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 (openvino/tools/mo/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_)
