def test_tf_depth_to_space_infer(self):
graph = build_graph(nodes, edges)
dts_node = Node(graph, 'DtS')
DepthToSpaceOp.depth_to_space_infer(dts_node)
exp_shape = np.array([1, 2048, 1152, 64])
res_shape = graph.node['out_data_node']['shape']
self.assertTrue(np.array_equal(exp_shape, res_shape))
def replace_pattern(graph: Graph, match: dict):
channel_splitting_reshape = match['reshape_0']
channel_concating_reshape = match['reshape_1']
initial_shape = channel_splitting_reshape.in_port(0).data.get_shape()
resulting_shape = channel_concating_reshape.in_port(1).data.get_value()
if initial_shape[0] != resulting_shape[0]:
return
channel_splitted_out_shape = channel_splitting_reshape.in_port(
1).data.get_value()
if not all([initial_shape[i] == channel_splitted_out_shape[j] for i, j in {0: 0, 2: 4, 3: 5}.items()]) or \
channel_splitted_out_shape[1] != channel_splitted_out_shape[2]:
return
block_size = channel_splitted_out_shape[2]
expected_output_shape = [
initial_shape[0], initial_shape[1] // (block_size * block_size),
initial_shape[2] * block_size, initial_shape[3] * block_size
]
if not np.array_equal(expected_output_shape, resulting_shape):
return
name = channel_concating_reshape.soft_get('name',
channel_concating_reshape.id)
depth_to_space = DepthToSpaceOp(graph, {
'name': name,
'block_size': block_size,
'mode': 'depth_first'
}).create_node()
channel_concating_reshape.out_port(0).get_connection().set_source(
depth_to_space.out_port(0))
depth_to_space.in_port(0).connect(
channel_splitting_reshape.in_port(0).get_source())
def extract(cls, node):
# update the attributes of the node
block_size = node.pb.attr['block_size'].i
data_format = node.pb.attr['data_format'].s.decode('utf-8')
DepthToSpaceOp.update_node_stat(node, {
'block_size': block_size,
'data_format': data_format
})
return cls.enabled
def test_tf_depth_to_space_infer_nchw(self):
graph = build_graph(nodes, edges)
graph.graph['layout'] = 'NCHW'
graph.node['in_data_node']['shape'] = np.array([1, 256, 1024, 576])
dts_node = Node(graph, 'DtS')
DepthToSpaceOp.infer(dts_node)
exp_shape = np.array([1, 64, 2048, 1152])
res_shape = graph.node['out_data_node']['shape']
self.assertTrue(np.array_equal(exp_shape, res_shape))
def extract(cls, node):
# update the attributes of the node
node_name = node.soft_get('name', node.id)
block_size = onnx_attr(node, 'blocksize', 'i', default=None)
assert block_size is not None, \
'DepthToSpace should have "blocksize" attribute specified for node {}'.format(node_name)
onnx_mode = onnx_attr(node, 'mode', 's', default=b'DCR').decode()
assert onnx_mode in [
'DCR', 'CRD'
], 'Unrecognized mode provided for DepthToSpace node {}'.format(
node_name)
if onnx_mode == 'DCR':
mode = 'blocks_first'
else:
mode = 'depth_first'
DepthToSpaceOp.update_node_stat(node, {
'block_size': block_size,
'mode': mode
})
return cls.enabled
def test_tf_depth_to_space_infer_error_1(self):
graph = build_graph(nodes, edges)
graph.node['in_data_node']['shape'] = np.array([1, 1024, 576, 255])
dts_node = Node(graph, 'DtS')
self.assertRaises(Error, DepthToSpaceOp.depth_to_space_infer(dts_node))