add caffe emitter v1
@@ -164,7 +164,7 @@ class CaffeEmitter(Emitter):
node.layer_type, params)
assert len(node.inputs
) == 1, 'The count of Convolution node\'s input is not 1.'
- input = self.graph.get_bottom_node(node, idx=0)
+ input = self.graph.get_bottom_node(node, idx=0, copy=True)
attr = {
'filter_size': kernel,
'num_filters': channel,
@@ -192,7 +192,7 @@ class CaffeEmitter(Emitter):
node.layer_type, params)
assert len(node.inputs
) == 1, 'The count of Deconvolution node\'s input is not 1.'
- input = self.graph.get_bottom_node(node, idx=0)
+ input = self.graph.get_bottom_node(node, idx=0, copy=True)
attr = {
'output_size': None,
'filter_size': kernel,
@@ -220,7 +220,7 @@ class CaffeEmitter(Emitter):
pool_type = 'avg'
assert len(
node.inputs) == 1, 'The count of Pooling node\'s input is not 1.'
- input = self.graph.get_bottom_node(node, idx=0)
+ input = self.graph.get_bottom_node(node, idx=0, copy=True)
attr = {
'pool_size': kernel,
'pool_stride': stride,
@@ -238,7 +238,7 @@ class CaffeEmitter(Emitter):
def ReLU(self, node):
assert len(
node.inputs) == 1, 'The count of ReLU node\'s input is not 1.'
- input = self.graph.get_bottom_node(node, idx=0)
+ input = self.graph.get_bottom_node(node, idx=0, copy=True)
attr = {'name': string(node.layer_name)}
node.fluid_code.add_layer("relu",
inputs=input,
@@ -255,7 +255,7 @@ class CaffeEmitter(Emitter):
# just scales by alpha (as does Krizhevsky's paper).
# We'll account for that here.
alpha = params.alpha / float(params.local_size)
- input = self.graph.get_bottom_node(node, idx=0)
+ input = self.graph.get_bottom_node(node, idx=0, copy=True)
attr = {
'n': params.local_size,
'k': 1.0,
@@ -288,7 +288,7 @@ class CaffeEmitter(Emitter):
params = node.layer.inner_product_param
assert params.axis == 1
assert params.bias_term == True
- input = self.graph.get_bottom_node(node, idx=0)
+ input = self.graph.get_bottom_node(node, idx=0, copy=True)
attr = {
'size': params.num_output,
'name': string(node.layer_name),
@@ -304,7 +304,7 @@ class CaffeEmitter(Emitter):
def Softmax(self, node):
assert len(
node.inputs) == 1, 'The count of Softmax node\'s input is not 1.'
- input = self.graph.get_bottom_node(node, idx=0)
+ input = self.graph.get_bottom_node(node, idx=0, copy=True)
params = node.layer.softmax_param
axis = params.axis
shape = node.input_shape[0]
