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# Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved.
#
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the License); you may
# not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an AS IS BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Description:
# Functions used to read from a TensorFlow Lite format file.
import os.path
import numpy as np
from .tflite.Model import Model
from .tflite.BuiltinOperator import BuiltinOperator
from .nn_graph import Graph, Subgraph
from .operation import Operation
from .tensor import Tensor, QuantizationParameters
from .tflite_mapping import builtin_operator_map, datatype_map, datatype_map_numpy, DataType
def decode_str(s):
if s is None:
return ""
return s.decode("utf-8")
def reshape_tensor_add_const_op(tens, reorder):
if not tens.reshaped:
original_shape = tens.shape
tens.name = tens.name + "_reshape"
tens.shape = [original_shape[idx] for idx in reorder]
tens.bandwidth_shape = tens.shape
tens.storage_shape = tens.shape
if tens.values is not None:
tens.values = tens.values.transpose(reorder)
if tens.quant_values is not None:
tens.quant_values = tens.quant_values.transpose(reorder)
op = Operation("Const", tens.name)
op.outputs = [tens]
tens.ops = [op]
tens.reshaped = True
class TFLiteSubgraph:
def __init__(self, graph, subgraph):
self.graph = graph
self.name = decode_str(subgraph.Name())
self.tensors = []
for idx in range(subgraph.TensorsLength()):
self.tensors.append(self.parse_tensor(subgraph.Tensors(idx)))
for idx in range(subgraph.OperatorsLength()):
self.parse_operator(subgraph.Operators(idx))
self.outputs = [self.tensors[idx] for idx in subgraph.OutputsAsNumpy()]
self.inputs = [self.tensors[idx] for idx in subgraph.InputsAsNumpy()]
# Fix up tensors without operations. Generate either Placeholder or Constant ops
for tens in self.inputs:
assert not tens.ops
op = Operation("Placeholder", tens.name)
op.outputs = [tens]
tens.ops = [op]
for tens in self.tensors:
if not tens.ops:
op = Operation("Const", tens.name)
op.outputs = [tens]
tens.ops = [op]
def parse_tensor(self, tens_data):
np_shape = tens_data.ShapeAsNumpy()
shape = list(np_shape) if type(np_shape) is np.ndarray else []
name = decode_str(tens_data.Name())
dtype = datatype_map[tens_data.Type()]
tens = Tensor(shape, dtype, name)
quant = tens_data.Quantization()
def len1_array_to_scalar(arr):
# The following flatbuffer quantisation fields all return a scalar value of 0 if they are not definied in
# the input buffer. This is represented in Vela by using None.
# Otherwise, the fields returned are a single or multi-element array. In which case, single element arrays
# are converted to scalars
if isinstance(arr, int) and arr == 0:
return None
if len(arr) == 1:
return arr[0]
return arr
tens.quantization = QuantizationParameters()
tens.quantization.min = len1_array_to_scalar(quant.MinAsNumpy())
tens.quantization.max = len1_array_to_scalar(quant.MaxAsNumpy())
tens.quantization.scale_f32 = len1_array_to_scalar(quant.ScaleAsNumpy())
tens.quantization.zero_point = len1_array_to_scalar(quant.ZeroPointAsNumpy())
if dtype == DataType.uint8:
tens.quantization.quant_min = 0
tens.quantization.quant_max = (1 << dtype.bits) - 1
elif dtype in set((DataType.int8, DataType.int16, DataType.int32, DataType.int64)):
tens.quantization.quant_min = -(1 << (dtype.bits - 1))
tens.quantization.quant_max = (1 << (dtype.bits - 1)) - 1
else:
raise Exception("DataType '" + str(dtype) + "' is not supported for quantization.")
if tens.quantization.scale_f32 is None and tens.quantization.zero_point is None:
tens.quantization = None
tens.values = None
buf = self.graph.buffers[tens_data.Buffer()]
if buf is not None:
tens.values = np.array(buf.view(datatype_map_numpy[tens_data.Type()]).reshape(shape))
if tens.quantization is not None:
tens.quant_values = tens.values
tens.values = tens.quantization.dequantize(tens.quant_values)
return tens
def parse_operator(self, op_data):
op_type, opt_serializer = self.graph.operator_codes[op_data.OpcodeIndex()]
inputs = [self.tensors[idx] for idx in op_data.InputsAsNumpy()]
outputs = [self.tensors[idx] for idx in op_data.OutputsAsNumpy()]
name = "unknown_op_name"
if len(outputs):
name = outputs[0].name
op = Operation(op_type, name)
op.inputs = inputs
op.outputs = outputs
for out in op.outputs:
out.ops = [op]
activation_function_to_split_out = None
if op_type.startswith("DepthwiseConv2d") or op_type.startswith("Conv2D"):
reshape_tensor_add_const_op(inputs[1], (1, 2, 3, 0))
if op_type.startswith("FullyConnected"):
reshape_tensor_add_const_op(inputs[1], (1, 0))
if opt_serializer is not None:
op.attrs = opt_serializer.deserialize(op_data.BuiltinOptions(), op_data.CustomOptionsAsNumpy())
if op_type.startswith("ResizeBilinear"):
upscaled_shape = [op.inputs[0].shape[1] * 2, op.inputs[0].shape[2] * 2]
out_shape = op.outputs[0].shape[1:3]
if not op.attrs['align_corners'] and out_shape == upscaled_shape:
# this means the output is supposed to be a x2 upscale,
# so we need to do SAME padding
op.attrs.update({'padding': b'SAME'})
elif (op.attrs['align_corners']
and out_shape == [upscaled_shape[0] - 1, upscaled_shape[1] - 1]):
# here we can just run the avg pool without padding and
# produce a (M * 2 - 1, N * 2 - 1) sized output
op.attrs.update({'padding': b'VALID'})
else:
assert False, "Only 2x upscaling is supported"
op.attrs.update({'filter_width': 2, 'filter_height': 2, 'stride_w': 1, 'stride_h': 1,})
if "stride_w" in op.attrs:
op.attrs["strides"] = (1, op.attrs["stride_h"], op.attrs["stride_w"], 1)
if "filter_width" in op.attrs:
op.attrs["ksize"] = (1, op.attrs["filter_height"], op.attrs["filter_width"], 1)
if "dilation_w_factor" in op.attrs:
op.attrs["dilation"] = (1, op.attrs["dilation_h_factor"], op.attrs["dilation_w_factor"], 1)
if "depth_multiplier" in op.attrs:
op.attrs["channel_multiplier"] = op.attrs["depth_multiplier"]
if "fused_activation_function" in op.attrs:
if op_type in set(("ConcatTFLite",)):
act = op.attrs["fused_activation_function"]
del op.attrs["fused_activation_function"]
if act is not None:
activation_function_to_split_out = act
if activation_function_to_split_out is not None:
act_op = Operation(activation_function_to_split_out, name + activation_function_to_split_out)
out_tens = op.outputs[0]
intermediate_tens = out_tens.clone("_act_intermediate")
out_tens.ops = [act_op]
act_op.outputs = [out_tens]
intermediate_tens.ops = [op]
op.outputs[0] = intermediate_tens
act_op.inputs = [intermediate_tens]
class TFLiteGraph:
def __init__(
self, filename, batch_size=1, feed_dict={}, output_node_names=[], initialisation_nodes=[],
):
self.op_times = {}
if batch_size is None:
batch_size = 1
self.batch_size = batch_size
self.name = os.path.splitext(os.path.basename(filename))[0]
self.initialisation_nodes = initialisation_nodes
with open(filename, "rb") as f:
buf = bytearray(f.read())
model = Model.GetRootAsModel(buf, 0)
self.buffers = []
for idx in range(model.BuffersLength()):
self.buffers.append(self.parse_buffer(model.Buffers(idx)))
self.operator_codes = []
for idx in range(model.OperatorCodesLength()):
self.operator_codes.append(self.parse_operator_code(model.OperatorCodes(idx)))
self.subgraphs = []
for idx in range(model.SubgraphsLength()):
self.subgraphs.append(TFLiteSubgraph(self, model.Subgraphs(idx)))
self.nng = Graph(self.name, self.batch_size)
for tflite_sg in self.subgraphs:
sg = Subgraph(tflite_sg.name)
sg.original_inputs = tflite_sg.inputs # Preserve the original input order
sg.output_tensors = tflite_sg.outputs
self.nng.subgraphs.append(sg)
def parse_buffer(self, buf_data):
if buf_data.DataLength() == 0:
return None
data = buf_data.DataAsNumpy()
return data
def parse_operator_code(self, code):
c = code.BuiltinCode()
op_type, ser = builtin_operator_map[c]
if c == BuiltinOperator.CUSTOM:
op_type += decode_str(code.CustomCode())
return op_type, ser
def read_tflite(
filename, batch_size=1, feed_dict={}, output_node_names=[], initialisation_nodes=[],
):
tflite_graph = TFLiteGraph(filename, batch_size, feed_dict, output_node_names, initialisation_nodes)
nng = tflite_graph.nng
nng.refresh_after_modification()
return nng
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