def test_get_tensor_data_success(self):
"""Get tensor data success."""
test_tag_name = self._complete_tag_name
processor = TensorProcessor(self._mock_data_manager)
results = processor.get_tensors([self._train_id], [test_tag_name],
step='1',
dims='[0,0,:-1,:]',
detail='data')
recv_metadata = results.get('tensors')[0].get("values")
for recv_values, expected_values in zip(recv_metadata, self._tensors):
assert recv_values.get('wall_time') == expected_values.get(
'wall_time')
assert recv_values.get('step') == expected_values.get('step')
dims = expected_values.get('value').get("dims")
expected_data = np.array(
expected_values.get('value').get("float_data")).reshape(dims)
recv_tensor = np.array(recv_values.get('value').get("data"))
expected_tensor = TensorUtils.get_specific_dims_data(
expected_data, (0, 0, slice(None, -1, None), slice(None)))
# Compare tensor shape when recv_tensor shape is not empty.
if recv_tensor.shape != (0, ):
assert recv_tensor.shape == expected_tensor.shape
assert np.allclose(recv_tensor, expected_tensor, rtol=1e-6)
def test_get_tensor_data_success(self):
"""Get tensor data success."""
test_tag_name = self._complete_tag_name
processor = TensorProcessor(self._mock_data_manager)
results = processor.get_tensors([self._train_id], [test_tag_name], step='1', dims='[0,0,:,:]', detail='data')
recv_metadata = results.get('tensors')[0].get("values")
for recv_values, expected_values in zip(recv_metadata, self._tensors):
assert recv_values.get('wall_time') == expected_values.get('wall_time')
assert recv_values.get('step') == expected_values.get('step')
dims = expected_values.get('value').get("dims")
expected_data = np.array(expected_values.get('value').get("float_data")).reshape(dims)
recv_tensor = np.array(recv_values.get('value').get("data"))
expected_tensor = TensorUtils.get_specific_dims_data(expected_data, [0, 0, None, None], dims)
assert np.sum(np.isclose(recv_tensor, expected_tensor, rtol=1e-6) == 0) == 0
def _get_tensors_data(self, step, dims, tensors):
"""
Builds a JSON-serializable object with information about tensor dims data.
Args:
step (int): Specify step of tensor.
dims (str): Specify dims of tensor.
tensors (list): The list of _Tensor data.
Returns:
dict, a dict including the `wall_time`, `step`, and `value' for each tensor.
{
"wall_time": 0,
"step": 0,
"value": {
"dims": [1],
"data_type": "DT_FLOAT32",
"data": [[0.1]]
"statistics": {
"max": 0,
"min": 0,
"avg": 0,
"count": 1,
"nan_count": 0,
"neg_inf_count": 0,
"pos_inf_count": 0
}
}
}
Raises:
ResponseDataExceedMaxValueError, If the size of response data exceed max value.
StepTensorDataNotInCacheError, If query step is not in cache.
"""
values = []
step_in_cache = False
dims = TensorUtils.convert_array_from_str_dims(dims, limit=2)
for tensor in tensors:
# This value is an instance of TensorContainer
value = tensor.value
if step != tensor.step:
continue
step_in_cache = True
res_data = TensorUtils.get_specific_dims_data(
value.ndarray, dims, list(value.dims))
flatten_data = res_data.flatten().tolist()
if len(flatten_data) > MAX_TENSOR_RESPONSE_DATA_SIZE:
raise ResponseDataExceedMaxValueError(
"the size of response data: {} exceed max value: {}.".
format(len(flatten_data), MAX_TENSOR_RESPONSE_DATA_SIZE))
def transfer(array):
if not isinstance(array, np.ndarray):
# The list is used here so that len function can be used
# when the value of array is `NAN`、｀-INF｀ or ｀INF｀.
array = [array]
transfer_data = [None] * len(array)
for index, data in enumerate(array):
if isinstance(data, np.ndarray):
transfer_data[index] = transfer(data)
else:
if np.isnan(data):
transfer_data[index] = 'NAN'
elif np.isneginf(data):
transfer_data[index] = '-INF'
elif np.isposinf(data):
transfer_data[index] = 'INF'
else:
transfer_data[index] = float(data)
return transfer_data
stats = TensorUtils.get_statistics_from_tensor(res_data)
if stats.nan_count + stats.neg_inf_count + stats.pos_inf_count > 0:
tensor_data = transfer(res_data)
else:
tensor_data = res_data.tolist()
values.append({
"wall_time": tensor.wall_time,
"step": tensor.step,
"value": {
"dims": value.dims,
"data_type": anf_ir_pb2.DataType.Name(value.data_type),
"data": tensor_data,
"statistics": TensorUtils.get_statistics_dict(stats)
}
})
break
if not step_in_cache:
raise StepTensorDataNotInCacheError(
"this step: {} data may has been dropped.".format(step))
return values