Source code for objax.functional.core.ops

# Copyright 2020 Google LLC
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

__all__ = ['dynamic_slice', 'flatten', 'interpolate', 'one_hot', 'pad', 'rsqrt', 'scan', 'stop_gradient',
           'top_k', 'upsample_2d', 'upscale_nn']

from typing import Union, Tuple

import jax.nn
from jax import numpy as jn, lax

from objax import util
from objax.constants import Interpolate
from objax.typing import JaxArray

dynamic_slice = lax.dynamic_slice
one_hot = jax.nn.one_hot
pad = jn.pad
scan = lax.scan
stop_gradient = lax.stop_gradient
top_k = lax.top_k  # Current code doesn't work with gradient.
rsqrt = lax.rsqrt

[docs]def flatten(x: JaxArray) -> JaxArray: """Flattens input tensor to a 2D tensor. Args: x: input tensor with dimensions (n_1, n_2, ..., n_k) Returns: The input tensor reshaped to two dimensions (n_1, n_prod), where n_prod is equal to the product of n_2 to n_k. """ return x.reshape([x.shape[0], -1])
[docs]def interpolate(input: JaxArray, size: Union[int, Tuple[int, ...]] = None, scale_factor: Union[int, Tuple[int, ...]] = None, mode: Union[Interpolate, str] = Interpolate.BILINEAR) -> JaxArray: """ Function to interpolate JaxArrays by size or scaling factor Args: input: input tensor size: int or tuple for output size scale_factor: int or tuple scaling factor for each dimention mode:str or Interpolate interpolation method e.g. ['bilinear', 'nearest'] Returns: output : output JaxArray after interpolation """ assert size or scale_factor, f'both size: {size} and scale_factor: {scale_factor} can not be None .' assert bool(size) ^ bool(scale_factor), f'either size or scale_factor must be none ' \ f'scale: {size}, scale_factor: {scale_factor} .' input_shape = input.shape input_dim = len(input_shape) if scale_factor: if isinstance(scale_factor, int): size = (input_shape[0], *(jn.array(input_shape[1:]) * scale_factor)) if isinstance(scale_factor, Tuple): output_dim = len(scale_factor) size = (*input_shape[:input_dim - output_dim], *(jn.array(input_shape[input_dim - output_dim:]) * jn.array(scale_factor))) else: if isinstance(size, int): size = (*input_shape[:-1], size) if isinstance(size, Tuple): output_dim = len(size) assert input_dim >= output_dim, f'Number of dimensions of "{size}"' \ f' must be < = to input shape"{input_shape}" ' size = (*input_shape[:input_dim - output_dim], *size) output = jax.image.resize(input, shape=size, method=util.to_interpolate(mode)) return output
[docs]def upsample_2d(x: JaxArray, scale: Union[Tuple[int, int], int], method: Union[Interpolate, str] = Interpolate.BILINEAR) -> JaxArray: """Function to upscale 2D images. Args: x: input tensor. scale: int or tuple scaling factor method: str or UpSample interpolation methods e.g. ['bilinear', 'nearest']. Returns: upscaled 2d image tensor """ s = x.shape assert len(s) == 4, f'{s} must have 4 dimensions to be upsampled, or you can try interpolate function.' scale = util.to_tuple(scale, 2) y = jax.image.resize(x.transpose([0, 2, 3, 1]), shape=(s[0], s[2] * scale[0], s[3] * scale[1], s[1]), method=util.to_interpolate(method)) return y.transpose([0, 3, 1, 2])
[docs]def upscale_nn(x: JaxArray, scale: int = 2) -> JaxArray: """Nearest neighbor upscale for image batches of shape (N, C, H, W). Args: x: input tensor of shape (N, C, H, W). scale: integer scaling factor. Returns: Output tensor of shape (N, C, H * scale, W * scale). """ s = x.shape x = x.reshape(s[:2] + (s[2], 1, s[3], 1)) x = jn.tile(x, (1, 1, 1, scale, 1, scale)) return x.reshape(s[:2] + (scale * s[2], scale * s[3]))