def roipooling_infer(node: Node):
"""
Sets shape of output node according specified parameters input blobs and node
Sets number from the first input blob, channels from the second one, height and width are specified
Parameters
----------
node
"""
shapes = [node.in_node(i).shape for i in range(len(node.in_nodes()))]
if any(s is None for s in shapes):
return
if len(node.in_nodes()) == 4: # TensorFlow case of CropAndResize operation
crop_size = node.in_node(3).value
if crop_size is None:
log.error('The ROIPooling size is not known for node {}'.format(
node.soft_get('name')))
return
if not isinstance(crop_size, np.ndarray) or len(crop_size) != 2:
log.error(
'The ROIPooling size is should have 2 elements for node {}'.
format(node.soft_get('name')))
node.pooled_h = crop_size[0]
node.pooled_w = crop_size[1]
node.graph.remove_edge(node.in_node(3).id, node.id)
node.graph.remove_edge(node.in_node(2).id, node.id)
layout = node.graph.graph['layout']
assert len(layout) == 4
node.out_node().shape = shape_for_layout(
layout,
batch=shapes[1][get_batch_dim(layout, 4)],
features=shapes[0][get_features_dim(layout, 4)],
height=node.pooled_h,
width=node.pooled_w)
def infer(node: Node):
in_shape = node.in_port(0).data.get_shape()
if in_shape.size != 4:
raise Error(
'TensorFlow DepthToSpace operation is supported for 4D \'NHWC\' input layout only. '
'Current input shape is \'{}\''.format(in_shape))
layout = node.graph.graph['layout']
N = in_shape[get_batch_dim(layout, 4)]
H = in_shape[get_height_dim(layout, 4)]
W = in_shape[get_width_dim(layout, 4)]
C = in_shape[get_features_dim(layout, 4)]
block_size = node['block_size']
if C is not dynamic_dimension and C % (block_size**2):
raise Error(
'Feature dimensions of input tensor of DepthToSpace operation have to be divisible by square '
'of DepthToSpace \'block_size\' parameter. Input tensor shape = {}. Feature dimension = {}. '
'block_size = {}'.format(in_shape, C, block_size))
out_shape = shape_for_layout(layout,
batch=N,
features=C // (block_size * block_size),
height=H * block_size,
width=W * block_size)
if is_fully_defined(in_shape) and is_fully_defined(
out_shape) and np.prod(in_shape) != np.prod(out_shape):
raise Error(
'Number of input elements "{}" is not equal to number of output elements "" for node "{}"'
''.format(in_shape, out_shape, node.soft_get('name', node.id)))
node.out_port(0).data.set_shape(out_shape)
def infer(node: Node):
in_shape = node.in_node().shape
if in_shape.size != 4:
raise Error(
'TensorFlow SpaceToDepth operation is supported for 4D \'NHWC\' input layout only. '
'Current input shape is \'{}\''.format(in_shape))
layout = node.graph.graph['layout']
N = in_shape[get_batch_dim(layout, 4)]
H = in_shape[get_height_dim(layout, 4)]
W = in_shape[get_width_dim(layout, 4)]
C = in_shape[get_features_dim(layout, 4)]
block_size = node['block_size']
if (H is not dynamic_dimension
and H % block_size) or (W is not dynamic_dimension
and W % block_size):
raise Error(
'Spatial dimensions of input tensor of SpaceToDepth operation have to be divisible by '
'SpaceToDepth \'block_size\' parameter. Input tensor shape = {}. Spatial dimensions = {},{}. '
'block_size = {}'.format(in_shape, H, W, block_size))
out_shape = shape_for_layout(layout,
batch=N,
features=C * (block_size**2),
height=H // block_size,
width=W // block_size)
node.out_port(0).data.set_shape(out_shape)
def upsample_infer(node: Node):
layout = node.graph.graph['layout']
assert len(layout) == 4
input_shape = node.in_node(0).shape
if input_shape is None:
return
if len(node.in_nodes()) == 1:
in_height = input_shape[get_height_dim(layout, 4)]
in_width = input_shape[get_width_dim(layout, 4)]
assert node.has('width_scale') is not None and node.has(
'height_scale') is not None
out_height = math.floor(in_height * node.height_scale)
out_width = math.floor(in_width * node.width_scale)
node.out_node().shape = shape_for_layout(
layout,
batch=input_shape[get_batch_dim(layout, 4)],
features=input_shape[get_features_dim(layout, 4)],
height=out_height,
width=out_width)
else:
assert node.in_node(1).value is not None
eps = 1e-5 # This is to make rounding in case of very close number to round to closest instead of down
# generic output shape calculation to support 5D input shape case
node.out_node().shape = np.array(
(input_shape + eps) * node.in_node(1).value).astype(np.int64)
def upsample_infer(node: Node):
layout = node.graph.graph['layout']
assert len(layout) == 4
input_shape = node.in_node(0).shape
if input_shape is None:
return
if len(node.in_nodes()) == 1:
in_height = input_shape[get_height_dim(layout, 4)]
in_width = input_shape[get_width_dim(layout, 4)]
assert node.has('width_scale') is not None and node.has(
'height_scale') is not None
out_height = math.floor(in_height * node.height_scale)
out_width = math.floor(in_width * node.width_scale)
node.out_node().shape = shape_for_layout(
layout,
batch=input_shape[get_batch_dim(layout, 4)],
features=input_shape[get_features_dim(layout, 4)],
height=out_height,
width=out_width)
else:
assert node.in_node(1).value is not None
# generic output shape calculation to support 5D input shape case
node.out_node().shape = input_shape * node.in_node(1).value
def find_and_replace_pattern(self, graph: Graph):
layout = graph.graph['layout']
for eltwise_op_node in graph.get_op_nodes(is_eltwise=True):
out_shape = eltwise_op_node.out_port().data.get_shape()
if 4 <= len(out_shape) <= 5:
out_features = out_shape[get_features_dim(
layout, len(out_shape))]
for port, node in eltwise_op_node.in_nodes().items():
if len(node.shape) != len(out_shape) and len(
node.shape) == 1 and out_features == node.shape[0]:
new_shape = shape_for_layout(
layout,
batch=1,
features=out_features,
height=1,
width=1,
depth=1 if len(out_shape) == 5 else None)
dim_const = Const(graph, {
'value': new_shape,
'name': node.id + '/Dim'
}).create_node()
reshape_op = Reshape(graph,
attrs={
'dim': new_shape,
'name': node.id + '/Broadcast'
}).create_node()
eltwise_op_node.in_port(port).get_source(
).node.out_port(0).get_connection().set_destination(
reshape_op.in_port(0))
reshape_op.in_port(1).connect(dim_const.out_port(0))
reshape_op.out_port(0).connect(
eltwise_op_node.in_port(port))
def replace_pattern(self, graph: Graph, match: dict):
bias_add = match['BiasAdd']
# Replace BiasAdd by Add operation
new_add = Add(graph, {'name': bias_add.id + '/Add'}).create_node()
bias_add.in_port(0).get_connection().set_destination(
new_add.in_port(0))
bias_add.in_port(1).get_connection().set_destination(
new_add.in_port(1))
bias_add.out_port(0).get_connection().set_source(new_add.out_port(0))
if bias_add.data_format != 'NCHW':
return
input_shape = new_add.in_port(0).data.get_shape()
bias_shape = new_add.in_port(1).data.get_shape()
assert len(bias_shape) == 1
unsqueeze_dims = np.arange(len(input_shape))
channel_dim = get_features_dim('NCHW', len(input_shape))
unsqueeze_dims = np.delete(unsqueeze_dims, channel_dim, 0)
unsqueeze_node = Unsqueeze(graph, {
'name': new_add.id + '/BiasUnsqueeze'
}).create_node()
unsqueeze_dims_node = Const(graph, {
'name': new_add.id + '/Dims',
'value': unsqueeze_dims
}).create_node()
# Reconnecting nodes
unsqueeze_node.in_port(1).connect(unsqueeze_dims_node.out_port(0))
unsqueeze_node['override_output_shape'] = True
new_add.in_port(1).get_connection().insert_node(unsqueeze_node)
def pad_op_transform(graph: Graph, match: dict):
op = match['op']
pad_op = match['pad_op']
input_data = pad_op.in_node(0)
pads = pad_op.in_node(1).value if len(
pad_op.in_nodes()) == 2 else pad_op.pads
if pad_op.mode != 'constant':
log.info(
'The pad node "{}" with pad mode "{}" cannot be fused.'.format(
pad_op.soft_get('name'), pad_op.mode))
return
if pad_op.mode == 'constant' and pad_op.fill_value != 0.0:
log.info('The pad node "{}" with non-zero fill value cannot be fused.'.
format(pad_op.soft_get('name')))
return
input_tensor_dims = len(match['pad_output'].shape)
if np.any(pads[get_features_dim(op.graph.graph['layout'], input_tensor_dims)] != 0) or \
np.any(pads[get_batch_dim(op.graph.graph['layout'], input_tensor_dims)] != 0):
log.info(
'The pad node "{}" with padding over feature/batch dimension cannot be fused.'
.format(pad_op.soft_get('name')))
return
op.pad += pads
op.pad_spatial_shape = op.pad[op.spatial_dims]
op['auto_pad'] = None
if op.type == 'Pooling':
op['exclude_pad'] = False
assert (graph[match['pad_output'].node][match['op'].node][0]['in'] == 0)
edge_attrs = graph.get_edge_data(match['pad_output'].id, match['op'].id)[0]
graph.remove_edge(match['pad_output'].id, match['op'].id)
graph.add_edge(input_data.id, match['op'].id, **{'in': 0, **edge_attrs})
def infer(node: Node):
in_shape = node.in_node().shape
if in_shape.size != 4:
raise Error(
'TensorFlow SpaceToDepth operation is supported for 4D \'NHWC\' input layout only. '
'Current input shape is \'{}\''.format(in_shape))
layout = node.graph.graph['layout']
N = in_shape[get_batch_dim(layout, 4)]
H = in_shape[get_height_dim(layout, 4)]
W = in_shape[get_width_dim(layout, 4)]
C = in_shape[get_features_dim(layout, 4)]
block_size = node['block_size']
if H % block_size or W % block_size:
raise Error(
'Spatial dimensions of input tensor of SpaceToDepth operation have to be divisible by '
'SpaceToDepth \'block_size\' parameter. Input tensor shape = {}. Spatial dimensions = {},{}. '
'block_size = {}'.format(in_shape, H, W, block_size))
out_shape = shape_for_layout(layout,
batch=N,
features=int(C * (block_size**2)),
height=int(H / block_size),
width=int(W / block_size))
assert np.prod(in_shape) == np.prod(out_shape)
node.out_node().shape = int64_array(out_shape)
def infer(node: Node):
in_shape = node.in_node().shape
if in_shape.size != 4:
raise Error(
'TensorFlow DepthToSpace operation is supported for 4D \'NHWC\' input layout only. '
'Current input shape is \'{}\''.format(in_shape))
layout = node.graph.graph['layout']
N = in_shape[get_batch_dim(layout, 4)]
H = in_shape[get_height_dim(layout, 4)]
W = in_shape[get_width_dim(layout, 4)]
C = in_shape[get_features_dim(layout, 4)]
block_size = node['block_size']
if C % (block_size**2):
raise Error(
'Feature dimensions of input tensor of DepthToSpace operation have to be divisible by square '
'of DepthToSpace \'block_size\' parameter. Input tensor shape = {}. Feature dimension = {}. '
'block_size = {}'.format(in_shape, C, block_size))
out_shape = shape_for_layout(layout,
batch=N,
features=int(C / (block_size**2)),
height=int(H * block_size),
width=int(W * block_size))
assert np.prod(in_shape) == np.prod(out_shape)
node.out_node().shape = int64_array(out_shape)
def infer(node):
layout = node.graph.graph['layout']
node_name = node.soft_get('name', node.id)
assert len([port for port in node.in_ports().values() if not port.disconnected()]) == 3, \
'The node "{}" must 3 inputs'.format(node_name)
assert node.has_valid('pooled_w'), '"pooled_w" attribute is not set for node "{}"'.format(node_name)
assert node.has_valid('pooled_h'), '"pooled_h" attribute is not set for node "{}"'.format(node_name)
assert node.has_valid('mode'), '"mode" attribute is not set for node "{}"'.format(node_name)
assert node.mode in ['avg', 'max'], \
'"mode" attribute range of values is ["avg", "max"], got {} for node "{}"'.format(node.mode, node_name)
input_shape = node.in_port(0).data.get_shape()
rois_shape = node.in_port(1).data.get_shape()
indices_shape = node.in_port(2).data.get_shape()
assert input_shape is not None and rois_shape is not None and indices_shape is not None, \
'The node "{}" input shape is None'.format(node_name)
assert rois_shape[0] == indices_shape[0], 'The number of batch indices does not correspond to number of ROIs ' \
'for node "{}"'.format(node_name)
assert rois_shape[1] == 4, 'The size of ROI element must be 4 for node "{}"'.format(node_name)
assert len(input_shape) == 4, 'The rank of port 0 input tensor of node "{}" must be 4.'.format(node_name)
node.out_port(0).data.set_shape(
shape_for_layout(layout,
batch=rois_shape[0],
features=input_shape[get_features_dim(layout, 4)],
height=node.pooled_h,
width=node.pooled_w)
)
def infer(node: None):
input_shape = node.in_node(0).shape
name = node.soft_get('name', node.id)
if node.axes is not None and node.across_channels is not None:
raise Error('Either axes or across_channels can be set for the MVN in node "{}".'.format(name))
if node.across_channels is None:
if node.axes is not None:
# normalizing (replacing -1 with actual index)
axes_data_value = node.axes
axes = [axes_data_value.item()] if axes_data_value.size == 1 else axes_data_value
axes = [get_canonical_axis_index(input_shape, a) for a in axes]
# deduce across_channels from the axes, e.g. if the first axis is included (assuming batch is zero axis)
feature_dim = get_features_dim(node.graph.graph['layout'], len(input_shape)) \
if (4 <= len(input_shape) <= 5) \
else 1
node.across_channels = int(feature_dim in axes)
if 0 in axes:
raise Error('Reduction over the batch dimension in node "{}" '
'is not supported by the backend.'.format(name))
for i in range(2, len(input_shape)):
if i not in axes:
raise Error(
'Reduction over spatial dimensions in node "{}" '
'is obligatory for the backend.'.format(name))
else:
node.across_channels = 0 # default
copy_shape_infer(node)
def insert_pre_processing(graph: Graph, input_node: Node, node_mean_scale_values: np.array,
preprocessing_name: str):
assert preprocessing_name in ['scale', 'mean']
if node_mean_scale_values.get(preprocessing_name) is None:
return
user_value = node_mean_scale_values[preprocessing_name]
value = 1 / user_value if preprocessing_name == 'scale' else user_value * (-1)
optimize_value = int(preprocessing_name == 'scale')
op = Mul if preprocessing_name == 'scale' else Add
if all([x == optimize_value for x in value]):
return
assert input_node.has_valid('shape')
features_dim_idx = get_features_dim(graph.graph['layout'], len(input_node.shape))
assert value.size == input_node.shape[features_dim_idx] or value.size == 1
shape = np.ones(len(input_node.shape), dtype=np.int64)
shape[features_dim_idx] = value.size
value = value.reshape(shape)
name = input_node.soft_get('name', input_node.id) + '/' + preprocessing_name
preprocessing = create_op_with_const_inputs(graph, op=op, port_value_dict={1: value}, op_attrs={'name': name})
for dst in input_node.out_port(0).get_destinations():
if dst.node.soft_get('type') != 'ShapeOf':
dst.get_connection().set_source(preprocessing.out_port(0))
input_node.out_port(0).connect(preprocessing.in_port(0))
def infer(node: Node):
assert [port.idx for port in node.in_ports().values() if not port.disconnected()] == [0], \
'Wrong input nodes number for node {} with type ExtractImagePatches'.format(node.soft_get('name', node.id))
input_shape = node.in_port(0).data.get_shape()
name = node.soft_get('name', node.id)
assert input_shape is not None, 'Input shape is not set for node {} with type ExtractImagePatches'.format(name)
assert len(input_shape) == 4, 'ExtractImagePatches operation supports only 4D tensors'
layout = node.graph.graph['layout']
N = input_shape[get_batch_dim(layout, 4)]
C = input_shape[get_features_dim(layout, 4)]
size_spatial = int64_array(node.sizes)[node.spatial_dims]
input_spatial_shape = input_shape[node.spatial_dims]
stride_spatial_shape = node.strides[node.spatial_dims]
size_extent = node.rates[node.spatial_dims] * (size_spatial - 1) + 1
pad_spatial_shape, output_spatial_shape = tf_window_op_pad_infer(input_spatial_shape,
size_extent,
stride_spatial_shape,
node.auto_pad,
False)
out_shape = shape_for_layout(layout,
batch=N,
features=C * np.prod(size_spatial),
height=output_spatial_shape[0],
width=output_spatial_shape[1])
node.out_port(0).data.set_shape(int64_array(out_shape))
def infer(node: Node):
layout = node.graph.graph['layout']
assert len(layout) == 4
assert len(
[p for p in node.in_ports().values() if not p.disconnected()])
assert node.has_valid('mode')
assert node.has_valid('axes')
src_shape = node.in_port(0).data.get_shape()
assert src_shape is not None
dst_shape = node.in_port(1).data.get_value()
assert dst_shape is not None
out_height = dst_shape[0]
out_width = dst_shape[1]
node.out_node().shape = shape_for_layout(
layout,
batch=src_shape[get_batch_dim(layout, 4)],
features=src_shape[get_features_dim(layout, 4)],
height=out_height,
width=out_width)
PermuteAttrs.create_permute_attrs(node, attrs=[('axes', 'input:0')])
def insert_pre_processing(graph: Graph, input_node: Node, node_mean_scale_values: np.array,
preprocessing_name: str):
assert preprocessing_name in ['scale', 'mean']
if node_mean_scale_values.get(preprocessing_name) is None:
return
user_value = node_mean_scale_values[preprocessing_name]
value = 1 / user_value if preprocessing_name == 'scale' else user_value * (-1)
optimize_value = int(preprocessing_name == 'scale')
op = Mul if preprocessing_name == 'scale' else Add
if all([x == optimize_value for x in value]):
return
assert input_node.has_valid('shape')
features_dim_idx = get_features_dim(graph.graph['layout'], len(input_node.shape))
assert value.size == input_node.shape[features_dim_idx] or value.size == 1
shape = np.ones(len(input_node.shape), dtype=np.int64)
shape[features_dim_idx] = value.size
value = value.reshape(shape)
name = input_node.soft_get('name', input_node.id) + '/' + preprocessing_name
preprocessing = create_op_with_const_inputs(graph, op=op, port_value_dict={1: value}, op_attrs={'name': name})
for dst in input_node.out_port(0).get_destinations():
if dst.node.soft_get('type') != 'ShapeOf':
# After the insertion of additional operations model optimizer
# should keep the link to the input layer. Parameter node in framework
# should map to parameter node in IR.
# For this reason 'fw_tensor_debug_info' should be kept in data node.
dst.get_connection().set_source(preprocessing.out_port(0), "source")
input_node.out_port(0).connect(preprocessing.in_port(0))
def resample_infer(node: Node):
layout = node.graph.graph['layout']
assert len(layout) == 4
input_shape = node.in_node(0).shape
if input_shape is None:
return
in_height = input_shape[get_height_dim(layout, 4)]
in_width = input_shape[get_width_dim(layout, 4)]
if node.has('fw') and node.fw == 'tf':
dst_shape = node.in_node(1).value
if dst_shape is None or len(input_shape) != 4 or len(
dst_shape) != 2:
log.error(
'Node {} with op {} cannot be converted to Resample layer because there is no enough info about '
'src/dst shapes: src_shape = {}, dst_shape = {}'.format(
node.name, node.op, input_shape, dst_shape))
node.type = None # prevent translation to a valid IE layer
return
out_height = dst_shape[0]
out_width = dst_shape[1]
node.graph.remove_edge(node.in_node(1).id, node.id)
else:
if len(node.in_nodes()) == 1:
if node.has('width') and node.has('height'):
out_height = node.height
out_width = node.width
else:
out_height = node.factor * in_height
out_width = node.factor * in_width
else:
out_height = node.in_node(1).shape[get_height_dim(layout, 4)]
out_width = node.in_node(1).shape[get_width_dim(layout, 4)]
node.factor = factor_update(
node.factor,
[float(out_height) / in_height,
float(out_width) / in_width], [in_height, in_width],
[out_height, out_width], node.soft_get('name'))
node.out_node().shape = shape_for_layout(
layout,
batch=input_shape[get_batch_dim(layout, 4)],
features=input_shape[get_features_dim(layout, 4)],
height=out_height,
width=out_width)
def find_and_replace_pattern(self, graph: Graph):
layout = graph.graph['layout']
for n in list(graph.nodes()):
if 'type' in graph.node[n] and graph.node[n]['type'] == 'Eltwise' and get_value_id(Node(graph, n)) is None:
eltwise_op_node = Node(graph, n)
out_shape = eltwise_op_node.out_node().shape
if 4 <= len(out_shape) <= 5:
out_features = out_shape[get_features_dim(layout, len(out_shape))]
for port, node in eltwise_op_node.in_nodes().items():
if len(node.shape) != len(out_shape) and len(node.shape) == 1 and out_features == node.shape[0]:
in_atts = deepcopy(graph.get_edge_data(node.id, n)[0])
graph.remove_edge(node.id, n)
new_shape = shape_for_layout(layout, batch=1, features=out_features, height=1, width=1,
depth=1 if len(out_shape) == 5 else None)
reshape_data_op = Reshape(graph, attrs={'dim': new_shape, 'name': node.id + '/Broadcast'})
reshape_data_node = reshape_data_op.create_node_with_data([node])
graph.add_edge(reshape_data_node.id, eltwise_op_node.id, **in_atts)
def upsample_infer(node: Node):
node_name = node.soft_get('name', node.id)
layout = node.graph.graph['layout']
assert len(
layout
) == 4, 'Input tensor rank must be equal to 4 for node "{}"'.format(
node_name)
input_shape = node.in_port(0).data.get_shape()
if len(node.in_nodes()) == 1:
in_height = input_shape[get_height_dim(layout, 4)]
in_width = input_shape[get_width_dim(layout, 4)]
assert node.has('width_scale') is not None and node.has(
'height_scale') is not None
if in_height is not dynamic_dimension:
out_height = math.floor(in_height * node.height_scale)
else:
out_height = dynamic_dimension
if in_width is not dynamic_dimension:
out_width = math.floor(in_width * node.width_scale)
else:
out_width = dynamic_dimension
node.out_port(0).data.set_shape(
shape_for_layout(layout,
batch=input_shape[get_batch_dim(layout, 4)],
features=input_shape[get_features_dim(
layout, 4)],
height=out_height,
width=out_width))
else:
scales = node.in_port(1).data.get_value()
assert scales is not None, 'The input with scales for node "{}" is not constant'.format(
node_name)
eps = 1e-5 # This is to make rounding in case of very close number to round to closest instead of down
# generic output shape calculation to support 5D input shape case
output_shape = shape_array(
[dynamic_dimension for _ in range(len(input_shape))])
for idx in range(len(output_shape)):
if input_shape[idx] is not dynamic_dimension:
output_shape[idx] = int(
(input_shape[idx] + eps) * scales[idx])
else:
output_shape[idx] = dynamic_dimension_value
node.out_port(0).data.set_shape(output_shape)
def pad_op_transform(graph: Graph, match: dict):
op = match['op']
pad_op = match['pad_op']
input_data = pad_op.in_node(0)
if pad_op.mode != 'constant':
log.info(
'The pad node "{}" with pad mode "{}" cannot be fused.'.format(
pad_op.soft_get('name'), pad_op.mode))
return
if op.type == 'Pooling' and op.pool_method == 'max':
return
if pad_op.mode == 'constant':
fill_value = pad_op.in_port(3).data.get_value()
if fill_value is None or fill_value != 0.0:
log.info(
'The pad node "{}" with non-zero fill value cannot be fused.'.
format(pad_op.soft_get('name')))
return
input_tensor_dims = len(match['pad_output'].shape)
for in_port in [1, 2]:
pads = pad_op.in_port(in_port).data.get_value()
if pads[get_features_dim(op.graph.graph['layout'], input_tensor_dims)] != 0 or \
pads[get_batch_dim(op.graph.graph['layout'], input_tensor_dims)] != 0:
log.info(
'The pad node "{}" with padding over feature/batch dimension cannot be fused.'
.format(pad_op.soft_get('name')))
return
op.pad += np.concatenate([
pad_op.in_port(1).data.get_value().reshape([-1, 1]),
pad_op.in_port(2).data.get_value().reshape([-1, 1])
],
axis=1)
op.pad_spatial_shape = op.pad[op.spatial_dims]
op['auto_pad'] = None
if op.type == 'Pooling':
op['exclude_pad'] = False
assert (graph[match['pad_output'].node][match['op'].node][0]['in'] == 0)
edge_attrs = graph.get_edge_data(match['pad_output'].id, match['op'].id)[0]
graph.remove_edge(match['pad_output'].id, match['op'].id)
graph.add_edge(input_data.id, match['op'].id, **{'in': 0, **edge_attrs})
def replace_pattern(graph: Graph, match: dict):
reshape = match['reshape']
assert len(reshape.in_nodes()) > 0
if graph.graph['layout'] == 'NCHW' or reshape.has_and_set('nchw_layout') or\
reshape.soft_get('correct_data_layout') is True:
return
input_node = reshape.in_node()
output_node = reshape.out_node()
input_shape = input_node.shape
output_shape = output_node.shape
if len(input_shape) >= 4 and len(output_shape) == 3:
# Check that we will permute some shapes in this Reshape by our permutation pass
layout = 'NCHW'
c_idx = get_features_dim(layout, len(input_shape))
hw_idx = [
get_width_dim(layout, len(input_shape)),
get_height_dim(layout, len(input_shape))
]
if input_shape[c_idx] != 1 and np.any(
input_shape[hw_idx] != [1, 1]):
# then nhwc -> nchw permutation can change shapes significantly
# We need to wrap up node with NCHW -> NHWC permutes and don't touch it later
permutation = PermuteAttrs.get_nchw_to_nhwc_permutation(
len(input_shape))
permutation_back = PermuteAttrs.get_nchw_to_nhwc_permutation(
len(input_shape))
# 1. Insert input Permute
# This Permute will permute input from original input layout to operation layout
edge_attrs = graph.get_edge_data(input_node.id, reshape.id)[0]
graph.remove_edge(input_node.id, reshape.id)
permute_op = Permute(graph, {
'order': permutation.perm,
'name': reshape.name + '/Permute_'
})
permute_data_node = permute_op.create_node_with_data(
[input_node])
graph.add_edge(permute_data_node.id, reshape.id, **edge_attrs)
def replace_pattern(self, graph: Graph, match: dict):
reshape1 = match['reshape1']
reshape2 = match['reshape2']
transpose = match['transpose']
# Check that Reshape->Transpose->Reshape shuffle only feature channel
input_shape = np.array(reshape1.in_node(0).shape)
reshape1_shape = np.array(reshape1.out_node().shape)
output_shape = np.array(reshape2.out_node().shape)
# Check that input shape is 4D
if len(input_shape) != 4:
log.warning(
'Can\'t convert Reshape->Transpose({})->Reshape sequence due to input shape should be 4D '
'(instead of {}D)'.format(transpose.name, len(input_shape)))
return
# Check that output shape the same as input shape
if not np.prod(input_shape) == np.prod(output_shape):
log.warning(
'Can\'t convert Reshape->Transpose({})->Reshape sequence due to output shape should be equal '
'to input shape: {} and {}'.format(transpose.name, input_shape,
output_shape))
return
# Input shapes can be either NCHW or NHWC, so in case of channel split, feature channel can be splited as
# follows in comments below
# So feature_dims_split list contains possible dims responsible for feature dim
layout = graph.graph['layout']
feature_dim = get_features_dim(layout, len(input_shape))
spatial_dims = [
get_height_dim(layout, len(input_shape)),
get_width_dim(layout, len(input_shape))
]
if layout == 'NCHW':
# NC1C2HW or NC1C2(H*W)
feature_dims_split = np.array([feature_dim, feature_dim + 1])
else:
# NHWC1C2 or N(H*W)C1C2 or (N*H*W)C1C2
feature_dims_split = np.array(
[len(reshape1_shape) - 2,
len(reshape1_shape) - 1])
# Check that feature_dims_split suits reshape layer shape
for dim in feature_dims_split:
if dim < 0 or dim >= len(reshape1_shape):
log.warning(
'Can\'t convert Reshape({}:{})->Transpose->Reshape sequence. Can\'t detect feature shuffle.'
''.format(reshape1.shape, reshape1_shape))
return
if not np.prod(np.delete(reshape1_shape,
feature_dims_split)) == np.prod(
np.delete(input_shape, feature_dim)):
log.warning(
'Can\'t convert Reshape->Transpose->Reshape sequence. Can\'t detect feature shuffle. {} '
'should be equal to {}'.format(
np.prod(np.delete(reshape1_shape, feature_dims_split)),
np.prod(np.delete(input_shape, feature_dim))))
return
# Check transpose order
if not np.array_equal(feature_dims_split[::-1],
transpose.order[feature_dims_split]):
log.warning(
'Can\'t convert Reshape->Transpose({})->Reshape sequence. Transpose operation should witch '
'feature order (given order: {})'.format(
transpose.name, transpose.order))
return
# Now we are sure that Reshape->Transpose->Reshape shuffle feature dims
# So, then we change Reshape and Transpose attrs to suite NCHW layout
# The resulting shape for Reshape1 layer : [N,C1,C2,(H*W)]
new_reshape1_shape = np.concatenate(
(np.array([input_shape[0]]),
np.array(reshape1_shape[feature_dims_split]),
np.array([np.prod(input_shape[spatial_dims])])))
new_transpose_order = np.array([0, 2, 1, 3])
new_transpose_shape = np.array(new_reshape1_shape[new_transpose_order])
reshape1.out_node().shape = new_reshape1_shape
reshape1.dim = np.copy(new_reshape1_shape)
transpose.order = new_transpose_order
transpose.out_node().shape = new_transpose_shape
# Preserve layers from conversion to NCHW (in case of NHWC topology layout)
reshape1['nchw_layout'] = True
reshape1.out_node()['nchw_layout'] = True
transpose['nchw_layout'] = True
transpose.out_node()['nchw_layout'] = True
def replace_pattern(self, graph: Graph, match: dict):
layout = graph.graph['layout']
if layout != 'NHWC':
return
reshape1 = match['reshape1']
softmax = match['softmax']
# Check that Reshape->Softmax->Reshape shuffle only feature channel
input_shape = np.array(reshape1.in_node(0).shape)
reshape1_shape = np.array(reshape1.out_node().shape)
# Check that input shape is 4D
if len(input_shape) != 4:
log.warning(
'Can\'t convert Reshape({})->Softmax->Reshape sequence due to input shape should be 4D '
'(instead of {}D {})'.format(reshape1.name, len(input_shape),
input_shape))
return
if len(reshape1_shape) != 2:
log.warning(
'This pass expect 2D output tensor for first Reshape {} layer (given shape: {})'
''.format(reshape1.name, reshape1_shape))
return
# Define feature dim
feature_dim = get_features_dim(layout, len(input_shape))
spatial_dims = [
get_height_dim(layout, len(input_shape)),
get_width_dim(layout, len(input_shape))
]
# Skip transform in case if spatial dims in input shape are equal to [1,1]
if np.array_equal(input_shape[spatial_dims], np.array([1, 1])):
log.info('Skip this transformation due to spatial dims are [1,1]')
return
# Check that Reshape1 has out dims [-1, feature_dims]
if not (reshape1_shape[-1] == input_shape[-1] and reshape1_shape[0]
== np.prod(np.delete(input_shape, feature_dim))):
log.warning(
'Output shape for Reshape operation should be [{},{}] instead of {}'
.format(np.prod(np.delete(input_shape, feature_dim)),
input_shape[-1], reshape1_shape))
return
# Now we are sure that Reshape->Softmax suits for this transformation
# The resulting shape for Reshape1 layer : [N,C,(H*W)]
new_reshape1_shape = np.concatenate(
(np.array([input_shape[0]]), np.array([reshape1_shape[-1]]),
np.array([np.prod(input_shape[spatial_dims])])))
# update 'dim' attribute but preserve batch dimension size which could be -1
reshape1.dim = int64_array([reshape1.dim[0], *new_reshape1_shape[1:]])
old_shape = np.array(reshape1.out_node().shape)
reshape1.out_node().shape = new_reshape1_shape
softmax.out_node().shape = new_reshape1_shape
# Preserve layers from conversion to NCHW (in case of NHWC topology layout)
reshape1['nchw_layout'] = True
reshape1.out_node()['nchw_layout'] = True
softmax['nchw_layout'] = True
softmax.out_node()['nchw_layout'] = True
# Create final Reshape to keep original shape for softmax output if softmax is not the last node
softmax_out_data = softmax.out_node()
if len(softmax_out_data.out_nodes()) != 0:
next_operation = softmax_out_data.out_node()
# Save edge attributes & remove edge
edge_attrs = graph.get_edge_data(softmax_out_data.id,
next_operation.id)[0]
graph.remove_edge(softmax_out_data.id, next_operation.id)
reshape_op = Reshape(
graph,
dict(name=softmax.id + "/Reshape",
dim=np.array(old_shape),
nchw_layout=True))
reshape_out_data = reshape_op.create_node_with_data(
inputs=[softmax_out_data])
graph.add_edges_from([(reshape_out_data.id, next_operation.id,
edge_attrs)])
def replace_pattern(self, graph: Graph, match: dict):
y = match['maximum'].in_port(0).data.get_value()
if y is None:
y = match['maximum'].in_port(1).data.get_value()
if y is None or y.shape != ():
log.debug(
'The value of the "maximum_y_data" is not defined or is not constant'
)
return
# We need to check axes which performed reduction because IE supports only 2D, 3D, 4D inputs and
# reduction only along spatial and channel dimensions.
input_rank = len(match['sum'].in_port(0).data.get_shape())
if input_rank not in [2, 3, 4]:
log.debug(
'IE supports L2 normalization only for 2D, 3D and 4D tensors.')
return
axes = match['sum'].in_port(1).data.get_value()
axes = int64_array(axes)
if axes.shape == ():
axes = int64_array([axes])
axes = int64_array(
[axis if axis >= 0 else axis + input_rank for axis in axes])
axes.sort()
transformation_applicable = False
# check for case C + all spatial dims. Works for 2D (NC), 3D (NCH) and 4D (NCHW and NHWC)
if len(axes) + 1 == input_rank and np.array_equal(
axes, int64_array(np.arange(start=1, stop=input_rank))):
transformation_applicable = True
# check for pure C channel normalization
if len(axes) == 1 and ((input_rank == 4 and get_features_dim(
graph.graph['layout'], input_rank) == axes[0]) or
(input_rank != 4 and axes[0] == 1)):
transformation_applicable = True
if not transformation_applicable:
log.debug(
'IE doesn\'t support l2 normalization with reduction along axes {}.'
.format(axes))
return
output_name = match['l2_normalize'].soft_get('name',
match['l2_normalize'].id)
normalize_node = create_op_node_with_second_input(
graph, NormalizeL2Op, axes, {
'name': output_name,
'eps_mode': 'max',
'eps': y
})
match['square'].in_port(0).get_source().connect(
normalize_node.in_port(0))
match['square'].in_port(0).disconnect()
if match['l2_normalize'].in_port(
0).get_source().node.id == match['rsqrt'].id:
match['l2_normalize'].in_port(1).disconnect()
else:
match['l2_normalize'].in_port(0).disconnect()
match['l2_normalize'].out_port(0).get_connection().set_source(
normalize_node.out_port(0))
rename_nodes([(match['l2_normalize'], output_name + "/TBR"),
(normalize_node, output_name)])
def interp_infer(node: Node):
layout = node.graph.graph['layout']
assert len(layout) == 4
if len(node.in_nodes()) == 2:
src_shape = node.in_node(0).shape
dst_shape = node.in_node(1).shape
# in Caffe can be 2 inputs too, but shape should be got from shape of the second input
if node.parse_2nd_input == 'shape':
dst_shape = [
dst_shape[get_height_dim(layout, 4)],
dst_shape[get_width_dim(layout, 4)]
]
else:
# it is TF case
dst_shape = node.in_node(1).value
if src_shape is None or dst_shape is None or len(
src_shape) != 4 or len(dst_shape) != 2:
log.error(
'Node {} with op {} cannot be converted to Resample layer because there is no enough info about '
'src/dst shapes: src_shape = {}, dst_shape = {}'.format(
node.name, node.op, src_shape, dst_shape))
node.type = None # prevent translation to a valid IE layer
return
in_height = src_shape[get_height_dim(layout, 4)]
in_width = src_shape[get_width_dim(layout, 4)]
out_height = dst_shape[0]
out_width = dst_shape[1]
node.factor = factor_update(
node.factor,
[float(out_height) / in_height,
float(out_width) / in_width], [in_height, in_width],
[out_height, out_width], node.soft_get('name'))
if node.factor is None:
node['width'] = out_width
node['height'] = out_height
node.out_node().shape = shape_for_layout(
layout,
batch=src_shape[get_batch_dim(layout, 4)],
features=src_shape[get_features_dim(layout, 4)],
height=out_height,
width=out_width)
node.graph.remove_edge(node.in_node(1).id, node.id)
else:
outn = node.out_node(0)
in_shape = node.in_node(0)
num_ = in_shape.shape[get_batch_dim(layout, 4)]
channels_ = in_shape.shape[get_features_dim(layout, 4)]
height_in_ = in_shape.shape[get_height_dim(layout, 4)]
width_in_ = in_shape.shape[get_width_dim(layout, 4)]
height_out_ = height_in_ + node.pad_beg + node.pad_end
width_out_ = width_in_ + node.pad_beg + node.pad_end
if node.shrink_factor != 1 and node.zoom_factor == 1:
shrink_factor = node.shrink_factor
if shrink_factor < 1:
log.error(
'Shrink factor should be positive in node {}'.format(
node.id))
return None
height_out_ = (height_out_ - 1) / shrink_factor + 1
width_out_ = (width_out_ - 1) / shrink_factor + 1
elif node.shrink_factor == 1 and node.zoom_factor != 1:
zoom_factor = node.zoom_factor
if zoom_factor < 1:
log.error(
'Zoom factor should be positive in node {}'.format(
node.id))
return None
node['debug_message'] = 'Interp layer shape inference function may be wrong, please, try to update ' \
'layer shape inference function in the file (extensions/ops/interp.op at the ' \
'line {}).'.format(inspect.currentframe().f_lineno) + refer_to_faq_msg(100)
# Reshape methods can be different in some cases
# Commented out section represents reshape that used in deeplab-caffe
# Uncomment the following lines, if your model was trained with deeplab-caffe
# or have the same reshape method
# height_out_ = height_out_ + (height_out_ - 1) * (zoom_factor - 1)
# width_out_ = width_out_ + (width_out_ - 1) * (zoom_factor - 1)
# Comment out the following lines if you use the reshape method from previous section
height_out_ = height_out_ * zoom_factor
width_out_ = width_out_ * zoom_factor
elif node.width != 0 and node.height != 0:
height_out_ = node.height
width_out_ = node.width
elif node.shrink_factor != 1 and node.zoom_factor != 1:
shrink_factor = node.shrink_factor
zoom_factor = node.zoom_factor
if shrink_factor < 1:
log.error(
'Shrink factor should be positive in node {}'.format(
node.id))
return None
if zoom_factor < 1:
log.error(
'Zoom factor should be positive in node {}'.format(
node.id))
return None
height_out_ = (height_out_ - 1) / shrink_factor + 1
width_out_ = (width_out_ - 1) / shrink_factor + 1
height_out_ = height_out_ + (height_out_ - 1) * (zoom_factor -
1)
width_out_ = width_out_ + (width_out_ - 1) * (zoom_factor - 1)
outn.shape = shape_for_layout(layout,
batch=num_,
features=channels_,
height=height_out_,
width=width_out_)
def replace_pattern(self, graph: nx.MultiDiGraph, match: dict):
node = match['reduce']
if not node.has_valid('reduce_type') or node.reduce_type.lower() not in self.supported_reduce_types:
log.error("Reduce type {} is not supported for node {}".format(node.soft_get('reduce_type'), node.id))
return
reduce_type = node.reduce_type.lower()
if reduce_type not in self.pool_method_map:
log.error("Reduce type {} is not included in pool_method_map. Please update pool_method_map with new key "
"{}".format(reduce_type, reduce_type))
return
input_data = node.in_node()
output_data = node.out_node()
input_shape = node.in_node().shape
output_shape = node.out_node().shape
# normalize node.axis to exclude negative indices
node.axis = [get_canonical_axis_index(input_shape, a) for a in node.axis]
axis = node.axis
# Check that values in axis list are consecutive
for idx in range(1, len(axis)):
if axis[idx] != (axis[idx - 1] + 1):
log.error("Reduce with not consecutive axes {} is not supported ".format(axis))
return
layout = graph.graph['layout']
# So now we are sure that we can convert Reduce to appropriate operation
# 1. Calculate shape that will be used in reduction
reduction_dim = np.prod([input_shape[idx] for idx in axis])
begin_dims = np.array([input_shape[idx] for idx in range(axis[0])])
end_dim = np.prod([input_shape[idx] for idx in range(axis[-1] + 1, len(input_shape))])
# 2. Create reshape with appropriate shape
if layout == 'NCHW':
if len(begin_dims) > 2:
begin_dims = np.array([np.prod(begin_dims[0:-1]), begin_dims[-1]], dtype=np.int64)
else:
# Expand begin_dims to 2
begin_dims = np.array(np.append(begin_dims, [1] * (2 - len(begin_dims))), dtype=np.int64)
reshape_shape = np.array([*begin_dims, reduction_dim, end_dim], dtype=np.int64)
pool_window = np.array([1, 1, reduction_dim, 1], dtype=np.int64)
elif layout == 'NHWC':
begin_dims = np.prod(begin_dims)
reshape_shape = np.array([begin_dims, reduction_dim, 1, end_dim], dtype=np.int64)
pool_window = np.array([1, reduction_dim, 1, 1], dtype=np.int64)
else:
log.error('{} layout currently is not supported'.format(layout))
return
# 3. Reduce => Reshape->Pooling->Reshape
reshape_op = Reshape(graph, {'name': node.id + '/Reshape', 'dim': reshape_shape})
final_reshape_op = Reshape(graph, {'name': node.id + '/FinalReshape', 'dim': output_shape})
pooling_op = Pooling(graph,
dict(name=node.id + '/Pool',
window=pool_window,
output_spatial_shape=None,
batch_dims=np.array([get_batch_dim(layout, 4)], dtype=np.int64),
channel_dims=np.array([get_features_dim(layout, 4)], dtype=np.int64),
exclude_pad='false', pool_method=self.pool_method_map[reduce_type]))
graph.remove_edge(input_data.id, node.id)
graph.remove_edge(node.id, output_data.id)
final_reshape_op.create_node_with_data(
inputs=[pooling_op.create_node_with_data(
inputs=[reshape_op.create_node_with_data(
inputs=[input_data]
)]
)],
data_nodes=output_data)
# 4. If it is reduction with summation, we need to multiply by size of the reduction slice with Mul op
if reduce_type == 'sum':
output_data.in_node().insert_node_with_data_after(
output_data,
Power,
{'name': node.name + '/Mul', 'scale': float(reduction_dim)}
)
def test_get_features_dim_NDHWC(self):
self.assertEqual(get_features_dim('NHWC', 5), 4)
def test_get_features_dim_NCDHW(self):
self.assertEqual(get_features_dim('NCHW', 5), 1)
