# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
__all__ = ['dynamic_slice', 'pad', 'rsqrt', 'stop_gradient', 'top_k',
'flatten', 'one_hot', 'upscale_nn']
import jax.nn.functions as jnnf
from jax import numpy as jn, lax
from objax.typing import JaxArray
dynamic_slice = lax.dynamic_slice
one_hot = jnnf.one_hot
pad = jn.pad
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 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]))