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42 lines
1.7 KiB
42 lines
1.7 KiB
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Functions for quantized training and evaluation."""
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import tensorflow as tf
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def build(graph_rewriter_config, is_training):
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"""Returns a function that modifies default graph based on options.
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Args:
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graph_rewriter_config: graph_rewriter_pb2.GraphRewriter proto.
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is_training: whether in training of eval mode.
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"""
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def graph_rewrite_fn():
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"""Function to quantize weights and activation of the default graph."""
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if (graph_rewriter_config.quantization.weight_bits != 8 or
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graph_rewriter_config.quantization.activation_bits != 8):
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raise ValueError('Only 8bit quantization is supported')
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# Quantize the graph by inserting quantize ops for weights and activations
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if is_training:
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tf.contrib.quantize.create_training_graph(
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input_graph=tf.get_default_graph(),
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quant_delay=graph_rewriter_config.quantization.delay)
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else:
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tf.contrib.quantize.create_eval_graph(input_graph=tf.get_default_graph())
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tf.contrib.layers.summarize_collection('quant_vars')
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return graph_rewrite_fn
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