walsvid / coordconv Goto Github PK

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?

did you try coordinate regression task in the original paper?

For example, batch_size_shape not used, leading to batch_size mismatch.

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?

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 :

code to be:

conv_layer = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=(9, 9), padding=4, stride=1, bias=False)