1 """
2 Generic inference function for ONNX, BIE, or NEF model.
3 """
4 import pathlib
5 import sys
6
7 ROOT_FOLDER = pathlib.Path(__file__).resolve().parent.parent
8 sys.path.append(str(ROOT_FOLDER))
9
10 import python_flow.common.exceptions as exceptions
11 import python_flow.nef.nef as nef
12 import python_flow.dynasty.dynasty as dynasty
13
14 def kneron_inference(pre_results, nef_file="", onnx_file="", bie_file="", model_id=None,
15 input_names=[], radix=8, data_type="float", reordering=[],
16 ioinfo_file="", dump=False, platform=520):
17 """Performs inference on the input model given the specified parameters.
18
19 Input pre_results should be in format (1, h, w, c).
20
21 Arguments:
22 pre_results: List of NumPy arrays in channel last format from preprocessing
23 nef_file: Path to NEF model for inference
24 onnx_file: Path to ONNX model for inference, unused if nef_file is specified
25 bie_file: Path to BIE model for inference, unused if nef_file/onnx_file is specified
26 model_id: Integer of model to run inference, only necessary for NEF with multiple models
27 input_names: List of input node names of BIE/ONNX model, unused if nef_file is specified
28 radix: Integer radix to convert from float to fixed input, DEPRECATED
29 data_type: String format of the resulting output, "fixed" or "float"
30 dump: Boolean flag to dump intermediate nodes
31 reordering: List of node names/integers specifying the output order
32 ioinfo_file: String path to file mapping output node number to name, only used with NEF
33 platform: Integer indicating platform of Dynasty fixed model
34 """
35 dump = 2 if dump else 0
36 try:
37 if nef_file:
38 nef_path = pathlib.Path(nef_file).resolve()
39 platform, model_name, input_folder, output_folder = nef.setup_nef(nef_path, model_id)
40 input_files = nef.prep_csim_inputs(
41 pre_results, input_folder, str(platform), nef_path.parent, model_name)
42 output = nef.nef_inference(
43 model_name, str(platform), data_type, nef_path.parent, input_files, input_folder,
44 output_folder, reordering, ioinfo_file, str(dump))
45 elif onnx_file:
46 onnx = pathlib.Path(onnx_file).resolve()
47 input_folder = onnx.parent / "out/onnx/inputs" / onnx.name
48 output_folder = onnx.parent / "out/onnx/outputs" / onnx.name
49
50 input_files = dynasty.prep_dynasty(
51 pre_results, input_folder, output_folder, input_names, platform, False, str(onnx))
52 output = dynasty.dynasty_inference(
53 str(onnx), "Float", str(platform), data_type, input_files, input_names,
54 str(output_folder), reordering, dump)
55 elif bie_file:
56 bie = pathlib.Path(bie_file).resolve()
57 input_folder = bie.parent / "out/bie/inputs" / bie.name
58 output_folder = bie.parent / "out/bie/outputs" / bie.name
59
60 input_files = dynasty.prep_dynasty(
61 pre_results, input_folder, output_folder, input_names, platform, True, str(bie))
62 output = dynasty.dynasty_inference(
63 str(bie), "bie", str(platform), data_type, input_files, input_names,
64 str(output_folder), reordering, dump)
65 else:
66 raise exceptions.RequiredConfigError("No input model selected for inference.")
67 except exceptions.ConfigError as error:
68 sys.exit(error)
69
70 return output
