not supported yet.') - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 2, "input tensors length should be 2" @@ -1779,12 +1660,10 @@ def convert_pack(self, op): """Convert TFLite pack""" try: from tflite.BuiltinOptions import BuiltinOptions - from tflite.Operator import Operator from tflite.PackOptions import PackOptions except ImportError: raise ImportError("The tflite package must be installed") - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) >= 1, "input tensors should greater than 1" in_exprs = [self.get_expr(input_tensor.tensor_idx) for input_tensor in input_tensors] @@ -1806,12 +1685,10 @@ def convert_unpack(self, op): """Convert TFLite unpack""" try: from tflite.BuiltinOptions import BuiltinOptions - from tflite.Operator import Operator from tflite.UnpackOptions import UnpackOptions except ImportError: raise ImportError("The tflite package must be installed") - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 1, "input tensors length should be 1" input_tensor = input_tensors[0] @@ -1848,12 +1725,7 @@ def convert_unpack(self, op): def convert_batch_to_space_nd(self, op): """batch_to_space_nd implementation.""" - try: - from tflite.Operator import Operator - except ImportError: - raise ImportError("The tflite package must be installed") - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 3, "input tensors length should be 3" @@ -1901,12 +1773,6 @@ def convert_batch_to_space_nd(self, op): def convert_space_to_batch_nd(self, op): """space_to_batch_nd implementation.""" - try: - from tflite.Operator import Operator - except ImportError: - raise ImportError("The tflite package must be installed") - - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 3, "input tensors length should be 3" @@ -1960,12 +1826,10 @@ def convert_depth_to_space(self, op): """Convert TFLite DEPTH_TO_SPACE""" try: from tflite.BuiltinOptions import BuiltinOptions - from tflite.Operator import Operator from tflite.DepthToSpaceOptions import DepthToSpaceOptions except ImportError: raise ImportError("The tflite package must be installed") - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 1, "input tensors length should be 1" @@ -1985,12 +1849,10 @@ def convert_space_to_depth(self, op): """Convert TFLite SPACE_TO_DEPTH""" try: from tflite.BuiltinOptions import BuiltinOptions - from tflite.Operator import Operator from tflite.SpaceToDepthOptions import SpaceToDepthOptions except ImportError: raise ImportError("The tflite package must be installed") - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 1, "input tensors length should be 1" @@ -2008,12 +1870,6 @@ def convert_space_to_depth(self, op): def convert_prelu(self, op): """Convert TFLite PReLU""" - try: - from tflite.Operator import Operator - except ImportError: - raise ImportError("The tflite package must be installed") - - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 2, "input tensors length should be 2" @@ -2033,13 +1889,11 @@ def convert_transpose_conv(self, op): try: from tflite.BuiltinOptions import BuiltinOptions from tflite.TensorType import TensorType - from tflite.Operator import Operator from tflite.TransposeConvOptions import TransposeConvOptions from tflite.Padding import Padding except ImportError: raise ImportError("The tflite package must be installed") - assert isinstance(op, Operator) input_tensors = self.get_input_tensors(op) assert len(input_tensors) == 3, "input tensors length should be 3"