def forward_numpyop(ops: List[NumpyOp],
data: MutableMapping[str, Any],
state: Dict[str, Any],
batched: bool = False) -> None:
"""Call the forward function for list of NumpyOps, and modify the data dictionary in place.
Args:
ops: A list of NumpyOps to execute.
data: The data dictionary.
state: Information about the current execution context, ex. {"mode": "train"}. Must contain at least the mode.
batched: Whether the `data` is batched or not.
"""
for op in ops:
op_data = get_inputs_by_op(op, data, copy_on_write=op.in_place_edits)
try:
op_data = op.forward_batch(
op_data, state) if batched else op.forward(op_data, state)
except ValueError as err:
if err.args[0] == 'assignment destination is read-only':
# If the numpy error text changes we'll need to make adjustments in the future
op.in_place_edits = True
op_data = get_inputs_by_op(op,
data,
copy_on_write=op.in_place_edits)
op_data = op.forward_batch(
op_data, state) if batched else op.forward(op_data, state)
else:
raise err
if isinstance(op, Delete):
for key in op.inputs:
del data[key]
if op.outputs:
write_outputs_by_op(op, data, op_data)
def _forward_batch(batch: MutableMapping[str, Any], state: Dict[str, Any], ops: List[TensorOp]) -> None:
"""Run a forward pass through the network's Op chain given a `batch` of data.
Args:
batch: A batch of input data. Predictions from the network will be written back into this dictionary.
state: A dictionary holding information about the current execution context. The TF gradient tape, for
example will be stored here.
ops: Which ops to execute.
"""
for op in ops:
data = get_inputs_by_op(op, batch)
data = op.forward(data, state)
if op.outputs:
write_outputs_by_op(op, batch, data)
def forward_numpyop(ops: List[NumpyOp], data: MutableMapping[str, Any], mode: str) -> None:
"""Call the forward function for list of NumpyOps, and modify the data dictionary in place.
Args:
ops: A list of NumpyOps to execute.
data: The data dictionary.
mode: The current execution mode ("train", "eval", "test", or "infer").
"""
for op in ops:
op_data = get_inputs_by_op(op, data)
op_data = op.forward(op_data, {"mode": mode})
if isinstance(op, Delete):
for key in op.inputs:
del data[key]
if op.outputs:
write_outputs_by_op(op, data, op_data)
def forward_numpyop(ops: List[NumpyOp],
data: MutableMapping[str, Any],
state: Dict[str, Any],
batched: Optional[str] = None) -> Optional[FilteredData]:
"""Call the forward function for list of NumpyOps, and modify the data dictionary in place.
Args:
ops: A list of NumpyOps to execute.
data: The data dictionary.
state: Information about the current execution context, ex. {"mode": "train"}. Must contain at least the mode.
batched: Whether the `data` is batched or not. If it is batched, provide the string ('tf', 'torch', or 'np')
indicating which type of tensors the batch contains.
"""
if batched:
# Cast data to Numpy before performing batch forward
for key, val in data.items():
data[key] = to_tensor(val, target_type='np')
for op in ops:
op_data = get_inputs_by_op(op, data, copy_on_write=op.in_place_edits)
try:
op_data = op.forward_batch(
op_data, state) if batched else op.forward(op_data, state)
except ValueError as err:
if err.args[0] == 'assignment destination is read-only':
# If the numpy error text changes we'll need to make adjustments in the future
op.in_place_edits = True
op_data = get_inputs_by_op(op,
data,
copy_on_write=op.in_place_edits)
op_data = op.forward_batch(
op_data, state) if batched else op.forward(op_data, state)
else:
raise err
if isinstance(op_data, FilteredData):
return op_data
if isinstance(op, Delete):
for key in op.inputs:
del data[key]
if op.outputs:
write_outputs_by_op(op, data, op_data)
if batched:
# Cast data back to original tensor type after performing batch forward
for key, val in data.items():
data[key] = to_tensor(val, target_type=batched, shared_memory=True)
return None
def test_multi_key_multi_data(self):
batch = {}
write_outputs_by_op(op=Op(outputs=["x", "y"]),
store=batch,
outputs=[1, [1, 2]])
self.assertEqual(batch, {"x": 1, "y": [1, 2]})
def test_single_key_multi_data(self):
batch = {}
write_outputs_by_op(op=Op(outputs="x"), store=batch, outputs=[1, 2])
self.assertEqual(batch, {"x": [1, 2]})
def test_single_key_single_data(self):
batch = {}
write_outputs_by_op(op=Op(outputs="x"), store=batch, outputs=1)
self.assertEqual(batch, {"x": 1})