# TensorFlow Frontend¶

The TensorFlow frontend helps in importing TensorFlow models into TVM.

Supported versions:

1.12 and below

Tested models:

Inception (V1/V2/V3/V4)

Resnet (All)

Mobilenet (V1/V2 All)

Vgg (16/19)

BERT (Base/3-layer)

## Preparing a Model for Inference¶

### Remove Unneeded Nodes¶

The export process will remove many nodes that are not needed for inference, but unfortunately will leave some remaining. The nodes that should be manually removed are:

Dropout, including Dropout and DropoutWrapper

### Convert None Dimensions to Constants¶

TVM has minimal support for dynamic tensor shapes. Dimensions that are `None`

should be replaced with constants. For example, a model may accept an input with shape `(None,20)`

. This should be converted to a shape like `(1,20)`

. The model should be modified accordingly to ensure that these shapes match throughout the graph.

### Export¶

TensorFlow frontend expects a frozen protobuf (.pb) or saved model as input. It currently does not support checkpoint (.ckpt). The graphdef needed by the TensorFlow frontend can be extracted from the active session, or by using the TFParser helper class.

The model should be exported with a number of transformations to prepare the model for inference. It is also important to set ``add_shapes=True``

, as this will embed the output shapes of each node into the graph. Here is one function to export a model as a protobuf given a session:

```
import tensorflow as tf
from tensorflow.tools.graph_transforms import TransformGraph
def export_pb(session):
with tf.gfile.GFile("myexportedmodel.pb", "wb") as f:
inputs = ["myinput1", "myinput2"] # replace with your input names
outputs = ["myoutput1"] # replace with your output names
graph_def = session.graph.as_graph_def(add_shapes=True)
graph_def = tf.graph.util.convert_variables_to_constants(session, graph_def, outputs)
graph_def = TransformGraph(
graph_def,
inputs,
outputs,
[
"remove_nodes(op=Identity, op=CheckNumerics, op=StopGradient)",
"sort_by_execution_order", # sort by execution order after each transform to ensure correct node ordering
"remove_device",
"sort_by_execution_order",
"fold_batch_norms",
"sort_by_execution_order",
"fold_old_batch_norms",
"sort_by_execution_order"
]
)
f.write(graph_def.SerializeToString())
```

Another method is to export and freeze the graph.

## Import the Model¶

### Explicit Shape:¶

To ensure shapes can be known throughout the entire graph, pass the ``shape``

argument to ``from_tensorflow``

. This dictionary maps input names to input shapes. Please refer to these test cases for examples.

### Data Layout¶

Most TensorFlow models are released with NHWC layout. NCHW layout often provides better performance, especially on GPU. The TensorFlow frontend can automatically convert the model’s data layout by passing the argument ``layout='NCHW'``

to ``from_tensorflow``

.

## Best Practices¶

Use static tensor shapes instead of dynamic shapes (remove

``None``

dimensions).Use static RNN instead of dynamic RNN, as

``TensorArray``

isn’t supported yet.

## Supported Ops¶

Abs

Add

AddN

All

Any

ArgMax

ArgMin

AvgPool

BatchMatMul

BatchMatMulV2

BatchNormWithGlobalNormalization

BatchToSpaceND

BiasAdd

BroadcastTo

Cast

Ceil

CheckNumerics

ClipByValue

Concat

ConcatV2

Conv2D

Cos

Tan

CropAndResize

DecodeJpeg

DepthwiseConv2dNative

DepthToSpace

Dilation2D

Equal

Elu

Enter

Erf

Exit

Exp

ExpandDims

Fill

Floor

FloorDiv

FloorMod

FusedBatchNorm

FusedBatchNormV2

Gather

GatherNd

GatherV2

Greater

GreaterEqual

Identity

IsFinite

IsInf

LeakyRelu

LeftShift

Less

LessEqual

Log

Log1p

LoopCond

LogicalAnd

LogicalOr

LogicalNot

LogSoftmax

LRN

LSTMBlockCell

MatMul

Max

MaxPool

Maximum

Mean

Merge

Min

Minimum

MirrorPad

Mod

Mul

Neg

NextIteration

NotEqual

OneHot

Pack

Pad

PadV2

Pow

Prod

Range

Rank

RealDiv

Relu

Relu6

Reshape

ResizeBilinear

ResizeBicubic

ResizeNearestNeighbor

ReverseV2

RightShift

Round

Rsqrt

Select

Selu

Shape

Sigmoid

Sign

Sin

Size

Slice

Softmax

Softplus

SpaceToBatchND

SpaceToDepth,

Split

SplitV

Sqrt

Square

SquareDifference

Squeeze

StridedSlice

Sub

Sum

Switch

Tanh

TensorArrayV3

TensorArrayScatterV3

TensorArrayGatherV3

TensorArraySizeV3

TensorArrayWriteV3

TensorArrayReadV3

TensorArraySplitV3

TensorArrayConcatV3

Tile

TopKV2

Transpose

TruncateMod

Unpack

UnravelIndex

Where

ZerosLike