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
in_height = input_shape[get_height_dim(layout, 4)]
in_width = input_shape[get_width_dim(layout, 4)]
if len(node.in_nodes()) == 1:
assert node.has('width_scale') is not None and node.has(
'height_scale') is not None
out_height_scale = node.height_scale
out_width_scale = node.width_scale
else:
assert node.in_node(1).value is not None
out_height_scale = node.in_node(1).value[get_height_dim(layout, 4)]
out_width_scale = node.in_node(1).value[get_width_dim(layout, 4)]
out_height = math.floor(in_height * out_height_scale)
out_width = math.floor(in_width * out_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)
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 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 = np.array(
input_shape * node.in_node(1).value).astype(np.int64)
def priorbox_infer(node: Node):
layout = node.graph.graph['layout']
data_shape = node.in_node(0).shape
# calculate all different aspect_ratios (the first one is always 1)
# in aspect_ratio 1/x values will be added for all except 1 if flip is True
ar_seen = [1.0]
ar_seen.extend(node.aspect_ratio.copy())
if node.flip:
for s in node.aspect_ratio:
ar_seen.append(1.0 / s)
ar_seen = np.unique(np.array(ar_seen).round(decimals=6))
num_ratios = 0
if len(node.min_size) > 0:
num_ratios = len(ar_seen) * len(node.min_size)
if node.has_valid('fixed_size') and len(node.fixed_size) > 0:
num_ratios = len(ar_seen) * len(node.fixed_size)
if node.has_valid('density') and len(node.density) > 0:
for d in node.density:
if node.has_valid('fixed_ratio') and len(node.fixed_ratio) > 0:
num_ratios = num_ratios + len(
node.fixed_ratio) * (pow(d, 2) - 1)
else:
num_ratios = num_ratios + len(ar_seen) * (pow(d, 2) - 1)
num_ratios = num_ratios + len(node.max_size)
res_prod = data_shape[get_height_dim(
layout, 4)] * data_shape[get_width_dim(layout, 4)] * num_ratios * 4
node.out_node(0).shape = np.array([1, 2, res_prod], dtype=np.int64)
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 tf_resize_infer(node: Node):
input_shape = node.in_port(0).data.get_shape()
if input_shape is None:
return
attrs_msg = "If half_pixel_centers attribute of the node {} with op {} is True, " \
"the attribute align_corners must be False"
node_name = node.soft_get('name', node.id)
assert not node.half_pixel_centers or (node.half_pixel_centers and not node.align_corners), \
attrs_msg.format(node_name, node.op)
connected_in_ports = [port for port in node.in_ports().values() if not port.disconnected()]
assert len(connected_in_ports) == 2, \
"Node {} with op {} number of inputs must be equal to 2.".format(node_name, node.op)
new_sizes_value = node.in_port(1).data.get_value()
assert new_sizes_value is not None, "Node {} with op {} has no value in input port 1".format(node_name, node.op)
input_rank = len(input_shape)
assert input_rank == 4, \
"Resized input data of the node {} with op {} must be 4D tensor".format(node_name, node.op)
len_msg = "Op {} with name {} supports only resize with respect to height and width dimension simultaneously"
assert len(new_sizes_value) == 2, len_msg.format(node_name, node.op)
output_shape = int64_array(input_shape.copy())
layout = node.graph.graph['layout']
output_shape[get_height_dim(layout, input_rank)] = new_sizes_value[0]
output_shape[get_width_dim(layout, input_rank)] = new_sizes_value[1]
node.out_port(0).data.set_shape(output_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 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 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 is_spatial_squeeze(layout: str, input_shape: np.ndarray, squeeze_dims: np.ndarray):
"""
Checks that the squeeze operation removes all spatial dimensions.
:param layout: graph layout.
:param input_shape: numpy array with input shape.
:param squeeze_dims: numpy array with dims to squeeze.
:return: result of the check.
"""
if len(input_shape) < 4 or len(input_shape) > 5:
return False
spatial_dims = [get_height_dim(layout, len(input_shape)), get_width_dim(layout, len(input_shape))]
if len(input_shape) == 5:
spatial_dims.append(get_depth_dim(layout, len(input_shape)))
for dim in spatial_dims:
if input_shape[dim] != 1:
log.debug('The reshape from "{}" with squeezed dims "{}" is not a spatial squeeze'.format(input_shape,
squeeze_dims))
return False
if len(squeeze_dims) != len(spatial_dims):
log.debug('The reshape from "{}" with squeezed dims "{}" is not a spatial squeeze'.format(input_shape,
squeeze_dims))
return False
log.debug('The reshape from "{}" with squeezed dims "{}" is not a spatial squeeze'.format(input_shape,
squeeze_dims))
return True
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 priorbox_clustered_infer(node: Node):
layout = node.graph.graph['layout']
data_shape = node.in_node(0).shape
num_ratios = len(node.width)
res_prod = data_shape[get_height_dim(
layout, 4)] * data_shape[get_width_dim(layout, 4)] * num_ratios * 4
node.out_node(0).shape = np.array([1, 2, res_prod], dtype=np.int64)
def priorbox_clustered_infer(node: Node):
layout = node.graph.graph['layout']
data_shape = node.in_node(0).shape
num_ratios = len(node.width)
if node.has_and_set('V10_infer'):
assert node.in_node(0).value is not None
node.out_node(0).shape = np.array([2, np.prod(node.in_node(0).value) * num_ratios * 4], dtype=np.int64)
else:
res_prod = data_shape[get_height_dim(layout, 4)] * data_shape[get_width_dim(layout, 4)] * num_ratios * 4
node.out_node(0).shape = np.array([1, 2, res_prod], dtype=np.int64)
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 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 regionyolo_infer(node: Node):
input_shape = node.in_node(0).shape
if input_shape is None:
return
axis = get_canonical_axis_index(input_shape, node.axis)
end_axis = get_canonical_axis_index(input_shape, node.end_axis)
node.axis = axis
node.end_axis = end_axis
if node.do_softmax:
flat_dim = np.prod(input_shape[axis: end_axis + 1])
node.out_node().shape = np.array([*input_shape[:axis], flat_dim, *input_shape[end_axis + 1:]])
else:
layout = node.graph.graph['layout']
assert len(layout) == 4
node.out_node().shape = shape_for_layout(layout,
batch=input_shape[get_batch_dim(layout, 4)],
features=(node.classes + node.coords + 1) * len(node.mask),
height=input_shape[get_height_dim(layout, 4)],
width=input_shape[get_width_dim(layout, 4)])
def regionyolo_infer(node: Node):
input_shape = node.in_port(0).data.get_shape()
axis = get_canonical_axis_index(input_shape, node.axis)
end_axis = get_canonical_axis_index(input_shape, node.end_axis)
node.axis = axis
node.end_axis = end_axis
if node.do_softmax:
dims_to_flatten = input_shape[axis: end_axis + 1]
if is_fully_defined(dims_to_flatten):
flat_dim = np.ma.prod(dims_to_flatten)
else:
flat_dim = dynamic_dimension_value
node.out_port(0).data.set_shape([*input_shape[:axis], flat_dim, *input_shape[end_axis + 1:]])
else:
layout = node.graph.graph['layout']
assert len(layout) == 4
node.out_port(0).data.set_shape(shape_for_layout(layout,
batch=input_shape[get_batch_dim(layout, 4)],
features=(node.classes + node.coords + 1) * len(node.mask),
height=input_shape[get_height_dim(layout, 4)],
width=input_shape[get_width_dim(layout, 4)]))
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_resize(graph: Graph, resize: Node):
log.debug("Converting of ONNX Resize-11 to Interpolate-4 "
"is triggered for node {}.".format(
resize.soft_get('name', resize.id)))
input_shape = resize.in_port(0).data.get_shape()
input_rank = len(input_shape)
resize_name = resize.soft_get('name', resize.id)
if input_rank not in {4, 5}:
log.warning(
'The input shape is not 4D or 5D for op with name {}'.format(
resize_name))
return
num_of_inputs = len([
port for port in resize.in_ports().values() if not port.disconnected()
])
assert num_of_inputs in {3, 4}, \
"Number of inputs of ONNXResize (with name {}) should be equal to 3 or 4".format(resize_name)
assert resize.soft_get('coordinate_transformation_mode') != 'tf_crop_and_resize', \
'Mode tf_crop_and_resize is not supported for op {} with name {}'.format(resize.op, resize_name)
layout = graph.graph['layout']
if input_rank == 4:
begin_dim = get_height_dim(layout, input_rank)
end_dim = get_width_dim(layout, input_rank) + 1
else:
begin_dim = get_depth_dim(layout, input_rank)
end_dim = get_width_dim(layout, input_rank) + 1
sizes_ss = create_op_with_const_inputs(
graph, StridedSlice, {
1: int64_array([begin_dim]),
2: int64_array([end_dim]),
3: int64_array([1])
}, {
'name': resize_name + '/StridedSlice_sizes',
'begin_mask': int64_array([1]),
'end_mask': int64_array([1]),
'new_axis_mask': int64_array([0]),
'shrink_axis_mask': int64_array([0]),
'ellipsis_mask': int64_array([0])
})
scales_ss = create_op_with_const_inputs(
graph, StridedSlice, {
1: int64_array([begin_dim]),
2: int64_array([end_dim]),
3: int64_array([1])
}, {
'name': resize_name + '/StridedSlice_scales',
'begin_mask': int64_array([1]),
'end_mask': int64_array([1]),
'new_axis_mask': int64_array([0]),
'shrink_axis_mask': int64_array([0]),
'ellipsis_mask': int64_array([0])
})
axes_node = Const(
graph, {
'name': resize_name + '/axis',
'value': int64_array(np.arange(begin_dim, end_dim))
}).create_node()
shape_calculation_mode = 'scales' if num_of_inputs == 3 else 'sizes'
interpolate_node = Interpolate(
graph, {
'version': 'opset4',
'mode': convert_mode(resize.mode),
'coordinate_transformation_mode':
resize.coordinate_transformation_mode,
'cube_coeff': resize.cube_coeff,
'nearest_mode': resize.nearest_mode,
'pads_begin': int64_array([0]),
'pads_end': int64_array([0]),
'antialias': 0,
'shape_calculation_mode': shape_calculation_mode,
'in_ports_count': 4
}).create_node()
axes_node.out_port(0).connect(interpolate_node.in_port(3))
shape_of = Shape(graph, {'name': resize_name + '/ShapeOf'}).create_node()
add_node = create_op_with_const_inputs(graph, Add,
{1: float_array([1.0e-5])},
{'name': resize_name + '/Add'})
input_data_type = data_type_str_to_np(graph.graph['cmd_params'].data_type)
if num_of_inputs == 3:
cast_shape_to_float = Cast(graph, {
'dst_type': input_data_type
}).create_node()
mul_node = Mul(graph, {'name': resize_name + '/Mul'}).create_node()
shape_of.out_port(0).connect(cast_shape_to_float.in_port(0))
cast_shape_to_float.out_port(0).connect(mul_node.in_port(0))
cast_add_result_to_int = Cast(graph, {
'dst_type': np.int64
}).create_node()
floor_node = Floor(graph, {
'name': resize_name + '/Floor'
}).create_node()
mul_node.out_port(0).connect(add_node.in_port(0))
add_node.out_port(0).connect(floor_node.in_port(0))
floor_node.out_port(0).connect(cast_add_result_to_int.in_port(0))
cast_add_result_to_int.out_port(0).connect(sizes_ss.in_port(0))
sizes_ss.out_port(0).connect(interpolate_node.in_port(1))
scales_ss.out_port(0).connect(interpolate_node.in_port(2))
connection_of_resize_input = resize.in_port(0).get_connection()
connection_of_resize_input.set_destination(interpolate_node.in_port(0))
connection_of_scales = resize.in_port(2).get_connection()
connection_of_scales.set_destination(scales_ss.in_port(0))
connection_of_resize_input.get_source().connect(shape_of.in_port(0))
connection_of_scales.get_source().connect(mul_node.in_port(1))
else:
cast_shape_to_float = Cast(graph, {
'dst_type': input_data_type
}).create_node()
cast_sizes_to_float = Cast(graph, {
'dst_type': input_data_type
}).create_node()
div_node = Div(graph, {'name': resize_name + '/Div'}).create_node()
cast_sizes_to_float.out_port(0).connect(div_node.in_port(0))
cast_shape_to_float.out_port(0).connect(div_node.in_port(1))
shape_of.out_port(0).connect(cast_shape_to_float.in_port(0))
div_node.out_port(0).connect(add_node.in_port(0))
add_node.out_port(0).connect(scales_ss.in_port(0))
scales_ss.out_port(0).connect(interpolate_node.in_port(2))
sizes_ss.out_port(0).connect(interpolate_node.in_port(1))
connection_of_resize_input = resize.in_port(0).get_connection()
connection_of_resize_input.set_destination(interpolate_node.in_port(0))
connection_of_sizes = resize.in_port(3).get_connection()
connection_of_sizes.set_destination(sizes_ss.in_port(0))
connection_of_resize_input.get_source().connect(shape_of.in_port(0))
connection_of_sizes.get_source().connect(
cast_sizes_to_float.in_port(0))
rename_nodes([(resize, resize_name + '/delete'),
(interpolate_node, resize_name)])
resize.out_port(0).get_connection().set_source(
interpolate_node.out_port(0))
def test_get_width_dim_NDHWC(self):
self.assertEqual(get_width_dim('NHWC', 5), 3)
def test_get_width_dim_NCDHW(self):
self.assertEqual(get_width_dim('NCHW', 5), 4)
def test_get_width_dim_NHWC(self):
self.assertEqual(get_width_dim('NHWC', 4), 2)
def replace_pattern(self, graph: Graph, match: Dict[str, Node]):
log.debug('UpsampleToResample is triggered')
upsample = match['upsample']
upsample_name = upsample.soft_get('name', upsample.id)
input_shape = upsample.in_port(0).data.get_shape()
input_shape_rank = len(input_shape)
if input_shape_rank not in [4, 5]:
log.warning('The input shape is not 4D or 5D for op {}'.format(
upsample.soft_get('name')))
return
depth_scale = None
if len(upsample.in_nodes()) == 2:
if upsample.in_node(1).value is None:
return
scales = upsample.in_node(1).value
assert len(scales) in (
4, 5
), 'Supported scales rank is 4 or 5, but it is {} for node {}'.format(
len(scales), upsample_name)
if not (math.isclose(scales[0], 1, rel_tol=1e-5)
and math.isclose(scales[1], 1, rel_tol=1e-5)):
return
height_scale = scales[2]
width_scale = scales[3]
if len(scales) == 5:
depth_scale = scales[4]
else:
height_scale = upsample['height_scale']
width_scale = upsample['width_scale']
if not math.isclose(height_scale, width_scale, rel_tol=1e-5):
log.debug(
'Width and height scales are not equal: {} vs {} for node {}'.
format(width_scale, height_scale, upsample_name))
return
if depth_scale is not None and not math.isclose(
height_scale, depth_scale, rel_tol=1e-5):
log.debug(
'Depth and height scales are not equal: {} vs {} for node {}'.
format(depth_scale, height_scale, upsample_name))
return
if 1 in upsample.in_ports() and not upsample.in_port(1).disconnected():
upsample.in_port(1).disconnect()
shape = Shape(graph, {'name': upsample_name + '/0_port'}).create_node()
layout = graph.graph['layout']
if input_shape_rank == 4:
begin_value = int64_array(
[get_height_dim(layout, input_shape_rank)])
factor_value = np.array([height_scale, width_scale])
else:
begin_value = int64_array(
[get_depth_dim(layout, input_shape_rank)])
factor_value = np.array([depth_scale, height_scale, width_scale])
ss = create_op_with_const_inputs(
graph, StridedSlice, {
1: begin_value,
2: int64_array([get_width_dim(layout, input_shape_rank) + 1]),
3: int64_array([1])
}, {
'name': upsample_name + '/ss_0_port',
'begin_mask': int64_array([1]),
'end_mask': int64_array([1]),
'new_axis_mask': int64_array([0]),
'shrink_axis_mask': int64_array([0]),
'ellipsis_mask': int64_array([0])
})
mul = create_op_node_with_second_input(
graph, Mul, factor_value, {'name': upsample_name + '/factor_mul_'})
source = upsample.in_port(0).get_connection().get_source()
source.connect(shape.in_port(0))
shape.out_port(0).connect(ss.in_port(0))
ss.out_port(0).connect(mul.in_port(0))
# Create Interpolate operation
if input_shape_rank == 4:
axes = int64_array([
get_height_dim(layout, input_shape_rank),
get_width_dim(layout, input_shape_rank)
])
else:
axes = int64_array([
get_depth_dim(layout, input_shape_rank),
get_height_dim(layout, input_shape_rank),
get_width_dim(layout, input_shape_rank)
])
resample_op = Interpolate(
graph,
dict(name=upsample_name + '/Interpolate',
axes=axes,
mode=upsample.attrs()['mode'],
antialias=0,
convert_to_resample=True)).create_node()
upsample.add_input_port(1, skip_if_exist=True)
assert upsample.in_port(1).disconnected()
mul.out_port(0).connect(resample_op.in_port(1))
upsample.in_port(0).get_connection().set_destination(
resample_op.in_port(0))
upsample.out_port(0).get_connection().set_source(
resample_op.out_port(0))
convert_to_float = Cast(graph, dict(dst_type=np.float32)).create_node()
convert_to_int = Cast(graph, dict(dst_type=np.int64)).create_node()
mul.in_port(0).get_connection().insert_node(convert_to_float)
mul.out_port(0).get_connection().insert_node(convert_to_int)
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: Graph, match: Dict[str, Node]):
log.debug('UpsampleToResample is triggered')
upsample = match['upsample']
input_shape = upsample.in_port(0).data.get_shape()
input_shape_rank = len(input_shape)
if input_shape_rank not in [4, 5]:
log.warning('The input shape is not 4D or 5D for op {}'.format(
upsample.soft_get('name')))
return
if len(upsample.in_nodes()) == 2:
if upsample.in_node(1).value is None:
return
scales = upsample.in_node(1).value
assert scales.shape == (4, )
if not (math.isclose(scales[0], 1, rel_tol=1e-5)
and math.isclose(scales[1], 1, rel_tol=1e-5)):
return
height_scale = scales[2]
width_scale = scales[3]
else:
height_scale = upsample['height_scale']
width_scale = upsample['width_scale']
if 1 in upsample.in_ports() and not upsample.in_port(1).disconnected():
upsample.in_port(1).disconnect()
factor = Const(graph, {
'value': np.array([height_scale, width_scale])
}).create_node()
shape = Shape(graph, {'name': upsample.name + '/0_port'}).create_node()
layout = graph.graph['layout']
if input_shape_rank == 4:
begin = Const(graph, {
'value':
int64_array([get_height_dim(layout, input_shape_rank)])
}).create_node()
else:
begin = Const(graph, {
'value':
int64_array([get_depth_dim(layout, input_shape_rank)])
}).create_node()
end = Const(graph, {
'value':
int64_array([get_width_dim(layout, input_shape_rank) + 1])
}).create_node()
stride = Const(graph, {'value': int64_array([1])}).create_node()
ss = StridedSlice(
graph, {
'name': upsample.name + '/ss_0_port',
'begin_mask': np.array([1]),
'end_mask': np.array([0]),
'new_axis_mask': np.array([0]),
'shrink_axis_mask': int64_array([0]),
'ellipsis_mask': int64_array([0])
}).create_node()
mul = Mul(graph, {
'name': upsample.name + '/factor_mul_'
}).create_node()
source = upsample.in_port(0).get_connection().get_source()
source.connect(shape.in_port(0))
shape.out_port(0).connect(ss.in_port(0))
begin.out_port(0).connect(ss.in_port(1))
end.out_port(0).connect(ss.in_port(2))
stride.out_port(0).connect(ss.in_port(3))
ss.out_port(0).connect(mul.in_port(0))
factor.out_port(0).connect(mul.in_port(1))
# Create Interpolate operation
if input_shape_rank == 4:
axes = int64_array([
get_height_dim(layout, input_shape_rank),
get_width_dim(layout, input_shape_rank)
])
else:
axes = int64_array([
get_depth_dim(layout, input_shape_rank),
get_height_dim(layout, input_shape_rank),
get_width_dim(layout, input_shape_rank)
])
resample_op = Interpolate(
graph,
dict(name='Interpolate/{}'.format(upsample.name),
axes=axes,
mode=upsample.attrs()['mode'],
antialias=0,
convert_to_resample=True)).create_node()
upsample.add_input_port(1, skip_if_exist=True)
assert upsample.in_port(1).disconnected()
mul.out_port(0).connect(resample_op.in_port(1))
upsample.in_port(0).get_connection().set_destination(
resample_op.in_port(0))
upsample.out_port(0).get_connection().set_source(
resample_op.out_port(0))
def replace_tf_resize(graph: Graph, resize: Node, interpolation_mode: str):
resize_name = resize.soft_get('name', resize.id)
log.debug(
"Converting of {} to Interpolate-4 is triggered for node {}.".format(
resize.op, resize_name))
num_of_inputs = len([
port for port in resize.in_ports().values() if not port.disconnected()
])
assert num_of_inputs == 2, \
"Number of inputs of {} (with name {}) should be equal to 2".format(resize.op, resize_name)
attrs_msg = "If half_pixel_centers attribute of the node {} with op {} is True, " \
"the attribute align_corners must be False"
assert not resize.half_pixel_centers or (resize.half_pixel_centers and not resize.align_corners), \
attrs_msg.format(resize_name, resize.op)
shape = Shape(graph, {'name': resize_name + '/shapeof'}).create_node()
layout = graph.graph['layout']
height_dim = get_height_dim(layout, 4)
width_dim = get_width_dim(layout, 4)
ss = create_op_with_const_inputs(
graph, StridedSlice, {
1: int64_array([height_dim]),
2: int64_array([width_dim + 1]),
3: int64_array([1])
}, {
'name': resize_name + '/StridedSlice',
'begin_mask': int64_array([1]),
'end_mask': int64_array([1]),
'new_axis_mask': int64_array([0]),
'shrink_axis_mask': int64_array([0]),
'ellipsis_mask': int64_array([0])
})
div_node = Div(graph, {'name': resize_name + '/Div'}).create_node()
shape_to_float = Cast(graph, dict(dst_type=np.float32)).create_node()
size_to_float = Cast(graph, dict(dst_type=np.float32)).create_node()
size_to_float.out_port(0).connect(div_node.in_port(0))
shape_to_float.out_port(0).connect(div_node.in_port(1))
ss.out_port(0).connect(shape_to_float.in_port(0))
shape.out_port(0).connect(ss.in_port(0))
align_corners = resize.align_corners
half_pixel_centers = resize.half_pixel_centers
nearest_mode = 'floor' if interpolation_mode == 'nearest' else 'round_prefer_floor'
if align_corners:
coordinate_transformation_mode = 'align_corners'
if interpolation_mode == 'nearest':
nearest_mode = 'round_prefer_ceil'
elif half_pixel_centers:
coordinate_transformation_mode = 'tf_half_pixel_for_nn' if interpolation_mode == 'nearest' else 'half_pixel'
else:
coordinate_transformation_mode = 'asymmetric'
interpolate4 = create_op_with_const_inputs(
graph, Interpolate, {3: int64_array([height_dim, width_dim])}, {
'name': resize_name + '/interpolate_4',
'mode': interpolation_mode,
'antialias': False,
'coordinate_transformation_mode': coordinate_transformation_mode,
'pads_begin': int64_array([0]),
'pads_end': int64_array([0]),
'nearest_mode': nearest_mode,
'cube_coeff': -0.75,
'shape_calculation_mode': 'sizes',
'version': 'opset4',
'in_ports_count': 4,
})
resize_input_connection = resize.in_port(0).get_connection()
resize_input_connection.set_destination(interpolate4.in_port(0))
resize_input_connection.get_source().connect(shape.in_port(0))
div_node.out_port(0).connect(interpolate4.in_port(2))
sizes_connection = resize.in_port(1).get_connection()
sizes_connection.set_destination(interpolate4.in_port(1))
sizes_connection.get_source().connect(size_to_float.in_port(0))
resize.out_port(0).get_connection().set_source(interpolate4.out_port(0))
rename_nodes([(resize, resize_name + '/delete'),
(interpolate4, resize_name)])
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[str, Node]):
log.debug('UpsampleToResample is triggered')
upsample = match['upsample']
upsample_name = upsample.soft_get('name', upsample.id)
input_shape = upsample.in_port(0).data.get_shape()
input_shape_rank = len(input_shape)
if input_shape_rank not in [4, 5]:
log.warning('The input shape is not 4D or 5D for op {}'.format(
upsample.soft_get('name')))
return
depth_scale = None
layout = graph.graph['layout']
if len(upsample.in_nodes()) == 2:
if upsample.in_node(1).value is None:
return
scales = upsample.in_node(1).value
assert len(scales) in (
4, 5
), 'Supported scales rank is 4 or 5, but it is {} for node {}'.format(
len(scales), upsample_name)
if not (math.isclose(scales[0], 1, rel_tol=1e-5)
and math.isclose(scales[1], 1, rel_tol=1e-5)):
return
height_scale = scales[get_height_dim(layout, input_shape_rank)]
width_scale = scales[get_width_dim(layout, input_shape_rank)]
if len(scales) == 5:
depth_scale = scales[get_depth_dim(layout, input_shape_rank)]
else:
height_scale = upsample['height_scale']
width_scale = upsample['width_scale']
if 1 in upsample.in_ports() and not upsample.in_port(1).disconnected():
upsample.in_port(1).disconnect()
upsample_name = upsample.soft_get('name', upsample.id)
shape = Shape(graph, {'name': upsample_name + '/0_port'}).create_node()
layout = graph.graph['layout']
if input_shape_rank == 4:
begin_value = int64_array(
[get_height_dim(layout, input_shape_rank)])
factor_value = float32_array([height_scale, width_scale])
else:
begin_value = int64_array(
[get_depth_dim(layout, input_shape_rank)])
factor_value = float32_array(
[depth_scale, height_scale, width_scale])
ss = create_op_with_const_inputs(
graph, StridedSlice, {
1: begin_value,
2: int64_array([get_width_dim(layout, input_shape_rank) + 1]),
3: int64_array([1])
}, {
'name': upsample_name + '/ss_0_port',
'begin_mask': int64_array([1]),
'end_mask': int64_array([1]),
'new_axis_mask': int64_array([0]),
'shrink_axis_mask': int64_array([0]),
'ellipsis_mask': int64_array([0])
})
mul = create_op_node_with_second_input(
graph, Mul, factor_value, {'name': upsample_name + '/factor_mul'})
source = upsample.in_port(0).get_connection().get_source()
source.connect(shape.in_port(0))
shape.out_port(0).connect(ss.in_port(0))
ss.out_port(0).connect(mul.in_port(0))
# Create Interpolate operation
if input_shape_rank == 4:
axes = int64_array([
get_height_dim(layout, input_shape_rank),
get_width_dim(layout, input_shape_rank)
])
else:
axes = int64_array([
get_depth_dim(layout, input_shape_rank),
get_height_dim(layout, input_shape_rank),
get_width_dim(layout, input_shape_rank)
])
axes_node = Const(graph, {
'name': upsample_name + '/axis',
'value': axes
}).create_node()
interpolate = Interpolate(
graph, {
'mode': upsample.attrs()['mode'],
'antialias': 0,
'pads_begin': int64_array([0]),
'pads_end': int64_array([0]),
'coordinate_transformation_mode': 'half_pixel',
'nearest_mode': 'round_prefer_floor',
'cube_coeff': -0.75,
'shape_calculation_mode': 'scales',
'version': 'opset4',
'in_ports_count': 4
}).create_node()
upsample.add_input_port(1, skip_if_exist=True)
assert upsample.in_port(1).disconnected()
mul.out_port(0).connect(interpolate.in_port(1))
axes_node.out_port(0).connect(interpolate.in_port(3))
scales_node = Const(graph, {
'name': upsample_name + '/scales',
'value': factor_value
}).create_node()
scales_node.out_port(0).connect(interpolate.in_port(2))
upsample.in_port(0).get_connection().set_destination(
interpolate.in_port(0))
upsample.out_port(0).get_connection().set_source(
interpolate.out_port(0))
rename_nodes([(upsample, upsample_name + '/delete'),
(interpolate, upsample_name)])
convert_to_float = Cast(graph, dict(dst_type=np.float32)).create_node()
convert_to_int = Cast(graph, dict(dst_type=np.int64)).create_node()
mul.in_port(0).get_connection().insert_node(convert_to_float)
mul.out_port(0).get_connection().insert_node(convert_to_int)
