def test_two_inputs_dynamic_value_infer(self):
in_value = shape_array([dynamic_dimension_value, 3]).reshape(
(1, 1, 1, 2))
graph = build_graph(
self.node_attrs,
self.edge_attrs + [('pads_begin', 'pad'), ('pads_end', 'pad')],
{'data_in': {
'value': in_value,
'shape': in_value.shape
}},
nodes_with_edges_only=True,
)
out_shape = (1, 1, 5, 8)
mask = np.zeros(out_shape, dtype=np.bool)
mask[0][0][1][2] = True
ref_value = np.ma.masked_array(np.zeros(out_shape, dtype=np.int64),
mask=mask,
dtype=np.int64)
ref_value[0][0][1][3] = 3
pad_node = Node(graph, 'pad')
Pad.infer(pad_node)
output_value = Node(graph, 'data_out').value
self.assertTrue(
np.array_equal(Node(graph, 'data_out').shape, ref_value.shape))
self.assertTrue(strict_compare_tensors(output_value, ref_value))
self.assertTrue(isinstance(output_value, np.ma.masked_array))
self.assertTrue(output_value[0][0][1][2] is dynamic_dimension)
def test_two_inputs_value_infer(self):
in_value = np.random.rand(*self.node_attrs['data_in']['shape']).astype(
np.float32)
graph = build_graph(
self.node_attrs,
self.edge_attrs + [('pads_begin', 'pad'), ('pads_end', 'pad')],
{'data_in': {
'value': in_value
}},
nodes_with_edges_only=True,
)
pads = np.insert(
self.node_attrs['pads_end']['value'],
np.arange(len(self.node_attrs['pads_begin']['value'])),
self.node_attrs['pads_begin']['value'])
pads = np.reshape(pads,
(len(self.node_attrs['pads_begin']['value']), 2))
ref_value = np.pad(in_value, pads, constant_values=0, mode='constant')
pad_node = Node(graph, 'pad')
Pad.infer(pad_node)
self.assertTrue(
np.array_equal(
Node(graph, 'data_out').shape,
np.array([1, 3, 100 + 1 + 3, 200 + 2 + 4])))
self.assertTrue(
np.array_equal(Node(graph, 'data_out').value, ref_value))
def test_not_enough_inputs(self):
graph = build_graph(
self.node_attrs,
self.edge_attrs + [('pads_begin', 'pad')],
nodes_with_edges_only=True,
)
pad_node = Node(graph, 'pad')
with self.assertRaisesRegex(AssertionError,
".*must have 3 or 4 inputs.*"):
Pad.infer(pad_node)
def test_one_input_and_no_pads(self):
graph = build_graph(
self.node_attrs,
self.edge_attrs,
nodes_with_edges_only=True,
)
pad_node = Node(graph, 'pad')
with self.assertRaisesRegex(AssertionError,
".*pads attribute is missing.*"):
Pad.infer(pad_node)
def test_two_inputs(self):
graph = build_graph(
self.node_attrs,
self.edge_attrs + [('pads_begin', 'pad'), ('pads_end', 'pad')],
nodes_with_edges_only=True,
)
pad_node = Node(graph, 'pad')
Pad.infer(pad_node)
self.assertTrue(
np.array_equal(
Node(graph, 'data_out').shape,
np.array([1, 3, 100 + 1 + 3, 200 + 2 + 4])))
def test_two_inputs_and_pads(self):
graph = build_graph(
self.node_attrs,
self.edge_attrs + [('data_pads', 'pad')],
{
'pad': {
'pads':
np.array([[0, 0], [0, 0], [1, 3], [2, 4]], dtype=np.int64)
}
},
nodes_with_edges_only=True,
)
pad_node = Node(graph, 'pad')
with self.assertRaisesRegex(
AssertionError, ".*unexpected additional input argument.*"):
Pad.infer(pad_node)
def test_one_input(self):
graph = build_graph(
self.node_attrs,
self.edge_attrs,
{
'pad': {
'pads':
np.array([[0, 0], [0, 0], [1, 3], [2, 4]], dtype=np.int64)
}
},
nodes_with_edges_only=True,
)
pad_node = Node(graph, 'pad')
Pad.infer(pad_node)
self.assertTrue(
np.array_equal(
Node(graph, 'data_out').shape,
np.array([1, 3, 100 + 1 + 3, 200 + 2 + 4])))