def test_region_infer_dynamic_flatten(self):
graph = build_graph(
nodes_attributes, [('node_1', 'region'), ('region', 'node_3'),
('node_3', 'op_output')],
{
'node_3': {
'shape': None,
'value': None
},
'node_1': {
'shape': shape_array(
[1, dynamic_dimension_value, 227, 227])
},
'region': {
'end_axis': 1,
'axis': 0,
'do_softmax': 1,
**layout_attrs()
}
})
graph.graph['layout'] = 'NCHW'
reorg_node = Node(graph, 'region')
RegionYoloOp.regionyolo_infer(reorg_node)
exp_shape = shape_array([dynamic_dimension_value, 227, 227])
res_shape = graph.node['node_3']['shape']
self.assertTrue(strict_compare_tensors(exp_shape, res_shape))
def test_region_infer_do_softmax(self):
graph = build_graph(
nodes_attributes, [('node_1', 'region'), ('region', 'node_3'),
('node_3', 'op_output')], {
'node_3': {
'shape': None
},
'node_1': {
'shape': np.array([1, 227, 227, 3])
},
'region': {
'do_softmax': 0,
'end_axis': -1,
'axis': 1,
'classes': 80,
'coords': 4,
'mask': np.array([6, 7, 8]),
**layout_attrs()
}
})
graph.graph['layout'] = 'NHWC'
reorg_node = Node(graph, 'region')
RegionYoloOp.regionyolo_infer(reorg_node)
exp_shape = np.array([1, 227, 227, (80 + 4 + 1) * 3])
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_region_infer_flatten(self):
graph = build_graph(
nodes_attributes, [('node_1', 'region'), ('region', 'node_3'),
('node_3', 'op_output')], {
'node_3': {
'shape': None,
'value': None
},
'node_1': {
'shape': np.array([1, 3, 227, 227])
},
'region': {
'end_axis': 1,
'axis': 0,
'do_softmax': 1,
**layout_attrs()
}
})
graph.graph['layout'] = 'NCHW'
reorg_node = Node(graph, 'region')
RegionYoloOp.regionyolo_infer(reorg_node)
exp_shape = np.array([1 * 3, 227, 227])
res_shape = graph.node['node_3']['shape']
for i in range(0, len(exp_shape)):
self.assertEqual(exp_shape[i], res_shape[i])