def test_attrs(self):
params = {
'type_str': 'Deconv2D',
'padding': [10, 10],
'stride': [12, 12],
'kernel': [11, 11],
'dilate': [13, 13],
'group': 14,
'output': 13,
'bias_term': True
}
res = conv_create_attrs(params)
exp_res = {
'pad': np.array([[0, 0], [0, 0], [10, 10], [10, 10]]),
'pad_spatial_shape': np.array([[10, 10], [10, 10]]),
'stride': np.array([1, 1, 12, 12]),
'kernel_spatial': np.array([11, 11]),
'dilation': np.array([1, 1, 13, 13]),
'group': 14,
'bias_addable': True,
'output_spatial_shape': None,
'output_shape': None,
'output': 13,
}
for key in exp_res.keys():
if key in ('pad', 'pad_spatial_shape', 'stride', 'kernel_spatial',
'dilation'):
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