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[Frontend][Darknet] L2 normalization support in darknet #1916

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Oct 24, 2018
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[Frontend][Darknet] L2 normalization support in darknet #1916

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83db15e
l2 normalization
PariksheetPinjari909 Oct 17, 2018
564a6e3
retrigger CI
PariksheetPinjari909 Oct 18, 2018
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12 changes: 12 additions & 0 deletions nnvm/python/nnvm/frontend/darknet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -267,6 +267,13 @@ def _darknet_upsampling(inputs, attrs):
new_attrs['scale'] = attrs.get('scale', 1)
return _darknet_get_nnvm_op(op_name)(*inputs, **new_attrs), None

def _darknet_l2normalize(inputs, attrs):
"""Process the l2 normalization operation."""
op_name, new_attrs = 'l2_normalize', {}
new_attrs['eps'] = attrs.get('eps', 0)
new_attrs['axis'] = attrs.get('axis', 1)
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I don't really know darknet, would appreciate if you could point out where these two attrs come from. The overall functionality looks good to me.

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Sure. The darknet computes the l2 normalization always along the channel axis. So it doesn't store the axis information anywhere. And while dividing by sum it doesn't check if sum is greater than eps or not.
So i have made default eps as 0 and axis as 1 to make it compatible with TVM's L2 normalization implementation. You can have look at darknet's l2 normalization implementation here

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got it. thanks!

return _darknet_get_nnvm_op(op_name)(*inputs, **new_attrs), None

def _darknet_softmax_output(inputs, attrs):
"""Process the softmax operation."""
temperature = attrs.get('temperature', 1)
Expand Down Expand Up @@ -370,6 +377,7 @@ def _darknet_op_not_support(inputs, attrs):
LAYERTYPE.REGION : _darknet_region,
LAYERTYPE.SHORTCUT : _darknet_shortcut,
LAYERTYPE.UPSAMPLE : _darknet_upsampling,
LAYERTYPE.L2NORM : _darknet_l2normalize,
LAYERTYPE.YOLO : _darknet_yolo,
LAYERTYPE.DETECTION : _darknet_op_not_support,
LAYERTYPE.CROP : _darknet_op_not_support,
Expand Down Expand Up @@ -630,6 +638,10 @@ def _get_darknet_attrs(self, layer, layer_num):

elif LAYERTYPE.UPSAMPLE == layer.type:
attr.update({'scale' : layer.stride})

elif LAYERTYPE.L2NORM == layer.type:
pass

else:
err = "Darknet layer type {} is not supported in nnvm.".format(layer.type)
raise NotImplementedError(err)
Expand Down
1 change: 1 addition & 0 deletions nnvm/python/nnvm/testing/darknet.py
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Expand Up @@ -512,6 +512,7 @@ class ACTIVATION(object):
layer make_lstm_layer(int batch, int inputs, int outputs, int steps, int batch_normalize, int adam);
layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_normalize, int adam);
layer make_upsample_layer(int batch, int w, int h, int c, int stride);
layer make_l2norm_layer(int batch, int inputs);
void free_network(network *net);
"""
)
16 changes: 15 additions & 1 deletion nnvm/tests/python/frontend/darknet/test_forward.py
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Original file line number Diff line number Diff line change
Expand Up @@ -107,7 +107,7 @@ def get_darknet_output(net, img):
out.insert(0, attributes)
out.insert(0, _read_memory_buffer((layer.total*2, ), layer.biases))
out.insert(0, _read_memory_buffer((layer.n, ), layer.mask, dtype='int32'))
layer_ou tshape = (layer.batch, layer.out_c,
layer_outshape = (layer.batch, layer.out_c,
layer.out_h, layer.out_w)
out.insert(0, _read_memory_buffer(layer_outshape, layer.output))
elif i == net.n-1:
Expand Down Expand Up @@ -361,6 +361,19 @@ def test_forward_upsample():
test_forward(net)
LIB.free_network(net)

def test_forward_l2normalize():
'''test l2 normalization layer'''
net = LIB.make_network(1)
layer = LIB.make_l2norm_layer(1, 224*224*3)
layer.c = layer.out_c = 3
layer.h = layer.out_h = 224
layer.w = layer.out_w = 224
net.layers[0] = layer
net.w = net.h = 224
LIB.resize_network(net, 224, 224)
test_forward(net)
LIB.free_network(net)

def test_forward_elu():
'''test elu activation layer'''
net = LIB.make_network(1)
Expand Down Expand Up @@ -520,6 +533,7 @@ def test_forward_activation_logistic():
test_forward_region()
test_forward_yolo_op()
test_forward_upsample()
test_forward_l2normalize()
test_forward_elu()
test_forward_rnn()
test_forward_crnn()
Expand Down