Tensorflow Object Detection API 源码分析之 builders/graph_rewriter_builder.py

protos/graph_rewriter.proto

syntax = "proto2";

package object_detection.protos;

// Message to configure graph rewriter for the tf graph.
message GraphRewriter {
optional Quantization quantization = 1;
}

// Message for quantization options. See
// tensorflow/contrib/quantize/python/quantize.py for details.
message Quantization {
// Number of steps to delay before quantization takes effect during training.
optional int32 delay = 1 [default = 500000];

// Number of bits to use for quantizing weights.
// Only 8 bit is supported for now.
optional int32 weight_bits = 2 [default = 8];

// Number of bits to use for quantizing activations.
// Only 8 bit is supported for now.
optional int32 activation_bits = 3 [default = 8];
}

builders/graph_rewriter_builder.py

# 量化的函数, 仅支持 8bit 量化 返回的是 graph_rewrite_fn 函数
"""Functions for quantized training and evaluation."""

import tensorflow as tf

def build(graph_rewriter_config, is_training):
"""Returns a function that modifies default graph based on options.

Args:
graph_rewriter_config: graph_rewriter_pb2.GraphRewriter proto.
is_training: whether in training of eval mode.
"""
def graph_rewrite_fn():
"""Function to quantize weights and activation of the default graph."""
if (graph_rewriter_config.quantization.weight_bits != 8 or
graph_rewriter_config.quantization.activation_bits != 8):
raise ValueError('Only 8bit quantization is supported')

# Quantize the graph by inserting quantize ops for weights and activations
if is_training:
tf.contrib.quantize.create_training_graph(
input_graph=tf.get_default_graph(),
quant_delay=graph_rewriter_config.quantization.delay)
else:
tf.contrib.quantize.create_eval_graph(input_graph=tf.get_default_graph())

tf.contrib.layers.summarize_collection('quant_vars')
return graph_rewrite_fn