Breaking Changes
- Renamed the `-qcind` option to the `-cind` option and changed it to be available as test data for both the `-cotof` and `-oiqt` options.
-cind INPUT_NAME NUMPY_FILE_PATH MEAN STD, \
--custom_input_op_name_np_data_path INPUT_NAME NUMPY_FILE_PATH MEAN STD
Input name of OP and path of data file (Numpy) for custom input for -cotof or -oiqt,
and mean (optional) and std (optional).
<Usage in -cotof>
When using -cotof, custom input defined by the user, instead of dummy data, is used.
In this case, mean and std are omitted from the input.
-cind {input_op_name} {numpy_file_path}
e.g. -cind onnx::Equal_0 test_cind/x_1.npy -cind onnx::Add_1 test_cind/x_2.npy -cotof
The input_op_name must be the same as in ONNX,
and it may not work if the input format is different between ONNX and TF.
<Usage in -oiqt>
INPUT Name of OP and path of calibration data file (Numpy) for quantization
and mean and std.
The specification can be omitted only when the input OP is a single 4D tensor image data.
If omitted, it is automatically calibrated using 20 normalized MS-COCO images.
The type of the input OP must be Float32.
Data for calibration must be pre-normalized to a range of 0 to 1.
-cind {input_op_name} {numpy_file_path} {mean} {std}
Numpy file paths must be specified the same number of times as the number of input OPs.
Normalize the value of the input OP based on the tensor specified in mean and std.
(input_value - mean) / std
Tensors in Numpy file format must be in dimension order after conversion to TF.
Note that this is intended for deployment on low-resource devices,
so the batch size is limited to 1 only.
e.g.
The example below shows a case where there are three input OPs.
Assume input0 is 128x128 RGB image data.
In addition, input0 should be a value that has been divided by 255
in the preprocessing and normalized to a range between 0 and 1.
input1 and input2 assume the input of something that is not an image.
Because input1 and input2 assume something that is not an image,
the divisor is not 255 when normalizing from 0 to 1.
"n" is the number of calibration data.
ONNX INPUT shapes:
input0: [n,3,128,128]
mean: [1,3,1,1] -> [[[[0.485]],[[0.456]],[[0.406]]]]
std: [1,3,1,1] -> [[[[0.229]],[[0.224]],[[0.225]]]]
input1: [n,64,64]
mean: [1,64] -> [0.1, ..., 0.64]
std: [1,64] -> [0.05, ..., 0.08]
input2: [n,5]
mean: [1] -> [0.3]
std: [1] -> [0.07]
TensorFlow INPUT shapes (Numpy file ndarray shapes):
input0: [n,128,128,3]
mean: [1,1,1,3] -> [[[[0.485, 0.456, 0.406]]]]
std: [1,1,1,3] -> [[[[0.229, 0.224, 0.225]]]]
input1: [n,64,64]
mean: [1,64] -> [0.1, ..., 0.64]
std: [1,64] -> [0.05, ..., 0.08]
input2: [n,5]
mean: [1] -> [0.3]
std: [1] -> [0.07]
-cind "input0" "../input0.npy" [[[[0.485, 0.456, 0.406]]]] [[[[0.229, 0.224, 0.225]]]]
-cind "input1" "./input1.npy" [0.1, ..., 0.64] [0.05, ..., 0.08]
-cind "input2" "input2.npy" [0.3] [0.07]
<Using -cotof and -oiqt at the same time>
To use -cotof and -oiqt simultaneously,
you need to enter the Input name of OP, path of data file, mean, and std all together.
And the data file must be in Float32 format,
and {input_op_name}, {numpy_file_path}, {mean}, and {std} must all be entered.
Otherwise, an error will occur during the -oiqt stage.
What's Changed
* new parameter `-cind`, `—custom_input_op_name_np_data_path` by On-JungWoan in https://github.com/PINTO0309/onnx2tf/pull/296
* README Adjustments by PINTO0309 in https://github.com/PINTO0309/onnx2tf/pull/305
New Contributors
* On-JungWoan made their first contribution in https://github.com/PINTO0309/onnx2tf/pull/296
**Full Changelog**: https://github.com/PINTO0309/onnx2tf/compare/1.8.25...1.9.0