def testArrayViewSlicingDownNumericTensorToOneElement(self):
x = np.array([[1.1, 2.2, np.inf], [-np.inf, 3.3, np.nan]],
dtype=np.float32)
dtype, shape, data = tensor_helper.array_view(x, slicing='[0,0]')
self.assertEqual('float32', dtype)
self.assertEqual(tuple(), shape)
self.assertTrue(np.allclose(1.1, data))
def query(self, time_indices):
"""Query the values at given time indices.
Args:
time_indices: 0-based time indices to query, as a `list` of `int`.
Returns:
Values as a list of `numpy.ndarray` (for time indices in memory) or
`None` (for time indices discarded).
"""
if self._disposed:
raise ValueError(
'Cannot query: this _WatchStore instance is already disposed')
if not isinstance(time_indices, (tuple, list)):
time_indices = [time_indices]
output = []
for time_index in time_indices:
if isinstance(self._data[time_index], _TensorValueDiscarded):
output.append(None)
else:
data_item = self._data[time_index]
if (hasattr(data_item, 'dtype') and
tensor_helper.translate_dtype(data_item.dtype) == 'string'):
_, _, data_item = tensor_helper.array_view(data_item)
data_item = np.array(
tensor_helper.process_buffers_for_display(data_item),
dtype=np.object)
output.append(data_item)
return output
def query(self, watch_key, time_indices=None, slicing=None, mapping=None):
"""Query tensor store for a given watch_key.
Args:
watch_key: The watch key to query.
time_indices: A numpy-style slicing string for time indices. E.g.,
`-1`, `:-2`, `[::2]`. If not provided (`None`), will use -1.
slicing: A numpy-style slicing string for individual time steps.
mapping: An mapping string or a list of them. Supported mappings:
`{None, 'image/png', 'health-pill'}`.
Returns:
The potentially sliced values as a nested list of values or its mapped
format. A `list` of nested `list` of values.
Raises:
ValueError: If the shape of the sliced array is incompatible with mapping
mode. Or if the mapping type is invalid.
"""
if watch_key not in self._tensor_data:
raise KeyError("watch_key not found: %s" % watch_key)
if time_indices is None:
time_indices = "-1"
time_slicing = tensor_helper.parse_time_indices(time_indices)
all_time_indices = list(range(self._tensor_data[watch_key].num_total()))
sliced_time_indices = all_time_indices[time_slicing]
if not isinstance(sliced_time_indices, list):
sliced_time_indices = [sliced_time_indices]
recombine_and_map = False
step_mapping = mapping
if len(sliced_time_indices) > 1 and mapping not in (None,):
recombine_and_map = True
step_mapping = None
output = []
for index in sliced_time_indices:
value = self._tensor_data[watch_key].query(index)[0]
if value is not None and not isinstance(
value, debug_data.InconvertibleTensorProto
):
output.append(
tensor_helper.array_view(
value, slicing=slicing, mapping=step_mapping
)[2]
)
else:
output.append(None)
if recombine_and_map:
if mapping == "image/png":
output = tensor_helper.array_to_base64_png(output)
elif mapping and mapping != "none":
logger.warn(
"Unsupported mapping mode after recomining time steps: %s",
mapping,
)
return output
def testArrayViewOnScalarString(self):
# Construct a numpy scalar that corresponds to a TensorFlow string tensor
# value.
x = np.array('foo', dtype=np.object)
dtype, shape, data = tensor_helper.array_view(x)
self.assertEqual('string', dtype)
self.assertEqual(tuple(), shape)
self.assertEqual('foo', data)
def testArrayViewOnScalarString(self):
# Construct a numpy scalar that corresponds to a TensorFlow string tensor
# value.
x = np.array('foo', dtype=np.object)
dtype, shape, data = tensor_helper.array_view(x)
self.assertEqual('string', dtype)
self.assertEqual(tuple(), shape)
self.assertEqual('foo', data)
def query(self,
watch_key,
time_indices=None,
slicing=None,
mapping=None):
"""Query tensor store for a given watch_key.
Args:
watch_key: The watch key to query.
time_indices: A numpy-style slicing string for time indices. E.g.,
`-1`, `:-2`, `[::2]`. If not provided (`None`), will use -1.
slicing: A numpy-style slicing string for individual time steps.
mapping: An mapping string or a list of them. Supported mappings:
`{None, 'image/png', 'health-pill'}`.
Returns:
The potentially sliced values as a nested list of values or its mapped
format. A `list` of nested `list` of values.
Raises:
ValueError: If the shape of the sliced array is incompatible with mapping
mode. Or if the mapping type is invalid.
"""
if watch_key not in self._tensor_data:
raise KeyError("watch_key not found: %s" % watch_key)
if time_indices is None:
time_indices = '-1'
time_slicing = tensor_helper.parse_time_indices(time_indices)
all_time_indices = list(range(self._tensor_data[watch_key].num_total()))
sliced_time_indices = all_time_indices[time_slicing]
if not isinstance(sliced_time_indices, list):
sliced_time_indices = [sliced_time_indices]
recombine_and_map = False
step_mapping = mapping
if len(sliced_time_indices) > 1 and mapping not in (None, ):
recombine_and_map = True
step_mapping = None
output = []
for index in sliced_time_indices:
value = self._tensor_data[watch_key].query(index)[0]
if (value is not None and
not isinstance(value, debug_data.InconvertibleTensorProto)):
output.append(tensor_helper.array_view(
value, slicing=slicing, mapping=step_mapping)[2])
else:
output.append(None)
if recombine_and_map:
if mapping == 'image/png':
output = tensor_helper.array_to_base64_png(output)
elif mapping and mapping != 'none':
tf.logging.warn(
'Unsupported mapping mode after recomining time steps: %s',
mapping)
return output
def testImagePngMappingWorksForArrayWithOnlyOneElement(self):
x = np.array([[-42]], dtype=np.int16)
dtype, shape, data = tensor_helper.array_view(x, mapping="image/png")
self.assertEqual("int16", dtype)
self.assertEqual((1, 1), shape)
decoded_x = im_util.decode_png(base64.b64decode(data))
self.assertEqual((1, 1, 3), decoded_x.shape)
self.assertEqual(np.uint8, decoded_x.dtype)
self.assertAllClose(np.zeros([1, 1, 3]), decoded_x)
def testArrayViewSlicingStringTensorToScalar(self):
# Construct a numpy array that corresponds to a TensorFlow string tensor
# value.
x = np.array([['foo', 'bar', 'qux'], ['baz', 'corge', 'grault']],
dtype=np.object)
dtype, shape, data = tensor_helper.array_view(x, slicing='[1, 1]')
self.assertEqual('string', dtype)
self.assertEqual((1, 1), shape)
self.assertEqual([['corge']], data)
def testArrayViewSlicingStringTensorToScalar(self):
# Construct a numpy array that corresponds to a TensorFlow string tensor
# value.
x = np.array([['foo', 'bar', 'qux'], ['baz', 'corge', 'grault']],
dtype=np.object)
dtype, shape, data = tensor_helper.array_view(x, slicing='[1, 1]')
self.assertEqual('string', dtype)
self.assertEqual((1, 1), shape)
self.assertEqual([['corge']], data)
def testImagePngMappingWorksForArrayWithOnlyOneElement(self):
x = np.array([[-42]], dtype=np.int16)
dtype, shape, data = tensor_helper.array_view(x, mapping="image/png")
self.assertEqual("int16", dtype)
self.assertEqual((1, 1), shape)
decoded_x = im_util.decode_png(base64.b64decode(data))
self.assertEqual((1, 1, 3), decoded_x.shape)
self.assertEqual(np.uint8, decoded_x.dtype)
self.assertAllClose(np.zeros([1, 1, 3]), decoded_x)
def testArrayView2DWithSlicingAndImagePngMapping(self):
x = np.ones([15, 16], dtype=np.int32)
dtype, shape, data = tensor_helper.array_view(x,
slicing="[:15:3, :16:2]",
mapping="image/png")
self.assertEqual("int32", dtype)
self.assertEqual((5, 8), shape)
decoded_x = im_util.decode_png(base64.b64decode(data))
self.assertEqual((5, 8, 3), decoded_x.shape)
def testArrayViewSlicingStringTensorToScalar(self):
# Construct a numpy array that corresponds to a TensorFlow string tensor
# value.
x = np.array([["foo", "bar", "qux"], ["baz", "corge", "grault"]],
dtype=np.object)
dtype, shape, data = tensor_helper.array_view(x, slicing="[1, 1]")
self.assertEqual("string", dtype)
self.assertEqual((1, 1), shape)
self.assertEqual([["corge"]], data)
def testArrayView2DWithSlicingAndImagePngMapping(self):
x = np.ones([15, 16], dtype=np.int32)
dtype, shape, data = tensor_helper.array_view(
x, slicing="[:15:3, :16:2]", mapping="image/png")
self.assertEqual("int32", dtype)
self.assertEqual((5, 8), shape)
decoded_x = im_util.decode_png(base64.b64decode(data))
self.assertEqual((5, 8, 3), decoded_x.shape)
self.assertEqual(np.uint8, decoded_x.dtype)
self.assertAllClose(np.zeros([5, 8, 3]), decoded_x)
def _comm_tensor_data(device_name,
node_name,
maybe_base_expanded_node_name,
output_slot,
debug_op,
tensor_value,
wall_time):
"""Create a dict() as the outgoing data in the tensor data comm route.
Args:
device_name: Name of the device that the tensor is on.
node_name: (Original) name of the node that produces the tensor.
maybe_base_expanded_node_name: Possbily base-expanded node name.
output_slot: Output slot number.
debug_op: Name of the debug op.
tensor_value: Value of the tensor, as a numpy.ndarray.
wall_time: Wall timestamp for the tensor.
Returns:
A dict representing the tensor data.
"""
output_slot = int(output_slot)
tf.logging.info(
'Recording tensor value: %s, %d, %s', node_name, output_slot, debug_op)
tensor_values = None
if isinstance(tensor_value, debug_data.InconvertibleTensorProto):
if not tensor_value.initialized:
tensor_dtype = UNINITIALIZED_TAG
tensor_shape = UNINITIALIZED_TAG
else:
tensor_dtype = UNSUPPORTED_TAG
tensor_dtype = UNSUPPORTED_TAG
tensor_values = NA_TAG
else:
tensor_dtype = str(tensor_value.dtype)
tensor_shape = tensor_value.shape
# The /comm endpoint should respond with tensor values only if the tensor is
# small enough. Otherwise, the detailed values sould be queried through a
# dedicated tensor_data that supports slicing.
if tensor_helper.numel(tensor_shape) < 5:
_, _, tensor_values = tensor_helper.array_view(tensor_value)
return {
'type': 'tensor',
'timestamp': wall_time,
'data': {
'device_name': device_name,
'node_name': node_name,
'maybe_base_expanded_node_name': maybe_base_expanded_node_name,
'output_slot': output_slot,
'debug_op': debug_op,
'dtype': tensor_dtype,
'shape': tensor_shape,
'values': tensor_values,
},
}
def _comm_tensor_data(device_name,
node_name,
maybe_base_expanded_node_name,
output_slot,
debug_op,
tensor_value,
wall_time):
"""Create a dict() as the outgoing data in the tensor data comm route.
Args:
device_name: Name of the device that the tensor is on.
node_name: (Original) name of the node that produces the tensor.
maybe_base_expanded_node_name: Possbily base-expanded node name.
output_slot: Output slot number.
debug_op: Name of the debug op.
tensor_value: Value of the tensor, as a numpy.ndarray.
wall_time: Wall timestamp for the tensor.
Returns:
A dict representing the tensor data.
"""
output_slot = int(output_slot)
tf.logging.info(
'Recording tensor value: %s, %d, %s', node_name, output_slot, debug_op)
tensor_values = None
if isinstance(tensor_value, debug_data.InconvertibleTensorProto):
if not tensor_value.initialized:
tensor_dtype = UNINITIALIZED_TAG
tensor_shape = UNINITIALIZED_TAG
else:
tensor_dtype = UNSUPPORTED_TAG
tensor_dtype = UNSUPPORTED_TAG
tensor_values = NA_TAG
else:
tensor_dtype = str(tensor_value.dtype)
tensor_shape = tensor_value.shape
# The /comm endpoint should respond with tensor values only if the tensor is
# small enough. Otherwise, the detailed values sould be queried through a
# dedicated tensor_data that supports slicing.
if tensor_helper.numel(tensor_shape) < 5:
_, _, tensor_values = tensor_helper.array_view(tensor_value)
return {
'type': 'tensor',
'timestamp': wall_time,
'data': {
'device_name': device_name,
'node_name': node_name,
'maybe_base_expanded_node_name': maybe_base_expanded_node_name,
'output_slot': output_slot,
'debug_op': debug_op,
'dtype': tensor_dtype,
'shape': tensor_shape,
'values': tensor_values,
},
}
def testArrayViewFloat2DWithSlicing(self):
x = np.ones([4, 4], dtype=np.float64)
y = np.zeros([4, 4], dtype=np.float64)
float_array = np.concatenate((x, y), axis=1)
dtype, shape, values = tensor_helper.array_view(float_array,
slicing="[2:, :]")
self.assertEqual("float64", dtype)
self.assertEqual((2, 8), shape)
self.assertAllClose(
[[1, 1, 1, 1, 0, 0, 0, 0], [1, 1, 1, 1, 0, 0, 0, 0]], values)
def testArrayViewInt3DWithSlicing(self):
x = np.ones([4, 4], dtype=np.int32)
int_array = np.zeros([3, 4, 4], dtype=np.int32)
int_array[0, ...] = x
int_array[1, ...] = 2 * x
int_array[2, ...] = 3 * x
dtype, shape, values = tensor_helper.array_view(
int_array, slicing="[:, :, 2]")
self.assertEqual("int32", dtype)
self.assertEqual((3, 4), shape)
self.assertEqual([[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]], values)
def testArrayViewFloat2DWithSlicing(self):
x = np.ones([4, 4], dtype=np.float64)
y = np.zeros([4, 4], dtype=np.float64)
float_array = np.concatenate((x, y), axis=1)
dtype, shape, values = tensor_helper.array_view(
float_array, slicing="[2:, :]")
self.assertEqual("float64", dtype)
self.assertEqual((2, 8), shape)
self.assertAllClose(
[[1, 1, 1, 1, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 0, 0]], values)
def testArrayViewInt3DWithSlicing(self):
x = np.ones([4, 4], dtype=np.int32)
int_array = np.zeros([3, 4, 4], dtype=np.int32)
int_array[0, ...] = x
int_array[1, ...] = 2 * x
int_array[2, ...] = 3 * x
dtype, shape, values = tensor_helper.array_view(int_array,
slicing="[:, :, 2]")
self.assertEqual("int32", dtype)
self.assertEqual((3, 4), shape)
self.assertEqual([[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]], values)
def testImagePngMappingWorksForArrayWithOnlyInfAndNaN(self):
x = np.array([[np.nan, -np.inf], [np.inf, np.nan]], dtype=np.float32)
dtype, shape, data = tensor_helper.array_view(x, mapping="image/png")
self.assertEqual("float32", dtype)
self.assertEqual((2, 2), shape)
decoded_x = im_util.decode_png(base64.b64decode(data))
self.assertEqual((2, 2, 3), decoded_x.shape)
self.assertEqual(np.uint8, decoded_x.dtype)
self.assertAllClose(tensor_helper.NAN_RGB, decoded_x[0, 0, :]) # nan.
self.assertAllClose(tensor_helper.NEGATIVE_INFINITY_RGB,
decoded_x[0, 1, :]) # -infinity.
self.assertAllClose(tensor_helper.POSITIVE_INFINITY_RGB,
decoded_x[1, 0, :]) # +infinity.
self.assertAllClose(tensor_helper.NAN_RGB, decoded_x[1, 1, :]) # nan.
def testImagePngMappingWorksForArrayWithOnlyInfAndNaN(self):
x = np.array([[np.nan, -np.inf], [np.inf, np.nan]], dtype=np.float32)
dtype, shape, data = tensor_helper.array_view(x, mapping="image/png")
self.assertEqual("float32", dtype)
self.assertEqual((2, 2), shape)
decoded_x = im_util.decode_png(base64.b64decode(data))
self.assertEqual((2, 2, 3), decoded_x.shape)
self.assertEqual(np.uint8, decoded_x.dtype)
self.assertAllClose(tensor_helper.NAN_RGB, decoded_x[0, 0, :]) # nan.
self.assertAllClose(tensor_helper.NEGATIVE_INFINITY_RGB,
decoded_x[0, 1, :]) # -infinity.
self.assertAllClose(tensor_helper.POSITIVE_INFINITY_RGB,
decoded_x[1, 0, :]) # +infinity.
self.assertAllClose(tensor_helper.NAN_RGB, decoded_x[1, 1, :]) # nan.
def testImagePngMappingRaisesExceptionForEmptyArray(self):
x = np.zeros([0, 0])
with six.assertRaisesRegex(
self, ValueError, r"Cannot encode an empty array .* \(0, 0\)"):
tensor_helper.array_view(x, mapping="image/png")
def testArrayViewFloat2DNoSlicing(self):
float_array = np.ones([3, 3], dtype=np.float32)
dtype, shape, values = tensor_helper.array_view(float_array)
self.assertEqual("float32", dtype)
self.assertEqual((3, 3), shape)
self.assertEqual(float_array.tolist(), values)
def testImagePngMappingRaisesExceptionForEmptyArray(self):
x = np.zeros([0, 0])
with six.assertRaisesRegex(
self, ValueError, r"Cannot encode an empty array .* \(0, 0\)"):
tensor_helper.array_view(x, mapping="image/png")
def _comm_tensor_data(device_name,
node_name,
maybe_base_expanded_node_name,
output_slot,
debug_op,
tensor_value,
wall_time):
"""Create a dict() as the outgoing data in the tensor data comm route.
Note: The tensor data in the comm route does not include the value of the
tensor in its entirety in general. Only if a tensor satisfies the following
conditions will its entire value be included in the return value of this
method:
1. Has a numeric data type (e.g., float32, int32) and has fewer than 5
elements.
2. Is a string tensor and has fewer than 5 elements. Each string element is
up to 40 bytes.
Args:
device_name: Name of the device that the tensor is on.
node_name: (Original) name of the node that produces the tensor.
maybe_base_expanded_node_name: Possbily base-expanded node name.
output_slot: Output slot number.
debug_op: Name of the debug op.
tensor_value: Value of the tensor, as a numpy.ndarray.
wall_time: Wall timestamp for the tensor.
Returns:
A dict representing the tensor data.
"""
output_slot = int(output_slot)
tf.logging.info(
'Recording tensor value: %s, %d, %s', node_name, output_slot, debug_op)
tensor_values = None
if isinstance(tensor_value, debug_data.InconvertibleTensorProto):
if not tensor_value.initialized:
tensor_dtype = UNINITIALIZED_TAG
tensor_shape = UNINITIALIZED_TAG
else:
tensor_dtype = UNSUPPORTED_TAG
tensor_shape = UNSUPPORTED_TAG
tensor_values = NA_TAG
else:
tensor_dtype = tensor_helper.translate_dtype(tensor_value.dtype)
tensor_shape = tensor_value.shape
# The /comm endpoint should respond with tensor values only if the tensor is
# small enough. Otherwise, the detailed values sould be queried through a
# dedicated tensor_data that supports slicing.
if tensor_helper.numel(tensor_shape) < 5:
_, _, tensor_values = tensor_helper.array_view(tensor_value)
if tensor_dtype == 'string' and tensor_value is not None:
tensor_values = tensor_helper.process_buffers_for_display(
tensor_values, limit=STRING_ELEMENT_MAX_LEN)
return {
'type': 'tensor',
'timestamp': wall_time,
'data': {
'device_name': device_name,
'node_name': node_name,
'maybe_base_expanded_node_name': maybe_base_expanded_node_name,
'output_slot': output_slot,
'debug_op': debug_op,
'dtype': tensor_dtype,
'shape': tensor_shape,
'values': tensor_values,
},
}
def testArrayViewSlicingDownNumericTensorToOneElement(self):
x = np.array([[1.1, 2.2, np.inf], [-np.inf, 3.3, np.nan]], dtype=np.float32)
dtype, shape, data = tensor_helper.array_view(x, slicing='[0,0]')
self.assertEqual('float32', dtype)
self.assertEqual(tuple(), shape)
self.assertTrue(np.allclose(1.1, data))
def _comm_tensor_data(device_name, node_name, maybe_base_expanded_node_name,
output_slot, debug_op, tensor_value, wall_time):
"""Create a dict() as the outgoing data in the tensor data comm route.
Note: The tensor data in the comm route does not include the value of the
tensor in its entirety in general. Only if a tensor satisfies the following
conditions will its entire value be included in the return value of this
method:
1. Has a numeric data type (e.g., float32, int32) and has fewer than 5
elements.
2. Is a string tensor and has fewer than 5 elements. Each string element is
up to 40 bytes.
Args:
device_name: Name of the device that the tensor is on.
node_name: (Original) name of the node that produces the tensor.
maybe_base_expanded_node_name: Possbily base-expanded node name.
output_slot: Output slot number.
debug_op: Name of the debug op.
tensor_value: Value of the tensor, as a numpy.ndarray.
wall_time: Wall timestamp for the tensor.
Returns:
A dict representing the tensor data.
"""
output_slot = int(output_slot)
tf.logging.info('Recording tensor value: %s, %d, %s', node_name,
output_slot, debug_op)
tensor_values = None
if isinstance(tensor_value, debug_data.InconvertibleTensorProto):
if not tensor_value.initialized:
tensor_dtype = UNINITIALIZED_TAG
tensor_shape = UNINITIALIZED_TAG
else:
tensor_dtype = UNSUPPORTED_TAG
tensor_shape = UNSUPPORTED_TAG
tensor_values = NA_TAG
else:
tensor_dtype = tensor_helper.translate_dtype(tensor_value.dtype)
tensor_shape = tensor_value.shape
# The /comm endpoint should respond with tensor values only if the tensor is
# small enough. Otherwise, the detailed values sould be queried through a
# dedicated tensor_data that supports slicing.
if tensor_helper.numel(tensor_shape) < 5:
_, _, tensor_values = tensor_helper.array_view(tensor_value)
if tensor_dtype == 'string' and tensor_value is not None:
tensor_values = tensor_helper.process_buffers_for_display(
tensor_values, limit=STRING_ELEMENT_MAX_LEN)
return {
'type': 'tensor',
'timestamp': wall_time,
'data': {
'device_name': device_name,
'node_name': node_name,
'maybe_base_expanded_node_name': maybe_base_expanded_node_name,
'output_slot': output_slot,
'debug_op': debug_op,
'dtype': tensor_dtype,
'shape': tensor_shape,
'values': tensor_values,
},
}
def testArrayViewFloat2DNoSlicing(self):
float_array = np.ones([3, 3], dtype=np.float32)
dtype, shape, values = tensor_helper.array_view(float_array)
self.assertEqual("float32", dtype)
self.assertEqual((3, 3), shape)
self.assertEqual(float_array.tolist(), values)
