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import math
import torch
def compute_same_pad(kernel_size, stride):
if isinstance(kernel_size, int):
kernel_size = [kernel_size]
if isinstance(stride, int):
stride = [stride]
assert len(stride) == len(
kernel_size
), "Pass kernel size and stride both as int, or both as equal length iterable"
return [((k - 1) * s + 1) // 2 for k, s in zip(kernel_size, stride)]
def uniform_binning_correction(x, n_bits=8):
"""Replaces x^i with q^i(x) = U(x, x + 1.0 / 256.0).
Args:
x: 4-D Tensor of shape (NCHW)
n_bits: optional.
Returns:
x: x ~ U(x, x + 1.0 / 256)
objective: Equivalent to -q(x)*log(q(x)).
"""
b, c, h, w = x.size()
n_bins = 2**n_bits
chw = c * h * w
x += torch.zeros_like(x).uniform_(0, 1.0 / n_bins)
objective = -math.log(n_bins) * chw * torch.ones(b, device=x.device)
return x, objective
def split_feature(tensor, type="split"):
"""
type = ["split", "cross"]
"""
C = tensor.size(1)
if type == "split":
# return tensor[:, : C // 2, ...], tensor[:, C // 2 :, ...]
return tensor[:, :1, ...], tensor[:, 1:, ...]
elif type == "cross":
# return tensor[:, 0::2, ...], tensor[:, 1::2, ...]
return tensor[:, 0::2, ...], tensor[:, 1::2, ...]
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