def _freeze_requantization_ranges(self):
self._tmp_graph_def = freeze_max(self._tmp_graph_def,
self._requant_min_max_log)
self._tmp_graph_def = freeze_min(self._tmp_graph_def,
self._requant_min_max_log)
self._tmp_graph_def = freeze_requantization_range(
self._tmp_graph_def, self._requant_min_max_log)
if self.debug:
write_graph(self._tmp_graph_def, self._int8_frozen_range_graph)
def _insert_logging(self):
int8_dynamic_range_graph_def = graph_pb2.GraphDef()
int8_dynamic_range_graph_def.CopyFrom(self._tmp_graph_def)
InsertLogging(self._tmp_graph_def,
ops=[
"RequantizationRange{}".format(
"PerChannel" if self.per_channel else "")
],
message="__requant_min_max:").do_transformation()
InsertLogging(self._tmp_graph_def, ops=["Min"],
message="__min:").do_transformation()
InsertLogging(self._tmp_graph_def, ops=["Max"],
message="__max:").do_transformation()
write_graph(self._tmp_graph_def, self._int8_logged_graph)
self._tmp_graph_def.CopyFrom(int8_dynamic_range_graph_def)
def _fuse_requantize_with_fused_quantized_conv(self):
self._tmp_graph_def = fuse_quantized_conv_and_requantize(
self._tmp_graph_def)
# strip_unused_nodes with optimize_for_inference
dtypes = self._get_dtypes(self._tmp_graph_def)
# self._tmp_graph_def = optimize_for_inference(self._tmp_graph_def, self.inputs, self.outputs, dtypes, False)
self._tmp_graph_def = StripUnusedNodes(self._tmp_graph_def,
self.inputs, self.outputs,
dtypes).do_transform()
self._tmp_graph_def = graph_util.remove_training_nodes(
self._tmp_graph_def, self.outputs)
self._tmp_graph_def = FoldBatchNormNodes(
self._tmp_graph_def).do_transform()
RerangeQuantizedConcat(self._tmp_graph_def).do_transformation()
write_graph(self._tmp_graph_def, self.output_graph)
logging.info('Converted graph file is saved to: %s', self.output_graph)
def _optimize_frozen_fp32_graph(self):
"""Optimize fp32 frozen graph."""
self._tmp_graph_def = read_graph(self.input_graph,
self.input_graph_binary_flag)
dtypes = self._get_dtypes(self._tmp_graph_def)
# self._tmp_graph_def = optimize_for_inference(self._tmp_graph_def, self.inputs, self.outputs, dtypes, False)
self._tmp_graph_def = FuseColumnWiseMul(
self._tmp_graph_def).do_transformation()
self._tmp_graph_def = StripUnusedNodes(self._tmp_graph_def,
self.inputs, self.outputs,
dtypes).do_transform()
self._tmp_graph_def = graph_util.remove_training_nodes(
self._tmp_graph_def, self.outputs)
self._tmp_graph_def = FoldBatchNormNodes(
self._tmp_graph_def).do_transform()
write_graph(self._tmp_graph_def, self._fp32_optimized_graph)
def _quantize_graph(self):
"""quantize graph."""
if not self._tmp_graph_def:
self._tmp_graph_def = read_graph(self.input_graph,
self.input_graph_binary_flag)
g = ops.Graph()
with g.as_default():
importer.import_graph_def(self._tmp_graph_def)
intel_quantizer = QuantizeGraphForIntel(
self._tmp_graph_def,
self.outputs,
self.per_channel,
excluded_ops=self.excluded_ops,
excluded_nodes=self.excluded_nodes)
self._tmp_graph_def = intel_quantizer.do_transform()
if self.debug:
write_graph(self._tmp_graph_def, self._int8_dynamic_range_graph)