def test_pooling_infer_decrement_input_spatial(self):
graph = build_graph(nodes_attributes,
[('node_1', 'pool'),
('pool', 'node_2'),
('node_2', 'op_output')
],
{'node_2': {'shape': None},
'node_1': {'shape': np.array([1, 3, 224, 224])},
'pool': {'window': np.array([1, 1, 1, 1]), 'stride': np.array([1, 1, 3, 3]),
'pad': np.array([[0, 0], [0, 0], [3, 3], [3, 3]]),
'pad_spatial_shape': np.array([[1, 1], [1, 1]]),
'pool_method': 'avg', 'exclude_pad': 'false', 'global_pool': 0,
'output_spatial_shape': None, 'output_shape': None,
'kernel_spatial': np.array([3, 3]), 'spatial_dims': np.array([2, 3]),
'channel_dims': np.array([1]), 'batch_dims': np.array([0]),
'pooling_convention': 'full'}
})
pool_node = Node(graph, 'pool')
Pooling.infer(pool_node)
exp_shape = np.array([1, 3, 75, 75])
res_shape = graph.node['node_2']['shape']
for i in range(0, len(exp_shape)):
self.assertEqual(exp_shape[i], res_shape[i])
def test_pooling_infer_wrong_input_shape(self):
graph = build_graph(
nodes_attributes, [('node_1', 'pool'), ('pool', 'node_2'),
('node_2', 'op_output')],
{
'node_2': {
'shape': None
},
'node_1': {
'shape': np.array([1, 3, 1, 1])
},
'pool': {
'window': np.array([1, 1, 5, 5]),
'stride': np.array([1, 1, 2, 2]),
'pad': np.array([[0, 0], [0, 0], [1, 1], [1, 1]]),
'pad_spatial_shape': np.array([[1, 1], [1, 1]]),
'pool_method': 'avg',
'exclude_pad': False,
'global_pool': False,
'output_spatial_shape': None,
'output_shape': None,
'kernel_spatial': np.array([3, 3]),
'spatial_dims': np.array([2, 3]),
'channel_dims': np.array([1]),
'batch_dims': np.array([0]),
'pooling_convention': 'full'
}
})
pool_node = Node(graph, 'pool')
with self.assertRaises(Error):
Pooling.infer(pool_node)
def test_pooling_infer_no_shape(self):
graph = build_graph(
nodes_attributes, [('node_1', 'pool'), ('pool', 'node_2'),
('node_2', 'op_output')],
{
'node_2': {
'shape': None
},
'node_1': {
'shape': None
},
'pool': {
'window': np.array([1, 1, 1, 1]),
'stride': np.array([1, 1, 2, 2]),
'pad': np.array([[0, 0], [0, 0], [3, 3], [3, 3]]),
'pad_spatial_shape': np.array([[3, 3], [3, 3]]),
'pool_method': 'avg',
'exclude_pad': False,
'output_spatial_shape': None,
'output_shape': None,
'kernel_spatial': np.array([3, 3]),
'spatial_dims': np.array([2, 3]),
'channel_dims': np.array([1]),
'batch_dims': np.array([0]),
'pooling_convention': 'full'
}
})
pool_node = Node(graph, 'pool')
Pooling.infer(pool_node)
res_shape = graph.node['node_2']['shape']
self.assertIsNone(res_shape)
def infer(cls, node: Node):
input_shape = node.in_node(0).shape
input_h = input_shape[2]
input_w = input_shape[3]
output_h = node.output_size[0]
output_w = node.output_size[1]
stride_h = input_h // output_h
stride_w = input_w // output_w
kernel_h = input_h - (output_h - 1) * stride_h
kernel_w = input_w - (output_w - 1) * stride_w
data = {
'window': int64_array([1, 1, kernel_h, kernel_w]),
'stride': int64_array([1, 1, stride_h, stride_w]),
'pad': int64_array([[0, 0], [0, 0], [0, 0], [0, 0]]),
'pad_spatial_shape': int64_array([[0, 0], [0, 0]]),
'pool_method': 'avg',
'exclude_pad': 'false',
'output_spatial_shape': None,
'spatial_dims': None,
'channel_dims': int64_array([1]),
'batch_dims': int64_array([0]),
'layout': 'NCHW',
'rounding_type': 'floor',
'pooling_convention': 'valid'
}
# update the attributes of the node
Pooling.update_node_stat(node, data)
Pooling.infer(node)
def test_pooling_dynamic_infer(self):
graph = build_graph(nodes_attributes,
[('node_1', 'pool'),
('pool', 'node_2'),
('node_2', 'op_output')
],
{'node_2': {'shape': None},
'node_1': {'shape': shape_array([1, dynamic_dimension_value, dynamic_dimension_value,
256])},
'pool': {'window': np.array([1, 1, 1, 1]), 'stride': np.array([1, 1, 2, 2]),
'pad': np.array([[0, 0], [0, 0], [3, 3], [3, 3]]),
'pad_spatial_shape': np.array([[3, 3], [3, 3]]),
'pool_method': 'avg', 'exclude_pad': False, 'global_pool': False,
'output_spatial_shape': None, 'output_shape': None,
'kernel_spatial': np.array([3, 3]), 'spatial_dims': np.array([2, 3]),
'channel_dims': np.array([1]), 'batch_dims': np.array([0]),
'pooling_convention': 'full'}
})
pool_node = Node(graph, 'pool')
Pooling.infer(pool_node)
exp_shape = shape_array([1, dynamic_dimension_value, dynamic_dimension_value, 131])
res_shape = graph.node['node_2']['shape']
self.assertTrue(strict_compare_tensors(exp_shape, res_shape))
def test_pooling_infer_with_dilations(self):
graph = build_graph(nodes_attributes,
[('node_1', 'pool'),
('pool', 'node_2'),
('node_2', 'op_output')
],
{'node_2': {'shape': None},
'node_1': {'shape': np.array([1, 3, 256, 256])},
'pool': {'window': np.array([1, 1, 2, 2]), 'stride': np.array([1, 1, 2, 2]),
'pad': np.array([[0, 0], [0, 0], [0, 0], [1, 1]]),
'pad_spatial_shape': np.array([[0, 0], [1, 1]]),
'pool_method': 'max', 'exclude_pad': False, 'global_pool': False,
'output_spatial_shape': None, 'output_shape': None,
'kernel_spatial': np.array([2, 2]), 'spatial_dims': np.array([2, 3]),
'channel_dims': np.array([1]), 'batch_dims': np.array([0]),
'pooling_convention': 'full', 'dilation': np.array([1, 1, 2, 2]),
'auto_pad': 'valid'}
})
pool_node = Node(graph, 'pool')
Pooling.infer(pool_node)
exp_shape = np.array([1, 3, 127, 127])
res_shape = graph.node['node_2']['shape']
for i in range(0, len(exp_shape)):
self.assertEqual(exp_shape[i], res_shape[i])
