def dequantize(self, G: NNGraph):
qrecs = G.quantization
LOG.info("dequantizing graph parameters")
for _, node, _, fnode in G.nodes_iterator():
qrec = qrecs[NodeId(node, fnode)]
anode = node if fnode is None else fnode
handler = self.handlers[1].get(anode.__class__)
if handler:
handler.dequantize(anode, qrec)
def dequantize(self, G: NNGraph):
qrecs = G.quantization
LOG.info("dequantizing graph parameters")
for _, node, _, fnode in G.nodes_iterator():
qrec = qrecs[NodeId(node, fnode)]
if isinstance(node, ConstantInputParameters):
node.value = qrec.out_q[0].dequantize(node.value)
else:
anode = node if fnode is None else fnode
if isinstance(anode, (FcParameters, Conv2DParameters)):
if anode.has_bias:
anode.biases = qrec.biases_q.dequantize(anode.biases)
anode.weights = qrec.weights_q.dequantize(anode.weights)
def report(self, G: NNGraph, stats):
dump_stats = OrderedDict()
for step_idx, node, fusion_idx, fnode in G.nodes_iterator(self._yield_fusions):
stat = stats[NodeId(node, fnode)]
stat = copy.deepcopy(stat)
if fusion_idx:
name = "{}_{}".format(node.name, fusion_idx)
idx = "{}_{}".format(step_idx, fusion_idx)
else:
name = node.name
idx = str(step_idx)
dump_stats[name] = stat
stat['idx'] = idx
return dump_stats_table(dump_stats, do_totals=self._do_totals, threshold=self._threshold)