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Nnir2nnef fix #52

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Nnir2nnef fix #52

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23cdd72
nnef_to_nnir
hansely123 Feb 15, 2019
df54d6c
nnir_to_nnef bug fix
hansely123 Feb 15, 2019
8fe2b9c
Bug fix
hansely123 Feb 15, 2019
257e2bd
test
hansely123 Feb 16, 2019
daf13b7
Fix
hansely123 Feb 16, 2019
3835c68
Merge branch 'nnef_to_nnir' into nnir2nnef_fix
hansely123 Feb 16, 2019
92ee8d2
Fix
hansely123 Feb 16, 2019
fb50d19
Fixed parameters
hansely123 Feb 21, 2019
85e4050
Merge branch 'master' into nnir2nnef_fix
hansely123 Feb 21, 2019
acbf472
Param Fix
hansely123 Feb 21, 2019
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22 changes: 13 additions & 9 deletions model_compiler/python/nnir_to_nnef.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,7 @@ def generateGraph(graph,outputFolder,label):
with open(fileName, 'wb') as f:
f.write( \
"""# This file is generated by nnir2nnef.py
version 1.0;

graph nnir (%s) -> (%s) {
""" % (', '.join([tensor.name for tensor in graph.inputs]), ', '.join([tensor.name for tensor in graph.outputs])))
Expand Down Expand Up @@ -60,30 +61,33 @@ def generateGraph(graph,outputFolder,label):
""" %s = conv(%s, %s, %sstride=[%d,%d], dilation=[%d,%d], padding=[(%d,%d),(%d,%d)], groups=%d, border = 'ignore');
""" % (node.outputs[0], node.inputs[0], node.inputs[1], node.inputs[2] + ', ' if len(node.inputs) == 3 else '', \
strides[0], strides[1], dilations[0], dilations[1], pads[0], pads[1], pads[2], pads[3], group))
elif node.type == 'avg_pool' or 'max_pool':
elif node.type == 'avg_pool' or node.type == 'max_pool':
kernel_shape = node.attr.get('kernel_shape')
pads = node.attr.get('pads')
padding = '(0,0),(0,0),(%d,%d),(%d,%d)' % (pads[0], pads[1], pads[2], pads[3]) if len(pads) != 0 else ''
strides = node.attr.get('strides')
stride = '1,1,%d,%d' % (strides[0], strides[1]) if len(strides) != 0 else ''
dilations = node.attr.get('dilations')
dilation = '1,1,%d,%d' % (dilations[0], dilations[1]) if len(dilations) != 0 else ''
f.write( \
""" %s = %s(%s, size=[%d,%d], stride=[%d,%d], dilation=[%d,%d], padding=[(%d,%d),(%d,%d)], border = 'ignore');
""" %s = %s(%s, size=[1,1,%d,%d], stride=[%s], dilation=[%s], padding=[%s], border = 'ignore');
""" % (node.outputs[0], node.type, node.inputs[0], kernel_shape[0], kernel_shape[1], \
strides[0], strides[1], dilations[0], dilations[1], pads[0], pads[1], pads[2], pads[3]))
elif node.type == 'relu' or 'softmax':
stride, dilation, padding))
elif node.type == 'relu' or node.type == 'softmax':
f.write( \
""" %s = %s(%s);
""" % (node.outputs[0], node.type, node.inputs[0]))
elif node.type == 'add':
elif node.type == 'sum':
f.write( \
""" %s = %s(%s, %s);
""" % (node.outputs[0], node.type, node.inputs[0], node.inputs[1]))
""" %s = add(%s, %s);
""" % (node.outputs[0], node.inputs[0], node.inputs[1]))
elif node.type == 'batch_norm':
f.write( \
""" %s = batch_normalization(%s, mean = %s, variance = %s, offset = %s, scale = %s, epsilon = %ef);
""" %s = batch_normalization(%s, %s, %s, %s, %s, epsilon = %e);
""" % (node.outputs[0], node.inputs[0], node.inputs[3], node.inputs[4], node.inputs[2], node.inputs[1], node.attr.get('epsilon')))
elif node.type == 'gemm':
f.write( \
""" %s = matmul(%s, %s, trA = %s, trB = %s);
""" %s = matmul(%s, %s, transposeA = %s, transposeB = %s);
""" % (node.outputs[0], node.inputs[0], node.inputs[1], \
'true' if node.attr.get('transA') == 1 else 'false', \
'true' if node.attr.get('transB') == 1 else 'false'))
Expand Down