3dgrid_vqgan/training/vq_gan_3d/utils.py

""" Adapted from https://github.com/SongweiGe/TATS"""
# Copyright (c) Meta Platforms, Inc. All Rights Reserved

import warnings
import torch
import imageio

import math
import numpy as np

import sys
import pdb as pdb_original
# import SimpleITK as sitk
import logging

import imageio.core.util
logging.getLogger("imageio_ffmpeg").setLevel(logging.ERROR)


class ForkedPdb(pdb_original.Pdb):
    """A Pdb subclass that may be used
    from a forked multiprocessing child

    """

    def interaction(self, *args, **kwargs):
        _stdin = sys.stdin
        try:
            sys.stdin = open('/dev/stdin')
            pdb_original.Pdb.interaction(self, *args, **kwargs)
        finally:
            sys.stdin = _stdin


# Shifts src_tf dim to dest dim
# i.e. shift_dim(x, 1, -1) would be (b, c, t, h, w) -> (b, t, h, w, c)
def shift_dim(x, src_dim=-1, dest_dim=-1, make_contiguous=True):
    n_dims = len(x.shape)
    if src_dim < 0:
        src_dim = n_dims + src_dim
    if dest_dim < 0:
        dest_dim = n_dims + dest_dim

    assert 0 <= src_dim < n_dims and 0 <= dest_dim < n_dims

    dims = list(range(n_dims))
    del dims[src_dim]

    permutation = []
    ctr = 0
    for i in range(n_dims):
        if i == dest_dim:
            permutation.append(src_dim)
        else:
            permutation.append(dims[ctr])
            ctr += 1
    x = x.permute(permutation)
    if make_contiguous:
        x = x.contiguous()
    return x


# reshapes tensor start from dim i (inclusive)
# to dim j (exclusive) to the desired shape
# e.g. if x.shape = (b, thw, c) then
# view_range(x, 1, 2, (t, h, w)) returns
# x of shape (b, t, h, w, c)
def view_range(x, i, j, shape):
    shape = tuple(shape)

    n_dims = len(x.shape)
    if i < 0:
        i = n_dims + i

    if j is None:
        j = n_dims
    elif j < 0:
        j = n_dims + j

    assert 0 <= i < j <= n_dims

    x_shape = x.shape
    target_shape = x_shape[:i] + shape + x_shape[j:]
    return x.view(target_shape)


def accuracy(output, target, topk=(1,)):
    """Computes the accuracy over the k top predictions for the specified values of k"""
    with torch.no_grad():
        maxk = max(topk)
        batch_size = target.size(0)

        _, pred = output.topk(maxk, 1, True, True)
        pred = pred.t()
        correct = pred.eq(target.reshape(1, -1).expand_as(pred))

        res = []
        for k in topk:
            correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res


def tensor_slice(x, begin, size):
    assert all([b >= 0 for b in begin])
    size = [l - b if s == -1 else s
            for s, b, l in zip(size, begin, x.shape)]
    assert all([s >= 0 for s in size])

    slices = [slice(b, b + s) for b, s in zip(begin, size)]
    return x[slices]


def adopt_weight(global_step, threshold=0, value=0.):
    weight = 1
    if global_step < threshold:
        weight = value
    return weight

def comp_getattr(args, attr_name, default=None):
    if hasattr(args, attr_name):
        return getattr(args, attr_name)
    else:
        return default


def visualize_tensors(t, name=None, nest=0):
    if name is not None:
        print(name, "current nest: ", nest)
    print("type: ", type(t))
    if 'dict' in str(type(t)):
        print(t.keys())
        for k in t.keys():
            if t[k] is None:
                print(k, "None")
            else:
                if 'Tensor' in str(type(t[k])):
                    print(k, t[k].shape)
                elif 'dict' in str(type(t[k])):
                    print(k, 'dict')
                    visualize_tensors(t[k], name, nest + 1)
                elif 'list' in str(type(t[k])):
                    print(k, len(t[k]))
                    visualize_tensors(t[k], name, nest + 1)
    elif 'list' in str(type(t)):
        print("list length: ", len(t))
        for t2 in t:
            visualize_tensors(t2, name, nest + 1)
    elif 'Tensor' in str(type(t)):
        print(t.shape)
    else:
        print(t)
    return ""