def infer(node: Node):
"""
Infers shape of convolution node as it is done in ONNX.
It is very similar to one that Caffe does, but slightly different.
We made a complete fork of this function because they are supposed to be
supported differently by different people.
Args:
node: graph convolution node
"""
input_shape = node.in_port(0).data.get_shape()
if input_shape is None:
raise Error('Input data shape is None for node {}'.format(
node.soft_get('name', node.id)))
# bias_term cannot be deduced earlier for frameworks that represent
# convolution weights/biases as regular inputs; so the number of inputs
# is being checked here and restore correct value for bias_term to
# have the rest of the code unchanged. It will be used after we merge
# several infer functions for convolution in different FWs to a single one.
if not node.has_valid('bias_term'):
node['bias_term'] = len(node.in_nodes()) == 3
weights_index = node.weights_index if node.has_valid(
'weights_index') else 1
# Reshape weights kernel to original shape
# In case of caffe or MXNet framework, values for weights have no structured shape like OIHW
# so we have to reshape weights to normal shape
# For this case, Convolution node should have attribute reshape_kernel = True
if node.has_valid('reshape_kernel') and node.reshape_kernel:
if not (node.has_valid('output') and node.has_valid('channel_dims')
and node.has_valid('group')
and node.has_valid('kernel_spatial')):
log.error(
'Cannot reshape kernel due to not all required attrs was set to {} node'
.format(node.id))
return
# layout for Convolution weights is OIHW
kernel_shape = shape_array([
node.output,
input_shape[node.channel_dims].item() / node.group, *[
node.kernel_spatial[i]
for i in range(len(node.kernel_spatial))
]
])
if node.type == 'Deconvolution': # layout for Deconvolution weights is IOHW
kernel_shape[[0, 1]] = kernel_shape[[1, 0]]
if is_fully_defined(
kernel_shape) and np.prod(kernel_shape) != np.prod(
node.in_node(weights_index).value.shape):
log.error(
"Size of weights {} does not match kernel shape: {}\n"
"".format(np.prod(node.in_node(weights_index).value.shape),
kernel_shape) +
" Possible reason is wrong channel number in input shape\n"
)
raise Error("Cannot reshape weights to kernel shape")
if not is_fully_defined(kernel_shape):
num_undefined = np.count_nonzero(kernel_shape.mask is True) # pylint: disable=no-member
if num_undefined > 1:
raise Error(
'Too many undefined dimensions of the kernel shape for node {}. Use --input_shape '
'command line parameter to specify model input shapes'.
format(node.soft_get('name', node.id)))
kernel_size = np.prod(node.in_node(weights_index).value.shape)
# calculate undefined dimension using fully defined shape of the weights input and known kernel_shape
# dimensions
kernel_shape[np.where(kernel_shape == np.ma.masked)[0]
[0]] = kernel_size // np.prod(kernel_shape)
node.in_node(weights_index).shape = shape_array(kernel_shape)
node.in_node(weights_index).value = np.reshape(
node.in_node(weights_index).value, kernel_shape)
node.reshape_kernel = False
# Pass weights shape to node attribute kernel_shape
kernel_shape = node.in_node(weights_index).shape
node['kernel_shape'] = kernel_shape
# Calculate kernel_spatial_idx and spatial_dims if it is not specified
# It is necessary for ONNX dut to convolution can be 1D/2D/3D
if not node.has_valid('kernel_spatial_idx'):
node['kernel_spatial_idx'] = np.delete(
[x for x in range(len(kernel_shape))],
(node.input_feature_channel, node.output_feature_channel))
if not node.has_valid('spatial_dims'):
node['spatial_dims'] = np.delete(
[x for x in range(len(input_shape))],
(node.channel_dims[0], node.batch_dims[0]))
node['kernel_spatial'] = kernel_shape[node.kernel_spatial_idx]
if not node.has_valid('output'):
# restore the number of output feature maps from the second argument that is weights
if node.type in [
'Convolution', 'Deconvolution', 'DeformableConvolution',
'BinaryConvolution'
]:
node['output'] = kernel_shape[node.output_feature_channel]
else:
raise Error(
'Convolution infer function was called for a node {} with unsupported type {}',
node.soft_get('name'), node.type)
# Set default values for dilation, strides and pads if not set
if not node.has_valid('dilation'):
node['dilation'] = np.full([len(input_shape)], 1, dtype=np.int64)
if not node.has_valid('stride'):
node['stride'] = np.full([len(input_shape)], 1, dtype=np.int64)
if not node.has_valid('pad'):
node['pad'] = int64_array([[0, 0]] * len(input_shape))
node['pad_spatial_shape'] = node.pad[node.spatial_dims]
if not node.has_valid('output_padding'):
node['output_padding'] = np.full([len(input_shape)],
0,
dtype=np.int64)
if node.has_valid('output_padding') and len(input_shape) > len(
node['output_padding']):
output_padding = np.zeros(len(input_shape), dtype=np.int64)
for i in range(len(node['output_padding'])):
output_padding[i] = node['output_padding'][i]
node['output_padding'] = output_padding
input_spatial_shape = input_shape[node.spatial_dims]
stride_spatial_shape = node.stride[node.spatial_dims]
kernel_extent = node.dilation[node.spatial_dims] * (
node.kernel_spatial - 1) + 1
# TensorFlow always has auto_pad attribute that can be either valid or same_upper
# In ONNX auto_pad attribute is deprecated but appears in some models (could be valid, same_upper or same_lower)
# Caffe do not use auto_pad attribute
if node.has_valid(
'auto_pad'
) and node.auto_pad != 'explicit' and not node.has_valid(
'output_spatial_shape'):
node['pad_spatial_shape'], node[
'output_spatial_shape'] = tf_window_op_pad_infer(
input_spatial_shape, kernel_extent, stride_spatial_shape,
node.auto_pad, node.type == 'Deconvolution')
pad = np.zeros((len(input_shape), 2), dtype=np.int64)
pad[node.spatial_dims] = node.pad_spatial_shape
node.pad = pad
else:
pad_spatial_shape = np.add.reduce(node.pad_spatial_shape, axis=1)
if node.type in ('Convolution', 'BinaryConvolution'):
float_spatial = Convolution.calc_convolution(
input_spatial_shape, stride_spatial_shape,
pad_spatial_shape, kernel_extent)
node['output_spatial_shape'] = shape_array(float_spatial)
elif node.type == 'Deconvolution':
# In case of given output_spatial_shape we calculate pads spatial
if node.has_valid('output_spatial_shape'):
if node.has_valid('get_pad'):
node['pad'] = node.get_pad(node, input_shape,
kernel_shape)
else:
log.debug(
'Can\'t calculate paddings due to missing lambda get_pad in {} node'
.format(node.id))
return
else:
output_padding = node.output_padding[
node.spatial_dims] if node.has_valid(
'output_padding') else None
if output_padding is not None and any(output_padding):
pad_spatial_shape -= output_padding
for dim in range(len(pad_spatial_shape)):
node.pad_spatial_shape[dim][
1] -= pad_spatial_shape[dim]
float_spatial = Convolution.calc_deconvolution(
node, input_spatial_shape, pad_spatial_shape,
kernel_extent)
node['output_spatial_shape'] = shape_array(float_spatial)
elif node.type == 'DeformableConvolution':
# get the output spatial shape from the second input with offsets
node['output_spatial_shape'] = int64_array(
[node.in_node(1).shape[2:4]])
else:
assert 'Unsupported layer type "{}"'.format(node.type)
# For cases when group attribute wasn't set in extractor we should specify get_group attribute
# this attribute should store lambda node: ... (check tf convolution extractor)
if node.has_valid('get_group'):
node['group'] = node.get_group(node)
output_shape = shape_array(
[dynamic_dimension_value for _ in range(len(input_shape))])
output_shape[node.batch_dims] = input_shape[node.batch_dims] # pylint: disable=unsupported-assignment-operation
output_shape[node.spatial_dims] = node.output_spatial_shape # pylint: disable=unsupported-assignment-operation
# For cases when output attribute wasn't set in extractor we should specify get_output_feature_dim attribute
# this attribute should store lambda node: ... (check tf convolution extractor)
if node.has_valid('get_output_feature_dim'):
node['output'] = node.get_output_feature_dim(node)
output_shape[node.channel_dims] = node.output # pylint: disable=unsupported-assignment-operation
node['output_shape'] = output_shape
node.out_port(0).data.set_shape(output_shape)
# bin attribute is used for pre-processing, but it will be deleted in BlobNormalizer transformation
# and the blobs (weights, biases) will be represented as inputs to the node
mark_input_bins(
node, start_port=1 if node.type != 'DeformableConvolution' else 2)
assign_dims_to_weights(node.in_node(weights_index),
node.kernel_spatial_idx,
node.input_feature_channel,
node.output_feature_channel, len(kernel_shape))
PermuteAttrs.create_permute_attrs(
node,
attrs=[
('pad', 'input:0'),
('stride', 'input:0'),
('dilation', 'input:0'),
('output_shape', 'input:0'),
('batch_dims', 'input:0'),
('channel_dims', 'input:0'),
('spatial_dims', 'input:0'),
('kernel_shape', 'input:{}'.format(weights_index)),
('kernel_spatial_idx', 'input:{}'.format(weights_index)),
('input_feature_channel', 'input:{}'.format(weights_index)),
('output_feature_channel', 'input:{}'.format(weights_index)),
])
# is needed to permute Conv weights from the original TF [H, W, C_IN, C_OUT] into IE [C_OUT, C_IN, H, W]
# but for other nodes in weights subgraph permutations must turned off
# by marking with MarkSubGraphsWithCorrectLayout even if graph layout is NCHW.
PermuteAttrs.set_permutation(
node.in_node(weights_index), node,
node.soft_get('get_weights_permute', None))
PermuteInputs().set_input_permutation(node.in_node(weights_index),
node,
'input:{}'.format(weights_index),
'transpose')
