crowsonkb

import argparse
import csv
from pathlib import Path

import torch
from torch import optim, nn
from torch.nn import functional as F
from torch.utils import data
from torchvision import datasets, transforms, utils
from torchvision.transforms import functional as TF

crowsonkb

#!/usr/bin/env python3

import argparse
from collections import defaultdict
import csv
import math

import torch
from torch import nn, optim
from torch.nn import functional as F

crowsonkb

import torch
from torch import nn


class PseudoHuberLoss(nn.Module):
    """The Pseudo-Huber loss."""

    reductions = {'mean': torch.mean, 'sum': torch.sum, 'none': lambda x: x}
    
    def __init__(self, beta=1, reduction='mean'):

crowsonkb

from torch import nn
from torch.nn import functional as F


class SoftPool2d(nn.Module):
    """Applies a 2D soft pooling over an input signal composed of several
    input planes. See https://arxiv.org/abs/2101.00440"""

    def __init__(self, kernel_size, ceil_mode=False, temperature=1.):
        super().__init__()

crowsonkb

"""Trains IMLE on the MNIST dataset."""

import torch
from torch import optim, nn
from torch.utils import data
from torchvision import datasets, transforms, utils
from torchvision.transforms import functional as TF
from tqdm import tqdm

from vgg_loss import vgg_loss

crowsonkb

"""Trains IMLE on the MNIST dataset."""

import torch
from torch import optim, nn
from torch.nn import functional as F
from torch.utils import data
from torchvision import datasets, transforms, utils
from torchvision.transforms import functional as TF
from tqdm import tqdm

crowsonkb

"""Good differentiable image resampling for PyTorch."""

from functools import update_wrapper
import math

import torch
from torch.nn import functional as F


def sinc(x):

crowsonkb

from math import ceil

import torch
from torch import nn
from torch.nn import functional as F


class Downsample2d(nn.Module):
    kernels = {
        'binomial2': [0.25, 0.5, 0.25],

crowsonkb

import abc

import torch
from torch import nn, optim


class Constraint(nn.Module, metaclass=abc.ABCMeta):
    def __init__(self, fn, damping):
        super().__init__()
        self.fn = fn

crowsonkb

import torch
from torch import nn, optim


class Constraint(nn.Module):
    def __init__(self, fn, maximum, damping=1e-2):
        super().__init__()
        self.fn = fn
        self.register_buffer('maximum', torch.as_tensor(maximum))
        self.register_buffer('damping', torch.as_tensor(damping))