Comments (1)
AlexanderLutsenko
commented on May 23, 2024
Hey, thanks for bringing this up! I fixed what I could in v0.12.2, but there are still problems with TFJS. It only works when groups == 1 or groups == in_channels. As a last resort, you can always express grouped convolution as normal one, missing out on efficiency. In fact, that's how ConvTranspose1d/ConvTranspose2d are converted, Tensorflow just completely botched it: tensorflow/tensorflow#45216.
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Related Issues (20)
- Tensorflow warns that variables were used in Lambda layers but are not present in tracked objects HOT 2
- I am getting 'Unimplemented nodes' exception HOT 5
- Imprecise conversion for custom log_softmax converter HOT 4
- Keras symbolic inputs/outputs do not implement `__len__` HOT 8
- Custom Softplus Layer and Einsum Error when load_model with Keras HOT 7
- Debugging tensor shapes when using dynamic axes HOT 8
- Convolution layers: Add support for `same` padding. HOT 2
- Parameter count mismatch when converting `nn.TransformerEncoderLayer` HOT 6
- ModuleNotFoundError: No module named 'keras.src.engine' HOT 3
- raise Exception(f'Failed conversion: {self.original_node}') HOT 2
- Troubleshooting Evaluation and Compilation Issues with Keras_yolov5s.h5 Conversion
- Failed to conversion a convolution when padding argument is 'valid' or default HOT 2
- When groups equal input channels then PyTorch Conv2D is mapped to Keras DepthwiseConv2D HOT 5
- Convert pretrained .torch model (including weights) HOT 2
- ValueError from adding tf.Tensor and None when exporting model with dynamic shapes
- Conversion fails randomly on models with multiple inputs HOT 7
- Identity layer is considered as 'Unimplemented nodes' HOT 1
- Is the MultiheadAttention converter outdated? HOT 2
- Preserve node naming/namespacing
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