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dnn/native: add native support for avg_pool

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Not support pooling strides in channel dimension yet.

Signed-off-by: Ting Fu <ting.fu@intel.com>
Reviewed-by: Guo, Yejun <yejun.guo@intel.com>
This commit is contained in:
Ting Fu
2020-08-10 00:33:13 +08:00
committed by Guo, Yejun
parent 40597add98
commit 91efc41a69
7 changed files with 223 additions and 3 deletions
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37
tools/python/convert_from_tensorflow.py
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@ -67,10 +67,12 @@ class TFConverter:
self.edges = {}
self.conv_activations = {'Relu':0, 'Tanh':1, 'Sigmoid':2, 'None':3, 'LeakyRelu':4}
self.conv_paddings = {'VALID':0, 'SAME':1}
self.pool_paddings = {'VALID':0, 'SAME':1}
self.converted_nodes = set()
self.conv2d_scope_names = set()
self.conv2d_scopename_inputname_dict = {}
self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3, 'Maximum':4, 'MathBinary':5, 'MathUnary':6}
self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3, 'Maximum':4,
'MathBinary':5, 'MathUnary':6, 'AvgPool':7}
self.mathbin2code = {'Sub':0, 'Add':1, 'Mul':2, 'RealDiv':3, 'Minimum':4}
self.mathun2code = {'Abs':0, 'Sin':1, 'Cos':2, 'Tan':3, 'Asin':4,
'Acos':5, 'Atan':6, 'Sinh':7, 'Cosh':8, 'Tanh':9, 'Asinh':10,
@ -300,6 +302,37 @@ class TFConverter:
np.array([output_operand_index],dtype=np.uint32).tofile(f)
def dump_avg_pool_to_file(self, node, f):
assert(node.op == 'AvgPool')
self.layer_number = self.layer_number + 1
self.converted_nodes.add(node.name)
node0 = self.name_node_dict[node.input[0]]
strides = node.attr['strides']
# Tensorflow do not support pooling strides in batch dimension and
# current native NN do not support pooling strides in channel dimension, added assert() here.
assert(strides.list.i[1]==strides.list.i[2])
assert(strides.list.i[0]==1)
assert(strides.list.i[3]==1)
strides = strides.list.i[1]
filter_node = node.attr['ksize']
input_name = node.input[0]
# Tensorflow do not support pooling ksize in batch dimension and channel dimension.
assert(filter_node.list.i[0]==1)
assert(filter_node.list.i[3]==1)
filter_height = filter_node.list.i[1]
filter_width = filter_node.list.i[2]
padding = node.attr['padding'].s.decode("utf-8")
np.array([self.op2code[node.op], strides, self.pool_paddings[padding], filter_height],
dtype=np.uint32).tofile(f)
input_operand_index = self.add_operand(input_name, Operand.IOTYPE_INPUT)
output_operand_index = self.add_operand(node.name, Operand.IOTYPE_OUTPUT)
np.array([input_operand_index, output_operand_index],dtype=np.uint32).tofile(f)
def dump_layers_to_file(self, f):
for node in self.nodes:
if node.name in self.converted_nodes:
@ -313,6 +346,8 @@ class TFConverter:
if node.op == 'Conv2D':
self.dump_simple_conv2d_to_file(node, f)
if node.op == 'AvgPool':
self.dump_avg_pool_to_file(node, f)
elif node.op == 'DepthToSpace':
self.dump_depth2space_to_file(node, f)
elif node.op == 'MirrorPad':