def _weight_to_image(
weight: Tensor,
kernel_channels_last: bool = False) -> Optional[Tensor]:
"""Logs a weight as a TensorBoard image.
Implementation from TensorFlow codebase, would have invoked theirs directly but they didn't make it a static
method.
"""
w_img = squeeze(weight)
shape = backend.int_shape(w_img)
if len(shape) == 1: # Bias case
w_img = reshape(w_img, [1, shape[0], 1, 1])
elif len(shape) == 2: # Dense layer kernel case
if shape[0] > shape[1]:
w_img = permute(w_img, [0, 1])
shape = backend.int_shape(w_img)
w_img = reshape(w_img, [1, shape[0], shape[1], 1])
elif len(shape) == 3: # ConvNet case
if kernel_channels_last:
# Switch to channels_first to display every kernel as a separate images
w_img = permute(w_img, [2, 0, 1])
w_img = expand_dims(w_img, axis=-1)
elif len(shape) == 4: # Conv filter with multiple input channels
if kernel_channels_last:
# Switch to channels first to display kernels as separate images
w_img = permute(w_img, [3, 2, 0, 1])
w_img = reduce_sum(
abs(w_img),
axis=1) # Sum over the each channel within the kernel
w_img = expand_dims(w_img, axis=-1)
shape = backend.int_shape(w_img)
# Not possible to handle 3D convnets etc.
if len(shape) == 4 and shape[-1] in [1, 3, 4]:
return w_img
def _convert_for_visualization(tensor: Tensor,
tile: int = 99) -> np.ndarray:
"""Modify the range of data in a given input `tensor` to be appropriate for visualization.
Args:
tensor: Input masks, whose channel values are to be reduced by absolute value summation.
tile: The percentile [0-100] used to set the max value of the image.
Returns:
A (batch X width X height) image after visualization clipping is applied.
"""
if isinstance(tensor, torch.Tensor):
channel_axis = 1
else:
channel_axis = -1
flattened_mask = reduce_sum(abs(tensor),
axis=channel_axis,
keepdims=True)
non_batch_axes = list(range(len(flattened_mask.shape)))[1:]
vmax = percentile(flattened_mask,
tile,
axis=non_batch_axes,
keepdims=True)
vmin = reduce_min(flattened_mask, axis=non_batch_axes, keepdims=True)
return clip_by_value((flattened_mask - vmin) / (vmax - vmin), 0, 1)