walsvid / coordconv Goto Github PK
View Code? Open in Web Editor NEWPytorch implementation of "An intriguing failing of convolutional neural networks and the CoordConv solution" - https://arxiv.org/abs/1807.03247
License: MIT License
Pytorch implementation of "An intriguing failing of convolutional neural networks and the CoordConv solution" - https://arxiv.org/abs/1807.03247
License: MIT License
Although it does not affect the final results,
please consider this minor change in your code to reflect the original paper contents
It will be helpful for debugging
paper :
## bias is basically neglected in tensorflow
onehots = np.pad(np.eye(3136).reshape((3136, 56, 56, 1)), ((0,0), (4,4), (4,4), (0,0)), "constant");
images = tf.nn.conv2d(onehots, np.ones((9, 9, 1, 1)), [1]*4, "SAME")
code as is :
CoordConv/experiments/generate_data.py
Line 25 in 647bcfe
code to be:
conv_layer = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=(9, 9), padding=4, stride=1, bias=False)
did you try coordinate regression task in the original paper?
Hi, thanks for sharing the code. The jupyter notebook is very helpful indeed, but it doesn't seem to work on the quadrant data whereas the same code works fine for the uniform data. Anybody else facing this problem?
Currently, I have been using this implementation for one of my object detection models.
As the model is training, batch processing time increases linearly. I still haven't figured out why.
Does anyone see a similar behavior?
Line 71 in 3f86264
For example, batch_size_shape
not used, leading to batch_size mismatch.
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