def try_permute(arr, shape):
try:
perm = FormatManager.permutation(FormatManager.determine_format(arr.shape), FormatManager.determine_format(shape))
G_LOGGER.verbose("Permuting shape: {:} using permutation {:}".format(arr.shape, perm))
arr = np.transpose(arr, perm)
except Exception as err:
# FormatManager may not recognize the format or be able generate the permutation for the format combination
G_LOGGER.extra_verbose("Skipping permutation due to {:}".format(err))
return arr
def __call__(self):
uff_model, input_names, input_shapes, output_names = self.uff_loader()
builder = trt.Builder(TRT_LOGGER)
network = builder.create_network()
parser = trt.UffParser()
# Input names should come from the converter, as a preprocessing script may have been applied to the frozen model.
for name, shape in zip(input_names, input_shapes):
# Default order is NCHW, only set to NHWC if we're reasonably certain that it is.
input_order = self.uff_order
if not self.uff_order:
input_order = trt.UffInputOrder.NCHW
if FormatManager.determine_format(shape) == DataFormat.NHWC:
input_order = trt.UffInputOrder.NHWC
shape = shape[1:]
G_LOGGER.verbose(
"Registering UFF input: {:} with shape: {:} and input order: {:}"
.format(name, shape, input_order))
parser.register_input(name, shape, input_order)
if output_names and output_names != constants.MARK_ALL:
for name in output_names:
G_LOGGER.verbose("Registering UFF output: " + str(name))
parser.register_output(name)
G_LOGGER.info(
"Parsing UFF model with inputs: {:} and outputs: {:}".format(
input_names, output_names))
success = parser.parse_buffer(uff_model, network)
if not success:
G_LOGGER.critical("Could not parse UFF correctly")
return builder, network, parser, input_shapes[0][0]
def test_format_deduction(test_case):
assert test_case.format == FormatManager.determine_format(test_case.shape)