def verify_net_in_out(
net_id: Optional[str] = None,
meta_file: Optional[Union[str, Sequence[str]]] = None,
config_file: Optional[Union[str, Sequence[str]]] = None,
device: Optional[str] = None,
p: Optional[int] = None,
n: Optional[int] = None,
any: Optional[int] = None,
args_file: Optional[str] = None,
**override,
):
"""
Verify the input and output data shape and data type of network defined in the metadata.
Will test with fake Tensor data according to the required data shape in `metadata`.
Typical usage examples:
.. code-block:: bash
python -m monai.bundle verify_net_in_out network --meta_file <meta path> --config_file <config path>
Args:
net_id: ID name of the network component to verify, it must be `torch.nn.Module`.
meta_file: filepath of the metadata file to get network args, if `None`, must be provided in `args_file`.
if it is a list of file paths, the content of them will be merged.
config_file: filepath of the config file to get network definition, if `None`, must be provided in `args_file`.
if it is a list of file paths, the content of them will be merged.
device: target device to run the network forward computation, if None, prefer to "cuda" if existing.
p: power factor to generate fake data shape if dim of expected shape is "x**p", default to 1.
n: multiply factor to generate fake data shape if dim of expected shape is "x*n", default to 1.
any: specified size to generate fake data shape if dim of expected shape is "*", default to 1.
args_file: a JSON or YAML file to provide default values for `net_id`, `meta_file`, `config_file`,
`device`, `p`, `n`, `any`, and override pairs. so that the command line inputs can be simplified.
override: id-value pairs to override or add the corresponding config content.
e.g. ``--_meta#network_data_format#inputs#image#num_channels 3``.
"""
_args = _update_args(
args=args_file,
net_id=net_id,
meta_file=meta_file,
config_file=config_file,
device=device,
p=p,
n=n,
any=any,
**override,
)
_log_input_summary(tag="verify_net_in_out", args=_args)
config_file_, meta_file_, net_id_, device_, p_, n_, any_ = _pop_args(
_args,
"config_file",
"meta_file",
net_id="",
device="cuda:0" if is_available() else "cpu",
p=1,
n=1,
any=1)
parser = ConfigParser()
parser.read_config(f=config_file_)
parser.read_meta(f=meta_file_)
# the rest key-values in the _args are to override config content
for k, v in _args.items():
parser[k] = v
try:
key: str = net_id_ # mark the full id when KeyError
net = parser.get_parsed_content(key).to(device_)
key = "_meta_#network_data_format#inputs#image#num_channels"
input_channels = parser[key]
key = "_meta_#network_data_format#inputs#image#spatial_shape"
input_spatial_shape = tuple(parser[key])
key = "_meta_#network_data_format#inputs#image#dtype"
input_dtype = get_equivalent_dtype(parser[key], torch.Tensor)
key = "_meta_#network_data_format#outputs#pred#num_channels"
output_channels = parser[key]
key = "_meta_#network_data_format#outputs#pred#dtype"
output_dtype = get_equivalent_dtype(parser[key], torch.Tensor)
except KeyError as e:
raise KeyError(
f"Failed to verify due to missing expected key in the config: {key}."
) from e
net.eval()
with torch.no_grad():
spatial_shape = _get_fake_spatial_shape(input_spatial_shape,
p=p_,
n=n_,
any=any_) # type: ignore
test_data = torch.rand(*(1, input_channels, *spatial_shape),
dtype=input_dtype,
device=device_)
output = net(test_data)
if output.shape[1] != output_channels:
raise ValueError(
f"output channel number `{output.shape[1]}` doesn't match: `{output_channels}`."
)
if output.dtype != output_dtype:
raise ValueError(
f"dtype of output data `{output.dtype}` doesn't match: {output_dtype}."
)
logger.info("data shape of network is verified with no error.")
def test_value(self, argments, image, expected_data):
result = HistogramNormalized(**argments)(image)["img"]
assert_allclose(result, expected_data)
self.assertEqual(
get_equivalent_dtype(result.dtype, data_type=np.ndarray),
argments.get("dtype", np.float32))
