* ONNX supports similar op ReduceLogSumExp. Conversions are added when
exporting.
* Refactored CNTKToONNXHelper::BroadcastInputsIfNeeded to support more
generalized cases.
Previously, to resolve issue of gather producing incorrect gradient
values, validity mask check was added to ensure we don't count non-valid
cells as 0.
However, this check is needed only for input that has dynamic axis, i.e.
inputs that have MBLayout.
- Previous implementation has the assumption that (0 <= dk < width).
This assumption doesn't stand when lo >(kernel - 1) / 2.
The updated calculation supports arbitrary lo & hi non-negative
integer value. The new calculation has dk in range (0, width + hi +
lo].
- Enables onnx backend test {averagepool_2d_pads, maxpool_2d_pads} to
pass.
- Refactor function CalcPaddingForSameLowerOrUpperAutoPad in conv/pool import,
changing parameter "const Variable& input" to "const NDShape& inputWithBatchAxisShape",
to specify the required shape format as [N x C x H x W].
pad values are explicitly computed based on ONNX spec equations during import in the following cases:
- case 1: when auto_pad is SAME_UPPER | SAME_LOWER for convolution, convolution transpose and pooling.
- case 2: when output_shape is explicitly set for convolution transpose.
note: output_shape in ONNX spec can have the two below format:
1. [X1 * X2 * ... * Xn]
2. [N * O * X1 * X2 * ... * Xn]
- Update exporting of conv/pooling to always export pad values.
- Enable correct exporting of multiple pretrained models (ResNet50/ResNet101/ResNet152_ImageNet_Caffe, etc).
- Overhaul convtranspose pads exporting
- Support conv weight export with omitted out channel axis (LRN).
- Add tests in onnx_op_test to cover the above changes
- Windows OOBE (pip) tests & Linus OOBE tests: skip onnx_model_test. This test requires
onnx to be installed. Skip Until we decide to add onnx dependencies to
OOBE test environment.
TJ
Bowen Bao
Spandan Tiwari
Peyman Manikashani
Ke Deng
liqfu
Sergii Dymchenko