2.0.4

v2.0.4

Background

• Running onnx2tf.py -i densenet-12.onnx -cotof failed at Mul (sng_Mul_1) with a broadcast shape mismatch.
• Actual TF shapes at failure:
  • x: (1, 112, 112, 64)
  • y: (1, 64, 1, 1) (channel-first style)

Root Cause

• In shape_unmatched_special_avoidance_workaround, some transpose paths converted constant np.ndarray inputs into tf.Tensor.
• After this conversion, downstream explicit_broadcast behavior changed and the constant was no longer normalized to NHWC-compatible shape.
• As a result, the constant remained (1, 64, 1, 1) instead of (1, 1, 1, 64), causing tf.math.multiply to fail.

Implemented Fix

• File: onnx2tf/utils/common_functions.py
• Function: shape_unmatched_special_avoidance_workaround
• Added helper:
  • _transpose_preserve_array(tensor, perm)
  • Uses np.transpose when tensor is np.ndarray
  • Uses existing transpose_with_flexing_deterrence for tensor-like inputs
• Replaced internal transpose call sites in this function with _transpose_preserve_array so constants keep np.ndarray type through alignment.

Effect

• Constant layout alignment now preserves array semantics and allows explicit_broadcast to correctly reshape constants to channel-last form when needed.
• For the failing case, y is now normalized to (1, 1, 1, 64), and Mul executes successfully.

Validation

• Reproduced and re-ran:
  • python onnx2tf/onnx2tf.py -i densenet-12.onnx -cotof
• Result:
  • Conversion completed successfully (exit code 0)
  • Validation logs reported matching outputs for the model conversion path.

What's Changed

• fix-shape_unmatched_special_avoidance_workaround by @PINTO0309 in #836

Full Changelog: 2.0.3...2.0.4