Comments (4)
I will not go along with the onnxruntime issue.
sit4onnx -if _pi.original.onnx -oep cpu -fs 384 128
INFO: file: _pi.original.onnx
INFO: providers: ['CPUExecutionProvider']
INFO: input_name.1: input shape: [384, 128] dtype: float32
INFO: test_loop_count: 10
INFO: total elapsed time: 9.15980339050293 ms
INFO: avg elapsed time per pred: 0.915980339050293 ms
INFO: output_name.1: output shape: [384, 76] dtype: float32
onnx2tf -i _pi.original.onnx -cotof -ois input:384,128
from onnx2tf.
Ah, apologies, I think I understand: n*m
implies any n
and any m
, but actually it is not true (the model only supports specific values of n
and m
), and onnx2tf
cannot (simply) know this, hence the error. So manually specifying a valid input to onnx2tf
is required. Thank you!
from onnx2tf.
That is correct.
from onnx2tf.
(Thank you so much for your open source work - you have created incredibly valuable tools.)
from onnx2tf.
Related Issues (20)
- Discrepancies between Conv layer outputs of the ONNX model and converted TensorFlow models HOT 3
- Resize operation fails (['unk__0', 'unk__1', 'unk__2', 'unk__3']) and raises UnboundLocalError: local variable 'new_size' referenced before assignment HOT 20
- FULL INTEGER QUANTIZED MODEL (INT8) infers always the same value HOT 2
- Error performing quantization-aware training (QAT) with keras onnx2tf generated model HOT 4
- how to fuse activation into conv: fused_activation_function=NONE HOT 7
- Resize conversion bug HOT 2
- Op Error about "Transpose" HOT 7
- [docTR] Can it support from ONNX NHWC to TF NHWC? HOT 1
- Constant outputs removed from ONNX during conversion HOT 4
- [ConvNext-Det] Quantized ConvNext HOT 7
- [ONNX to TFLite] Cannot reshape from slice to unsqueeze. HOT 6
- Wrong shape on a specific node into TFLITE 32 model generated HOT 12
- Android GPU inference falls with TRANSPOSE_CONV: Max version supported: 3. Requested version 4. HOT 2
- Conversion error for Resize layer when using flag "-nuo" HOT 2
- Incorrect arguments in tf.nn.convolution HOT 5
- FusedConv error HOT 3
- RTMDet int8 quantization HOT 10
- [Phi3] List index out of range error on converting microsoft Phi3 onnx models to TfLite HOT 2
- matmul uint8 converts to flexbatchmatmul HOT 4
- Custom LSTM PyTorch model Full integer quantization problem HOT 3
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from onnx2tf.