[singa] branch master updated: fix

[wa...@apache.org]

This is an automated email from the ASF dual-hosted git repository.

wangwei pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/singa.git


The following commit(s) were added to refs/heads/master by this push:
     new 645af8e  fix
     new 9c100ac  Merge pull request #569 from joddiy/fix-batchnom-scale-and-bias-as-parameter
645af8e is described below

commit 645af8e9bc9986f228851d1ca6b6d5ca7dc517a1
Author: joddiy <jo...@qq.com>
AuthorDate: Thu Dec 19 20:10:13 2019 +0800

    fix
---
 python/singa/autograd.py |  9 +++------
 python/singa/sonnx.py    | 26 +-------------------------
 2 files changed, 4 insertions(+), 31 deletions(-)

diff --git a/python/singa/autograd.py b/python/singa/autograd.py
index cc5bb6f..37cb431 100644
--- a/python/singa/autograd.py
+++ b/python/singa/autograd.py
@@ -1498,16 +1498,13 @@ class BatchNorm2d(Layer):
 
 
 class _BatchNorm2d(Operation):
-    def __init__(self, handle, running_mean, running_var, scale, bias, name=None):
+    def __init__(self, handle, running_mean, running_var, name=None):
         super(_BatchNorm2d, self).__init__(name)
         self.handle = handle
         self.running_mean = running_mean.data
         self.running_var = running_var.data
-        self.scale = scale.data
-        self.bias = bias.data
 
-    def forward(self, x):
-        scale, bias = self.scale, self.bias
+    def forward(self, x, scale, bias):
         if training:
             if (type(self.handle) == singa.BatchNormHandle):
                 y, mean, var = singa.CpuBatchNormForwardTraining(
@@ -1566,7 +1563,7 @@ class _BatchNorm2d(Operation):
 
 
 def batchnorm_2d(handle, x, scale, bias, running_mean, running_var):
-    return _BatchNorm2d(handle, running_mean, running_var, scale, bias)(x)[0]
+    return _BatchNorm2d(handle, running_mean, running_var)(x, scale, bias)[0]
 
 
 class _Pooling2d(Operation):
diff --git a/python/singa/sonnx.py b/python/singa/sonnx.py
index e124135..5b175ba 100755
--- a/python/singa/sonnx.py
+++ b/python/singa/sonnx.py
@@ -485,29 +485,12 @@ class SingaFrontend(object):
         # then we add nodes of scal, bias, mean, var 
         nodes = []
         running_values = {
-            "scale": op.scale,
-            "bias": op.bias,
             "mean": op.running_mean,
             "var": op.running_var
         }
         for tmp_name, running_value in running_values.items():
             node_name = op.name+":"+tmp_name
             bn_node.input.append(node_name)
-            # running_value.ToHost()
-            # running_value = running_value.GetFloatValue(int(running_value.Size()))
-            # node = NodeProto()
-            # node.name = node_name
-            # node.op_type = cls._rename_operators.get("Dummy", "Dummy")
-            # node.output.extend([node_name])
-            # node.attribute.extend([helper.make_attribute(
-            #     'value', helper.make_tensor(
-            #         name=node_name,
-            #         data_type=TensorProto.FLOAT,
-            #         dims=[len(running_value)],
-            #         vals=running_value,
-            #     )
-            # )])
-            # nodes.append(node)
 
         nodes.append(bn_node)
         return nodes
@@ -664,8 +647,6 @@ class SingaFrontend(object):
             elif yid in input_tensors and optype == '_BatchNorm2d': 
                 # batchnorm add scale, bias, mean, var as inputs
                 running_values = {
-                    "scale": op.scale,
-                    "bias": op.bias,
                     "mean": op.running_mean,
                     "var": op.running_var
                 }
@@ -1396,11 +1377,6 @@ class SingaBackend(Backend):
                 inputs = [tensor_map[x].clone() for x in node.inputs]
                 handle, forward = cls._onnx_node_to_singa_op(node, inputs, opset_version)
                 singa_ops.extend([singa_op(node.name, node, handle, forward)])
-                # we must know the shape of ouput
-                # becasue it will become the input of next layer
-                # so we need to init a new tensor with the same shape with the output
-                # outputs = cls._run_node(node, inputs, handle, forward, opset_version)
-                # tensor_map.update(outputs)
         return weights, singa_ops
 
     @classmethod
@@ -1482,7 +1458,7 @@ class SingaRep(BackendRep):
         ret_outputs = collections.OrderedDict()
         # run the handle by the order of the list(the list is Topological Sorting)
         for x, val in zip(self.model.graph.input, inputs):
-            self.tensor_map[x.name] = val        
+            self.tensor_map[x.name] = val
         for _, op, handle, forward in self.singa_ops[:last_layers]:
             inputs = [self.tensor_map[x] for x in op.inputs]
             outputs = _run_node(op, inputs, handle, forward)