alper111

{
    "editor.minimap.enabled": false,
    "editor.fontFamily": "SF Mono",
    "editor.bracketPairColorization.enabled": false,
    "editor.acceptSuggestionOnEnter": "off",
    "editor.inlineSuggest.enabled": true,
    "files.autoSave": "onFocusChange",
    "files.associations": {
        "*.pro": "prolog",
        "*.pl": "perl"

alper111

import torch
import matplotlib.pyplot as plt


def sample_gumbel_diff(*shape):
    eps = 1e-20
    u1 = torch.rand(shape)
    u2 = torch.rand(shape)
    diff = torch.log(torch.log(u2+eps)/torch.log(u1+eps)+eps)
    return diff

alper111

from PIL import Image

def save_with_color(x, filename, n_per_row, pad_value=[0., 0., 0.], padding=2):
    N, C, H, W  = x.shape
    n_per_col = N // n_per_row
    if N > n_per_row * n_per_col:
        n_per_col += 1
    canvas = torch.empty(H*n_per_col+(n_per_col-1)*padding,
                         W*n_per_row+(n_per_row-1)*padding,
                         3,

alper111

import torch
import numpy as np

def p2dist_pytorch(x, y):
    y_dim = len(y.shape)
    return torch.pow(x, 2).sum(dim=-1).view(x.shape[:-1]+(1,)) - \
        2 * torch.matmul(x, y.permute(list(range(y_dim-2))+[y_dim-1, y_dim-2])) + \
            torch.pow(y, 2).sum(dim=-1).view(y.shape[:-2]+(1,y.shape[-2]))

def p2dist_numpy(x, y):

alper111

import torch

class SoftNode(torch.nn.Module):
    def __init__(self, in_features, out_features, depth, projection="constant"):
        super(SoftNode, self).__init__()
        self.projection = projection
        if depth > 0:
            self.left = SoftNode(in_features, out_features, depth-1, projection=projection)
            self.right = SoftNode(in_features, out_features, depth-1, projection=projection)
            self.gating = torch.nn.Linear(in_features, 1)

alper111

import torch
import torchvision

class VGGPerceptualLoss(torch.nn.Module):
    def __init__(self, resize=True):
        super(VGGPerceptualLoss, self).__init__()
        blocks = []
        blocks.append(torchvision.models.vgg16(pretrained=True).features[:4].eval())
        blocks.append(torchvision.models.vgg16(pretrained=True).features[4:9].eval())
        blocks.append(torchvision.models.vgg16(pretrained=True).features[9:16].eval())

alper111

# MIT License
# 
# Copyright (c) 2024 Alper Ahmetoglu
# 
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

alper111

import torch

class SoftTree(torch.nn.Module):
    
    def __init__(self, in_features, out_features, depth, projection='constant', dropout=0.0):
        super(SoftTree, self).__init__()
        self.proj = projection
        self.depth = depth
        self.in_features = in_features
        self.out_features = out_features