def test_caffe_interp_infer_wh(self):
graph = build_graph(
nodes_attributes, [('node_1', 'interp'), ('interp', 'node_3'),
('node_3', 'op_output')], {
'node_3': {
'shape': None
},
'node_1': {
'shape': np.array([1, 1024, 1, 1])
},
'interp': {
'width': 65,
'height': 33,
'zoom_factor': 1,
'shrink_factor': 1,
'pad_beg': 0,
'pad_end': 0
}
})
graph.graph['layout'] = 'NCHW'
interp_node = Node(graph, 'interp')
InterpOp.interp_infer(interp_node)
exp_shape = np.array([1, 1024, 33, 65])
res_shape = graph.node['node_3']['shape']
for i in range(0, len(exp_shape)):
self.assertEqual(exp_shape[i], res_shape[i])
def test_caffe_interp_2_blobs(self):
graph = build_graph(
nodes_attributes, [('node_1', 'interp'), ('node_2', 'interp'),
('interp', 'node_3'), ('node_3', 'op_output')],
{
'node_3': {
'shape': None
},
'node_1': {
'shape': np.array([1, 256, 33, 66])
},
'node_2': {
'shape': np.array([1, 1, 3, 6])
},
'interp': {
'zoom_factor': 1,
'shrink_factor': 1,
'pad_beg': 0,
'pad_end': 0,
'parse_2nd_input': 'shape',
}
})
graph.graph['layout'] = 'NCHW'
interp_node = Node(graph, 'interp')
InterpOp.interp_infer(interp_node)
exp_shape = np.array([1, 256, 3, 6])
res_shape = graph.node['node_3']['shape']
for i in range(0, len(exp_shape)):
self.assertEqual(exp_shape[i], res_shape[i])
def extract(node):
mapping_rule = {
'pad_end': 0,
'pad_beg': 0,
'align_corners': int(node.pb.attr['align_corners'].b)
}
InterpOp.update_node_stat(node, mapping_rule)
return __class__.enabled
def replace_pattern(graph: Graph, match: dict):
node = match['interpolate']
# common
mode = node.mode
assert mode in ['linear', 'nearest', 'cubic', 'area']
in_shape = node.in_port(0).data.get_shape()
assert in_shape is not None and len(in_shape) == 4
out_shape = node.out_port(0).data.get_shape()
assert out_shape is not None and len(out_shape) == 4
in_height, in_width = in_shape[2], in_shape[3]
out_height, out_width = out_shape[2], out_shape[3]
factor = factor_update(
None if not node.has_valid('factor') else node.factor,
[float(out_height) / in_height,
float(out_width) / in_width], [in_height, in_width],
[out_height, out_width], node.soft_get('name'))
update_attrs = {
'width': out_width,
'height': out_height,
'factor': factor,
}
if (node.has_valid('shrink_factor')
and node.has_valid('zoom_factor')) or factor is None:
del update_attrs['factor']
if node.has('factor'):
del node['factor']
if ((node.has_valid('shrink_factor') and node.shrink_factor != 1) or
(node.has_valid('zoom_factor') and node.zoom_factor != 1) or 'factor' in update_attrs) \
and ((not node.has_valid('width') or node.width == 0) and
(not node.has_valid('height') or node.height == 0)):
update_attrs['width'] = 0
update_attrs['height'] = 0
# specific
if mode in ['nearest', 'cubic', 'area'
] or node.has_and_set('convert_to_resample'):
assert not node.align_corners
assert node.pads_begin == 0 and node.pads_end == 0
update_attrs[
'resample_type'] = InterpolateToInterpOrResample.type_map[mode]
ResampleOp.update_node_stat(node, update_attrs)
node.in_port(1).disconnect()
elif mode == 'linear':
update_attrs.update({
'pad_beg': node.pads_begin,
'pad_end': node.pads_end,
'align_corners': node.align_corners,
})
InterpOp.update_node_stat(node, update_attrs)
node.in_port(1).disconnect()
node['force_precision_in_ports'] = None
def test_tf_interp_infer_two_inputs(self):
graph = build_graph(
nodes_attributes, [('node_1', 'interp'), ('node_2', 'interp'),
('interp', 'node_3')], {
'node_1': {
'shape': np.array([1, 20, 30, 100])
},
'node_2': {
'shape': np.array([2]),
'value': np.array([2, 3])
}
})
graph.graph['layout'] = 'NHWC'
interp_node = Node(graph, 'interp')
InterpOp.interp_infer(interp_node)
exp_shape = np.array([1, 2, 3, 100])
res_shape = graph.node['node_3']['shape']
for i in range(0, len(exp_shape)):
self.assertEqual(exp_shape[i], res_shape[i])
def test_tf_interp_infer_one_input_hw(self):
graph = build_graph(
nodes_attributes, [('node_1', 'interp'), ('interp', 'node_3')], {
'node_1': {
'shape': np.array([1, 20, 30, 100])
},
'interp': {
'height': 4,
'width': 6,
'pad_beg': 0,
'pad_end': 0,
'zoom_factor': None,
'shrink_factor': None
}
})
graph.graph['layout'] = 'NHWC'
interp_node = Node(graph, 'interp')
InterpOp.interp_infer(interp_node)
exp_shape = np.array([1, 4, 6, 100])
res_shape = graph.node['node_3']['shape']
for i in range(0, len(exp_shape)):
self.assertEqual(exp_shape[i], res_shape[i])
def test_caffe_interp_infer_zoom_shrink_error(self):
graph = build_graph(
nodes_attributes, [('node_1', 'interp'), ('interp', 'node_3'),
('node_3', 'op_output')], {
'node_3': {
'shape': None
},
'node_1': {
'shape': np.array([1, 256, 33, 65])
},
'interp': {
'zoom_factor': 0,
'height': 0,
'width': 0,
'shrink_factor': 0,
'pad_beg': 0,
'pad_end': 0
}
})
graph.graph['layout'] = 'NCHW'
interp_node = Node(graph, 'interp')
InterpOp.interp_infer(interp_node)
self.assertIsNone(graph.node['node_3']['shape'])
def replace_pattern(graph: Graph, match: dict):
node = match['interpolate']
assert 1 in node.in_ports() and not node.in_port(1).disconnected() and \
node.in_port(1).data.get_value() is not None, 'Interpolate node {} is corrupted: no 1-port input found'
# common
mode = node.mode
assert mode in ['linear', 'nearest', 'cubic', 'area']
in_shape = node.in_port(0).data.get_shape()
assert in_shape is not None and len(in_shape) in [4, 5]
out_shape = node.out_port(0).data.get_shape()
assert out_shape is not None and len(out_shape) in [4, 5]
in_height, in_width = in_shape[2], in_shape[3]
out_height, out_width = out_shape[2], out_shape[3]
factor = factor_update(
None if not node.has_valid('factor') else node.factor,
[float(out_height) / in_height,
float(out_width) / in_width], [in_height, in_width],
[out_height, out_width], node.soft_get('name'))
update_attrs = {
'width': out_width,
'height': out_height,
'factor': factor,
}
if (node.has_valid('shrink_factor')
and node.has_valid('zoom_factor')) or factor is None:
del update_attrs['factor']
if node.has('factor'):
del node['factor']
if ((node.has_valid('shrink_factor') and node.shrink_factor != 1) or
(node.has_valid('zoom_factor') and node.zoom_factor != 1) or 'factor' in update_attrs) \
and ((not node.has_valid('width') or node.width == 0) and
(not node.has_valid('height') or node.height == 0)):
update_attrs['width'] = 0
update_attrs['height'] = 0
# specific
if mode in ['nearest', 'cubic', 'area'
] or node.has_and_set('convert_to_resample'):
assert not node.align_corners
assert node.pads_begin == 0 and node.pads_end == 0
update_attrs[
'resample_type'] = InterpolateToInterpOrResample.type_map[mode]
ResampleOp.update_node_stat(node, update_attrs)
if not graph.graph[
'cmd_params'].keep_shape_ops or graph.graph['fw'] != 'tf':
node.in_port(1).disconnect()
else:
# we avoid making resample non-reshapable for tf version
shape = Shape(graph, {}).create_node()
node.in_port(0).get_source().connect(shape.in_port(0))
batch = node_to_get_batch_value(shape)
features = node_to_get_features_dimension_value(shape)
full_shape = new_shape_node_from_shape_nodes(
[batch, features,
node.in_port(1).get_source().node])
node.in_port(1).get_connection().set_source(
full_shape.out_port(0))
full_shape['override_output_shape'] = True
elif mode == 'linear':
assert len(in_shape) == 4, 'Interp does not support 5D input'
update_attrs.update({
'pad_beg': node.pads_begin,
'pad_end': node.pads_end,
'align_corners': node.align_corners,
})
InterpOp.update_node_stat(node, update_attrs)
node.in_port(1).disconnect()