Larger input size about brain-tumor-segmentation HOT 4 CLOSED

self.default_transformation = transforms.Compose([
            transforms.Grayscale(),
            transforms.Resize((512, 512))
        ])

self.default_transformation = transforms.Compose([
            transforms.Grayscale(),
            transforms.Resize((1024, 1024))
        ])

Inside model.py DynamicUNet class.

def summary(self, input_size=(1, 512, 512), batch_size=-1, device='cuda'):
        """ Get the summary of the network in a chart like form
        with name of layer size of the inputs and parameters 
        and some extra memory details.
        This method uses the torchsummary package.
        For more information check the link.
        Link :- https://github.com/sksq96/pytorch-summary
        Parameters:
            input_size(tuple): Size of the input for the network in
                                 format (Channel, Width, Height).
                                 Default: (1,512,512)
            batch_size(int): Batch size for the network.
                                Default: -1
            device(str): Device on which the network is loaded.
                            Device can be 'cuda' or 'cpu'.
                            Default: 'cuda'
        Returns:
            A printed output for IPython Notebooks.
            Table with 3 columns for Layer Name, Input Size and Parameters.
            torchsummary.summary() method is used.
        """