def extract(cls, node):
attrs = tf_create_attrs(node, 2, 3)
def get_num_groups(node):
if 'group' in node:
return node.group
elif node.in_node(0).shape is not None and node.kernel_shape is not None \
and node.in_node(0).shape[node.channel_dims[0]] is not dynamic_dimension \
and node.kernel_shape[node.input_feature_channel] is not dynamic_dimension:
# if group attribute is not defined, number of groups is calculated
# from number of input channels and filter channel size
return node.in_node(0).shape[
node.channel_dims] // node.kernel_shape[
node.input_feature_channel]
else:
return 1
attrs.update({
'op':
__class__.op,
'get_group':
get_num_groups,
'get_output_feature_dim':
lambda node: node.kernel_shape[node.output_feature_channel],
'get_weights_permute':
PermuteAttrs.Permutation(perm=int64_array([3, 2, 0, 1]),
inv=int64_array([2, 3, 1, 0]))
})
# update the attributes of the node
Convolution.update_node_stat(node, attrs)
return cls.enabled
def extract(cls, node):
attrs = tf_create_attrs(node, 3, 4)
attrs.update({
'op':
__class__.op,
'get_group':
lambda node: 1,
'get_output_feature_dim':
lambda node: node.kernel_shape[node.output_feature_channel],
'get_weights_permute':
PermuteAttrs.Permutation(perm=int64_array([4, 3, 0, 1, 2]),
inv=int64_array([2, 3, 4, 1, 0]))
})
# update the attributes of the node
Convolution.update_node_stat(node, attrs)
return cls.enabled
def extract(cls, node):
attrs = tf_create_attrs(node, 4, 3)
attrs.update({
'op':
cls.op,
'get_weights_permute':
PermuteAttrs.Permutation(perm=int64_array([4, 3, 0, 1, 2]),
inv=int64_array([2, 3, 4, 1, 0])),
'swap_0_and_2_inputs':
True,
'shape_input':
True,
})
# update the attributes of the node
Deconvolution.update_node_stat(node, attrs)
return cls.enabled
def extract(cls, node):
attrs = tf_create_attrs(node, 2, 2)
attrs.update({
'op':
__class__.op,
'kernel_spatial_idx':
np.array([0, 1], dtype=np.int64),
'get_group':
lambda node: node.kernel_shape[node.output_feature_channel],
'get_output_feature_dim':
lambda node: node.kernel_shape[-1] * node.kernel_shape[-2],
'get_weights_permute':
PermuteAttrs.Permutation(perm=int64_array([2, 3, 0, 1]),
inv=int64_array([2, 3, 0, 1]))
})
# update the attributes of the node
Convolution.update_node_stat(node, attrs)
return cls.enabled
def extract(cls, node):
attrs = tf_create_attrs(node, 2, 3)
attrs.update({
'op':
__class__.op,
'get_group':
lambda node: node.group
if 'group' in node and node.group is not None else node.in_node(0).
shape[node.channel_dims] // node.kernel_shape[
node.input_feature_channel],
'get_output_feature_dim':
lambda node: node.kernel_shape[node.output_feature_channel],
'get_weights_permute':
PermuteAttrs.Permutation(perm=int64_array([3, 2, 0, 1]),
inv=int64_array([2, 3, 1, 0]))
})
# update the attributes of the node
Convolution.update_node_stat(node, attrs)
return cls.enabled
**valued_data('split_2_data1', np.zeros([3, 3, 4, 4])),
**valued_data('split_2_data2', np.zeros([3, 3, 4, 4])),
**valued_data('split_2_data3', np.zeros([3, 3, 4, 4])),
**valued_data('split_2_data4', np.zeros([3, 3, 4, 4])),
**regular_op_with_shaped_data(
'conv2d_1', [1, 4, 8, 8], {
'type':
'Convolution',
'channel_dims':
np.array([1]),
'pad':
np.array([2, 2]),
'stride':
np.array([2, 2]),
'get_weights_permute':
PermuteAttrs.Permutation(perm=int64_array([3, 2, 0, 1]),
inv=int64_array([2, 3, 1, 0])),
'group':
1,
'output':
4,
'output_shape': [1, 4, 8, 8],
'can_be_fused':
True
}),
**regular_op_with_shaped_data(
'conv2d_2', [1, 4, 8, 8], {
'type': 'Convolution',
'pad': np.array([2, 2]),
'stride': np.array([2, 2]),
'can_be_fused': True
}),