GitHub - Sean0123456789/graffitist: Graph Transforms to Quantize and Retrain Deep Neural Nets in TensorFlow.

Graph Transforms to Quantize and Retrain Deep Neural Nets in TensorFlow

Graffitist is a flexible and scalable framework built on top of TensorFlow to process low-level graph descriptions of deep neural networks (DNNs) for accurate and efficient inference on fixed-point hardware. It comprises of a (growing) library of transforms to apply various neural network compression techniques such as quantization, pruning, and compression. Each transform consists of unique pattern matching and manipulation algorithms that when run sequentially produce an optimized output graph.

Graffitist stands on the shoulders of giants, and the interface is inspired in part by earlier tools from TensorFlow. It is developed with tight integration to the static data-flow graph semantics of TensorFlow. This comes with many benefits of a mature ML framework, such as strong low-level graph processing API, accelerated kernels for bit-accurate emulation, extensive pretrained model-zoo, large-scale distributed training, production readiness, clean documentation and great support from TensorFlow developers and the open-source community.

Graffitist uses a novel quantization threshold training technique called Adaptive-Gradient Log-domain Threshold Training (ALT) which results in highly accurate and efficient 8-bit and 4-bit quantized networks, amenable to most generic fixed-point hardware. For details, please refer to our paper:

Trained Uniform Quantization for Accurate and Efficient Neural Network Inference on Fixed-Point Hardware,
Sambhav R. Jain, Albert Gural, Michael Wu, Chris Dick,
arXiv preprint arXiv:1903.08066, 2019.

Quantization

Graffitist allows for quantization in two modes:

Static Mode. Quantization thresholds (hence scale factors) are determined based on statistics of activations derived from a calibration dataset^. This results in quantized performance (INT8) that is usually competitive with floating-point baselines (FP32) without retraining. Note that while calibration can run on CPU within tens of minutes, use of GPU is recommended due to its ~O(n^2) runtime complexity.
Retrain Mode. Quantization thresholds and weights are simultaneously trained (ALT method) for improved accuracy and further reduced precision (e.g. INT4). This approach yields highly accurate and compact DNN implementations on a fixed-point target. In many cases, INT8 retrained networks match FP32 accuracy while INT4 retrained networks reach within 1-3% of it depending on network topology. Recovery is achieved within 5 epochs of ALT retraining.

^small randomly chosen subset of the validation set with appropriate pre-processing applied

Quantization scheme

For simplicity and ease of mapping on generic fixed-point hardware, the quantization scheme is constrained to use linear (affine symmetric) mapping with:

no zero-points
strict powers-of-2 scale-factors
per-tensor quantization (for both weights and activations)
mid-tread quantization

Supported ops

compute: Conv2D, MatMul, DepthwiseConv2dNative
normalization: FusedBatchNorm
activation: Relu, Relu6, Maximum (for leaky-relu)
scale preserving: ConcatV2, BiasAdd, Add (eltwise-add), Maximum (for leaky-relu)
pool: MaxPool, AvgPool
classifier: Softmax

Supported layer topologies

compute -> normalization/bias-add
compute -> normalization/bias-add -> activation
compute -> normalization/bias-add -> eltwise-add
compute -> normalization/bias-add -> eltwise-add -> activation (relu/relu6)

Supported optimizations

normalization layers following compute layers are folded in
concat-of-concat layers are collapsed
identity nodes without control edges are spliced
avgpool is transformed to depthwise conv with reciprocal multiplier as weights
input scale sharing is enforced for concat, bias-add, eltwise-add and max ops
relu/relu6 outputs are quantized to uint8 instead of int8

Supported precisions

compute layers are quantized as q₈( q₁₆( sum( q_8/4(w)*q₈(x) ) ) + q₁₆(b) )
leaky-relu is quantized as q₈( max( q₁₆(x), q₁₆( q₁₆(a)*q₁₆(x) ) ) )
eltwise-add is quantized as q₈( q₈(x) + q₈(y) )
avgpool is quantized as q₈( sum( q₁₈(r)*q₈(x) ) )

Graffitist is in experimental stages as we continue to add support for more operation types, layer topologies, network styles, graph optimizations, and compression techniques. To request support for options not mentioned above, please submit an issue with details.

Imagenet performance

Static Mode	FP32	INT8 (static)
	top-1 / top-5	top-1 / top-5
vgg16	70.9 / 89.8	70.4 / 89.7
vgg19	71.0 / 89.8	70.4 / 89.7
resnet_v1_50	75.2 / 92.2	74.3 / 91.7
resnet_v1_101	76.4 / 92.9	74.8 / 92.0
resnet_v1_152	76.8 / 93.2	76.2 / 93.0
inception_v1	69.8 / 89.6	68.6 / 88.9
inception_v2	74.0 / 91.8	73.1 / 91.3
inception_v3	78.0 / 93.9	76.8 / 93.3
inception_v4	80.2 / 95.2	79.4 / 94.6
mobilenet_v1	71.0 / 90.0	0.6 / 3.6
mobilenet_v2	70.1 / 89.5	0.3 / 1.2
darknet19	73.0 / 91.4	68.7 / 89.7

Retrain Mode	FP32 (retrain wt)	INT8 (retrain wt)	INT8 (retrain wt,th)	INT4^ (retrain wt,th)
	top-1 / top-5 [#ep]	top-1 / top-5 [#ep]	top-1 / top-5 [#ep]	top-1 / top-5 [#ep]
vgg16	71.9 / 90.5 [1.0]	71.8 / 90.5 [1.0]	71.7 / 90.4 [0.9]	71.5 / 90.3 [4.0]
vgg19	71.8 / 90.4 [1.0]	71.7 / 90.4 [1.0]	71.7 / 90.4 [1.0]	71.2 / 90.1 [2.0]
resnet_v1_50	75.4 / 92.5 [3.7]	75.3 / 92.3 [1.0]	75.4 / 92.3 [1.9]	74.4 / 91.7 [2.0]
resnet_v1_101	76.6 / 93.2 [1.2]	76.3 / 93.0 [1.0]	76.4 / 93.1 [0.9]	75.7 / 92.5 [2.0]
resnet_v1_152	76.8 / 93.3 [1.0]	76.7 / 93.3 [1.5]	76.7 / 93.3 [1.4]	76.0 / 93.0 [1.9]
inception_v1	70.3 / 90.0 [2.8]	70.6 / 90.3 [3.5]	70.7 / 90.2 [2.4]	67.2 / 88.2 [4.0]
inception_v2	74.3 / 92.2 [3.3]	74.4 / 92.3 [4.7]	74.4 / 92.4 [2.5]	71.9 / 90.8 [4.8]
inception_v3	78.3 / 94.2 [2.1]	78.2 / 94.1 [2.0]	78.3 / 94.3 [1.2]	76.4 / 93.1 [4.4]
inception_v4	80.2 / 95.2 [n/a]	80.1 / 95.3 [1.7]	80.1 / 95.2 [1.5]	78.9 / 94.7 [4.2]
mobilenet_v1	71.1 / 90.0 [3.4]	67.0 / 87.9 [4.6]	71.1 / 90.0 [2.1]	n/a
mobilenet_v2	71.7 / 90.7 [3.2]	68.2 / 89.0 [2.7]	71.8 / 90.6 [2.2]	n/a
darknet19	74.4 / 92.3 [3.1]	72.9 / 91.6 [3.8]	74.5 / 92.3 [1.8]	73.2 / 91.6 [2.8]

^INT4 weights, INT8 activations. First/last layer weights are kept as INT8.

Changelog

The following modifications were done before exporting the graph definitions to serialized TensorFlow protocol buffers:

Replaced reduce_mean with avg_pool
Removed dropout
Removed auxiliary logits layers

Reproducibility

The top-1/top-5 accuracy metrics are evaluated on Imagenet validation set (50k images) downloaded from here and processed into TFRecord format using this script. We expect to see some degree of randomness (+-0.1%) between runs on:

different platforms
- when dataset is processed using different image library versions
- due to non-deterministic thread scheduling
- due to driver version differences
same platform
- when more than one valid topological orders are feasible
- due to inexact floating point math

Citations

If you find our work useful for your research, please cite:

Paper

@article{alt2019,
  title={Trained Uniform Quantization for Accurate and Efficient Neural Network Inference on Fixed-Point Hardware},
  author={Jain, Sambhav R and Gural, Albert and Wu, Michael and Dick, Chris},
  journal={arXiv preprint arXiv:1903.08066},
  year={2019}
}

Code

@misc{graffitist2019,
  author = {Xilinx},
  title = {Graffitist: Graph Transforms in TensorFlow to Quantize and Retrain Deep Neural Nets},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Xilinx/graffitist}}
}

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Python API

Graffitist uses a Python interface and is invoked as follows:

python graffitize.pyc \
          --in_graph        <path_to_in_graph.pb> \
          --out_graph       <path_to_out_graph.pb> \
          --inputs          <input_node_name> \
          --outputs         <output_node_name> \
          --input_shape     <H,W,C> \
          --transforms      <list_of_transforms_to_apply>

For a full list of arguments and available transforms, use the help option: python graffitize.pyc -h.

We also provide utility scripts for end-to-end ALT training and validation of Graffitist quantized networks on ImageNet.

Training

python utils/train_imagenet_tf.py \
          --data_dir        <path_to_tfrecords_dir> \
          --ckpt_dir        <path_to_ckpt_meta_dir> \
          --image_size      <size> \
          --batch_size_t    <N>

Validation

python utils/validate_imagenet_tf.py \
          --data_dir        <path_to_tfrecords_dir> \
          --model_dir       <path_to_model_dir> \
          --image_size      <size> \
          --batch_size      <N>

Calibration set generation

python utils/validate_imagenet_tf.py \
          --data_dir        <path_to_tfrecords_dir> \
          --model_dir       <path_to_model_dir> \
          --image_size      <size> \
          --calib_set_size  <N> \
          --gen_calib_set

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Requirements

Graffitist is packaged with custom quantization kernels (C++/CUDA) that are pre-compiled on the following configuration:

Ubuntu 16.04
Python 3.6
TensorFlow 1.12 (CPU or GPU)
CUDA 9.0, cuDNN 7 (if GPU)

Kernels

To load the pre-compiled kernels, prepend the following code (update paths as necessary) to your Python scripts. For example, see the provided validation script.

import tensorflow as tf

cpu_kernel_path = './kernels/quantize_ops.so'
gpu_kernel_path = './kernels/quantize_ops_cuda.so'

if tf.test.is_built_with_cuda() and tf.test.is_gpu_available(cuda_only=True):
  tf.load_op_library(gpu_kernel_path)
else:
  tf.load_op_library(cpu_kernel_path)

Installation

Install Anaconda3

Download and install Anaconda3(-5.3.1).

wget https://repo.anaconda.com/archive/Anaconda3-5.3.1-Linux-x86_64.sh
bash ./Anaconda3-5.3.1-Linux-x86_64.sh

When prompted, allow Anaconda to setup your .bashrc appropriately. Then source it to load the conda installation path.

source ~/.bashrc

Create a new conda environment venv with Python 3.6 interpreter and activate it.

conda create -n venv pip python=3.6
conda activate venv

Install TensorFlow 1.12

CPU-only:

pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.12.0-cp36-cp36m-linux_x86_64.whl

GPU:

pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.12.0-cp36-cp36m-linux_x86_64.whl

(Optional) Install CUDA 9.0, cuDNN 7

The following NVIDIA® software is required to use TensorFlow with GPU support:

NVIDIA® GPU drivers CUDA 9.0 requires 384.x or higher.
CUDA® Toolkit TensorFlow supports CUDA 9.0.
CUPTI ships with the CUDA Toolkit.
cuDNN SDK (>= 7.2)

Clone Graffitist

Clone the master branch of Graffitist locally.

git clone https://github.com/Xilinx/graffitist.git
cd ./graffitist

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How to run

Prepare models

To get started, we provide a set of standard networks including graph descriptions (.pb/.meta), pre-trained FP32 weights (.ckpt) and calibration datasets (.npy) with applied preprocessing. Both eval and training graphs are included (for static and retrain mode respectively). This is because training specific layers (batch_normalization, dropout, etc) behave differently in the two modes.

Network	Download
vgg16	static / retrain
vgg19	static / retrain
resnet_v1_50	static / retrain
resnet_v1_101	static / retrain
resnet_v1_152	static / retrain
inception_v1	static / retrain
inception_v2	static / retrain
inception_v3	static / retrain
inception_v4	static / retrain
mobilenet_v1	static / retrain
mobilenet_v2	static / retrain
darknet19	static / retrain

If using one of the provided models, simply download and extract to ./models/ dir and proceed to next step.

If bringing your own model (BYOM), ensure the following files are saved out to ./models/my_model_dir/:

my_model.pb
checkpoint
my_model.ckpt.data-00000-of-00001
my_model.ckpt.index
my_model.ckpt.meta (only needed in retrain mode)
calibration_set.npy

The graph .pb is a serialized TensorFlow protocol buffer containing the nodes and edges. Refer here for details on TensorFlow protocol buffers. Note that the input graph to Graffitist should NOT contain frozen weights, as they are read from a separate checkpoint.

The checkpoint file points to the specific .ckpt to use. For example:

model_checkpoint_path: "my_model.ckpt"
all_model_checkpoint_paths: "my_model.ckpt"

Refer here for details on saving TensorFlow checkpoints.

The metagraph .meta contains the graph and training metadata (e.g. variable collections, weight regularization). It is only required in retrain mode. Refer here for details on exporting TensorFlow metagraph.

The calibration set .npy contains N randomly sampled images with applied data pre-processing, stored as a numpy array of shape [N, H, W, C]. See the included validation script for more details on generating a calibration set.

UPDATE: SavedModel interface is on the way!

Set paths

Activate conda env and set paths:

conda activate venv

groot=`find -maxdepth 3 -name 'graffitize.pyc' -printf '%h\n'`
mroot=`find -maxdepth 1 -name 'models'`

To retrain for INT4, set INT4_MODE as follows (ignored in static mode):

INT4_MODE="1"

Configure options

Choose a network and configure for either static or retrain mode:

Network	Config
vgg16	static / retrain
vgg19	static / retrain
resnet_v1_50	static / retrain
resnet_v1_101	static / retrain
resnet_v1_152	static / retrain
inception_v1	static / retrain
inception_v2	static / retrain
inception_v3	static / retrain
inception_v4	static / retrain
mobilenet_v1	static / retrain
mobilenet_v2	static / retrain
darknet19	static / retrain

For a full list of default config options see examples.

If BYOM, set config options based on your model (similar to the examples provided).

Pick a recipe and run

Recipe 1: Optimized inference graph

This recipe applies various layer optimizations and pre-processing to generate a simplified inference graph (not quantized yet).

python $groot/graffitize.pyc \
    --in_graph $in_graph \
    --out_graph $opt_graph \
    --inputs $input_node \
    --outputs $output_node \
    --input_shape $input_shape \
    --transforms 'fix_input_shape' \
                 'fold_batch_norms' \
                 'remove_training_nodes' \
                 'strip_unused_nodes' \
                 'preprocess_layers'

Recipe 2: Quantized inference graph (static mode)

This recipe quantizes the inference graph using static calibration for efficient fixed-point implementation.

python $groot/graffitize.pyc \
    --in_graph $in_graph \
    --out_graph $quant_graph \
    --inputs $input_node \
    --outputs $output_node \
    --input_shape $input_shape \
    --transforms 'fix_input_shape' \
                 'fold_batch_norms' \
                 'remove_training_nodes' \
                 'strip_unused_nodes' \
                 'preprocess_layers' \
                 'quantize(weight_bits='$wb', activation_bits='$ab', layer_bits='$lb', relu_bits='$rb', avgpool_bits='$pb', avgpool_reciprocal_bits='$prb')'

Recipe 3: Quantized training graph (retrain mode)

Follow these steps to retrain quantized networks:

Step 1: Generate the quantized training graph.

python $groot/graffitize.pyc \
    --in_graph $in_metagraph \
    --out_graph $trainquant_graph \
    --inputs $input_node \
    --outputs $output_node \
    --input_shape $input_shape \
    --transforms 'fix_input_shape' \
                 'fold_batch_norms(is_training=True)' \
                 'preprocess_layers' \
                 'quantize(is_training=True, weight_bits='$wb', activation_bits='$ab', layer_bits='$lb', relu_bits='$rb', avgpool_bits='$pb', avgpool_reciprocal_bits='$prb', first_layer='$first_layer', last_layer='$last_layer')'

Step 2: Retrain. For the example networks, use the included training script. Once converged, ensure checkpoint points to the correct retrained ckpt.

Step 3: Generate the equivalent quantized inference graph using retrained variables from previous step.

python $groot/graffitize.pyc \
    --in_graph $in_graph \
    --out_graph $infquant_graph \
    --inputs $input_node \
    --outputs $output_node \
    --input_shape $input_shape \
    --transforms 'fix_input_shape' \
                 'fold_batch_norms' \
                 'remove_training_nodes' \
                 'strip_unused_nodes' \
                 'preprocess_layers' \
                 'quantize(calibrate_quant_layers=False, weight_bits='$wb', activation_bits='$ab', layer_bits='$lb', relu_bits='$rb', avgpool_bits='$pb', avgpool_reciprocal_bits='$prb', first_layer='$first_layer', last_layer='$last_layer')'

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Common errors

Error Message	Possible Fix
`RuntimeError: Bad magic number in .pyc file`	Use correct Python version; see Requirements
`tensorflow.python.framework.errors_impl.NotFoundError: graffitist/kernels/quantize_ops.so: undefined symbol: _ZN10tensorflow22CheckNotInComputeAsyncEPNS_15OpKernelContextEPKc`	Use correct TensorFlow version; see Requirements
`tensorflow.python.framework.errors_impl.NotFoundError: graffitist/kernels/quantize_ops_cuda.so: undefined symbol: _ZN10tensorflow22CheckNotInComputeAsyncEPNS_15OpKernelContextEPKc`	Use correct TensorFlow version; see Requirements
`ImportError: libcublas.so.9.0: cannot open shared object file: No such file or directory`	Use correct CUDA version with TF for GPU; see Requirements
`RecursionError: maximum recursion depth exceeded in comparison`	Stack limit set to 3k (default: 1k) to avoid stack overflows for lack of tail-call optimization in Python; get in touch

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Default model config

vgg16 (static)

mdir=$mroot/vgg16_slim_pretrained
in_graph=$mdir/vgg16_slim_pretrained.pb
opt_graph=$mdir/vgg16_slim_pretrained_opt.pb
quant_graph=$mdir/vgg16_slim_pretrained_quant.pb
input_node=input
output_node=vgg_16/fc8/squeezed
input_shape=224,224,3
wb=-8; ab=-8; lb=-16; rb=8; pb=8; prb=18;

[continue]

vgg16 (retrain)

mdir=$mroot/vgg16_slim_pretrained_train
in_metagraph=$mdir/vgg16_slim_pretrained.ckpt.meta
in_graph=$mdir/vgg16_slim_pretrained.pb
opt_graph=$mdir/vgg16_slim_pretrained_opt.pb
trainquant_graph=$mdir/vgg16_slim_pretrained_trainquant.pb
infquant_graph=$mdir/vgg16_slim_pretrained_infquant.pb
input_node=input
output_node=vgg_16/fc8/squeezed
input_shape=224,224,3
[ "$INT4_MODE" = 1 ] && wb=-4 || wb=-8; ab=-8; lb=-16; rb=8; pb=8; prb=18;
first_layer=vgg_16/conv1/conv1_1/Conv2D
last_layer=vgg_16/fc8/Conv2D

[continue]

vgg19 (static)

mdir=$mroot/vgg19_slim_pretrained
in_graph=$mdir/vgg19_slim_pretrained.pb
opt_graph=$mdir/vgg19_slim_pretrained_opt.pb
quant_graph=$mdir/vgg19_slim_pretrained_quant.pb
input_node=input
output_node=vgg_19/fc8/squeezed
input_shape=224,224,3
wb=-8; ab=-8; lb=-16; rb=8; pb=8; prb=18;

[continue]

vgg19 (retrain)

mdir=$mroot/vgg19_slim_pretrained_train
in_metagraph=$mdir/vgg19_slim_pretrained.ckpt.meta
in_graph=$mdir/vgg19_slim_pretrained.pb
opt_graph=$mdir/vgg19_slim_pretrained_opt.pb
trainquant_graph=$mdir/vgg19_slim_pretrained_trainquant.pb
infquant_graph=$mdir/vgg19_slim_pretrained_infquant.pb
input_node=input
output_node=vgg_19/fc8/squeezed
input_shape=224,224,3
[ "$INT4_MODE" = 1 ] && wb=-4 || wb=-8; ab=-8; lb=-16; rb=8; pb=8; prb=18;
first_layer=vgg_19/conv1/conv1_1/Conv2D
last_layer=vgg_19/fc8/Conv2D

[continue]

resnet_v1_50 (static)

mdir=$mroot/resnet_v1_50_slim_pretrained
in_graph=$mdir/resnet_v1_50_slim_pretrained.pb
opt_graph=$mdir/resnet_v1_50_slim_pretrained_opt.pb
quant_graph=$mdir/resnet_v1_50_slim_pretrained_quant.pb
input_node=input
output_node=resnet_v1_50/predictions/Softmax
input_shape=224,224,3
wb=-8; ab=-8; lb=-16; rb=8; pb=8; prb=18;

[continue]

resnet_v1_50 (retrain)

mdir=$mroot/resnet_v1_50_slim_pretrained_train
in_metagraph=$mdir/resnet_v1_50_slim_pretrained.ckpt.meta
in_graph=$mdir/resnet_v1_50_slim_pretrained.pb
opt_graph=$mdir/resnet_v1_50_slim_pretrained_opt.pb
trainquant_graph=$mdir/resnet_v1_50_slim_pretrained_trainquant.pb
infquant_graph=$mdir/resnet_v1_50_slim_pretrained_infquant.pb
input_node=input
output_node=resnet_v1_50/predictions/Softmax
input_shape=224,224,3
[ "$INT4_MODE" = 1 ] && wb=-4 || wb=-8; ab=-8; lb=-16; rb=8; pb=8; prb=18;
first_layer=resnet_v1_50/conv1/Conv2D
last_layer=resnet_v1_50/logits/Conv2D

[continue]

resnet_v1_101 (static)

mdir=$mroot/resnet_v1_101_slim_pretrained
in_graph=$mdir/resnet_v1_101_slim_pretrained.pb
opt_graph=$mdir/resnet_v1_101_slim_pretrained_opt.pb
quant_graph=$mdir/resnet_v1_101_slim_pretrained_quant.pb
input_node=input
output_node=resnet_v1_101/predictions/Softmax
input_shape=224,224,3
wb=-8; ab=-8; lb=-16; rb=8; pb=8; prb=18;

[continue]

resnet_v1_101 (retrain)

mdir=$mroot/resnet_v1_101_slim_pretrained_train
in_metagraph=$mdir/resnet_v1_101_slim_pretrained.ckpt.meta
in_graph=$mdir/resnet_v1_101_slim_pretrained.pb
opt_graph=$mdir/resnet_v1_101_slim_pretrained_opt.pb
trainquant_graph=$mdir/resnet_v1_101_slim_pretrained_trainquant.pb
infquant_graph=$mdir/resnet_v1_101_slim_pretrained_infquant.pb
input_node=input
output_node=resnet_v1_101/predictions/Softmax
input_shape=224,224,3
[ "$INT4_MODE" = 1 ] && wb=-4 || wb=-8; ab=-8; lb=-16; rb=8; pb=8; prb=18;
first_layer=resnet_v1_101/conv1/Conv2D
last_layer=resnet_v1_101/logits/Conv2D

[continue]

resnet_v1_152 (static)

mdir=$mroot/resnet_v1_152_slim_pretrained
in_graph=$mdir/resnet_v1_152_slim_pretrained.pb
opt_graph=$mdir/resnet_v1_152_slim_pretrained_opt.pb
quant_graph=$mdir/resnet_v1_152_slim_pretrained_quant.pb
input_node=input
output_node=resnet_v1_152/predictions/Softmax
input_shape=224,224,3
wb=-8; ab=-8; lb=-16; rb=8; pb=8; prb=18;

[continue]

resnet_v1_152 (retrain)

mdir=$mroot/resnet_v1_152_slim_pretrained_train
in_metagraph=$mdir/resnet_v1_152_slim_pretrained.ckpt.meta
in_graph=$mdir/resnet_v1_152_slim_pretrained.pb
opt_graph=$mdir/resnet_v1_152_slim_pretrained_opt.pb
trainquant_graph=$mdir/resnet_v1_152_slim_pretrained_trainquant.pb
infquant_graph=$mdir/resnet_v1_152_slim_pretrained_infquant.pb
input_node=input
output_node=resnet_v1_152/predictions/Softmax
input_shape=224,224,3
[ "$INT4_MODE" = 1 ] && wb=-4 || wb=-8; ab=-8; lb=-16; rb=8; pb=8; prb=18;
first_layer=resnet_v1_152/conv1/Conv2D
last_layer=resnet_v1_152/logits/Conv2D

[continue]

inception_v1 (static)

mdir=$mroot/inception_v1_bn_slim_pretrained
in_graph=$mdir/inception_v1_bn_slim_pretrained.pb
opt_graph=$mdir/inception_v1_bn_slim_pretrained_opt.pb
quant_graph=$mdir/inception_v1_bn_slim_pretrained_quant.pb
input_node=input
output_node=InceptionV1/Logits/Predictions/Softmax
input_shape=224,224,3
wb=-8; ab=-8; lb=-16; rb=8; pb=8; prb=18;

[continue]

inception_v1 (retrain)

mdir=$mroot/inception_v1_bn_slim_pretrained_train
in_metagraph=$mdir/inception_v1_bn_slim_pretrained.ckpt.meta
in_graph=$mdir/inception_v1_bn_slim_pretrained.pb
opt_graph=$mdir/inception_v1_bn_slim_pretrained_opt.pb
trainquant_graph=$mdir/inception_v1_bn_slim_pretrained_trainquant.pb
infquant_graph=$mdir/inception_v1_bn_slim_pretrained_infquant.pb
input_node=input
output_node=InceptionV1/Logits/Predictions/Softmax
input_shape=224,224,3
[ "$INT4_MODE" = 1 ] && wb=-4 || wb=-8; ab=-8; lb=-16; rb=8; pb=8; prb=18;
first_layer=InceptionV1/InceptionV1/Conv2d_1a_7x7/Conv2D
last_layer=InceptionV1/Logits/Conv2d_0c_1x1/Conv2D

[continue]

inception_v2 (static)

mdir=$mroot/inception_v2_slim_pretrained
in_graph=$mdir/inception_v2_slim_pretrained.pb
opt_graph=$mdir/inception_v2_slim_pretrained_opt.pb
quant_graph=$mdir/inception_v2_slim_pretrained_quant.pb
input_node=input
output_node=InceptionV2/Predictions/Softmax
input_shape=224,224,3
wb=-8; ab=-8; lb=-16; rb=8; pb=8; prb=18;

[continue]

inception_v2 (retrain)

mdir=$mroot/inception_v2_slim_pretrained_train
in_metagraph=$mdir/inception_v2_slim_pretrained.ckpt.meta
in_graph=$mdir/inception_v2_slim_pretrained.pb
opt_graph=$mdir/inception_v2_slim_pretrained_opt.pb
trainquant_graph=$mdir/inception_v2_slim_pretrained_trainquant.pb
infquant_graph=$mdir/inception_v2_slim_pretrained_infquant.pb
input_node=input
output_node=InceptionV2/Predictions/Softmax
input_shape=224,224,3
[ "$INT4_MODE" = 1 ] && wb=-4 || wb=-8; ab=-8; lb=-16; rb=8; pb=8; prb=18;
first_layer=InceptionV2/InceptionV2/Conv2d_1a_7x7/separable_conv2d/depthwise
last_layer=InceptionV2/Logits/Conv2d_1c_1x1/Conv2D

[continue]

inception_v3 (static)

mdir=$mroot/inception_v3_slim_pretrained
in_graph=$mdir/inception_v3_slim_pretrained.pb
opt_graph=$mdir/inception_v3_slim_pretrained_opt.pb
quant_graph=$mdir/inception_v3_slim_pretrained_quant.pb
input_node=input
output_node=InceptionV3/Predictions/Softmax
input_shape=299,299,3
wb=-8; ab=-8; lb=-16; rb=8; pb=8; prb=18;

[continue]

inception_v3 (retrain)

mdir=$mroot/inception_v3_slim_pretrained_train
in_metagraph=$mdir/inception_v3_slim_pretrained.ckpt.meta
in_graph=$mdir/inception_v3_slim_pretrained.pb
opt_graph=$mdir/inception_v3_slim_pretrained_opt.pb
trainquant_graph=$mdir/inception_v3_slim_pretrained_trainquant.pb
infquant_graph=$mdir/inception_v3_slim_pretrained_infquant.pb
input_node=input
output_node=InceptionV3/Predictions/Softmax
input_shape=299,299,3
[ "$INT4_MODE" = 1 ] && wb=-4 || wb=-8; ab=-8; lb=-16; rb=8; pb=8; prb=18;
first_layer=InceptionV3/InceptionV3/Conv2d_1a_3x3/Conv2D
last_layer=InceptionV3/Logits/Conv2d_1c_1x1/Conv2D

[continue]

inception_v4 (static)

mdir=$mroot/inception_v4_slim_pretrained
in_graph=$mdir/inception_v4_slim_pretrained.pb
opt_graph=$mdir/inception_v4_slim_pretrained_opt.pb
quant_graph=$mdir/inception_v4_slim_pretrained_quant.pb
input_node=input
output_node=InceptionV4/Logits/Predictions
input_shape=299,299,3
wb=-8; ab=-8; lb=-16; rb=8; pb=8; prb=18;

[continue]

inception_v4 (retrain)

mdir=$mroot/inception_v4_slim_pretrained_train
in_metagraph=$mdir/inception_v4_slim_pretrained.ckpt.meta
in_graph=$mdir/inception_v4_slim_pretrained.pb
opt_graph=$mdir/inception_v4_slim_pretrained_opt.pb
trainquant_graph=$mdir/inception_v4_slim_pretrained_trainquant.pb
infquant_graph=$mdir/inception_v4_slim_pretrained_infquant.pb
input_node=input
output_node=InceptionV4/Logits/Predictions
input_shape=299,299,3
[ "$INT4_MODE" = 1 ] && wb=-4 || wb=-8; ab=-8; lb=-16; rb=8; pb=8; prb=18;
first_layer=InceptionV4/InceptionV4/Conv2d_1a_3x3/Conv2D
last_layer=InceptionV4/Logits/Logits/MatMul

[continue]

mobilenet_v1 (static)

mdir=$mroot/mobilenet_v1_slim_pretrained
in_graph=$mdir/mobilenet_v1_slim_pretrained.pb
opt_graph=$mdir/mobilenet_v1_slim_pretrained_opt.pb
quant_graph=$mdir/mobilenet_v1_slim_pretrained_quant.pb
input_node=input
output_node=MobilenetV1/Predictions/Softmax
input_shape=224,224,3
wb=-8; ab=-8; lb=-16; rb=8; pb=8; prb=18;

[continue]

mobilenet_v1 (retrain)

mdir=$mroot/mobilenet_v1_slim_pretrained_train
in_metagraph=$mdir/mobilenet_v1_slim_pretrained.ckpt.meta
in_graph=$mdir/mobilenet_v1_slim_pretrained.pb
opt_graph=$mdir/mobilenet_v1_slim_pretrained_opt.pb
trainquant_graph=$mdir/mobilenet_v1_slim_pretrained_trainquant.pb
infquant_graph=$mdir/mobilenet_v1_slim_pretrained_infquant.pb
input_node=input
output_node=MobilenetV1/Predictions/Softmax
input_shape=224,224,3
[ "$INT4_MODE" = 1 ] && wb=-4 || wb=-8; ab=-8; lb=-16; rb=8; pb=8; prb=18;
first_layer=MobilenetV1/MobilenetV1/Conv2d_0/Conv2D
last_layer=MobilenetV1/Logits/Conv2d_1c_1x1/Conv2D

[continue]

mobilenet_v2 (static)

mdir=$mroot/mobilenet_v2_slim_pretrained
in_graph=$mdir/mobilenet_v2_slim_pretrained.pb
opt_graph=$mdir/mobilenet_v2_slim_pretrained_opt.pb
quant_graph=$mdir/mobilenet_v2_slim_pretrained_quant.pb
input_node=input
output_node=MobilenetV2/Predictions/Softmax
input_shape=224,224,3
wb=-8; ab=-8; lb=-16; rb=8; pb=8; prb=18;

[continue]

mobilenet_v2 (retrain)

mdir=$mroot/mobilenet_v2_slim_pretrained_train
in_metagraph=$mdir/mobilenet_v2_slim_pretrained.ckpt.meta
in_graph=$mdir/mobilenet_v2_slim_pretrained.pb
opt_graph=$mdir/mobilenet_v2_slim_pretrained_opt.pb
trainquant_graph=$mdir/mobilenet_v2_slim_pretrained_trainquant.pb
infquant_graph=$mdir/mobilenet_v2_slim_pretrained_infquant.pb
input_node=input
output_node=MobilenetV2/Predictions/Softmax
input_shape=224,224,3
[ "$INT4_MODE" = 1 ] && wb=-4 || wb=-8; ab=-8; lb=-16; rb=8; pb=8; prb=18;
first_layer=MobilenetV2/Conv/Conv2D
last_layer=MobilenetV2/Logits/Conv2d_1c_1x1/Conv2D

[continue]

darknet19 (static)

mdir=$mroot/darknet19_dw2tf_pretrained
in_graph=$mdir/darknet19.pb
opt_graph=$mdir/darknet19_opt.pb
quant_graph=$mdir/darknet19_quant.pb
input_node=darknet19/net1
output_node=darknet19/softmax1/Softmax
input_shape=256,256,3
wb=-8; ab=-8; lb=-16; rb=8; pb=-8; prb=18;

[continue]

darknet19 (retrain)

mdir=$mroot/darknet19_dw2tf_pretrained_train
in_metagraph=$mdir/darknet19.ckpt.meta
in_graph=$mdir/darknet19.pb
opt_graph=$mdir/darknet19_opt.pb
trainquant_graph=$mdir/darknet19_trainquant.pb
infquant_graph=$mdir/darknet19_infquant.pb
input_node=darknet19/net1
output_node=darknet19/softmax1/Softmax
input_shape=256,256,3
[ "$INT4_MODE" = 1 ] && wb=-4 || wb=-8; ab=-8; lb=-16; rb=8; pb=-8; prb=18;
first_layer=darknet19/convolutional1/Conv2D
last_layer=darknet19/convolutional19/Conv2D

[continue]

[back to ToC]

Name		Name	Last commit message	Last commit date
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docs/img		docs/img
graph_transforms		graph_transforms
kernels		kernels
utils		utils
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
graffitize.pyc		graffitize.pyc

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Sean0123456789/graffitist

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Graph Transforms to Quantize and Retrain Deep Neural Nets in TensorFlow

Contents

Quantization

Quantization scheme

Supported ops

Supported layer topologies

Supported optimizations

Supported precisions

Imagenet performance

Changelog

Reproducibility

Citations

Paper

Code

Python API

Training

Validation

Calibration set generation

Requirements

Kernels

Installation

Install Anaconda3

Install TensorFlow 1.12

(Optional) Install CUDA 9.0, cuDNN 7

Clone Graffitist

How to run

Prepare models

Set paths

Configure options

Pick a recipe and run

Recipe 1: Optimized inference graph

Recipe 2: Quantized inference graph (static mode)

Recipe 3: Quantized training graph (retrain mode)

Common errors

Default model config

vgg16 (static)

vgg16 (retrain)

vgg19 (static)

vgg19 (retrain)

resnet_v1_50 (static)

resnet_v1_50 (retrain)

resnet_v1_101 (static)

resnet_v1_101 (retrain)

resnet_v1_152 (static)

resnet_v1_152 (retrain)

inception_v1 (static)

inception_v1 (retrain)

inception_v2 (static)

inception_v2 (retrain)

inception_v3 (static)

inception_v3 (retrain)

inception_v4 (static)

inception_v4 (retrain)

mobilenet_v1 (static)

mobilenet_v1 (retrain)

mobilenet_v2 (static)

mobilenet_v2 (retrain)

darknet19 (static)

darknet19 (retrain)

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