def test_0d_not_type_shape(self):
pb_tensor = PB({
'dtype': 3,
'tensor_content': b'\x01\x00\x00\x00',
})
tf_dtype = pb_tensor.dtype
shape = np.array([3])
ref = [1, 1, 1]
res = tf_tensor_content(tf_dtype, shape, pb_tensor)
self.assertTrue(np.all(res == ref))
def test_0d_type_no_shape(self):
pb_tensor = PB({
'dtype': 3,
'int_val': [5],
})
tf_dtype = pb_tensor.dtype
shape = np.array([])
ref = 5
res = tf_tensor_content(tf_dtype, shape, pb_tensor)
self.assertTrue(res == ref)
def extract(cls, node):
pb_tensor = node.pb.attr["value"].tensor
shape = tf_tensor_shape(pb_tensor.tensor_shape)
attrs = {
'shape': shape,
'value': tf_tensor_content(pb_tensor.dtype, shape, pb_tensor),
'data_type': tf_dtype_extractor(pb_tensor.dtype),
}
Const.update_node_stat(node, attrs)
return cls.enabled
def test_list_type_shape(self):
pb_tensor = PB({
'dtype': 3,
'int_val': np.array([1, 2, 3, 4, 5], dtype=np.int32)
})
tf_dtype = pb_tensor.dtype
shape = np.array([10])
ref = [1, 2, 3, 4, 5, 5, 5, 5, 5, 5]
res = tf_tensor_content(tf_dtype, shape, pb_tensor)
self.assertTrue(np.all(res == ref))
def test_nd_type_no_shape(self):
pb_tensor = PB({
'dtype': 3,
'int_val': [[1, 2, 3], [4, 5, 6]],
})
tf_dtype = pb_tensor.dtype
shape = np.array([2, 3])
ref = [[1, 2, 3], [4, 5, 6]]
res = tf_tensor_content(tf_dtype, shape, pb_tensor)
self.assertTrue(np.all(res == ref))
def tf_const_ext(pb):
pb_tensor = pb.attr["value"].tensor
result = {
'data_type': tf_dtype_extractor(pb_tensor.dtype),
'shape': tf_tensor_shape(pb_tensor.tensor_shape),
'infer': tf_const_infer
}
result['value'] = tf_tensor_content(pb_tensor.dtype, result['shape'], pb_tensor)
log.debug('Constant extractor for node gives shape = {} and value.shape = {}'.format(result['shape'],
result['value'].shape))
return result
def test_nd_not_type_no_shape(self):
pb_tensor = PB({
'dtype': 3,
'tensor_content':
b'\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00\x04\x00\x00\x00\x05\x00\x00\x00\x06\x00\x00\x00',
})
tf_dtype = pb_tensor.dtype
shape = np.array([2, 3])
ref = [[1, 2, 3], [4, 5, 6]]
res = tf_tensor_content(tf_dtype, shape, pb_tensor)
self.assertTrue(np.all(res == ref))
def test_list_not_type_shape(self):
pb_tensor = PB({
'dtype':
3,
'tensor_content':
b'\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00\x04\x00\x00\x00\x05\x00\x00\x00'
})
tf_dtype = pb_tensor.dtype
shape = np.array([10])
ref = [1, 2, 3, 4, 5, 5, 5, 5, 5, 5]
res = tf_tensor_content(tf_dtype, shape, pb_tensor)
self.assertTrue(np.all(res == ref))
def test_list_not_type_no_shape(self):
pb_tensor = PB(
dict(
dtype=3,
tensor_content=
b'\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00\x04\x00\x00\x00\x05\x00\x00\x00'
))
tf_dtype = pb_tensor.dtype
shape = np.array([5])
ref = [1, 2, 3, 4, 5]
res = tf_tensor_content(tf_dtype, shape, pb_tensor)
self.assertTrue(np.all(res == ref))
def test_str_decode_list(self):
pb_tensor = PB({
'dtype': 7,
'string_val': [b'\377\330\377\377\330\377'],
})
shape = int64_array([])
warning_message = 'ERROR:root:Failed to parse a tensor with Unicode characters. Note that Inference Engine ' \
'does not support string literals, so the string constant should be eliminated from the ' \
'graph.'
with self.assertLogs(log.getLogger(), level="ERROR") as cm:
result = tf_tensor_content(pb_tensor.dtype, shape, pb_tensor)
self.assertEqual([warning_message, warning_message], cm.output)
def test_str_decode(self):
pb_tensor = PB({
'dtype': 7,
'string_val': [b"\037\000\036\000\002\000\303\237\035\000\002"]
})
tf_dtype = pb_tensor.dtype
shape = int64_array([1])
warning_message = 'ERROR:root:Failed to parse a tensor with Unicode characters. Note that Inference Engine ' \
'does not support string literals, so the string constant should be eliminated from the ' \
'graph.'
ref_val = np.array([b'\x1f\x00\x1e\x00\x02\x00\xc3\x9f\x1d\x00\x02'])
with self.assertLogs(log.getLogger(), level="ERROR") as cm:
result = tf_tensor_content(tf_dtype, shape, pb_tensor)
self.assertEqual([warning_message], cm.output)
self.assertEqual(ref_val, result)
