def test_params_creation(self):
params = {
'pad': None,
'kernel_size': None,
'stride': None,
'dilation': None,
'group': 14,
'num_output': 15,
'bias_term': True,
'pad_w': 3,
'pad_h': 4,
'kernel_w': 5,
'kernel_h': 6,
'stride_h': 3,
'stride_w': 2,
}
exp_res = {
'padding': [3, 4],
'stride': [2, 3],
'kernel': [5, 6],
'dilate': [1, 1],
'group': 14,
'output': 15
}
res = conv_set_params(
FakeConvProtoLayer(FakeMultiParam(params)).convolution_param,
'Conv2D')
for key in exp_res.keys():
if key in ('padding', 'stride', 'stride', 'kernel', 'dilate'):
np.testing.assert_equal(res[key], exp_res[key])
else:
self.assertEqual(res[key], exp_res[key])
def extract(node):
proto_layer, model_layer = node.pb, node.model_pb
if not proto_layer:
raise Error('Protobuf layer can not be empty')
conv_param = proto_layer.convolution_param
conv_type = 'ConvND' if len(proto_layer.bottom) > 1 else 'Conv2D'
params = conv_set_params(conv_param, conv_type)
attrs = conv_create_attrs(params)
attrs.update({
'op': __class__.op,
'get_group': lambda node: node.group,
'get_output_feature_dim': lambda node: node.output,
'weights_index': 1 if conv_type == 'Conv2D' else 2
})
# Embed weights and biases as attributes
# It will be moved to a separate nodes in special pass
attrs.update(
weights_biases(conv_param.bias_term,
model_layer,
start_index=len(proto_layer.bottom),
proto=conv_param))
attrs.update(layout_attrs())
# update the attributes of the node
Convolution.update_node_stat(node, attrs)
return __class__.enabled