Build_Mask function - missing image size in Keypoints dataset

[kkoripl]

Hi,

Another one with shapes, as I was almost killing myself few times, why losses are throwing incompatible shapes error:

InvalidArgumentError:  Incompatible shapes: [2,320,320,30] vs. [2,512,512,30]
	 [[node gradients_1/loss_2/softmax_loss/dice_loss_plus_1focal_loss/mul_grad/BroadcastGradientArgs (defined at /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3009) ]] [Op:__inference_keras_scratch_graph_156811]

when all my pictures are of 512x512 size. Losses are getting 2 arguments of ground-truth and predictions, which are tensors of shapes: batch_size, image_height, image_width, classes, so the change need to be made beforehand and I think it's on the preprocessing staff.

In Keypoints dataset general class Dataset has function getItem and inside of it:

        mask = _build_mask(keypoints)

        # extract certain classes from mask (e.g. cars)
        masks = [(mask == v) for v in self.class_values]
        mask = np.stack(masks, axis=-1).astype("float")

        # add background if mask is not binary
        if mask.shape[-1] != 1:
            background = 1 - mask.sum(axis=-1, keepdims=True)
            mask = np.concatenate((mask, background), axis=-1)

        # apply augmentations
        if self.augmentation:
            sample = self.augmentation(image=image, mask=mask)
            image, mask = sample["image"], sample["mask"]

Everything looks fine, but, _build_mask_utils has some default values:

def _build_mask(keypoints, mask_shape=(320, 320), nb_of_mask=29):

I don't know what exactly module of augumentation does, but it looks like mask is changing shape of a validation image, so when to compare against old one, incompatible shapes error is thrown.

My idea is to invoke _build_mask like so:

mask = _build_mask(keypoints, mask_shape = image.shape)

[DonsetPG]

Hey,

Again yes good catch. I am very sorry I taught I removed such hardcoded shape in every training scripts...
Anyway, this should be corrected with the last commit.

Feel free to open a pull request next time haha!
Cheers