NCTC / models /research /object_detection /builders /graph_rewriter_builder.py
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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# 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
#
# http://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.
# ==============================================================================
"""Functions for quantized training and evaluation."""
import tensorflow.compat.v1 as tf
import tf_slim as slim
# pylint: disable=g-import-not-at-top
try:
from tensorflow.contrib import quantize as contrib_quantize
except ImportError:
# TF 2.0 doesn't ship with contrib.
pass
# pylint: enable=g-import-not-at-top
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:
contrib_quantize.experimental_create_training_graph(
input_graph=tf.get_default_graph(),
quant_delay=graph_rewriter_config.quantization.delay
)
else:
contrib_quantize.experimental_create_eval_graph(
input_graph=tf.get_default_graph()
)
slim.summarize_collection('quant_vars')
return graph_rewrite_fn