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Handle multiple dimension case for BatchNormalization #106
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vinitra-zz
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Jun 6, 2019
keras2onnx/ke2onnx/batch_norm.py
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| @@ -62,4 +69,4 @@ def convert_keras_batch_normalization(scope, operator, container): | |||
| momentum=momentum, spatial=spatial) | |||
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| # Permute [N,C,H,W] to [N,H,W,C] | |||
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Clarify that this comment is for the 4D input shape case
vinitra-zz
approved these changes
Jun 6, 2019
vinitra-zz
reviewed
Jun 6, 2019
| perm_1 = list(range(1, len(op.input_shape) - 1)) | ||
| perm_1 = [0, len(op.input_shape) - 1] + perm_1 | ||
| perm_2 = list(range(2, len(op.input_shape))) | ||
| perm_2 = [0] + perm_2 + [1] |
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Also, might not ever need to calculate perm_1 and perm_2 if skip_transpose is False. Perhaps move these into the respective sections for converter efficiency?
wenbingl
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Oct 22, 2019
lock tf to 1.9 ... travis build having issues with 1.10
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