jizongFox

# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
#
# 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
#
#     http://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,

jizongFox

import time
import typing as t
from typing import Union

import torch
from torch import nn, Tensor
from torch.nn import functional as F
from torch.nn.common_types import _size_2_t
from torch.nn.modules.utils import _pair

jizongFox

from functools import partial

import matplotlib.pyplot as plt
import torch
from torch import nn

from models.efficientsplitformer.efficientsplitcmtformer2 import _Meta4D, Meta4D, SelfSeparableMeta4D
from models.nn.tokenmixer import MultiHeadSelfAttention, _TokenMixer
from models.nn.mbblock import MBConv, MBConvAttention

jizongFox

#!/bin/bash
#
# script to extract ImageNet dataset
# ILSVRC2012_img_train.tar (about 138 GB)
# ILSVRC2012_img_val.tar (about 6.3 GB)
# make sure ILSVRC2012_img_train.tar & ILSVRC2012_img_val.tar in your current directory
#
#  https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md
# 
#  train/

jizongFox

# demo for https://arxiv.org/pdf/1708.00489.pdf and Fig. 3 of https://arxiv.org/pdf/2106.08265.pdf
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm

sample_num = 1000
x_sample = np.random.uniform(-1, 1, sample_num)
y_sample = np.random.uniform(-1, 1, sample_num)
dataset = np.stack([x_sample, y_sample], axis=1)