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Hello. I convert weights from a pytorch model to tf2 model. To verify the result, I export the feature maps after the conv2d for both pytorch original model and tf2 model. However, the feature map from tf2 model has black bars on the top and left hand side of the map, whereas the pytorch feature map does not have these black bars. The pytorch code of conv2d is nn.Conv2d(1, 56, kernel_size=5, padding=5//2) The tensorflow build is tensorflow.keras.layers.Conv2D(56, 5, padding="same")(inp) According to https://stackoverflow.com/questions/68165375/manualy-convert-pytorch-weights-to-tf-keras-weights-for-convolutional-layer# advise, I even add the zeropadding layer to the tensorflow model x = tensorflow.keras.layers.ZeroPadding2D(padding=((2,2)))(inp) x2 = tensorflow.keras.layers.Conv2D(56, 5, padding="valid")(x) However, the black bar is still there. I have no clue for this problem > <. For the converting process for pytorch weights to tensorflow weights is as follow: onnx_1_w_num = onnx_l.weight.data.permute(2,3,1,0).numpy() onnx_1_b_num = onnx_l.bias.data.numpy() tf_l.set_weights([onnx_1_w_num,onnx_1_b_num]) submitted by /u/mrgreen3325 |
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